Applying design knowledge and machine learning to scada data for classification of wind turbine operating regimes

Barahona, Braulio; Hoelzl, Cyprien; Chatzi, Eleni

doi:10.1109/ssci.2017.8280947

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…However, hardware sensors in SCADA systems cannot directly measure some physical variables such as bending moments and drive-train torques, which are important indicators for wind turbine failures. Consequently, the techniques of soft sensors have been applied to estimate the immeasurable information from the measurable physical variables (Barahona et al, 2017;Alvarez and Ribaric, 2018). A soft sensor is basically a predictive model that is used to infer critical but difficult-to-measure physical variables (Kadlec et al, 2009;Kadlec and Gabrys, 2011).…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Lang

2020

Control Engineering Practice

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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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