免疫粒子群算法在梯级电站短期优化调度中的应用

将免疫原理引入粒子群算法(PSO)中,利用其免疫记忆与自我调节机制保持各适应度层次的粒子维持一定的浓度,保证种群的多样性;引入疫苗接种等操作,对算法的进化过程进行有目的、有选择地指导,提高算法的搜索性能.随后在分析梯级电站短期优化调度数学模型及该算法特点的基础上,建立了基于免疫粒子群(IPSO)算法的梯级电站短期优化调度数学模型,并给出其具体的求解步骤.最后应用该方法进行仿真计算,并与常规调度及PSO算法进行对比,结果表明,该算法可获得较优的优化调度方案,并可提高解的精度,加快其收敛速度.

Application of immune particle swarm optimization algorithm to short term optimal dispatch of cascade hydropower stations

The mathematical model for short term optimal dispatch of cascade hydropower stations was established referring the global maximum output energy as the objective function and considering the restriction of output of hydropower stations, balance of water volume and control of reservoir level. The principle of immune was introduced to improve the particle swarm optimization (IPSO) algorithm for searching the optimal dispatch scheme. The improved method utilize the function of immune memory and the self adjustment mechanism to maintain the concentration of particles at a certain level in every layer to guarantee the diversity of population, and adopts the operation of vaccine inoculation to accelerate the optimization searching speed. So that the accuracy of solution is elevated and the convergence speed of calculation is higher than that of using particle swarm optimal (PSO) algorithm.