An improved prediction method of subsequent commutation failure of an LCC-HVDC considering sequential control response

• Published in: Protection and control of modern power systems Vol. 8; no. 1; pp. 46 - 11
• Main Authors: Ouyang, Jinxin, Pan, Xinyu, Ye, Junjun, Xiao, Chao, Diao, Yanbo, Zhang, Qingwu
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.12.2023; Springer Nature B.V; SpringerOpen
• Subjects: Circuits; Commutation; Controllers; Converter station; Electric power; Electric power systems; Electrical Machines and Networks; Electricity distribution; Energy; Energy Systems; Failure; High voltage; Line-commutated converter based high-voltage direct-current (LCC-HVDC); Mutation; Original Research; Power Electronics; Prediction; Recovery; Renewable and Green Energy; Sequential control; Subsequent commutation failure (SCF); Threshold voltage; Line-commutated converter based high-voltage direct-current (LCC-HVDC); Converter station; Subsequent commutation failure (SCF); Prediction
• ISSN: 2367-2617; 2367-0983
• DOI: 10.1186/s41601-023-00323-9

Subsequent commutation failure (SCF) can be easily generated during the first commutation failure (CF) recovery process in a line-commutated converter-based high voltage direct-current system. SCF poses a significant threat to the safe and stable operation of power systems, and accurate prediction of CF is thus important. However, SCF is affected by the operating characteristics of the main circuit and the coupling effects of sequential control response in the inverter station. These are difficult to predict accurately. In this paper, a new SCF prediction method considering the control response is proposed based on the physical principle of SCF. The time sequence and switching conditions of the controllers at different stages of the first CF recovery process are described, and the corresponding equations of commutation voltage affected by different controllers are derived. The calculation method of the SCF threshold voltage is proposed, and the prediction method is established. Simulations show that the proposed method can predict SCF accurately and provide useful tools to suppress SCF.