State Estimation of Power Using the Whale Optimization Algorithm

Abstract

In power systems, the process of attaining a better prediction from a set of variables from state variables is called state estimation (SE). These variables consist of magnitudes of bus voltage and the corresponding angles of all the buses. Because of the non-linearity and intricacy of ever-developing power systems, it has become important to apply upgraded techniques for the dissolution and supervision in practical environments. The discussed analysis evaluates the appositeness of a new metaheuristic technique called the whale optimization algorithm (WOA) which is a population-based algorithm, to reduce the measurement errors so as to gauge the optimal point of the power system when some susceptible values are inadequate. WOA displays admirable attainment in global optimization. It employs a bubble-net hunting approach and it mimics the social behaviour of humpback whales to get the best candidate solution. The approach is tested on IEEE-14, IEEE-30, and IEEE-57 bus test systems and the potency is validated by comparison with the biogeography based optimization algorithm (BBO).