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Article type: Research Article
Authors: Xu, Bichuana | Guo, Leib | Pan, Zhuohongc; * | Liu, Yutinga | Tong, Taoa | Li, Tangbinga | Ding, Liangd | Wan, Huaa | Zhang, Jinga
Affiliations: [a] State Grid Jiangxi Electric Power Research Institute, Nanchang, Jiangxi, China | [b] State Grid Henan Electric Power Research Institute, Zhengzhou, Henan, China | [c] North China Electric Power University (Baoding), Baoding, Hebei, China | [d] Shaoxing Power Supply Bureau of State Grid Zhejiang Electric Power Co., Shaoxing, Zhejiang, China
Correspondence: [*] Corresponding author: Zhuohong Pan, North China Electric Power University (Baoding), Baoding, Hebei 071003, China. E-mail: panzhuohong@ncepu.edu.cn.
Abstract: With the wide application of high voltage/ultra-high voltage (HV/UHV) DC transmission technology, the impact of DC grounding electrode location selection on the surrounding power grid has become increasingly prominent, especially the problem of DC bias hazard caused by DC grounding electrodes at provincial grid boundaries needs to be solved urgently. This paper studies the assessment and prevention of trans-regional DC bias risk, and proposes an inversion method of earth resistivity model based on the measured data of neutral current in the provincial boundary area. Firstly, the DC bias risk of provincial boundary power grid is simulated and calculated, and the influence of reasonable selection of earth model on the accuracy of risk assessment results is explained. Based on the classical Fletcher-Reeves conjugate gradient method and the measured neutral current data, a reduced-order inversion method for the earth resistivity parameters of the provincial boundary power grid is proposed. On this basis, a set of DC bias risk control scheme is formulated for the actual project of Gannan power grid. Finally, the feasibility of the scheme is verified by simulation and measured data.
Keywords: DC grounding electrode location, DC bias, earth resistivity model, mitigation, neutral series capacitor/resistor
DOI: 10.3233/JCM-225945
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 22, no. 3, pp. 857-869, 2022
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