Incremental particle swarm optimizer with local search for optimal power flow subjected to digital GCC based on neural network

In effort to get an economics and save optimal operation of power system generation, Incremental particle swarm optimizer with local search (IPSOLS) is employed to solve optimal power flow (OPF) problems. IPSOLS is hybrid method among incremental social learning (ISL), particle swarm optimization (PSO) and local search algorithm that having good ability in finding global optimal solution faster and also in avoiding trapped into local optimal solution. The optimal solution of OPF problems can be getting by two ways. The first is choice the appropriate optimization methods (in this paper IPSOLS) and the second is alleviate the constraint. To alleviate constraint the digital generator capability curve (GCC) is used as an OPF constraint replacing the rectangular (P _min-Pmax and Q _min- Q _max) constraint only. To minimize the complicated mathematics problem in accounting GCC as an OPF constraint, neural network (NN) is employed in designing digital GCC and in checking security algorithm. The algorithm is very simple and flexible especially for representing non linear generation operation limit near steady state stability limit and under excitation operation area. To verify performance of the proposed method the Java Bali 500 kV power systems that containing of 8 generators and 23 buses is used as sample test systems. The simulation result shows that the proposed method has more economics compared to the rectangular constraint only (OPF-IPSOLS with rectangular constraint). Also the proposed method is faster in getting optimal solution than OPF-PSO.