Abstract
Dissolved gas analysis (DGA) method is widely used to detect the incipient fault of power transformers. This paper presents a novel DGA method for power transformer fault diagnosis based on Harris-Hawks-optimization (HHO) algorithm optimized kernel extreme learning machine (KELM). The non-code ratios of the gases are used as the characterizing vector for the KELM model, and the Harris-Hawks-optimization (HHO) algorithm is introduced to optimize the KELM parameters, which promotes the fault diagnostic performance of KELM. Based on dataset collected from IEC TC 10, the fault diagnosis capability of the proposed method is validated by different characterizing vectors and is compared with conventional KELM and other optimized KELM. Moreover, the generalization ability of the proposed method is confirmed by China DGA data. The results demonstrate that the proposed method is superior to other methods and is more effective and stable for power transformer fault diagnosis with high accuracy.
Highlights
The power transformer plays a vital role in power systems, and it serves as the connection of transmission and distribution networks at different voltage levels [1]
Various approaches have been used to solve transformer fault diagnosis problem such as frequency response analysis [4], the vibration analysis [5], dissolved gas analysis (DGA) [6], etc. Among these methods, dissolved gas analysis (DGA) is a widely used technique to detect the incipient faults in power transformers [7, 8]
These samples are used to evaluate the performance of the proposed fault diagnosis method
Summary
The power transformer plays a vital role in power systems, and it serves as the connection of transmission and distribution networks at different voltage levels [1]. Traditional methods based on DGA such as IEEE key gases [9], Rogers ratios [10], IEC standard code [11], Dornenburg ratios [12], Duval triangle [13], IEC 60599 [14], have been applied to transformer fault diagnosis. The various proposed methods have produced good results and made an important contribution in the field of transformer fault diagnosis. By analyzing the reviewed articles, despite different diagnosis models have been presented by various scholars and experts, a precise diagnosis model is still needed yet For this purpose, a novel fault diagnosis model based on HHOKELM for power transformers is proposed in this study.
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