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
Authors | Duan, F and Živanović, R |
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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. |
Keywords | induction motor; condition monitoring; stator fault detection; parameter estimation algorithms |
Year | 2016 |
Journal | Electric Power Components and Systems |
Journal citation | 44 (10) |
Publisher | Taylor & Francis |
Digital Object Identifier (DOI) | https://doi.org/10.1080/15325008.2015.1089336 |
Publication dates | |
26 May 2016 | |
Publication process dates | |
Deposited | 01 Dec 2017 |
Accepted | 22 Aug 2015 |
Accepted author manuscript | License File Access Level Open |
https://openresearch.lsbu.ac.uk/item/873zy
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