Generator Fault Diagnosis with Bit-Coding Support Vector Regression Algorithm
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 regressi...
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| Vydáno v: | Energies (Basel) Ročník 16; číslo 8; s. 3582 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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MDPI AG
01.04.2023
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| ISSN: | 1996-1073, 1996-1073 |
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| 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. |
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| AbstractList | 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. |
| Audience | Academic |
| Author | Lin, Whei-Min |
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| SubjectTerms | Algorithms bit-coding support vector regression (BSVR) Classification Comparative analysis Design Fault diagnosis Fuzzy logic generator fault diagnosis (GFD) Neural networks support vector machine (SVM) support vector regression (SVR) Turbines Vibration |
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