Open Access

ARTICLE

A Disturbance Localization Method for Power System Based on Group Sparse Representation and Entropy Weight Method

Zeyi Wang¹, Mingxi Jiao¹, Daliang Wang¹, Minxu Liu¹, Minglei Jiang², He Wang³, Shiqiang Li^3,*
1 Changchun Power Supply Company, State Grid Jilin Electric Power Co., Ltd., Changchun, 130021, China
2 Economic and Technical Research Institute, State Grid Jilin Electric Power Co., Ltd., Changchun, 130022, China
3 Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Northeast Electric Power University, Jilin, 132012, China
* Corresponding Author: Shiqiang Li. Email:

Energy Engineering https://doi.org/10.32604/ee.2024.028223

Received 06 December 2022; Accepted 24 February 2023; Published online 03 April 2024

Abstract

This paper addresses the problem of complex and challenging disturbance localization in the current power system operation environment by proposing a disturbance localization method for power systems based on group sparse representation and entropy weight method. Three different electrical quantities are selected as observations in the compressed sensing algorithm. The entropy weighting method is employed to calculate the weights of different observations based on their relative disturbance levels. Subsequently, by leveraging the topological information of the power system and pre-designing an overcomplete dictionary of disturbances based on the corresponding system parameter variations caused by disturbances, an improved Joint Generalized Orthogonal Matching Pursuit (J-GOMP) algorithm is utilized for reconstruction. The reconstructed sparse vectors are divided into three parts. If at least two parts have consistent node identifiers, the node is identified as the disturbance node. If the node identifiers in all three parts are inconsistent, further analysis is conducted considering the weights to determine the disturbance node. Simulation results based on the IEEE 39-bus system model demonstrate that the proposed method, utilizing electrical quantity information from only 8 measurement points, effectively locates disturbance positions and is applicable to various disturbance types with strong noise resistance.

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Keywords

Disturbance location; compressed sensing; group sparse representation; entropy power method; GOMP algorithm

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