RETRACTED: Artificial neural networks applications in wind energy systems: a review

Ata, Raşit

doi:10.1016/j.rser.2015.04.166

Cited by 206 publications

(86 citation statements)

References 150 publications

(294 reference statements)

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“…the final layer weight, θ j hidden layer bias, θ k the final layer bias, and f (·) is the activation function sigmoid type. The sigmoid function, Figure 3, is used as the activation function of the ANN, given by Equation (15). It provides an output in the range [0, 1].…”

Section: Artificial Neuronal Network (Ann): Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Machine Learning for Wind Turbine Blades Maintenance Management

2017

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Delamination in Wind Turbine Blades (WTB) is a common structural problem that can generate large costs. Delamination is the separation of layers of a composite material, which produces points of stress concentration. These points suffer greater traction and compression forces in working conditions, and they can trigger cracks, and partial or total breakage of the blade. Early detection of delamination is crucial for the prevention of breakages and downtime. The main novelty presented in this paper has been to apply an approach for detecting and diagnosing the delamination WTB. The approach is based on signal processing of guided waves, and multiclass pattern recognition using machine learning. Delamination was induced in the WTB to check the accuracy of the approach. The signal is denoised by wavelet transform. The autoregressive Yule-Walker model is employed for feature extraction, and Akaike's information criterion method for feature selection. The classifiers are quadratic discriminant analysis, k-nearest neighbors, decision trees, and neural network multilayer perceptron. The confusion matrix is employed to evaluate the classification, especially the receiver operating characteristic analysis by: recall, specificity, precision, and F-score.

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Section: Artificial Neuronal Network (Ann): Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Machine Learning for Wind Turbine Blades Maintenance Management

2017

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“…Artificial neural networks, used in different areas of science (CATALOGNA et al, 2012;WERE et al, 2015;ATA, 2015) including wood technology (FINI et al, 2015;IGLESIAS et al, 2015;TIRYAKI et al, 2014a), can accurately predict results. Artificial neural networks are biologically-inspired computation models, consisting of simple processing elements that implement a particular mathematical function data when activated, generating the results desired (SCHUMAN;BIRDWEl, 2013).…”

Section: Scimentioning

confidence: 99%

Prediction of the physical, mechanical and colorimetric properties of Eucalyptus grandis heat-treated wood using artificial neural networks

Zanuncio¹,

Carvalho²,

Silva³

et al. 2017

Sci. For.

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“…By using advanced SCADA data mining methods, various models predicting normal behavior have been developed to detect common anomalies in most important WT components. These models were established by employing data-driven approaches that involve NNs [24][25][26], support vector machine (SVM) [27], ANFIS [28], deep neural network (DNN) [29], and nonlinear state estimate technique [30]. In [31], different WT performance curves, such as the power curve, rotor curve, and blade pitch curve were modeled for monitoring performance of WTs.…”

Section: Introductionmentioning

confidence: 99%

Condition Assessment for Wind Turbines with Doubly Fed Induction Generators Based on SCADA Data

Sun¹,

Li²,

Wang³

et al. 2017

Journal of Electrical Engineering and Technology

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-This paper presents an effective approach for wind turbine (WT) condition assessment based on the data collected from wind farm supervisory control and data acquisition (SCADA) system. Three types of assessment indices are determined based on the monitoring parameters obtained from the SCADA system. Neural Networks (NNs) are used to establish prediction models for the assessment indices that are dependent on environmental conditions such as ambient temperature and wind speed. An abnormal level index (ALI) is defined to quantify the abnormal level of the proposed indices. Prediction errors of the prediction models follow a normal distribution. Thus, the ALIs can be calculated based on the probability density function of normal distribution. For other assessment indices, the ALIs are calculated by the nonparametric estimation based cumulative probability density function. A Back-Propagation NN (BPNN) algorithm is used for the overall WT condition assessment. The inputs to the BPNN are the ALIs of the proposed indices. The network structure and the number of nodes in the hidden layer are carefully chosen when the BPNN model is being trained. The condition assessment method has been used for real 1.5 MW WTs with doubly fed induction generators. Results show that the proposed assessment method could effectively predict the change of operating conditions prior to fault occurrences and provide early alarming of the developing faults of WTs.

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RETRACTED: Artificial neural networks applications in wind energy systems: a review

Cited by 206 publications

References 150 publications

Machine Learning for Wind Turbine Blades Maintenance Management

Machine Learning for Wind Turbine Blades Maintenance Management

Prediction of the physical, mechanical and colorimetric properties of Eucalyptus grandis heat-treated wood using artificial neural networks

Condition Assessment for Wind Turbines with Doubly Fed Induction Generators Based on SCADA Data

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