自然降雨下光伏组件积灰预测方法研究

为了预测光伏组件上的积灰量,以相关气象因素为输入变量,基于最小二乘支持向量机(LSSVM)预测算法,并通过改进粒子群算法(PSO)中惯性因子的衰减方式提高预测算法寻优鲁棒性。在积灰预测模型中考虑当地时空因素,引入强相关的降雨量,并结合光伏组件发电功率衰减率,建立自然降雨清洗下的积灰预测模型。在杭州地区进行测量实验,通过实例计算表明此模型可快速预测统计周期内光伏组件积灰量以及积灰引起功率衰减率,为精准预测光伏发电功率和制定组件清洁频率提供依据。

STUDY ON PREDICTION ALGORITHM OF DUSTFALL ON PV MODULES UNDER NATURAL RAINFALL

To predict the amount of dust accumulated on the surface of photovoltaic modules,the meteorological factors were used as variables,the Least Squares Support Vector Machine(LSSVM)was used as Prediction algorithm,and by improving the attenuation method of the inertia factor in the particle swarm optimization(PSO)to improve the algorithm’s optimization robustness.In the prediction model of dust accumulation,local time and space factors were taken account,combined the rainfall which was strongly correlated and the power attenuation rate of photovoltaic modules to establish a dust accumulation prediction model under natural rainfall.Measurement experiments were carried out in Hangzhou,and the example calculation showed that the model could quickly predict the amount of dust accumulation of photovoltaic modules and the rate of power attenuation caused by dust accumulation during the statistical period,which could provide a basis for accurately predicting the power generation of photovoltaic modules and formulating the cleaning frequency of modules.