沈阳农业大学学报2018，Vol.49Issue(3)：363-370,8.DOI:10.3969/j.issn.1000-1700.2018.03.016
分布式光伏发电特性与气象影响因子诊断分析
Diagnostic Analysis of Distributed Photovoltaic Power Characteristics and the Impact of Meteorological Factors
摘要
Abstract
In order to realize the safe and stable operation of the rural power grid after the PV power plant is integrated into the grid and the formulation of the power generation plan for the rural power system, accurate prediction of the power generated by the photovoltaic power plant is indispensable. Through the acquisition of power generation and meteorological field test data of distributed photovoltaic power stations in Shenyang from October 2014 to September 2016, the Pearson correlation analysis method was used to analyze the correlation between photovoltaic power generation and meteorological factors in the same period. The correlation between sunshine hours and daily maximum temperature and PV integrated output was the highest, and the correlation coefficients were 0.902, 0.782 and 0.364, respectively. On this basis, the degree of correlation between the three meteorological factors and photovoltaic power generation in different seasons was analyzed. The correlations between radiation and sunshine duration and power generation was the highest, which were 0.972 and 0.641, respectively. The highest correlation between the maximum daily temperature and generation power in autumn was 0.382. Based on different seasons, the degree of disturbance of power generation under different weather types (sunny, cloudy/partly cloudy, partly cloudy/sunny, rainy, partly cloudy, sunny/fog and snow/partly cloudy) was analyzed, and daily power generation was conducted under different seasons and weather types. The power curves showed a normal distribution. Among them, the minimum power generation disturbance occurred in sunny days and the largest power disturbance occurred in rainy days, and the average standard deviations for the four seasons were 1.40, 2.81, 3.12, 3.36, and 3.51 for sunny, partly cloudy, partly cloudy/sunny, cloudy, and rainy days, respectively. The standard deviation of both sunny/fog and snow days was 1.91. With solar radiation, sunshine hours, and daily maximum temperature as inputs, a multivariate linear regression model for different types of weather generation in different seasons was established to predict the power generation in October 2016. The test results showed that the prediction errors were all less than 20％, and the grid power requirements were accurately met. Forecasting can better achieve the management and dispatch of rural power grids.关键词
分布式光伏/发电功率/气象因子/功率预测Key words
distributed PV/PV power/meteorological factors/power prediction分类
信息技术与安全科学引用本文复制引用
曹英丽,方诗琦,王洋,于炳新,邹焕成,许童羽..分布式光伏发电特性与气象影响因子诊断分析[J].沈阳农业大学学报,2018,49(3):363-370,8.基金项目
国家科技支撑计划项目(2012BAJ26B00) （2012BAJ26B00）
辽宁省博士启动基金项目(20131098) （20131098）
