Online wind turbine fault detection through automated SCADA data analysis

Zaher, Ammar; McArthur, S.D.J.; Infield, David; Patel, Yash

doi:10.1002/we.319

Cited by 359 publications

(245 citation statements)

References 18 publications

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“…Among the most commonly deployed ICM techniques has been artificial neural networks (ANNs), which can be used for detecting both anomalous sensor readings and diagnosing these as specific faults. 7,8 Using only SCADA data, Zaher et al successfully applied ANNs to detect anomalous temperature readings within a turbine's gearbox and cooling oil, 9 while Kusiak and Li also applied ANNs for diagnosing the severity of a fault. 10 Yan demonstrated that the use of Random Forests (RF) 11 for classifying faults outperformed conventional decision tree classifiers and support vector machines, as well as producing comparable performance to ANNs.…”

Section: Technology Reviewmentioning

confidence: 99%

Intelligent integrated maintenance for wind power generation

et al. 2015

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A novel architecture and system for the provision of Reliability Centred Maintenance (RCM) for offshore wind power generation is presented. The architecture was developed by conducting a bottom-up analysis of the data required to support RCM within this specific industry, combined with a top-down analysis of the required maintenance functionality. The architecture and system consists of three integrated modules for intelligent condition monitoring, reliability and maintenance modelling, and maintenance scheduling that provide a scalable solution for performing dynamic, efficient and cost-effective preventative maintenance management within this extremely demanding renewable energy generation sector. The system demonstrates for the first time the integration of state-of-the-art advanced mathematical techniques: Random Forests, dynamic Bayesian networks and memetic algorithms in the development of an intelligent autonomous solution. The results from the application of the intelligent integrated system illustrated the automated detection of faults within a wind farm consisting of over 100 turbines, the modelling and updating of the turbines' survivability and creation of a hierarchy of maintenance actions, and the optimizing of the maintenance schedule with a view to maximizing the availability and revenue generation of the turbines.

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Section: Technology Reviewmentioning

confidence: 99%

Intelligent integrated maintenance for wind power generation

et al. 2015

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“…From [5][6][7], we know that abnormal temperature changes or rises are an effective indication of incipient gearbox failure. In [17], authors gives a proof that the gearbox temperature rise will be proportional to the power output if the gearbox works normally, that is, the gearbox transmission efficiency has not changes.…”

Section: Gearbox Failure Analysismentioning

confidence: 99%

“…Unfortunately, in the SCADA data we have, there is no gearbox failure record. Based on [5][6][7]17], it is representive to simulate a real incipient gearbox failure by adding extra temperature drift on the initial SCADA data. And these manual drift data is used to test the effectiveness of this AAKR CM method in the flowing two cases.…”

Section: Gearbox Failure Analysismentioning

confidence: 99%

“…An unexpected increase in component temperature may indicate an overload, poor lubrication or possibly ineffective passive or active cooling [5]. Previous work used a Back Propagation (BP) neural network to construct the normal behavior temperature models of gearbox based on SCADA data [6]. When the residual between the model prediction and the measured value becomes very large, a potential fault is identified.…”

Section: Introductionmentioning

confidence: 99%

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Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods

Guo

Bai

2011

Energies

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Condition Monitoring (CM) of wind turbines can greatly reduce the maintenance costs for wind farms, especially for offshore wind farms. A new condition monitoring method for a wind turbine gearbox using temperature trend analysis is proposed. Autoassociative Kernel Regression (AAKR) is used to construct the normal behavior model of the gearbox temperature. With a proper construction of the memory matrix, the AAKR model can cover the normal working space for the gearbox. When the gearbox has an incipient failure, the residuals between AAKR model estimates and the measurement temperature will become significant. A moving window statistical method is used to detect the changes of the residual mean value and standard deviation in a timely manner. When one of these parameters exceeds predefined thresholds, an incipient failure is flagged. In order to simulate the gearbox fault, manual temperature drift is added to the initial Supervisory Control and Data Acquisitions (SCADA) data. Analysis of simulated gearbox failures shows that the new condition monitoring method is effective.

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“…Both of these strategies require a full understanding of the wind turbine system and a detailed analysis of its failure mechanisms. WT SCADA systems provide a rich resource to achieve this capability as it archives comprehensive signal information, historical alarms and detailed fault logs, as well as environmental and operational conditions [1,2,6,8]. A wind turbine's systematic performance can be monitored through a proper analysis of the information collected by the SCADA system which covers all the major WT sub-assemblies.…”

Section: Introductionmentioning

confidence: 99%

Wind turbine SCADA alarm pattern recognition

Chen¹,

Qiu²,

Feng³

et al. 2011

IET Conference on Renewable Power Generation (RPG 2011)

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractCurrent wind turbine (WT) studies focus on improving their reliability and reducing the cost of energy, particularly when they are operated offshore. WT Supervisory Control and Data Acquisition (SCADA) systems contain alarm signals providing significant important information. Pattern recognition embodies a set of promising techniques for intelligently processing WT SCADA alarms. This paper presents the feasibility study of SCADA alarm processing and diagnosis method using an artificial neural network (ANN). The back-propagation network (BPN) algorithm was used to supervise a three layers network to identify a WT pitch system fault, known to be of high importance, from pitch system alarm. The trained ANN was then applied on another 4 WTs to find similar pitch system faults. Based on this study, we have found the general mapping capability of the ANN help to identify those most likely WT faults from SCADA alarm signals, but a wide range of representative alarm patterns are necessary for supervisory training.

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Online wind turbine fault detection through automated SCADA data analysis

Cited by 359 publications

References 18 publications

Intelligent integrated maintenance for wind power generation

Intelligent integrated maintenance for wind power generation

Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods

Wind turbine SCADA alarm pattern recognition

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