摘要
针对多目标粒子群优化算法求解负荷优化分配问题时所出现的最优解分布不均、局部最优等问题,引入了精英交叉算子并基于拥挤度对非劣解集进行排序,给出了精确计及网损时的机组出力等式不等式约束处理方法。在忽略和计及网损两种情况下针对3机组系统进行负荷优化分配,仿真结果表明改进后的粒子群优化算法寻优能力得到提升。同样利用模糊隶属度函数筛选Pareto解集,所提方法得到的结果明显优于常规粒子群优化算法,在降低发电成本及污染物排放的同时使得求解结果严格满足约束条件。
Aiming at the problem of uneven distribution of the optimal solution and local optimality when the multi-objective particle swarm optimization algorithm is used to solve the problem of optimal load distribution,the elite crossover operator is introduced and the non-inferior solution sets are ranked based on the congestion.This paper presents a method to deal with the equality and inequality constraints of the unit output when the network loss is taken into account accurately.Load optimization distribution is performed for the three-unit system with or without network loss.Simulation results show that the improved particle swarm optimization algorithm has improved the optimization ability.When the fuzzy membership function is used to screen Pareto solution set,the result of the method proposed in this paper is obviously better than that of the conventional particle swarm optimization algorithm,which can reduce the cost of power generation and pollutant emission while making the solution strictly meet the constraints.
作者
魏家柱
潘庭龙
Wei Jiazhu;Pan Tinglong(School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,Jiangsu,China)
出处
《电测与仪表》
北大核心
2022年第10期117-122,129,共7页
Electrical Measurement & Instrumentation
基金
国家自然科学基金资助项目(61672266)。
关键词
经济环保负荷分配
粒子群优化算法
精英交叉算子
拥挤距离排序
economic and environmental load distribution
particle swarm optimization algorithm
elite crossover operator
congestion distance ranking
