Combined SA PSO method for transmission constrained maintenance scheduling using levelized risk method

Abstract

This paper presents a model for maintenance scheduling (MS) of generators using combined simulated annealing and binary particle swarm optimization (CSABPSO) based stochastic approach. The objective function of this paper is to reduce the loss of load probability (LOLP) for a power system. The hybrid algorithm combines BPSO with simulated annealing behavior. The CSABPSO algorithm takes both of the advantages, good solution quality of SA and fast searching ability of BPSO. As stochastic optimization algorithms are sensitive to their parameters, proper weight update procedure for parameters selection is introduced in this paper to improve solution quality. Weight update operators are introduced in the BPSO in order to obtain the diversified solutions in the search space. In this paper the impacts of transmission constrained MS is considered. Moreover, the CSABPSO based economic dispatch (ED) and load flow have been decomposed as a sub-problem in the maintenance model that results to a more practical transmission constrained maintenance schedule. A case study for IEEE reliability test system (RTS) is used for detailed analysis and further results are presented for roy billinton test system (RBTS) which shows the proposed algorithm can accomplish a significant levelization in the reliability indices over the planning horizon and demonstrates the usefulness of the proposed approach. The simulation results obtained by proposed algorithm indicate transmission line constraint influence on establishment of optimal MS. The CSABPSO could have higher efficiency, better quality than the other compared algorithms.