风机最大功率点跟踪的湍流影响机理研究与性能优化

发布时间：2018-11-07 18:39

【摘要】：风力发电作为最具有商业化潜力的可再生能源形式之一,逐渐受到各国的广泛关注。如何使风电机组最大效率地吸收和转化风能是风力发电需要解决的首要问题。随着高风速低湍流的理想风场资源逐渐开发殆尽,幅员辽阔且同样适用于风力发电的低风速地区逐渐受到关注。然而,低风速地区的风速幅值低、湍流强度大,为风电机组运行带来了不利于风能捕获的环境。面对应用场景从理想风场向低风速风场的转变,风力机慢动态性能与湍流风速快速变化的矛盾愈发显著,使得传统最大功率点跟踪控制难以获得满意的控制效果。因此,本文在不改变风力机结构和控制器结构设计的前提下,围绕湍流对最大功率点跟踪的影响,以及考虑湍流的最大功率点跟踪的控制系统参数优化两个方面展开研究,以期进一步提升低风速条件下最大功率点跟踪的性能。主要获得以下结果： 1.探索和分析了最大功率点跟踪的影响因素及其作用机理。具体地,将影响跟踪效果的因素归为动态性能和跟踪要求两大方面,并以此为指导,围绕湍流特性和风机结构特性提取出多个具体的影响因素,包括平均风速、湍流强度等。研究结果表明,湍流特性和风力机结构都会影响最大功率点跟踪的效果。因此,在应用最大功率点跟踪控制时,需要全面考虑上述因素的影响,并相应调整控制系统参数。 2.针对湍流导致的自适应转矩控制中转矩增益系数异常的问题,对其搜索范围进行限定。该方法以湍流特性与转矩增益系数的统计关系为指导,设定转矩增益系数的上下限值,以此剔除转矩增益系数的异常值。研究结果表明,该方法能够提高风能捕获效率。 3.对基于收缩跟踪区间的最大功率点跟踪控制的跟踪区间进行优化。鉴于跟踪区间的优化与湍流特性具有复杂的非线性关系而难以解析描述,本文采用径向基函数神经网络建立平均风速、湍流强度与最优跟踪区间的映射关系,实现根据风速动态优化跟踪区间。研究结果表明,与传统方法相比,该方法具有更高的风能捕获效率,且具有良好的预测精度和泛化能力。 4.针对爬山算法受湍流干扰而出现搜索方向出错的问题,通过设置最大功率点检测和停止机制,使风力机跟踪至最大功率点附近时,不仅避免了转速振荡对系统机械部件的磨损,更克服了停止机制生效后风速再次变化时对搜索方向判断的干扰,从而提高了风能捕获效率。
[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.
【学位授予单位】：南京理工大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TM315

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