Reliability Models and Failure Detection Algorithms for Wind Turbines

Durante las pasadas décadas, la industria eólica ha sufrido un crecimiento muysignificativo en Europa llevando a la generación eólica al puesto más relevanteen cuanto a producción energética mediante fuentes renovables. Sin embargo, siconsideramos los aspectos económicos, el sector eólico todavía no ha alcanzadoel nivel competitivo necesario para batir a los sistemas de generación de energíaconvencionales.Los costes principales en la explotación de parques eólicos se asignan a lasactividades relacionadas con la Operación y Mantenimiento (O&M). Esto se debeal hecho de que, en la actualidad, la Operación y Mantenimiento está basadaprincipalmente en acciones correctivas o preventivas. Por tanto, el uso de técnicaspredictivas podría reducir de forma significativa los costes relacionados con lasactividades de mantenimiento mejorando así los beneficios globales de la explotaciónde los parques eólicos.Aunque los beneficios del mantenimiento predictivo se consideran cada díamás importantes, existen todavía la necesidad de investigar y explorar dichastécnicas. Modelos de fiabilidad avanzados y algoritmos de predicción de fallospueden facilitar a los operadores la detección anticipada de fallos de componentesen los aerogeneradores y, en base a ello, adaptar sus estrategias de mantenimiento.Hasta la fecha, los modelos de fiabilidad de turbinas eólicas se basan, casiexclusivamente, en la edad de la turbina. Esto es así porque fueron desarrolladosoriginalmente para máquinas que trabajan en entornos ‘amigables’, por ejemplo, enel interior de naves industriales. Los aerogeneradores, al contrario, están expuestosa condiciones ambientales altamente variables y, por tanto, los modelos clásicosde fiabilidad no reflejan la realidad con suficiente precisión. Es necesario, portanto, desarrollar nuevos modelos de fiabilidad que sean capaces de reproducir el comportamiento de los fallos de las turbinas eólicas y sus componentes, teniendoen cuenta las condiciones meteorológicas y operacionales en su emplazamiento.La predicción de fallos se realiza habitualmente utilizando datos que se obtienendel sistema de Supervisión Control y Adquisición de Datos (SCADA) o de Sistemasde Monitorización de Condición (CMS). Cabe destacar que en turbinas eólicasmodernas conviven ambos tipos de sistemas y la fusión de ambas fuentes de datospuede mejorar significativamente la detección de fallos. Esta tesis pretende mejorarlas prácticas actuales de Operación y Mantenimiento mediante: (1) el desarrollo demodelos avanzados de fiabilidad y detección de fallos basados en datos que incluyanlas condiciones ambientales y operacionales existentes en los parques eólicos y (2)la aplicación de nuevos algoritmos de detección de fallos que usen las condicionesambientales y operacionales del emplazamiento, así como datos procedentes tantode sistemas SCADA como CMS. Estos dos objetivos se han dividido en cuatrotareas.En la primera tarea se ha realizado un análisis exhaustivo tanto de los fallosproducidos en un amplio conjunto de aerogeneradores (amplio en número de turbinasy en longitud de los registros) como de sus tiempos de parada asociados. De estaforma, se han visualizado los componentes que más fallan en función de la tecnologíadel aerogenerador, así como sus modos de fallo. Esta información es vital para eldesarrollo posterior de modelos de fiabilidad y mantenimiento.En segundo lugar, se han investigado las condiciones meteorológicas previasa sucesos con fallos de los principales componentes de los aerogeneradores. Seha desarrollado un entorno de aprendizaje basado en datos utilizando técnicas deagrupamiento ‘k-means clustering’ y reglas de asociación ‘a priori’. Este entorno escapaz de manejar grandes cantidades de datos proporcionando resultados útiles yfácilmente visualizables. Adicionalmente, se han aplicado algoritmos de detecciónde anomalías y patrones para encontrar cambios abruptos y patrones recurrentesen la serie temporal de la velocidad del viento en momentos previos a los fallosde los componentes principales de los aerogeneradores. En la tercera tarea, sepropone un nuevo modelo de fiabilidad que incorpora directamente las condicionesmeteorológicas registradas durante los dos meses previos al fallo. El modelo usados procesos estadísticos separados, uno genera los sucesos de fallos, así comoceros ocasionales mientras que el otro genera los ceros estructurales necesarios paralos algoritmos de cálculo. Los posibles efectos no observados (heterogeneidad) en el parque eólico se tienen en cuenta de forma adicional. Para evitar problemas de‘over-fitting’ y multicolinearidades, se utilizan sofisticadas técnicas de regularización.Finalmente, la capacidad del modelo se verifica usando datos históricos de fallosy lecturas meteorológicas obtenidas en los mástiles meteorológicos de los parqueseólicos.En la última tarea se han desarrollado algoritmos de predicción basados encondiciones meteorológicas y en datos operacionales y de vibraciones. Se ha‘entrenado’ una red de Bayes, para predecir los fallos de componentes en unparque eólico, basada fundamentalmente en las condiciones meteorológicas delemplazamiento. Posteriormente, se introduce una metodología para fusionar datosde vibraciones obtenidos del CMS con datos obtenidos del sistema SCADA, conel objetivo de analizar las relaciones entre ambas fuentes. Estos datos se hanutilizado para la predicción de fallos en el eje principal utilizando varios algoritmosde inteligencia artificial, ‘random forests’, ‘gradient boosting machines’, modelosgeneralizados lineales y redes neuronales artificiales. Además, se ha desarrolladouna herramienta para la evaluación on-line de los datos de vibraciones (CMS)denominada DAVE (‘Distance Based Automated Vibration Evaluation’).Los resultados de esta tesis demuestran que el comportamiento de los fallos delos componentes de aerogeneradores está altamente influenciado por las condicionesmeteorológicas del emplazamiento. El entorno de aprendizaje basado en datos escapaz de identificar las condiciones generales y temporales específicas previas alos fallos de componentes. Además, se ha demostrado que, con los modelos defiabilidad y algoritmos de detección propuestos, la Operación y Mantenimiento delas turbinas eólicas puede mejorarse significativamente. Estos modelos de fiabilidady de detección de fallos son los primeros que proporcionan una representaciónrealística y específica del emplazamiento, al considerar combinaciones complejasde las condiciones ambientales, así como indicadores operacionales y de estadode operación obtenidos a partir de la fusión de datos de vibraciones CMS y datosdel SCADA. Por tanto, este trabajo proporciona entornos prácticos, modelos yalgoritmos que se podrán aplicar en el campo del mantenimiento predictivo deturbinas eólicas.<br /

A Review of using Data Mining Techniques in Power Plants

Data mining techniques and their applications have developed rapidly during the last two decades. This paper reviews application of data mining techniques in power systems, specially in power plants, through a survey of literature between the year 2000 and 2015. Keyword indices, articles’ abstracts and conclusions were used to classify more than 86 articles about application of data mining in power plants, from many academic journals and research centers. Because this paper concerns about application of data mining in power plants; the paper started by providing a brief introduction about data mining and power systems to give the reader better vision about these two different disciplines. This paper presents a comprehensive survey of the collected articles and classifies them according to three categories: the used techniques, the problem and the application area. From this review we found that data mining techniques (classification, regression, clustering and association rules) could be used to solve many types of problems in power plants, like predicting the amount of generated power, failure prediction, failure diagnosis, failure detection and many others. Also there is no standard technique that could be used for a specific problem. Application of data mining in power plants is a rich research area and still needs more exploration

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

Identifying health status of wind turbines by using self organizing maps and interpretation-oriented post-processing tools

Identifying the health status of wind turbines becomes critical to reduce the impact of failures on generation costs (between 25–35%). This is a time-consuming task since a human expert has to explore turbines individually. Methods: To optimize this process, we present a strategy based on Self Organizing Maps, clustering and a further grouping of turbines based on the centroids of their SOM clusters, generating groups of turbines that have similar behavior for subsystem failure. The human expert can diagnose the wind farm health by the analysis of a small each group sample. By introducing post-processing tools like Class panel graphs and Traffic lights panels, the conceptualization of the clusters is enhanced, providing additional information of what kind of real scenarios the clusters point out contributing to a better diagnosis. Results: The proposed approach has been tested in real wind farms with different characteristics (number of wind turbines, manufacturers, power, type of sensors, ...) and compared with classical clustering. Conclusions: Experimental results show that the states healthy, unhealthy and intermediate have been detected. Besides, the operational modes identified for each wind turbine overcome those obtained with classical clustering techniques capturing the intrinsic stationarity of the data.Peer ReviewedPostprint (published version

Automated On-line Fault Prognosis for Wind Turbine Monitoring using SCADA data

Current wind turbine (WT) studies focus on improving their reliability and reducing the cost of energy, particularly when WTs are operated offshore. A Supervisory Control and Data Acquisition (SCADA) system is a standard installation on larger WTs, monitoring all major WT sub-assemblies and providing important information. Ideally, a WT’s health condition or state of the components can be deduced through rigorous analysis of SCADA data. Several programmes have been made for that purpose; however, the resulting cost savings are limited because of the data complexity and relatively low number of failures that can be easily detected in early stages. This thesis develops an automated on-line fault prognosis system for WT monitoring using SCADA data, concentrating particularly on WT pitch system, which is known to be fault significant. A number of preliminary activities were carried out in this research. They included building a dedicated server, developing a data visualisation tool, reviewing the existing WT monitoring techniques and investigating the possible AI techniques along with some examples detailing applications of how they can be utilised in this research. The a-priori knowledge-based Adaptive Neuro-Fuzzy Inference System (APK-ANFIS) was selected to research in further because it has been shown to be interpretable and allows domain knowledge to be incorporated. A fault prognosis system using APK-ANFIS based on four critical WT pitch system features is proposed. The proposed approach has been applied to the pitch data of two different designs of 26 Alstom and 22 Mitsubishi WTs, with two different types of SCADA system, demonstrating the adaptability of APK-ANFIS for application to variety of technologies. After that, the Alstom results were compared to a prior general alarm approach to show the advantage of prognostic horizon. In addition, both results are evaluated using Confusion Matrix analysis and a comparison study of the two tests to draw conclusions, demonstrating that the proposed approach is effective

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Intelligent Data Fusion for Applied Decision Support

Data fusion technologies are widely applied to support a real-time decision-making in complicated, dynamically changing environments. Due to the complexity in the problem domain, artificial intelligent algorithms, such as Bayesian inference and particle swarm optimization, are employed to make the decision support system more adaptive and cognitive. This dissertation proposes a new data fusion model with an intelligent mechanism adding decision feedback to the system in real-time, and implements this intelligent data fusion model in two real-world applications. The first application is designing a new sensor management system for a real-world and highly dynamic air traffic control problem. The main objective of sensor management is to schedule discrete-time, two-way communications between sensors and transponder-equipped aircraft over a given coverage area. Decisions regarding allocation of sensor resources are made to improve the efficiency of sensors and communications, simultaneously. For the proposed design, its loop nature takes account the effect of the current sensor model into the next scheduling interval, which makes the sensor management system able to respond to the dynamically changing environment in real-time. Moreover, it uses a Bayesian network as the mission manager to come up with operating requirements for each region every scheduling interval, so that the system efficiently balances the allocation of sensor resources according to different region priorities. As one of this dissertation\u27s contribution in the area of Bayesian inference, the resulting Bayesian mission manager is shown to demonstrate significant performance improvement in resource usage for prioritized regions such as a runway in the air traffic control application for airport surfaces. Due to wind\u27s importance as a renewable energy resource, the second application is designing an intelligent data-driven approach to monitor the wind turbine performance in real-time by fusing multiple types of maintenance tests, and detect the turbine failures by tracking the turbine maintenance statistics. The current focus has been on building wind farms without much effort towards the optimization of wind farm management. Also, under performing or faulty turbines cause huge losses in revenue as the existing wind farms age. Automated monitoring for maintenance and optimizing of wind farm operations will be a key element in the transition of wind power from an alternative energy form to a primary form. Early detection and prediction of catastrophic failures helps prevent major maintenance costs from occurring as well. I develop multiple tests on several important turbine performance variables, such as generated power, rotor speed, pitch angle, and wind speed difference. Wind speed differences are particularly effective in the detection of anemometer failures, which is a very common maintenance issue that greatly impacts power production yet can produce misleading symptoms. To improve the detection accuracy of this wind speed difference test, I discuss a new method to determine the decision boundary between the normal and abnormal states using a particle swarm optimization (PSO) algorithm. All the test results are fused to reach a final conclusion, which describes the turbine working status at the current time. Then, Bayesian inference is applied to identify potential failures with a percentage certainty by monitoring the abnormal status changes. This approach is adaptable to each turbine automatically, and is advantageous in its data-driven nature to monitor a large wind farm. This approach\u27s results have verified the effectiveness of detecting turbine failures early, especially for anemometer failures

Wind Turbine Fault Detection: an Unsupervised vs Semi-Supervised Approach

The need for renewable energy has been growing in recent years for the reasons we all know, wind power is no exception. Wind turbines are complex and expensive structures and the need for maintenance exists. Conditioning Monitoring Systems that make use of supervised machine learning techniques have been recently studied and the results are quite promising. Though, such systems still require the physical presence of professionals but with the advantage of gaining insight of the operating state of the machine in use, to decide upon maintenance interventions beforehand. The wind turbine failure is not an abrupt process but a gradual one. The main goal of this dissertation is: to compare semi-supervised methods to at tack the problem of automatic recognition of anomalies in wind turbines; to develop an approach combining the Mahalanobis Taguchi System (MTS) with two popular fuzzy partitional clustering algorithms like the fuzzy c-means and archetypal analysis, for the purpose of anomaly detection; and finally to develop an experimental protocol to com paratively study the two types of algorithms. In this work, the algorithms Local Outlier Factor (LOF), Connectivity-based Outlier Factor (COF), Cluster-based Local Outlier Factor (CBLOF), Histogram-based Outlier Score (HBOS), k-nearest-neighbours (k-NN), Subspace Outlier Detection (SOD), Fuzzy c-means (FCM), Archetypal Analysis (AA) and Local Minimum Spanning Tree (LoMST) were explored. The data used consisted of SCADA data sets regarding turbine sensorial data, 8 to tal, from a wind farm in the North of Portugal. Each data set comprises between 1070 and 1096 data cases and characterized by 5 features, for the years 2011, 2012 and 2013. The analysis of the results using 7 different validity measures show that, the CBLOF al gorithm got the best results in the semi-supervised approach while LoMST won in the unsupervised scenario. The extension of both FCM and AA got promissing results.A necessidade de produzir energia renovável tem vindo a crescer nos últimos anos pelas razões que todos sabemos, a energia eólica não é excepção. As turbinas eólicas são es truturas complexas e caras e a necessidade de manutenção existe. Sistemas de Condição Monitorizada utilizando técnicas de aprendizagem supervisionada têm vindo a ser estu dados recentemente e os resultados são bastante promissores. No entanto, estes sistemas ainda exigem a presença física de profissionais, mas com a vantagem de obter informa ções sobre o estado operacional da máquina em uso, para decidir sobre intervenções de manutenção antemão. O principal objetivo desta dissertação é: comparar métodos semi-supervisionados para atacar o problema de reconhecimento automático de anomalias em turbinas eólicas; desenvolver um método que combina o Mahalanobis Taguchi System (MTS) com dois mé todos de agrupamento difuso bem conhecidos como fuzzy c-means e archetypal analysis, no âmbito de deteção de anomalias; e finalmente desenvolver um protocolo experimental onde é possível o estudo comparativo entre os dois diferentes tipos de algoritmos. Neste trabalho, os algoritmos Local Outlier Factor (LOF), Connectivity-based Outlier Factor (COF), Cluster-based Local Outlier Factor (CBLOF), Histogram-based Outlier Score (HBOS), k-nearest-neighbours (k-NN), Subspace Outlier Detection (SOD), Fuzzy c-means (FCM), Archetypal Analysis (AA) and Local Minimum Spanning Tree (LoMST) foram explorados. Os conjuntos de dados utilizados provêm do sistema SCADA, referentes a dados sen soriais de turbinas, 8 no total, com origem num parque eólico no Norte de Portugal. Cada um está compreendendido entre 1070 e 1096 observações e caracterizados por 5 caracte rísticas, para os anos 2011, 2012 e 2013. A ánalise dos resultados através de 7 métricas de validação diferentes mostraram que, o algoritmo CBLOF obteve os melhores resultados na abordagem semi-supervisionada enquanto que o LoMST ganhou na abordagem não supervisionada. A extensão do FCM e do AA originou resultados promissores

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)

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors