Wind Turbine Anomaly Detection Based on SCADA Data Mining

Liu, Xiaoyuan; Lu, Senxiang; Ren, Yan; Wu, Zhenlong

doi:10.3390/electronics9050751

Cited by 20 publications

(11 citation statements)

References 30 publications

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“…Considering low-frequency sampling in the SCADA systems, each sample reflects the statistical value of the conditional data rather than the instantaneous value. Accordingly, classical high-frequency signal processing technologies do not meet the time-series requirements of the SCADA data analysis with low sampling frequency and strong coupling (Liu et al , 2020). Investigations based on SCADA data can be mainly divided into two main categories, including regression-based models and classification-based models.…”

Section: Fault Detection Methods Based On Supervisory Control and Dat...mentioning

confidence: 99%

Application of SCADA data in wind turbine fault detection – a review

Yuan

2022

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Purpose With the rapid increase in the number of installed wind turbines (WTs) worldwide, requirements and expenses of maintenance have also increased significantly. The condition monitoring (CM) of WT provides a strong “soft guarantee” for preventive maintenance. The supervisory control and data acquisition (SCADA) system records a huge amount of condition data, which has become an effective means of CM. The main objective of the present study is to summarize the application of SCADA data to fault detection in wind turbines, analyze its advantages and disadvantages and predict the potential of future investigations on the use of SCADA data for fault detection. Design/methodology/approach The authors first review the means of WT CM and summarize the characteristics of CM based on SCADA data. To ensure the quality of SCADA data, data preprocessing methods are analyzed and compared. Then, the failure modes of the key components are discussed and the SCADA data used for fault detection of each component are compared. Moreover, the fault detection methods for WT are classified and a general framework for fault detection is proposed. Finally, the issues in the WT fault detection method based on SCADA data are reviewed. Findings Based on the performed analyses, it is found that although the fault detection accuracy based on SCADA data is relatively poor, it has low capital expenses and low computational cost. More specifically, when there is scarce fault data, the normal SCADA data can be used to detect the fault time. However, the specific fault type cannot be identified in this way. When a large amount of fault data are accumulated in the SCADA system, it can not only detect the occurrence time of the fault but also identify the specific fault type. Originality/value The main contribution of the present study is to summarize the pre-processing methods for SCADA data, the data required for fault detection of key components and the characteristics of the fault detection model. Then we propose a general fault detection framework for wind turbines based on SCADA data, where the maintenance workers can choose the appropriate fault detection method according to different fault detection requirements and data resources. This article is expected to provide guidance for fault detection based on time-series sensor signals and be of interest to researchers, maintenance workers and managers.

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Section: Fault Detection Methods Based On Supervisory Control and Dat...mentioning

confidence: 99%

Application of SCADA data in wind turbine fault detection – a review

Yuan

2022

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“…The inner part of the wind turbine system is fully equipped with sensors that are supposed to take the measurements and send readings to the control room. These data signals are called SCADA signals, and it has helped the management tremendously in monitoring the conditions of the WT system [101]. Next, to train the data, these signals were downloaded and transferred to Microsoft Excel to proceed to the next process.…”

Section: Feature Extractionmentioning

confidence: 99%

Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis

Shahrulhisham

Chong²,

Yaw³

et al. 2022

J. Phys.: Conf. Ser.

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Wind energies are one of the most used resources worldwide and favours the economy by not emitting harmful gases that could lead to global warming. It is a cost-efficient method and environmentally friendly. Hence, explains the popularity of wind energy production over the years. Unfortunately, a minor fault could be contagious by affecting the nearby components, then a more complicated problem might arise, which may be costly. Thus, this article conducted a machine learning technique, support vector machine (SVM) to monitor the health of the wind turbine system by classifying the class of healthy data and faulty data. Some SVM types were experimented with, including Linear, Quadratic, Cubic, Fine Gaussian, Medium Gaussian, and Coarse Gaussian. Then these models were trained under different validation schemes that are cross-validation, holdout validation, and re-substitution validation as an approach to evaluate the performance of each model. In the end, Cubic SVM is proven to outperformed other models under the provision of 10-fold cross-validation with an accuracy of 98.25%. The result showed that Cubic SVM has the best performance while Linear SVM has the least accuracy among other models. Hence choosing the default value is preferred as the final product to diagnose the fault in wind turbine systems.

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“…Her bir algoritma kendine ait tekniklerle verilen veri seti içerisinden aykırı olabilecek değerleri tespit etmeye çalışmıştır. Kümeleme yönteminde, içerisinde parametre olarak baz alınacak küme sayısı verilmektedir [10]. Burada optimum küme sayısını bulabilmek için Elbow ve Silhouette yöntemleri kullanılmıştır.…”

Section: Aykırı Değer Tespiti (Anomaly Detection)unclassified

Estimating Remaining Useful Life in Wind Power Plants

Bulut¹

2021

International Journal of 3D Printing Technologies and Digital Industry

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Dünya çapında sayısı artmakta olan rüzgar enerji santrallerinin (RES) işletme ve bakım maliyetleri, üretimin karlılığını belirleyen önemli bir kalemdir. Kestirimci bakım yöntemleriyle rüzgar türbinlerinin güvenli çalışma süreleri uzatılabilmesinin yanında, işletme ve bakım maliyetleri de düşürülebilir. Çalışmamızda, rüzgar türbini ve bileşenlerine dair bir kestirimci bakım yöntemi sunılmaktadır. Kalan faydalı ömür (RUL) yaklaşımıyla, arızaların ne kadar süre sonra gerçekleşeceği ve arızaların hangi alt sistemde meydana geleceği LSTM (Long Short-Term Memory) gibi sıralı derin öğrenme yöntemleriyle tahmin edilebilmektedir. Önerilen çözümde, faaliyetteki bir rüzgar çiftliği bünyesindeki türbinler değerlendirilmektedir.

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Wind Turbine Anomaly Detection Based on SCADA Data Mining

Cited by 20 publications

References 30 publications

Application of SCADA data in wind turbine fault detection – a review

Application of SCADA data in wind turbine fault detection – a review

Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis

Estimating Remaining Useful Life in Wind Power Plants

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