Identification of Inter-Turn Short-Circuits in Induction Motor Stator Winding Using Simulated Annealing

Marcin Tomczyk,Anna Plichta,Ryszard Mielnik,Maciej Sułowicz,Iwona Goldasz

doi:10.3390/en15010117

Abstract

This paper presents a method of inter-turn short-circuit identification in induction motors during load current variations based on a hybrid analytic approach that combines the genetic algorithm and simulated annealing. With this approach, the essence of the method relies on determining the reference matrices and calculating the distance between the reference matric values and the test matrix. As a whole, it is a novel approach to the process of identifying faults in induction motors. Moreover, applying a discrete optimization algorithm to search for alternative solutions makes it possible to obtain the true minimal values of the matrices in the identification process. The effectiveness of the applied method in the monitoring and identification processes of the inter-turn short-circuit in the early stage of its creation was confirmed in tests carried out for several significant state variables describing physical magnitudes of the selected induction motor model. The need for identification of a particular fault is related to a gradual increase in its magnitude in the process of the induction motor’s exploitation. The occurrence of short-circuits complicates the dynamic properties of the measured diagnostic signals of the system to a great extent.

Highlights

The basic issue in the exploitation of different types of devices, machines, or technical systems is to provide continuous and failure-free operation
Inter-Turn Short-Circuiting in Induction Motor Models In Tables 1–4, several cases of the inter-turn short-circuits that were investigated in the identification process are contained in the column labelled Test parameters and the correct results of the calculation of the matrices H1 and H2 obtained during the identification process of inter-turn short-circuiting are marked in bold
Based on on the the results results of of this this research, research, one one can can see see that that in in the the proposed proposed diagnostic diagnostic method, the extraction of information from obtained time series for the investigated physimethod, the extraction of information from obtained time series for the investigated physical magnitudes of induction motors leads to increased fault detection and identification capabilities

Summary

Introduction

The basic issue in the exploitation of different types of devices, machines, or technical systems is to provide continuous and failure-free operation. This reliability is the key requirement in the growing industry 4.0 era. It is required that the diagnostic system detects and identifies the occurrence of faults in real time or in early stages of their creation [1]. Nowadays, these requirements can only be satisfied for the systems built using high-tech technology, based on microprocessor techniques and software that implements effective methods of electric machine diagnostics

Methods

Results

Conclusion