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A Study of Cooperative Group Control of Generator for Wind Power Generating Systems

风力发电系统发电机组协同控制研究

基本信息

批准号：
08650358
负责人：
TOUMIYA Tatsumi
金额：
$ 1.41万
依托单位：
Anan College of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1996
资助国家：
日本
起止时间：
1996 至 1997
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-08650358/
关键词：
Wind power generating system Cooperative group control Output control Adaptive control Modelling Neural network Hill-climbing method プロペラ形風車

项目摘要

Not only about wind energy, natural energies of ocean, sun, etc. are clean and safe, but fluctuation of properties are large and has poor reproducibility, so that most important subject is to find out methods to take out those energies effectively.To simulate and control windmill power system by control sequence, we need to obtain the accurate model of the wind power generating system previously. In chapter 2, we discuss a new model of a windmill and a method for determination of the windmill torque loss coefficients of a windmill power system. Also, we apply the neural network method to model a small-scale windmill power system. We manifested the validity of the proposed method by simulation results from both the simulated results for sinusoidal change of wind speed and natural wind speed by random sequence.In chapter 3, we describe the development of a windmill simulator, to simulate accurately the movement of a windmill. The simulator has a model to facilitate the change of windmill … More parameters, such as power coefficients or moment of ineertia, by overcoming the weak points of research and development environment for wind utilization.In chapter 4, for the purpose of cooperative group control of genetators, a control method which is possible to take out wind energy effectively even system characteristics is unknown, is suggested. This control method is possible to follow quasi-maximum output only by measurements of load terminal voltage and current which are relatively simple to measure high accuracy. Hill-climbing method among adaptive control method was adopted. Secondly, fuzzy infrence is applied to the output control. The control method hereby proposed is the one to allow the measured wind speed and windmill speed to be the input of the fuzzy control rule by regarding these factors as ambiguous data and to permit the values of the load resistance to be the output. Finally, we proposed output control method by estimating system characteristics measuring wind speed and windmill speed. Less

不仅风能、海洋、太阳等自然能源清洁安全，而且性质波动大，再现性差，如何有效地提取这些能量是首要课题，而要通过控制顺序对风电系统进行仿真和控制，首先需要得到准确的风力发电系统模型。在第二章中，我们讨论了一种新的风车模型和一种确定风车电力系统风车转矩损失系数的方法。并将神经网络方法应用于一个小型风电系统的建模。通过随机序列模拟风速正弦变化和自然风速变化的仿真结果，验证了所提方法的有效性。第三章介绍了风车模拟器的研制，以准确模拟风车的运动。模拟器有一个模型，可以方便地改变风车…在第四章中，为了实现发电机组的协同群控，提出了一种即使在系统特性未知的情况下也能有效地获取风能的控制方法。这种控制方法仅通过测量负载端电压和电流就可以跟踪准最大输出，这两种测量相对简单，测量精度较高。在自适应控制方法中，采用爬山法。其次，将模糊推理应用到输出控制中。本文提出的控制方法是将测量的风速和风车风速作为模糊控制规则的输入，将这些因素视为模糊数据，并允许负载阻力的值作为输出。最后，通过估计测量风速和风车速度的系统特性，提出了输出控制方法。较少