【摘要】：風(fēng)力發(fā)電作為最具有商業(yè)化潛力的可再生能源形式之一,逐漸受到各國的廣泛關(guān)注。如何使風(fēng)電機(jī)組最大效率地吸收和轉(zhuǎn)化風(fēng)能是風(fēng)力發(fā)電需要解決的首要問題。 隨著高風(fēng)速低湍流的理想風(fēng)場資源逐漸開發(fā)殆盡,幅員遼闊且同樣適用于風(fēng)力發(fā)電的低風(fēng)速地區(qū)逐漸受到關(guān)注。然而,低風(fēng)速地區(qū)的風(fēng)速幅值低、湍流強(qiáng)度大,為風(fēng)電機(jī)組運(yùn)行帶來了不利于風(fēng)能捕獲的環(huán)境。 面對應(yīng)用場景從理想風(fēng)場向低風(fēng)速風(fēng)場的轉(zhuǎn)變,風(fēng)力機(jī)慢動態(tài)性能與湍流風(fēng)速快速變化的矛盾愈發(fā)顯著,使得傳統(tǒng)最大功率點(diǎn)跟蹤控制難以獲得滿意的控制效果。因此,本文在不改變風(fēng)力機(jī)結(jié)構(gòu)和控制器結(jié)構(gòu)設(shè)計(jì)的前提下,圍繞湍流對最大功率點(diǎn)跟蹤的影響,以及考慮湍流的最大功率點(diǎn)跟蹤的控制系統(tǒng)參數(shù)優(yōu)化兩個(gè)方面展開研究,以期進(jìn)一步提升低風(fēng)速條件下最大功率點(diǎn)跟蹤的性能。主要獲得以下結(jié)果： 1.探索和分析了最大功率點(diǎn)跟蹤的影響因素及其作用機(jī)理。具體地,將影響跟蹤效果的因素歸為動態(tài)性能和跟蹤要求兩大方面,并以此為指導(dǎo),圍繞湍流特性和風(fēng)機(jī)結(jié)構(gòu)特性提取出多個(gè)具體的影響因素,包括平均風(fēng)速、湍流強(qiáng)度等。研究結(jié)果表明,湍流特性和風(fēng)力機(jī)結(jié)構(gòu)都會影響最大功率點(diǎn)跟蹤的效果。因此,在應(yīng)用最大功率點(diǎn)跟蹤控制時(shí),需要全面考慮上述因素的影響,并相應(yīng)調(diào)整控制系統(tǒng)參數(shù)。 2.針對湍流導(dǎo)致的自適應(yīng)轉(zhuǎn)矩控制中轉(zhuǎn)矩增益系數(shù)異常的問題,對其搜索范圍進(jìn)行限定。該方法以湍流特性與轉(zhuǎn)矩增益系數(shù)的統(tǒng)計(jì)關(guān)系為指導(dǎo),設(shè)定轉(zhuǎn)矩增益系數(shù)的上下限值,以此剔除轉(zhuǎn)矩增益系數(shù)的異常值。研究結(jié)果表明,該方法能夠提高風(fēng)能捕獲效率。 3.對基于收縮跟蹤區(qū)間的最大功率點(diǎn)跟蹤控制的跟蹤區(qū)間進(jìn)行優(yōu)化。鑒于跟蹤區(qū)間的優(yōu)化與湍流特性具有復(fù)雜的非線性關(guān)系而難以解析描述,本文采用徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)建立平均風(fēng)速、湍流強(qiáng)度與最優(yōu)跟蹤區(qū)間的映射關(guān)系,實(shí)現(xiàn)根據(jù)風(fēng)速動態(tài)優(yōu)化跟蹤區(qū)間。研究結(jié)果表明,與傳統(tǒng)方法相比,該方法具有更高的風(fēng)能捕獲效率,且具有良好的預(yù)測精度和泛化能力。 4.針對爬山算法受湍流干擾而出現(xiàn)搜索方向出錯(cuò)的問題,通過設(shè)置最大功率點(diǎn)檢測和停止機(jī)制,使風(fēng)力機(jī)跟蹤至最大功率點(diǎn)附近時(shí),不僅避免了轉(zhuǎn)速振蕩對系統(tǒng)機(jī)械部件的磨損,更克服了停止機(jī)制生效后風(fēng)速再次變化時(shí)對搜索方向判斷的干擾,從而提高了風(fēng)能捕獲效率。
[Abstract]:Wind power generation, as one of the most commercialized renewable energy forms, has been paid more and more attention by many countries. How to make wind turbine absorb and convert wind energy efficiently is the most important problem for wind power generation. With the development of the ideal wind field resources with high wind speed and low turbulence, the low wind speed areas with vast area and also suitable for wind power generation have attracted more and more attention. However, the wind speed amplitude is low and the turbulence intensity is large in the low wind speed area, which brings the unfavorable environment to wind energy capture for the wind turbine operation. In the face of the change from ideal wind field to low wind speed, the contradiction between the slow dynamic performance of wind turbine and the rapid change of turbulent wind speed becomes more and more obvious, which makes the traditional maximum power point tracking control difficult to obtain satisfactory control effect. Therefore, without changing the structure of wind turbine and controller structure, this paper focuses on the influence of turbulence on maximum power point tracking and the optimization of control system parameters considering the maximum power point tracking of turbulence. In order to further improve the performance of maximum power point tracking under low wind speed. The main results are as follows: 1. The influencing factors and the mechanism of maximum power point tracking are explored and analyzed. Specifically, the factors affecting the tracking effect are classified into two aspects: dynamic performance and tracking requirements. Based on this, several specific factors are extracted around the turbulence characteristics and the structural characteristics of the fan, including the average wind speed, turbulence intensity and so on. The results show that the turbulence characteristics and wind turbine structure will affect the maximum power point tracking effect. Therefore, in the application of maximum power point tracking control, it is necessary to consider the influence of the above factors and adjust the control system parameters accordingly. 2. Aiming at the problem of abnormal torque gain coefficient in adaptive torque control caused by turbulence, the search range is limited. The method is guided by the statistical relationship between the turbulence characteristics and the torque gain coefficient, and sets the upper and lower limits of the torque gain coefficient, so as to eliminate the abnormal value of the torque gain coefficient. The results show that this method can improve the efficiency of wind energy capture. 3. The tracking interval of maximum power point tracking control based on contraction tracking interval is optimized. In view of the complex nonlinear relationship between the optimization of the tracking interval and the turbulent characteristics, the radial basis function neural network is used to establish the mapping relationship between the mean wind speed, the turbulence intensity and the optimal tracking interval. The tracking interval is optimized dynamically according to the wind speed. The results show that compared with the traditional method, this method has higher wind energy capture efficiency, better prediction accuracy and generalization ability. 4. Aiming at the problem that the search direction of mountain climbing algorithm is wrong due to turbulence disturbance, the maximum power point detection and stopping mechanism is set to make the wind turbine track to the maximum power point, which not only avoids the abrasion of rotating speed oscillation to the mechanical parts of the system, but also makes the wind turbine track to the maximum power point. It overcomes the disturbance of searching direction judgment when the wind speed changes again after the stop mechanism comes into effect and improves the efficiency of wind energy capture.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TM315

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