Defect Prediction for Capacitive Equipment in Power System
As a core component of the smart grid, capacitive equipment plays a critical role in modern power systems. When defects occur, they pose a significant threat to the safety of both other equipment and personnel. Hence, it is of great significance to predict whether defects occur in capacitive equipme...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/5/1968 |
| _version_ | 1797264853840166912 |
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| author | Qingjun Peng Zezhong Zheng Hao Hu |
| author_facet | Qingjun Peng Zezhong Zheng Hao Hu |
| author_sort | Qingjun Peng |
| collection | DOAJ |
| description | As a core component of the smart grid, capacitive equipment plays a critical role in modern power systems. When defects occur, they pose a significant threat to the safety of both other equipment and personnel. Hence, it is of great significance to predict whether defects occur in capacitive equipment in advance. To achieve this goal, we propose a novel method that integrates the weight of evidence (WOE) feature encoding with machine learning (ML). Five models, including support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron (MLP), and linear classification, are employed with WOE features for defect prediction. Furthermore, based on the prediction of equipment with defects, an additional prediction is conducted to determine the potential defect level of the equipment. Experimental results demonstrate that the performance of each algorithm significantly improves with WOE encoding features. Particularly, the RF model with WOE encoding features exhibits optimal performance. In conclusion, the proposed method offers a promising solution for predicting the occurrence of defects and the corresponding defect levels of capacitive equipment. It enables relevant personnel to focus on and inspect equipment predicted to be at risk of defects, thereby preventing major malfunctions. |
| first_indexed | 2024-04-25T00:35:30Z |
| format | Article |
| id | doaj.art-833978f17e644d4dbde9453bce7625cc |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-25T00:35:30Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-833978f17e644d4dbde9453bce7625cc2024-03-12T16:39:37ZengMDPI AGApplied Sciences2076-34172024-02-01145196810.3390/app14051968Defect Prediction for Capacitive Equipment in Power SystemQingjun Peng0Zezhong Zheng1Hao Hu2Electric Power Research Institute of Yunnan Power Grid Corporation, Kunming 650127, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, ChinaAs a core component of the smart grid, capacitive equipment plays a critical role in modern power systems. When defects occur, they pose a significant threat to the safety of both other equipment and personnel. Hence, it is of great significance to predict whether defects occur in capacitive equipment in advance. To achieve this goal, we propose a novel method that integrates the weight of evidence (WOE) feature encoding with machine learning (ML). Five models, including support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron (MLP), and linear classification, are employed with WOE features for defect prediction. Furthermore, based on the prediction of equipment with defects, an additional prediction is conducted to determine the potential defect level of the equipment. Experimental results demonstrate that the performance of each algorithm significantly improves with WOE encoding features. Particularly, the RF model with WOE encoding features exhibits optimal performance. In conclusion, the proposed method offers a promising solution for predicting the occurrence of defects and the corresponding defect levels of capacitive equipment. It enables relevant personnel to focus on and inspect equipment predicted to be at risk of defects, thereby preventing major malfunctions.https://www.mdpi.com/2076-3417/14/5/1968defect predictioncapacitive equipmentWOE encodingmachine learning |
| spellingShingle | Qingjun Peng Zezhong Zheng Hao Hu Defect Prediction for Capacitive Equipment in Power System Applied Sciences defect prediction capacitive equipment WOE encoding machine learning |
| title | Defect Prediction for Capacitive Equipment in Power System |
| title_full | Defect Prediction for Capacitive Equipment in Power System |
| title_fullStr | Defect Prediction for Capacitive Equipment in Power System |
| title_full_unstemmed | Defect Prediction for Capacitive Equipment in Power System |
| title_short | Defect Prediction for Capacitive Equipment in Power System |
| title_sort | defect prediction for capacitive equipment in power system |
| topic | defect prediction capacitive equipment WOE encoding machine learning |
| url | https://www.mdpi.com/2076-3417/14/5/1968 |
| work_keys_str_mv | AT qingjunpeng defectpredictionforcapacitiveequipmentinpowersystem AT zezhongzheng defectpredictionforcapacitiveequipmentinpowersystem AT haohu defectpredictionforcapacitiveequipmentinpowersystem |