XGBoost-Based Intelligent Decision Making of HVDC System with Knowledge Graph

Qiang Li, Qian Chen, Jiyang Wu, Youqiang Qiu, Changhong Zhang, Yilong Huang, Jianbao Guo and Bo Yang ()
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Qiang Li: EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Dali Bureau, Dali 671000, China
Qian Chen: EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510000, China
Jiyang Wu: EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510000, China
Youqiang Qiu: EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Dali Bureau, Dali 671000, China
Changhong Zhang: Maintenance and Test Center of CSG, EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510000, China
Yilong Huang: Maintenance and Test Center of CSG, EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510000, China
Jianbao Guo: Maintenance and Test Center of CSG, EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510000, China
Bo Yang: EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Dali Bureau, Dali 671000, China

Energies, 2023, vol. 16, issue 5, 1-21

Abstract: This study aims to achieve intelligent decision making in HVDC systems in the framework of knowledge graphs (KGs). First, the whole life cycle KG of an HVDC system was established by combining intelligent decision making. Then, fault diagnosis was studied as a typical case study, and an intelligent decision-making method for HVDC systems based on XGBoost that significantly improved the speed, accuracy, and robustness of fault diagnosis was designed. It is noteworthy that the dataset used in this study was extracted in the framework of KGs, and the intelligent decision making of KG and HVDC systems was accordingly combined. Four kinds of fault data extracted from KGs were firstly preprocessed, and their features were simultaneously trained. Then, sensitive weights were set, and the pre-computed sample weights were put into the XGBoost model for training. Finally, the trained test set was substituted into the XGBoost classification model after training to obtain the classification results, and the recognition accuracy was calculated by means of a comparison with the standard labels. To further verify the effectiveness of the proposed method, back propagation (BP) neural network, probabilistic neural network (PNN), and classification tree were adopted for validation on the same fault dataset. The experimental results show that the XGBoost used in this paper could achieve accuracy of over 87% in multiple groups of tests, with recognition accuracy and robustness being higher than those of its competitors. Therefore, the method proposed in this paper can effectively identify and diagnose faults in HVDC systems under different operation conditions.

Keywords: knowledge graph; intelligent decision making; HVDC; fault diagnosis; XGBoost (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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