ICPSO算法及其在经济负荷分配中的应用

提出一种改进的混沌粒子群优化ICPSO(improved chaotic particle swarm optimization)算法,用于求解非线性、非凸、不连续等复杂约束条件的电力系统经济负荷分配。通过修正粒子群迭代的行动策略,并引入Tent混沌映射加强部分粒子的全局搜索能力,可以提高优化算法的全局搜索性能。最后将该算法应用于3机6母线的电力系统经济负荷分配中,在计及阀点效应的情况下,分别以考虑网损和忽略网损为例进行仿真。仿真结果表明,该算法有较快的收敛速度和较强的全局搜索能力,验证了算法的有效性和优越性。

Improved Chaotic Particle Swarm Optimization Algorithm and Its Application in Economic Load Dispatch

An improved chaotic particle swarm optimization(ICPSO) algorithm was presented to solve the economic load dispatch problems in power systems,which were of complex constraints such as: nonlinear,non-convex,and discrete characteristics etc.The global search performance of optimization algorithm was improved by revising the iterative strategy of the particle swarm and introducing the Tent chaotic map to enhance the global ergodicity of some particles.Finally,the proposed algorithm was applied to the simulation in economic load dispatch of a three-generators with six-buses power system in two cases: neglecting and considering the transmission losses respectively under the valve point effect.The results of the simulation show that the algorithm has a faster constringency rate and better global optimization,and the effectiveness and superiority of this algorithm were proved.