Gravity Search Algorithm hybridized Recursive Least Square method for power system harmonic estimation

This paper presents a new hybrid method based on Gravity Search Algorithm (GSA) and Recursive Least Square (RLS), known as GSA-RLS, to solve the harmonic estimation problems in the case of time varying power signals in presence of different noises. GSA is based on the Newton's law of gravity and mass interactions. In the proposed method, the searcher agents are a collection of masses that interact with each other using Newton's laws of gravity and motion. The basic GSA algorithm strategy is combined with RLS algorithm sequentially in an adaptive way to update the unknown parameters (weights) of the harmonic signal. Simulation and practical validation are made with the experimentation of the proposed algorithm with real time data obtained from a heavy paper industry. A comparative performance of the proposed algorithm is evaluated with other recently reported algorithms like, Differential Evolution (DE), Particle Swarm Optimization (PSO), Bacteria Foraging Optimization (BFO), Fuzzy-BFO (F-BFO) hybridized with Least Square (LS) and BFO hybridized with RLS algorithm, which reveals that the proposed GSA-RLS algorithm is the best in terms of accuracy, convergence and computational time.