电力系统经济负荷分配的量子粒子群算法

Quantum-behaved particle swarm optimization algorithm for economic load dispatch of power system

本文首次将量子粒子群算法用于电力系统经济负荷分配中。该算法是以粒子群中粒子的收敛特性为基础,依据量子物理理论提出的,改变了传统粒子群算法的搜索策略,可使粒子在整个可行解空间中搜索寻求全局最优解。同时该算法的进化方程中不需要速度向量,而且进化方程的形式更简单,参数较少且容易控制。对两个算例进行仿真测试,证实该算法可有效解决经济负荷分配问题;性能对比显示,该算法求得的解优于已有的改进粒子群算法及其它优化算法所求得的解。本文为量子粒子群算法用于经济负荷分配的实用化研究奠定了必要的理论基础。

Quantum-behaved particle swarm optimization algorithm is firstly used in economic load dispatch of power system in this paper. The algorithm is based on convergence behavior of particles in particle swarm, which is presented according to quantum physics theory. It changes evolutionary search strategy of traditional particle swarm optimization algorithm. Particles can search global optimal solution in whole feasible solution. The evolution equation of new algorithm need no velocity vector, and the form of equation is simple, which parameters is less and is easier to control. The algorithm is simulated by two cases, which validates that it can effectively solve the problem of economic load dispatch. Through performance comparison, it is obvious that the solution is superior to that of improved particle swarm optimization algorithm and other optimization algorithm. This paper lays necessary theoretical foundation for practicality for economic load dispatch of power system based on quantum-behaved particle swarm optimization algorithm.