基于MAQPSO的电力系统无功优化研究

Reactive power optimization for power system based on MAQPSO

文中提出一种多智能体量子粒子群优化算法(Multi Agent Quantum Particle Swam Optimization,MAQPSO)求解电力系统无功优化问题,改善了传统量子粒子群算法后期收敛速度慢、易陷入局部最优解等缺点。该算法结合了量子粒子群算法和多智能体进化思想,每一个Agent相当于量子粒子群优化算法中的一个粒子,通过Agent的邻域竞争、自学习等操作,使得算法能够更迅速、更精确地收敛到全局最优解。通过对IEEE14、30、57和118节点系统的优化仿真,结果表明该算法有收敛精度高、寻优速度快等优点。

This paper proposed a novel quantum particle swam optimization algorithm based on multi agent system （MAQPSO） approach to solve the reactive power optimization. The algorithm overcomes the defects of conventional quantum particle swam optimization slow convergent speed and easy convergence to local minimum point of error function on later. This algorithm combined quantum particle swam optimization algorithm with multi-agent evolutionary thought. Every Agent serves as a particle of quantum particle swam, competition by the agent of the neighborhood and self-study, thus the algorithm can more quickly and accurately converge to global optimal solution. On the basis of the optimization simulation for IEEE14, 30, 57 and 118 nodes system, the results show that the algorithm has the advan- tages of high convergence precision and speed of optimization.