M. BERRABAH Fouad

ABSTRACT
This paper presents two robust controls techniques: current control in the dq
reference frame and direct power control (DPC) applied to a three-phase PWM
rectifier to achieve efficient dc voltage regulation, unit power factor control and
harmonic distortion rate reduction. Simulations were performed using
Matlab/Simulink software. According to these results, the DPC direct power control
seems to be more efficient than the current control in the dq reference frame, which
has a lower harmonic distortion rate, and the DC voltage follows its new reference
for the two developed controls. However, the response time, overshoot and
oscillations of the controlled quantities, as well as the exponential convergence of
the errors for the transient regime and the setpoint change, are different. It can be
seen that the best values of the latter are those obtained by applying the DPC
direct power control, which is considered to be the best performing and most
efficient control compared to the other control

Abstract. The aim of this work is to estimate the flashover voltage of a high-voltage insulator that has been deliberately polluted using fuzzy logic
(FL). Initially, experimental experiments on a high-voltage insulator were used to collect a data set that was then used to implement the idea of
artificial intelligence. These studies were conducted using varying degrees of fake pollution, namely saline distilled water. Each pollution level
indicated the quantity of artificial pollution, measured in millilitres, in each section of the insulator. The collecting database provides flashover voltage
measurements associated with varying levels of artificial pollution in each insulator zone and its conductivity. Furthermore, we have used fuzzy logic
(FL) to forecast the flashover voltage of the high-voltage insulator and assess the insulating condition of simulated pollution. The suggested
prediction model, which is based on Federated Learning (FL), is implemented using MATLAB's graphical user interface. Ultimately, a comparison
was conducted between the outcomes achieved by FL and real-world ones. The database used in this comparison differs from that used in concepts
based on programming language implementation taken from previous literature. The findings demonstrate the superior effectiveness of the FL
approach in predicting the flashover voltage of high-voltage insulators when compared to data acquired from practical testing.
Streszczenie. Celem pracy jest oszacowanie napięcia przeskoku izolatora wysokiego napięcia, który został celowo zanieczyszczony przy użyciu
logiki rozmytej (FL). Początkowo eksperymenty eksperymentalne na izolatorze wysokiego napięcia służyły zebraniu zbioru danych, który następnie
wykorzystano do wdrożenia idei sztucznej inteligencji. Badania te przeprowadzono przy użyciu różnego stopnia fałszywych zanieczyszczeń, a
mianowicie destylowanej wody solankowej. Każdy poziom zanieczyszczenia wskazywał ilość sztucznych zanieczyszczeń, mierzoną w mililitrach, w
każdej sekcji izolatora. Zbierana baza danych zapewnia pomiary napięcia przeskoku związane z różnymi poziomami sztucznych zanieczyszczeń w
każdej strefie izolatora i jego przewodnością. Ponadto wykorzystaliśmy logikę rozmytą (FL) do prognozowania napięcia przeskoku izolatora
wysokiego napięcia i oceny stanu izolacji symulowanego zanieczyszczenia. Sugerowany model predykcyjny, oparty na Federated Learning (FL), jest
implementowany przy użyciu graficznego interfejsu użytkownika MATLAB-a. Ostatecznie przeprowadzono porównanie wyników uzyskanych na
platformie FL z wynikami uzyskanymi w świecie rzeczywistym. Baza danych wykorzystana w tym porównaniu różni się od bazy danych stosowanej
w koncepcjach opartych na implementacji języka programowania zaczerpniętych z wcześniejszej literatury. Odkrycia wskazują na wyższą
skuteczność metody FL w przewidywaniu napięcia przeskoku izolatorów wysokiego napięcia w porównaniu z danymi uzyskanymi z testów
praktycznych. (Oszacowanie napięcia przeskoku izolatora przy użyciu logiki rozmytej (FL))

Abstract

Induction motors in modern industry are becoming more and more functional and complex. Unfortunately, these machines are not free from damages what make their fault diagnosis the most critical aspect of system monitoring and maintenance. Vibrational signal data yields relevant information about the state of the entire system, as well as specifically about one of its components that makes its analysis quite interesting. For this effect, the current paper aims to propose an automatic diagnosis and monitoring method for detecting and locating bearing faults in an induction motor based on vibration signal processing. The suggested method combines the discrete wavelet transform (DWT) with the envelope spectrum (ENV) as advanced signal processing, incorporating a machine learning algorithm based on random forest classifier. The discrete wavelet transforms (DWT), using the Haar wavelet, decomposes the vibrational signal to provide both approximations and details. Each detail is then reconstructed to avoid any missing of information. To precisely select the reconstructed detail (𝑅𝑒𝑐𝑑𝑘) that provides pertinent information about bearing faults, a statistical study is conducted. This study involves calculating four indicators (Root mean square (RMS), correlation coefficient (CC), energy coefficient (EC) and peak to peak (P2P) factor) is performed for each (𝑅𝑒𝑐𝑑𝑘). These indicators are compared with threshold indicators, and this criterion is met by the reconstructed details 1 and 3. The obtained reconstructed details are then subjected to the spectral envelope analysis to detect the fault frequencies, which are considered as new features entering the random forest classifier model. This combination of approaches allows better feature extraction and structuring of the dataset, leading to improved accuracy of the random forest classifier, achieving a higher classification rate of more than 99,53 %. The proposed DWT-ENV-RF method indicates well its efficiency when compared to other recent works, and the attained results are all confirmed by the experimental tests conducted in the CWRU laboratory.

Abstract. Most industrial applications use integer-order proportional integral derivative (IOPID) controllers due to well-known characteristics such as
simplicity and ease of implementation. However, because of their nonlinear nature and the underlying iso-damping feature of fractional-order
operators, fractional-order PID (FOPID) and fractionalized-order PID (FrOPID) controllers outperform the IOPID controllers. In this study, three
different controllers based on particle swarm optimization are used to regulate a stable system. While a FrOPID controller only has to optimize four
parameters and a normal PID controller only needs to optimize three parameters, a FOPID controller requires the optimization of five parameters.
Set-point tracking, and better disturbance rejection are obtained with the fractional PID controller, whereas fractionalized PID outperforms the other
controllers in terms of noise attenuation.

Abstract – In this paper, a Robust Adaptive Fuzzy Backstepping Control (RAFBC) is applied to
electromechanical system which called Twin Rotor Multi Input Multi Output System (TRMS) in
order to follow the desired trajectory. This strategy yields robustness to various kinds of
uncertainties and guaranteed stability of the closed-loop control system. The adaptive laws have
been used in order to ameliorate the robustness against uncertainties, wind effects and external
disturbances. The stability of system in the closed-loop has been demonstrated using Lyapunov
method. In the control design, type 2 fuzzy logic systems are used to approximate the unknown
functions. Hybrid adaptive robust tracking control schemes that are based upon a combination of
bounds of type 2 fuzzy approximation parameters and the backstepping design are developed such
that all the states and signals are bounded and the proposed approach alleviate the online
computation burden and improves the robustness to dynamic uncertainties and external
disturbances. In addition, the coupling effects between the horizontal and vertical subsystems of
TRMS are considered as uncertainties. Thus, precise trajectory tracking is maintained under
various operational conditions with the presence of various types of uncertainties. Unlike other
controllers, the proposed control algorithm can estimate model uncertainties online and improve
the robustness of the system. Experimental tests were carried out and the results demonstrate that
the proposed algorithm performs well in tracking and under model uncertainties. Copyright ©
2023 Praise Worthy Prize S.r.l. - All rights reserved.
Keywords: TRMS, Robust Control, Adaptive Control, Fuzzy, Backstepping, Uncertainties, Wind
Effects

This paper presents an active fault tolerant control (FTC) strategy based on the estimated fault information for a double star induction machine (DSIM) to compensate for faults effects and thus improve the reliability and availability of the machine. The DSIM is powered by two three-phase voltage source inverters (VSI) using pulse width modulation (PWM). A defective dynamic model of DSIM in the rotating synchronous d-q frame with a fieldoriented control strategy is developed. The proposed FTC design is based on a backstepping control (BSC) using a
nonlinear Thau observer with an adaptive fault estimation law. The Thau observer is used for fault detection and fault reconstruction at the same time. After that, the estimation value of the faults effect is introduced directly into the control law in order to guarantee the stability of the machine in post fault. The sufficient condition for the stability of the closed-loop system (machine + observer) in faulty operation is analyzed and verified using Lyapunov theory. Finally, the efficiency and robustness of the proposed FTC approach are validated in steady state by a
numerical simulation developed in MATLAB / Simulink. Obtained results show that the proposed FTC provides a strong fault tolerance where all closed-loop system signals are bounded and errors converge to a small neighborhood of the origin. Simulation results in healthy and faulty conditions confirm the reliability of the suggested framework.

Recently, fault detection in asynchronous motors has paid attention of many researchers. The monitoring of these machines is performed through of the use of several physical quantities, among them vibration analysis has a crucial importance for early detection of rolling bearing faults in induction motor (IM), which they represent about 41% of IM’s ensemble defects. Commonly, the induction motor operates under non-stationary operating conditions (varying speed, fluctuating load …), and that leads to the birth of non-stationary vibration signals. The vibration signals produced from the bearing cannot generate any information about the state of the machine. Therefore, a proper analysis of these signals by means of different signal processing tools allows us to determine if the entire rotating machinery is in a normal or abnormal state. In the field of bearing fault detection, signal processing though of fault diagnosis methods has taken preponderant place. Among these methods, Fast Fourier Transform is most frequently used enabling the signal decomposition without losing any information, but it is limited to non-stationary signals such as it cannot provide the temporal location of the appearance of another shock after the born of a first one. To overcome this limitation the Discret Wavelet Transform is used providing both of time and frequency location. In this paper, we propose a diagnosis method for the identification of bearing faults, which serves to combine the DWT (Discret Wavelet Transform) and the envelope analysis. The DWT decomposes the signals of the outer race defect and the inner race defect of the bearing to obtain details. These details will be then subjected to a statistical analysis based on the kurtosis and energy coefficient (EC) in order to select the optimum wavelet details including significant harmonics corresponding to the fault cases. The envelope analysis is then applied to the selected details for extracting the frequencies’ characteristics of bearing faults. The calculated theoretical faults following the equations (1)-(3) mentioned in [1] are the rotation frequency 𝑓𝑟=28.83 Hz; the inner race frequency 𝑓𝑖𝑟=156.34 Hz; the outer race frequency 𝑓𝑜𝑟=103.12 Hz, this all from exploiting the data of vibration signals that are measured at a sampling frequency of 12000 Hz and a motor speed of 1730 RPM, available in CWRU [2]. The selection of the details obtained from the signals corresponding to the outer race and the inner race defects must respond to the greatest value of both the kurtosis and the energy value. As shown in table I, detail 1 matches to the greatest value of the kurtosis and the EC. For the healthy bearing case, the chosen detail has the smallest values that confirms its undamaged case. Taken into account that a value of kurtosis lower than 3 belongs to a good state of the bearing. The results obtained by this combined method are compared with those obtained theoretically that demonstrates well its effectiveness.

The present paper proposes a diagnosis and monitoring method for detecting and locating bearing faults in an induction motor based on vibration signal processing. The proposed method served to combine Fast Fourier Transform as an advanced signal-processing tool with the Artificial Neural Network (ANN). This study starts in a first stage with the application of the FFT in order to extract frequencies characterizing the fault of the three vibration signals , and . These frequencies will be used in a second stage as inputs of the proposed ANN to locate the bearing’s undamaged components. The features extracted in this study for training the ANN model are the , , , and . Therefore, the results generated by ANN indicate a satisfactory outcome with a higher classification rate of 98.93 %. The suggested FFT-ANN method successfully demonstrates its effectiveness, and the acquired results are completely validated by experiments carried out in the CWRU.

Abstract. The maintenance of the induction motor by the mean of vibration analysis has advanced significantly in recent years, thanks to sophisticated signal processing techniques. These techniques allow detecting at an early state the existence of a fault. The present paper proposes a diagnosis method for detecting bearing faults. This method uses the Kurtogram for diagnosing the bearing fault based on the frequency characterizing fault of the inner and outer race. The results attained are all verified after many experimental tests conducted in the CWRU.

Abstract – Nowadays, the induction motors take preponderant place in modern industrial applications. However, due to hard operating conditions, these motors are exposed to different forms of damages that affects the stator, the rotor and the bearings. The bearing faults represent about 41 % of all other fault’s occurrences. In addition, the monitoring of these bearings can explored through the use of several physical quantities among them the vibration analysis is the most exploited to monitor and diagnose bearing defects. In this paper, we propose a diagnosis method for the identification of bearing faults by combining the Discret Wavelet Transform (DWT) and the Discret Fourier Transform (DFT). The DWT decomposes the signals corresponding to the two cases: outer race fault and inner race fault, to obtain details coefficients. A statistical study based on RMS and peak-to-peak factor, is then applied to the obtained details in order to select the optimum wavelet detail containing necessary harmonics conforming to the fault cases. The selection of the details obtained from the wavelet decomposition must respond to the highest value of both the RMS (Root Mean Square) and the peak-to-peak factor. The DFT is thereafter applied to the selected details to obtain the frequency spectrum for extracting the frequencies’ characteristics of bearing faults. The fault frequencies calculated theoretically that following the equations from (1) to (3) mentioned in [1], and exploiting the data of vibration signals measured at a sampling frequency of 12000 Hz and a motor speed of 1772 RPM, available in CWRU [2] are: the rotation frequency 𝑓𝑟 = 29.53 Hz, the outer race fault frequency 𝑓𝑜𝑟 =105.64 Hz and the inner race fault frequency 𝑓𝑖𝑟 =160.16 Hz. The results obtained by the Discret Fourier Transform are compared with those obtained theoretically that demonstrates well the effectiveness of the proposed method.

The main objective of this article is to contribute the automatic fault diagnosis of broken rotor bars in three-phase squirrel-cage induction motor using vibration analysis. In fact, two approaches are combined to do so, based on signal processing technique and artificial intelligence technique. The first technique is based on discrete wavelet transform (DWT) to detect the harmonics that characterize this fault, using the Daubechies wavelet vibration analysis according to three axes (X, Y, Z). This application permits having the approximation mode function and the details (recd). To exact choice of reconstruction details which contains the information of the broken rotor bars faults, two statistical studies based on the root mean square values (RMS) and Kurtosis shock factor calculation are carried out for each (recd). The choice of (recd) is conditioned by (RMS) and Kurtosis values as: RMSrecd1 < RMSrecd2 and Kurtosisrecd1 > Kurtosisrecd2. Experimental results showed that (recd1 and recd2) satisfied the condition set for (RMS) and Kurtosis values. At the end of first technique, a spectral envelop of recd1 is adopted to detect the broken rotor bars fault and the second technique based on artificial neural network (ANN) is used to identify the number of broken rotor bars. The characteristics of features used as input variables of ANN are the RMS of recd1 and recd2, and the Kurtosis shock factor of recd1 and recd2. The experimental results demonstrated the high efficiency of the proposed method with rotor broken bars fault classification rate of 98.66%.

A travers les recherches bibliographiques et les contacts que nous avons effectués avec les experts, nous avons remarqué que les appareils de détection par courants de Foucault conçus et commercialisés sont très sophistiqués et faciles à manipuler par un simple opérateur ; et cela grâce à l’imagerie 2D en temps réel des résultats d’inspection. Cependant, les résultats obtenus restent relativement qualitatifs car quelques paramètres essentiels restent inconnus. Pour cette raison, nous avons proposé de compléter certains résultats des scans en développant des algorithmes capables de caractériser en profondeur les régions affectées par les défauts de forme quelconque (corrosion, fissure, arrachement de matière, criques,..) et cela dans le cas des capteurs multiéléments alimentés en mode harmonique ou pulsé. De cette manière, les formes des régions affectées seront représentées en 3D (détermination de la profondeur de chaque point du défaut) ce qui va permettre aux contrôleurs d’évaluer leurs dangerosité. Dans les futurs travaux nous étendons cette technique pour la caractérisation d’un défaut aléatoire et la réalisation d'un dispositif de scan approprié. En effet, cela permettra de réduire le cout global des appareils importés.

direct torque control based on multi-level space vector modulation of double star asynchronous machine

In this paper a Passive Fault Tolerant Control (PFTC) based on non-linear backstepping control is proposed for a Double Star Induction Machine (DSIM) under Broken Rotor Bars (BRB) fault of a squirrel-cage in order to improve its reliability and availability. The proposed PFTC is able to maintain acceptable performance in the event of BRB. This control technique guarantees robustness against uncertainties and external disturbances and is also able to deal directly with faults by compensating for the effects of the BRB fault in the machine without prior knowledge on the fault, its location and its severity. The stability of the closed-loop is verified by the exploitation of the Lyapunov theory. a comparative study is made between the proposed Fault Tolerance Control (FTC) and Sliding Mode Control (SMC) for demonstrating the performance and effectiveness of the proposed controller. The results obtained show that the proposed FTC has a better robustness against the BRB fault where the DSIM operates with acceptable performance in both speed and torque.

This paper presents a new fault tolerant control (FTC) strategy for double star induction machine (DSIM) under stator and rotor faults. The proposed controller is developed in order to compensate the faults effect using a nonlinear backstepping controller combined with a novel fault detection and compensation block (FDCB) designed to on-line detect and compensate the faults. Simulation results are presented to show the effectiveness of the proposed FTC in terms of speed and flux responses using an estimator of rotor flux. Compared with backstepping control, the obtained results confirm the validity of the proposed FTC and its ability to ensure a ripple-free operation when the fault occurs.

This paper presents a fault-tolerant control (FTC) strategy for double-star induction machine subject to stator and rotor faults. To steer the speed and the flux to their desired references, a nonlinear sliding mode controller (SMC) is designed. However, the proposed SMC can’t deal with the faults effect which can achieve graceful system degradation. In order to compensate the faults effect, an appropriate combination between the proposed SMC and a new developed fault detection and compensation block is made. Simulation results are presented to show the effectiveness of the proposed FTC in terms of speed and flux responses using an estimator of rotor flux. Compared with SMC, the obtained results confirm the validity of the proposed FTC strategy and its ability to ensure a ripple-free operation when the fault occurs. In this kind of multiphase machines, the proposed controller is applied for the first time; its efficiency, robustness and simple design are promising for practical implementation and can be an alternative to the existing FTC.

The present paper describes the direct torque sensor less speed control of induction motor fed by three-level voltage source inverter. Inverter switches control is based on fuzzy logic control. Compared to conventional direct torque control (DTC), hysteresis controllers, flux position and voltage vector selection table are replaced by fuzzy logic blocks to realize a DTC-fuzzy control. The obtained results have showed high speed performance, reduced torque and flux fluctuations when the proposed DTC-fuzzy strategy is used for the control of three-level voltage source inverter associated with induction machine.

The present paper is focused on Direct Torque Control (DTC) Speed Sensorless of a double stator asynchronous machine (DSAM) fed by three-level six-phase inverter. The inverter switches control is based on Space Vector Modulation technique optimized by Artificial Neuronal Network (SVM-ANN). Compared to the conventional DTC technique, voltage vectors selection table is replaced by space vector modulation technique to realize a DTC-SVM speed sensorless control. Moreover, to validate the proposed control technique several tests were conducted by computer simulations. The obtained results have showed high speed performances and a reduction in torque and flux fluctuations compared to other techniques (DTC and DTC-SVM) when the proposed DTC-SVM-ANN control technique is applied to control a double stator asynchronous machine fed by a three-level six-phase inverter.