Chaotic-Opposition Whale optimization algorithm based load flow analysis of small-scale, median and broad critical power systems

Abstract

In power system phraseology load flow study deals with attainment of steady state explication of the power network and determined for steady state powers for various buses along with the bus voltages. For adhering to the load flow problem, a modified version of Whale Optimization Algorithm has been suggested by the authors in this paper. The algorithm is a trustworthy meta-heuristic optimization algorithm derived from nature and motivated by the humpback whale bubble-net hunting hypothesis. The accuracy and reliability has been enhanced by the introduction of chaos theory and opposition-based learning (OBL) to WOA so as to effectively cover the entire search region and thereby enhance the convergence of single or multi-objective metaheuristic algorithms. The new algorithm termed as Chaotic-Opposition based Whale Optimization (COWOA) uses the chaos theory for primary tuning of parameters of WOA by which the exploitation and exploration processes are adjusted and OBL is used to look for the solutions in reverse direction of indicated values in order to test if selects in reverse direction can provide even better solutions. Numerical and simulation results demonstrate that in discerning scenario when traditional load flow approaches flounder, COWOA is able to provide effective solutions.