【摘要】：可再生能源已成為我國應對世界能源危機和經(jīng)濟發(fā)展新形勢的戰(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得到了快速發(fā)展，而大規(guī)模隨機波動光伏發(fā)電的并網(wǎng)必將對電網(wǎng)的安全穩(wěn)定和調度運行產(chǎn)生不利影響。對光伏發(fā)電的輸出功率進行準確預測，是打破規(guī)�；夥l(fā)電并網(wǎng)應用瓶頸的有效措施。光伏發(fā)電功率受多元氣象因素的影響，其預測模型輸入變量選取的是否合理直接影響預測精度。目前，關于光伏發(fā)電氣象影響因子作用程度的定量研究很少，本文針對光伏發(fā)電功率與多元氣象影響因子之間的動態(tài)關聯(lián)關系開展研究，為預測模型輸入變量的識別優(yōu)化與合理選取提供科學依據(jù)，具有重要的理論意義和應用價值。 本文在對比分析光伏發(fā)電功率與多元氣象影響因子變化規(guī)律的基礎上，給出了氣象影響因子作用程度強弱的科學表示。首先，針對不同的氣象因素，通過散點圖和相關系數(shù)對其與光伏發(fā)電功率的相關性進行了分析，并討論了不同天氣類型對相關性的影響。根據(jù)相關性的大小，確定輻照度、組件溫度、環(huán)境溫度和風速為光伏發(fā)電功率的主氣象影響因子。在相關系數(shù)的基礎上，為了消除不同變量數(shù)值差異的影響，并考慮極值信息對關聯(lián)程度的作用，采用灰色關聯(lián)分析方法對氣象影響因子作用程度進行了趨勢分析。計算光伏發(fā)電功率與氣象影響因子的灰色關聯(lián)度和因子權重系數(shù)用以衡量它們之間的關聯(lián)程度，并對不同歸一化方法的計算結果進行了討論，指出0~1區(qū)間歸一化方法更適合。通過不同天氣類型下灰色關聯(lián)度和因子權重系數(shù)的對比，分析了氣象影響因子作用程度的變化趨勢。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系，利用線性的相關系數(shù)和灰色關聯(lián)度衡量氣象影響因子作用程度較難獲得滿意效果，為此，采用信息熵理論對光伏發(fā)電功率與氣象影響因子之間的動態(tài)關聯(lián)關系進行量化研究。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算其值，并對不同天氣類型下互信息值的大小進行了比較。從信息相對減少的角度，引入統(tǒng)計相關系數(shù)的概念，分析了光伏發(fā)電功率與氣象影響因子的相關性。利用互信息和統(tǒng)計相關系數(shù)給出了光伏發(fā)電功率與氣象影響因子動態(tài)關聯(lián)關系的科學度量，，并根據(jù)不同數(shù)據(jù)源的歷史數(shù)據(jù)，驗證了量化研究的結果。最后，通過綜合對比，對相關分析、趨勢分析和量化研究三種不同方法進行了評價。
[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.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM615

【參考文獻】

相關期刊論文 前10條

1 陳昌松;段善旭;殷進軍;;基于神經(jīng)網(wǎng)絡的光伏陣列發(fā)電預測模型的設計[J];電工技術學報;2009年09期

2 譚艷妮;譚忠富;;能源消費結構與經(jīng)濟增長關聯(lián)關系的灰色分析方法[J];電力學報;2009年01期

3 章堅民;章謙之;王娜;鄭凌蔚;謝小高;;光伏電站電能采集系統(tǒng)的發(fā)電模型及參數(shù)率定[J];電力系統(tǒng)自動化;2011年13期

4 朱永強;田軍;;最小二乘支持向量機在光伏功率預測中的應用[J];電網(wǎng)技術;2011年07期

5 王守相;張娜;;基于灰色神經(jīng)網(wǎng)絡組合模型的光伏短期出力預測[J];電力系統(tǒng)自動化;2012年19期

6 張艷霞;趙杰;;基于反饋型神經(jīng)網(wǎng)絡的光伏系統(tǒng)發(fā)電功率預測[J];電力系統(tǒng)保護與控制;2011年15期

7 楊志安，王光瑞，陳式剛;用等間距分格子法計算互信息函數(shù)確定延遲時間[J];計算物理;1995年04期

8 李芬;陳正洪;成馳;段善旭;;太陽能光伏發(fā)電量預報方法的發(fā)展[J];氣候變化研究進展;2011年02期

9 丁晶,王文圣,趙永龍;以互信息為基礎的廣義相關系數(shù)[J];四川大學學報(工程科學版);2002年03期

10 劉玉蘭;孫銀川;桑建人;左河疆;嚴曉瑜;馬篩艷;;影響太陽能光伏發(fā)電功率的環(huán)境氣象因子診斷分析[J];水電能源科學;2011年12期

本文編號：2254012