Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection

Schlechtingen, Meik; Santos, Ilmar

doi:10.1016/j.ymssp.2010.12.007

Cited by 301 publications

(211 citation statements)

References 7 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%

“…Zhang et al [14] investigated a wind turbine's main bearing faults also using the ANN method. Schlechtingen et al compared the detection efficiency for bearing faults and the stator temperature anomalies using a regression model and an ANN model [12]. Yang et al mainly focused on detecting incipient wind turbine blades and drive train faults by investigating the correlations among the relevant SCADA data.…”

Section: Related Workmentioning

confidence: 99%

“…After removing the errors, the SCADA data are re-sampled. Since the 10-min sampling interval has been widely adopted in existing SCADA-based fault prediction and diagnosis literature [7,12], we re-sampled the data using the ten-minute interval. All the entries in the re-sampled data are equated every ten minutes.…”

Section: Status Datamentioning

confidence: 99%

See 2 more Smart Citations

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%

Section: Related Workmentioning

confidence: 99%

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Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

et al. 2017

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“…The ANN was chosen as many studies have shown that it is the most effective classification model to predict the condition of offshore oil and gas pipelines on varieties of factors, including corrosion (El-Abbasy 2014; Schlechtingen and Santos 2011). Also these studies highlighted the effective use of Bayesian and decision tree approaches in condition-based maintenance of offshore wind turbines (Nielsen and Srensen 2011).…”

Section: Processingmentioning

confidence: 99%

Evolving ANN-based sensors for a context-aware cyber physical system of an offshore gas turbine

2017

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“…In [8], a neural network (NN) based normal behavior model of generator bearing temperature was developed to analyze bearing faults in WTs. A comparative analysis of two NN-based models and a regression-based model was presented in [9] to detect the anomalies in gearbox bearing temperature and generator stator temperature. Guo Peng et al used the method of temperature trend analysis to monitor the operation state of gearbox in wind turbines.…”

Section: Introductionmentioning

confidence: 99%

The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT

et al. 2017

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Abstract:In the working process of Double-Fed Wind Turbines (DFWT), it is very important to monitor and predict the temperature of the high-speed output shaft of the gearbox timely and effectively. Support vector machine has more advantages in the temperature prediction of wind turbines. Least squares support vector machine is suitable for online prediction due to reducing the computational complexity of support vector machine. In order to solve the sparsity of least squares support vector machine, an improved least squares support vector machine based on pruning algorithm is proposed in this paper to predict the temperature of the high-speed output shaft of gearbox using the practical data of Double-Fed Wind Turbines. At the same time, in order to improve the prediction accuracy and to solve the problem of few links between different feature parameters in common normalization method, the paper uses the method of joint normalization to preprocess the data. The principal component analysis is used to reduce the dimension of the data. Particle swarm optimization algorithm is used to optimize the parameters of the pruning least squares support vector machine. The proposed model that is established in this paper is a new model to forecast the temperature of the high-speed output shaft. The results show that its prediction accuracy is higher than that of other algorithms.

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Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection

Cited by 301 publications

References 7 publications

Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

Evolving ANN-based sensors for a context-aware cyber physical system of an offshore gas turbine

The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT

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