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This paper deals with the application of a new neuron, the TLS EXIN neuron, to AC induction motor drives. In particular, it addresses two important subjects of AC induction motor drives : the on-line estimation of the electrical parameters of the machine and the speed estimation in sensorless drives. On this basis, this work summarizes the parameter estimation and sensorless techniques already developed by the authors over these last few years, all based on the TLS EXIN. With regard to sensorless, two techniques are proposed : one based on the MRAS and the other based on the full-order Luenberger observer. The work show some of the most significant results obtained by the authors in these fields and stresses the important potentiality of this new neural technique in AC induction machine drives.

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Many researches dealt with the problem of induction motors fault detection and diagnosis. The major difficulty is the lack of an accurate model that describes a fault motor. Moreover, experienced engineers are often required to interpret measurement data that are frequently inconclusive. A fuzzy logic approach may help to diagnose induction motor faults. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. Therefore, this paper applies fuzzy logic to induction motors fault detection and diagnosis. The motor condition is described

using linguistic variables. Fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and data bases, is built to support the fuzzy inference. The induction motor condition is diagnosed using a compositional rule of fuzzy inference.

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This paper presents a simplified control model for stabilizing a load voltage using a switched reactor in parallel with a fixed capacitor of static VAR compensator. Two IGBT’s are used to control the reactance of the switched reactor. A uniform pulse width modulation is used for controlling the two switches. The compensator has a simple control circuit and structure. A complete modeling and numerical simulation for the proposed systems is presented. A high speed Digital Signal Processor is used for implementing proportional-integral (PI) and fuzzy load voltage controllers. Experimental results indicate the superiority of fuzzy logic control over the conventional proportional-integral control method. Simulation results are reported and proved to be in good agreement with the relevant experimental results.

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A novel concept of application of Artificial Neural Networks ( ANN) for generating the optimum switching functions for the voltage and harmonic control of DC-to-AC bridge inverters is presented. In many research, the neural network is trained off line using the desired switching angles given by the classic harmonic elimination strategy to any value of the modulation index. This limits the

utilisability and the precision in other modulation index values. In order to avoid this problem, a new training algorithm is developed without using the desired switching angles but it uses the desired solution of the elimination harmonic equation, i.e. first harmonics are equal to zero. Theoretical analysis of the proposed solving algorithm with neural networks is provided, and simulation results are given to show the high performance and technical advantages of the developed modulator.

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In this paper, an alternative battery charging control technique based on fuzzy logic for photovoltaic (PV) applications is presented. A PV module is connected to a buck type DC/DC power converter and a microcontroller based unit is used to control the lead acid battery charging voltage. The fuzzy control is used due to the simplicity of implementation, robustness and independence from the complex mathematical representation of the battery. The usefulness of this control method is confirmed by experiments.

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This paper deals with the application of non-linear predictive control with neural networks to Proton Exchange Membrane Fuel Cells (PEM-FC). The control objective is to regulate the cell voltage, acting on the hydrogen pressure, trying to reduce the variation of the input control variable. An analysis of the non-linearities of the fuel cell stack has been carried out, making use of a suitable fuel cell model.

The non-linear predictive control has been implemented by several neural networks (multi value perceptrons), after dividing the operating domain into three areas according to the cell current value (low loads, quasi-linear zone and high loads).

Simulation results have been provided and discussed, showing the goodness of the proposed non-linear control technique in reducing the variations of hydrogen pressure.

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This paper presents solution of optimal power flow ( OPF) problem of electrical power system via a genetic algorithm of real type. The objective is to minimize the total fuel cost of generation and environmental pollution caused by fossil based thermal generating units and also maintain an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors, transformers tap-setting and power flow of transmission lines.

CPU times can be reduced by decomposing the optimization constraints to active constraints that affect directly the cost function manipulated directly the GA, and passive constraints such as generator bus voltages and transformer tap setting maintained in their soft limits using a conventional constraint load flow. The algorithm was developed in an Object Oriented fashion, in the C++ programming language. This option satisfies the requirements of flexibility, extensibility,maintainability and data integrity. The economic power dispatch is applied to IEEE 30-bus model system ( 6-generator, 41-line and 20-load) . The numerical results have demonstrate the effectiveness of the stochastic search algorithms

because its can provide accurate dispatch solutions with reasonable time. Further analyses indicate that this method is effective for large-scale power systems.

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It is known that power electronics and its related subjects are not easy to understand for students taking them for first time. This is due to nature of the subjects which involve many areas and disciplines. The introduction of generalpurpose simulation package has helped the student a step further in understanding this subject. However, because of the generality of these tools and their drag-anddrop and ad-hoc features, the students still face problems in designing a converter circuit. In this paper, the problem above is addressed by introducing a learning aid

tool that guides the student over prescribed steps to design a power electronics circuit. The tool is knowledge-based system where its knowledge base encompasses

two types of knowledge ; topologies and switching devices. The first step in the design procedure is the selection of the application of the desired circuit. Then few steps are to be followed to come out with the appropriate topology with the optimum switching devices and parameters. System structure, its different modules and the detailed design procedure are explained in this paper

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The objective of this paper is to present an analysis of how to get high performance of an induction motor drive for both dynamic and steady state operation. In order to improve a good dynamic response in transient ( minimize the speed drop) , an algorithm for a maximum torque capability of the available maximum inverter current and voltage is presented. When the rotor speed reaches its reference, a loss minimization algorithm is developed to improve high efficiency. Simulation studies show the performance of the proposed work.

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This work presents a method for solving the problem of load flow in electric power

systems including a wind power station with asynchronous generators. For this type

of power station, the generated active power is only known and consequently the

absorbed reactive power must be determined. So we have used the circular diagram

at each iteration and by considering this node as a consuming node in the load flow

program. Since the wind speed is not constant, the generated power is neither

constant. To predict the state of the network in real time, we have used the

artificial neural networks after a stage of training using a rich base of data.

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