Abstract Fault detection based on data from the supervisory control and data acquisition (SCADA) system, which has been installed in most MW-scale wind turbines, has brought significant benefits for wind farm operators. However, the changes in the features of hardware sensor measurements, which are used in current SCADA systems, often cannot provide reliable early alarms. In order to resolve this problem, in this paper, a novel dynamic model sensor method is proposed for the SCADA data based wind turbine fault detection. A dynamic model representing the relationship between the generator temperature, wind speed, and ambient temperature is derived following the first principles and used as the basic structure of the model sensor. When the model sensor is applied for fault detection, its parameters are updated regularly using the generator temperature, wind speed, and ambient temperature data from the SCADA system. Then, from the updated model, the fault sensitive features of wind turbine system are extracted via performing system frequency analysis and used for the turbine fault detection. This novel model sensor method is applied to the SCADA data of a wind farm of 3 wind turbines currently operating in Spain. The results show that the proposed method can not only detect the turbine generator fault but also reveal the trend of ageing with the wind turbine generator, demonstrating its capability of failure prognosis for wind turbine system and components.

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基于 SCADA 数据的风力涡轮机故障检测：一种动态模型传感器方法

摘要 基于监控和​​数据采集 (SCADA) 系统数据的故障检测已安装在大多数兆瓦级风力涡轮机中，为风电场运营商带来了显着的好处。然而，当前 SCADA 系统中使用的硬件传感器测量特性的变化往往无法提供可靠的早期警报。为了解决这个问题，本文提出了一种基于SCADA数据的风力发电机故障检测动态模型传感器方法。根据第一原理推导出代表发电机温度、风速和环境温度之间关系的动态模型，并将其用作模型传感器的基本结构。当模型传感器用于故障检测时，其参数使用来自 SCADA 系统的发电机温度、风速和环境温度数据定期更新。然后，从更新后的模型中，通过系统频率分析提取风电机组系统的故障敏感特征，用于风电机组故障检测。这种新颖的模型传感器方法被应用于目前在西班牙运行的 3 台风力涡轮机的风电场的 SCADA 数据。结果表明，该方法不仅能检测出汽轮发电机故障，还能揭示风力发电机的老化趋势，证明其对风力发电机系统及部件的故障预测能力。通过系统频率分析提取风电机组故障敏感特征，用于风电机组故障检测。这种新颖的模型传感器方法被应用于目前在西班牙运行的 3 台风力涡轮机的风电场的 SCADA 数据。结果表明，该方法不仅能检测出汽轮发电机故障，还能揭示风力发电机的老化趋势，证明其对风力发电机系统及部件的故障预测能力。通过系统频率分析提取风电机组故障敏感特征，用于风电机组故障检测。这种新颖的模型传感器方法被应用于目前在西班牙运行的 3 台风力涡轮机的风电场的 SCADA 数据。结果表明，该方法不仅能检测出汽轮发电机故障，还能揭示风力发电机的老化趋势，证明其对风力发电机系统及部件的故障预测能力。