背包模型结合粒子群算法的泵站节能优化

Energy Saving Optimization of Pumping Station Based on Knapsack Model and Particle Swarm Optimization Algorithm

泵站节能优化的研究,对于泵站的经济运行有着积极的意义。论文提出用粒子群算法求解背包模型的方法,以泵站优化之后能耗最小效率最高为准则,分析了泵站经济运行的理论数学模型。采用背包模型代替传统的罚函数,将理论数学模型转化为背包优化模型。基于常熟某大型泵站的实际工况数据,在优化模型的基础上拟合出水利泵站性能曲线,得到拟合公式后利用粒子群算法寻优,求解出在特定水情之下的优化开机组合。结果表明,将泵站传统数学模型转化为背包优化模型切实可行,用粒子群算法求解出的运行功率比未优化之前下降5.9%,相比于传统的遗传算法迭代次数降低了2.2%,效率更高收敛效果更优,节能优化成果显著。

The study of pumping station energy saving has positive significance for the economic operation of pumping stations.This paper proposes a particle swarm optimization algorithm for knapsack model.Based on the principle of minimum energy consumption and maximum efficiency after optimization of pumping stations,the theoretical and mathematical models of economic operation of pumping stations are analyzed.The knapsack model is used to replace the traditional penalty function,and the theoretical mathematical model is transformed into a knapsack optimization model.Based on the actual working condition data of a large-scale pumping station in Changshu,the performance curve of the pumping station is fitted on the basis of the optimization model,and the fitting formula is obtained.Then the optimal start-up combination under specific water conditions is solved by particle swarm optimization algorithm.The results show that it is feasible to transform the traditional mathematical model of the pumping station into a knapsack optimization model.The operation power obtained by PSO is 5.9%lower than that before optimization.Compared with the traditional genetic algorithm,the number of iterations is reduced by 2.2%.The efficiency is higher,the convergence effect is better,and the energy saving optimization results are remarkable.