Prediction of Insulator ESDD Based on Meteorological Feature Mining and AdaBoost-MEA-ELM Model
In order to obtain the pollution condition of transmission line insulators in time, a method of insulator equivalent salt deposit density (ESDD) prediction based on meteorological data is proposed in this paper. The meteorological features that are more closely related to insulator pollution degree...
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State Grid Energy Research Institute
2023-09-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202303084 |
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| author | Yaoping WANG Te LI Kaihua JIANG Wenhui LI Qiang WU Yu WANG |
| author_facet | Yaoping WANG Te LI Kaihua JIANG Wenhui LI Qiang WU Yu WANG |
| author_sort | Yaoping WANG |
| collection | DOAJ |
| description | In order to obtain the pollution condition of transmission line insulators in time, a method of insulator equivalent salt deposit density (ESDD) prediction based on meteorological data is proposed in this paper. The meteorological features that are more closely related to insulator pollution degree are mined, and the importance of each meteorological feature is evaluated by the random forest algorithm. Combined with the sequential forward search method, the optimal subset of meteorological features for ESDD prediction model could be determined. Based on the natural pollution test data of Taizhou City, the basic ESDD prediction model was established by using extreme learning machine (ELM), and its initial weights and thresholds were optimized by the mind evolution algorithm (MEA). Then the AdaBoost algorithm was applied to further improve the accuracy of the model. The results show that the average absolute error of ESDD prediction of AdaBoost-MEA-ELM model is 0.0032 mg/cm2, which is 58.97% lower than that of the original ELM model. Compared with other common models, the performance of the proposed model and the rationality of the combination of these three algorithms are verified. The variation of prediction error when training data changed was obtained by k-fold verification method, which further prove the generalization performance and stability of the model. |
| format | Article |
| id | doaj-art-b1f24b9c99c44acca200faac779aa997 |
| institution | OA Journals |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2023-09-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-b1f24b9c99c44acca200faac779aa9972025-08-20T02:04:31ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492023-09-0156915716710.11930/j.issn.1004-9649.202303084zgdl-56-08-yaoPrediction of Insulator ESDD Based on Meteorological Feature Mining and AdaBoost-MEA-ELM ModelYaoping WANG0Te LI1Kaihua JIANG2Wenhui LI3Qiang WU4Yu WANG5School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaState Grid Zhejiang Electric Power Research Institute, Hangzhou 310014, ChinaState Grid Zhejiang Electric Power Research Institute, Hangzhou 310014, ChinaState Grid Taizhou Power Supply Company, Taizhou 318000, ChinaSchool of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaSchool of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaIn order to obtain the pollution condition of transmission line insulators in time, a method of insulator equivalent salt deposit density (ESDD) prediction based on meteorological data is proposed in this paper. The meteorological features that are more closely related to insulator pollution degree are mined, and the importance of each meteorological feature is evaluated by the random forest algorithm. Combined with the sequential forward search method, the optimal subset of meteorological features for ESDD prediction model could be determined. Based on the natural pollution test data of Taizhou City, the basic ESDD prediction model was established by using extreme learning machine (ELM), and its initial weights and thresholds were optimized by the mind evolution algorithm (MEA). Then the AdaBoost algorithm was applied to further improve the accuracy of the model. The results show that the average absolute error of ESDD prediction of AdaBoost-MEA-ELM model is 0.0032 mg/cm2, which is 58.97% lower than that of the original ELM model. Compared with other common models, the performance of the proposed model and the rationality of the combination of these three algorithms are verified. The variation of prediction error when training data changed was obtained by k-fold verification method, which further prove the generalization performance and stability of the model.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202303084esdd predictionmeteorological characteristicsrandom forestextreme learning machinemind evolution algorithmadaboost algorithm |
| spellingShingle | Yaoping WANG Te LI Kaihua JIANG Wenhui LI Qiang WU Yu WANG Prediction of Insulator ESDD Based on Meteorological Feature Mining and AdaBoost-MEA-ELM Model Zhongguo dianli esdd prediction meteorological characteristics random forest extreme learning machine mind evolution algorithm adaboost algorithm |
| title | Prediction of Insulator ESDD Based on Meteorological Feature Mining and AdaBoost-MEA-ELM Model |
| title_full | Prediction of Insulator ESDD Based on Meteorological Feature Mining and AdaBoost-MEA-ELM Model |
| title_fullStr | Prediction of Insulator ESDD Based on Meteorological Feature Mining and AdaBoost-MEA-ELM Model |
| title_full_unstemmed | Prediction of Insulator ESDD Based on Meteorological Feature Mining and AdaBoost-MEA-ELM Model |
| title_short | Prediction of Insulator ESDD Based on Meteorological Feature Mining and AdaBoost-MEA-ELM Model |
| title_sort | prediction of insulator esdd based on meteorological feature mining and adaboost mea elm model |
| topic | esdd prediction meteorological characteristics random forest extreme learning machine mind evolution algorithm adaboost algorithm |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202303084 |
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