Study of probabilistic available transfer capability by improved particle swarm optimization

This paper carries out the comprehensive studies, which concerning the problem of probabilistic Available Transfer Capability (abr. ATC) computation based on the improved particle swarm optimization (abr. IPSO). Firstly, the mathematic model of ATC based on optimal power flow is constructed and a new method of improved particle swarm optimization which is adaptive for the model of ATC is presented. A new self-adaptive adjustment inertia-weighted strategy factor, which elevates the adaptability of PSO and accelerates convergence-speed of PSO, is proposed. On consideration of the search characteristics of particle swarm optimization (abr. PSO), penalty function is assigned dynamically. Secondly, ATC can be computed by IPSO after selection of critical contingencies by modal analysis method. Finally, through the probabilistic analysis based on the probability of each contingency and the value of ATC corresponding to each contingency, the probabilistic ATC can be computed. A case study of IEEE-30 bus system demonstrates the rationality and validity of strategies proposed in this paper.