M. KHODJA Djalal eddine

L ' objectif visé dans ce travail est d'évaluer les performances de la commande DTC à base des techniques intelligents versus la DTC classique pour la commande des machines asynchrones.
Ces commandes sont implémentés sur un circuit logique programmable de type FPGA tout en minimisant les ressources hardwares occupées dans la carte FPGA.

This paper presents modeling and analysis of short-circuit faults between the stator windings of
a permanent magnet synchronous machine (PMSM). The studied method is based totally on growing and
developing a mathematical model that describes both the healthy and faulty states of the studied machine.
Furthermore, this dynamic model was simulated and Examined to study the kinetic behaviour under
different failure conditions. Also, frequency domain analysis based on the Fast Fourier transform (FFT) has
been established. The proposed approach analyzes the frequency spectrum of the stator current to detect
harmonic signatures that indicate the presence of short circuits between the windings, Total harmonic
distortion (THD) may also be used as a good indicator to predict the presence of a short circuit fault. The
simulation results confirm the effectiveness of this technique and show that it can accurately distinguish
between healthy and defective motor states.
Keywords: Permanent Magnet Synchronous Machine (PMSM), Fault diagnosis, Inter-turns short circuit,
Modeling, Fast Fourier transform (FFT).

Stator Winding Faults Analysis of Permanent Magnet Synchronous Motor Using Motor Signatures

This project develops an intelligent fault diagnosis system for asynchronous motors using
neural networks and decision trees, implemented on FPGA circuits. Neural networks use
multilayer perceptrons with backpropagation to classify faults from RMS voltage inputs.
Decision trees provide interpretable and efficient fault detection. The FPGA implementation
ensures rapid, real-time fault diagnosis. The study demonstrates improved fault detection
accuracy and responsiveness, highlighting the potential of combining AI algorithms with
FPGA technology to enhance the reliability of electromechanical systems.

FAULT TOLERANT OPERATION BASED ON DIRECTC TORQUE CONTROL FOR INDUCTION MACHINE WITH BROKEN BARS

REDUCTION OF POWER QUALITY PROBLEMS USING SHUNT ACTIVE FILTERS BASED ON THE HYSTERESIS CURRENT CONTROLL

Hysteresis Control for Compensation of Current Harmonics by Using Active Parallel Filter.

Evaluation of the Effectiveness of Fault-Tolerant Operation of Broken Bars Using Direct Torque Control in an Induction Machin.

Robust fault-tolerant control based on direct torque control DTC by using a multi-winding model for simulate broken bars.

Optimization Fuzzy Pid Regulator By Direct Torque Control (Dtc) Of Induction Machine

Currently, the analysis of the stator current signal
of induction motors (known as MCSA) has gained popularity as a
method to evaluate the operational condition of asynchronous
motors and prevent potential breakdowns. Traditional
approaches for detecting faults in rotating machinery, relying on
micro-programmed sequential systems like microprocessors and
DSPs, have demonstrated its limits in terms of real-time
requirements and processing speed. To address these challenges,
it has become essential to embrace cutting-edge technologies,
such as ASICs or FPGAs, which offer more efficient diagnostic
capabilities. The innovation in our research lies in the
development of a fault detection algorithm implemented on
FPGA hardware architecture for a induction machines. This
algorithm leverages fuzzy logic and analyzes the stator current
signal (MCSA), specifically by considering the "Root Mean
Square" (RMS) of the stator current as an indicator of potential
faults. To validate this architecture, we conducted hardware in
the loop validation procedure using Xilinx Virtex-4 ML402
FPGA board and XSG within the Matlab/Simulink environment.
Our hardware architecture has significantly enhanced the fault
detection performance in induction machines, notably in terms of
online real-time detection, detection speed, and resource
utilization.
Keywords— FPGA, Hardware Co-simulation, XGS, Fuzzy
current analysis, Diagnostics, Asynchronous machine

this work describes the development,
implementation and validation of hardware architecture on
FPGA for fuzzy DTC control of induction motor. This approach
enables addressing the primary challenges associated with this
control technique, which include mitigating torque ripples,
minimizing flux fluctuations, and achieving precise switching
frequency control. The introduction of this method has sparked
numerous research endeavors aimed at demonstrating its
effectiveness and resolving its inherent limitations. Specifically,
this study focuses on validating a fuzzy DTC implementation
hardware on the development card ML402, which utilizes an
FPGA circuit of the Xilinx Virtex-4 type. This validation is
carried out using Xilinx system generator and VHDL description
language. The results obtained show the effectiveness of the
proposed approach for controlling the induction machine and
reducing flux and torque ripples.
Keywords— Fuzzy Control, FPGA, DTC, power system, XSG

Presently, signal analysis of stator current of induction motor has become a popular technique to assess the health state of
asynchronous motor in order to avoid failures. The classical implementations of failure detection algorithms for rotating machines, based on
microprogrammed sequential systems such as microprocessors and digital signal processing have shown their limitations in terms of speed
and real time constraints, which requires the use of new technologies providing more efficient diagnostics such as application specific
integrated circuit or field programmable gate array (FPGA). The purpose of this work is to study the contribution of the implementation of
fuzzy logic on FPGA programmable logic circuits in the diagnosis of asynchronous machine failures for a phase unbalance and a missing
phase faults cases. Methodology. In this work, we propose hardware architecture on FPGA of a failure detection algorithm for asynchronous
machine based on fuzzy logic and motor current signal analysis by taking the RMS signal of stator current as a fault indicator signal. Results.
The validation of the proposed architecture was carried out by a co-simulation hardware process between the ML402 boards equipped with a
Virtex-4 FPGA circuit of the Xilinx type and Xilinx system generator under MATLAB/Simulink. Originality. The present work combined the
performance of fuzzy logic techniques, the simplicity of stator current signal analysis algorithms and the execution power of ML402 FPGA
board, for the fault diagnosis of induction machine achieving the best ratios speed/performance and simplicity/performance. Practical value.
The emergence of this method has improved the performance of fault detection for asynchronous machine, especially in terms of hardware
resource consumption, real-time online detection and speed of detection. References 22, tables 3, figures 19.
Key words: asynchronous machine, fuzzy current analysis, field programmable gate array, hardware co-simulation.

Abstract. In this paper, we will develop an adaptive control algorithm applied to the wind energy conversion system (WECS) based on a double-fed
induction machine (DFIM) driven by a turbine with variable blade pitch, and controlled through the rotor variables by two bidirectional converters. The
main function of these converters in the considered system is the connection of the wind generator to the power grid in two different ways: one on
the grid side converter which will allow continuous bus control and improve the power factor on the grid side; the other, on the converter on the rotor
side, which will allow the control and optimization of the energy flow generated by the stator during the periods of operation of this system. In the first
part we presented the individual modeling of the wind turbine chain, then we presented and developed the controls necessary to control the active
and reactive powers produced by this system in order to ensure optimum performance and production quality.
Streszczenie. W niniejszym artykule opracujemy algorytm sterowania adaptacyjnego zastosowany w systemie konwersji energii wiatru (SKEW)
oparty na dwustronnie zasilanej maszynie indukcyjnej (DZMI) napędzanej turbiną o zmiennym skoku łopatek i sterowanej poprzez zmienne wirnika
dwoma dwukierunkowymi konwertery. Główną funkcją tych przekształtników w rozpatrywanym systemie jest podłączenie generatora wiatrowego do
sieci elektroenergetycznej na dwa różne sposoby: jeden po stronie przekształtnika sieciowego, który umożliwi ciągłą kontrolę magistrali i poprawi
współczynnik mocy po stronie sieci; drugi, na przekształtniku po stronie wirnika, co pozwoli na sterowanie i optymalizację przepływu energii
generowanej przez stojan w okresach pracy tego układu. W pierwszej części przedstawiliśmy indywidualne modelowanie łańcucha turbiny wiatrowej,
następnie przedstawiliśmy i opracowaliśmy sterowanie niezbędne do sterowania mocą czynną i bierną wytwarzaną przez ten system w celu
zapewnienia optymalnej wydajności i jakości produkcji. (Adaptacyjne sterowanie rozmyte typu 1 systemem konwersji energii wiatrowej w
oparciu o dwustronnie zasilaną maszynę indukcyjną)
Keywords: Adaptive control, Modelling, Type 1 Fuzzy logic control, wind energy conversion system.
Słowa kluczowe: Sterowanie adaptacyjne, modelowanie, sterowanie Fuzzy logic typu 1, system konwersji energii wiatrowej

The automatic detection of errors is becoming increasingly important human operator weakness (inherent); which is a consequence of fatigue, lapses memory and sometimes due to environmental pressure (noise, heat...etc.). As a matter of fact, we are absolutely interested in automatic diagnostics; which enables early detection about anomalies, which is one of the sure means of contributing to improve the productivity of the various sectors. For this purpose, the proposed research work deals with the co-simulation using FPGA configured soft processor of Artificial Neural Network based diagnostic of induction machine faults. Once the ANN architecture is chosen and an off-line training is applied which makes the ANN able to identify the different induction machine faults, a method is proposed to implement the sigmoid shaped activation function using system generator toolbox provided by Xilinx. The system generator allows downloading the obtained ANN algorithm in the Virtex4 FPGA ship. The results obtained by co-simulation of the induction motor drive in Matlab/Simulink and the ANN on the FPGA are satisfactory and very promising.

Currently, the direct torque control is very popular in industry and is of great interest to scientists in the variable
speed drive of asynchronous machines. This technique provides decoupling between torque control and flux without the need to use
pulse width modulation or coordinate transformation. Nevertheless, this command presents two major importunities: the switching
frequency is highly variable on the one hand, and on the other hand, the amplitude of the torque and stator flux ripples remain
poorly controlled throughout the considered operating speed range. The novelty of this article proposes improvements in
performance of direct torque control of asynchronous machines by development of a fuzzy direct torque control algorithm. This latter
makes it possible to provide solutions to the major problems of this control technique, namely: torque ripples, flux ripples, and
failure to control switching frequency. Purpose. The emergence of this method has given rise to various works whose objective is to
show its performance, or to provide solutions to its limitations. Indeed, this work consists in validation of a fuzzy direct torque
control architecture implemented on the ML402 development kit (based on the Xilinx Virtex-4 type field programmable gate array
circuit), through hardware description language (VHDL) and Xilinx generator system. The obtained results showed the robustness of
the control and sensorless in front of load and parameters variation of induction motor control. The research directions of the model
were determined for the subsequent implementation of results with simulation samples. References 19, tables 5, figures 26.
Key words: fuzzy control, field programmable gate array, Xilinx system generator, direct torque control, power system.

Second order sliding mode control of a wind energy conversion system
based on a double fed asynchronous machine

Hybrid Sliding-fuzzy type 1 control of a permanent magnet
synchronous machine (PMSM)

HYBRID FUZZY SLIDING MODE CONTROL OF THE POWER GENERATED BY WIND ENERGY CONVERSION
SYSTEM (WECS) BASED OF A DOUBLY FED INDUCTION GENERATOR (DFIG).

Introduction. Newly, the design of a controller for speed control of double star induction motor as a research focus. Consequently,
backstepping technique is used to recursively construct a stable control law for speed and flux. Nevertheless, this control law coming
from backstepping requires the knowledge of speed and flux values; in practice the measurement sensors are expensive and fragile.
The novelty of this work consists to propose a control strategy which based on accurate Kalman filter observer that estimates speed,
flux and torque. This extended Kalman filter is an optimal state estimator and is usually applied to a dynamic system that involves a
random noise environment. Purpose. Apply a backstepping control of double star induction motor based on principle of rotor flux
orientation. This approach consists in finding a Lyapunov function that allows deducing a control law and a modified adaptation
rule is referred and sufficient conditions for the stability of the command-observer, in contrast to other techniques who use nonlinear
principle. Results. The simulation results are shown to illustrate the performance of the proposed scheme under parametric
uncertainties by simulation on MATLAB. The obtained results showed the robustness of the sensorless control in front of load and
parameters variation of double stator induction motor. The research directions of the model were determined for the subsequent
implementation of results with simulation samples. References 17, tables 1, figures 18.
Key words: double stator induction motor, model reference, backstepping control, extended Kalman filter.
Вступ. Новітня розробка контролера для регулювання швидкості асинхронного двигуна з подвійною зіркою є предметом
дослідження. Отже, метод відступу використовується для рекурсивної побудови стабільного закону керування швидкістю
та потоком. Тим не менш, цей закон керування, що випливає з відступу, вимагає знання значення швидкості та потоку; на
практиці вимірювальні датчики коштовні та недовговічні. Новизна даної роботи полягає в тому, щоб запропонувати
стратегію управління на основі точного спостерігача за фільтром Калмана, який оцінює швидкість, потік і крутний
момент. Цей розширений фільтр Калмана є оптимальним засобом оцінки стану і зазвичай застосовується до динамічної
системи, яка включає середовище випадкових шумів. Мета. Застосування підходу відступу до керування асинхронним
двигуном з подвійною зіркою на основі принципу орієнтації потоку ротора. Цей підхід полягає у знаходженні функції
Ляпунова, яка дозволяє вивести закон керування та модифіковане правило адаптації, а також достатні умови для
стабільності спостерігача команд, на відміну від інших методик, які використовують нелінійний принцип. Результати.
Результати моделювання наведені для ілюстрації роботи запропонованої схеми за параметричних невизначеностей
шляхом моделювання на MATLAB. Отримані результати показали надійність безсенсорного керування перед зміною
навантаження та параметрів асинхронного двигуна з подвійним статором. Визначені напрямки дослідження моделі для
подальшої реалізації результатів на прикладах моделювання. Бібл. 17, табл. 1, рис. 18.
Ключові слова: асинхронний двигун з подвійним статором, еталонна модель, керування на основі відступу,
розширений фільтр Калмана.

In this paper, Speed Sensorless Vector Control of Double star Induction machine
DSIM using sliding mode observer is presented. The search for the gains of
conventional Luenberger observer in the sense of stability Lyapunov, oriented to
sliding mode observer form, but the sign function caused the chattering effect, the
replace it by function smooth are adopted. As a result, application of DSIM speed
sensorless vector control using sliding mode has shown that is robust to load
disturbances and / or reference speed change. The proposed control scheme is verified
by simulation.

This work is part of the study and implementation of a fuzzy PWM control structure
(fuzzy pulse width modulation) of an induction machine (MAS) on a circuit-based
platform reconfigurable FPGA type. We first presented a strategy for the hardware
implementation of a fuzzy inference system on a programmable logic circuit of FPGA
type, through the hardware description language (VHDL) and Xilinx generator system
(XSG). Secondly we proposed Fuzzy-PWM architecture for the improvement of
response time of an asynchronous machine (MAS) and finally we validate the proposed
hardware co-simulation architecture in real time on the ML402 development kit (based
on FPGA Xilinx Virtex-4) and Simulink / Matlab.

This article provides a full hardware implementation for direct torque control (DTC)
of an asynchronous motor (AM) on the Field Programmable Gate Array (FPGA). Due
to its high processing frequency, the FPGA circuit presents an alternative for achieving
a high performance DTC implementation. This cannot be achieved by any DSP or
microcontroller application. The proposed hardware architecture implements three
components of DTC control strategy (the integral proportional speed regulator, the
estimation and the switching blocks). This implementation has the advantage of being
faster, more efficient and with minimum hardware resources on the target FPGA board.
The hardware architecture of the DTC control was designed and simulated in Matlab /
Simulink using XSG blocks, then synthesized with the Xilinx ISE 14.2 tool,
implemented and validated on the Xilinx Virtex-4 FPGA circuit by Hardware in the
Loop process.

Abstract: This article provides a hardware implementation of torque and flux estimators for direct torque control
(DTC) of an induction motor (IM) on the Field Programmable Gate Array (FPGA). Due to its high processing
frequency, the FPGA circuit presents an alternative strategy for achieving a high performance DTC implementation.
This cannot be achieved by Digital Signal Processing (DSP) or Microcontroller application. Optimized hardware
architecture is also proposed for implementation of a complete estimator with explicit determination of all the
quantities such as the flux phase, the flux amplitude and the torque amplitude. This work presents a precise hardware
estimator of the torque and the flux, it developed with negligible estimation error (0.0002 [Wb] RMS estimate error
for the stator flux and 0.0005 [Nm] RMS estimate error the torque), in addition to that this estimator is implemented
on the Virtex-4 FPGA board with minimal resources(less 5% of slices registers, 5% of DSP48Es and around 17% of
LUTS). This estimator is developed and synthesized independently of the command and can be reused for any task
which requires the estimation of these quantities. After this the estimator was used to complete a DTC control in a
hardware co-simulation procedure with the Xilinx system generator and MATLAB/SIMULINK tools on the Xilinx
ML402 development kit.
Keywords: FPGA, Hardware co-simulation, Xilinx generator system, Induction motor control, Flux and torque
estimatio

In this paper, we propose sensorless backstepping control of a double-star induction machine (DSIM). First, the backstepping approach is designed to steer the flux and
speed variables to their references and to compensate uncertainties. Lyapunov”s theory is
used and it demonstrates that the dynamic tracking of trajectories tracking is asymptotically
stable. Second, unfortunately, this law control called sophisticated is a major problem which
leads to the necessity of using a mechanical sensor (speed, load torque). This imposes an
additional cost and increases the complexity of the montage. In practice, this variable is
unknown and its measurement is expensive. To restrain this problem we estimate speed
and load torque by using a Luenberger observer (LO). Simulation results are provided to
illustrate the performance of the proposed approach in high and low variable speeds and
load torque disturbance

This study aims to display fuzzy logic (FL) technique for diagnosis of fault induction
machine. This allows monitoring of fuzzy information from different signals to give more
accurate judgment on the health of the engine, through using a multi-winding model of
induction machine for the simulation of broken bars. This model allows study the influence
of defects and appear the behavior of the machine in the different modes of running
conditions (healthy and fault). In this work, we focus the application of a fuzzy logic
technique based on the fast Fourier transformation (FFT) by analyzing the stator current
for fault detection. The results of the simulation obtained allowed us to show the
importance of the fuzzy logic approach based on classification of signals for detecting the
faulty.

This study offers the opportunity to extend the functioning of the most advanced protection
systems. The faults which can arise on the power transmission lines are numerous and
varied: Short-circuit; Overvoltage; Overloads, etc. In the context of short circuits, the
conventional sensor as the Mho distance relay also known as the admittance relay is
generally used. This relay will be discussed later in this study. By taking into account the
preventive risks of the Mho relay and discover the new techniques of artificial intelligence,
namely the neural network which can contribute to the precise and rapid detection of all
types of short-circuit faults. The results of the simulation tests demonstrate the
effectiveness of the methods proposed for the automatic diagnosis of faults.

In this paper, first of all the motor performance created by permanent magnetic in a slot
less air-gap of a surface mounted permanent-magnet synchronous motor with non
magnetic rotor and either sinusoidal or mixed (quasi-Halbatch) magnetization using polar
coordinates is presented. After that the analysis works for both internal and external rotor
motor topologies, the effect of stator slots is introduced by modulating the magnetic field
distribution in the less slot stator by the complex relative air-gap permeances and the
conformal transformation of the slot geometry. Finally, the predicted results of flux
density distribution and cogging torque with those obtained by finite-element are
analyzed.

This article is devoted to the adaptive control with robust reference model based on a
vector control applied to a dual-star asynchronous machine; this study is based on the
Landau stability theorem. The adaptive control is designed for the speed loop. The
identification techniques using closed-loop output error algorithm and MRAC (Model
Rerferance Adaptive Control) were reviewed. All these techniques have one thing in
common: they place in principle the link between robust control, closed-loop
identification and adaptive control. The reference model based on the Landau stability
theorem makes it possible to improve the performance of the adaptive control and
maintains the robust control with respect to the parametric change of the DSIM (Doubly
Star Induction Motor). The main is to ensure a minimum level of performance of the drive
system that is malfunctioning. The simulation results clearly show the robustness of the
proposed MRAC command against parametric variations of DSIM.

The objective of this study is to present artificial intelligence (AI) technique for detection and
localization of fault in induction machine fault, through a multi-winding model for the
simulation of four adjacent broken bars and three-phase model for the simulation of shortcircuit between turns. In this work, it was found that the application of artificial neural
networks (ANN) based on Root mean square values (RMS) plays a big role for fault detection
and localization. The simulation and obtained results indicate that ANN is able to detect the
faulty with high accuracy.

The purpose of this study is diagnosis the fault of induction machine, through detection of
broken bars by stator current analysis. Therefor we present a multi-winding model for the
simulation of faults as part of the fault detection study, and test the behavior of the machine in
the healthy and faulty state. In this paper it is focus the signal processing technique for
detecting defects. This technique is based on continuous wavelet transform (CWT) to detect
and locate defects, by using the multi-level decomposition for the detection of defects and the
location of the broken bars at the rotor. The findings of this research results show the
importance of this technique for the analysis of fault signatures in healthy and defective cases.

In this paper we will establish a sliding mode adaptive control based on the direct rotor field control applied to the double stars induction machine. Due to the performance limitations of conventional controllers due to their sensitivities, non-linearity and parametric variations, the indirect "self tuning" adaptive control is applied. Indeed, the recursive least squares algorithm is used to obtain the identification model in order to adapt the RST regulator parameters in real time, and then generate the control law. However, the location of the sliding mode regulators to the control chain by indirect rotor flow orientation offers good static and dynamic performance, and almost total rejection of the disturbance. Finally, all the results obtained showed the robustness of the control in front of the variation of the parameters of the system and load.

In this work, we proposed multi-winding model for the simulation of broken bars in squirrel cage asynchronous machine, this model allows to study the influence of the broken bar defects on the behavior general of machines in different operating conditions (healthy and faulty). The breaking of the most frequent bars of the rotor causes oscillations of the torque, speed, and the current, the increase of the resistance of the rotor creates the defects proportional with the number of breaks bar K .The diagnosis fault using technique of single processing based on Spectrum analysis for detection broken bar. The results of the simulation obtained allowed us to show the importance of this technique for detection broken bar.

The induction machine is the most used in industrial field because of their Safety, reliability, efficiency, and performance. The main objective of our work is the fault detection the rotor bar in induction machine, and describing the various defects that can happen on machine. We propose a multi-winding model of the asynchronous machine for the simulation of broken bar. This model makes to study the influence of a bar fault and evaluation machine behavior in healthy and faulty case. For detection fault using technique of single processing, is based on the spectrum analysis of the stator current. The results of the simulation obtained allowed us to show the importance of this technique and detect the presence of a fault and to locate its source.

In this paper, we discuss the diagnosis of stator and rotor defects in induction machines. After describing the various defects that can occur on these, we propose a multi-winding model of the asynchronous machine that simulates stator faults for short-circuiting or phase-out. Then, we present a model based on the coupled electrical circuit’s method. This model makes it possible to study the influence of a bar fault on the general behavior of the asynchronous motor. In addition, we have studied the phenomena created by these defects on the different temporary variables of the machine, then we particularly interested in the development of new identification techniques. The latter are based on the evaluation of stator current spectrum indices calculated from a Fourier transform in the case of a stator or rotor defect. Finally, the results obtained with this approach make it possible to detect the presence of a defect and to locate its origin.

In industry, the service continuity of an electrical system drives requires degrees of reliability of operation
and high safety. Thus, the system requires robust elements to avoid falling into the defects. But the appearance of
defects, it is inevitable. It is therefore necessary to solve this problem by developing a control technique that can
ensure continuity of operation in the presence of faults. Indeed, the sliding mode adaptive control based on fuzzy
logic ensures the stability of the system healthy and able to compensate certain types of defects: the defects of low
severity. In fact, is with the fuzzy sliding controller with varying control gains. This one is based on supervisory
fuzzy inference system using the adaptive tune in order to improve the performance of controller in presence of
current sensors faults. In addition, the sensors are the most sensitive elements and the seat of frequent faults, and
play a leading role in the closed loop control. An operation of a tolerant control the default intended to compensate
the default offset sensor. The developed algorithm is validated by simulation and the obtained results showed the
effectiveness and the robustness of the proposed approach under different scenarios.