SCADA-data-based wind turbine fault detection: A dynamic model sensor method

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.

Intelligent Predictive Maintenance and Remote Monitoring Framework for Industrial Equipment Based on Mixed Reality

“…However, this is a semi-manual approach that does not take into consideration the more complex dynamic behavioral patterns of the machinery, or the contextual data relating to the manufacturing process, thus lacking adaptability relative to the current status of the industrial equipment (Nicholson et al, 2012). What is more, in recent research works SCADA systems are integrated with ML algorithms, in order to extend their usability as well as to shift towards prognostics (Pang et al, 2020;Ruiming et al, 2020;Zhang and Lang, 2020). In the research work of Wang et al (2020), the authors have developed a framework based on Convolution Auto Encoder and Long-Short Term Memory (LSTM) in an attempt to estimate RUL more accurately in comparison to conventional methods.…”

Investigation of Inverter Temperature Prediction Model in Wind Farm Based on SCADA Data

“…Therefore, it has become a research hotspot to analyze the big data provided by SCADA system and how to realize the operation status identification and prediction of key components of WT. At present, the related researches mainly focus on the state prediction of key components such as wind turbine gearbox [17], generator [18], pitch system [19] by using SCADA data, mainly using long-short term memory (LSTM) [20], support vector machine [21], artificial neural network [22] and other methods, rarely involving complex electrical equipment such as inverter. The regression prediction model of wind turbine active power is establishes based on support vector regression (SVR) algorithm, and realizes the early prediction of wind turbine pitch system fault through the model prediction residual in [23].…”

SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

“…In [15][16][17], the targets are the stator winding temperature, the generator bearing temperature, and the generator slip ring temperatures. In [18], a test case of generator damage (rotor winding failure) is analyzed: the diagnosis is based on a dynamic model sensor method representing the relationship between the generator temperature, wind speed, and ambient temperature.…”