Applying thermophysics for wind turbine drivetrain fault diagnosis using SCADA data

Cost-effective wind turbine diagnosis using SCADA data is a promising technology for future intelligent wind farm operation and management. This paper presents a thermophysics based method for wind turbine drivetrain fault diagnosis. A synthesized thermal model is formed by incorporating thermal mechanisms of the drivetrain into a wind turbine system model. Applications of the model are demonstrated in case studies of the gearbox and generator comparing simulation results and SCADA data analysis. The results show nonlinearity of the gearbox oil temperature rise with wind speed/output power that can effectively indicate gearbox efficiency degradation, which may be attributed to gear transmission problems such as gear teeth wear. Electrical generator faults, such as ventilation failure and winding voltage unbalance will cause changes to heat transfer and result in temperature changes that can be used for diagnosis purposes. This is shown by different patterns of stator winding temperature associated with power generation, while the simulation reveals the thermal mechanism. The method can be applied to diagnose some failure modes which are hard to identify from vibration analysis. The developed thermal model can play a central role for the purpose of fault diagnosis, by deriving relationships between various SCADA signals and revealing changes in the thermophysics of wind turbine operation

wind turbine generator slip ring damage detection through temperature data analysis

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Fault Detection in a Wind Turbine Hydraulic Pitch System Using Deep Autoencoder Extracted Features

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Multivariate SCADA data analysis methods for real-world wind turbine power curve monitoring

Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible

Using SCADA data for wind turbine condition monitoring - a review

The ever increasing size of wind turbines and the move to build them offshore have accelerated the need for optimised maintenance strategies in order to reduce operating costs. Predictive maintenance requires detailed information on the condition of turbines. Due to the high costs of dedicated condition monitoring systems based on mainly vibration measurements, the use of data from the turbine Supervisory Control And Data Acquisition (SCADA) system is appealing. This review discusses recent research using SCADA data for failure detection and condition monitoring, focussing on approaches which have already proved their ability to detect anomalies in data from real turbines. Approaches are categorised as (i) trending, (ii) clustering, (iii) normal behaviour modelling, (iv) damage modelling and (v) assessment of alarms and expert systems. Potential for future research on the use of SCADA data for advanced turbine condition monitoring is discussed

Fault Diagnosis and Fault Tolerant Control of Wind Turbines: An Overview

Wind turbines are playing an increasingly important role in renewable power generation. Their complex and large-scale structure, however, and operation in remote locations with harsh environmental conditions and highly variable stochastic loads make fault occurrence inevitable. Early detection and location of faults are vital for maintaining a high degree of availability and reducing maintenance costs. Hence, the deployment of algorithms capable of continuously monitoring and diagnosing potential faults and mitigating their effects before they evolve into failures is crucial. Fault diagnosis and fault tolerant control designs have been the subject of intensive research in the past decades. Significant progress has been made and several methods and control algorithms have been proposed in the literature. This paper provides an overview of the most recent fault diagnosis and fault tolerant control techniques for wind turbines. Following a brief discussion of the typical faults, the most commonly used model-based, data-driven and signal-based approaches are discussed. Passive and active fault tolerant control approaches are also highlighted and relevant publications are discussed. Future development tendencies in fault diagnosis and fault tolerant control of wind turbines are also briefly stated. The paper is written in a tutorial manner to provide a comprehensive overview of this research topic

Machine learning and data-driven fault detection for ship systems operations

Well maintained vessels exhibit high reliability, safety and energy efficiency. Even though machinery failures are inevitable, their occurrence can be foreseen when predictive maintenance schemes are implemented. Predictive maintenance may be optimally applied through condition, performance, and process monitoring. Most importantly, it can include the detection of developing faults, which affect the performance of ship systems and hinder energy-efficient operations of ships. Under this viewpoint, this paper proposes a new data-driven fault detection methodology in a novel application for shipboard systems, by exploring the "learning potential" of recorded voyage data. The proposed methodology, combines the benefits of Expected Behaviour (EB) models, by selecting the optimal regression model, with the Exponentially Weighted Moving Average (EWMA) for fault detection, in novel ship applications. It is seen that a multiple polynomial ridge regression model, with testing R2 score of nearly 0.96 and can accurately detect certain developing faults manifesting in both the Main Engine (ME) cylinder Exhaust Gas (EG) temperature and the ME scavenging air pressure. The early detection of developing faults can be used to supplement the daily monitoring of ship operations and enable the planning of pre-emptive rectifying actions by reducing sub-optimal machinery conditions

Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms - A Review

In the wind energy industry, the power curve represents the relationship between the “wind speed” at the hub height and the corresponding “active power” to be generated. It is the most versatile condition indicator and of vital importance in several key applications, such as wind turbine selection, capacity factor estimation, wind energy assessment and forecasting, and condition monitoring, among others. Ensuring an effective implementation of the aforementioned applications mostly requires a modeling technique that best approximates the normal properties of an optimal wind turbines operation in a particular wind farm. This challenge has drawn the attention of wind farm operators and researchers towards the “state of the art” in wind energy technology. This paper provides an exhaustive and updated review on power curve based applications, the most common anomaly and fault types including their root-causes, along with data preprocessing and correction schemes (i.e., filtering, clustering, isolation, and others), and modeling techniques (i.e., parametric and non-parametric) which cover a wide range of algorithms. More than 100 references, for the most part selected from recently published journal articles, were carefully compiled to properly assess the past, present, and future research directions in this active domain

Ethercat tabanlı bir scada sisteminde kural ve makine öğrenmesine dayalı saldırı ve anomali tespiti

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Endüstriyel kontrol sistemleri (EKS) bulundukları konum ve bileşenleri bakımından kritik altyapıya sahip sistemler olup, bilişim teknolojilerinden (BT) bağımsız olarak uygulama alanına göre kendilerine ait kabul ve işleyişleri bulunmaktadır. Bu sistemler, günümüzde otomasyon hiyerarşisinde yer alan seviyeler arası yatay ve dikey entegrasyonun tek bir protokolle sağlanması fikrinden yola çıkılarak Ethernet ile de adapte edilmiş durumdadır. Dolayısıyla EKS'ler hem doğalarından hem de Ethernet üzerinden bilişim teknolojilerinin sunduğu hizmetlerin içerisine dahil edildiklerinden dolayı siber saldırılara karşı tehdit altındadır. Bu durum, çoğunlukla iletişim altyapısı üzerinden gelen saldırıların tespiti için özelinde EKS çözümlerini gerektirir. Bu çalışmada, otomasyon uygulamalarında yaygın bir kullanıma sahip olan, Ethernet tabanlı gerçek zamanlı EtherCAT protokolü için Snort saldırı tespit sistemi üzerinde bilinen ve bilinmeyen saldırıları tespit eden bütüncül bir yapı ve makine öğrenmesi teknikleriyle anomali tespiti olmak üzere ikisi kural biri anomali tespitine dayanan 3 farklı yaklaşım sunulmaktadır. Sistem, geliştirilen önişlemci yardımıyla, bilinen saldırılar için güvenli düğüm yaklaşımı, bilinmeyen saldırılar için ise saha veri yolu tekrar periyodunu tespit ederek istatistiksel tekniklerle ve özgün çözümlerle kural tabanlı olarak saldırı tespitini kapsamaktadır. Tespitler bir günlükleme ve izleme yapısı olan ELK yığını üzerinde kullanıcıya sunulmaktadır. Ayrıca, yine bilinmeyen saldırılar için oluşturulan su seviye kontrol otomasyonu test ortamı üzerinde olaylar gerçeklenerek bir veri seti hazırlanması ve çeşitli öğrenme tekniklerinin veri seti üzerinde anomali tespitini kapsamaktadır. Bilinmeyen saldırıların tespiti kapsamında uygulanan periyot tespitinin %95-%99 doğrulukla yapılabildiği görülmüştür. Önerilen sistem üzerinde ise MAC aldatma, veri enjeksiyonu, DoS, köle saldırıları gibi ataklar gerçeklenmiş, alarm ve günlüklemeler incelendiğinde saldırıların başarıyla tespit edildiği görülmüştür. Ayrıca, k-NN ve SVM GA tekniklerinin olay tespitinde başarılı sonuç verdikleri belirlenmiştir.Industrial control systems (ICS) are critical infrastructures in terms of their location and components. These systems have their own features and operation related to the application field independent from the information technologies (IT). They are also adapted with the Ethernet technologies based on the idea of providing horizontal and vertical integration between the levels in the automation hierarchy with a single protocol. Therefore, ICSs are threatened by cyber attacks, due to both their nature and support of IT services through Ethernet. This risk requires ICS specific solutions to detect and prevent attacks which use communication infrastructure. In this study, two rule based which detect known and unknown attacks on the Snort system and one anomaly based which uses machine learning techniques, in total of three different approaches were presented as a holistic structure for Ethernet based real-time EtherCAT protocol, which is widely used in automation applications. In the case of rule based intrusion detection, the EtherCAT preprocessor was proposed, which applies the trust node approach for known attacks, and identifies the field bus repetition period for unknown attacks, with statistical techniques and novel solutions. The findings were presented to the user on the ELK stack, which is a logging and monitoring structure. For anomaly based intrusion detection, the water level control automation testbed was developed, a dataset was prepared by generating events and various machine learning techniques were applied on the dataset. According to the findings obtained in this research, it was concluded that the period determination which was applied within the scope of unknown attack detection can be made with 95% - 99% accuracy. When the logs and alerts of the realized MAC spoofing, data injection, DoS, slave attacks were investigated, it was seen that the attacks were able to be detected successfully. For anomaly detection part of the study, k-NN and SVM GA techniques were found to be successful in detecting events

Improved wind turbine monitoring using operational data

With wind energy becoming a major source of energy, there is a pressing need to reduce all associated costs to be competitive in a market that might be fully subsidy-free in the near future. Before thousands of wind turbines were installed all over the world, research in e.g. understanding aerodynamics, developing new materials, designing better gearboxes, improving power electronics etc., helped to cut down wind turbine manufacturing costs. It might be assumed, that this would be sufficient to reduce the costs of wind energy as the resource, the wind itself, is free of costs. However, it has become clear that the operation and maintenance of wind turbines contributes significantly to the overall cost of energy. Harsh environmental conditions and the frequently remote locations of the turbines makes maintenance of wind turbines challenging. Just recently, the industry realised that a move from reactive and scheduled maintenance towards preventative or condition-based maintenance will be crucial to further reduce costs. Knowing the condition of the wind turbine is key for any optimisation of operation and maintenance. There are various possibilities to install advanced sensors and monitoring systems developed in recent years. However, these will inevitably incur new costs that need to be worthwhile and retro-fits to existing turbines might not always be feasible. In contrast, this work focuses on ways to use operational data as recorded by the turbine's Supervisory Control And Data Acquisition (SCADA) system, which is installed in all modern wind turbines for operating purposes -- without additional costs. SCADA data usually contain information about the environmental conditions (e.g. wind speed, ambient temperature), the operation of the turbine (power production, rotational speed, pitch angle) and potentially the system's health status (temperatures, vibration). These measurements are commonly recorded in ten-minutely averages and might be seen as indirect and top-level information about the turbine's condition. Firstly, this thesis discusses the use of operational data to monitor the power performance to assess the overall efficiency of wind turbines and to analyse and optimise maintenance. In a sensitivity study, the financial consequences of imperfect maintenance are evaluated based on case study data and compared with environmental effects such as blade icing. It is shown how decision-making of wind farm operators could be supported with detailed `what-if' scenario analyses. Secondly, model-based monitoring of SCADA temperatures is investigated. This approach tries to identify hidden changes in the load-dependent fluctuations of drivetrain temperatures that can potentially reveal increased degradation and possible imminent failure. A detailed comparison of machine learning regression techniques and model configurations is conducted based on data from four wind farms with varying properties. The results indicate that the detailed setup of the model is very important while the selection of the modelling technique might be less relevant than expected. Ways to establish reliable failure detection are discussed and a condition index is developed based on an ensemble of different models and anomaly measures. However, the findings also highlight that better documentation of maintenance is required to further improve data-driven condition monitoring approaches. In the next part, the capabilities of operational data are explored in a study with data from both the SCADA system and a Condition Monitoring System (CMS) based on drivetrain vibrations. Analyses of signal similarity and data clusters reveal signal relationships and potential for synergistic effects of the different data sources. An application of machine learning techniques demonstrates that the alarms of the commercial CMS can be predicted in certain cases with SCADA data alone. Finally, the benefits of having wind turbines in farms are investigated in the context of condition monitoring. Several approaches are developed to improve failure detection based on operational statistics, CMS vibrations or SCADA temperatures. It is demonstrated that utilising comparisons with neighbouring turbines might be beneficial to get earlier and more reliable warnings of imminent failures. This work has been part of the Advanced Wind Energy Systems Operation and Maintenance Expertise (AWESOME) project, a European consortium with companies, universities and research centres in the wind energy sector from Spain, Italy, Germany, Denmark, Norway and UK. Parts of this work were developed in collaboration with other fellows in the project (as marked and explained in footnotes)