光伏电站天空图像中云团运动速度的快速准确计算是进行光伏功率分钟级超短期预测的前提基础.针对目前采用图像分割匹配计算方法耗时较长的缺点,基于分钟级时间尺度下相邻天空图像中云团形变较小的条件,根据图像相位差的周期分布特性,提出一种基于傅里叶相位相关理论的云团分钟级运动速度计算方法,并定义针对其计算结果准确性的评估指标.实际算例对比结果表明,在相同计算精度前提下,该方法较现有方法可大幅减少运算时间,且鲁棒性与抗干扰能力更强.

The various meteorological factors affecting the output power of the photovoltaic(PV) power station are analyzed at first.Then the relationship between the meteorological factors and electrical characteristics of PV panel is formulated on the basis of physical theory.Finally,the output power of PV station is predicted using the PV cell diode model and the inverter loss model,and the measured data from an actual PV station are used to verify the prediction.The prediction results show that the method can improve the prediction precision and meet the requirements of practical application.Besides,the method is not restricted by the historical data,adaptable to the power prediction of newly-built PV power stations.

<正>随着社会经济的快速发展,能源已成为制约人类经济社会发展的主要因素之一,为了应对越来越紧张的能源供应和化石能源导致的气候影响,世界各国都十分重视可再生能源的开发和利用。欧盟提出了2020年可再生能源替代常规能源达到20%,2050年达到80%～100%的计划,中国也提出在2020年中国非化石能源占能源总量

The statistical method for predicting photovoltaic generation power was studied in this paper.Based on the analysis of various meteorological factors,such as solar radiation,temperature and cloudiness,the BP neural network was adopted to establish the statistical model,which realized the power prediction of a photovoltaic station for the next 24 hours.The proposed method was then used to establish the power prediction systems of the main photovoltaic stations in Shanghai.The results suggested that this method had higher prediction precision,and could meet the requirements of the practical engineering application.