Nonlinear system identification for model-based condition monitoring of wind turbines
Nonlinear system identification for model-based condition monitoring of wind turbines
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DOI：
10.1016/j.renene.2014.05.035
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发表时间：
2014-11
期刊：
影响因子：
8.7
通讯作者：
P. Cross;Xiandong Ma
中科院分区：
文献类型：
--
作者：
P. Cross;Xiandong Ma
本文提出了一种基于数据驱动模型的状态监测方案，适用于风力发电机组。该计划是基于一个非线性的数据为基础的建模方法，其中的模型参数作为系统变量的函数而变化。模型结构和参数直接从过程的输入和输出数据中识别。所提出的方法证明了从模拟的并网风力涡轮机，它是用来检测电网和电力电子故障的数据。该方法进一步评估与SCADA数据从一个运行的风电场，它是用来识别齿轮箱和发电机故障。与人工智能方法，如基于人工神经网络的模型相比，本文所采用的方法提供了一个参数化的非线性过程的有效表示。因此，它是相对简单的实现所提出的基于模型的方法在线使用现场可编程门阵列。
This paper proposes a data driven model-based condition monitoring scheme that is applied to wind turbines. The scheme is based upon a non-linear data-based modelling approach in which the model parameters vary as functions of the system variables. The model structure and parameters are identified directly from the input and output data of the process. The proposed method is demonstrated with data obtained from a simulation of a grid-connected wind turbine where it is used to detect grid and power electronic faults. The method is evaluated further with SCADA data obtained from an operational wind farm where it is employed to identify gearbox and generator faults. In contrast to artificial intelligence methods, such as artificial neural network-based models, the method employed in this paper provides a parametrically efficient representation of non-linear processes. Consequently, it is relatively straightforward to implement the proposed model-based method on-line using a field-programmable gate array.