Fast assessment method of power grid risk based on Huffman code and state identification

Chang Che; Yong Wang; Wenbo Liu; Guozheng Zhang

doi:10.1117/12.2689697

10 August 2023 Fast assessment method of power grid risk based on Huffman code and state identification

Chang Che, Yong Wang, Wenbo Liu, Guozheng Zhang

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127480W (2023) https://doi.org/10.1117/12.2689697
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

With the development of economy and society, people's requirements for power quality and reliability are gradually increasing, so it is necessary to carry out risk assessment on power system. Monte Carlo method is a common method for power system risk assessment, but there is a contradiction between the calculation speed and accuracy. In order to improve the calculation accuracy, a large number of state samples need to be calculated, which takes a long time, and there are many repetitive states. For this reason, an improved Monte Carlo method based on Huffman code and state identification is proposed, which can significantly improve the efficiency of power grid risk assessment from two aspects. The first is to uniquely identify the system states through Huffman code and record relevant data, so as to avoid recalculation of the same states. The second is the fast and efficient identification of system state based on the shortest weighted path length of Huffman code. The effectiveness of the method is verified by an example, and several factors that may affect the effectiveness of the method are analyzed.

Citation Download Citation

Chang Che, Yong Wang, Wenbo Liu, and Guozheng Zhang "Fast assessment method of power grid risk based on Huffman code and state identification", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127480W (10 August 2023); https://doi.org/10.1117/12.2689697

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