Resilience in Floating Offshore Wind Turbines: A Scoping Review

Background With climate change a looming global threat, offshore wind energy is a vital resource, and floating offshore wind turbines (FOWT) are essential to capture its full potential. Unfortunately, high operations and maintenance expenses pose an obstacle to widespread implementation of FOWT. Reducing maintenance needs by limiting FOWT damage or failure in harsh environments will undoubtedly contribute to lowering costs and to improving on-site personnel safety. Resilience, an important concept in the field of risk management, may be instrumental in achieving these goals. Objective The objective of this thesis was to develop a thorough understanding of how resilience is understood and its applications to FOWT design and operation. The following issues were of greatest interest: the degree to which FOWT literature addresses resilience, the various interpretations and definitions of resilience that are employed in FOWT research, and how those definitions of resilience are applied to FOWT. These issues and objectives led to the question this thesis sought to answer, in order to map the knowledge and potential gaps in FOWT resilience research: How is resilience understood and applied in the context of FOWT design and operation? Methodology In order to answer this research question, a scoping review was conducted, in which two databases – ScienceDirect and GreenFILE – were searched for sources that discussed resilience with respect to FOWT. In accordance with the JBI scoping review methodology, a search and screening strategy, including search terms and inclusion criteria, was determined in advance. The multi-stage screening process ensured that all relevant sources were included, and the entire process is described in such a way as to be transparent and repeatable. Results Thirteen sources, consisting of twelve articles and one report, were found to meet the inclusion criteria, and these were thematically analyzed in order to investigate the definitions/interpretations and applications of resilience to FOWT technology. Several trends were discovered among the included sources, including a dominant engineering perspective and a glaring lack of explicit resilience definitions. Despite this lack of definitions, however, several interpretations of resilience were found to be used among the thirteen sources, and these are discussed in depth. Furthermore, the various applications of resilience to FOWT were mapped in order to identify popular topics, and these findings were compared to trends noted elsewhere in the literature. Conclusions The results of this review provide valuable insight into the main interpretations of resilience that are used in relation to FOWT. They also provide a solid foundation for future work and for improvements in FOWT resilience research. Among these are the need for a clear definition of resilience in FOWT studies and the potential benefits that could come from the development of a risk management approach to enhance the strong engineering perspective within the field of FOWT resilience research

Studies in Electrical Machines & Wind Turbines associated with developing Reliable Power Generation

The publications listed in date order in this document are offered for the Degree of Doctor of Science in Durham University and have been selected from the author’s full publication list. The papers in this thesis constitute a continuum of original work in fundamental and applied electrical science, spanning 30 years, deployed on real industrial problems, making a significant contribution to conventional and renewable energy power generation. This is the basis of a claim of high distinction, constituting an original and substantial contribution to engineering science

Modelling offshore wind farm operation and maintenance with view to estimating the benefits of condition monitoring

Offshore wind energy is progressing rapidly and playing an increasingly important role in electricity generation. Since the Kyoto Protocol in February 2005, Europe has been substantially increasing its installed wind capacity. Compared to onshore wind, offshore wind allows the installation of larger turbines, more extensive sites, and encounters higher wind speed with lower turbulence. On the other hand, harsh marine conditions and the limited access to the turbines are expected to increase the cost of operation and maintenance (O&M costs presently make up approximately 20-25% of the levelised total lifetime cost of a wind turbine). Efficient condition monitoring has the potential to reduce O&M costs. In the analysis of the cost effectiveness of condition monitoring, cost and operational data are crucial. Regrettably, wind farm operational data are generally kept confidential by manufacturers and wind farm operators, especially for the offshore ones.To facilitate progress, this thesis has investigated accessible SCADA and failure data from a large onshore wind farm and created a series of indirect analysis methods to overcome the data shortage including an onshore/offshore failure rate translator and a series of methods to distinguish yawing errors from wind turbine nacelle direction sensor errors. Wind turbine component reliability has been investigated by using this innovative component failure rate translation from onshore to offshore, and applies the translation technique to Failure Mode and Effect Analysis for offshore wind. An existing O&M cost model has been further developed and then compared to other available cost models. It is demonstrated that the improvements made to the model (including the data translation approach) have improved the applicability and reliability of the model. The extended cost model (called StraPCost+) has been used to establish a relationship between the effectiveness of reactive and condition-based maintenance strategies. The benchmarked cost model has then been applied to assess the O&M cost effectiveness for three offshore wind farms at different operational phases.Apart from the innovative methodologies developed, this thesis also provides detailed background and understanding of the state of the art for offshore wind technology, condition monitoring technology. The methodology of cost model developed in this thesis is presented in detail and compared with other cost models in both commercial and research domains.Offshore wind energy is progressing rapidly and playing an increasingly important role in electricity generation. Since the Kyoto Protocol in February 2005, Europe has been substantially increasing its installed wind capacity. Compared to onshore wind, offshore wind allows the installation of larger turbines, more extensive sites, and encounters higher wind speed with lower turbulence. On the other hand, harsh marine conditions and the limited access to the turbines are expected to increase the cost of operation and maintenance (O&M costs presently make up approximately 20-25% of the levelised total lifetime cost of a wind turbine). Efficient condition monitoring has the potential to reduce O&M costs. In the analysis of the cost effectiveness of condition monitoring, cost and operational data are crucial. Regrettably, wind farm operational data are generally kept confidential by manufacturers and wind farm operators, especially for the offshore ones.To facilitate progress, this thesis has investigated accessible SCADA and failure data from a large onshore wind farm and created a series of indirect analysis methods to overcome the data shortage including an onshore/offshore failure rate translator and a series of methods to distinguish yawing errors from wind turbine nacelle direction sensor errors. Wind turbine component reliability has been investigated by using this innovative component failure rate translation from onshore to offshore, and applies the translation technique to Failure Mode and Effect Analysis for offshore wind. An existing O&M cost model has been further developed and then compared to other available cost models. It is demonstrated that the improvements made to the model (including the data translation approach) have improved the applicability and reliability of the model. The extended cost model (called StraPCost+) has been used to establish a relationship between the effectiveness of reactive and condition-based maintenance strategies. The benchmarked cost model has then been applied to assess the O&M cost effectiveness for three offshore wind farms at different operational phases.Apart from the innovative methodologies developed, this thesis also provides detailed background and understanding of the state of the art for offshore wind technology, condition monitoring technology. The methodology of cost model developed in this thesis is presented in detail and compared with other cost models in both commercial and research domains

Health monitoring of renewable energy systems

The offshore wind energy industry has grown exponentially; globally, there is 12GW of installed capacity of offshore wind, of which over 95% has been installed in the past ten years. Access and maintenance in offshore wind farms can be difficult and considerably more expensive than onshore wind farms. Additionally, with low availability levels and greater downtime due to failures, there is a growing interest in the optimisation of operation and maintenance (O&M) activities to maximise profitability. Traditionally, maintenance activities on critical components and subsystems have deployed two maintenance approaches; time-based preventative or corrective. Time-based preventative or scheduled maintenance approaches are based on intervening at fixed intervals, determined in advance for each component. Scheduling is based on failure statistics such as mean time between failures (MTBF), mean time to repair (MTTR) or mean time to failure (MTTF). These come either from publicly available databases or operational measurements. As part of preventive maintenance activities, there are annual services of the turbine to replace and maintain any component or assembly based on manufacturers’ indications. On the other hand, the corrective maintenance approach involves operating equipment until it fails and then restoring it, repairing it, or replacing it. Due to conservative estimates regarding the probability of failure, preventive and corrective maintenance approaches have financial implications associated with them. In the preventive approach, components are frequently replaced before they reach the end of their working life. In contrast, corrective maintenance guarantees that the serviceable life of a component is maximised, but it is subjected to long downtime, which is expensive regarding energy generation loss. Additionally, failure of the component may cause consequential damage to other parts of the wind turbine system, resulting in even greater repair costs, downtime and loss of revenue. A comprehensive literature review has been undertaken in the areas of maintenance, turbine reliability, turbine failure modes and causes, physics of failure, condition monitoring techniques, and costs. The limitations and disadvantages of current operation and maintenance practices are identified, and new approaches combining the knowledge of the condition of components and historical data are proposed and compared to achieve optimal turbine availability and maintenance cost reduction. A Failure Modes and Effects Analysis (FMEA) was performed for the functional modes of each system, subsystem, assembly and component following the British standard BS EN 60812:2006. Currently, the most common offshore wind turbine uses three blades, a 3-stage gearbox, induction generator and a fully rated power converter. The Siemens 3.6MW -120 turbine is selected for this project as an example of this configuration. The main objectives of undertaking this comprehensive FMEA are to identify critical components and their failures with significant impact on the wind turbine operation in terms of maintainability, safety and availability. The assessment identified 500 components and almost 1000 failure causes. The most critical assemblies identified in terms of severity, occurrence and undetectability of the failure are; the frequency converter, pitch system, yaw system and gearbox. The implementation of a condition-based maintenance philosophy, including the development of real predictive approaches which estimate the remaining useful life of degrading critical components has been analysed by the recent literature. However, developing such capabilities for the critical assemblies identified is a significant technical challenge. This study aims to develop and demonstrate the implementation of a methodology and appropriate algorithms to optimise O&M of offshore wind farms, by estimating the remaining useful life of critical components with greater accuracy using a combination of physics-based models, statistical-based models and data mining approaches. A register of trends and likely the main causes of failures of the power converter, gearbox, yaw system and pitch system was generated through a thorough literature search and participation in conferences and workshops during the project. The main sources of failure of the power converter and gearbox have been represented by algorithms and physics-based models developed in Python and proprietary software, respectively. These algorithms comprise two phases: diagnosis or learning phase using historical data (such as SCADA or digital information recorded by condition monitoring systems) and prognosis phase using simulated data (using as a basis the wind turbine aero-elastic software FASTv8). The pitch system failure mechanisms were explored using a combination of data mining approaches and subject matter expert knowledge. Examples of approaches investigated and implemented include: Support Vector Machine (SVM) to define normal behaviour and K Nearest Neighbour (KNN) to classify new observations regarding operation state (green for normal operation, amber for abnormal operation, red for failure). New observations with amber or red colours need to be analysed further, to diagnose potential failure modes using a decision tree algorithm with more variables related to the pitch system. The goals of developing a well-defined strategy for maintenance interventions and optimised management of wind farm logistics are required to effectively improve wind farm availability while reducing the cost of operations. Additionally, a clear identification of uncertainties inherent in stochastic processes, necessary for estimating access, failure prognosis and failure probabilities is required for operators to make informed decisions. The final output of this work is an O&M cost model which analyses and compares a conventional O&M strategy using a combination of preventive and reactive maintenance against an O&M strategy using the approaches described above for failure prognosis and diagnosis. The analysis is performed for a fictitious offshore wind farm with one-year operational data. The results include availability, downtime, the cost of repair, loss of production, revenue losses and the hidden CO2 emissions of the maintenance activities taking into account a combined probability level to account for the uncertainties

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

Advanced reliability analysis of complex offshore Energy systems subject to condition based maintenance.

As the demand for energy in our world today continues to increase and conventional reserves become less available, energy companies find themselves moving further offshore and into more remote locations for the promise of higher recoverable reserves. This has been accompanied by increased technical, safety and economic risks as the unpredictable and dynamic conditions provide a challenge for the reliable and safe operation of both oil and gas (O&G) and offshore wind energy assets. Condition-based maintenance (CBM) is growing in popularity and application in offshore energy production, and its integration into the reliability analysis process allows for more accurate representation of system performance. Advanced reliability analysis while taking condition-based maintenance (CBM) into account can be employed by researchers and practitioners to develop a better understanding of complex system behaviour in order to improve reliability allocation as well as operation and maintenance (O&M). The aim of this study is therefore to develop models for reliability analysis which take into account dynamic offshore conditions as well as condition-based maintenance (CBM) for improved reliability and O&M. To achieve this aim, models based on the stochastic petri net (SPN) and dynamic Bayesian network (DBN) techniques are developed to analyse the reliability and optimise the O&M of complex offshore energy assets. These models are built to take into account the non-binary nature, maintenance regime and repairability of most offshore energy systems. The models are then tested using benchmark case studies such as a subsea blowout preventer, a floating offshore wind turbine (FOWT), an offshore wind turbine (OWT) gearbox and an OWT monopile. Results from these analyses reveal that the incorporation of degradation and CBM can indeed be done and significantly influence the reliability analysis and O&M planning of offshore energy assets.Shafiee, Mahmood (Associate)PhD in Energy and Powe

State-of-the-Art Review and Synthesis: A Requirement-based Roadmap for Standardized Predictive Maintenance Automation Using Digital Twin Technologies

Recent digital advances have popularized predictive maintenance (PMx), offering enhanced efficiency, automation, accuracy, cost savings, and independence in maintenance. Yet, it continues to face numerous limitations such as poor explainability, sample inefficiency of data-driven methods, complexity of physics-based methods, and limited generalizability and scalability of knowledge-based methods. This paper proposes leveraging Digital Twins (DTs) to address these challenges and enable automated PMx adoption at larger scales. While we argue that DTs have this transformative potential, they have not yet reached the level of maturity needed to bridge these gaps in a standardized way. Without a standard definition for such evolution, this transformation lacks a solid foundation upon which to base its development. This paper provides a requirement-based roadmap supporting standardized PMx automation using DT technologies. A systematic approach comprising two primary stages is presented. First, we methodically identify the Informational Requirements (IRs) and Functional Requirements (FRs) for PMx, which serve as a foundation from which any unified framework must emerge. Our approach to defining and using IRs and FRs to form the backbone of any PMx DT is supported by the track record of IRs and FRs being successfully used as blueprints in other areas, such as for product development within the software industry. Second, we conduct a thorough literature review spanning fields to determine the ways in which these IRs and FRs are currently being used within DTs, enabling us to point to the specific areas where further research is warranted to support the progress and maturation of requirement-based PMx DTs.Comment: (1)This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Machine Learning based Wind Power Forecasting for Operational Decision Support

To utilize renewable energy efficiently to meet the needs of mankind's living demands becomes an extremely hot topic since global warming is the most serious global environmental problem that human beings are facing today. Burning of fossil fuels, such as coal and oil directly for generating electricity leads to environment pollution and exacerbates global warning. However, large-scale development of hydropower increases greenhouse gas emissions and greenhouse effects. This research is related to knowledge of wind power forecasting (WPF) and machine learning (ML). This research is built around one central research question: How to improve the accuracy of WPF by using AI methods? A pilot conceptual system combining meteorological information and operations management has been formulated. The main contribution is visualized in a proposed new framework, named Meteorological Information Service Decision Support System, consisting of a meteorological information module, wind power prediction module and operations management module. This conceptual framework has been verified by quantitative analysis in empirical cases. This system utilizes meteorological information for decision-making based on condition-based maintenance in operations and management for the purpose of optimizing energy management. It aims to analyze and predict the variation of wind power for the next day or the following week to develop scheduling planning services for WPEs based on predicting wind speed for every six hours, which is short-term wind speed prediction, through training, validating, and testing dataset. Accurate prediction of wind speed is crucial for weather forecasting service and WPF. This study presents a carefully designed wind speed prediction model which combines fully-connected neural network (FCNN), long short-term memory (LSTM) algorithm with eXtreme Gradient Boosting (XGBoost) technique, to predict wind speed. The performance of each model is tested by using reanalysis data from European Center for Medium-Range Weather Forecasts (ECMWF) for Meteorological observatory located in Vaasa in Finland. The results show that XGBoost algorithm has similar improved prediction performance as LSTM algorithm, in terms of RMSE, MAE and R2 compared to the commonly used traditional FCNN model. On the other hand, the XGBoost algorithm has a significant advantage on training time while comparing to the other algorithms in this case study. Additionally, this sensitivity analysis indicates great potential of the optimized deep learning (DL) method, which is a subset of machine learning (ML), in improving local weather forecast on the coding platform of Python. The results indicate that, by using Meteorological Information Service Decision Support System, it is possible to support effective decision-making and create timely actions within the WPEs. Findings from this research contribute to WPF in WPEs. The main contribution of this research is achieving decision optimization on a decision support system by using ML. It was concluded that the proposed system is very promising for potential applications in wind (power) energy management

Aeronautical engineering: A continuing bibliography with indexes (supplement 303)

This bibliography lists 211 reports, articles, and other documents introduced into the NASA scientific and technical information database. Subject coverage includes: design, construction, and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Design Optimization of Wind Energy Conversion Systems with Applications

Modern and larger horizontal-axis wind turbines with power capacity reaching 15 MW and rotors of more than 235-meter diameter are under continuous development for the merit of minimizing the unit cost of energy production (total annual cost/annual energy produced). Such valuable advances in this competitive source of clean energy have made numerous research contributions in developing wind industry technologies worldwide. This book provides important information on the optimum design of wind energy conversion systems (WECS) with a comprehensive and self-contained handling of design fundamentals of wind turbines. Section I deals with optimal production of energy, multi-disciplinary optimization of wind turbines, aerodynamic and structural dynamic optimization and aeroelasticity of the rotating blades. Section II considers operational monitoring, reliability and optimal control of wind turbine components