Use of SCADA Data for Failure Detection in Wind Turbines

Kim, Kyu-Sung; Parthasarathy, Girija; Uluyol, Önder; Foslien, Wendy; Sheng, Shuangwen; Fleming, Paul

doi:10.1115/es2011-54243

Cited by 112 publications

(66 citation statements)

References 16 publications

(15 reference statements)

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“…Two comprehensive reviews of existing approaches for fault diagnosis are provided by Lu et al [2] and Márquez et al [5]. These methods focused on detecting gearbox faults [7][8][9], blades/pitch faults [10,11], drive train faults [9,10], and main bearings faults [12][13][14].…”

Section: Related Workmentioning

confidence: 99%

Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

et al. 2017

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Abstract:The fast-growing wind power industry faces the challenge of reducing operation and maintenance (O&M) costs for wind power plants. Predictive maintenance is essential to improve wind turbine reliability and prolong operation time, thereby reducing the O&M cost for wind power plants. This study presents a solution for predictive maintenance of wind turbine generators. The proposed solution can: (1) predict the remaining useful life (RUL) of wind turbine generators before a fault occurs and (2) diagnose the state of the wind turbine generator when the fault occurs. Moreover, the proposed solution implies low-deployment costs because it relies solely on the information collected from the widely available supervisory control and data acquisition (SCADA) system. Extra sensing hardware is needless. The proposed solution has been deployed and evaluated in two real-world wind power plants located in China. The experimental study demonstrates that the RUL of the generators can be predicted 18 days ahead with about an 80% prediction accuracy. When faults occur, the specific type of generator fault can be diagnosed with an accuracy of 94%.

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Section: Related Workmentioning

confidence: 99%

Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

et al. 2017

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“…An operational wind farm typically generates vast quantities of data. The SCADA data contain information about every aspect of a wind farm, from power output and wind speed to any errors registered within the system [20]. SCADA data may be effectively used to ''tune'' a wind farm, providing early warnings of possible failures and optimizing power outputs across many turbines in all conditions.…”

Section: Scada Data Based Cms For Wtmentioning

confidence: 99%

SCADA data based condition monitoring of wind turbines

2014

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Wind turbines (WTs) are quite expensive pieces of equipment in power industry. Maintenance and repair is a critical activity which also consumes lots of time and effort, hence making it a costly affair. Carefully planning the maintenance based upon condition of the equipment would make the process reasonable. Mostly the WTs are equipped with some kind of condition monitoring device/system, which provides the information about the device to the central data base i.e., supervisory control and data acquisition (SCADA) data base. These devices/systems make use of data processing techniques/methods in order to detect and predict faults. The information provided by condition monitoring equipments keeps on recoding in the SCADA data base. This paper dwells upon the techniques/methods/algorithms developed, to carry out diagnosis and prognosis of the faults, based upon SCADA data. Subsequently data driven approaching for SCADA data interpretation has been reviewed and an artificial intelligence (AI) based framework for fault diagnosis and prognosis of WTs using SCADA data is proposed.

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“…Recently, Qiu YN and Feng YH [4,5] prove the relationship between the SCADA data trending and WT failures based on physical principle analysis, which reveals that for certain failure modes SCADA data is useful on the fault detection and diagnosis. Fault features can be captured by anomaly detection algorithms developed on SCADA data [6].…”

Section: Introductionmentioning

confidence: 99%

Study of Wind Turbine Fault Diagnosis Based on Unscented Kalman Filter and SCADA Data

et al. 2016

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Effective wind turbine fault diagnostic algorithms are crucial for wind turbine intelligent condition monitoring. An unscented Kalman filter approach is proposed to successfully detect and isolate two types of gearbox failures of a wind turbine in this paper. The state space models are defined for the unscented Kalman filter model by a detailed wind turbine nonlinear systematic principle analysis. The three failure modes being studied are gearbox damage, lubrication oil leakage and pitch failure. The results show that unscented Kalman filter model has special response to online input parameters under different fault conditions. Such property makes it effective on fault identification. It also shows that properly defining unscented Kalman filter state space vectors and control vectors are crucial for improving its sensitivity to different failures. Online fault detection capability of this approach is then proved on SCADA data. The developed unsented Kalman filter model provides an effective way for wind turbine fault detection using supervisory control and data acquisition data. This is essential for further intelligent WT condition monitoring.

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Use of SCADA Data for Failure Detection in Wind Turbines

Cited by 112 publications

References 16 publications

Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

SCADA data based condition monitoring of wind turbines

Study of Wind Turbine Fault Diagnosis Based on Unscented Kalman Filter and SCADA Data

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