Probability Prediction of Low-voltage Tripping Failures in Distribution Transformer Station Areas Based on XGBoost Algorithm

doi:10.11930/j.issn.1004-9649.201908140

Abstract

Abstract: In view of the frequent low-voltage tripping faults in the distribution transformer station areas during summer season, a machine learning-based warning model is proposed for low-voltage tripping in this paper. Firstly, a combination method based on isolated forest and synthetic minority oversampling technique-nominal continuous (SMOTE-NC) algorithm is proposed in data pre-processing phase. The aim of this combination method is to oversample the representative trip fault samples to adjust imbalance rate of dataset based on outlier separation. Secondly, the extreme gradient boosting (XGBoost) classifier model is trained using the optimized data sets to predict the probability of low-voltage tripping faults in the target station area. Finally, the proposed algorithm is validated by the measured data in an area of Guangzhou, and the results show it has good application effects.

Key words: distribution transformer station area, low-voltage tripping fault, machine learning, imbalanced dataset, XGBoost

WU Qiong, YU Wencheng, HONG Haisheng, YU Lei, DUAN Lian, SHANG Mingyuan, LIU Zhe. Probability Prediction of Low-voltage Tripping Failures in Distribution Transformer Station Areas Based on XGBoost Algorithm[J]. Electric Power, 2020, 53(4): 105-113.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201908140

https://www.electricpower.com.cn/EN/Y2020/V53/I4/105

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