A new teaching-learning-based optimization algorithm for distribution system state estimation

Abstract

Distribution system state estimation (DSSE) is of vital importance to the monitoring and control of recent active distribution networks. This paper proposes a new teaching-learning-based optimization algorithm (TLBO) for estimating state variables of radial distribution systems. In this method, the state variables are estimated by minimizing the sum of weighted squared errors considering either real or pseudo measurements. TLBO is a successful recently-proposed optimization technique that simulates the educational system in a classroom. In this paper, an effective mutation has been incorporated into original TLBO algorithm to evade trapping in local minima and develop search process. Nevertheless, developing the search process does not considerably lessen the speed of the algorithm. So the proposed method is an efficient algorithm for DSSE. Finally, the proposed method is studied on three radial distribution test systems. The numerical results have been depicted to demonstrate the efficiency and accuracy of the method for solving DSSE.