Title: Extracting abnormal frequency signals from power grid based on spectrum analysis

Authors: Chuning Peng; Jun Zhang; Xiaodong Yin; Yi Cao; Quan Wang; Haibin Chen; Jiachuan Long

Addresses: State Grid Corporation of China, Xicheng District, Beijing, China ' China Electric Power Research Institute, Wuhan, China ' China Electric Power Research Institute, Wuhan, China ' State Grid Shanghai Municipal Electric Power Company, Shanghai, China ' China Electric Power Research Institute, Wuhan, China ' State Grid Shanghai Municipal Electric Power Company, Shanghai, China ' School of Electronics and Information Engineering, Wuhan Donghu University, Wuhan, Hubei, China

Abstract: Owing to the low accuracy and efficiency of extracting abnormal signals in traditional methods, a power grid abnormal frequency signal extraction method based on spectrum analysis is proposed. Secondly, for the denoised power grid signal, the bispectral interpolation correction algorithm is used to correct the power grid signal. Finally, utilising the observation, mixing and source matrices derived from the power grid signal, the ratio of the power frequency to the anomalous frequency within the grid signal is determined. Subsequently, the anomalous frequency component is isolated from the amalgamated signal comprising both the grid and anomalous frequencies. The outcomes of the experiments conducted affirm that the methodology presented herein adeptly attenuates noise in power grid signals, achieving an impressive average extraction accuracy of 98.23% for frequency components.

Keywords: spectrum analysis; power grid abnormal frequency signal; signal extraction; singular value decomposition.

DOI: 10.1504/IJCAT.2024.143301

International Journal of Computer Applications in Technology, 2024 Vol.74 No.4, pp.298 - 306

Received: 25 Dec 2023
Accepted: 07 Jun 2024
Published online: 12 Dec 2024 *

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