Analysis of SCADA data for early fault detection, with application to the maintenance management of wind turbines

Bangalore, Pramod; Patriksson, Michael

doi:10.1016/j.renene.2017.08.073

Cited by 93 publications

(45 citation statements)

References 26 publications

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“…The problem that the traditional models only provide either an age-based or a condition-based preventive maintenance schedule can be solved using the proposed framework. A modified mathematical model called preventive maintenance scheduling problem with interval costs (PMSPIC) was presented for maintenance optimization, considering both age-based and condition-based failure rate models [200]. IO&M has a more important influence on the structural safety and reliability of OWTs.…”

Section: Intelligent Operation and Maintenancementioning

confidence: 99%

Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development

et al. 2019

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With the depletion of fossil energy, offshore wind power has become an irreplaceable energy source for most countries in the world. In recent years, offshore wind power generation has presented the gradual development trend of larger capacity, taller towers, and longer blades. The more flexible towers and blades have led to the structural operational safety of the offshore wind turbine (OWT) receiving increasing worldwide attention. From this perspective, health monitoring systems and operational safety evaluation techniques of the offshore wind turbine structure, including the monitoring system category, data acquisition and transmission, feature information extraction and identification, safety evaluation and reliability analysis, and the intelligent operation and maintenance, were systematically investigated and summarized in this paper. Furthermore, a review of the current status, advantages, disadvantages, and the future development trend of existing systems and techniques was also carried out. Particularly, the offshore wind power industry will continue to develop into deep ocean areas in the next 30 years in China. Practical and reliable health monitoring systems and safety evaluation techniques are increasingly critical for offshore wind farms. Simultaneously, they have great significance for strengthening operation management, making efficient decisions, and reducing failure risks, and are also the key link in ensuring safe energy compositions and achieving energy development targets in China. The aims of this article are to inform more scholars and experts about the status of the health monitoring and safety evaluation of the offshore wind turbine structure, and to contribute toward improving the efficiency of the corresponding systems and techniques.

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Section: Intelligent Operation and Maintenancementioning

confidence: 99%

Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development

et al. 2019

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“…Wind energy is one of the main renewable energy sources [1,2]. It is harvested using wind turbines and to be converted into electric power.…”

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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“…Typically, the documents extracting the normal behavior data can be grouped into two categories: Combined use of alarm log information. In practice, the status data or alarm logs are often used to label SCADA data and to construct models . Theoretically, the data processed via the labeled records of faults are suitable for training a relatively perfect model.…”

Section: Introductionmentioning

confidence: 99%

Quantile regression neural network‐based fault detection scheme for wind turbines with application to monitoring a bearing

Fan

Jia³

et al. 2019

Wind Energy

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Under the framework of normal behavior modeling, this paper develops a novel scheme for fault detection via quantile regression neural networks (QRNNs). The QRNN model is a combination of quantile regressions and neural networks. It is able to identify the normal status or extract the normal behavior data accurately and quickly through lower and upper regression quantiles.Additionally, it is flexible to explore the potential nonlinear patterns contained in the normal status by taking advantage of neural networks. Finally, we monitor the residuals produced from QRNN to detect faults by using the exponentially weighted moving average (EWMA) control chart. The utility of our scheme is illustrated by empirical analyses of bearing fault detection based on Supervisory Control and Data Acquisition (SCADA) data from a wind turbine. We find that the QRNN model outperforms the multiple linear regression (MLR) and back propagation neural networks (BPNNs) in terms of mean absolute error (MAE). Besides, the obtained relationship between the width of control limits in EWMA and the number of alarms provides an important and convenient way for practical applications. KEYWORDS exponentially weighted moving average, fault detection, normal behavior modeling, quantile regression neural networks, wind turbine INTRODUCTIONWind energy is one of the most significant clean renewable energies for electrical generation. An increasing number of wind farms have been built to fulfill human's growing power demand. However, wind turbines (WTs) are typically deployed in a remote or offshore location with abundant wind energy, where performing regular inspections to maintain normal operations will cost a lot. In addition, unscheduled and reactive maintenances could increase the meaningless downtime, causing the loss of revenue. Therefore, how to perform more effective operations and maintenance procedures via monitoring remotely with lower cost has attracted more and more attentions from both academics and practitioners.Condition monitoring systems (CMSs) require not only high-level knowledge about the systems but also the high-cost vibration sensors, which hinders from the prevalence of CMSs. 1-3 On the contrary, the data collected via Supervisory Control and Data Acquisition (SCADA) systems that archive useful information in a convenient manner is universally available. Extensive efforts have been devoted to developing various methods for fault detection of WTs based on SCADA data.Based on SCADA data, the residuals produced from three models including a generalized mapping regressor (GMR), a general regression neural network (GRNN), and a feed-forward multilayer perceptron (MLP) have already been used to monitor the power curve of WTs. 4 The copula approach, estimating bivariate probability distribution function, is proposed to model the power curve so that the deviation between the existing and expected behavior can be checked. 5 The Mahalanobis distance is applied to detect faults in the optional curves containing the power curve, rotor curv...

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Analysis of SCADA data for early fault detection, with application to the maintenance management of wind turbines

Cited by 93 publications

References 26 publications

Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development

Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development

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

Quantile regression neural network‐based fault detection scheme for wind turbines with application to monitoring a bearing

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