Evaluation, Analysis and Diagnosis for HVDC Transmission System Faults via Knowledge Graph under New Energy Systems Construction: A Critical Review

Jiyang Wu, Qiang Li, Qian Chen, Guangqiang Peng, Jinyu Wang, Qiang Fu and Bo Yang ()
Additional contact information
Jiyang Wu: CSG EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510000, China
Qiang Li: CSG EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510000, China
Qian Chen: CSG EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510000, China
Guangqiang Peng: Maintenance and Test Center of CSG EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510663, China
Jinyu Wang: CSG EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Dali Bureau, Dali 671000, China
Qiang Fu: CSG EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Dali Bureau, Dali 671000, China
Bo Yang: Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China

Energies, 2022, vol. 15, issue 21, 1-20

Abstract: High voltage direct current (HVDC) transmission systems play a critical role to optimize resource allocation and stabilize power grid operation in the current power grid thanks to their asynchronous networking and large transmission capacity. To ensure the operation reliability of the power grid and reduce the outage time, it is imperative to realize fault diagnosis of HVDC transmission systems in a short time. Based on the prior research on fault diagnosis methods of HVDC systems, this work comprehensively summarizes and analyzes the existing fault diagnosis methods from three different angles: fault type, fault influence, and fault diagnosis. Meanwhile, with the construction of the digital power grid system, the type, quantity, and complexity of power equipment have considerably increased, thus, traditional fault diagnosis methods can basically no longer meet the development needs of the new power system. Artificial intelligence (AI) techniques can effectively simplify solutions’ complexity and enhance self-learning ability, which are ideal tools to solve this problem. Therefore, this work develops a knowledge graph technology-based fault diagnosis framework for HVDC transmission systems to remedy the aforementioned drawbacks, in which the detailed principle and mechanism are introduced, as well as its technical framework for intelligent fault diagnosis decision.

Keywords: high voltage direct current; fault diagnosis; knowledge graph; digital power grid (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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