A Data-Driven Approach for Condition Monitoring of Wind Turbine Pitch Systems

Cong, Yang; Zheng, Qian; Pei, Yan; Wei, Lu

doi:10.3390/en11082142

Cited by 15 publications

(10 citation statements)

References 24 publications

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“…The temperature of wind‐turbine components is often used to evaluate wind‐turbine health. The temperature of the key components was monitored and recorded by the SCADA system; the pitch‐motor temperature is one of these [32]. As the pitch motor ages, part of the input electrical energy is lost thermally, as described earlier.…”

Section: Aging Indicatorsmentioning

confidence: 99%

Comprehensive aging assessment of pitch systems combining SCADA and failure data

Wei

Zheng

Zareipour

et al. 2021

IET Renewable Power Gen

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Due to the recent rapid development of the wind energy industry, many wind turbines that have been operational over long periods will face degradation caused by aging effects; hence, an appropriate aging assessment method for wind turbines and their components is essential for optimizing the asset management and maintenance strategy of a wind farm. An aging assessment method is proposed for wind‐turbine electric‐pitch systems by introducing four individual aging indicators based on the examination of supervisory control and data acquisition (SCADA) and failure data, i.e. function, energy‐consumption, temperature, and reliability indicators. To obtain a reliable comprehensive assessment, an information‐fusion method has been developed based on a given reference value; the weighting factors in the information fusion were calculated based on the reference value, while reliability and robustness were verified using the SCADA and failure data from a wind farm over three years.

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Section: Aging Indicatorsmentioning

confidence: 99%

Comprehensive aging assessment of pitch systems combining SCADA and failure data

Wei

Zheng

Zareipour

et al. 2021

IET Renewable Power Gen

Self Cite

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“…It was able to detect failure ahead of time, but it was unclear as to how long. Yang et al [16] presented a data-driven approach for wind turbine pitch system condition monitoring. This used three feature selection techniques: sequential forward selection, gradient boosted decision trees, and mutual information, to independently select features, and then choose the common top five features among them.…”

Section: Pitch System Condition Monitoringmentioning

confidence: 99%

Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

et al. 2021

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Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. This novel technique compared several models per turbine, each trained on a different number of months of data. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed.

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“…Several machine learning-based investigations have been performed for fault detection of pitch systems. Yang et al [7] used pitch motor temperature as an indicator of the health state of an electric pitch system. The pitch motor temperature was modeled by support vector regression and three different types of anomalies, including limit-switch failure, angleencoder failure, and slip-ring failure, which were detected prior to the SCADA system by residual analysis based on an exponentially weighted moving average control chart.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of Wind Turbine Pitch Motors Based on Ensemble Learning Approach

Yin

Fan²,

Qu³

et al. 2022

J. Phys.: Conf. Ser.

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Machine learning-based condition monitoring of wind turbines’ critical components is an active area of research, especially for pitch systems, which suffer from a high failure rate. In this work, we successfully predicted and detected the high-temperature fault of the electric pitch motor by analyzing SCADA data through the ensemble learning-based approach. For that, normal behavior models to predict pitch motor temperature were constructed respectively for three pitch motors by gradient boosting tree regression. Residual evolution before the reported high-temperature fault was studied by the sliding window approach. A Shewhart control chart was applied to detect the anomalies of temperature. The proposed approach successfully gave an early warning for the potential high-temperature fault of electric pitch motors around ten days prior to the SCADA system.

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A Data-Driven Approach for Condition Monitoring of Wind Turbine Pitch Systems

Cited by 15 publications

References 24 publications

Comprehensive aging assessment of pitch systems combining SCADA and failure data

Comprehensive aging assessment of pitch systems combining SCADA and failure data

Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

Fault Detection of Wind Turbine Pitch Motors Based on Ensemble Learning Approach

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