Hybrid power system state estimation using Taguchi differential evolution algorithm

Hybridising of different optimisation techniques provides a scope to improve global exploration capability of the resulting method. In this study, an enhanced differential evolution (DE) algorithm, called hybrid Taguchi-differential evolution (TDE) algorithm is proposed to solve power system state estimation problem as an optimisation problem. TDE combines the positive properties of the Taguchi's method to the classical DE algorithm for improving the accuracy and reliability of state estimation problem. The systematic reasoning ability of the Taguchi method is incorporated after crossover operation of DE algorithm to obtain the potential chromosome, better convergence rate and subsequently, to improve the robustness of the results. The proposed method is tested on IEEE test bus systems along with two ill-conditioned systems under different simulated conditions. The results reveal that solutions yield towards global optimum and it compares far better than conventional DE, particle swarm optimisation, gravitational search algorithm and weighted least square based state estimation techniques in terms of optimisation performance, solution quality and the statistical error analysis.