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A global optimization framework for parameter estimation of a wind generation unit model

机译:风力发电机组模型参数估计的全局优化框架

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This paper purposes a global optimization method that could be applied in parameter estimation of wind generation unit model. When complex nonlinear models, like a wind generation unit model are used, the parameter estimation based on local optimization methods, such as nonlinear least squares method or Newton's method, may not be able to find parameter values with acceptable accuracy. The global optimization method, named Hyperbolic Cross Point (HCP) method, is proposed to find good initial parameter values that are used as the starting values, to make parameter estimation based on iterative local optimization methods, converge to accurate parameter values. The paper concludes with two case studies which demonstrate application of the HCP in conjunction with local optimization method, where the results obtained are confirmed to be the globally best solution.
机译:本文旨在提出一种可应用于风力发电机组模型参数估计的全局优化方法。当使用复杂的非线性模型(如风力发电单元模型)时,基于局部优化方法(例如非线性最小二乘法或牛顿法)的参数估计可能无法找到可接受的精度参数值。提出了一种全局优化方法,称为双曲线交叉点(HCP)方法,以寻找良好的初始参数值作为起始值,并基于迭代局部优化方法进行参数估计,以收敛到准确的参数值。本文以两个案例研究作为结束,这些案例研究证明了HCP与局部优化方法结合的应用,其中所获得的结果被证实是全球最佳的解决方案。

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