Optimal power flow solution using fuzzy evolutionary and swarm optimization

The optimal power flow is an important problem of power systems in which certain control variables are adjusted to minimize an objective function such as the cost of active power generation or the losses, while satisfying physical and operating limits on various controls, dependent variables and function of variables. This paper presents an efficient and reliable evolutionary based approach to solve the optimal power flow (OPF) problems. The proposed approach employs the integration of Fuzzy Systems with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm for optimal setting of OPF problem control variables. The proposed approach has been tested on the modified IEEE 30-bus test system with objective function that reflects fuel cost minimization with different linear and non-linear constraints. The proposed approach results have been compared with the results those reported in the literature. The results of proposed approaches are promising and it shows the effectiveness and robustness of proposed methods.

Highlights
► Evolutionary algorithms and swarm optimization techniques have been used in the past.
► Paper deals with integration of fuzzy system with GA and PSO techniques.
► These synergetic approaches used for OPF.
► The systems considered for OPF have various non-linearities and constraints.
► The results are compared with earlier published work and found better.