Improved Brain Storm Optimization with Differential Evolution strategies for Load Adjustment Distribution State Estimation Using Correntropy

Abstract

Abstract This paper proposes improved brain storm optimization with differential evolution strategies (IBSODE) for load adjustment distribution state estimation (DSE) using correntropy. If failure of the sensors and communication systems occur, outliers may exist in the measured values. If the outliers exist in measurement values, correntropy is an effective method for DSE. Since the objective function of DSE has a nonlinear characteristic because of various distribution system equipment, evolutionary computation techniques have been applied to the load adjustment DSE. For example, Hybrid Particle Swarm Optimization (HPSO), Differential Evolutionary PSO (DEEPSO), and Modified BSO (MBSO) have been applied to the DSE problem. However, accuracy of distribution state estimation should be improved. The proposed correntropy based DSE method using IBSODE is verified to be more accurate than the conventional weighted least square based method using DEEPSO, the conventional correntropy based methods using DEEPSO, MBSO, and the original BSODE with 33 bus system.