基于最小二乘支持向量回归的光伏电站清洗时间动态优化方法

DYNAMIC OPTIMIZATION OF PV PLANT CLEANING TIME BASED ON LS-SVR

在现有研究基础上提出基于最小二乘支持向量回归的清洗时间优化方法,通过构建发电功率积灰衰减模型和峰值小时数气象预测模型,对特定间隔周期内不同清洗时间的经济效益进行动态评估,从而确定使光伏电站收益最大化的清洗时间。以上方法在户外光伏电站上进行了工程验证,基于实测数据与预测数据的计算结果对比分析表明,该方法能较准确地预测光伏电站最佳清洗时间,而清洗收益则还与清洗周期的选择以及清洗成本相关。该文提出的动态优化方法具有一定的可行性和适用性,为光伏电站的后期运维提供了有效的手段和依据。

This paper proposes a decision method using least square support vector regression(LS-SVR)to find the optimal situation.Algorithm models have been developed to predict the output power of PV plants with dust accumulation,as well as the peak sun hours according to weather classification.The economic benefits of PV plants could be evaluated dynamically for different time schedules using these models,and thus the best cleaning time during specific period is just the one that maximizes the benefits.Field experiments are carried out to verify the feasibility of our method.When the results evaluated from measured data are compared with those evaluated from prediction data,it is clear that our method is capable of predicting the optimal cleaning time of PV plants.Besides,it is also noticed that the economic benefits of optimized cleaning is also related to the length of period and cost of cleaning.The method proposed in this work provides a promising tool for the real-time optimization of dust cleaning strategy,which would be of great meaning during the maintenance and operation of PV plants.