Sparse Bayesian Learning-Based Topology Reconstruction Under Measurement Perturbation for Fault Location

Topology reconstruction is of great importance for fault location in smart grids. However, with the increasing development of grid infrastructure, the measurement perturbation increases and the topology reconstruction accuracy decreases. By analyzing the topology sparsity variation of the power grid before and after faults, a new method for fault location based on topology recognition is proposed. Traditional Bayesian inference with synthesized noise term is introduced to solve the measurement perturbation problem, and a fixed-point iteration method is adopted for inference to solve intractable posterior in Bayesian learning. Extensive simulations are conducted to verify the feasibility of the method, which show that the root mean square error (RMSE) of the proposed method is lower than 0.03 when the noise level varies from 5e(-6) to 5e(-3). The fault location results are more accurate compared with traditional methods when facing ground fault. The proposed method is of great significance for fault location under unknown topology information.