Optimal static state estimation using improved particle swarm optimization and gravitational search algorithm

• Improved particle swarm optimization is applied to solve state estimation problem.
• Gravitational search algorithm is also applied to solve state estimation problem.
• Improved minimizations of fitness functions have been achieved.
• Performance indices have been improved using the proposed techniques.
• Statistical error analysis proves the superiority of the proposed techniques.

In this paper, two novel evolutionary search techniques based on Improved Particle Swarm Optimization (IPSO) algorithm and Gravitational Search Algorithm (GSA), have been proposed to solve the static State Estimation (SE) problem as an optimization problem. The proposed methods are tested on five IEEE standard test systems along with two ill-conditioned test systems under different simulated conditions and the results are compared with the same of standard Weighted Least Square State Estimation (WLS-SE) technique, Particle Swarm Optimization (PSO) based SE and Hybrid Particle Swarm Optimization Gravitational Search Algorithm (PSOGSA) based SE technique. The optimization performance and the statistical error analysis show the superiority of the proposed GSA based SE technique over the other two techniques.