An improved micro-particle swarm optimization algorithm and its application in transient stability constrained optimal power flow

In this paper, an improved micro-particle swarm optimization (mPSO) algorithm is proposed and applied to the development of a computational method for solving Transient Stability Constrained Optimal Power Flow (TSCOPF) problem. The improvement includes two novel intelligent strategies: the dynamic space adjustment and the selective re-initialization. The first strategy is designed to accelerate the convergence speed by confining the search in a reduced space, while the second strategy is to reduce the possible overabundance by re-initializing the particles only in a non-overlapping space. The simulation results demonstrate the superiority of the improved mPSO algorithm over both the original mPSO algorithm and the standard PSO algorithm. The proposed method is validated through case studies for solving TSCOPF problems on IEEE 39-bus system and IEEE 162-bus system. The results show that the algorithm can utilize a small population to obtain an equally good or better optimization outcomes compared to those of the original mPSO algorithm and the standard PSO algorithm. Copyright (C) 2012 John Wiley & Sons, Ltd.