Prediction of Insulator ESDD Based on Meteorological Feature Mining and AdaBoost-MEA-ELM Model

doi:10.11930/j.issn.1004-9649.202303084

Abstract

Abstract: 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.

Key words: ESDD prediction, meteorological characteristics, random forest, extreme learning machine, mind evolution algorithm, AdaBoost algorithm

WANG Yaoping, LI Te, JIANG Kaihua, LI Wenhui, WU Qiang, WANG Yu. Prediction of Insulator ESDD Based on Meteorological Feature Mining and AdaBoost-MEA-ELM Model[J]. Electric Power, 2023, 56(9): 157-167.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202303084

https://www.electricpower.com.cn/EN/Y2023/V56/I9/157

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