M. BENYOUNES Abdelhafid

MCB

Directory of teachers

Department

DEPARTEMENT OF: ELECTRICAL ENGINEERING

Research Interests

Specialized in DEPARTEMENT OF: ELECTRICAL ENGINEERING. Focused on academic and scientific development.

Contact Info

University of M'Sila, Algeria

Email : abdelhafid.benyounes@univ-msila.dz

On the Web:

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Recent Publications

2024-12-18

Fault Detection and Isolation of Wind Turbine using Minimal Learning Machine

Wind turbines, as one of the fastest-growing renew-
able energy technologies, require advanced fault detection and
diagnostic methods to maintain cost-effective and reliable energy
production. The field of fault detection includes model-based and
data-driven approaches, with recent advancements emphasizing
data-driven techniques that leverage machine learning for more
robust performance. In this paper, we aim to apply the Minimal
Learning Machine (MLM) for fault detection and isolation
(FDI) in wind turbines. MLM is a supervised machine learning
technique that relies on distance-based learning to predict outputs
by approximating relationships in the input space. This ap-
proach offers a simpler and computationally efficient alternative
to traditional machine learning methods while still providing
accurate fault detection capabilities. For the fault detection phase,
we employ indices to identify anomalies, SPE index (Squared
Prediction Error) for indicating potential sensor/ actuator faults
in the system. Fault isolation is achieved through structured
residuals, using the principle of reconstruction to pinpoint specific
faults. Our application concerned the benchmark Wind Turbine
Supervisory Control and Data Acquisition (SCADA) system,
which provides a comprehensive set of variables to evaluate
MLM’s performance in terms of fault prediction and isolation.
Index Terms—wind turbines, supervisory control and data
acquisition (SACADA), fault detection and isolation, Fault re-
construction, machine learning
Citation

M. BENYOUNES Abdelhafid, (2024-12-18), "Fault Detection and Isolation of Wind Turbine using Minimal Learning Machine", [international] The 1st International Conference on Applications and Technologies of Renewable Energy Systems (ICATRES2024) , algeria

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Decentralized Sensor Fault Detection and Isolation Using Robust Observer for a DC Microgrid

Sensor faults are common problems in an islanded DC Microgrid, which significantly compromise the performance
and operational integrity of the Microgrid. Aiming to detect and isolate sensor faults in islanded DC Microgrids,
this paper proposes a robust fault detection and isolation scheme for an islanded DC Microgrid with uncertainties.
Therein, a model considering the converter uncertainties is established and utilized to design the observer. Then, a
residual-based function is generated that utilizes the estimation error of the observers to detect and identify faults.
Model uncertainties are minimized on the estimation error by incorporating an H∞ uncertainty attenuation in to
the observer design. The sufficient condition for stability is derived and expressed as Linear Matrix inequality
(LMI). Simulation using Matlab/Simscape results are presented validating the accurate fault detection and
identification
Citation

M. BENYOUNES Abdelhafid, (2024-12-18), "Decentralized Sensor Fault Detection and Isolation Using Robust Observer for a DC Microgrid", [international] The 1st International Conference on Applications and Technologies of Renewable Energy Systems (ICATRES2024) , algeria

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2024-12-02

Traveux pratique Systèmes Asservis Linéaires et Continus

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Citation

M. BENYOUNES Abdelhafid, (2024-12-02), "Traveux pratique Systèmes Asservis Linéaires et Continus", [national] university of m'sila

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2024-10-31

Improve efficiency of Perovskite-Based Solar Cell by photon recycling

In recent years, significant advancements have been made in thin-film planar
heterojunction solar cells, emerging as cost-effective photovoltaic devices with high power
conversion efficiency. Among the materials utilized, organometal trihalide perovskite
(CH3NH3PbI3) stands out as a promising absorber material. Its appeal lies in the affordability
of organic-inorganic perovskite compounds, readily available in nature, ease of fabrication, and
compatibility with large-scale processing at low temperatures [1-2].
In addition to its effective absorption in the ultraviolet range, this material exhibits captivating
optoelectronic properties, including high crystallinity, elevated carrier mobility, and extensive
carrier diffusion lengths. Despite these advantages, the highest reported power conversion
efficiency for perovskite solar cells is currently at 26.1%, as of 2022 [3].
This study introduces a thin-film organometal trihalide perovskite solar cell featuring hybrid
interfaces between carefully chosen materials. These selections are the result of an in-depth
study aimed at minimizing recombination and optimizing performance. Furthermore, we
enhance the absorption of the incident solar spectrum by incorporating a 1D photonic crystal at
the cell's bottom, facilitating the photon recycling process.
The proposed solar cell parameters are numerically computed using the rigorous coupled wave
algorithm through the SYNOPSYS RSOFT CAD tool. The thickness of each layer in the
structure is optimized using the MOST scanning and optimization module of the RSOFT CAD
tool, achieving the highest power conversion efficiency at a minimal device thickness
(approximately 2.5 μm).
Remarkably, the power conversion efficiency achieved is 27.5%, with a fill factor of 87.4% at
AM 1.5, showcasing great promise. This demonstrates the remarkable potential of the proposed
design to achieve efficiencies exceeding 5%, positioning it as a competitive contender in the
existing crystalline silicon photovoltaic market.
Citation

M. BENYOUNES Abdelhafid, (2024-10-31), "Improve efficiency of Perovskite-Based Solar Cell by photon recycling", [international] 4th International Conference on Nanomaterials and Applications nanoMAT2024, Tunisia, 2024 , Tunisia

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Efficient Prediction of Modal Indices in Silicon Waveguides Using Machine Learning Techniques

This study presents a novel machine learning approach for predicting modal indices in silicon
waveguides. Our methodology employs a deep neural network (DNN) architecture to
establish a robust link between waveguide geometric characteristics and their corresponding
effective refractive indices (neff) for both transverse electric (TE) and transverse magnetic
(TM) modes.
The DNN is trained on a comprehensive data set generated by precise finite differenceeigen
mode (FDE) simulations. The input features include waveguide width, height, and operating
wavelength, while the outputs consist of the fundamental TE and TM mode indices. We
employ a dual training methodology and a dynamic learning rate to improve model
convergence and g
Our methodology demonstrates remarkable accuracy, achieving a mean absolute error of less
than 10-4 for neff predictions a cross many geometries relevant to silicon photonics. Notably,
post-training, our method can predict modal indices for arbitrary waveguide dimensions
within milliseconds, achieving a speed improvement beyond 1000 times relative to
conventional si
We evaluate our model's effectiveness using experimental data and demonstrate its
application in accelerated design space exploration for silicon photonic devices. Furthermore,
we illustrate the application of this strategy to complex waveguide designs, including multi-
layer and slot waveguides, thereby enabling the efficient optimization of advanced photonic
integrated circuits.
Citation

M. BENYOUNES Abdelhafid, (2024-10-31), "Efficient Prediction of Modal Indices in Silicon Waveguides Using Machine Learning Techniques", [international] 4th International Conference on Nanomaterials and Applications nanoMAT2024, Tunisia, 2024 , Tunisia

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2024-06-15

Control of a Variable Speed Wind Turbine

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Citation

M. BENYOUNES Abdelhafid, (2024-06-15), "Control of a Variable Speed Wind Turbine", [international] 3rd International Conference on Frontiers in Academic Research , Turkey

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2024-05-16

A variable speed electric drive based on a permanent magnet synchronous motor (PMSM)

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Citation

M. BENYOUNES Abdelhafid, (2024-05-16), "A variable speed electric drive based on a permanent magnet synchronous motor (PMSM)", [national] 3rd International Conference on Engineering, Natural and Social Sciences ICENSOS 2024 , Turky

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2024-04-18

A Decision Tree Approach for Detecting and Classifying Rolling Element Bearing Faults in Asynchronous Machines

A Decision Tree Approach for Detecting and Classifying Rolling Element Bearing Faults in Asynchronous Machines
Citation

M. BENYOUNES Abdelhafid, (2024-04-18), "A Decision Tree Approach for Detecting and Classifying Rolling Element Bearing Faults in Asynchronous Machines", [international] 2nd International Conference on Scientific and Innovative Studies ICSIS 2024 , Turkey

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Vector Control of Induction Motor Using Type-1 Fuzzy Logic Controller

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Citation

M. BENYOUNES Abdelhafid, (2024-04-18), "Vector Control of Induction Motor Using Type-1 Fuzzy Logic Controller", [international] 2nd International Conference on Scientific and Innovative Studies ICSIS 2024 , Turkey

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2023-06-01

A Comparative Modeling Study of Gas Turbine Using Adaptive Neural Network, Nonlinear Autoregressive Exogenous, and Fuzzy Logic Approaches for Modeling and Control

- This paper focuses on the identification and modeling of gas turbine dynamics, specifically those used in power
generation plants. The approach utilizes experimental data and employs fuzzy reasoning systems. The resulting model serves
the purpose of approximating nonlinear gas turbine systems and ensuring reliable system control. By incorporating
uncertainties associated with human reasoning, such as fuzzy systems based on Takagi-Sugeno reasoning, it is possible to
achieve highly reliable control systems. The primary goal of this paper is to increase the effective monitoring system by
employing nonlinear identification techniques, namely fuzzy systems and neuro-fuzzy systems, based on real-time on-site
experimental data. Additionally, the proposed identification approaches are evaluated through a comparative study, where the
results obtained using the Nonlinear Autoregressive Exogenous Neural Networks (NARX-NN) modeling technique are
compared with those obtained using the Adaptive Inference System combined with the techniques of Neuro-Fuzzy renowned
ANFIS concept. The obtained investigation results further facilitate the comprehension and analysis of the nonlinearities
present in these complex systems, ultimately aiding in the prediction of their dynamic behavior
Citation

M. BENYOUNES Abdelhafid, (2023-06-01), "A Comparative Modeling Study of Gas Turbine Using Adaptive Neural Network, Nonlinear Autoregressive Exogenous, and Fuzzy Logic Approaches for Modeling and Control", [national] INTERNATIONAL JOURNAL OF SMART GRID , INTERNATIONAL JOURNAL OF SMART GRID

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2022

Real Time Object Detection With Drone Using Deep Learning Algorithm

Abstract—In that work, a type of an unmanned aerial vehicle
(UAV) called quadrotor with an object detection system is the
subject that we will talk about. The main objectives of our work
is the detection of chosen objects from the quadrotor. For that,
there are two parts presented, the first one, a detailed description
of the mathematical effects that applied to the structure of our
system and how we implemented our system with showing all the
parts, and we used the PID controller to stabilize the system. In
the second part, we will talk about artificial intelligence generally
and deep learning specifically and we will show how exactly the
detection happen using the right technics and algorithms with
Yolov5 and which version we will use. Finally we will make a
real test with the real time showing our prototype working in
the field.
Citation

M. BENYOUNES Abdelhafid, (2022), "Real Time Object Detection With Drone Using Deep Learning Algorithm", [international] the 2022 International Conference of advanced Technology in Electronic and Electrical Engineering (ICATEEE) , Univ de M'sila algeria

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2018

Diagnosis of uncertain nonlinear systems using interval kernel principal components analysis: Application to a weather station

The Principal Component Analysis (PCA) is one of the most known and used linear statistical methods for process monitoring. However, the PCA algorithm is not designed to handle the uncertainty of the sensor measurements that is represented by an interval type data. Including uncertainty of the sensors measurements in the analysis requires extending the PCA methodology to the Symbolic Data Analysis (SDA). The SDA refers to a paradigm where statistical units are described by interval-valued variables. In this regard, Symbolic Principal Component Analysis (SPCA), particularly Midpoints-Radii PCA (MRPCA) technique, is investigated for modeling and diagnosis of uncertain data. The aim of the present paper is to propose an extended version of the linear SPCA technique, based on midpoints and radii, to the nonlinear case of kernel PCA method (MR-KPCA). The basic idea is to construct a robust KPCA model from midpoints and radii of the nonlinear uncertain process data. Then, the robust KPCA model is used for diagnosis (FDI) purpose. In fact, the FDI decisions are improved by taking in to account the uncertainties on the nonlinear data. The MR-KPCA algorithm is applied for sensor fault detection and isolation of an automatic weather station. The results of applying this algorithm show its feasibility and advantageous performances.
Citation

M. BENYOUNES Abdelhafid, (2018), "Diagnosis of uncertain nonlinear systems using interval kernel principal components analysis: Application to a weather station", [national] ISA Transactions , elsevier

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2017

Encyclopaedia of Gas Turbines: Materials, Modeling and Performance

This book presents current research in the area of gas turbines for different applications. It is a highly useful book providing a variety of topics ranging from basic understanding about the materials and coatings selection, designing and modeling of gas turbines to advanced technologies for their ever increasing efficiency, which is the need of the hour for modern gas turbine industries
Citation

M. BENYOUNES Abdelhafid, (2017), "Encyclopaedia of Gas Turbines: Materials, Modeling and Performance", [national] , Auris Reference Limited

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FUZZY MODELING AND SIMULATION OF GAS TURBINE USING FUZZY CLUSTERING ALGORITHM

FUZZY MODELING AND SIMULATION OF GAS TURBINE USING FUZZY CLUSTERING ALGORITHM
Citation

M. BENYOUNES Abdelhafid, (2017), "FUZZY MODELING AND SIMULATION OF GAS TURBINE USING FUZZY CLUSTERING ALGORITHM", [international] SIXTH INTERNATIONAL SCIENTIFIC CONFERENCE “ENGINEERING, TECHNOLOGIES AND SYSTEMS” TECHSYS 2017 , SOFIA, PLOVDIV BRANCH, Bulgaria

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Gas turbine modeling using adaptive fuzzy neural network approach based on measured data classification

The use of gas turbines is widespread in several industries such as; hydrocarbons, aerospace, power generation. However, despite to their many advantages, they are subject to multiple exploitation problem that need to be solved. Indeed, the purpose of the present paper is to develop mathematical models of this industrial system using an adaptive fuzzy neural network inference system. Where the knowledge variables in this complex system are determined from the real time input/output data measurements collected from the plant of the examined gas turbine. It is obvious that the advantage of the neuro-fuzzy modeling is to obtain robust model, which enable a decomposition of a complex system into a set of linear subsystems. On the other side, by focusing on the membership functions for residual generator to get consistent settings based on the used data structure classification and selection, where the main goal is to obtain a robust system information to ensure the supervision of the examined gas turbine.
Citation

M. BENYOUNES Abdelhafid, (2017), "Gas turbine modeling using adaptive fuzzy neural network approach based on measured data classification", [national] Mathematics-in-Industry Case Studies , Springer International Publishing

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2016

Commande floue tolérante aux défauts appliquée à la supervision des vibrations dans les turbines à gaz: Application sur une turbine TITAN 130

Automation production systems evolution makes the failures diagnosing essential for industrial development. The work developed in this thesis is a contribution to the study of methods of control to faults tolerant, for the detection and localization of defects based on fuzzy models. This work aims to identify and model the dynamics of a gas turbine, used in industrial plants, from experimental data to approximate variable of this nonlinear system, by integrating the inaccuracies of human reasoning as rules and linguistic variables. This is to achieve an effective implementation of this system based on the use of models obtained in their strategy of fault tolerant control. The obtained results are satisfactory and give justification to further the applicability of fuzzy control fault tolerant approach in industry, especially for problems of diagnosis and monitoring of complex processes.
Citation

M. BENYOUNES Abdelhafid, (2016), "Commande floue tolérante aux défauts appliquée à la supervision des vibrations dans les turbines à gaz: Application sur une turbine TITAN 130", [national] Université de Djelfa

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Fuzzy logic addresses turbine vibration on Algerian gas line

Traditional techniques for addressing vibration in gas turbines are unable to adapt to complex modern operating environments. Uncontrolled dynamic vibration can lead to premature aging of turbine components, or unacceptable noise and vibration. To achieve operational efficiency in gas turbine control, this article proposes new methods based on artificial intelligence tools and applies them to Sonatrach’s gas compression station Medjebara SC3 in Djelfa, Algeria, on the Hassi R’mel-Bejaia (GG1) pipeline, part of a 1,400-km natural gas line connecting Algeria to Europe. This article proposes using fuzzy-logic techniques to examine a gas turbine system that includes several interacting components, the failure of which could lead to both lost revenues and lost lives.
Citation

M. BENYOUNES Abdelhafid, (2016), "Fuzzy logic addresses turbine vibration on Algerian gas line", [national] Oil & Gas Journal , PennWell Publishing Co. Energy Group, Tulsa, USA

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Gas Turbine Modeling Based on Fuzzy Clustering Algorithm Using Experimental Data

The development of reliable mathematical models for nonlinear systems has been a primary topic in several industrial applications. This work proposes to examine the application of fuzzy logic to represent the control parameters of a gas turbine based on the fuzzy clustering method using Gustafson–Kessel algorithms. The results obtained from data classification of construction with associated models indicate applications in modeling the examined system.
Citation

M. BENYOUNES Abdelhafid, (2016), "Gas Turbine Modeling Based on Fuzzy Clustering Algorithm Using Experimental Data", [national] Applied Artificial Intelligence , Taylor and francis

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2015

Fuzzy modeling and control of an industrial Gas turbine

Fuzzy modeling and control of an industrial Gas turbine
Citation

M. BENYOUNES Abdelhafid, (2015), "Fuzzy modeling and control of an industrial Gas turbine", [international] 9ème Conférence sur le Génie Electrique , Alger-Algeira

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Decentralized Fuzzy Sliding Mode Control With Chattering Elimination for the Stabilisation of a Quadrotor Helicopter Attitude

This paper presents a decentralized control strategy for the stabilization of a Quadrotor helicopter attitude, based on the combining of the fuzzy logic control and sliding mode control (SMC). The main purpose of this work is to reduce the chattering phenomenon. To achieve our purpose we have used a fuzzy logic control to generate the discontinue part of control signal in SMC, the results of our simulations indicate that the control performance of the stabilization of the Quadrotor are satisfactory and the proposed fuzzy sliding mode control (FSMC) can achieve favorable performance.
Citation

M. BENYOUNES Abdelhafid, (2015), "Decentralized Fuzzy Sliding Mode Control With Chattering Elimination for the Stabilisation of a Quadrotor Helicopter Attitude", [international] The 1st International Conference on Applied Automation and Industrial Diagnostics (ICAAID 2015), , Djelfa ,Algérie

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Takagi Sugeno models identification based on fuzzy data construction: Gas turbine investigation

The development of mathematical models for industrial systems is an important topic in many
disciplines of science and engineering. The implementation of the laws governing equations such
industrial systems, leads to a model of knowledge too complex and their use in control is very delicate.
This work, present fuzzy Takagi Sugeno models identification developed to an examined gas turbine,
using experimental data in real time. The obtained results show that the proposed technique is efficient
to approximate the model of the examined gas turbine.
Citation

M. BENYOUNES Abdelhafid, (2015), "Takagi Sugeno models identification based on fuzzy data construction: Gas turbine investigation", [international] International Conference on Applied Automation and Industrial Diagnostics (ICAAID 2015), , Djelfa ,Algérie

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Control of an industrial gas turbine based on fuzzy model

Today, gas turbines are one of the major parts of modern industry. They have played very imported in aeronautical industry, power generation, and main mechanical drivers for large pumps and compressors, In this paper a proportional-integral (PI) control design of an industrial gas turbine based on fuzzy modeling is constructed, this work addressed the major problem of the gas turbine, the system modelling, a fuzzy modeling is used to build the system model, a PI speed control is proposed, a comparison with the mathematical model proposed by Rowen is discussed, the simulations results show that the proposed fuzzy model is reliable and can be used for gas turbine control and diagnosis.
Citation

M. BENYOUNES Abdelhafid, (2015), "Control of an industrial gas turbine based on fuzzy model", [international] 16th IFAC Conference on Technology, Culture and International Stability , Sozopol, Bulgaria

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Adaptive neuro-Fuzzy modeling of an industrial Gas turbine Based a exprimental data

Nowadays, gas turbines are one of the major parts of Modern industry. They have played very important role in aeronautical industry, power generation and main mechanical drivers for large pumps and compressors. This study addressed the modeling and the simulation of the Industrial Gas Turbine Solar TITAN 130 with two shafts, located in the gas injection station of Djelfa in Algeria. The used method for modeling of this gas turbine is based on adaptive neuro-fuzzy inference system (ANFIS) with the use of fuzzy c-mean clustering (FCM) algorithm.
Citation

M. BENYOUNES Abdelhafid, (2015), "Adaptive neuro-Fuzzy modeling of an industrial Gas turbine Based a exprimental data", [international] International Conference on Automatics and Mechatronics CIAM’2015 , Oran ,Algérie

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2013

Fuzzy modeling of Multiple-Input Multiple-Output systems using Takagi-Sugeno models based on Gustafson-Kessel clustering

Fuzzy identification and modeling is one of the best approaches for the representation of complex systems. In this article we use the Takagi-Sugeno fuzzy model for some class of
nonlinear system, in order to use this proposed approach in various industrial applications. The validation of the proposed model was tested by the clustering technique, based on
Gustafson-Kessel algorithm, to a multivariable industrial system. .
Key words: Fuzzy modeling, Takagi-Sugeno fuzzy model, Gustafson-Kessel algorithm, complex systems, industrial applications, multivariable industrial system.
Citation

M. BENYOUNES Abdelhafid, (2013), "Fuzzy modeling of Multiple-Input Multiple-Output systems using Takagi-Sugeno models based on Gustafson-Kessel clustering", [national] The International Journal on Advanced Electrical Engineering , The International Journal on Advanced Electrical Engineering ISSN:2335-1209

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