Study of Probabilistic Available Transfer Capability by Improved Particle Swarm Optimization

This paper presents a new model of probabilistic available transfer capability (abr. ATC) computation, which is based on the improved particle swarm optimization (abr. IPSO). Firstly, pointing to the search characteristics of particle swarm optimization (abr. PSO) penalty function is assigned dynamically according to the amount overrunning the upper-limit or lower- limit of inequality constraints during the calculating process, which accelerates the speed of convergence of PSO. A new self-adaptive adjustment strategy of inertia weight is also proposed, which elevates the adaptability of PSO and accelerates convergence-speed of PSO all the more. Secondly, ATC can be computed by IPSO after critical contingencies are selected by modal analysis method. Lastly, through the probabilistic analysis based on the probability of each contingency happening and ATC corresponding to each contingency, the probabilistic ATC can be gotten. A case study of IEEE-30 bus system demonstrates the rationality and the validity of strategies proposed in this paper.