APPLICATION OF PARTICLE SWARM OPTIMIZATION ALGORITHM FOR OPTIMAL REACTIVE POWER PLANNING

摘要

This paper investigates the applicability of the particle swarm optimization (PSO) algorithm to the optimal reactive power planning (ORPP) problem. The paper uses the fuel cost minimization approach to solve the ORPP problem. The problem is decomposed into the real power (P) and the reactive power (Q) optimization subproblems. The P optimization minimizes the operation cost by adjusting P generation, while Q optimization adjusts transformer tap settings. Q generation and VAR source investment minimizes the operation cost and the investment on VARs. The P and Q subproblems are each optimized by the PSO in an iterative manner until the global minimum is obtained. The effectiveness of the proposed PSO is tested on the IEEE 30-bus system and the results are compared with those of evolutionary programming, evolutionary strategy, and linear programming.