Solving Optimal Power Flow Problems Using Chaotic Self-adaptive Differential Harmony Search Algorithm

Optimal power flow is the basic tool that allows an electric utility to determine the economic and secure operating conditions of an electric power system. This article presents a chaotic self-adaptive differential harmony search algorithm to solve optimal power flow problems with non-smooth and non-convex cost functions. The searching capacity of the proposed chaotic self-adaptive differential harmony search algorithm has been improved by introducing a chaotic self-adaptive differential mutation operator instead of a pitch adjustment operator in the harmony search algorithm. The effectiveness of the proposed chaotic self-adaptive differential harmony search algorithm has been tested with IEEE 30-bus, IEEE 300-bus, and 66-bus Indian utility systems. The simulation results obtained using the proposed algorithm are compared with other variants of the improved harmony search algorithm, such as the differential harmony search algorithm, the chaotic differential harmony search algorithm, the interior point method, and other techniques reported in the literature, to show its effectiveness. The results obtained by the proposed algorithm are found to be better than the results obtained by the differential harmony search, chaotic differential harmony search, interior point method, and other algorithms reported in the literature in terms of solution quality and standard deviation of generation cost. In terms of speed of convergence and computational time, the proposed algorithm is better than the differential harmony search and chaotic differential harmony search algorithms.