INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET)
E-ISSN: 2395-0056
VOLUME: 08 ISSUE: 05 | MAY 2021
P-ISSN: 2395-0072
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PARAMETER ESTIMATION OF THREE PHASE INDUCTION MOTOR WITH HYBRID SOFT COMPUTING TECHNIQUES D. K. Chaturvedi1, Mayank Pratap Singh2, O.P. Malik3 1Professor,
Dept. of Electrical Engineering, D.E.I., Dayalbagh, Agra, U. P., INDIA Professor, Dept. of Electrical Engineering, G. L. A. University, Mathura, U. P., INDIA 3Emeritus Professor, Dept. of Electrical and Computer Engineering, University of Calgary, AB, Canada 2Assistant
---------------------------------------------------------------------------***-----------------------------------------------------------------------------However, it has certain inherent short falls as well. Abstract-Three phase induction motors (TPIMs) are the ANA needs large number of examples for good training and most commonly used motors for rotating mechanical loads large training time. There is no guide to specific ANA in the industrial environment. TPIM behavior is totally structure and configuration for a problem at hand. The dependent on its parameters. The TPIM parameter neuron structure, such as summation type, or product type information tells about the health of the induction motor or combination, etc. can also be a variable. To overcome and is also necessary for precise control of its behavior. these drawbacks, a generalized neural network (GNA) is proposed in [4-5]. GNA performance can be further A Generalized Neural Approach (GNA) trained using a improved using Quantum inspired Genetic Algorithm hybrid approach, Quantum Genetic Algorithm (QGA), is used (QGA) to overcome the learning problems. in this paper to estimate the parameters of the TPIM. The QGA trained GNA (Q-GNA) is then deployed for parameter A GNA, trained using QGA, is used for parameter estimation of a squirrel cage TPIM in the Electrical Power estimation. Introduction of the work is provided in section Research Lab, D.E.I. (Deemed University) Dayalbagh, Agra, one. The next four sections describe the development of India. Performance of The proposed method, Q-GNA, is QGA trained GNA, and the determination of equivalent compared with a common neural network trained using circuit parameters of an induction motor is described in Levenberg-Marquet learning algorithm and a GNA trained section six. Laboratory set up followed by experimental using back-propagation. results and parameter estimation using Q-GNA, its comparison with LevenbergMarquet learning Keywords—Parameter Estimation, Three Phase algorithmtrained ANA and back-propagation algorithm Induction Motor, Artificial Neural Network, Equivalent trained GNA, and experimental results are described in Circuit Parameters, Quantum Computing. section seven followed by conclusions in section eight. I. INTRODUCTION II. GENERALISED NEURAL APPROACH Soft computing approaches have been broadly The GNA is built with the help of diverse used for three phase induction motor (TPIM) fault compensatory functions for aggregation and different nondiagnosis. These approaches may be categorized as expert linear functions for activation of the GNA as shown in Fig. systems [1], fuzzy logic system(FLS) [2], artificial neural 1. The GNA trained using back-propagation-algorithm (BPnetwork (ANA) [ 3-8], wavelet transform [9-11], and GNA) is used for TPIM parameter estimation. genetic approach [12]. The TPIM parameters are not constant during its operation and change non-linearly. As the parameters vary with operating temperature and weather conditions, magnetic, electrical and mechanical couplings, etc., the parameters estimated using the methods mentioned above do not give good results [13]. Fig-1: GNA Model
In the area of electrical machines and power systems, ANA has been widely used over the past few decades [13-15, 26, 27]. ANA can handle large size information at a time because of its parallel processing capability. It can do non-linear mapping of input-output very well and extrapolate the results for ill-defined or noisy data.Thus, it can offer a viable approach for TPIM parameter estimation.
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III.
GENETIC ALGORITHM
Training and performance of an ANA depend heavily on starting weights. If the starting weights are not good, the optimization may take a long time or it may not converge at all. Also, optimization using back-propagation algorithm for training needs error derivative. These hurdles motivated the researchers to devise a method which does not require a derivative and the solution does |
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