摘要
针对基于线性加权和处理成单目标优化问题的传统方法存在的缺陷,提出使用粒子群优化算法求解EELD多目标优化问题。该方法通过对粒子群算法个体极值和全局极值选取方式的改进,实现了对EELD多目标优化问题的非劣最优解集的搜索,为决策者提供了丰富的参考信息。在此基础上,应用模糊满意度方法求出的最优折衷解为调度运行人员提供了最佳调度折衷方案。最后,对一个三机系统进行了测试,并与线性加权人工神经网络法进行了比较分析,仿真结果验证了该方法的有效性。
To handle the existing defects of traditional methods which are based on the linear weighting and treated as single objective optimization problem, an improved particle swarm optimization（PSO） algorithm to solve EELD problems is proposed. Through the improvement of the modes of gBest and pBest of PSO algorithm, this method can implement the search for Pareto optimal set of EELD multi-objective optimization problems, and provide abundance of reference information for decisionmaker, On this basis, optimal compromise solution by approach of fuzzy satisfaction supplies the best compromise scheme for dispatcher. Finally, the effectiveness of this method is validated by sample system.
出处
《江西电力职业技术学院学报》
CAS
2009年第1期8-11,共4页
Journal of Jiangxi Vocational and Technical College of Electricity
关键词
电力系统
环境经济负荷调度
多目标优化
粒子群优化算法
power system
economic emission load dispatch
multi-objective optimization
particle swarm optimization
作者简介
陈茂迁（1983-），男，广西阳江人，硕士研究生，主要研究方向为调度自动化及计算机信息处理．
