Keeping the blades turning

Becker, E.; Poste, Paul

doi:10.1016/s1471-0846(06)70544-3

Cited by 22 publications

(1 citation statement)

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“…Mechanical, electrical, and cooling system failures are the three basic types of generator failures. Typically, all of these types of failure result in the overheating of a generator and the downtime of a wind turbine [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Limits of Early Predictive Maintenance in Wind Turbines Applying an Anomaly Detection Technique

Jankauskas

Serackis

Šapurov

et al. 2023

Sensors

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The aim of the presented investigation is to explore the time gap between an anomaly appearance in continuously measured parameters of the device and a failure, related to the end of the remaining resource of the device-critical component. In this investigation, we propose a recurrent neural network to model the time series of the parameters of the healthy device to detect anomalies by comparing the predicted values with the ones actually measured. An experimental investigation was performed on SCADA estimates received from different wind turbines with failures. A recurrent neural network was used to predict the temperature of the gearbox. The comparison of the predicted temperature values and the actual measured ones showed that anomalies in the gearbox temperature could be detected up to 37 days before the failure of the device-critical component. The performed investigation compared different models that can be used for temperature time-series modeling and the influence of selected input features on the performance of temperature anomaly detection.

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Section: Introductionmentioning

confidence: 99%

Exploring the Limits of Early Predictive Maintenance in Wind Turbines Applying an Anomaly Detection Technique

Jankauskas

Serackis

Šapurov

et al. 2023

Sensors

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Online wind turbine fault detection through automated SCADA data analysis

Zaher¹,

McArthur²,

Infield³

et al. 2009

Wind Energy

372

251

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This paper describes a set of anomaly-detection techniques and their applicability to wind turbine fault identification. It explains how the anomaly-detection techniques have been adopted to analyse supervisory control and data acquisition data acquired from a wind farm, automating and simplifying the operators' analysis task by interpreting the volume of data available. The techniques are brought together into one system to collate their output and provide a single decision support environment for an operator. The framework used is a novel multi-agent system architecture that offers the opportunity to corroborate the output of the various interpretation techniques in order to improve the accuracy of fault detection. The results presented demonstrate that the interpretation techniques can provide performance assessment and early fault identification, thereby giving the operators sufficient time to make more informed decisions regarding the maintenance of their machines

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A generalized model for wind turbine anomaly identification based on SCADA data

et al. 2016

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This paper presents a generalized model for wind turbine (WT) anomaly identification based on the data collected from wind farm supervisory control and data acquisition (SCADA) system. Neural networks (NNs) are used to establish prediction models of the WT condition parameters that are dependent on environmental conditions such as ambient temperature and wind speed. Input parameters of the prediction models are selected based on the domain knowledge. Three types of sample data, namely the WT's current SCADA data, the WT's historical SCADA data, and other similar WTs' current SCADA data, are used to train the condition parameter prediction models. Prediction accuracy of the models trained by these sample data is compared and discussed in the paper. Mean absolute error (MAE) index is used to select the prediction models trained by historical and other similar WTs' current SCADA data. Abnormal level index (ALI) is defined to quantify the abnormal level of prediction error of each selected model. To improve the accuracy of anomaly identification, a fuzzy synthetic evaluation method is used to integrate the identification results obtained from the different selected models. The proposed method has been used for real 1.5 MW WTs with doubly fed induction generators. The results show that the proposed method is more effective in WT anomaly identification than traditional methods.

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Keeping the blades turning

Cited by 22 publications

References 0 publications

Exploring the Limits of Early Predictive Maintenance in Wind Turbines Applying an Anomaly Detection Technique

Exploring the Limits of Early Predictive Maintenance in Wind Turbines Applying an Anomaly Detection Technique

Online wind turbine fault detection through automated SCADA data analysis

A generalized model for wind turbine anomaly identification based on SCADA data

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