Using a Probabilistic Breaker Malfunction Model to Account for Uncertainty in the Network Restoration Process

Abstract

Due to changes in weather conditions, severe natural disasters with widespread and profound impacts seriously threaten power grids, necessitating an increase in resilience through an intelligent and rapid network recovery approach following such incidents. The subject of this paper focuses on the probability of breakers malfunction (PBM) after receiving a close command in the phase of grid restoration, assessing the system's resilience from this perspective. Financial and human resource limitations on repair teams have necessitated proactive repair methods for prioritized circuit breakers with a resilience-enhancing approach. In this paper, based on the breakers monitored data, especially the waveform of their closing coil current and the relevant healthy index, an optimization problem is formulated. The non-sequential Monte Carlo probabilistic technique has been then utilized to solve the problem and evaluate the role of breakers' failure action in the supplied network load during the restoration phase. Circuit breakers are divided into different groups based on the breaker's malfunction to examine their impact on the supplied load fully. The method demonstrates that achieving a highly resilient power grid requires considering not only the health condition of the breakers but also their location in the network. Based on the simulation results, a novel approach is proposed to provide the maintenance priority ranking list of network breakers, aiming to increase system resilience against hazardous events.