本文選題：河西地區(qū) + 風力發(fā)電　； 參考：《蘭州大學》2014年博士論文

【摘要】：面對煤炭、石油等傳統(tǒng)能源資源的日益枯竭,以及日益嚴峻的環(huán)境問題,風能、太陽能等可再生能源已在世界范圍內(nèi)受到重視。其中風能作為重要的可再生能源資源,具有蘊藏量豐富、可再生、分布廣、無污染等特性,經(jīng)過近些年的發(fā)展,風力發(fā)電在電力發(fā)展中占據(jù)著不可忽視的地位。風電具有波動性和間歇性特點,大規(guī)模風電的接入對電力系統(tǒng)的安全穩(wěn)定運行帶來了挑戰(zhàn)。風電功率預測對于電力調(diào)度部門根據(jù)風電功率變化及時調(diào)整調(diào)度計劃、保證電能質(zhì)量、減少系統(tǒng)的備用容量、降低系統(tǒng)運行成本都是至關重要的。而風電場風速預測是風電功率預測的基礎,因此,提高風電場風速預測的精度,對于風力發(fā)電的發(fā)展起著十分關鍵的作用。根據(jù)預測周期的不同,風速預測通常可以分為長期風速預測、中期風速預測和短期風速預測。長期風速預測主要用于風電場規(guī)劃設計;中期風速預測主要用于電力系統(tǒng)的功率平衡和調(diào)度、交易、暫態(tài)穩(wěn)定評估;短期風速預測主要用于發(fā)電系統(tǒng)的控制,其對于及時糾正電網(wǎng)并網(wǎng)計劃中的偏差,完善電網(wǎng)并網(wǎng)計劃,充分利用風能,減少因中長期預測中的偏差而限發(fā)的電量,并保證電網(wǎng)安全,有著重要的意義。但目前短期風速預測精度依然不足,提高短期風速預測精度成為目前亟待解決的問題。本文的重點集中在短期風速統(tǒng)計預測方法的開發(fā)研究上。本文以河西地區(qū)的風速為研究對象,系統(tǒng)分析了該地區(qū)不同站點的風速和風向的統(tǒng)計規(guī)律,并探討了其變化特征。根據(jù)其變化特征,開發(fā)了三類較高精度的短期風速統(tǒng)計預報方法,分別為基于周期矯正(SAM)的短期風速預測模型、基于經(jīng)驗模式分解(EMD)的風速預測模型和模型重組的新預測方法。這就為風力發(fā)電系統(tǒng)的控制和風電場的短期風功率預測系統(tǒng)的開發(fā)提供指導。其主要結(jié)果如下:1)開發(fā)的第一類研究方法,針對實際風速的復雜周期變化,將SAM應用于風速預測模型中。這類方法提出了兩種基于SAM的短期風速預測模型,一種是將SAM和指數(shù)平滑法(ESM)相結(jié)合,我們稱之為SAM-ESM模型,另一種是將SAM和小波神經(jīng)網(wǎng)絡(WNN)相結(jié)合,并用遺傳算法(GA)對WNN進行了學習訓練,我們稱之為SAM-GA-WNN模型,利用這兩種模型對河西地區(qū)的風速進行了短期預測,并將預測結(jié)果與傳統(tǒng)的持續(xù)法(PM)預測結(jié)果進行了對比分析,結(jié)果表明,基于SAM的短期風速預測方法是一類較優(yōu)的預測方法,能夠提高預測精度。2)開發(fā)的第二類研究方法,針對風速數(shù)據(jù)序列的非平穩(wěn)性,將處理非平穩(wěn)信號的EMD方法應用于風速預測模型中。這類方法提出了兩種基于EMD分解的風速預測模型,一種是將EMD分解和自回歸移動平均(ARMA)模型相結(jié)合,我們稱之為EMD-ARMA模型,另一種是將EMD分解和BP神經(jīng)網(wǎng)絡(BPNN)相結(jié)合,并用粒子群優(yōu)化算法(PSO)對BPNN進行了學習訓練,我們稱之為EMD-PSO-BPNN模型,利用這兩種模型對河西地區(qū)的風速進行了短期預測,并將預測結(jié)果與PM模型的預測結(jié)果進行了對比分析,結(jié)果表明,本研究所開發(fā)的第二類短期風速預測方法是一類較優(yōu)的預測方法,能夠提高預測精度。3)開發(fā)的第三類研究方法,針對風速變化的不同模式,將模型重組的思想應用到風速預測模型中。這類方法提出了兩種模型重組的新預測模型,一種是將ESM模型和WNN模型相結(jié)合,ESM模型主要是用來捕獲風速變化的線性模式,WNN模型是來捕獲非線性模式,并考慮到WNN建模預測的復雜性,采用GA對WNN進行學習訓練,我們稱之為ESM-GA-WNN模型,另一種是ARMA和BPNN模型相結(jié)合,并用PSO對BPNN進行學習訓練,我們稱之為ARMA-PSO-BPNN模型,利用這兩種組合模型對河西地區(qū)的風速進行了短期預測,并將預測結(jié)果與PM模型的預測結(jié)果進行了對比分析,結(jié)果表明,本研究所開發(fā)的第三類短期風速預測方法是一類較優(yōu)的預測方法,能夠提高預測精度。4)基于上述開發(fā)的三類短期風速統(tǒng)計預測方法,對比分析了它們的預測結(jié)果,并對它們的適用性進行了研究,整體上來說,SAM-GA-WNN模型和EMD-PSO-BPNN模型是兩種較優(yōu)的模型。
[Abstract]:In the face of the increasingly exhaustion of traditional energy resources such as coal and oil, and the increasingly severe environmental problems, the renewable energy, such as wind and solar energy, has been paid much attention in the world. Wind energy is an important renewable energy resource, which has the characteristics of rich, renewable, widely distributed, and no pollution. After recent development, wind energy has been developed. Power generation plays an important role in the development of electric power. Wind power has the characteristics of volatility and intermittence. The access of large-scale wind power brings challenges to the safe and stable operation of the power system. The prediction of wind power is timely adjusted for the adjustment plan according to the change of wind power, and the quality of power is guaranteed and the system is reduced. It is very important to use capacity to reduce the operating cost of the system. The wind speed prediction is the basis of wind power prediction. Therefore, improving the precision of wind speed prediction is very important for the development of wind power generation. According to the different forecast period, the wind speed prediction can be divided into long term wind speed prediction and medium wind speed. Prediction and short-term wind speed prediction. Long term wind speed forecast is mainly used for wind farm planning and design; medium wind speed forecast is mainly used for power balance and scheduling, transaction, transient stability assessment, short-term wind speed prediction is mainly used for power generation system control, which can correct the deviation in grid connection plan in time and improve grid grid plan. It is of great significance to make full use of wind energy to reduce the limit of electricity in the medium and long term prediction and to ensure the safety of the power grid. However, the accuracy of short-term wind speed prediction is still insufficient and the accuracy of short-term wind speed prediction is an urgent problem to be solved at present. On the basis of the wind speed in Hexi area, this paper systematically analyzes the statistical laws of wind speed and wind direction of different stations in this area, and discusses its change characteristics. According to the characteristics of the wind speed, three kinds of high precision short-term wind speed statistical forecasting methods are developed, which are the short-term wind speed prediction models based on SAM. The wind speed prediction model of empirical mode decomposition (EMD) and the new prediction method of model reengineering. This provides guidance for the control of wind power system and the development of short-term wind power prediction system for wind farms. The main results are as follows: 1) the first kind of research method developed is applied to the wind speed according to the complex periodic changes of the real wind speed. In the prediction model, this method proposes two SAM based short-term wind speed forecasting models. One is combining SAM with exponential smoothing (ESM), which we call SAM-ESM model. The other is combining SAM with wavelet neural network (WNN), and using genetic algorithm (GA) to train WNN, which we call the SAM-GA-WNN model. The two models predict the wind speed in Hexi area, and compare the prediction results with the traditional PM prediction results. The results show that the short-term wind speed prediction method based on SAM is a better prediction method and can improve the prediction precision.2) of the second kinds of research methods for wind speed data sequence. The non stationarity of the column is used to apply the EMD method dealing with non-stationary signals to the wind speed prediction model. Two kinds of wind speed prediction models based on EMD decomposition are proposed. One is combining the EMD decomposition and the autoregressive moving average (ARMA) model. We call it the EMD-ARMA model, the other is the EMD decomposition and the BP neural network (BPNN) phase. Combining and using the particle swarm optimization algorithm (PSO) for learning and training of BPNN, we call it the EMD-PSO-BPNN model, using these two models to predict the wind speed in the Hexi region, and compare the prediction results with the prediction results of the PM model. The results show that the second kinds of short-term wind speed predictor developed by this research have been developed. The method is a kind of better prediction method, which can improve the prediction precision.3). In view of the different modes of wind speed, the thought of model reorganization is applied to the wind speed prediction model. This kind of method puts forward two new prediction models of model reorganization, one is to combine the ESM model with the WNN model, and the ESM model is the main model. It is a linear mode used to capture wind speed change. The WNN model is to capture the nonlinear model and take into account the complexity of the WNN modeling prediction. GA is used to learn and train WNN. We call it the ESM-GA-WNN model. The other is the combination of ARMA and BPNN model, and the PSO is used to train BPNN. We call it the ARMA-PSO-BPNN model. The two combination models are used to predict the wind speed in Hexi area, and the prediction results are compared with the prediction results of PM model. The results show that the third kinds of short-term wind speed prediction methods developed by this study are a kind of better prediction method and can improve the prediction precision of.4) based on the three types of short-term winds developed above. The prediction results of fast statistical prediction are compared and analyzed, and their applicability is studied. On the whole, the SAM-GA-WNN model and the EMD-PSO-BPNN model are two better models.
【學位授予單位】：蘭州大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TM614

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