A disturbance localization method for power system based on group sparse representation of compressed sensing

This paper proposes a new disturbance localization method for power system based on group sparse representation of compressed sensing. Based on the analysis of disturbance characteristics of power system, the sparse dictionary of compressed sensing group is constructed by the admittance matrix, impedance matrix, and power variation matrix. In addition, the traditional generalized orthogonal matching pursuit reconstruction algorithm is improved to obtain the group sparse coefficients corresponding to the above dictionaries for compressive sensing sparse reconstruction, and the corresponding weights of each class in the group sparse dictionary are determined based on the entropy weight method to realize the accurate location of power system disturbances. In this paper, the IEEE 39 node power systems are built on the PSCAD/EMTDC simulation platform for experimental verification, and three common disturbances of cutting generator capacity, cutting load capacity and single-phase grounding short circuit are analyzed respectively. The simulation results show that the proposed method can achieve the accurate location of common disturbances in power systems with limited measurement device configurations, and has the advantages of less computation and strong robustness to noise.