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)

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

Exploiting Robust Multivariate Statistics and Data Driven Techniques for Prognosis and Health Management

This thesis explores state of the art robust multivariate statistical methods and data driven techniques to holistically perform prognostics and health management (PHM). This provides a means to enable the early detection, diagnosis and prognosis of future asset failures. In this thesis, the developed PHM methodology is applied to wind turbine drive train components, specifically focussed on planetary gearbox bearings and gears. A novel methodology for the identification of relevant time-domain statistical features based upon robust statistical process control charts is presented for high frequency bearing accelerometer data. In total, 28 time-domain statistical features were evaluated for their capabilities as leading indicators of degradation. The results of this analysis describe the extensible multivariate “Moments’ model” for the encapsulation of bearing operational behaviour. This is presented, enabling the early degradation of detection, predictive diagnostics and estimation of remaining useful life (RUL). Following this, an extended physics of failure model based upon low frequency SCADA data for the quantification of wind turbine gearbox condition is described. This extends the state of the art, whilst defining robust performance charts for quantifying component condition. Normalisation against loading of the turbine and transient states based upon empirical data is performed in the bivariate domain, with extensibility into the multivariate domain if necessary. Prognosis of asset condition is found to be possible with the assistance of artificial neural networks in order to provide business intelligence to the planning and scheduling of effective maintenance actions. These multivariate condition models are explored with multivariate distance and similarity metrics for to exploit traditional data mining techniques for tacit knowledge extraction, ensemble diagnosis and prognosis. Estimation of bearing remaining useful life is found to be possible, with the derived technique correlating strongly to bearing life (r = .96

Challenges in using operational data for reliable wind turbine condition monitoring

Operational data of wind turbines recorded by the Supervisory Control And Data Acquisition (SCADA) system originally intended only for operation and performance monitoring show promise also for assessing the health of the turbines. Using these data for monitoring mechanical components, in particular the drivetrain subassembly with gearbox and bearings, has recently been investigated with multiple techniques. In this paper the advantages and drawbacks of suggested approaches as well as general challenges and limitations are discussed focusing on automated and farm-wide condition monitoring

PCA-based accelerometer data transformation in offshore jacket-type wind turbine support structures for incipient damage detection

Global power capacity is increasingly being comprised by renewable energy sources, where wind farms stand out as paramount power stations. Therefore, the structural health of wind turbines (WTs) represents an essential factor in the energy industry. Specifically, offshore jacket-type WT supports are under critical operational and environmental conditions. Hence, a damage detection strategy is stated, considering several types of structural states and limitations in the quantity of acquired data. The proposed methodology consists of a PCA-based data transformation, in which initially known healthy data are used to be compared with a set of data to be diagnosed; then the damage or healthy states are predicted based on the Mahalanobis distance and threshold value. Because it is a semi-supervised technique, there is no requirement to have damage data on hand to construct the model. The strategy is tested in a scaled-down WT experimental tower.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

A data-mining approach for wind turbine fault detection based on scada data analysis using artificial neural networks

Wind energy has shown significant growth in terms of installed power in the last decade. However, one of the most critical problems for a wind farm is represented by Operation and Maintenance (O&M) costs, which can represent 20-30% of the total costs related to power generation. Various monitoring methodologies targeted to the identification of faults, such as vibration analysis or analysis of oils, are often used. However, they have the main disadvantage of involving additional costs as they usually entail the installation of other sensors to provide real-time control of the system. In this paper, we propose a methodology based on machine learning techniques using data from SCADA systems (Supervisory Control and Data Acquisition). Since these systems are generally already implemented on most wind turbines, they provide a large amount of data without requiring extra sensors. In particular, we developed models using Artificial Neural Networks (ANN) to characterize the behavior of some of the main components of the wind turbine, such as gearbox and generator, and predict operating anomalies. The proposed method is tested on real wind turbines in Italy to verify its effectiveness and applicability, and it was demonstrated to be able to provide significant help for the maintenance of a wind farm

Integration of mahalanobis-taguchi system and activity based costing in decision making for remanufacturing

Classifying components at the end of life (EOL) into remanufacture, repair or dispose is still a major concern to automotive industries. Prior to this study, no specific approach is reported as a guide line to determine critical crankpins that justifying economical remanufacturing process. Traditional cost accounting (TCA) has been used widely by remanufacturing industries but this is not a good measure of estimating the actual manufacturing costs per unit as compared to activity based costing (ABC). However, the application of ABC method in estimating remanufactured cost is rarely reported. These issues were handled separately without a proper integration to make remanufacturing decision which frequently results into uneconomical operating cost and finally the decision becomes less accurate. The aim of this work is to develop a suitable pattern recognition method for classifying crankshaft into three different EOL groups and subsequently evaluates the critical and non-critical crankpins of the used crankshaft using Mahalanobis-Taguchi System (MTS). A remanufacturability assessment technique was developed using Microsoft Excel spreadsheet on pattern recognition and critical crankpins evaluation, and finally integrates these information into a similar spreadsheet with ABC to make decision whether the crankshaft is to be remanufactured, repaired or disposed. The developed scatter diagram was able to recognize group pattern of EOL crankshaft which later was successfully used to determine critical crankpins required for remanufacturing process. The proposed method can serve as a useful approach to the remanufacturing industries for systematically evaluate and decide EOL components for further processing. Case study on six engine models, the result shows that three engines can be securely remanufactured at above 40% profit margin while another two engines are still viable to remanufacture but with less profit margin. In contrast, only two engines can be securely remanufactured due overcharge when using TCA. This inaccuracy affects significantly the overall remanufacturing activities and revenue of the industry. In conclusion, the proposed integration on pattern recognition, parameter evaluation and costing assists the decision making process to effectively remanufacture EOL automotive components as confirmed by Head of workshop of Motor Teknologi Industri Sdn. Bhd

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

Condition monitoring of wind turbine drive trains by normal behaviour modelling of temperatures

Condition monitoring and early failure detection are needed to reduce operational costs of wind turbines, particularly for offshore farms where accessibility is restricted. Failure detection technologies should be simple and reliable in order to contribute to the overall aim of cost reduction. Operational data from the Supervisory Control And Data Acquisition (SCADA) system are a potential source of information for condition monitoring and have the advantage of being recorded at each turbine without the costs of additional sensors. Detection of drivetrain failures using these ten-minute data has been successfully demonstrated in the last five years. This paper summarises and evaluates different ways of so-called normal behaviour modelling of temperature using SCADA data, i.e. the prediction of a measured temperature under the assumption that the system is behaving normally. After training, the residual of modelled and measured temperature acts as an indicator for possible wear and failures. Multiple approaches are discussed: linear modelling, artificial neural networks in auto-regressive, feedforward and layer recurrent configurations, adaptive neuro-fuzzy inference systems and state estimation techniques. A case study with real data reveals differences of approaches, sensitivity to training data and settings of algorithms. Early failure detection of a gearbox failure is demonstrated, although challenges in achieving reliable monitoring without many false alarms become apparent

FAULT DETECTION AND PROGNOSTICS OF INSULATED GATE BIPOLAR TRANSISTOR (IGBT) USING A K-NEAREST NEIGHBOR CLASSIFICATION ALGORITHM

Insulated Gate Bipolar Transistor (IGBT) is a power semiconductor device commonly used in medium to high power applications from household appliances, automotive, and renewable energy. Health assessment of IGBT under field use is of interest due to costly system downtime that may be associated with IGBT failures. Conventional reliability approaches were shown by experimental data to suffer from large uncertainties when predicting IGBT lifetimes, partly due to their inability to adapt to varying loading conditions and part-to-part differences. This study developed a data-driven prognostic method to individually assess IGBT health based on operating data obtained from run-to-failure experiments. IGBT health was classified into healthy and faulty using a K-Nearest Neighbor Centroid Distance classification algorithm. A feature weight optimization method was developed to determine the influence of each feature toward classifying IGBT's health states