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提出应用多目标遗传算法解决电力系统经济负荷分配问题。对负荷分配的数学模型进行了分析,将这一带约束的单目标优化问题转换成总煤耗函数和违反约束条件的程度函数两个目标函数优化问题。该算法采用实数编码技术,Pareto强度值作为个体的评价指标,利用遗传算法实现种群的进化,最终找到最优解。将该方法分别应用于某5台机组组成的发电系统和3台机组组成的发电系统进行负荷优化计算,结果与基于惩罚函数的单目标优化算法进行比较,分析表明该算法在确保满足各约束条件的前提下具备较好的寻优性能,证实了该算法的可行性与有效性。
A multi-objective genetic algorithm is proposed to solve the economic load distribution in power system. The mathematical model of load distribution is analyzed, and the single objective optimization problem with constraint is transformed into two objective function optimization problems with total coal consumption function and degree function violating constraints. The algorithm uses real number encoding technology, Pareto intensity value as an individual evaluation index, the use of genetic algorithms to achieve population evolution, and ultimately find the optimal solution. The proposed method is applied to a power generation system consisting of 5 units and 3 units for load optimization. The results are compared with the single objective optimization algorithm based on penalty function. The results show that the proposed algorithm can meet the constraints of each constraint Under the premise of better search performance, confirmed the feasibility and effectiveness of the algorithm.