Title: Risk assessment of power system network security based on RBF neural network

Authors: Yunhao Yu; Chameiling Di; Xiang Guo

Addresses: Power Dispatching Control Center of Guizhou Power Grid Co., Ltd., Guiyang, 550002, China ' Power Dispatching Control Center of Guizhou Power Grid Co., Ltd., Guiyang, 550002, China ' Power Dispatching Control Center of Guizhou Power Grid Co., Ltd., Guiyang, 550002, China

Abstract: In order to improve the accuracy and efficiency of power system network security risk assessment, this paper proposes a new power system network security risk assessment method based on RBF neural network. Firstly, establish the safety risk assessment index system and build the risk assessment matrix. Secondly, the risk value of the evaluation index is calculated according to the information entropy of the index. Finally, taking the risk value of the evaluation index as the input and the result of the power system network security risk assessment as the output, the RBF neural network is used to build the power system network security risk assessment model and the network security risk assessment result is obtained. The experiment shows that the maximum safety risk assessment accuracy of this method can reach 96% and the maximum assessment efficiency can reach 98%.

Keywords: RBF neural network; power system; network security risk; risk assessment.

DOI: 10.1504/IJPEC.2023.134871

International Journal of Power and Energy Conversion, 2023 Vol.14 No.2/3, pp.148 - 158

Received: 11 Jan 2023
Accepted: 21 Apr 2023
Published online: 15 Nov 2023 *

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