Influence of statistical uncertainty of component reliability estimations on offshore wind farm availability

Offshore wind turbine reliability, one of the industry's biggest sources of uncertainty, is the focus of the present paper. Specifically the impact of uncertain component failure distributions at constant failure rates has been investigated with respect to its implications for wind farm availability. A fully probabilistic offshore wind simulation model has been applied to quantify results; effects shown in this paper underline the significant impact that failure probability distributions have on asset performance evaluation. It was found that wind farm availability numbers may vary in the range up to 20 % just by changing the distributions of failure to a different pattern; in particular those scenarios in which extensive failure accumulation occurred led to significant losses in production. Results are interpreted and discussed mainly from the viewpoint of an offshore wind farm developer, owner and operator, with implications underlined for application in state-of-the-art offshore wind O&M (Operations and Maintenance) models and simulation tools

Scenario Analysis of Cost-Effectiveness of Maintenance Strategies for Fixed Tidal Stream Turbines in the Atlantic Ocean

This paper has developed an operation and maintenance (O&M) model for projected 20 MW tidal stream farm case studies at two sites in the northeast Atlantic in France and at EMEC’s Fall of Warness site in the UK. The annual energy production, number of incidents, and downtimes of the farms for corrective and planned (preventive) maintenance strategies are estimated using Monte Carlo simulations that vary weather windows, repair vessel availabilities, and mean annual failure rates modelled by Weibull distributions. The trade-offs between the mean annual failure rates, time availability, O&M costs, and energy income minus the variable O&M costs were analysed. For all scenarios, a 5-year planned maintenance strategy could considerably decrease the mean annual failure rates by 37% at both sites and increase the net energy income. Based on a detailed sensitivity analysis, the study has suggested a simple decision-making method that examines how the variation in the mean annual failure rate and changes in spare-part costs would reduce the effectiveness of a preventive maintenance strategy. This work provides insights into the most important parameters that affect the O&M cost of tidal stream turbines and their effect on tidal energy management. The output of the study will contribute to decision-making concerning maintenance strategies

Sensitivity analysis of offshore wind farm availability and operations & maintenance costs subject to uncertain input factors

"This thesis is jointly awarded by the University of Edinburgh, the University of Exeter and the University of Strathclyde".The deployment of offshore wind farms (OWFs) has increased in response to the threat of diminishing fossil fuel resources, climate change and the need for security of supply. The cost of offshore wind generation has not reached parity with established forms of electricity production. Operators need to simultaneously decrease the total project costs and increase energy yield to achieve a levelised cost of energy of £100/MWh. However, aspects of the Operations and Maintenance (O&M) remain uncertain, either through stochastic processes or through inexperience in the field. One way to handle uncertainty is to define how much the variance in these aspects affect the cost and availability. The thesis in hand introduces an O&M model and seeks to quantify the effects of uncertain inputs using complex sensitivity analysis methods.The sensitivity analysis is applied to an O&M computer simulation model for offshore wind that was developed prior to this project. Case study OWFs are identified to assess if the important factors are different when projects are comprised of a large number of wind turbine generators (WTGs) and are further offshore from the O&M hub port. The set of cases for the global sensitivity analysis comprises of three projects, to provide information applicable to the industry and demonstrate pertinence of sensitivity analysis on a case by case basis. A screening analysis, using the Morris method, is conducted to identify the most important factors on project cost and availability. This resulted in a list of twenty factors, relating to failure rates; duration of operations and information relating to vessels costs. An in-depth uncertainty analysis is conducted with the important factors to establish their distributions where possible. A global, variance-based sensitivity analysis, using the Sobol' method, is performed to quantify the effect on the variance of the two outputs.No single factor dominated the effect on O&M cost and availability for all cases. For each case, one to five factors contributed most to output variances. As an example, for a case of 30 WTGs located 20km offshore from the O&M hub port, the output variances are mainly a result of the change of number of crew transfer vessels and heavy lift vessel mobilisation time for nacelle component replacement. For an OWF with more WTGs, further from shore; the availability variance is dominated by more routine repair operations. Moreover, costs are largely dominated by WTG reliability. This work has confirmed that O&M costs are affected by the cost of deploying heavy-lift vessels even though only a small proportion of repairs require them. Significant factors are inconsistent across all the scenarios, supporting the conclusion that sensitivity analysis of each case is a necessary part of O&M costs and availability simulation. Using the most up-to-date information on current O&M practices, the analysis provides an indication of where to focus efforts for O&M cost reduction and improved availability.The deployment of offshore wind farms (OWFs) has increased in response to the threat of diminishing fossil fuel resources, climate change and the need for security of supply. The cost of offshore wind generation has not reached parity with established forms of electricity production. Operators need to simultaneously decrease the total project costs and increase energy yield to achieve a levelised cost of energy of £100/MWh. However, aspects of the Operations and Maintenance (O&M) remain uncertain, either through stochastic processes or through inexperience in the field. One way to handle uncertainty is to define how much the variance in these aspects affect the cost and availability. The thesis in hand introduces an O&M model and seeks to quantify the effects of uncertain inputs using complex sensitivity analysis methods.The sensitivity analysis is applied to an O&M computer simulation model for offshore wind that was developed prior to this project. Case study OWFs are identified to assess if the important factors are different when projects are comprised of a large number of wind turbine generators (WTGs) and are further offshore from the O&M hub port. The set of cases for the global sensitivity analysis comprises of three projects, to provide information applicable to the industry and demonstrate pertinence of sensitivity analysis on a case by case basis. A screening analysis, using the Morris method, is conducted to identify the most important factors on project cost and availability. This resulted in a list of twenty factors, relating to failure rates; duration of operations and information relating to vessels costs. An in-depth uncertainty analysis is conducted with the important factors to establish their distributions where possible. A global, variance-based sensitivity analysis, using the Sobol' method, is performed to quantify the effect on the variance of the two outputs.No single factor dominated the effect on O&M cost and availability for all cases. For each case, one to five factors contributed most to output variances. As an example, for a case of 30 WTGs located 20km offshore from the O&M hub port, the output variances are mainly a result of the change of number of crew transfer vessels and heavy lift vessel mobilisation time for nacelle component replacement. For an OWF with more WTGs, further from shore; the availability variance is dominated by more routine repair operations. Moreover, costs are largely dominated by WTG reliability. This work has confirmed that O&M costs are affected by the cost of deploying heavy-lift vessels even though only a small proportion of repairs require them. Significant factors are inconsistent across all the scenarios, supporting the conclusion that sensitivity analysis of each case is a necessary part of O&M costs and availability simulation. Using the most up-to-date information on current O&M practices, the analysis provides an indication of where to focus efforts for O&M cost reduction and improved availability

openO&M : Robust O&M open access tool for improving operation and maintenance of offshore wind turbines

With O&M costs accounting between 25-30% of life-cycle costs, it becomes pertinent to model related activities analytically, accounting for all downtime-contributing factors and at the same time incorporating in the analysis practicalities of operations. Related analysis should be able to account for the accurate prediction of weather data, classification of maintenance interventions and modelling of failure rates, and finally, apply realistic strategies with respect to planned and unplanned maintenance activities. This paper reports the development of the initial version of an open-access tool, which allows the estimation of availability of a given wind farm with specified characteristics throughout its service life, allowing for the simulation of a number of scenarios related to reliability parameters, vessels specifications and availability, number of technicians etc, towards optimising a wind farm maintenance strategy. Here, we present initial results for a reference case study, showing applicability and responsiveness of the tool and sensitivity analysis of the jack-up vessel mobilisation time as a varying parameter as it was found to have a significant impact in the estimated availability

Data-driven weather forecasting models performance comparison for improving offshore wind turbine availability and maintenance

Wind power is highly dependent on wind speed and operations offshore are affected by wave height; these together called turbine weather datasets that are variable and intermittent over various time-scales and signify offshore weather conditions. In contrast to onshore wind, offshore wind requires improved forecasting since unfavorable weather prevents repair and maintenance activities. This study proposes two data-driven models for long-term weather conditions forecasting to support operation and maintenance (O&M) decision-making process. These two data-driven approaches are long short-term memory network, abbreviated as LSTM, and Markov chain. An LSTM is an artificial recurrent neural network, capable of learning long-term dependencies within a sequence of data and is typically used to avoid the long-term dependency problem. While, Markov is another data-driven stochastic model, which assumes that, the future states depend only on the current states, not on the events that occurred before. The readily available weather FINO3 datasets are used to train and validate the performance of these models. A performance comparison between these weather forecasted models would be carried out to determine which approach is most accurate and suitable for improving offshore wind turbine availability and support maintenance activities. The entire study outlines the weakness and strength associated with proposed models in relations to offshore wind farms operational activities

Wind Farm Management Decision Support Systems For Short Term Horizon

Wind energy is one of the fastest growing energy sources and its technology maturity level is already higher than the majority of other renewables. Therefore, many countries started to change their financial support policies in an unfavourable way for the wind energy. This unsubsidised new era forces the wind industry to re-visit its expenditure components and to make improvements in operating strategies in order to minimise operational and maintenance (O&M) costs. The classical maintenance strategies focus on a year advanced programming of calendar based maintenance visits and corrective interventions. In this classical approach the maintenance programming flexibility is quite limited, since this kind of programming ignores dynamic environment of the wind farm and real time data-driven indicators. Then, downtimes, and corresponding revenue losses, due to wind turbine inaccessibility occur because wind turbines are exposed to challenging dynamic environmental conditions and located in remote areas. Low accessibility is one of the predominant problems, and remote control not always solves the problems. The cost optimal O&M strategies for the wind energy must consider condition based maintenance and a timely programming of wind turbine visit.Thus, an elaborate and flexible approach, which is capable of considering condition and accessibility of wind turbines using meteorological measurements and operational records is highly needed for the wind farm O&M management. The core objective of this thesis is the investigation of decision-making processes in wind farm management, and the generation of Decision Support Systems (DSSs) for O&M of wind farms. In order to develop practical and feasible DSSs, the research is conducted prioritising data-driven approaches. There still exist various inefficiently used data sources in an operational wind farm, therefore there is a room for an improvement to use efficiently available data. Generally, in a wind farm, two types of condition monitoring data can be collected as online inspection and offline inspection data. Online inspection data can be obtained from both condition monitoring system (CMS) and Supervisory Control and Data Acquisition (SCADA). CMS data require an additional investment in the turbines while, on the contrary, SCADA data are already available in the turbines. As a third source, offline inspection data consist of the records of all O&M visits to the wind farm, which are available but poorly recorded. In this study, the answer for the question of how to change a classical O&M strategy to an enhanced one using only the existing data sources without the need for an additional investment is searched.Firstly, analysis of key factors influencing in wind farm maintenance decisions is performed. In this regard, exploratory data analysis was considered to understand the monthly seasonality and the dependencies of day ahead hourly electricity market price, which is one of the decisive parameters for the wind farm revenue. Then, the connection between wind turbine failures, atmospheric variables and downtime is studied in order to provide additional information to a maintenance team and a maintenance planner for the intervention day. For the first part, well-structured and analysed electricity market price, electricity generation and demand data are needed. Therefore, the existing databases are reviewed for the case countries and a relevant analysis period is chosen. The electricity market data can be easily interpreted as time series data. To exhibit the characteristics of different electricity markets, various time series comparison tools are combined as an analysis guideline. By using this guideline, the drivers of the electricity market price are summarised for each case country. For the second part, available atmospheric and failure data for the relevant wind turbine components are gathered and combined. Then, convenient approaches among unsupervised learning models are selected. By combining the available tools and considering the needed information level for different purposes, the failure rules of prior to failure occurrence per month, in hours and in ten minutes increments are mined.Then, what-if analysis for revenue tracking of maintenance decisions is performed in order to generate a DSS for the evaluation of the major maintenance decisions taken in wind farms. To this purpose, the impact of country dynamics and subsidy frameworks considering the electricity market conditions are modelled. The impact of the intervention timing is analysed and the sensitivity of financial losses to environmental causes of underperformance are estimated.Finally, generation of decision support tool for planning of a maintenance day is studied to provide a useful maintenance DSS for in situ applications. The safe working rules considering the wind speed constraints for the accessibility to the wind turbine are reviewed taking into account the turbine manufacturer's O&M guidelines. The characteristics of the maintenance visits are summarised. Wind turbine accessibility trials using numerical weather prediction forecasting techniques for wind speed variable and synthetic forecasts for wind speed and wind gust variables are presented. An intervention decision pool considering safe working rules is generated, containing a list of plans capable of providing the optimal sequence of various tasks and ranked for revenue prioritised timing.This work has been part of the “Advanced Wind Energy Systems Operation and Maintenance Expertise" project, a European consortium with companies, universities and research centres from the wind energy sector. Parts of this work were developed in collaboration with other fellows in the project.<br /

Addressing Complexity and Intelligence in Systems Dependability Evaluation

Engineering and computing systems are increasingly complex, intelligent, and open adaptive. When it comes to the dependability evaluation of such systems, there are certain challenges posed by the characteristics of “complexity” and “intelligence”. The first aspect of complexity is the dependability modelling of large systems with many interconnected components and dynamic behaviours such as Priority, Sequencing and Repairs. To address this, the thesis proposes a novel hierarchical solution to dynamic fault tree analysis using Semi-Markov Processes. A second aspect of complexity is the environmental conditions that may impact dependability and their modelling. For instance, weather and logistics can influence maintenance actions and hence dependability of an offshore wind farm. The thesis proposes a semi-Markov-based maintenance model called “Butterfly Maintenance Model (BMM)” to model this complexity and accommodate it in dependability evaluation. A third aspect of complexity is the open nature of system of systems like swarms of drones which makes complete design-time dependability analysis infeasible. To address this aspect, the thesis proposes a dynamic dependability evaluation method using Fault Trees and Markov-Models at runtime.The challenge of “intelligence” arises because Machine Learning (ML) components do not exhibit programmed behaviour; their behaviour is learned from data. However, in traditional dependability analysis, systems are assumed to be programmed or designed. When a system has learned from data, then a distributional shift of operational data from training data may cause ML to behave incorrectly, e.g., misclassify objects. To address this, a new approach called SafeML is developed that uses statistical distance measures for monitoring the performance of ML against such distributional shifts. The thesis develops the proposed models, and evaluates them on case studies, highlighting improvements to the state-of-the-art, limitations and future work