Estimation of Wind Turbine Rotor Power Coefficient Using RMP Model

G. Son, Hee-Jin Lee, Jung-Wook Park
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引用次数: 13

Abstract

This paper presents an estimation of the rotor power coefficient (Cp) curve, which is useful for pitch angle control of a wind turbine system. The Cp curve is affected by several factors such as the structure of a wind turbine, surrounding environment in which the wind turbine built, and its method of control, etc. Therefore, it is necessary to estimate this curve in real-time using direct measurements from the generator and wind turbine. To achieve the optimal estimation for the Cp curve, the reduced multivariate polynomial (RMP) model is applied because it can be basically represented in a polynomial form. Unlike general neural network algorithms, the RMP model avoids a training process. This characteristic makes it possible to apply to the real-time estimation in a practical situation. Also, the first-order partial derivatives of the Cp curve are easily computed by using the RMP model. This derivative information can be effectively used to maximize turbine output power by a proper pitch angle control. The simulation results show that the proposed RMP model provides a good estimation performance in a fast and effective manner.
基于RMP模型的风力机转子功率系数估计
本文提出了一种转子功率系数(Cp)曲线的估计方法,为风电系统的俯仰角控制提供了依据。Cp曲线受风力机结构、风力机周围环境、风力机控制方法等因素的影响。因此,有必要利用发电机和风力涡轮机的直接测量实时估计该曲线。为了实现对Cp曲线的最优估计,由于RMP模型基本上可以用多项式形式表示,因此采用了RMP模型。与一般的神经网络算法不同,RMP模型避免了训练过程。这一特性使得它可以应用于实际情况下的实时估计。此外,利用RMP模型可以很容易地计算出Cp曲线的一阶偏导数。通过适当的俯仰角控制,可以有效地利用这些导数信息来最大化涡轮输出功率。仿真结果表明,所提出的RMP模型具有快速有效的估计性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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