發(fā)布時間：2018-05-31 12:57

  本文選題：光伏功率預(yù)測 + 灰色關(guān)聯(lián)分析��； 參考：《東北電力大學(xué)》2017年碩士論文

【摘要】：太陽能發(fā)電受到日照,季節(jié)變化及天氣波動等氣候條件的影響,使得發(fā)電系統(tǒng)的輸出功率具有間斷性、周期性和不確定性的特點。光伏發(fā)電功率預(yù)測技術(shù)作為光伏電站建站必備的技術(shù)條件,關(guān)系到電站并網(wǎng)及電網(wǎng)調(diào)度的準(zhǔn)確性和合理性,若能準(zhǔn)確掌握短期內(nèi)光伏電站的輸出功率,可大大降低電站并網(wǎng)風(fēng)險,提高電網(wǎng)運行的安全性和穩(wěn)定性。本文在對氣象因素如何影響光伏發(fā)電功率輸出以及功率預(yù)測技術(shù)基礎(chǔ)進(jìn)行了簡要介紹之后,將光伏發(fā)電功率短期預(yù)測問題分為超短期和短期兩部分。針對超短期輸出功率的預(yù)測,提出一種基于氣象因素的相似日選取方法:利用光伏發(fā)電系統(tǒng)的歷史氣象信息建立氣象特征向量,通過計算灰色關(guān)聯(lián)度尋找到預(yù)測日的相似歷史日。然后使用相似日歷史數(shù)據(jù)和小波神經(jīng)網(wǎng)絡(luò)(WNN,Wavelet Neural Network)構(gòu)建一種光伏發(fā)電功率的超短期預(yù)測模型,通過使用某光伏發(fā)電系統(tǒng)的歷史數(shù)據(jù)進(jìn)行建模,對所選兩類不同天氣類型的預(yù)測日的出力情況進(jìn)行逐時刻預(yù)測,預(yù)測結(jié)果顯示模型預(yù)測效果較好,尤其對于理想晴天條件下預(yù)測的更加精確。針對短期輸出功率的預(yù)測,提出一種基于思維進(jìn)化算法(MEA,Mind Evolutionary Algorithm)優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的光伏功率短期預(yù)測模型,通過我國青海省錫鐵山裝機量100MW的光伏電站為期一年的歷史運行數(shù)據(jù)進(jìn)行建模,按照季節(jié)劃分為四個預(yù)測單元分別對預(yù)測模型進(jìn)行訓(xùn)練和電站出力預(yù)測,通過與電站的實際出力情況和電站所配備的預(yù)測系統(tǒng)短期預(yù)測值的比較分析,由BP算法和MEA-BP算法所構(gòu)建的模型均達(dá)到了一定的預(yù)測精度,其中MEA-BP模型有效的降低了BP網(wǎng)絡(luò)模型的預(yù)測誤差。最后將相似日與神經(jīng)網(wǎng)絡(luò)結(jié)合,建立一個基于相似日和神經(jīng)網(wǎng)絡(luò)的光伏功率短期預(yù)測模型:通過設(shè)置兩組對照實驗:一組使用相似歷史日的數(shù)據(jù)來訓(xùn)練網(wǎng)絡(luò)并進(jìn)行預(yù)測(實驗組),一組使用相鄰歷史日數(shù)據(jù)來訓(xùn)練網(wǎng)絡(luò)并進(jìn)行預(yù)測(對照組),對比實驗的結(jié)果顯示實驗組的預(yù)測效果更為準(zhǔn)確。經(jīng)過反復(fù)預(yù)測實驗,驗證了課題所提出的預(yù)測模型能夠?qū)τ行У念A(yù)測光伏發(fā)電系統(tǒng)的輸出功率,預(yù)測結(jié)果也表明基于神經(jīng)網(wǎng)絡(luò)的預(yù)測模型在一定程度上能夠滿足實際應(yīng)用需求,在光伏電站建站時對功率預(yù)測技術(shù)的設(shè)計具有一定的參考價值。
[Abstract]:Solar power generation is affected by the weather conditions such as sunshine, seasonal variation and weather fluctuation, which makes the output power of the power generation system have the characteristics of discontinuity, periodicity and uncertainty. As a necessary technical condition for the construction of photovoltaic power station, PV generation power prediction technology is related to the accuracy and rationality of grid connection and grid dispatching. If the output power of photovoltaic power station can be accurately grasped in the short term, It can greatly reduce the risk of grid connection and improve the security and stability of power grid operation. After a brief introduction of how meteorological factors affect the output of photovoltaic power generation and the technical basis of power prediction, the short-term prediction of photovoltaic power generation is divided into two parts: ultra short term and short term. According to the prediction of ultra-short-term output power, a similar day selection method based on meteorological factors is proposed: the meteorological characteristic vector is established by using the historical meteorological information of photovoltaic power generation system. The similar historical days of the predicted days are found by calculating the grey correlation degree. Then, using the similar daily historical data and wavelet neural network (WNNN) to construct an ultra-short-term prediction model of photovoltaic power generation, using the historical data of a photovoltaic power generation system to model. The prediction results show that the forecasting effect of the model is better, especially for the ideal sunny weather conditions. In order to predict the short-term output power, a short-term PV power prediction model based on the thinking evolutionary algorithm (MEA ind Evolutionary algorithm) is proposed to optimize the BP neural network. Based on the historical operation data of 100MW photovoltaic power station in Xitieshan of Qinghai Province for one year, the forecasting model is divided into four forecasting units according to the seasons, and the forecasting model is trained and the power generation of the power station is predicted. By comparing with the actual force of the power station and the short-term prediction value of the forecasting system equipped with the power station, the model constructed by BP algorithm and MEA-BP algorithm has achieved a certain prediction accuracy. MEA-BP model can effectively reduce the prediction error of BP network model. Finally, the similar days are combined with neural networks. Establish a short-term photovoltaic power prediction model based on similar days and neural networks: train and predict the network by setting up two groups of controlled experiments: one group uses data from similar historical days to train the network and make prediction (experimental group, group using adjacent data) Historical daily data to train the network and predict (control group, the results of comparative experiments show that the experimental group is more accurate prediction results. After repeated prediction experiments, it is verified that the proposed prediction model can effectively predict the output power of photovoltaic power generation system. The prediction results also show that the prediction model based on neural network can meet the practical application needs to a certain extent. It has certain reference value for the design of power prediction technology in the construction of photovoltaic power station.
【學(xué)位授予單位】：東北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP183;TM615

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本文編號：1959754