Block-Sparse Bayesian Learning Method for Fault Location in Active Distribution Networks With Limited Synchronized Measurements

Abstract

Timely and accurate fault location techniques can reduce power outages and improve power supply reliability. This paper proposes a new fault location method for active distribution networks (ADNs) which utilizes limited synchronized measurements (μPMUs). The proposed method formulates the fault location problem as the estimation of a block-sparse fault injection current signal which can inherently reveal fault types and locations. A modified block-sparse Bayesian learning (BSBL) algorithm is utilized to estimate this block-sparse signal by exploiting its both block structure and intra-block correlation. In addition, the proposed method provides a strategy for the optimal placement of much fewer μPMUs that will not yield a complete observable system. The proposed method is able to pinpoint the fault position accurately, and is applicable to both balanced and unbalanced ADNs. The effectiveness of the proposed method is examined on the IEEE 123-node distribution network with DGs and the results are discussed thoroughly.