Fault Diagnosis of Power Equipment Based on Improved SVM Algorithm
Fault diagnosis of power equipment is a crucial task to credit the safe and stable operation of equipment. However, fault diagnosis of power equipment faces challenges such as high dimensionality, complexity, and nonlinearity. Therefore, this study proposes an improved support vector machine model,...
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| Format: | Article |
| Language: | English |
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European Alliance for Innovation (EAI)
2025-07-01
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| Series: | EAI Endorsed Transactions on Energy Web |
| Subjects: | |
| Online Access: | https://publications.eai.eu/index.php/ew/article/view/7185 |
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| author | Youle Song Yuting Duan Tong Rao |
| author_facet | Youle Song Yuting Duan Tong Rao |
| author_sort | Youle Song |
| collection | DOAJ |
| description | Fault diagnosis of power equipment is a crucial task to credit the safe and stable operation of equipment. However, fault diagnosis of power equipment faces challenges such as high dimensionality, complexity, and nonlinearity. Therefore, this study proposes an improved support vector machine model, combined with grey wolf optimization algorithm, aimed at improving the accuracy and efficiency of power equipment fault diagnosis. To validate the model’s performance, this study divided a dataset of 3870 power equipment defects into training and testing sets using an 8:2 ratio, with evaluation metrics including accuracy, recall, and F1 score. The results showed that the fault recognition rate of the support vector machine model based on the improved grey wolf optimization algorithm reached 92.76%, with an accuracy close to 0.95 and a loss rate of 0.13. The model exhibited faster convergence speed, as well as better stability and convergence. At the same time, the optimized model had good feature extraction ability on different types of model faults, and the comprehensive recognition error of the model was basically stable in the interval of (-0.005, 0.005). The experiment validates that the research model improves the optimization algorithm through modal decomposition strategy. Meanwhile, the improvement of support vector machine parameter selection has strengthened the recognition and analysis of fault characteristics, providing an effective solution for power equipment fault diagnosis.
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| format | Article |
| id | doaj-art-f8925c30ac2c450e8c9a192f77c110f3 |
| institution | Matheson Library |
| issn | 2032-944X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Energy Web |
| spelling | doaj-art-f8925c30ac2c450e8c9a192f77c110f32025-07-08T21:05:17ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Energy Web2032-944X2025-07-011210.4108/ew.7185Fault Diagnosis of Power Equipment Based on Improved SVM AlgorithmYoule Song0Yuting Duan1Tong Rao2Electric Power Research Institute of Yunnan Power Grid, Kunming, 650000, ChinaElectric Power Research Institute of Yunnan Power GridElectric Power Research Institute of Yunnan Power GridFault diagnosis of power equipment is a crucial task to credit the safe and stable operation of equipment. However, fault diagnosis of power equipment faces challenges such as high dimensionality, complexity, and nonlinearity. Therefore, this study proposes an improved support vector machine model, combined with grey wolf optimization algorithm, aimed at improving the accuracy and efficiency of power equipment fault diagnosis. To validate the model’s performance, this study divided a dataset of 3870 power equipment defects into training and testing sets using an 8:2 ratio, with evaluation metrics including accuracy, recall, and F1 score. The results showed that the fault recognition rate of the support vector machine model based on the improved grey wolf optimization algorithm reached 92.76%, with an accuracy close to 0.95 and a loss rate of 0.13. The model exhibited faster convergence speed, as well as better stability and convergence. At the same time, the optimized model had good feature extraction ability on different types of model faults, and the comprehensive recognition error of the model was basically stable in the interval of (-0.005, 0.005). The experiment validates that the research model improves the optimization algorithm through modal decomposition strategy. Meanwhile, the improvement of support vector machine parameter selection has strengthened the recognition and analysis of fault characteristics, providing an effective solution for power equipment fault diagnosis. https://publications.eai.eu/index.php/ew/article/view/7185Support vector machineGrey wolf optimization algorithmModal decompositionPower equipmentFault diagnosis |
| spellingShingle | Youle Song Yuting Duan Tong Rao Fault Diagnosis of Power Equipment Based on Improved SVM Algorithm EAI Endorsed Transactions on Energy Web Support vector machine Grey wolf optimization algorithm Modal decomposition Power equipment Fault diagnosis |
| title | Fault Diagnosis of Power Equipment Based on Improved SVM Algorithm |
| title_full | Fault Diagnosis of Power Equipment Based on Improved SVM Algorithm |
| title_fullStr | Fault Diagnosis of Power Equipment Based on Improved SVM Algorithm |
| title_full_unstemmed | Fault Diagnosis of Power Equipment Based on Improved SVM Algorithm |
| title_short | Fault Diagnosis of Power Equipment Based on Improved SVM Algorithm |
| title_sort | fault diagnosis of power equipment based on improved svm algorithm |
| topic | Support vector machine Grey wolf optimization algorithm Modal decomposition Power equipment Fault diagnosis |
| url | https://publications.eai.eu/index.php/ew/article/view/7185 |
| work_keys_str_mv | AT youlesong faultdiagnosisofpowerequipmentbasedonimprovedsvmalgorithm AT yutingduan faultdiagnosisofpowerequipmentbasedonimprovedsvmalgorithm AT tongrao faultdiagnosisofpowerequipmentbasedonimprovedsvmalgorithm |