Association Relationship Analysis And Research Of PV Power Generation And Meteorological Impact Factors

Renewable energy has become a strategic emerging industry coping the worldenergy crisis and new situation of economic development in China. As an importantpart of renewable energy, the photovoltaic power has been developing rapidly inrecent years, and large-scale random fluctuating PV power grid is bound to affectthe safe&stability and dispatching operation of the grid adversely. The accurateprediction of the PV power output is an effective measure to break the applicationbottleneck of scale PV power grid. PV power generation is impacted by multiplemeteorological factors, yet whether the selected input variables of its predictionmodel is reasonable, affects the prediction accuracy directly. Currently thequantitative study on the effect extent of meteorological impact factors is few, thisthesis directs at the dynamic association relationship of PV power generation andmultiple meteorological impact factors, providing a scientific basis for theoptimized identification and reasonable selection of the input variables ofprediction model, with theoretical significance and application value.In this thesis, on the basis of the comparative analysis of the variation of PVpower generation and multiple meteorological impact factors, the scientificrepresentation of the effect extent of meteorological impact factors has been given.First, for different meteorological factors, through the scatter plot and correlationcoefficient, their correlations with PV power generation have been analyzed, and ithas discussed the effects of different weather types on correlation. According to thesize of correlation, the irradiance, module temperature, ambient temperature andwind speed have been determined as the main meteorological impact factors of PVpower generation. On the basis of the correlation coefficient, in order to eliminatethe influence of numerical differences of different variables, and considering therole of extremum information on the association degree, the trend analysis of theeffect extent of meteorological impact factors has been proceeded employing theGrey Relational Analysis(GRA). Computing the grey relational degrees and factorweighting coefficients of PV power generation and meteorological impact factors,which measure the association degree of them, and the results of differentnormalization methods have been discussed, nothing the normalization method ofrange0to1is more suitable. By comparing the grey relational degrees and factorweighting coefficients under different weather types, it has analyzed thetrends of the effect extent of meteorological impact factors. Second, due to themulti-coupling nonlinear relationship of PV power generation and meteorologicalimpact factors, utilizing the linear correlation coefficient and grey relational degree to evaluate the effect extent of meteorological impact factors is difficult to obtain asatisfactory result, therefore, the information entropy theory has been used to studythe dynamic association relationship of PV power generation and meteorologicalimpact factors quantitatively. From the point of information loss, it has defined themutual information of PV power generation and meteorological impact factors, forwhich the equidistant method has been selected to calculate its valueapproximatively, and it has compared the values of the mutual information of PVpower generation and meteorological impact factors under different weather types.From the point of relative information reduction, introducing the concept ofstatistical correlation coefficient, the correlation of PV power generation andmeteorological impact factors has been analyzed. It has provided the scientificmeasurement of the dynamic association relationship of PV power generation andmeteorological impact factors employing the mutual information and statisticalcorrelation coefficient, and according to historical data from different sources, theresults of quantitative study have been verified. Finally, the three different methods,correlation analysis, trend analysis and quantitative study, have been evaluatedthrough a comprehensive comparison.