Wind Turbine Condition Assessment Through Power Curve Copula Modeling

Gill, Simon; Stephen, Bruce; Galloway, Stuart

doi:10.1109/tste.2011.2167164

Cited by 163 publications

(93 citation statements)

References 14 publications

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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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“…Furthermore, temperature signals can also provide key information on the health condition of mechanical transmission system in wind turbines [9][10][11]. However, in previous work, the relationship between the temperature rise and the operating power has not been considered yet.…”

Section: Introductionmentioning

confidence: 99%

Estimating Health Condition of the Wind Turbine Drivetrain System

Qian

Zhang

2017

Energies

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Condition Monitoring (CM) has been considered as an effective method to enhance the reliability of wind turbines and implement cost-effective maintenance. Thus, adopting an efficient approach for condition monitoring of wind turbines is desirable. This paper presents a data-driven model-based CM approach for wind turbines based on the online sequential extreme learning machine (OS-ELM) algorithm. A physical kinetic energy correction model is employed to normalize the temperature change to the value at the rated power output to eliminate the effect of variable speed operation of the turbines. The residual signal, obtained by comparing the predicted values and practical measurements, is processed by the physical correction model and then assessed with a Bonferroni interval method for fault diagnosis. Models have been validated using supervisory control and data acquisition (SCADA) data acquired from an operational wind farm, which contains various types of temperature data of the gearbox. The results show that the proposed method can detect more efficiently both the long-term aging characteristics and the short-term faults of the gearbox.

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“…Among these, the following can be recalled: the copula power curve model [16], cubic spline interpolation [9], different types of artificial neural network [8,10,[17][18][19], multi-layer perceptron, random forest, k-nearest neighbors and support vector machines [8,15,20]. Among the non-parametric methods, there is the Method Of Bins on which the IEC 61400-12-1 standard is based [21].…”

Section: Introductionmentioning

confidence: 99%

A Simple and Effective Approach for the Prediction of Turbine Power Production From Wind Speed Forecast

Marrocu

Massidda

2017

Energies

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An accurate forecast of the power generated by a wind turbine is of paramount importance for its optimal exploitation. Several forecasting methods have been proposed either based on a physical modeling or using a statistical approach. All of them rely on the availability of high quality measures of local wind speed, corresponding generated power and on numerical weather forecasts. In this paper, a simple and effective wind power forecast technique, based on the probability distribution mapping of wind speed forecast and observed power data, is presented and it is applied to two turbines located on the island of Borkum (Germany) in the North Sea. The wind speed forecast of the ECMWF model at 100 m from the ground is used as the prognostic meteorological parameter. Training procedures are based entirely on relatively short time series of power measurements. Results show that our approach has skills that are similar or better than those obtained using more standard methods when measured with mean absolute error.

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Wind Turbine Condition Assessment Through Power Curve Copula Modeling

Cited by 163 publications

References 14 publications

Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

Estimating Health Condition of the Wind Turbine Drivetrain System

A Simple and Effective Approach for the Prediction of Turbine Power Production From Wind Speed Forecast

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