Multi-population differential evolutionary particle swarm optimization for distribution state estimation using correntropy in electric power systems

This paper proposes Multi-population differential evolutionary particle swarm optimization (DEEPSO) for distribution state estimation (DSE) using correntropy in electric power systems. Practical equipment in distribution systems causes nonlinear characteristics in an objective function and evolutionary computation methods have been applied to DSE so far. This paper applies Multi-population DEEPSO in order to improve estimation quality. Minimization of sum of square errors by the weighted least mean square (WLMS) has a problem when outliers exist in the measure values. Quality of estimated results is largely affected by the outliers using the WLMS, while correntropy has a possibility not to be affected by the outliers. The proposed method applies to a typical distribution system. The results indicate that the proposed DEEPSO based method can improve estimation results compared with conventional DEEPSO based method, and the correntropy based proposed method can estimate distribution system conditions more accurately than the conventional WLMS with the outliers.