An Ensemble Learning Solution for Predictive Maintenance of Wind Turbines Main Bearing

Beretta, Mattia; Julian, Anatole; Sepúlveda, José; Cusidó, J.; Porro, Olga

doi:10.3390/s21041512

Cited by 31 publications

(32 citation statements)

References 40 publications

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“…The different models that compound the ensemble should provide complementary information, see [17]. Thus, it is important to analyze the correlation between the indicators provided by each of the individual models: the anomaly detection model vs. the normality model.…”

Section: Ensemble Vs Single Modelsmentioning

confidence: 99%

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Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis

Beretta

Vidal

Sepúlveda³

et al. 2021

Applied Sciences

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The goal of this paper is to develop, implement, and validate a methodology for wind turbines’ main bearing fault prediction based on an ensemble of an artificial neural network (normality model designed at turbine level) and an isolation forest (anomaly detection model designed at wind park level) algorithms trained only on SCADA data. The normal behavior and the anomalous samples of the wind turbines are identified and several interpretable indicators are proposed based on the predictions of these algorithms, to provide the wind park operators with understandable information with enough time to plan operations ahead and avoid unexpected costs. The stated methodology is validated in a real underproduction wind park composed by 18 wind turbines.

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Section: Ensemble Vs Single Modelsmentioning

confidence: 99%

“…Within the typical wear mechanisms affecting the main bearing, micro-pitting, spalling, and smearing can be listed [16]. Main bearing failures can be anticipated through vibration analysis, but also using predictive models based on SCADA data and analysis of temperature signals, as reported in [17].…”

Section: Introductionmentioning

confidence: 99%

Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis

Beretta

Vidal

Sepúlveda³

et al. 2021

Applied Sciences

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“…This goal can, e.g., be pursued by physical models [31] or neural networks [32]. Alternative approaches based on anomaly detection and fusion of multiple indicators and alarm logs, addressing generator and main bearing failures, have been proposed in [33,34]. When located in a wind farm, also adjacent turbines can serve as a reference value [31].…”

Section: Previous Workmentioning

confidence: 99%

Quantification of the Information Loss Resulting from Temporal Aggregation of Wind Turbine Operating Data

et al. 2021

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SCADA operating data are more and more used across the wind energy domain, both as a basis for power output prediction and turbine health status monitoring. Current industry practice to work with this data is by aggregating the signals at coarse resolution of typically 10-min averages, in order to reduce data transmission and storage costs. However, aggregation, i.e., downsampling, induces an inevitable loss of information and is one of the main causes of skepticism towards the use of SCADA operating data to model complex systems such as wind turbines. This research aims to quantify the amount of information that is lost due to this downsampling of SCADA operating data and characterize it with respect to the external factors that might influence it. The issue of information loss is framed by three key questions addressing effects on the local and global scale as well as the influence of external conditions. Moreover, recommendations both for wind farm operators and researchers are provided with the aim to improve the information content. We present a methodology to determine the ideal signal resolution that minimized storage footprint, while guaranteeing high quality of the signal. Data related to the wind, electrical signals, and temperatures of the gearbox resulted as the critical signals that are largely affected by an information loss upon aggregation and turned out to be best recorded and stored at high resolutions. All analyses were carried out using more than one year of 1 Hz SCADA data of onshore wind farm counting 12 turbines located in the UK.

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“…More recent research proposed the use of Self-Organizing Maps (SOM) to determine turbine faults [14]. For the purpose of this research, the authors used an adaptation of ensemble learning recently published [15]. The advantage of using fault indicators is that the proposed solution might be applied to any wind turbine.…”

Section: Faults In Sensorsmentioning

confidence: 99%

Fault-Tolerant Control of a Wind Turbine Generator Based on Fuzzy Logic and Using Ensemble Learning

2021

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Wind energy is a form of renewable energy with the highest installed capacity. However, it is necessary to reduce the operation and maintenance costs and extend the lifetime of wind turbines to make wind energy more competitive. This paper presents a power-derating-based Fault-Tolerant Control (FTC) model in 2 MW three-bladed wind turbines implemented using the National Renewable Energy Laboratory’s (NREL) Fatigue, Aerodynamics, Structures, and Turbulence (FAST) wind turbine simulator. This control strategy is potentially supported by the health status of the gearbox, which was predicted by means of algorithms and quantified in an indicator denominated as a merge developed by SMARTIVE, a pioneering of in this idea. Fuzzy logic was employed in order to decide whether to down-regulate the output power or not, and to which level to adjust to the needs of the turbines. Simulation results demonstrated that a reduction in the power output resulted in a safer operation, since the stresses withstood by the blades and tower significantly decreased. Moreover, the results supported empirically that a diminution in the generator torque and speed was acheived, resulting in a drop in the gearbox bearing and oil temperatures. By implementing this power-derating FTC, the downtime due to failure stops could be controlled, and thus the power production noticeably grew. It has been estimated that more than 325,000 tons of CO2 could be avoided yearly if implemented globally.

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An Ensemble Learning Solution for Predictive Maintenance of Wind Turbines Main Bearing

Cited by 31 publications

References 40 publications

Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis

Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis

Quantification of the Information Loss Resulting from Temporal Aggregation of Wind Turbine Operating Data

Fault-Tolerant Control of a Wind Turbine Generator Based on Fuzzy Logic and Using Ensemble Learning

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