State Estimation of Electrical Distribution Power System Using the Grey Wolf Optimizer

Abstract

State estimation is a crucial activity in power systems to ensure efficient operation and maintain system security. This article presents an innovative approach using the Grey Wolf Optimizer (GWO) for state estimation in electric power systems. The GWO algorithm is a population-based metaheuristic inspired by the social behavior of grey wolves in hunting. In this approach, the GWO algorithm is applied to estimate the state variables of the power system, including voltage magnitudes and phase angles at all buses. The proposed method is validated using the IEEE 14-bus and 33-bus test systems and compared with the Extended Optimal Power Flow (E-OPF) approach. The results demonstrate the effectiveness of the GWO-based state estimator, especially with increased measurement redundancy. Additionally, the performance of the estimator was evaluated with randomly varying load values from light to heavy, highlighting its ability to handle diverse system conditions. Despite its promising performance, the proposed estimator exhibits a relatively high execution time, suggesting the need for optimization in computational efficiency.