【摘要】：可再生能源已成為我國(guó)應(yīng)對(duì)世界能源危機(jī)和經(jīng)濟(jì)發(fā)展新形勢(shì)的戰(zhàn)略新興產(chǎn)業(yè)。光伏發(fā)電作為可再生能源的重要組成部分近年來(lái)得到了快速發(fā)展，而大規(guī)模隨機(jī)波動(dòng)光伏發(fā)電的并網(wǎng)必將對(duì)電網(wǎng)的安全穩(wěn)定和調(diào)度運(yùn)行產(chǎn)生不利影響。對(duì)光伏發(fā)電的輸出功率進(jìn)行準(zhǔn)確預(yù)測(cè)，是打破規(guī)模化光伏發(fā)電并網(wǎng)應(yīng)用瓶頸的有效措施。光伏發(fā)電功率受多元?dú)庀笠蛩氐挠绊�，其預(yù)測(cè)模型輸入變量選取的是否合理直接影響預(yù)測(cè)精度。目前，關(guān)于光伏發(fā)電氣象影響因子作用程度的定量研究很少，本文針對(duì)光伏發(fā)電功率與多元?dú)庀笥绊懸蜃又g的動(dòng)態(tài)關(guān)聯(lián)關(guān)系開(kāi)展研究，為預(yù)測(cè)模型輸入變量的識(shí)別優(yōu)化與合理選取提供科學(xué)依據(jù)，具有重要的理論意義和應(yīng)用價(jià)值。 本文在對(duì)比分析光伏發(fā)電功率與多元?dú)庀笥绊懸蜃幼兓?guī)律的基礎(chǔ)上，給出了氣象影響因子作用程度強(qiáng)弱的科學(xué)表示。首先，針對(duì)不同的氣象因素，通過(guò)散點(diǎn)圖和相關(guān)系數(shù)對(duì)其與光伏發(fā)電功率的相關(guān)性進(jìn)行了分析，并討論了不同天氣類型對(duì)相關(guān)性的影響。根據(jù)相關(guān)性的大小，確定輻照度、組件溫度、環(huán)境溫度和風(fēng)速為光伏發(fā)電功率的主氣象影響因子。在相關(guān)系數(shù)的基礎(chǔ)上，為了消除不同變量數(shù)值差異的影響，并考慮極值信息對(duì)關(guān)聯(lián)程度的作用，采用灰色關(guān)聯(lián)分析方法對(duì)氣象影響因子作用程度進(jìn)行了趨勢(shì)分析。計(jì)算光伏發(fā)電功率與氣象影響因子的灰色關(guān)聯(lián)度和因子權(quán)重系數(shù)用以衡量它們之間的關(guān)聯(lián)程度，并對(duì)不同歸一化方法的計(jì)算結(jié)果進(jìn)行了討論，指出0~1區(qū)間歸一化方法更適合。通過(guò)不同天氣類型下灰色關(guān)聯(lián)度和因子權(quán)重系數(shù)的對(duì)比，分析了氣象影響因子作用程度的變化趨勢(shì)。其次，由于光伏發(fā)電功率與氣象影響因子之間是多重耦合的非線性關(guān)系，利用線性的相關(guān)系數(shù)和灰色關(guān)聯(lián)度衡量氣象影響因子作用程度較難獲得滿意效果，為此，采用信息熵理論對(duì)光伏發(fā)電功率與氣象影響因子之間的動(dòng)態(tài)關(guān)聯(lián)關(guān)系進(jìn)行量化研究。從信息損失的角度，定義了光伏發(fā)電功率與氣象影響因子的互信息，選擇等間距法近似計(jì)算其值，并對(duì)不同天氣類型下互信息值的大小進(jìn)行了比較。從信息相對(duì)減少的角度，引入統(tǒng)計(jì)相關(guān)系數(shù)的概念，分析了光伏發(fā)電功率與氣象影響因子的相關(guān)性。利用互信息和統(tǒng)計(jì)相關(guān)系數(shù)給出了光伏發(fā)電功率與氣象影響因子動(dòng)態(tài)關(guān)聯(lián)關(guān)系的科學(xué)度量，，并根據(jù)不同數(shù)據(jù)源的歷史數(shù)據(jù)，驗(yàn)證了量化研究的結(jié)果。最后，通過(guò)綜合對(duì)比，對(duì)相關(guān)分析、趨勢(shì)分析和量化研究三種不同方法進(jìn)行了評(píng)價(jià)。
[Abstract]:Renewable energy has become a strategic emerging industry in China to deal with the world energy crisis and the new situation of economic development. Photovoltaic power generation as an important part of renewable energy has been rapidly developed in recent years, and large-scale random fluctuations of photovoltaic power grid will inevitably have a negative impact on the security and stability of the grid and dispatching operation. Accurate prediction of the output power of photovoltaic power generation is an effective measure to break the bottleneck of grid-connected application of large-scale photovoltaic power generation. The power of photovoltaic generation is affected by multiple meteorological factors, and whether the input variables of the prediction model is reasonable or not has a direct impact on the prediction accuracy. At present, there are few quantitative studies on the effect of meteorological impact factors on photovoltaic power generation. This paper focuses on the dynamic correlation between photovoltaic power generation and multiple meteorological impact factors. It is of great theoretical significance and practical value to provide scientific basis for the identification, optimization and reasonable selection of input variables of prediction model. On the basis of comparing and analyzing the variation law of photovoltaic power generation power and multivariate meteorological influence factors, the scientific expression of the degree of action of meteorological influence factors is given in this paper. Firstly, according to different meteorological factors, the correlation between PV power and scattered plot and correlation coefficient is analyzed, and the influence of different weather types on the correlation is discussed. According to the correlation, the irradiance, module temperature, ambient temperature and wind speed are the main meteorological factors of photovoltaic power generation. Based on the correlation coefficient, in order to eliminate the influence of different variables and consider the effect of extreme value information on the correlation degree, the grey correlation analysis method is used to analyze the trend of meteorological influence factors. The grey correlation degree and factor weight coefficient of photovoltaic power and meteorological influence factors are calculated to measure the correlation degree between them. The results of different normalization methods are discussed and it is pointed out that the normalization method is more suitable in 0 ~ 1 interval. Based on the comparison of grey correlation degree and factor weight coefficient under different weather types, the change trend of the action degree of meteorological influence factors is analyzed. Secondly, because of the nonlinear relationship between photovoltaic power and meteorological impact factors, it is difficult to obtain satisfactory results by using linear correlation coefficient and grey correlation degree to measure the effect of meteorological impact factors. The information entropy theory is used to quantify the dynamic correlation between photovoltaic power and meteorological factors. From the point of view of information loss, the mutual information between photovoltaic power generation and meteorological influence factors is defined, and the value of mutual information under different weather types is calculated by the equal-distance method. From the point of view of relative reduction of information, the concept of statistical correlation coefficient is introduced to analyze the correlation between photovoltaic power generation and meteorological factors. Based on mutual information and statistical correlation coefficient, a scientific measure of dynamic correlation between photovoltaic power generation and meteorological impact factors is presented, and the results of quantitative research are verified according to historical data from different data sources. Finally, three different methods of correlation analysis, trend analysis and quantitative research are evaluated by comprehensive comparison.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM615

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