Fault detection of a wind turbine generator bearing using interpretable machine learning

A wind turbine is subjected to a number of degradation mechanisms during its operational lifetime. If left unattended, the degradation of components will result in poor performance and potential failure. Hence, to mitigate the risk of failures, it is imperative that the wind turbines are regularly monitored, inspected, and optimally maintained. Offshore wind turbines are normally inspected and maintained at fixed intervals (generally six-month intervals) and the maintenance program (list of tasks) is prepared using experience or risk-based reliability analysis, like risk-based inspection (RBI) and reliability-centered maintenance (RCM). This time-based maintenance program can be improved by incorporating results from condition monitoring (CM) involving data acquisition using sensors and fault detection using data analytics. It is important to ensure quality and quantity of data and to use correct procedures for data interpretation for fault detection to properly carry out condition assessment. This thesis contains the work carried out to develop a machine learning (ML) based methodology for detecting faults in a wind turbine generator bearing. The methodology includes application of ML using supervisory control and data acquisition (SCADA) data for predicting the operating temperature of a healthy bearing, and then comparing the predicted bearing temperature with the actual bearing temperature. Consistent abnormal differences between predicted and actual temperatures may be attributed to the degradation and presence of a fault in the bearing. This fault detection can then be used for rescheduling the maintenance tasks. The methodology is discussed in detail using a case study. In this thesis, interpretable ML tools are used to identify faults in a wind turbine generator bearing. Furthermore, variables affecting the generator bearing temperature are investigated. The analysis used two years of operational data from a 2 MW offshore wind turbine located in the Gulf of Guinea off the west coast of Africa. Out of the four ML models that were evaluated, the XGBoost model was determined to be the most effective performer. After utilizing the Shapley additive explanations (SHAP) to analyze the XGBoost model, it was determined that the temperature in the generator phase windings had the most significant effect on the model's predictions. Finally, based upon the deviation between the actual and the predicted temperatures, an anomaly in the generator bearing was successfully identified two months prior to a generator failure occurring.Masteroppgave i havteknologiHTEK3995MAMN-HTEKMAMN-HTE

Predictive maintenance with industrial sensor data

The Norwegian Ministry of Petroleum and Energy Commissions report shows that the government is making a large step closer to its ambition of allocating regions for 30,000 MW offshore wind via way of means of 2040. According to a report by IRENA, offshore wind operation and maintenance (O & M) costs make up a significant portion of the overall cost of electricity for offshore wind farms in G20 countries, ranging from 16-25%. To address this issue, it is essential to explore methods for improving operational reliability and reducing the maintenance costs of wind turbines. One promising approach is predictive maintenance, which involves leveraging data collected from sensors already equipped with the turbines to detect and address potential issues before they become more serious. Predictive maintenance is important in wind farms to reduce downtime and optimize the performance of wind turbines. Various rotating components in wind turbines make them complicated machinery, and if any of those parts fails, it can cause the entire turbine to shut down. This can result in lost revenue for the wind farm operator and lead to higher maintenance costs if the problem is not addressed quickly. This can be possible through a Supervisory Control and Data Acquisition (SCADA) system, which collects and analyzes data from various turbine components. We have developed a method for detecting and monitoring failures in critical components such as the gearbox and generator, based on historical SCADA data. Our approach utilizes machine learning models, specifically extreme gradient boosting (XGBoost), and has been tested on two real-world case studies involving eight different turbines. The outcomes show both the effectiveness and usefulness of our technique for boosting wind turbine reliability and minimizing maintenance costs

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

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)

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements

Investigating renewable energy systems using artifcial intelligence techniques

This research investigated applying Artificial Intelegence (AI) and Machine Learning (ML) to renewable energy through three studies. The first study characterized and mapped the recent research landscape in the field of AI applications for various renewable energy systems using Natural Language Prcoessing (NLP) and ML models. It considered published documetns at Scopus database in the period (2000-2021). The second study built hybrid Catboost-CNN-LSTM architecture pipeline to predict an industrial-scale biogas plant’s daily biogas production and investigate the feedstock components importance on it. The third study investigated prediciting biogas yield of various subtrates and the significance of each organic component (carbohydrates, proteins, fats/lipids, and legnin) in biogas production using hybrid VAE-XGboost model. The first study showed seven main metatopics and ascent of "deep learning (DL)" as a prominent methodology led to an increase in intricate subjects, including the optimization of power costs and the prediction of wind patterns. Also, a growing utilization of DL approaches for the analysis of renewable energy data, particularly in the context of wind and solar photovoltaic systems. The research themes and trends observed in the first study signify substantial recent investments in advanced AI learning techniques. The developed Catboost-CNN-LSTM pipeline achived a significant results and presented a superior approach when compared to previous relevant studies by eliminating the requirement for feature engineering, enabling direct prediction of biogas yield without the need for converting it into a classification task. The VAE-XGboost pipeline could ovcercome data limitation in the field and produced significant results. It has shown that the "fats" category is the most influential group on the methane production in biogas plants, however, “proteins” illustrated the lowest impact on biogas production

Development of numerical and data models for the support of digital twins in offshore wind engineering

Error on title page. Date of award is 2022.As offshore wind farms grow there is a continued demand for reduced costs. Maintenance costs and downtime can be reduced through greater information on the asset in relation to its operational loads and structural resistance to damage and so there is an increasing interest in digital twin technologies. Through digital twins, an operational asset can be replicated computationally, thus providing more information. Modelling these aspects requires a wide variety of models in different fields. To advance the feasibility of digital twin technology this thesis aims to develop the multi-disciplinary set of modelling domains which help form the basis of future digital twins. Throughout this work, results have been validated against operational data recorded from sensors on offshore structures. This has provided value and confidence to the results as it shows how well the mix of state-of-the art models compare to real world engineering systems. This research presents a portfolio of five research areas which have been published in a mix of peer-reviewed journal articles and conference papers. These areas are: 1) A computational fluid dynamics (CFD) model of an offshore wind farm conducted using a modified solver in the opensource software. This work implements actuator disk turbine models and uses Reynolds averaged Naiver Stokes approaches to represent the turbulence. This investigates the impact of modelling choices and demonstrates the impact of varied model parameters. The results are compared to operational site data and the modelling errors are quantified. There is good agreement between the models and site data. 2) An expansion on traditional CFD approaches through incorporating machine learning (ML). These ML models are used to approximate the results of the CFD and thereby allow for further analysis which retains the fidelity of CFD at comparatively negligible computational cost. The results are compared to operational site data and the errors at each step are quantified for validation. 3) A time-series forecasting of weather variables based on past measured data. A novel approach for forecasting time-series is developed and compared to two existing methods: Markov-Chains and Gradient Boosting. While this new method is more complex and requires more time to train, it has the desirable feature of incorporating seasonality at multiple timescales and thus providing a more representative time-series. 4) An investigation of the change in modal parameters in an offshore wind jacket structure from damages or from changing operational conditions. In this work the detailed design model of the structure from Ramboll is used. This section relates the measurable modal parameters to the operational condition through a modelling approach. 5) A study conducted using accelerometer data from an Offshore Substation located in a wind farm site. Operational data from 12 accelerometers is used to investigate the efficacy of several potential sensor layouts and therefore to quantify the consequence of placement decisions. The results of these developments are an overall improvement in the modelling approaches necessary towards the realisation of digital twins as well as useful development in each of the component areas. Both areas related to wind loading as well as structural dynamics have been related to operational data. The validation of this link between the measured and the modelled domains facilitates operators and those in maintenance in gaining more information and greater insights into the conditions of their assets.As offshore wind farms grow there is a continued demand for reduced costs. Maintenance costs and downtime can be reduced through greater information on the asset in relation to its operational loads and structural resistance to damage and so there is an increasing interest in digital twin technologies. Through digital twins, an operational asset can be replicated computationally, thus providing more information. Modelling these aspects requires a wide variety of models in different fields. To advance the feasibility of digital twin technology this thesis aims to develop the multi-disciplinary set of modelling domains which help form the basis of future digital twins. Throughout this work, results have been validated against operational data recorded from sensors on offshore structures. This has provided value and confidence to the results as it shows how well the mix of state-of-the art models compare to real world engineering systems. This research presents a portfolio of five research areas which have been published in a mix of peer-reviewed journal articles and conference papers. These areas are: 1) A computational fluid dynamics (CFD) model of an offshore wind farm conducted using a modified solver in the opensource software. This work implements actuator disk turbine models and uses Reynolds averaged Naiver Stokes approaches to represent the turbulence. This investigates the impact of modelling choices and demonstrates the impact of varied model parameters. The results are compared to operational site data and the modelling errors are quantified. There is good agreement between the models and site data. 2) An expansion on traditional CFD approaches through incorporating machine learning (ML). These ML models are used to approximate the results of the CFD and thereby allow for further analysis which retains the fidelity of CFD at comparatively negligible computational cost. The results are compared to operational site data and the errors at each step are quantified for validation. 3) A time-series forecasting of weather variables based on past measured data. A novel approach for forecasting time-series is developed and compared to two existing methods: Markov-Chains and Gradient Boosting. While this new method is more complex and requires more time to train, it has the desirable feature of incorporating seasonality at multiple timescales and thus providing a more representative time-series. 4) An investigation of the change in modal parameters in an offshore wind jacket structure from damages or from changing operational conditions. In this work the detailed design model of the structure from Ramboll is used. This section relates the measurable modal parameters to the operational condition through a modelling approach. 5) A study conducted using accelerometer data from an Offshore Substation located in a wind farm site. Operational data from 12 accelerometers is used to investigate the efficacy of several potential sensor layouts and therefore to quantify the consequence of placement decisions. The results of these developments are an overall improvement in the modelling approaches necessary towards the realisation of digital twins as well as useful development in each of the component areas. Both areas related to wind loading as well as structural dynamics have been related to operational data. The validation of this link between the measured and the modelled domains facilitates operators and those in maintenance in gaining more information and greater insights into the conditions of their assets

Use of advanced analytics for health estimation and failure prediction in wind turbines

Tesi en modalitat de tesi per compendiThe energy sector has undergone drastic changes and critical revolutions in the last few decades. Renewable energy sources have grown significantly, now representing a sizeable share of the energy production mix. Wind energy has seen increasing rate of adoptions, being one of the more convenient and sustainable mean of producing energy. Research and innovation have helped greatly in driving down production and operation costs of wind energy, yet important challenges still remain open. This thesis addresses predictive maintenance and monitoring of wind turbines, aiming to present predictive frameworks designed with the necessities of the industry in mind. More concretely: interpretability, scalability, modularity and reliability of the predictions are the objectives —together with limited data requirements— of this project. Of all the available data at the disposal of wind turbine operators, SCADA is the principal source of information utilized in this research, due to its wide availability and low cost. Ensemble models played an important role in the development of the presented predictive frameworks thanks to their modular nature which allows to combine very diverse algorithms and data types. Important insights gained from these experiments are the beneficial effect of combining multiple and diverse sources of data —for example SCADA and alarms logs—, the easiness of combining different algorithms and indicators, and the noticeable gain in predicting performance that it can provide. Finally, given the central role that SCADA data plays in this thesis, but also in the wind energy industry, a detailed analysis of the limitations and shortcomings of SCADA data is presented. In particular, the ef- fect of data aggregation —a common practice in the wind industry— is determined developing a methodological framework that has been used to study high–frequency SCADA data. This lead to the conclusion that typical aggregation periods, i.e. 5–10 minutes that are the standard in wind energy industry are not able to capture and maintain the information content of fast–changing signals, such as wind and electrical measurements.El sector energètic ha experimentat importants canvis i revolucions en les últimes dècades. Les fonts d’energia renovables han crescut significativament, i ara representen una part important en el conjunt de generació. L’energia eòlica ha augmentat significativament, convertint-se en una de les millors alternatives per produir energia verda. La recerca i la innovació ha ajudat a reduir considerablement els costos de producció i operació de l’energia eòlica, però encara hi ha oberts reptes importants. Aquesta tesi aborda el manteniment predictiu i el seguiment d’aerogeneradors, amb l’objectiu de presentar solucions d’algoritmes de predicció dissenyats tenint en compte les necessitats de la indústria. Més concretament conceptes com, la interpretabilitat, escalabilitat, modularitat i fiabilitat de les prediccions ho són els objectius, juntament amb els requisits limitats per les de dades disponibles d’aquest projecte. De totes les dades disponibles a disposició dels operadors d’aerogeneradors, les dades del sistema SCADA són la principal font d’informació utilitzada en aquest projecte, per la seva àmplia disponibilitat i baix cost. En el present treball, els models de conjunt tenen un paper important en el desenvolupament dels marcs predictius presentats gràcies al seu caràcter modular que permet l’ús d’algoritmes i tipus de dades molt diversos. Resultats importants obtinguts d’aquests experiments són l’efecte beneficiós de combinar múltiples i diverses fonts de dades, per exemple, SCADA i dades d’alarmes, la facilitat de combinar diferents algorismes i indicadors i el notable guany en predir el rendiment que es pot oferir. Finalment, donat el paper central que SCADA l’anàlisi de dades juga en aquesta tesi, però també en la indústria de l’energia eòlica, una anàlisi detallada de la es presenten les limitacions i les mancances de les dades SCADA. En particular es va estudiar l’efecte de l’agregació de dades -una pràctica habitual en la indústria eòlica-. Dins d’aquest treball es proposa un marc metodològic que s’ha utilitzat per estudiar dades SCADA d’alta freqüència. Això va portar a la conclusió que els períodes d’agregació típics, de 5 a 10 minuts que són l’estàndard a la indústria de l’energia eòlica, no són capaços de capturar i mantenir el contingut d’informació de senyals que canvien ràpidament, com ara mesures eòliques i elèctriquesPostprint (published version