In the final decade of the last century, there was enormous intellectual and engineering activity surrounding the recently invented induction motor, especially their efficiency increasing. The interest in improving the efficiency of electric motors stems from the fact that they represent 60 to 70% of the total industrial and commercial load. A knowledge the motors operating efficiency in an industrial plant is necessary, when deciding whether standard motors should be advantageously replaced with more efficient motors. A new approach is presented for analysis and design of closed rotor slot induction motors in this paper. The main idea is illustrated as follows: first based the computed machine parameters and motor geometry optimization will be carried out. Then, to validate the conceived machines, dynamical performance analysis will be achieved by MATLAB environment. Finally using finite element electromagnetic field analysis, the comparison results will be discussed and commented.
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.
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.
Keywords:Induction machine, faults detection and localization, broken bars, artificial neural network (ANN), root mean square (RMS), multi winding, three-phase model
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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.
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.
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