Oppositional brain storm optimization for fault section location in distribution networks

Abstract

Fault section location (FSL) is an important role in facilitating quick repair and restoration of distribution networks. In this chapter, an oppositional brain storm optimization referred to as OBSO is proposed to effectively solve the FSL problem. The FSL problem is transformed into a 0–1 integer programming problem. The difference between the reported overcurrent and expected overcurrent states of the feeder terminal units (FTUs) is used as the objective function. BSO has been shown to be competitive to other population-based algorithms. But its convergence speed is relatively slow. In OBSO, opposition-based learning method is utilized for population initialization and also for generation jumping to accelerate the convergence rate. The effectiveness of OBSO is comprehensively evaluated on different fault scenarios including single and multiple faults with lost and/or distorted fault signals. The experimental results show that OBSO is able to achieve more promising performance.