计及风电场随机特性的SVG模型参数智能辨识方法研究

Intelligent Identification of Static Var Generator Model With Stochastic Characters of Wind Farm

为了获得更准确的静止无功发生器(static var generator,SVG)模型参数以满足风电并网系统安全稳定运行的要求,提出一种计及风电场随机特性的SVG模型参数智能辨识方法。首先,通过分析SVG的动作特性建立其数学模型。然后,研究了风电场随机特性对辨识结果的影响途径和机理。最后,针对风电场随机特性引起的辨识结果不准确问题,提出一种考虑风电场随机特性的SVG模型参数多方式混合辨识方法,为准确辨识风电场SVG模型参数提供了新的方法。参数辨识仿真实验结果验证了所提方法的可行性。

In order to obtain accurate model parameters static var generator(SVG) to meet the safe and stable operation of wind power grid-connected system, a method of SVG model parameter intelligent identification with stochastic characters of wind farm was proposed in this paper. Firstly, the SVG mathematic model was established by analyzing its action characteristics. Secondly, the influence ways and mechanism of wind farm stochastic characteristics on identification results were studied. Finally, according to the problem of inaccurate identification results caused by stochastic characters of wind farm, a multimode hybrid identification method of SVG model parameters with wind farm stochastic characters was proposed, providing a new identification strategy for accurate identification of SVG model parameters. Simulation results of parameter identification verify the feasibility of the proposed method.