A fault detection method for pitch actuators faults in wind turbines

Tutivén, Christian; Vidal, Yolanda; Acho, Leonardo; Benedé, José Rodellar

doi:10.24084/repqj13.455

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Regarding fault detection and diagnosis for wind turbines, there are different models and methods developed which can be generally categorized as model-based [3][4][5][6] and databased [6][7][8][9][10][11][12][13] approaches. The model-based approach is mainly used for fault mechanism analysis, and the data-based approach is for condition assessment and prediction.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Method for Fault Detection of Wind Turbine Converter

et al. 2021

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The converter is an important component in wind turbine power drive-train systems, and usually, it has a higher failure rate. Therefore, detecting the potential faults for prediction of its failure has become indispensable for condition-based maintenance and operation of wind turbines. This paper presents an approach to wind turbine converter fault detection using convolutional neural network models which are developed by using wind turbine Supervisory Control and Data Acquisition (SCADA) system data. The approach starts with the selection of fault indicator variables, and then the fault indicator variables data are extracted from a wind turbine SCADA system. Using the data, radar charts are generated, and the convolutional neural network models are applied to feature extraction from the radar charts and characteristic analysis of the feature for fault detection. Based on the analysis of the Octave Convolution (OctConv) network structure, an improved AOctConv (Attention Octave Convolution) structure is proposed in this paper, and it is applied to the ResNet50 backbone network (named as AOC–ResNet50). It is found that the algorithm based on AOC–ResNet50 overcomes the issues of information asymmetry caused by the asymmetry of the sampling method and the damage to the original features in the high and low frequency domains by the OctConv structure. Finally, the AOC–ResNet50 network is employed for fault detection of the wind turbine converter using 10 min SCADA system data. It is verified that the fault detection accuracy using the AOC–ResNet50 network is up to 98.0%, which is higher than the fault detection accuracy using the ResNet50 and Oct–ResNet50 networks. Therefore, the effectiveness of the AOC–ResNet50 network model in wind turbine converter fault detection is identified. The novelty of this paper lies in a novel AOC–ResNet50 network proposed and its effectiveness in wind turbine fault detection. This was verified through a comparative study on wind turbine power converter fault detection with other competitive convolutional neural network models for deep learning.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Method for Fault Detection of Wind Turbine Converter

et al. 2021

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“…The damage to the pitch system not only increases the operation and maintenance cost, but also reduces the production efficiency of the wind turbine [7]. In recent years, there are a number of studies reported on fault diagnosis and prediction for wind turbine pitch system, e.g., References [7][8][9][10][11][12][13][14]. Based on literature survey, it is found that the published studies can be generally classified into two categories: One is physical model-based approach, e.g., References [12][13][14][15] and the other is based on measurement data, e.g., References [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…A set of possible faults affecting system dynamics is described. In Reference [13], a model-based variable pitch actuator fault detection method is proposed. When the pitch actuator is dynamically changed, the normalized gradient method is used to estimate the parameters of the pitch actuator and obtain the residual signal.…”

Section: Introductionmentioning

confidence: 99%

On Fault Prediction for Wind Turbine Pitch System Using Radar Chart and Support Vector Machine Approach

et al. 2019

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In order to reduce operation and maintenance cost and improve fault diagnosis and detection accuracy for wind turbines, a study on advanced methods has been carried out. The purpose of this paper is to present a new method developed using radar chart and support vector machine (SVM) approach for fault diagnosis and prediction of wind turbine pitch system as it usually has a higher failure rate. In the study, the supervisory control and data acquisition (SCADA) system data are utilized as source data for SVM prediction. First of all, the characteristics of the indicator variable data collected by the SCADA system are analyzed, and the radar charts corresponding to the normal and faulty operation of the wind turbine pitch system are constructed using the indicator variable data. Secondly, the SVM method is used to extract the gray-level co-occurrence matrix (GLCM) features and histogram of oriented gradients (HOG) features of the radar charts, and the SVM classifier is trained. Then, the operational status is predicted, the classification effect is evaluated by the confusion matrix, and the prediction evaluation index is calculated. Thirdly, the support vector regression method is used to analyze the SCADA indicator variable data, the input and output of the regression model are determined, and the training prediction model is established, and the prediction accuracy of the test model is analyzed using the test sample data. Finally, the forecasting evaluation indexes obtained by the above two methods are compared. It proves that the proposed method using SVM to analyze the system radar charts has a higher prediction accuracy of 91.24% than the support vector regression method. The prediction accuracy is improved by 8.6%. Hence, it is verified that the new method using a radar chart and SVM approach has superiority over the support vector regression method.

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Modelling of Actuators and Generators

Gambier

2022

Advances in Industrial Control

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A fault detection method for pitch actuators faults in wind turbines

Cited by 5 publications

References 13 publications

Deep Learning Method for Fault Detection of Wind Turbine Converter

Deep Learning Method for Fault Detection of Wind Turbine Converter

On Fault Prediction for Wind Turbine Pitch System Using Radar Chart and Support Vector Machine Approach

Modelling of Actuators and Generators

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