State Monitoring and Fault Diagnosis of HVDC System via KNN Algorithm with Knowledge Graph: A Practical China Power Grid Case

Qian Chen, Qiang Li, Jiyang Wu, Jingsong He, Chizu Mao, Ziyou Li and Bo Yang ()
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Qian Chen: EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510000, China
Qiang Li: EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510000, China
Jiyang Wu: Maintenance and Test Center of CSG EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510000, China
Jingsong He: Maintenance and Test Center of CSG EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510000, China
Chizu Mao: Maintenance and Test Center of CSG EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510000, China
Ziyou Li: EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Dali Bureau, Dali 671000, China
Bo Yang: EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Dali Bureau, Dali 671000, China

Sustainability, 2023, vol. 15, issue 4, 1-23

Abstract: Based on the four sets of faults data measured in the practical LCC-HVDC transmission project of China Southern Power Grid Tianshengqiao (Guangxi Province, China)–Guangzhou (Guangdong Province, China) HVDC transmission project, a fault diagnosis method based on the K-nearest neighbor (KNN) algorithm is proposed for an HVDC system. This method can effectively and accurately identify four different fault types, aiming to contribute to construction of a future HVDC system knowledge graph (KG). First, function and significance of fault diagnosis for KG are introduced, along with four specific fault scenarios. Then, the fault data are normalized, classified into a training set and a test set, and labeled. Based on this, the KNN fault diagnosis model is established and Euclidean distance (ED) is selected as the metric function of the KNN algorithm. Finally, the training data are conveyed to the model for training and testing, upon which the diagnosis result obtained by the KNN algorithm with a knowledge graph is compared with that of the support vector machine (SVM) algorithm and Bayesian classifier (BC). The simulation results show that the KNN algorithm can achieve the highest diagnosis accuracy, with more than 83.3% diagnostic accuracy under multiple test sets among all three diagnosis methods.

Keywords: HVDC; KNN; fault diagnosis; knowledge graph; Euclidean distance (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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