Optimization strategy for oil-paper insulation features based on correlation information entropy and light gradient boosting machine
LAI Wenhong1, LIU Qingzhen1, YAN Renwu2
1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108; 2. Fujian Provincial University Engineering Research Center for Simulation Analysis and Integrated Control of Smart Grid, Fujian University of Technology, Fuzhou 350118
Abstract：In order to fully explore the aging features, which are beneficial for comprehensive diagnosis results of transformer oil-paper insulation, a feature optimization strategy based on correlation information entropy and light gradient boosting machine (LightGBM) is proposed. Firstly, the initial high-dimensional feature space is formed with various time-domain features, which are extracted from the measured data of dielectric response of transformers in different aging states. Secondly, the correlation and redundancy of feature subsets is measured by correlation information entropy. Then the importance of features is evaluated according to LightGBM, so as to obtain the optimal feature space. Finally, the diagnostic performance of the optimal feature space is compared and analyzed against different control groups, and the superiority of the optimal feature space determined through the proposed optimization strategy is effectively verified.
赖汶鸿, 刘庆珍, 鄢仁武. 基于关联信息熵和轻量级梯度提升机的油纸绝缘特征优选策略[J]. 电气技术, 2024, 25(1): 34-41.
LAI Wenhong, LIU Qingzhen, YAN Renwu. Optimization strategy for oil-paper insulation features based on correlation information entropy and light gradient boosting machine. Electrical Engineering, 2024, 25(1): 34-41.
2024, Vol. 25 
