Generator Fault Diagnosis with Bit-Coding Support Vector Regression Algorithm

Whei-Min Lin ()
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Whei-Min Lin: School of Mechanical and Electrical Engineering, Tan Kah Kee College, Xiamen University, Zhangzhou 361005, China

Energies, 2023, vol. 16, issue 8, 1-14

Abstract: Generator fault diagnosis has a great impact on power networks. With the coupling effects, some uncertain factors, and all the complexities of generator design, fault diagnosis is difficult using any theoretical analysis or mathematical model. This paper proposes a bit-coding support vector regression (BSVR) algorithm for turbine generator fault diagnosis (GFD) based on a support vector machine (SVM) capable of processing multiple classification problems of fault diagnosis. The BSVR can simplify the design architecture and reduce the processing time for detection, where m classifier is needed for m class problems compared to the [ m ( m − 1)]/2 size of the original multi-class SVM. Compared with conventional methods, numerical test results showed a high accuracy, good robustness, and a faster processing performance.

Keywords: generator fault diagnosis (GFD); support vector machine (SVM); support vector regression (SVR); bit-coding support vector regression (BSVR) (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: 2023
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