M. CHOUDER Aissa

Prof

Directory of teachers

Department

DEPARTEMENT OF: ELECTRICAL ENGINEERING

Research Interests

Power Electronics Photovoltaic Systems DC and AC microgrids Application of Artificail Inteleggence in Energy Systems

Contact Info

University of M'Sila, Algeria

Email : aissa.chouder@univ-msila.dz

On the Web:

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Recent Publications

2024-12-23

Comparative Analysis of Machine Learning Models for Fault Detection in Photovoltaic Systems

Fault detection (FD) in photovoltaic (PV) systems is crucial for ensuring efficient energy production, minimizing maintenance costs, and maintaining system reliability. In this study, we conducted a comprehensive evaluation of several machine learning techniques for FD in PV systems, including Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), Decision Trees (DT), and Random Forest (RF). The performance of these models was analyzed based on their ability to handle the dynamic and nonlinear behavior of PV systems. Results from our experiments affirm that RF outperformed the other models in terms of robustness to noisy data and overall accuracy. MLP and ANN exhibited strong capabilities in capturing complex patterns, while SVR and KNN showed promise in handling specific data structures. This study offers valuable insights into the application of machine learning techniques for fault detection in PV systems, with RF emerging as the most reliable solution for enhancing system performance and reducing downtime.
Citation

M. CHOUDER Aissa, (2024-12-23), "Comparative Analysis of Machine Learning Models for Fault Detection in Photovoltaic Systems", [international] 5th International Conference on Scientific and Academic Research on 23-24 December in 2024 a , Konya/Turkey.

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2024-12-06

Improving Photovoltaic Power Prediction: Insights through Computational Modeling and Feature Selection

This work identifies the most effective machine learning techniques and supervised learning models to estimate power output from photovoltaic (PV) plants precisely. The performance of various regression models is analyzed by harnessing experimental data, including Random Forest regressor, Support Vector regression (SVR), Multi-layer Perceptron regressor (MLP), Linear regressor (LR), Gradient Boosting, k-Nearest Neighbors regressor (KNN), Ridge regressor (Rr), Lasso regressor (Lsr), Polynomial regressor (Plr) and XGBoost regressor (XGB). The methodology applied starts with meticulous data preprocessing steps to ensure dataset integrity. Following the preprocessing phase, which entails eliminating missing values and outliers using Isolation Feature selection based on a correlation threshold is performed to identify relevant parameters for accurate prediction in PV systems. Subsequently, Isolation Forest is employed for outlier detection, followed by model training and evaluation using key performance metrics such as Root-Mean-Squared Error (RMSE), Normalized Root-Mean-Squared Error (NRMSE), Mean Absolute Error (MAE), and R-squared (R 2), Integral Absolute Error (IAE), and Standard Deviation of the Difference (SDD). Among the models evaluated, Random Forest emerges as the top performer, highlighting promising results with an RMSE of 19.413, NRMSE of 0.048%, and an R 2 score of 0.968. Furthermore, the Random Forest regressor (the best-performing model) is integrated into a MATLAB application for real-time predictions, enhancing its usability and accessibility for a wide range of applications in renewable energy.
Citation

M. CHOUDER Aissa, (2024-12-06), "Improving Photovoltaic Power Prediction: Insights through Computational Modeling and Feature Selection", [national] Energies , MDPI

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2024-12-04

Optimal sizing of a hybrid microgrid system using solar, wind, diesel, and battery energy storage to alleviate energy poverty in a rural area of Biskra, Algeria

This paper presents a model for designing a stand-alone hybrid system consisting of photovoltaic sources, wind turbines, a storage system, and a diesel generator. The aim is to determine the optimal size to reduce the cost of electricity and ensure the provision of electricity at lower and more reliable prices for isolated rural areas. Three scenarios for five, fifteen, and twenty rural homes were sized and analyzed using actual weather data in the rural and pastoral area of Biskra, Algeria. The SFS and SOS algorithms were used for the first time, in addition to the commonly used PSO algorithm, to compare the performance of each algorithm and achieve the goal of meeting all energy load requirements at the lowest cost. Three factors have been taken into consideration within the proposed objective function, which are the levelized cost of energy (LCOE), loss of power supply probability LPSP, and the amount of energy consumed by the dummy load. The results of the scenarios showed that the SFS algorithm outperformed the other algorithms, with the lowest cost of electricity for each of the mentioned scenarios. The levelized cost of energy (LCOE), respectively, was 0.09138 $/kWh, 0.16588 $/kWh, and 0.24862 $/kWh.
Citation

M. CHOUDER Aissa, (2024-12-04), "Optimal sizing of a hybrid microgrid system using solar, wind, diesel, and battery energy storage to alleviate energy poverty in a rural area of Biskra, Algeria", [national] Journal of Energy Storage , Elsevier

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2024-11-06

Design and analysis of virtual impedance control scheme based on MESOGI for improving harmonic sharing of nonlinear loads

Under the presence of nonlinear load, the most existing virtual impedance (VI) methods-based control solution performs poorly in reactive power sharing among droop-operated VSIs in microgrids (MGs). This may be due to the involved estimation techniques for extracting the current harmonics at selected frequencies, which suffer from either poor accuracy of the harmonic estimation and/or the effect of DC offset in the measurements. Such an issue may affect the performance of the virtual impedance control, hence, the system stability. To bridge this gap, the implementation of the virtual impedance based on multiple enhanced second-order generalized integrator (MESOGI) suitable for harmonics and DC-offset estimation/rejection, is proposed in this paper. The MESOGI can offer an accurate estimation of the current quadrature components free from DC offset at selected frequencies, required to implement the virtual impedance control. Therefore, it makes the designed virtual impedance-based control scheme robust to voltage distortions, immune to DC disturbance, and capable of sharing properly the power harmonics. As a result, this may contribute to improving the reactive and harmonic power-sharing between droop-controlled VSIs within an islanded MG. The modeling of the MESOGI scheme and its performance investigation is carried out. In addition, the mathematical model of the implemented virtual impedance is derived. Further, analysis based on the obtained model of the equivalent output impedance including virtual impedance is established to study its effect. Simulation and experimental tests are performed to prove the effectiveness of the control proposal in improving the reactive power sharing under nonlinear load operating conditions.
Citation

M. CHOUDER Aissa, (2024-11-06), "Design and analysis of virtual impedance control scheme based on MESOGI for improving harmonic sharing of nonlinear loads", [national] Scientific Reports , Springer Nature

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2024-10-31

Enhanced Parameters Extraction Procedure of PV Module Using Electrical Fish Optimization

Accurate and reliable fault detection procedures are crucial to ensure normal operation of photovoltaic (PV) systems. To this end, the use of trusted model is the major step and an essential tool for monitoring and supervision the system under consideration. In his paper, the unknown parameters of the one diode model (ODM) in outdoor conditions are accurately identified using an enhanced methodology. The proposed methodology combines a novel translation method to correct the I-V curves to reference conditions and analytical formulations to derive the considered parameters in any operating condition of irradiance and temperature. For determining the five unknown parameters at standard test conditions, an optimization algorithm namely the electrical fish optimization (EFO) is used. Based on the extracted parameters, the evolution of maximum power point model was modeled and simulated versus measurements of a grid connected real MPP system . The obtain results show the effectiveness of the proposed strategy.
Citation

M. CHOUDER Aissa, (2024-10-31), "Enhanced Parameters Extraction Procedure of PV Module Using Electrical Fish Optimization", [international] 2024 IEEE International Multi-Conference on Smart Systems & Green Process , Tunisia

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2024-10-10

Comparative Performance Analysis of Metaheuristic Optimization Algorithms for Parameter Identification of Photovoltaic Cell/Module

PV (photovoltaic) cells are key elements in the conversion process of the sunlight into electrical energy. Therefore, the accurate modeling of PV cells or modules is of amount importance to not only predict their performance in terms of energy production but also to analyse their degradation process and detect their faults, which may be of different types. In this regard, various metaheuristic algorithms have been proposed over the past years to identify the parameters of the PV cells/modules model with the aim to accurately predict their behaviour under different operation conditions. This paper intends to compare three parameters extraction algorithms: Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO). From random weather conditions, the measured I-V curves are first translated into Standard Test Conditions (STC) of 1000 W/m2 and 25°C. Thereafter, the five standard parameters of the one-diode model are extracted using the aforementioned algorithms. A comparative study is carried out based on experimental data of an Isofoton 106W-12V monocrystalline PV module. It reveals the superiority of the GA in terms of accuracy. It shows also that the PSO outperforms the remaining algorithms from the point of view computational complexity. Moreover, the worst performances in terms of accuracy and computational complexity are obtained with the GWO.
Citation

M. CHOUDER Aissa, (2024-10-10), "Comparative Performance Analysis of Metaheuristic Optimization Algorithms for Parameter Identification of Photovoltaic Cell/Module", [international] 2024 IEEE International Conference on Artificial Intelligence & Green Energy (ICAIGE) , Yasmine Hammamet, Tunisia

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2024-02-04

Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model

Renewable energies have become pivotal in the global energy landscape. Their adoption is crucial for phasing out fossil fuels and promoting environmentally friendly energy solutions. In recent years, the energy management system (EMS) concept has emerged to manage the power grid. EMS optimizes electric grid operations through advanced metering, automation, and communication technologies. A critical component of EMS is power forecasting, which facilitates precise energy grid scheduling. This research paper introduces a deep learning hybrid model employing convolutional neural network–long short-term memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting. The proposed method integrates the variational mode decomposition (VMD) algorithm with the CNN-LSTM model to predict PV power output from a solar farm in Boussada, Algeria, spanning 1 January 2019, to 31 December 2020. The performance of the developed model is benchmarked against other deep learning models across various time horizons (15, 30, and 60 min): variational mode decomposition–convolutional neural network (VMD-CNN), variational mode decomposition–long short-term memory (VMD-LSTM), and convolutional neural network–long short-term memory (CNN-LSTM), which provide a comprehensive evaluation. Our findings demonstrate that the developed model outperforms other methods, offering promising results in solar power forecasting. This research contributes to the primary goal of enhancing EMS by providing accurate solar energy forecasts.
Citation

M. CHOUDER Aissa, (2024-02-04), "Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model", [national] Energies , MDPI

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2024-01-24

Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)

The meticulous monitoring and diagnosis of faults in photovoltaic (PV) systems enhances their reliability and facilitates a smooth transition to sustainable energy. This paper introduces a novel application of deep learning for fault detection and diagnosis in PV systems, employing a three-step approach. Firstly, a robust PV model is developed and fine-tuned using a heuristic optimization approach. Secondly, a comprehensive database is constructed, incorporating PV model data alongside monitored module temperature and solar irradiance for both healthy and faulty operation conditions. Lastly, fault classification utilizes features extracted from a combination consisting of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU). The amalgamation of parallel and sequential processing enables the neural network to leverage the strengths of both convolutional and recurrent layers concurrently, facilitating effective fault detection and diagnosis. The results affirm the proposed technique’s efficacy in detecting and classifying various PV fault types, such as open circuits, short circuits, and partial shading. Furthermore, this work underscores the significance of dividing fault detection and diagnosis into two distinct steps rather than employing deep learning neural networks to determine fault types directly.
Citation

M. CHOUDER Aissa, (2024-01-24), "Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)", [national] Sustainability , MDPI

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2023-12-15

Effects of Temperature and Solar Radiation on Photovoltaic Modules Performances Installed in Oued Keberit Power Plant, Algeria

This study presents the measured data of the "Oued Keberit" PV plant over a period of
four months (January to April 2022) after nearly 6 years of outdoor exposure to the
climate of Souk Ahras, eastern Algeria, in order to evaluate the performance of the
solar PV system. This evaluation includes the calculation of PV system performance
variables such as module performance, final performance and performance of the
module references, system losses, photovoltaic friendly efficiency, system efficiency,
performance ratio and capacity factor based on measured data, allowing comparison
of actual PV system performance and reference values determined by manifacturers.
The results showed that the four-month average values of the parameters Eac, Yr, Yf,
PR, , CF are 1.06375125MWh, 4.08h, 4.25h, 1.06, 16.40%, 60.28% respectively. Also,
the high efficiency of the photovoltaic system was obtained during winter, due to the
low temperature and the sufficient amount of solar radiation. However, the
photovoltaic system generates a lot of energy during summer, although there is less
output than during the winter season. This is because summer has maximum values for
sunshine duration and solar radiation.
Citation

M. CHOUDER Aissa, Aissa Chouder, , (2023-12-15), "Effects of Temperature and Solar Radiation on Photovoltaic Modules Performances Installed in Oued Keberit Power Plant, Algeria", [national] Journal of Advanced Research in Fluid Mechanics and Thermal Sciences , Journal of Advanced Research in Fluid Mechanics and Thermal Sciences

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2023-12-02

Observer current based deadbeat control for Single-phase UPS Inverter

This study introduces a deadbeat control method designed specifically for a single-phase inverter used in uninterruptible power supply (UPS) applications. The proposed control method necessitates the measurement of both capacitor current and output voltage in order to maintain sinusoidal output voltage and achieve high dynamic performance, even in the presence of load variations. The deadbeat controller aims to eliminate discrepancies between the output voltage and voltage reference, there by optimising the controller's performance without requiring additional current sensors. Furthermore, the deadbeat controller aims to counteract load voltage distortion and restore the system's state in the event of an external closed-loop disturbance. In order to address this drawback, we suggest employing a Luenberger observer to estimate the current of the capacitor.
Citation

M. CHOUDER Aissa, (2023-12-02), "Observer current based deadbeat control for Single-phase UPS Inverter", [national] THE 1ST NATIONAL CONFERENCE ON PHYSICS AND IT’S APPLICATIONS , Boussaada

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A Deadbeat Current Controller for Single Phase PV Grid Connected Inverters

Distributed photovoltaic units are frequently interconnected with the electric grid through the utilisation of single-phase inverters. The voltage and frequency at which the grid operates are not fixed, but rather can vary within the limits set by international norms. In addition, there is a continuous tightening of standards for power factor correction, total harmonic distortion, and dependability. Since grid operating conditions are dynamic, it is crucial to implement control algorithms that are both dependable and efficient, with flexible control parameters. With the goal of improving the efficiency and reducing the total harmonic distortion of the output current, this paper explores the topology of a single-phase inverter's configuration and its control algorithm. Therefore, deadbeat controllers that are continuously tuned based on the actual grid frequency are proposed as a solution to the control architecture with an appropriate Phase Locked Loop PLL, which addresses some practical issues with the control algorithm.
Citation

M. CHOUDER Aissa, (2023-12-02), "A Deadbeat Current Controller for Single Phase PV Grid Connected Inverters", [national] THE 1ST NATIONAL CONFERENCE ON PHYSICS AND IT’S APPLICATIONS , Boussaada

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Realization of a Solar Radiation Measurement System Using an Arduino Uno Card and a Reference Cell

We propose a new low-cost device to measure total solar radiation using a reference photovoltaic cell. This device is based on the proportional relation between the solar radiation and the short-circuit current of the photovoltaic cell. The device consists of a circuit that measures the voltage and current of the photovoltaic cell by a shunt resistor, and anADC that converts analog data into digital. The Arduino UNO platform is used to process data and display incident solar radiation on the reference cell. This device can be used to monitor solar radiation in an stand-alone or grid-connected photovoltaic installation, and also to evaluate the performance of solar panels.
Citation

M. CHOUDER Aissa, (2023-12-02), "Realization of a Solar Radiation Measurement System Using an Arduino Uno Card and a Reference Cell", [national] THE 1ST NATIONAL CONFERENCE ON PHYSICS AND IT’S APPLICATIONS , Boussaada

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Realization of a Solar Irradiation Measurement Device based on a Reference Cell and an Arduino Uno.

This work presents the design and implementation of a low-cost device for measuring global solar irradiation based on a reference photovoltaic cell. The device operates by measuring the short-circuit current of the photovoltaic cell, which is proportional to the incident solar irradiation. The short-circuit current is measured using a shunt resistor, and the resulting voltage across the shunt resistor is scaled using an operational amplifier circuit. The scaled voltage is then fed into an analog-to-digital converter (ADC) for further processing. The Arduino Uno platform is used to process the ADC data and display the measured solar irradiation in real time.
The device is implemented using the Proteus simulation environment. The design has been successfully tested and validated, and the device is shown to be accurate and reliable. The device can be used in a number of applications, such as monitoring solar irradiation in stand-alone or grid-connected photovoltaic power plants.
Citation

M. CHOUDER Aissa, (2023-12-02), "Realization of a Solar Irradiation Measurement Device based on a Reference Cell and an Arduino Uno.", [national] THE 1ST NATIONAL CONFERENCE ON PHYSICS AND IT’S APPLICATIONS , Boussaada

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2023-11-14

An Improved Fault Diagnosis in Stand-Alone Photovoltaic System Using Artificial Neural Network

This paper proposes an improved fault diagnosis for stand-alone photovoltaic (SAPV) system using artificial neural network (ANN) and power loss parameters as inputs. Unlike the classical power analysis approach that fails to find precisely the fault type, this method can accurately identify the fault class and can be used in real-time applications. The development of the ANN fault diagnosis model goes through data of both normal and faulty operation of the SAPV system. These data are obtained from either a real measurement to represent the normal operation where simplicity and safety are concerned or from the simulation in which the data of faults could hardly and costly be obtained. Three common types of ANN were trained, tested, and compared to choose the most efficient network to predict the faults in the system. The results indicate that multi-layer-perceptron network type is the most accurate network to recognize the faults with 95%. In addition, some test has been carried out in real-time to show their effectiveness.
Citation

M. CHOUDER Aissa, (2023-11-14), "An Improved Fault Diagnosis in Stand-Alone Photovoltaic System Using Artificial Neural Network", [national] Iranian Journal of Science and Technology Transactions of Electrical Engineering , Springer

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Comparative Analysis of Metaheuristic Algorithms for Extracting Electrical Parameters of PV Modules

This paper presents a comparison of
various metaheuristic algorithms used for extracting
electrical parameters from specific photovoltaic (PV)
panels. The focus lies on assessing and comparing the
efficiency of eight chosen algorithms in precisely
deriving electrical parameters from PV modules,
selected for their novelty and widespread use. The
ODM mathematical model was utilized due to its
exceptional precision and simplicity to accomplish this
aim. Next, a series of simulations were executed to
establish the optimal electrical parameters with the best
root mean square (RMS) values for each algorithm. The
resulting dataset from these simulations was then
analysed and compared to draw the final conclusions.
The primary goal of this comparative analysis is to
obtain valuable knowledge about the abilities of various
new metaheuristic algorithms in determining the
electrical parameters of photovoltaic modules.
Citation

M. CHOUDER Aissa, (2023-11-14), "Comparative Analysis of Metaheuristic Algorithms for Extracting Electrical Parameters of PV Modules", [international] 6 th International Conference on Electrotechnics 2023– ETT/FGE/USTO-MBAt: Oran Algeria , Oran Algeria

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2023-10-28

Prediction Model of PV Module Based on Artificial Neural Networks for the Energy Production

The accurate characterization and prediction of current-voltage characteristics of photovoltaic (PV) modules under different operating conditions is the main step for energy prediction and an important tool for monitoring and supervision the system. However, one of the problems of this technology is that as yet there are no models in the literature to directly calculate the daily dynamic maximum power of these kinds of PV systems. The development of models is an important task to support the application of this technology because it allows the prediction of the energy yield. In this paper a model based on artificial neural networks has been developed to address this important issue. The model takes into account the main important parameters that influence the electrical output of these kinds of systems which are direct irradiance, and module temperature. Comparative study with The simulated dynamic MPP model using the single diode model is presented to demonstrate the effectiveness of the considered approach. The obtained results show that the proposed model can be used for estimating the maximum power of a grid connected system located in the Centre de Developpement des Energies Renouvelables (CDER) in Algiers with an adequate margin of error.
Citation

M. CHOUDER Aissa, (2023-10-28), "Prediction Model of PV Module Based on Artificial Neural Networks for the Energy Production", [international] 5th Novel Intelligent and Leading Emerging Sciences Conference (NILES) , Egypt

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2023-10-27

A Very Short-Term Photovoltaic Power Forecasting Model Using Linear Discriminant Analysis Method and Deep Learning Based on Multivariate Weather Datasets

Photovoltaic (PV)-system-generated solar energy has inconsistent and variable properties, which makes controlling electric power distribution and preserving grid stability extremely difficult. A photovoltaic (PV) system’s performance is profoundly affected by the amount of sunlight that reaches the solar cell, the season of the year, the ambient temperature, and the humidity of the air. Every renewable energy technology, sadly, has its problems. As a result, the system is unable to function at its highest or best level. To combat the unstable and intermittent performance of solar power output, it is essential to achieve a precise PV system output power. This work introduces a new approach to enhancing accuracy and extending the time range of very short-term solar energy forecasting (15 min step ahead) by using multivariate time series inputs in different seasons. First, Linear Discriminant Analysis (LDA) is used to select the relevant factors from the mixed meteorological input data. Secondly, two very short-term deep learning prediction models, CNN and LSTM, are used to predict PV power for a shuffled and reduced database of weather inputs. Finally, the predicted outputs from the two models are combined using a classification strategy. The proposed method is applied to one year of real data collected from a solar power plant located in southern Algeria to demonstrate that this technique can improve the forecasting accuracy compared to other techniques, as determined through the use of statistical analysis involving normalized root mean square error (NRMSE), mean absolute error (MAE), mean bias error (MBE), and determination coefficient. (R2).
Citation

M. CHOUDER Aissa, (2023-10-27), "A Very Short-Term Photovoltaic Power Forecasting Model Using Linear Discriminant Analysis Method and Deep Learning Based on Multivariate Weather Datasets", [national] Engineering Proceedings , MDPI

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2023-08-08

Efficient Deadbeat Control of Single-Phase Inverter with Observer for High Performance Applications

In this paper, a deadbeat control technique for single-phase inverters used in UPS applications is presented. For the suggested
control approach to maintain sinusoidal output voltage for high dynamic performance even with load fluctuations, measurements of capacitor
current and output voltage are necessary. By reducing the error between the output voltage and the voltage reference without adding more
current sensors, the deadbeat controller improves the performance of the proposed controller. It also reduces load voltage distortion and
restores the system state in the event of an external shutdown-loop road interference. We suggest a capacitor current estimation based on the
Luenberger observer to address this flaw. PROCESSOR-IN-THE-LOOP The "P.I.L" test method, which can be thought of as an expensive
system, enables us to create and evaluate controllers by running built-in C code on the DSP scheduled for the controller during simulated
PSIM power phase control. To address this drawback, we propose a capacitor current estimation based on the Luenberger observer.
Citation

M. CHOUDER Aissa, (2023-08-08), "Efficient Deadbeat Control of Single-Phase Inverter with Observer for High Performance Applications", [national] Przegląd Elektrotechniczny , Wydawnictwo SIGMA

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2023-06-18

Dispositif de mesure de rayonnement solaire à base d'une cellule de référence

Ce dipositif permet la mesure du rayonement solaire a base d'une cellule photovoltaique de reference.
Ce disposif mesure le courant de court-circuit de la cellule de référence et par une formule de translation on obtient le rayomment solaire globale.
Citation

M. CHOUDER Aissa, (2023-06-18), "Dispositif de mesure de rayonnement solaire à base d'une cellule de référence", [national] Université Mohamed Boudiaf M'sila

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2023-06-14

Performance Investigation of a Large-Scale Grid-Tied PV Plant under High Plateau Climate Conditions: Case Study Ain El-Melh, Algeria

This paper investigates the performance of a large-scale 20 MW photovoltaic (PV) plant
in Ain El-Melh, Algeria. The evaluation is based on experimental data collected from
January to December 2019. The PV plant consists of 40 sub-fields with 500 kW inverters
and 1936 PV modules of 250 Wp each. Performance parameters including output energy,
module temperature, final yield, module/system efficiencies, performance ratio (PR),
corrected PR, and other loss-related indicators are assessed. The study focuses on the
unique high plateau climate conditions (HPCC) in Algeria and their relevance to largescale grid-tied PV plants. The high plateau climate is characterized by specific
environmental factors such as temperature fluctuations, high altitude, and variations in
solar irradiance. These factors play a crucial role in determining the performance of largescale grid-tied PV plants in this region. In this study, the CR1000X monitoring device is
employed to capture the environment’s data, while the NARI SJ-30 monitoring system
collects electrical data. During this study, the main environmental factors, such as
temperature, radiation, wind speed, precipitation, and relative humidity, which may affect
the PV plant's efficiency are considered to assess the PV system's performance. The results
show that the PV plant supplied 827.9 MWh to the grid in 2019. The final yield ranged
from 3.99 h/day in December to 5.897 h/day in April, and the PR varied from 64.8% to
79.34%. The annual capacity factor ranged from 16.65% to 24.57%. A soiling effect of
4.8% on the performance ratio was observed in a selected subfield. The findings are
valuable for researchers, investors, and policymakers involved in PV projects in similar
climates, advancing renewable energy utilization.
Citation

M. CHOUDER Aissa, (2023-06-14), "Performance Investigation of a Large-Scale Grid-Tied PV Plant under High Plateau Climate Conditions: Case Study Ain El-Melh, Algeria", [national] Journal Européen des Systèmes Automatisés , IIETA

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2023-05-24

Refroidisseur Portable fonctionnant a l'energie solaire

L'invention consiste en un refroidisseur d'eau construit autour des modules Peliers.
Il sert a garder la temperature a une temperature pour assuer la fraicheur de l'eau.
Citation

M. CHOUDER Aissa, (2023-05-24), "Refroidisseur Portable fonctionnant a l'energie solaire", [national] Université Mohamed Boudiaf M'isla

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2023-05-03

The Design and Processor-In-The-Loop Implementation of a Super-Twisting Control Algorithm Based on a Luenberger Observer for a Seamless Transition between Grid-Connected and Stand-Alone Modes in Microgrids May 2023Energies 16(9)

The abrupt transfer from grid-connected (GC) to stand-alone (SA) operation modes is one of the major issues that may threaten the stability of a distributed generation (DG) system. Furthermore, if the islanding mode happens, it is vital to take into consideration the load voltages or load current waveforms as soon as feasible. This paper develops an advanced control technique based on a super-twisting sliding mode controller (ST-SMC) for a three-phase inverter operating in both the GC and SA modes. This control scheme is proposed to ensure a smooth transition from the GC to SA mode and enhance the load voltage waveforms under the islanding mode. In addition, to minimize the operational costs of the system and the complexity of the studied model, a digital Luenberger observer (DLO) with a proper design is adopted for estimating the inverter-side current. The control scheme of the whole system switches between a current control mode during the GC mode and a voltage control mode during the SA mode. The super-twisting control algorithm is applied to the outer voltage control loop involved in the cascaded voltage/current control scheme in the SA mode. Simulation tests of a three-phase inverter are performed for the purpose of assessing the suggested control performance by using the PowerSim (PSIM) software and comparing it with a classical PI controller. Furthermore, a processor-in-the-loop (PIL) implementation in a DSP board TMS32F28335 while debugging is conducted using code composer studio 6.2.0. The obtained results show efficient control properties, such as a smooth transition among the microgrid (MG) operating modes, as well as effectiveness and robustness during both the GC and SA operation modes.
Citation

M. CHOUDER Aissa, (2023-05-03), "The Design and Processor-In-The-Loop Implementation of a Super-Twisting Control Algorithm Based on a Luenberger Observer for a Seamless Transition between Grid-Connected and Stand-Alone Modes in Microgrids May 2023Energies 16(9)", [national] Energies , MDPI

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2023-04-22

Revolutionizing Photovoltaic Systems: An Innovative Approach to Maximum Power Point Tracking Using Enhanced Dandelion Optimizer in Partial Shading Conditions

Partial shading (PS) is a prevalent phenomenon that often affects photovoltaic (PV) installations, leads to the appearance of numerous peaks in the power-voltage characteristics of PV cells, caused by the uneven distribution of solar irradiance on the PV module surface, known as global and local maximum power point (GMPP and LMPP). In this paper, a new technique for achieving GMPP based on the dandelion optimizer (DO) algorithm is proposed, inspired by the movement of dandelion seeds in the wind. The proposed technique aimed to enhance the efficiency of power generation in PV systems, particularly under PS conditions. However, the DO-based MPPT is compared with other advanced maximum power point tracker (MPPT) algorithms, such as Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CSA), and Bat Algorithm (BA). Simulation results establish the superiority and effectiveness of the used MPPT in terms of tracking efficiency, speed, robustness, and simplicity of implementation. Additionally, these results reveal that the DO algorithm exhibits higher performance, with a root mean square error (RMSE) of 1.09 watts, a convergence time of 2.3 milliseconds, and mean absolute error (MAE) of 0.13 watts.
Citation

M. CHOUDER Aissa, (2023-04-22), "Revolutionizing Photovoltaic Systems: An Innovative Approach to Maximum Power Point Tracking Using Enhanced Dandelion Optimizer in Partial Shading Conditions", [national] Energies , MDPI

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2023-02-05

Processor-in-the-Loop Validation of an Observer Current-based Dead-Beat Control for a Single-Phase UPS Inverter

This paper presents a dead-beat control algorithm for Uninterruptible Power Supply (UPS) applications of
single-phase inverters. The proposed control method requires the measurement of capacitor current and
output voltage in order to keep the output voltage sinusoidal ensuring high dynamic performance even
under load changes. The dead-beat controller optimizes the behavior of the system by eliminating the error
between the output and the reference voltage without increasing the number of current sensors, which are
costly, and eliminates load voltage distortions and restores the system state in the event of external
shutdown-loop road interference. In this paper, we propose a capacitor current estimation based on the
Luenberger observer. Processor-In-the-Loop (PIL) is a test method that allows us to create and evaluate
controllers by running built-in C code on the DSP scheduled for the controller during simulated PSIM
power phase control. It can be seen that the simulation results match the PIL test results, which proves the
validity of the proposed controller.
Citation

M. CHOUDER Aissa, (2023-02-05), "Processor-in-the-Loop Validation of an Observer Current-based Dead-Beat Control for a Single-Phase UPS Inverter", [national] Engineering, Technology and Applied Science Research , EOS Association

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2023-02-01

Robust Nonsingular Terminal Sliding Mode Control of a Buck Converter Feeding a Constant Power Load

In recent years, DC microgrid systems feeding constant power loads (CPLs) have been given a particular focus due to their effect on the overall system stability caused by their electrical characteristics behaving as negative incremental impedance. To address this issue, this paper investigates the stabilization of a DC bus voltage in a DC microgrid (MG) feeding a CPL. The output voltage of the main DC bus is stabilized by using a robust nonsingular terminal sliding mode controller that is characterized by the elimination of the singularity problem that arises from the conventional terminal sliding mode controller. The CPL is emulated by a boost converter where its output voltage is tightly regulated. The system is investigated in terms of voltage following and disturbance rejection. The robustness and effectiveness of the proposed control strategy are assessed against input voltage fluctuations and power demand variations. The proposed controller is validated through simulations and an experimental setup.
Citation

M. CHOUDER Aissa, (2023-02-01), "Robust Nonsingular Terminal Sliding Mode Control of a Buck Converter Feeding a Constant Power Load", [national] Electronics , MDPI

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Deep learning method based on autoencoder neural network applied to faults detection and diagnosis of photovoltaic system

The paper presents an application of deep learning for fault detection in PV systems located in Algiers –Algeria which has a nominal power of 9.54 kW. Each PV array comprises 30 PV modules each string contains 15 PV modules in series. Fault detection and diagnosis of Photovoltaic Systems (PVS) is an important task for successful solar power generation. Several faults have been occurred such as short-circuit cases and open-circuit string cases in PV generator can leads to fire and risk. The design of efficient defect detection methods for PV systems presents a challenge especially for small scale PV farms. Faults detection methods are failed under the presence of noisy signals. This paper presents a deep learning (DL) based method for detection, diagnosis and classification of the aforementioned defects. The proposed procedure consists of four fundamental steps, first a heuristic optimization approach based on Coyote Optimization Algorithm (COA) the five-unknown electrical parameters of the One Diode Model (ODM) and their insertion into a PSIM-based simulation that aims to mimic the operating PV system. Second, database construction that includes current, voltage and power at the Maximum Power Point (MPP), module temperature and solar irradiance for the PV system is supposed under healthy and faulty operations condition at optimal operational conditions. Then, this is followed by the extraction of new features of the old database using unsupervised learning characteristics of the Auto-Encoder (AE). and last, supervised learning using the new database based on Artificial Neural Network (ANN) construction for PV fault detection and classification. The proposed technique has been validated using monitored data from a real operating PV system situated in Algeria. The obtained results have shown the effectiveness of the proposed technique detection and classification of various PV fault types.
Citation

M. CHOUDER Aissa, (2023-02-01), "Deep learning method based on autoencoder neural network applied to faults detection and diagnosis of photovoltaic system", [national] Simulation Modelling Practice and Theory , Elsevier

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2023-01-12

A Developed Algorithm Inspired from the Classical KNN for Fault Detection and Diagnosis PV Systems

During the operation of photovoltaic systems, various faults can occur and result in serious problems, such as energy loss or system shutdown. Therefore, it is crucial to identify and diagnose these faults in order to improve system performance. The purpose of this work is to propose an efficient and simple procedure for the early detection and diagnosis of faults on the direct current side of photovoltaic systems. These faults include the short circuit of three modules, short circuit of ten modules, and string disconnection. Therefore, it is necessary to distinguish between four classes: the healthy class and three classes representing different types of faults. A dataset representing the four classes and comprising four measured attributes—cell temperature, solar irradiance, and current and voltage at the maximum power point—is utilized in the developed approach. The idea is to transform the multiclassification problem into a binary classification problem and utilize a modified version of the well-known K-nearest neighbors (KNN) classifier. In this proposed version, the training dataset is divided into two hyperspheres, each representing a distinct class. The Giza pyramid construction algorithm is then utilized to determine the optimal center coordinates of these hyperspheres. To classify a new data point using the proposed classifier, which combines the KNN classifier and the Giza pyramid construction algorithm, distances are computed only between the new data point and the center of each sphere. Unlike the classical version of the KNN classifier, which involves computing distances between the new data point and the entire dataset. To assess the efficiency of the proposed approach, a comparative study was conducted, including the classical version of the KNN, support vector machine, decision tree, and random forest algorithms. The evaluation criteria considered were accuracy, precision, recall, and execution time. The results of the carried-out study demonstrated the remarkable superiority of the proposed algorithm over these alternative methods.
Citation

M. CHOUDER Aissa, (2023-01-12), "A Developed Algorithm Inspired from the Classical KNN for Fault Detection and Diagnosis PV Systems", [national] Journal of Control Automation and Electrical Systems , Springer

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2023-01-03

Improved power computation method for droop-controlled single-phase VSIs in standalone microgrid considering non-linear loads

Computation of active and reactive powers is a crucial step in droop-controlled single-phase voltage source inverters (VSIs) in standalone microgrid since the performance and stability of the power-sharing strategy are strongly influenced by its speed and accuracy, especially in the case of non-linear loads. Here, an improved performance of power-sharing among single-phase droop-controlled VSIs in an islanded microgrid, considering DC component and nonlinear loads is presented. To achieve this goal, an enhanced power-sharing control scheme including a Multiple Enhanced Second-Order Generalized Integrator Frequency-Locked Loop (MESOGI-FLL) for power calculation is proposed. As a result, the proposed power computation technique provides high rejection capability of DC component and current harmonics, hence, perfect estimation of the fundamental component of the inverter output current and its 90◦ phase-shifted component. This strategy makes the power calculation method-based control scheme immune to disturbance effects of the DC component and the high current harmonics. Detailed analysis, mathematical modelling of MESOGI, as well as a comparison with recent methods, are also provided. Simulation and experimental tests were carried out and the obtained results have shown the effectiveness and robustness of the proposed power-sharing controller even under nonlinear load operating conditions.
Citation

M. CHOUDER Aissa, (2023-01-03), "Improved power computation method for droop-controlled single-phase VSIs in standalone microgrid considering non-linear loads", [national] IET, Generation ,Transmission & Distribution , Wiley

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2022-11-26

Faults Detection of PV Systems Based on Extracted Parameters using Modified Grey Wolf Algorithm

Accurate and reliable fault detection procedures are crucial to ensure normal operation of photovoltaic (PV) systems. To this end, the use of trusted model is the major step and an essential tool for monitoring and supervision the system under consideration. In his paper a suggested procedure based on three main steps is presented. Firstly, the unknown parameters of the one diode model (ODM) are accurately identified using modified grey wolf (MGW) algorithm. Subsequently, based on the extracted parameters, the evolution of maximum power point model was modeled and simulated versus measurements of a grid connected real MPP system . Finally, the PV array is simulated to take out the MPP coordinates by using a PSIMTM/MatlabTM co-simulation, as well as an efficient fault detection process based on simple approach is implemented. The obtain results show the effectiveness of this method in detecting and diagnosing faults for real time application.
Citation

M. CHOUDER Aissa, (2022-11-26), "Faults Detection of PV Systems Based on Extracted Parameters using Modified Grey Wolf Algorithm", [international] the 2022 International Conference of advanced Technology in Electronic and Electrical Engineering (ICATEEE) , M'sila University, Algeria,

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2022

Small signal modeling of power converters and practical implementation of linear controller in DSP plateforms

workshop a été organisé par "Power Quality in Electrical Networks Laboratory" et qui a traité de la modélisation faible signaux des convertisseur de puissance et de l'implémentation des controlleur PI et PID dans des plateformes DSP.
Citation

M. CHOUDER Aissa, (2022), "Small signal modeling of power converters and practical implementation of linear controller in DSP plateforms", [national] Power Quality in Electrical Networks , Université Setif1

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Fault detection in GCPV systems

Ce workshop a été destiné au etudiants de doctorat en génie electrique. Ce workshop a porté sur les techniques de detection et de diagnistic des sytems photovoltaique connectés au réseau.
Citation

M. CHOUDER Aissa, (2022), "Fault detection in GCPV systems", [national] Journée Doctorale en Génie Electrique , Université de M'sila

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A Comparative Study for Stator Winding Inter-Turn Short-Circuit Fault Detection Based on Harmonic Analysis of Induction Machine Signatures

This article deals with inter-turn short-circuit fault detection in stator windings of squirrel cage induction machines. The main aim is to perform harmonic analysis of different electrical signatures namely the stator phase current, external magnetic flux and electromagnetic torque at different levels of mechanical load in order to develop an efficient fault detection approach of this kind of defect in induction machines. The proposed approach is based on the analysis of saturation related harmonics at rank
, where
is an odd number, and magnetomotive force (MMF)-related harmonics at rank
, with
in stator phase current and stray flux and harmonics at rank
in electromagnetic torque. The amplitudes of these last harmonics in healthy condition are compared with 3% power supply unbalance, 16.6% (40 turns) and 33% (80 turns) levels of inter-turn short-circuit fault in frequency range from 0 Hz to 2500 Hz under different levels of mechanical load. Besides, the stand-still test is also investigated in this work. Simulation study is carried out based on 2.2kW squirrel cage IM using finite element method (FEM). This method provides accurate and inexpensive tool for evaluating the performance of induction machine under healthy and faulty conditions. The obtained results demonstrate that the stray flux is the most sensitive signature to the stator winding inter-turn short-circuit fault, and it is robust against the power supply unbalance in comparison with both stator current and electromagnetic torque.
Citation

M. CHOUDER Aissa, (2022), "A Comparative Study for Stator Winding Inter-Turn Short-Circuit Fault Detection Based on Harmonic Analysis of Induction Machine Signatures", [national] Mathematics and Computers in Simulation , Elsevier

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A simple and effective methodology for sizing electrical energy storage (EES) systems based on energy balance

The use of the electrical energy storage (EES) plays an important role in the transition of energy generation towards renewable energy sources (RESs). An effective sizing of EES systems is very important in order to cope with the volatility of RESs and to ensure a reliable energy supply. The present paper provides a methodology which helps to determine the minimum required EES size for conceiving a fully standalone system. Its approach is based on the evaluation of the energy balance for a given design period, and it can also be applied for sizing the EES system in grid-connected applications. The methodology was validated using measurement data obtained from two different systems corresponding to: a) a near-zero energy building with local generation sources, and b) a large-scale battery energy storage system (BESS) installed in a factory and used for peak-shaving. The obtained results confirmed the effectiveness of the proposed methodology by estimating the required size of the EES system. A good correlation was found between the estimated and the installed BESS size in the considered systems. The deviation between real and estimated BESS capacities was found to be less than 5%
Citation

M. CHOUDER Aissa, (2022), "A simple and effective methodology for sizing electrical energy storage (EES) systems based on energy balance", [national] Journal of Energy Storage , Elsevier

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An Improved Grey Wolf Optimizer for Parameter Extraction of Photovoltaic Cells

The exact parameter estimation of the fuel cell model is considered a critical stage in delivering a consistent emulation for the fuel cell system characteristics. The aim of this is to suggeste a robust methodology based on the Gradient-based Optimizer (GBO) to identify the best parameters of PEM fuel cell (PEMFC). Three distinct types of PEM fuel cells: 250 W FC stack, BCS 500 W, and SR-12 500 W, were used to demonstrate the superiority of the GBO. To confirm the superiority of GBO, the results were compared with those obtained using different optimizers such as salp swarm algorithm (SSA), heap-based optimizer (HBO), differential evolution (DE), whale optimization algorithm (WOA), moth-flame optimization algorithm (MFO), sine cosine algorithm (SCA), and Harris's hawk optimizer (HHO). During the optimization process, the unknown parameters of PEM fuel cells are used as decision variables, whereas the objective function needs to be minimum is represented by the sum square error between the measured data and estimated data. In addition, the obtained results by GBO are compared with other methods achieved in the literature. The superiority of GBO in determining the optimal parameters of different PEM fuel cells is proved.
Citation

M. CHOUDER Aissa, (2022), "An Improved Grey Wolf Optimizer for Parameter Extraction of Photovoltaic Cells", [international] 3 rd International Conference on Applied Engineering and Natural Sciences , Konya, Turkey

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Optimal parameter estimation strategy of PEM fuel cell using gradient-based optimizer

The exact parameter estimation of the fuel cell model is considered a critical stage in delivering a consistent emulation for the fuel cell system characteristics. The aim of this is to suggeste a robust methodology based on the Gradient-based Optimizer (GBO) to identify the best parameters of PEM fuel cell (PEMFC). Three distinct types of PEM fuel cells: 250 W FC stack, BCS 500 W, and SR-12 500 W, were used to demonstrate the superiority of the GBO. To confirm the superiority of GBO, the results were compared with those obtained using different optimizers such as salp swarm algorithm (SSA), heap-based optimizer (HBO), differential evolution (DE), whale optimization algorithm (WOA), moth-flame optimization algorithm (MFO), sine cosine algorithm (SCA), and Harris's hawk optimizer (HHO). During the optimization process, the unknown parameters of PEM fuel cells are used as decision variables, whereas the objective function needs to be minimum is represented by the sum square error between the measured data and estimated data. In addition, the obtained results by GBO are compared with other methods achieved in the literature. The superiority of GBO in determining the optimal parameters of different PEM fuel cells is proved.
Citation

M. CHOUDER Aissa, (2022), "Optimal parameter estimation strategy of PEM fuel cell using gradient-based optimizer", [national] Energy , Elsevier

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Adaptive Droop based Control Strategy for DC Microgrid Including Multiple Batteries Energy Storage Systems

In a microgrid architecture that includes energy storage systems based on parallel batteries, the inequalities in the batteries’ state of charge may cause inconsistency in the residual capacity of each battery. As a consequence, the battery cells may be degraded owing to overcharging or deep discharging. This paper presents an optimized load-sharing approach-based droop control strategy for parallel batteries operating in a DC microgrid. The main aim of the proposed control approach is to include the real battery capacity, which may be affected during its lifecycle, in the control algorithm in order to prevent non-matching conditions. As a result, proportional power-sharing will be allowed according to the actual capacity. In addition, all the SoCs will be equalized and the parallel batteries, present in the system, will operate equally in terms of SoC when delivering or absorbing power. Hence, the batteries lifecycle will be extended while power-sharing is performed in the best way. To this end, the identification of the actual battery capacity has been carried out using a metaheuristic optimization algorithm called Salp Swarm Algorithm (SSA). Each battery output is controlled by bidirectional DC/DC converters that ensure the charging and discharging process. The control approach has been evaluated under different scenarios such as similar and different capacities and a sudden disconnection of a battery. The obtained results prove the ability of the proposed control strategy to ensure proportional power-sharing while handling the inconsistency of residual energy between battery cells and improve the battery state of health.
Citation

M. CHOUDER Aissa, (2022), "Adaptive Droop based Control Strategy for DC Microgrid Including Multiple Batteries Energy Storage Systems", [national] Journal of Energy Storage , Elsevier

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DeadBeat Controller Based Luenberger Current Observer for Single-phase Islanded Inverter

The control of a DC/AC converter to provide a cost sinusoidal wave required in many application, is a challenging task. In the present paper, the deadbeat control algorithm has been proposed to generate a nearly sinusoidal waveform with zero steady-state error and low total harmonic distortion (THD). The controller is based on the output measurements to generate the required modulating signal (pulse-width), for controlling a single phase inverter. The derived deadbeat control algorithm requires the measurement of both capacitor current and output voltage which can be considered as a costly system. To tackle this disadvantage, we propose an estimation of the capacitor current based on Luenberger observer. The simulation results have confirmed the validity of the deadbeat control based current observer to enhance the output voltage, in the linear and nonlinear loads.
Citation

M. CHOUDER Aissa, (2022), "DeadBeat Controller Based Luenberger Current Observer for Single-phase Islanded Inverter", [international] 2022 19th International Multi-Conference on Systems, Signals & Devices (SSD) , Sétif, Algeria

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An Enhanced Primary Control Level for a DC Microgrid Systems

in the last few years, DC Microgrid is gained more attention than AC Microgrid due to its advantage associated with high efficiency, more reliability, and control simplicity. Despite the benefits of the DC Microgrid, power sharing is concerned as the major constraint in DC Microgrid. In this work, a parallel circuit including two DC-DC Buck converters, which are connected with a single resistive load, was designed. A basic droop control strategy is suggested to realize the power sharing between the converters, which is tested under different disturbances. The proposed method is characterized by enhanced power-sharing and better voltage regulation. The effectiveness and the robustness are confirmed using an experimental setup.
Citation

M. CHOUDER Aissa, (2022), "An Enhanced Primary Control Level for a DC Microgrid Systems", [international] 2022 19th International Multi-Conference on Systems, Signals & Devices (SSD) , Sétif, Algeria

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Control of a Three-Phase Grid-Connected Inverter based on Super-Twisting Sliding mode Algorithm

This paper deals with the robust current control for three-phase Grid-Connected Inverters (GCI) of distributed generation (DG) systems based on a Super-Twisting Sliding mode controller (ST-SMC) during injecting active and reactive power into the grid. This approach is capable of decreasing the chattering phenomena and improving the system's accuracy. The proposed controller is realized for the inner current controller to guarantee proper regulation, such as short-time response, small steady-state error, and so on. To achieve the appropriate synchronization desired between current injected and grid voltage, a phase locked loop (PLL) based on a synchronous reference frame (SRF) is applied. Finally, simulation results are examined by Powersim (PSIM) software and are compared with those of a conventional controller to validate the proposed controller's effectiveness.
Citation

M. CHOUDER Aissa, (2022), "Control of a Three-Phase Grid-Connected Inverter based on Super-Twisting Sliding mode Algorithm", [international] 2022 19th International Multi-Conference on Systems, Signals & Devices (SSD) , Sétif, Algeria

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Control of a Voltage Source Inverter in a Microgrid Architecture using PI and PR Controllers

This paper presents a voltage control of a three-phase inverter operating in stand-alone mode using proportional-integral (PI) and proportional-resonant (PR) controllers. The aim of this paper is to present a comparative study between these two controller types. The voltage and the current control loops, as well as the VSI's mathematical model, are presented in the dq and the αβ frames. The evaluation of the PI and PR controllers is discussed. The simulations study is caring out using PSIM software. The simulation results have shown the advantages of the PR controller and PI controller based in terms of total harmonic distortion (THD) reduction.
Citation

M. CHOUDER Aissa, (2022), "Control of a Voltage Source Inverter in a Microgrid Architecture using PI and PR Controllers", [international] 2022 19th International Multi-Conference on Systems, Signals & Devices (SSD) , Sétif, Algeria

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A novel hybrid GWO–PSO-based maximum power point tracking for photovoltaic systems operating under partial shading conditions

One of the major challenges in photovoltaic (PV) systems is extracting the maximum power from the PV array, especially when they operate under partial shading conditions (PSCs). To address this challenge, this paper introduces a novel hybrid maximum power point tracking (MPPT) method based on grey wolf optimization and particle swarm optimization (GWO–PSO) techniques. The developed MPPT technique not only avoids the common disadvantages of conventional MPPT techniques (such as perturb and observe (P&O) and incremental conductance) but also provides a simple and robust MPPT scheme to effectively handle partial shading in PV systems, since it requires only two control parameters, and its convergence to the global maximum power point (GMPP) is independent of the search process's initial conditions. The feasibility and effectiveness of the hybrid GWO–PSO-based MPPT method are verified via a co-simulation technique that combines MATLAB/SIMULINK and PSIM software environments, while comparing its performance against GWO, PSO and P&O based MPPT methods. The simulation results carried out under dynamic environmental conditions have shown the satisfactory effectiveness of the hybrid MPPT method in terms of tracking accuracy, convergence speed to GMPP and efficiency, compared to other methods.
Citation

M. CHOUDER Aissa, (2022), "A novel hybrid GWO–PSO-based maximum power point tracking for photovoltaic systems operating under partial shading conditions", [national] Scientific Reports , Springer

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Enhanced energy output from a PV system under partial shaded conditions through grey wolf optimizer

Due to its several powerful characteristics, the Grey Wolf Optimization (GWO) algorithm has recently become widely used in numerous optimization problems. In photovoltaic (PV) systems, power-voltage (P–V) curves present multiple maxima in case of partial shadow or modules mismatch which can be viewed as an optimization problem. In this paper, a GWO-based maximum power tracking (MPPT) algorithm for PV systems operating under partial shading conditions (PSCs) is proposed. With a direct control method, the proposed GWO-based MPPT allows successfully catching the global maximum power point (GMPP) of the PV array in case of uniform and non-uniform solar irradiation. To evaluate the effectiveness of the proposed MPPT scheme, a co-simulation combining Matlab/Simulink™ and Cadence/Pspice™ software environments is carried out and tested under various static and dynamic shading conditions. Furthermore, the tracking performance is compared with that of the particle swarm optimization (PSO) and classical perturb and observe (P&O) based MPPT methods under transient PSCs and also under inhomogeneous real conditions of Algerian sky. The simulation results have demonstrated the satisfactory performances of the proposed approach in terms of tracking efficiency and tracking speed over the other MPPT methods.
Citation

M. CHOUDER Aissa, (2022), "Enhanced energy output from a PV system under partial shaded conditions through grey wolf optimizer", [national] Cleaner Engineering and Technology , Elsevier

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Artificial Intelligence for Smart Photovoltaic Technologies

Photovoltaic (PV) systems operating in real conditions of work are very often subject to several faults that may lower significantly the produced energy and shorten their availability. Therefore, powerful and trusted fault detection procedures are necessary to enable early maintenance and avoid excessive energy losses. The large increase in PV power installed worldwide in recent years, especially in systems connected to electricity distribution networks: Grid-connected PV systems, has led to the development of strategies and tools for automatic supervision of these systems to detect faults and diagnose the source of these failures. This chapter presents a review of most relevant existing methodologies applied in fault detection and diagnosis of PV systems.
Citation

M. CHOUDER Aissa, (2022), "Artificial Intelligence for Smart Photovoltaic Technologies", [national] , AIP Publishing

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Advanced control scheme and dynamic phasor modelling of grid-tied droop-controlled inverters

his paper develops an advanced scheme, modelling, and analysis of power flow control intended for grid-connected droop-controlled VSIs within a single-phase microgrid (MG). The proposed control scheme includes a power calculation method based on an enhanced second-order generalized integrator frequency-locked loop (ESOGI-FLL). Contrary to the existing power calculation methods that use low-pass filter (LPF) with a low cutoff frequency to reject the grid voltage distortion and achieve average power, which may result in reducing the power calculation speed, the involved ESOGI-FLL can offer a fast transient response and benefits from high filtering capability of sub- and low-order harmonics. In addition, the ESOGI-FLL provides total rejection of the DC offset, which is another issue that may adversely affect the accuracy of the traditional methods. Thus, this proposal can contribute to improving the speed and accuracy of the power computation and make the power calculation scheme immune to DC disturbance, thereby enhancing the performance of the power control. On the other hand, a dynamic phasor modelling approach is adopted considering the dynamics of the ESOGI-based power calculation, instead of the LPF transfer function used to describe the power computation dynamic in the related works. The small-signal model of the grid-connected VSI power flow considering the line impedance R/X ratio as well as that describing the dynamics of the ESOGI-based power calculation are derived. Using these models, the closed-loop model of the grid-interactive inverter including the power controller dynamics is obtained. The system stability is assessed, which helps to determine properly the controller's gains. A simulation study of a grid-connected VSI is carried out in MATLAB/Simulink™ and PSIM's processor in the loop (PIL) platforms to assess the effectiveness of the proposed control approach. The results confirm the effectiveness of the proposed control to regulate real and reactive powers with good transient performances when the grid is subject to several working conditions.
Citation

M. CHOUDER Aissa, (2022), "Advanced control scheme and dynamic phasor modelling of grid-tied droop-controlled inverters", [national] IET Renewable Power Generation , IET

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Parameters Extraction of Photovoltaic Module Using Giza Pyramid Construction Optimization Algorithm

The widespread integration of renewable energy sources with the traditional power systems causes a considerable impact, such as the decrease of total inertia, damping properties and large frequency deviation. To tackle this issue, several literature proposed the virtual synchronous generator (VSG) emulation as an effective solution applied to power electronics inverters, i.e., grid forming and grid supporting power converters. In this paper, the VSG emulator in grid feeding structure is investigated within a simple week grid in order to bring support capabilities to the frequency and amplitude deviation. The frequency nadir at the point of common cohesion (PCC) can be reduced when more active power is required, whereas the voltage amplitude is reduced with reactive power demand. The small signal modeling of the VSG is addressed, taking into consideration inertia and damping variations to assess the transient response of active power and frequency during power variation. In order to validate the theoretical concepts, simulation tests have been carried out using PSIM platform.
Citation

M. CHOUDER Aissa, (2022), "Parameters Extraction of Photovoltaic Module Using Giza Pyramid Construction Optimization Algorithm", [international] 2022 2nd International Conference on Advanced Electrical Engineering (ICAEE) , Constantine

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Optimal Parameters Identification Strategy of a Lead Acid Battery Model for Photovoltaic Applications

Extracting the parameters of a lead-acid battery under real-world operating conditions is a significant part of solar photovoltaic (PV) engineering. Usually, the battery management system handles the battery system based on its model. However, its model's parameters can change due to its electrochemical nature. Hence, enhancing the model parameters' accuracy is required to achieve a reliable and accurate model. This research employs an improved methodology for extracting lead-acid battery data outdoors. The suggested method combines numerical and analytical formulations of parametric battery models for solar PV energy storage. The Shepherd model, which considers the battery's non-linear properties, is selected in this paper. Based on a modern meta-heuristic marine predator algorithm, the parameters of two solar lead-acid batteries are discovered using an optimal parameter identification technique (MPA). The MPA exhibits its capability in terms of fast convergence and accuracy. The acquired test results are compared to those produced by the salp swarm algorithm, artificial eco-system optimizer, hunger games search, a new optimization meta-heuristic method that inspires the behavior of the swarm of birds called COOT, and honey badger algorithm in terms of efficiency, convergence speed, and identification accuracy. The findings demonstrated that the MPA outperformed the other optimizers in identifying ability. This optimizer achieved 99.99% identification efficiency for both Bergan and Banner battery types, making it an excellent battery identification option.
Citation

M. CHOUDER Aissa, (2022), "Optimal Parameters Identification Strategy of a Lead Acid Battery Model for Photovoltaic Applications", [national] Energy Storage , Wiley

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2019

Comparison study aComparison study and parameter identification of three battery models for an off-grid photovoltaic system

There is growing interest in solar batteries, especially for photovoltaic (PV) applications. Therefore, an accurate battery model is required for the PV system because of its influence on system efficiency. Several mathematical models of batteries have been described in the scientific literature. However, this paper reviews three electrochemical models most commonly used for PV systems, such as Shepherd, Manegon and Coppetti, in order to define the most appropriate model for PV systems. This paper discusses an application of the pattern search optimization technique to extract the parameters of three battery models derived from experimental test results obtained from sealed gelled lead acid batteries for both charge and discharge modes. A comparative case and regression analysis based on statistical tests and a quantitative method were conducted to demonstrate the effectiveness and accuracy of the updated model from the three aforementioned. The simulation results and tests performed on the battery charge and discharge modes lead us as well to approve the algorithm’s accuracy regarding the updated model.
Citation

M. CHOUDER Aissa, Aicha Degla, Madjid Chick, Farid Bouchafa, , (2019), "Comparison study aComparison study and parameter identification of three battery models for an off-grid photovoltaic system", [national] International Journal of Green Energy , Taylor&Francis

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Simple and efficient approach to detect and diagnose electrical faults and partial shading in photovoltaic systems

Photovoltaic (PV) systems are continuously exposed to many potential faults, causing significant power generation losses. Accurate fault detection procedures are crucial to ensure the reliable operating condition. This paper presents simple, efficient and easy to implement approach to detect the most common failures, due to short-circuit (SC) and open-circuit (OC) faults, inverter disconnection (ID) and partial shading (PS). The proposed procedure introduces three indicators namely, current indicator , voltage indicator and power indicator with the main function to distinguish healthy and faulty operating conditions. To this end, single-diode model is adopted to generate a trusted PV model in combination with the best-so-far artificial bees colony (ABC) optimization algorithm in order to extract the unknown model parameters. Subsequently, the maximum power point (MPP) coordinates are estimated to mimic the real operating PV system. Measured and predicted MPP coordinates allow the calculation of current, voltage and power indicators. For each indicator, an upper and lower thresholds has been established by trial and error. The computed value of each indicator will reveal the healthy or faulty operation of the PV system when it is within or outside the predefined threshold respectively. An experimental evaluation is presented by using the monitored data of the 3.2 kWp grid-connected PV system located at the roof of the Renewable Energy Development Centre (CDER), Algeria.
Citation

M. CHOUDER Aissa, Y.Chaibia, M.Malvonib, M.Boussetta, M.Salhi, , (2019), "Simple and efficient approach to detect and diagnose electrical faults and partial shading in photovoltaic systems", [national] Energy Conversion and Management , Elsevier

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New modeling approach of secondary control layer for autonomous single-phase microgrids

In a microgrid (MG) topology, the secondary control is introduced to compensate for the voltage amplitude and frequency deviations, mainly caused by the inherent characteristics of the droop control strategy. This paper proposes an accurate approach to derive small signal models of the frequency and amplitude voltage at the point of common coupling (PCC) of a single-phase MG by analyzing the dynamics of the second-order generalized integrator-based frequency-locked loop (SOGI-FLL). The frequency estimate model is then introduced in the frequency restoration control loop, while the derived model of the amplitude estimate is introduced for the voltage restoration loop. Based on the obtained models, the MG stability analysis and proposed controllers’ parameters tuning are carried out. Also, this study includes the modeling and design of the synchronization control loop that enables a seamless transition from island mode to grid-connected mode operation. Simulation and practical experiments of a hierarchical control scheme, including traditional droop control and the proposed secondary control for two single-phase parallel inverters, are implemented to confirm the effectiveness and the robustness of the proposal under different operating conditions. The obtained results validate the proposed modeling approach to provide the expected transient response and disturbance rejection in the MG.
Citation

M. CHOUDER Aissa, Ahmed Bendib, Kamel Kara, Abdelhammid Kherbachi, Walid Issa, , (2019), "New modeling approach of secondary control layer for autonomous single-phase microgrids", [national] Journal of the Franklin Institute , Elsevier

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Enhanced structure of second-order generalized integrator frequency-locked loop suitable for DC-offset rejection in single-phase systems

For the proper operation of a microgrid, an accurate estimation of the grid parameters such as phase angle, operating frequency and amplitude under various disturbances is of prime interest. In this paper, an enhanced second-order generalized integrator-based frequency-locked loop (ESOGI-FLL) intended for DC-offset rejection in both grid-connected and islanded operation of a single-phase voltage source inverter (VSI) is presented. The main contribution in this work is the ability of the proposed scheme to remove the overall uninspected distortion in the orthogonal component and oscillations that affect the estimated frequency and phase caused mainly by the inherent DC-offset in the input voltage. Unlike the most popular PLLs dealing with DC-offset rejection, the proposed scheme has a great advantage in term of structure complexity and computational time. Numerical simulations as well as practical assessments are carried out to highlight the effectiveness and robustness of the proposed scheme in term of parameters estimation of highly shifted input voltage. In addition, the proposed scheme is tested against corrupted input voltage such as frequency change, phase jump and voltage sags. The paper also reports a detailed comparative study with conventional SOGI-FLL and third-order generalized integrator based FLL. The obtained results have shown best performances of the proposed ESOGI-FLL in term of DC-offset rejection in the orthogonal component, ripples elimination in the estimated frequency and less computational time.
Citation

M. CHOUDER Aissa, Karbachi Abdelhamid, Ahmed Bendib, Kamel Kara, Said Barkat, , (2019), "Enhanced structure of second-order generalized integrator frequency-locked loop suitable for DC-offset rejection in single-phase systems", [national] Electric Power Systems Research , Elsevier

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A prediction Model Based on Nelder-Mead Algorithm for the Energy Production of PV Module

The use of an adequate model of photovoltaic module for the energy prediction is an important tool. To this end, PV modeling primarily involves the formulation of the non-linear current versus voltage (I-V) curve. This paper presents an application of the Nelder-Mead simplex search method for identifying the parameters of solar cell and photovoltaic module models. The proposed technique is used to identify the unknown model parameters, namely, the generated photocurrent, saturation current, series resistance, shunt resistance, and ideality factor that govern the current-voltage relationship of a solar cell. The extracted parameters have been tested against several static IV characteristics of the PV module collected at different operating condition. Comparative study among different parameter estimation techniques is presented to demonstrate the effectiveness of the proposed approach. A dynamic MPP model has also been derived and simulated using the extracted parameters against MPP real dynamic measurements of a grid connected system located in the Centre de Developpement des Energies Renouvelables (CDER) in Algiers.
Citation

M. CHOUDER Aissa, (2019), "A prediction Model Based on Nelder-Mead Algorithm for the Energy Production of PV Module", [national] International Journal of Information Science & Technology, IJIST, , ijist

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2018

Removal of a textile dye using photovoltaic electrocoagulation

The main objective of this study was to investigate the operation of a continuous photovoltaic electrocoagulation process (PVEC) comprising an electrocoagulation (EC) part provided with aluminum electrodes and a settling zone, for the removal of a textile dye: the overall EC cell had a volume of 8.5 L. The effect of the operating parameters has been followed, e.g. inlet pH and dye concentration, residence time, current density, solar irradiance and energy consumption. The results showed that when the inlet concentration was increased from 50 to 1400 mg/L and for the following operating conditions: current density ranging from 100 to 400 A/m² , inlet flow rate at 15 L/h, electrode gap of 1 cm, inlet pH near 7, the removal rates of the color and turbidity were decreased from 96% to 87% and from 96% to 93%, respectively. Use of the photovoltaic cell in the place of the electric supply allowed to reach more than 99% of elimination of turbidity and 95% of the color when the initial concentration of the dye was 1400 mg/L. The specific electrical energy consumption was found at 16 kWh per kilogram of removed dye for EC using direct current, and at comparable levels upon use of photovoltaic cells. The consumption of the electrodes is at comparable levels for the two sources of energy, respectively at 0.45 and 0.6 kg Al per kg of removed dye).
Citation

M. CHOUDER Aissa, Billal Khemila, Rabah Zidelkhir, Jean-Pierre Leclerc, François Lapicque, , (2018), "Removal of a textile dye using photovoltaic electrocoagulation", [national] Sustainable Chemistry and Pharmacy , Elsevier

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0024-05-11

An Integrated Multi-Objective Optimization Algorithm for Parameter Estimation and Maximum Power Point Tracking in Photovoltaic Systems

The Maximum Power Point Tracking (MPPT) algorithm subjected the PV parameters to changes under faulty and weather operations conditions during runtime, which influenced the results obtained. The aim of this research is to provide an interconnected algorithm for online optimization of PV parameters. The MPPT algorithm will dynamically adjust its operations, ensuring continual optimization in response to each update of PV parameters. We choose a custom block over other simulation techniques to allow parameter updating and provide more flexibility and customization options. This paper proposes a combined meta-heuristic algorithm based on multi-objective optimization for both parameter estimation and MPPT. The simulation results under different algorithms show the efficacy of this combination in online optimization and the capability of its implementation in the real-time optimization controller. The paper also discusses the results of simulations using different candidate algorithms. Finally, this paper recommends the adaptation of this approach for multiple problems that include parameter estimation at the first level and suggests the use of advanced algorithms and techniques for the sake of accelerating time processing.
Citation

M. CHOUDER Aissa, (0024-05-11), "An Integrated Multi-Objective Optimization Algorithm for Parameter Estimation and Maximum Power Point Tracking in Photovoltaic Systems", [international] 2024 3rd International Conference on Advanced Electrical Engineering (ICAEE) , Sidi-Bel-Abbes, Algeria

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0024-01-22

Faults detection and diagnosis of PV systems based on machine learning approach using random forest classifier

Accurate and reliable fault detection procedures are crucial for optimizing photovoltaic (PV) system performance. Establishing a trustworthy PV array model is the primary step and a vital tool for monitoring and diagnosing PV systems. This paper outlines a two-step approach for creating a reliable PV array model and implementing a fault detection procedure using Random Forest Classifiers (RFCs).
Firstly, we extracted the five unknown parameters of the one-diode model (ODM) by combining the current–voltage translation method to predict the reference curve and employing the modified grey wolf optimization (MGWO) algorithm. In the second step, we simulated the PV array to obtain maximum power point (MPP) coordinates and construct operational databases through co-simulations in PSIM/MATLAB. We developed two RFCs: one for fault detection (a binary classifier) and another for fault diagnosis (a multiclass classifier).
Our results confirmed the accuracy of the PV array modeling approach. We achieved a root mean square error (RMSE) value of 0.0122 for the ODM parameter extraction and RMSEs lower than 0.3 in dynamic PV array output current simulations under cloudy conditions. Regarding the fault detection procedure, our results demonstrate exceptional classification accuracy rates of 99.4% for both fault detection and diagnosis, surpassing other tested models like Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Neural Networks (MLP Classifier), Decision Trees (DT), and Stochastic Gradient Descent (SGDC).
Citation

M. CHOUDER Aissa, (0024-01-22), "Faults detection and diagnosis of PV systems based on machine learning approach using random forest classifier", [national] Energy Conversion and Management , Elsevier

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