摘要
对于求解复杂的并联泵站群优化运行模型,狼群算法(wolf pack algorithm,WPA)存在收敛性和鲁棒性差等问题。为改善这些问题,该文以某典型并联泵站群为例,以泵站系统主机组运行能耗最低为优化目标,考虑流量、叶片角度、开机台数等约束条件,建立了并联泵站群优化运行模型。将模拟退火算法引入WPA算法中,提出混合狼群算法(hybrid wolf pack algorithm,HWPA)用于求解建立的优化模型。选择最小值、平均值和标准差作为算法性能的评价指标。相较于粒子群算法(particle swarm optimization,PSO)和WPA算法,HWPA算法求解典型并联泵站群优化运行模型得出的运行能耗最小值平均降低了15.60、10.23 kW,平均值平均降低了36.94、14.30 kW,标准差平均降低了84.82%、72.90%。在HWPA算法的基础上,对算法中的游走步长、奔袭步长、围攻步长的最小值和最大值4个参数进行单因素分析和拉丁超立方抽样设计计算,确定出4个参数的最优组合为0.33、1.53、0.672和4.8×10^5,进而提出改进混合狼群算法(improved hybrid wolf pack algorithm,IHWPA)。相较于HWPA算法,IHWPA算法求解典型并联泵站群优化运行模型得出的运行能耗最小值和平均值平均降低了4.66和13.26 kW,标准差平均降低了94.02%。应用IHWPA算法确定典型并联泵站群6个不同运行工况优化方案,结果表明,采用引入模拟退火算法、优选WPA算法参数的方法提高了算法的全局收敛性与计算鲁棒性,泵站运行最优决策方案较实际方案的运行能耗平均降低9.80%,可为泵站工程提供合理有效的运行方案,降低运行能耗。
For solving the optimization operation model of parallel pumping stations,wolf pack algorithm(WPA)has some problems such as poor convergence and robustness.In order to improve these problems,taking a typical parallel pumping station group as an example,aiming at the lowest energy consumption of the main unit of the pumping station system,and considering the constraints of flow rate,blade angle and number of running units,a mathematical model for optimal operation of parallel pumping stations was established in this paper.Simulated annealing algorithm(SA)was introduced into WPA,named as hybrid wolf pack algorithm(HWPA),which was proposed to solve the established optimization model.Minimum value,average value and standard deviation of energy consumption were used to evaluate the performance of the algorithm.Compared with particle swarm optimization(PSO)and WPA,the minimum value of energy consumption based on HWPA was decreased by 15.60 kW and 10.23 kW on average of energy consumption,the average value of energy consumption was decreased by 36.94 kW and 14.30 kW on average respectively,and the standard deviation was decreased by an average of 84.82% and 72.90% respectively.On the basis of the HWPA,four parameters of walking step,running step,minimum and maximum of siege step in the algorithm were analyzed by single factor simulation.At the same time,the min-max standardization method was used to standardize the minimum value,average value and standard deviation of energy consumption.The standardized value was further weighted to get the comprehensive evaluation index(E)of the algorithm evaluation.Then,according to the trend of E,the reasonable range of the above four parameters was determined.According to the results of single factor analysis,the four parameters mentioned above were selected as independent variables,and latin hypercube sampling was used to design simulation.Considering the minimum value,average value and standard deviation of energy consumption,the optimal combination of parameters was determined to be 0.33,1.53,0.672 and 4.8×10^5,and then the improved hybrid wolf swarm algorithm(IHWPA)was proposed.Compared with HWPA,the minimum and average value of energy consumption based on IHWPA were reduced by 4.66 and 13.26 kW on average,and the standard deviation was reduced by an average of 94.02%.IHWPA was used to determine six optimization schemes of typical parallel pump stations under different operation conditions.The results showed that the global convergence and calculation robustness of the algorithm were improved by introducing SA algorithm and optimizing the parameters of WPA.The optimal scheme reduces the energy consumption by 9.80% on average compared with the actual operation scheme,and the energy saving effect was significant.When the total pumping flow rate was small,the optimization effect of the optimization scheme was more significant,conversely,the difference between the two schemes became smaller,but the energy consumption of the optimization scheme was still lower than that of the actual scheme.It can be concluded that IHWPA is suitable for solving optimal models for this kind of pumping stations,which can provide a reasonable and effective operation scheme for pumping station engineering and reduce the operation energy consumption.
作者
冯晓莉
王永兴
仇宝云
Feng Xiaoli;Wang Yongxing;Qiu Baoyun(College of Electrical,Energy and Power Engineering,Yangzhou University,Yangzhou 225127,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2020年第3期30-36,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金(51509217、51679208)。
关键词
泵站
优化
狼群算法
运行参数
单因素分析
拉丁超立方抽样
pumping station
optimization
wolf pack algorithm
operating parameter
single factor analysis
latin hypercube sampling
