Modelling the effect of maintenance strategies and reliability for long-term wind yield assessment

Where a number of onshore wind farm locations are being maintained by a single central Operation and Maintenance Contractor, an effective competition exists between those sites for the use of that maintenance resource. Any differentials between those sites in terms of the costs of repair to the contractor, or the potential return to the contractor for improving the site availability under the Operations and Maintenance Contract, may mean a variance in the level of operational availability achieved by each site. A review of UK contract terms illustrates the potential differentials that may occur. A maintenance optimisation model is created which is used to simulate the potential availabilities of a set of wind farms maintained from a central resource in response to typical published failure rates and restoration times. Monte Carlo methods are applied to this simulation to provide an illustrative set of sensitivities which may be used to adjust availability losses assumed during the energy yield analysis of a potential onshore wind farm location

A model for availability growth with application to new generation offshore wind farms

A model for availability growth is developed to capture the effect of systemic risk prior to construction of a complex system. The model has been motivated by new generation offshore wind farms where investment decisions need to be taken before test and operational data are available. We develop a generic model to capture the systemic risks arising from innovation in evolutionary system designs. By modelling the impact of major and minor interventions to mitigate weaknesses and to improve the failure and restoration processes of subassemblies, we are able to measure the growth in availability performance of the system. We describe the choices made in modelling our particular industrial setting using an example for a typical UK Round III offshore wind farm. We obtain point estimates of the expected availability having populated the simulated model using appropriate judgemental and empirical data. We show the relative impact of modelling systemic risk on system availability performance in comparison with estimates obtained (Lesley Walls) from typical system availability modelling assumptions used in offshore wind applications. While modelling growth in availability is necessary for meaningful decision support in developing complex systems such as offshore wind farms, we also discuss the relative value of explicitly articulating epistemic uncertainties

Diagnostics and prognostics utilising dynamic Bayesian networks applied to a wind turbine gearbox

The UK has the largest installed capacity of offshore wind and this is set to increase significantly in future years. The difficulty in conducting maintenance offshore leads to increased operation and maintenance costs compared to onshore but with better condition monitoring and preventative maintenance strategies these costs could be reduced. In this paper an on-line condition monitoring system is created that is capable of diagnosing machine component conditions based on an array of sensor readings. It then informs the operator of actions required. This simplifies the role of the operator and the actions required can be optimised within the program to minimise costs. The program has been applied to a gearbox oil testbed to demonstrate its operational suitability. In addition a method for determining the most cost effective maintenance strategy is examined. This method uses a Dynamic Bayesian Network to simulate the degradation of wind turbine components, effectively acting as a prognostics tool, and calculates the cost of various preventative maintenance strategies compared to purely corrective maintenance actions. These methods are shown to reduce the cost of operating wind turbines in the offshore environment

Modelling epistemic uncertainty in offshore wind farm production capacity to reduce risk

Financial stakeholders in offshore wind farm projects require predictions of energy production capacity to better manage the risk associated with investment decisions prior to construction. Predictions for early operating life are particularly important due to the dual effects of cash flow discounting and the anticipated performance growth due to experiential learning. We develop a general marked point process model for the times to failure and restoration events of farm subassemblies to capture key uncertainties affecting performance. Sources of epistemic uncertainty are identified in design and manufacturing effectiveness. The model then captures the temporal effects of epistemic and aleatory uncertainties across subassemblies to predict the farm availability‐informed relative capacity (maximum generating capacity given the technical state of the equipment). This performance measure enables technical performance uncertainties to be linked to the cost of energy generation. The general modeling approach is contextualized and illustrated for a prospective offshore wind farm. The production capacity uncertainties can be decomposed to assess the contribution of epistemic uncertainty allowing the value of gathering information to reduce risk to be examined

Exploring a Bayesian approach for structural modelling of common cause failures

Common Cause Failures (CCFs) are a class of dependent failures that occur to complex technological systems, such as nuclear power plants, where redundant components serve as multiple layers of defence. For the purposes of quantitative assessment of CCFs, parametric models are used. A common feature of all parametric models is the difficulty in parameter estimation due to limited available observational data.The Unified Partial Method (UPM) for CCF modelling is a systematic methodology that takes into consideration physical and operational system defences. This research explores the application of the Influence Diagram (ID) formalism in order to extend UPM, through an example of Emergency Diesel Generators from nuclear power plants. The proposed model incorporates intermediate stages in the modelling process, namely root causes and coupling factors, to allow for a representation of the CCF mechanisms. Moreover, it captures interactions existing amongst the system's defences, in their contribution to risk. With an underlying Bayesian approach to risk, the model quantifies operational experience, accounts for the epistemic uncertainty, and allows for a coherent combination of expert opinion with observations. This thesis proposes a model structure, which integrates with the ICDE generic database for CCFs. Finally, the ID formalism allows for the propagation of uncertainty within the model structure, and provides a tool for decision-making.The construction of the ID model has been entirely based on expert judgment: the model network has been constructed with the help of experts, whilst a suggested model quantification methodology has been explored. This thesis documents the building process, and explores the behaviour of the resulting model. Findings within this research suggest the feasibility of the proposed methodology for development of a CCF model with a structural and exploratory character.Common Cause Failures (CCFs) are a class of dependent failures that occur to complex technological systems, such as nuclear power plants, where redundant components serve as multiple layers of defence. For the purposes of quantitative assessment of CCFs, parametric models are used. A common feature of all parametric models is the difficulty in parameter estimation due to limited available observational data.The Unified Partial Method (UPM) for CCF modelling is a systematic methodology that takes into consideration physical and operational system defences. This research explores the application of the Influence Diagram (ID) formalism in order to extend UPM, through an example of Emergency Diesel Generators from nuclear power plants. The proposed model incorporates intermediate stages in the modelling process, namely root causes and coupling factors, to allow for a representation of the CCF mechanisms. Moreover, it captures interactions existing amongst the system's defences, in their contribution to risk. With an underlying Bayesian approach to risk, the model quantifies operational experience, accounts for the epistemic uncertainty, and allows for a coherent combination of expert opinion with observations. This thesis proposes a model structure, which integrates with the ICDE generic database for CCFs. Finally, the ID formalism allows for the propagation of uncertainty within the model structure, and provides a tool for decision-making.The construction of the ID model has been entirely based on expert judgment: the model network has been constructed with the help of experts, whilst a suggested model quantification methodology has been explored. This thesis documents the building process, and explores the behaviour of the resulting model. Findings within this research suggest the feasibility of the proposed methodology for development of a CCF model with a structural and exploratory character

A location-specific CCF model for supporting the system design process

This paper examines a location-specific CCF model for supporting the system design process

Foundations of the UPM common cause failure method

This paper examines the foundations of the UPM common cause failure method

Robustness of maintenance decisions : Uncertainty modelling and value of information

In this paper we show how sensitivity analysis for a maintenance optimisation problem can be undertaken by using the concept of expected value of perfect information (EVPI). This concept is important in a decision-theoretic context such as the maintenance problem, as it allows us to explore the effect of parameter uncertainty on the cost and the resulting recommendations. To reduce the computational effort required for the calculation of EVPIs, we have used Gaussian process (GP) emulators to approximate the cost rate model. Results from the analysis allow us to identify the most important parameters in terms of the benefit of ’learning’ by focussing on the partial expected value of perfect information for a parameter. The analysis determines the optimal solution and the expected related cost when the parameters are unknown and partially known. This type of analysis can be used to ensure that both maintenance calculations and resulting recommendations are sufficiently robust

Bayes geometric scaling model for common cause failure rates

This paper proposes a mathematical model to associate key operational, managerial and design characteristics of a system with the system's susceptibility towards common cause failure (CCF) events. The model, referred to as the geometric scaling (GS) model, is a mathematical form that allows us to investigate the effect of possible system modifications on risk. As such, the presented methodology results in a CCF model with a strong decision-making character. Based on a Bayesian framework, the GS model allows for the representation of epistemic uncertainty, the update of prior uncertainty in the light of operational data and the coherent use of observations coming from different systems. From a CCF perspective these are particularly useful model features, because CCF events are rare; hence, the operational data available is sparse and is characterised by considerable uncertainty, with databases typically containing events from nominally identical systems from different plants. The GS model also possesses an attractive modelling feature because it significantly decreases the amount of information elicited from experts required for quantification

Robustness of maintenance decisions : uncertainty modelling and value of information

In this paper we show how sensitivity analysis for a maintenance optimisation problem can be undertaken by using the concept of Expected Value of Perfect Information (EVPI). This concept is important in a decision-theoretic context such as the maintenance problem, as it allows us to explore the effect of parameter uncertainty on the cost and the resulting recommendations. To reduce the computational effort required for the calculation of EVPIs, we have used Gaussian Process (GP) emulators to approximate the cost rate model. Results from the analysis allow us to identify the most important parameters in terms of the benefit of ‘learning’ by focussing on the partial Expected Value of Perfect Information for a parameter. Assuming that a parameter can become completely known before a maintenance decision is made, the analysis determines the optimal decision and the expected related cost, for the different values of the parameter. This type of analysis can be used to ensure that both maintenance calculations and resulting recommendations are sufficiently robust