基于遗传算法优化粗糙决策模型的光伏电站发电量的影响因素研究

RESEARCH ON INFLUENCING FACTORS OF POWER GENERATION OF PV POWER STATION BASED ON ROUGH SETS DECISION MODEL WITH GENETIC ALGORITHM OPTIMIZATION

通常光伏电站设计时要考虑2个主要目标,一个是使光伏电站的发电量最大,另一个是使光伏电站的度电成本最低。影响光伏电站发电量的因素众多,不同因素之间的相互影响及其交互影响极其复杂。在粗糙决策模型的基础上,引入遗传算法来寻找光伏电站发电量的主要影响因素。以光伏电站发电量作为决策属性,选取8个参数作为条件属性,建立了光伏电站发电量的诊断模型,通过计算分析,得到了影响光伏电站发电量的决策规则。结果表明:光伏组件串联数、热交换系数及交流线损是影响光伏电站发电量的决定性因素;对热交换系数较大的大型地面光伏电站而言,通过控制交流线损和光伏组件串联数可以更有效地提高光伏电站的发电量。

There are generally two main objectives should be considered in the design of PV power stations:the maximum power generation and the minimum LCOE.The factors that affect the power generation of PV power station are numerous and complex,and the interaction between different factors is extremely complex.Based on the rough sets decision model,this paper introduces genetic algorithm to find out the main influent factors for the power generation of the PV power station.Taking the power generation of the PV power station as the decision attribute and selecting eight parameters as conditional attributes to establish a diagnosis model for power generation of PV power station.Through calculation and analysis,the decision rules affecting the power generation of PV power station are obtained.The results show that the series number of PV modules,heat exchange coefficient and AC line loss are the decisive factors affecting power generation of PV power station.For large-scale ground PV power stations with a large heat exchange coefficient,the power generation of PV power station can be more effectively increased by controlling the AC line loss and the series number of PV modules.