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System reliability evaluation of 12-pulse series converter station based on improved Dijkstra algorithm.

Authors :
Zhao, Jie
Li, Songhuan
Wu, Fangjie
Zhang, Huaixun
Shen, Xiaolin
Dou, Jinqiu
Liang, Yilin
Source :
Electric Power Systems Research. Jan2024, Vol. 226, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

• Simplicity of principle improves computational efficiency. • Methods to effectively assess reliability. • Incorporate engineering realities to ensure accuracy. With the rise of large-scale energy DC transmission system, more and more projects use UHVDC transmission system for long-distance and large-capacity power transmission, in which the converter station is the core of the whole UHV DC transmission system. In this paper, a reliability assessment method based on improved Dijkstra's algorithm is proposed for the main wiring system of the converter station of the UHVDC transmission system and for the components in the main wiring. In order to fully consider the different states of components in actual operation, a four-state model is adopted in the study. The model improves the accuracy of the model by comprehensively considering the component's state characteristics, switching sequence, operation mode and other factors. Based on the state model of the components, the main wiring system is calculated in accordance with the theory of complex distribution networks in order to reduce the computational complexity. Finally, the main wiring system of the double 12-pulse converter station is traversed through the minimum paths based on the improved Dijkstra search method, and the reliability indexes of the obtained minimum paths are calculated according to the theory of the minimum path cut set. A typical bipolar dual 12-pulse series connection is used to verify the validity of the method, and some guidance is given for practical engineering. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
03787796
Volume :
226
Database :
Academic Search Index
Journal :
Electric Power Systems Research
Publication Type :
Academic Journal
Accession number :
173559957
Full Text :
https://doi.org/10.1016/j.epsr.2023.109909