Study of Induction Motor Inter-Turn Fault Part II: Online Model-Based Fault Diagnosis Method

Seong-Hwan Im and Bon-Gwan Gu
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Seong-Hwan Im: School of Energy Engineering, Kyungpook National University, Daegu 41566, Korea
Bon-Gwan Gu: School of Energy Engineering, Kyungpook National University, Daegu 41566, Korea

Energies, 2022, vol. 15, issue 3, 1-15

Abstract: This paper (Part II) is a follow-up paper to our previous work on developing induction motor inter-turn fault (ITF) models (Part I). In this paper, an online ITF diagnosis method of induction motors is proposed by utilizing the negative sequence current as a fault signal based on the fault model of the previous study in part I. The relationships among fault parameters, negative sequence current, and fault copper loss are analyzed with the ITF model. The analyses show that the fault severity index, a function of fault parameters, is directly related to the negative sequence and the copper loss. Therefore, the proposed model-based fault diagnosis method estimates the fault severity index from the negative sequence current and recognizes the ITF. With the estimated fault severity index, the fault copper loss by the ITF, causing thermal degradation, can be calculated. Finally, experiments were performed in various fault conditions to verify the proposed fault diagnosis method.

Keywords: inter-turn fault; ITF model; negative sequence; fault parameter; fault diagnosis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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