Induction Motor Stator Fault Detection by a Condition Monitoring Scheme Based on Parameter Estimation Algorithms

Journal article


Duan, F and Živanović, R (2016). Induction Motor Stator Fault Detection by a Condition Monitoring Scheme Based on Parameter Estimation Algorithms. Electric Power Components and Systems. 44 (10). https://doi.org/10.1080/15325008.2015.1089336
AuthorsDuan, F and Živanović, R
Abstract

This is an Accepted Manuscript of an article published by Taylor & Francis in Electric Power Components and Systems on 26 May 2016, available online: http://www.tandfonline.com/10.1080/15325008.2015.1089336.

This article presents a simple, low-cost, and effective method for the early diagnosis of stator short-circuit faults. The approach relies on the combination of an induction motor mathematical model and parameter estimation algorithm. The kernel of the method is the efficient search for the characteristic parameters that indicate stator short-circuit faults. However, the non-linearity of a machine model may imply multiple local minima of an objective function implemented in the estimation algorithm. Taking this into consideration, the suitability of two industry-proven optimization algorithms (pattern search algorithm and genetic algorithm) as applied in the proposed condition monitoring method was investigated. Experimental results show that the proposed diagnosis method is capable of detecting stator short-circuit faults and estimating level and location of faults. The study also indicates that the proposed method is robust to motor parameters offset and unbalanced voltage supply. Application of the pattern search algorithm is suitable for a continuous monitoring system, where the previous result can be used as starting point of the new search. The genetic algorithm requires longer computation time and is suitable for the offline diagnostic system. It is not sensitive to the starting point, and achieving global solution is guaranteed.

Keywordsinduction motor; condition monitoring; stator fault detection; parameter estimation algorithms
Year2016
JournalElectric Power Components and Systems
Journal citation44 (10)
PublisherTaylor & Francis
Digital Object Identifier (DOI)https://doi.org/10.1080/15325008.2015.1089336
Publication dates
Print26 May 2016
Publication process dates
Deposited01 Dec 2017
Accepted22 Aug 2015
Accepted author manuscript
License
File Access Level
Open
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