Comprehensive evaluation of power quality based on combination of minibatch K-means algorithm and random forest algorithm

ChunLei Ma; LiBin Zheng; XinJun Li; HongMian Wang; Meng Zhao; ChangHao Zhai

doi:10.1117/12.2646813

7 September 2022 Comprehensive evaluation of power quality based on combination of minibatch K-means algorithm and random forest algorithm

ChunLei Ma, LiBin Zheng, XinJun Li, HongMian Wang, Meng Zhao, ChangHao Zhai

Author Affiliations +

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123291U (2022) https://doi.org/10.1117/12.2646813
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

As one of the most important energy, power quality is very important to the safe and stable operation of distribution network. According to national standards and evaluation requirements, a power quality evaluation method is proposed based on Minibatch K-Means algorithm and random forest algorithm. At first, abnormal data are processed. Processed data are clustered analysis by Minibatch K-Means algorithm. Imbalance sample is processed for clustering data by SMOTE. At last, labeled data are extracted features by random forest algorithm. The training model is saved. The simulation results show that power quality is evaluated by Minibatch K-Means algorithm and random forest algorithm, which solves the problem that labelless power quality data can't be classified by integrating multiple indexes. It achieves a comprehensive, efficient and rapid evaluation of power quality in distribution network.

Citation Download Citation

ChunLei Ma, LiBin Zheng, XinJun Li, HongMian Wang, Meng Zhao, and ChangHao Zhai "Comprehensive evaluation of power quality based on combination of minibatch K-means algorithm and random forest algorithm", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123291U (7 September 2022); https://doi.org/10.1117/12.2646813

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