本文選題：風(fēng)電場群　切入點：馬爾可夫鏈　出處：《華北電力大學(xué)(北京)》2017年博士論文

【摘要】：新世紀以來,全球經(jīng)濟快速增長,同時也帶來了能源需求的快速增長。伴隨著化石能源的日漸衰竭,大力開發(fā)利用新能源已經(jīng)成為當今能源革命的主題。而風(fēng)力發(fā)電是全球發(fā)展最為迅速的新能源發(fā)電形式,已經(jīng)實現(xiàn)了連續(xù)十年裝機容量20%左右的快速增長。然而,由于風(fēng)電自身的特點,波動性和隨機性大,不可控的問題嚴重,并網(wǎng)和消納正逐步成為制約風(fēng)電開發(fā)的最主要問題。由于我國國情所限,無論是風(fēng)資源條件還是系統(tǒng)調(diào)峰能力都與歐美等國差距很大�！皸夛L(fēng)”壓力格外明顯。在這樣的背景下,大型風(fēng)電場群需要提高自身的控制水平,使大型風(fēng)電場群向“電網(wǎng)友好型”電源過渡。圍繞這一目標,本文在風(fēng)電數(shù)據(jù)預(yù)處理、大型風(fēng)電場群平滑效應(yīng)、大型風(fēng)電場群聚類模型、短期風(fēng)功率預(yù)測和大型風(fēng)電場群優(yōu)化控制等方面進行了研究,主要研究工作如下:在數(shù)據(jù)挖掘的過程中,數(shù)據(jù)預(yù)處理具有重要意義。本文基于馬爾可夫鏈理論,建立了雙向缺失點補充模型,對風(fēng)電數(shù)據(jù)進行缺失點補充。仿真結(jié)果表明,基于高階馬爾可夫鏈的雙向缺失點補充模型,具有很高的精度,可以滿足數(shù)據(jù)預(yù)處理的要求。通過對大型風(fēng)電場群運行數(shù)據(jù)的分析,定量分析了平滑效應(yīng)在大型風(fēng)電場群中空間尺度上的表現(xiàn),獲得了如下兩條性質(zhì):在風(fēng)電場群內(nèi)部,風(fēng)電出力波動隨空間范圍的變大而下降;距離較遠的區(qū)域之間的組合能更好的平抑波動,具有更強的平滑效應(yīng)�；谔摂M發(fā)電廠的理論和模糊C聚類算法,建立了大型風(fēng)電場群聚類模型,將整個風(fēng)電場群化作多臺虛擬風(fēng)力發(fā)電機,在虛擬風(fēng)力發(fā)電機內(nèi)部,遵循“同調(diào)等值”的原理,進行整體調(diào)度和控制。鑒于風(fēng)功率信號在同一頻率上具有更為接近的性質(zhì)和表現(xiàn),選擇最優(yōu)小波包變換作為信號分析手段。本文的短期風(fēng)功率預(yù)測模型采用了最優(yōu)小波包變換與最小二乘支持向量機結(jié)合的方式。通過實際風(fēng)功率數(shù)據(jù)驗證,結(jié)果表明加入最優(yōu)小波包變換的預(yù)測方法,其預(yù)測精度有了明顯的提升。在風(fēng)機有功功率模型分析的基礎(chǔ)上,建立了虛擬風(fēng)機全程可調(diào)策略。在風(fēng)況允許的條件下,小范圍調(diào)節(jié)時通過發(fā)電機轉(zhuǎn)矩,大范圍調(diào)節(jié)時通過槳距角和轉(zhuǎn)速聯(lián)合調(diào)節(jié),實現(xiàn)對風(fēng)電機組功率全程可調(diào),進而實現(xiàn)虛擬風(fēng)機整體的全程可調(diào)。最后基于短期風(fēng)功率預(yù)測的結(jié)果和虛擬風(fēng)機整體的全程可調(diào),提出了一種基于粒子群優(yōu)化算法的大型風(fēng)電場群優(yōu)化控制策略,使得大型風(fēng)電場群向“電網(wǎng)友好型”電源過渡。
[Abstract]:Since the new century, the rapid growth of the global economy has also brought about a rapid growth in energy demand. The development and utilization of new energy has become the theme of the energy revolution today. Wind power is the most rapidly developing new energy generation form in the world. It has achieved a rapid growth of about 20 percent of installed capacity for ten consecutive years. However, Due to the characteristics of wind power itself, the problems of high volatility and randomness, and uncontrollable problems, grid connection and absorption are gradually becoming the most important problems restricting the development of wind power. Both the wind resource conditions and the peak shaving capacity of the system are very different from those in Europe and the United States and other countries. The pressure of "abandoning wind" is particularly obvious. In this context, large wind farms need to improve their control level. In this paper, the wind power data preprocessing, the large wind farm group smoothing effect, the large scale wind farm cluster model, the wind farm cluster model, the wind power data pretreatment, the large wind farm group smoothing effect, the large scale wind farm cluster model, Short-term wind power prediction and large-scale wind farm group optimization control are studied. The main research work is as follows: in the process of data mining, data preprocessing is of great significance. A bi-directional missing point supplement model is established to complement the wind power data. The simulation results show that the model based on high order Markov chain has a high accuracy. By analyzing the operation data of large wind farm group, we quantitatively analyze the performance of smoothing effect on spatial scale in large wind farm cluster, and obtain the following two properties: inside the wind farm group, The wind power output fluctuation decreases with the increase of the spatial range, and the combination between the distant regions can better suppress the fluctuation and has a stronger smoothing effect. Based on the theory of virtual power plant and fuzzy C clustering algorithm, The cluster model of large scale wind farm is established. The whole wind farm is grouped into several virtual wind turbines. In the virtual wind turbine, the principle of "homology equivalence" is followed. Overall scheduling and control. Given the closer nature and performance of wind power signals at the same frequency, The optimal wavelet packet transform is chosen as the signal analysis method. The short-term wind power prediction model in this paper adopts the combination of the optimal wavelet packet transform and the least squares support vector machine. The results show that the prediction accuracy of the optimal wavelet packet transform is improved obviously. Based on the analysis of the active power model of the fan, the full-range adjustable strategy of the virtual fan is established. When the generator torque is adjusted in a small range and the pitch angle and rotational speed are combined to adjust in a large range, the power of wind turbine can be adjusted in the whole process. Finally, based on the result of short-term wind power prediction and the whole range adjustable of virtual wind turbine, an optimal control strategy based on particle swarm optimization (PSO) algorithm for large wind farm group is proposed. Make large wind farm group to "grid friendly" power supply transition.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TM614

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