【摘要】：隨著能源的日益減少、環(huán)境污染越來越嚴(yán)重,尤其是霧霾正逐步覆蓋著我國大部分城市,使用清潔無污染的能源便成為解決這些問題的一個重要選擇。風(fēng)能以其可再生、綠色、清潔環(huán)保等特性使得風(fēng)力發(fā)電量在世界發(fā)電總量中所占比重正在不斷加大。但由于風(fēng)力發(fā)電的間歇性及不可控等特性使得電網(wǎng)接納風(fēng)電的能力受到抑制,風(fēng)力發(fā)電的發(fā)展速度較為緩慢。對風(fēng)功率進行較高精度的預(yù)測,可以有效地降低風(fēng)電場輸出波動對電力系統(tǒng)的不利影響,并且提高風(fēng)力發(fā)電在電力市場中的競爭實力。同時,風(fēng)電機組向更大容量、更高效率的趨勢發(fā)展,就要求提高對風(fēng)能的利用率,使得研究最大風(fēng)能追蹤具有重要意義。 本文針對風(fēng)功率預(yù)測和最大風(fēng)能追蹤這兩大風(fēng)力發(fā)電中的實際問題,進行了深入研究。針對風(fēng)功率預(yù)測,由于風(fēng)功率與風(fēng)速有著直接的函數(shù)關(guān)系,因此可以通過預(yù)測風(fēng)速得到風(fēng)功率的預(yù)測結(jié)果。對于風(fēng)速序列的非線性及不平穩(wěn)性,本文應(yīng)用小波分解來降低。風(fēng)速預(yù)測大多應(yīng)用神經(jīng)網(wǎng)絡(luò)理論,目前還沒有統(tǒng)一的神經(jīng)網(wǎng)絡(luò)輸入量的選取方法,本文采用時間序列建模選擇輸入量的方法為神經(jīng)網(wǎng)絡(luò)選擇輸入量。綜上所述,本文提出一種綜合了小波理論、時間序列及神經(jīng)網(wǎng)絡(luò)的預(yù)測方法,即小波時序神經(jīng)網(wǎng)絡(luò)預(yù)測方法。該方法首先將原始風(fēng)速進行小波分解為一個低頻趨勢信號和幾個高頻隨機信號,然后對高頻信號采用時間序列的方法建模,繼而對小波分解后的兩種信號利用BP神經(jīng)網(wǎng)絡(luò)進行建模：低頻信號采用常規(guī)BP神經(jīng)網(wǎng)絡(luò)(輸入量為最近的6個歷史值),高頻信號應(yīng)用時間神經(jīng)網(wǎng)絡(luò)(輸入量應(yīng)用時間序列建模選擇),最后將各信號的預(yù)測結(jié)果通過疊加進行重構(gòu),得到原始風(fēng)速序列的最終預(yù)測結(jié)果。將風(fēng)速預(yù)測結(jié)果輸入到擬合的風(fēng)機功率曲線,便可預(yù)測出風(fēng)機功率�；谖覈戏侥筹L(fēng)電場的實測數(shù)據(jù)進行驗證,本文提出的預(yù)測方法具有較好的預(yù)測精度。針對最大風(fēng)能追蹤問題,本文在分析風(fēng)力機運行特性的基礎(chǔ)上,研究變速恒頻雙饋風(fēng)力發(fā)電機組實現(xiàn)最大風(fēng)能追蹤的控制方法。為簡化模型難度,對雙饋異步發(fā)電機進行坐標(biāo)變換,并采用定子磁鏈定向的矢量變換技術(shù)。簡化后的模型可完成發(fā)電機的有功功率和無功功率的解耦控制,并且實現(xiàn)最大風(fēng)能追蹤的目標(biāo)。應(yīng)用Matlab/Simulink進行完整的風(fēng)力發(fā)電系統(tǒng)的仿真研究,結(jié)果證明了所建模型的正確性。
[Abstract]:With the decrease of energy sources, environmental pollution is becoming more and more serious, especially haze is gradually covering most cities in China. The use of clean and non-polluting energy has become an important choice to solve these problems. Because of its renewable, green, clean and environmental characteristics, wind power generation in the world is increasing. However, due to the intermittent and uncontrollable characteristics of wind power generation, the ability of wind power grid to accept wind power is restrained, and the development of wind power generation is slow. The prediction of wind power with high accuracy can effectively reduce the adverse effect of wind farm output fluctuation on power system and improve the competitive power of wind power generation in power market. At the same time, the trend of wind turbine towards greater capacity and higher efficiency requires to improve the utilization rate of wind energy, which makes the study of maximum wind energy tracking of great significance. In this paper, the actual problems of wind power prediction and maximum wind power tracking are studied. For wind power prediction, because wind power and wind speed have a direct functional relationship, wind power prediction results can be obtained by predicting wind speed. Wavelet decomposition is used to reduce the nonlinear and uneven stability of wind speed series. Most of wind speed prediction is based on neural network theory, but there is no uniform method for selecting input quantity of neural network. In this paper, the method of selecting input quantity by time series modeling is used to select input quantity for neural network. To sum up, this paper presents a prediction method based on wavelet theory, time series and neural network, which is called wavelet time series neural network prediction method. In this method, the original wind speed is decomposed into a low frequency trend signal and several high frequency random signals by wavelet transform, and then the time series method is used to model the high frequency signal. Then, two kinds of signals after wavelet decomposition are modeled by BP neural network: the low-frequency signal is based on conventional BP neural network (the input is the most recent six historical values). The high frequency signal is constructed by time neural network (input quantity is modeled and selected by time series). Finally, the prediction results of each signal are reconstructed by superposition, and the final prediction results of the original wind speed series are obtained. The wind power can be predicted by inputting the wind speed prediction results into the fitted fan power curve. Based on the measured data of a wind farm in the south of China, the prediction method presented in this paper has good prediction accuracy. Aiming at the problem of maximum wind energy tracking, this paper studies the control method of variable speed constant frequency doubly-fed wind turbine based on the analysis of wind turbine operating characteristics. In order to simplify the difficulty of the model, the coordinate transformation of doubly-fed asynchronous generator is carried out, and the vector transformation technique of stator flux orientation is adopted. The simplified model can decouple the active and reactive power of the generator and achieve the goal of maximum wind power tracking. The simulation of wind power system with Matlab/Simulink is carried out, and the results show that the model is correct.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM614

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本文編號：2388802