本文關鍵詞： 光伏發(fā)電 短期功率預測 超短期功率預測 相關系數 天氣類型　出處：《江蘇大學》2017年碩士論文　論文類型：學位論文

【摘要】：近年來,隨著相關科技的進步,光伏發(fā)電的單位造價持續(xù)下降,因而得到了快速發(fā)展。太陽能作為一種清潔可再生能源,利用太陽能進行光伏發(fā)電能夠大幅緩解能源危機,減輕由傳統(tǒng)化石燃料帶來的一系列環(huán)境問題。然而,地表太陽能屬于間歇性能源,使得光伏電站的發(fā)電功率呈現出波動性和間歇性特征。當并網光伏電站裝機容量較大時,電力系統(tǒng)的安全性與穩(wěn)定性將受到影響。因此,需要準確預測光伏電站的發(fā)電功率以配合電力部門進行合理的計劃和調度。本文依托兩座并網光伏電站的實際采集數據,分析總結了近年來國內外相關領域的研究進展,對光伏電站短期和超短期功率預測進行了較為詳細的分析研究,論文主要包括以下幾方面內容:(1)根據相關理論設計了一種地外輻照度計算器,將獲取到的原始數據通過異常值監(jiān)測、有效時間區(qū)間判定、插補缺失數據和歸一化等步驟進行數據預處理后,建立了用于光伏發(fā)電功率預測的數據庫;(2)提出了一種基于ELM-SVM的短期功率預測模型。首先,根據天氣預報給出的不同天氣類型中地外輻照度與發(fā)電功率間的相關系數,將各天氣類型合并成晴天、多云、雨天三種典型天氣類型,并分別建立子預測模型。之后,利用皮爾遜相關系數根據各典型天氣類型的特征,選取針對性較強的參數作為子預測模型的輸入。最后利用“積分競爭制”回歸模型選取法,選取ELM作為晴天條件下的回歸模型,SVM作為多云和雨天條件下的回歸模型。結果表明ELM-SVM混合預測模型能夠發(fā)揮不同回歸模型的優(yōu)勢,相比使用單一模型預測方法,該混合預測模型具有更強的適應能力和更好的預測效果;(3)使用歷史發(fā)電功率作為模型輸入,本文提出了基于ELM的超短期功率預測模型。相比BP神經網絡,ELM具有更好的預測效果。最后,根據ELM模型在不同時間區(qū)間內的誤差分布特征,將歷史發(fā)電功率分時段訓練并建立子預測模型,實驗結果表明,基于ELM的分段式功率預測模型在天氣波動較大的環(huán)境中表現更佳。
[Abstract]:In recent years, with the progress of related science and technology, the unit cost of photovoltaic power has been continuously reduced, so it has been rapidly developed. Solar energy as a clean and renewable energy. Solar photovoltaic power generation can significantly alleviate the energy crisis and alleviate a series of environmental problems caused by traditional fossil fuels. However, surface solar energy is an intermittent energy source. When the installed capacity of grid-connected photovoltaic power station is large, the security and stability of power system will be affected. It is necessary to accurately predict the generation power of photovoltaic power station in order to cooperate with the power department to plan and dispatch reasonably. This paper relies on the actual acquisition data of two grid-connected photovoltaic power stations. This paper analyzes and summarizes the research progress in the related fields at home and abroad in recent years, and makes a more detailed analysis and research on the short-term and ultra-short-term power prediction of photovoltaic power plants. This paper mainly includes the following aspects: 1) according to the relevant theory, a kind of extraterrestrial irradiance calculator is designed. The original data is monitored by outliers and the effective time interval is determined. After preprocessing the missing data and normalized data, the database of photovoltaic power prediction is established. In this paper, a short-term power prediction model based on ELM-SVM is proposed. Firstly, the correlation coefficient between external irradiance and generation power in different weather types is given. The weather types are combined into three typical weather types: sunny, cloudy and rainy, and sub-prediction models are established respectively. After that, Pearson correlation coefficient is used according to the characteristics of each typical weather type. The parameters are selected as the input of the sub-prediction model. Finally, the "integral competition system" regression model selection method is used to select ELM as the regression model under sunny conditions. SVM is a regression model under cloudy and rainy conditions. The results show that the ELM-SVM hybrid prediction model can play the advantages of different regression models, compared with the single model prediction method. The hybrid prediction model has stronger adaptability and better prediction effect. Using the historical generation power as the input of the model, this paper presents an ultra-short-term power prediction model based on ELM. It has better prediction effect than BP neural network. Finally. According to the error distribution characteristics of the ELM model in different time intervals, the historical generation power is trained in different periods and the sub-prediction model is established. The experimental results show that. The segmented power prediction model based on ELM performs better in the fluctuating weather environment.
【學位授予單位】：江蘇大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM615

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