An FMEA-Based Risk Assessment Approach for Wind Turbine Systems: A Comparative Study of Onshore and Offshore

Failure mode and effects analysis (FMEA) has been extensively used by wind turbine assembly manufacturers for analyzing, evaluating and prioritizing potential/known failure modes. However, several limitations are associated with its practical implementation in wind farms. First, the Risk-Priority-Number (RPN) of a wind turbine system is not informative enough for wind farm managers from the perspective of criticality; second, there are variety of wind turbines with different structures and hence, it is not correct to compare the RPN values of different wind turbines with each other for prioritization purposes; and lastly, some important economical aspects such as power production losses, and the costs of logistics and transportation are not taken into account in the RPN value. In order to overcome these drawbacks, we develop a mathematical tool for risk and failure mode analysis of wind turbine systems (both onshore and offshore) by integrating the aspects of traditional FMEA and some economic considerations. Then, a quantitative comparative study is carried out using the traditional and the proposed FMEA methodologies on two same type of onshore and offshore wind turbine systems. The results show that the both systems face many of the same risks, however there are some main differences worth considering

DESIGN AND IMPLEMENTATION OF INTELLIGENT MONITORING SYSTEMS FOR THERMAL POWER PLANT BOILER TRIPS

Steam boilers represent the main equipment in the power plant. Some boiler trips may lead to an entire shutdown of the plant, which is economically burdensome. An early detection and diagnosis of the boiler trips is crucial to maintain normal and safe operational conditions of the plant. Numbers of methodologies have been proposed in the literature for fault diagnosis of power plants. However, rapid deployment of these methodologies is difficult to be achieved due to certain inherent limitations such as system inability to learn or a dynamically improve the system performance and the brittleness of the system beyond its domain of expertise. As a potential solution to these problems, two artificial intelligent monitoring systems specialized in boiler trips have been proposed and coded within the MA TLAB environment in the present work. The training and validation of the two systems have been performed using real operational data which was captured from the plant integrated acquisition system of JANAMANJUNG coal-fired power plant. An integrated plant data preparation framework for seven boiler trips with related operational variables, has been proposed for the training and validation of the proposed artificial intelligent systems. The feedforward neural network methodology has been adopted as a major computational intelligent tool in both systems. The root mean square error has been widely used as a performance indicator of the proposed systems. The first intelligent monitoring system represents the use of the pure artificial neural network system for boiler trip detection. The final architecture for this system has been explored after investigation of various main neural network topology combinations which include one and two hidden layers, one to ten neurons for each hidden layer, three types of activation function, and four types of multidimensional minimization training algorithms. It has been found that there was no general neural network topology combination that can be applied for all boiler trips. All seven boiler trips under consideration had been detected by the proposed systems before or at the same time as the plant control system. The second intelligent monitoring system represents mergmg of genetic algorithms and artificial neural networks as a hybrid intelligent system. For this hybrid intelligent system, the selection of appropriate variables from hundreds of boiler operation variables with optimal neural network topology combinations to monitor boiler trips was a major concern. The encoding and optimization process using genetic algorithms has been applied successfully. A slightly lower root mean square error was observed in the second system which reveals that the hybrid intelligent system performed better than the pure neural network system. Also, the optimal selection of the most influencing variables was performed successfully by the hybrid intelligent system. The proposed artificial intelligent systems could be adopted on-line as a reliable controller of the thermal power plant boiler

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

Forecasting Wind Turbine Failures and Associated Costs

Electricity demand is rapidly increasing with growth of population, development of technologies and electrically intensive industries. Also, emerging climate change concerns compel governments to seek environmentally friendly ways to produce electricity such as wind energy systems. In 2018, the wind energy reached 600 GW total capacity globally. However, this corresponds to only about 6% of global electricity demand and there is a need to increase wind energy penetration in electricity grids. One way to enhance the competitiveness of wind energy is to improve its reliability and availability and reduce associated maintenance costs. This study utilizes a database entitled “Wind Monitor and Evaluation Program (WMEP)” to investigate, model and improve wind turbine reliability and availability. The WMEP database consists of maintenance data of 575 wind turbines in Germany during 1989-2008. It is unique as it includes details of turbine model and size, affected subsystem and component, cause of failure, date and time of maintenance, location, and energy production from the wind turbines. Additional parameters such as climatic regions, geography number of previous failures and mean annual wind speed are added to the database in this study. In this research, two metrics are considered and developed such as time-to-failure or failure rate and time-to-repair or downtime for reliability and availability, respectively. This study investigated failure causes, effects and criticalities of wind turbine subsystems and components, assessed the risk factors impacting wind turbine reliability, modeled the reliability of wind turbines based on assessed risk factors, and predicted the cost of wind turbine failures under various operational and environmental conditions. A well-established reliability assessment technique - Failure Modes, Effects and Criticality Analysis is applied on the WMEP maintenance data from 109 wind turbines and three different climatic regions to understand the impacts of climate and wind turbine design type on wind turbine reliability and availability. First, climatic region impacts on identical wind turbine failures are investigated, then impacts of wind turbine design type are examined for the same climatic region. Furthermore, we compared the results of this investigation with results from previous FMECA studies which neglected impacts of climatic region and turbine design type in section 5.4. Two-step cluster and survival analyses are used to determine risk factors that affect wind turbine reliability. Six operational and environmental factors are considered for this approach, namely capacity factor (CF), wind turbine design type, number of previous failures (NOPF), geographical location, climatic region and mean annual wind speed (MAWS). Data are classified as frequent (time-to-failure80 days) failures and we identified 615 operations listing all these factor and energy production from 21 wind turbines in the WMEP data base. These factors are examined for their impact on wind turbine reliability and results are compared. In addition, wind turbine reliability is modeled by machine learning methods, namely logistic regression (LR) and artificial neural network (ANN), using the considered 615 operations. The objective of this investigation is to model and predict probability of frequently-failing wind turbines based on wind turbines’ known operational and environmental conditions. The models are evaluated and cross validated with 10-fold cross validation and prediction performances and compared with other algorithms such as k-nearest neighbor and support vector machines. Also, prediction performances of LR and ANN are discussed along with their easiness to interpret and share with others. Lastly, using data from 753 operations, a decision support tool for predicting cost of wind turbine failures is developed. The tool development includes machine learning application for estimating probability of failures in 60 days of operation and time-to-repair probabilities for divisions of 0-8hrs, 8-16hrs, 16-24hrs and more than 1 day based on operational and environmental conditions of wind turbines. Prediction for cost of wind turbine failures for 60 days of operation is calculated using assumed costs from time-to-repair divisions. The decision support tool can be updated by the user’s discretion on the cost of failures. This study provides a better understanding of wind turbine failures by investigating associated risk factors, modeling wind turbine reliability and predicting the future cost of failures by applying state-of-the art reliability and data analysis techniques. Wind energy developers and operators can be guided by this study in improving the reliability of wind turbines. Also, wind energy investors, operators and maintenance service managers can predict the cost of wind turbine failures with the decision support tool provided in this study

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

Decommissioning strategy to reduce the cost and risk-driving factors in the offshore wind industry.

With the increasing number of wind turbines approaching their end of life, there has to be a decommissioning strategy in place as the removal of these assets is not as direct as reverse installation. Offshore asset decommissioning involves technical, financial, operational, safety, policy, and environmental considerations on handling offshore marine assets at their end-of-life, with phases from the planning to site clean-up and monitoring. Offshore decommissioning activities cost significantly more than onshore; thus, adequate financial and safety provisions are essential, and more research required in this area. Decommissioning projects have hitherto been performed on a small scale, but with large-scale aging structures, they must be optimised for lowered costs and risks. In terms of planning, execution and costs, there have been significant cost overruns on decommissioning projects, which are not profit-generating projects. These forecasted large-scale decommissioning activities also have associated risks. Although risk management is a well-researched area, there is limited literature on offshore wind decommissioning risk management. This research thus, applies risk management methods and strategies to develop a robust decommissioning risk framework. In addition, to improve decommissioning processes and technologies, there is a need to develop new protocols for decommissioning. This research identifies potentials for computational simulations and automations that need to be developed to identify and manage the highest cost and risk-drivers. This study seeks to close the research gap in understanding how to decrease decommissioning costs and risks. This research addresses potential opportunities in cost and risk estimation research, impact analysis and reduction frameworks that can be adapted to decommissioning activities specific to the offshore wind industry.Shafiee, Mahmood (Associate)PhD in Energy and Powe

Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review

YesSystem safety, reliability and risk analysis are important tasks that are performed throughout the system lifecycle to ensure the dependability of safety-critical systems. Probabilistic risk assessment (PRA) approaches are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to, Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). Growing complexity of modern systems and their capability of behaving dynamically make it challenging for classical PRA techniques to analyse such systems accurately. For a comprehensive and accurate analysis of complex systems, different characteristics such as functional dependencies among components, temporal behaviour of systems, multiple failure modes/states for components/systems, and uncertainty in system behaviour and failure data are needed to be considered. Unfortunately, classical approaches are not capable of accounting for these aspects. Bayesian networks (BNs) have gained popularity in risk assessment applications due to their flexible structure and capability of incorporating most of the above mentioned aspects during analysis. Furthermore, BNs have the ability to perform diagnostic analysis. Petri Nets are another formal graphical and mathematical tool capable of modelling and analysing dynamic behaviour of systems. They are also increasingly used for system safety, reliability and risk evaluation. This paper presents a review of the applications of Bayesian networks and Petri nets in system safety, reliability and risk assessments. The review highlights the potential usefulness of the BN and PN based approaches over other classical approaches, and relative strengths and weaknesses in different practical application scenarios.This work was funded by the DEIS H2020 project (Grant Agreement 732242)

Application of the multigraph software architecture to intelligent patient monitoring

This project is concerned with the development of intelligent agents for use in the Intensive Care Unit (ICU), based on the Multigraph Architecture (MA). The objectives of the work are twofold: i. ) To consider the design issues of intelligent systems, using a model-based approach of adding intelligence to existing instrumentation and information systems, irrespective of their implementation. ii. ) More specifically, to investigate the role of MA in the implementation of such systems. The first objective will be achieved by writing a specification for an Integrated Model-based Development Environment (IMDE), identifying its functionality, user requirements, etc. The second objective is to implement a prototype of one such development environment. This will borrow concepts from a framework for intelligent process control for chemical plants, previously implemented in the MA. Finally an ICU application will be developed, using this IMDE, to monitor the respiratory system. The benefits associated with using such a model-based framework include modular design, reusability of software components, automatic synthesis of run-time system from the models and simpler consistency and validation procedures. Another benefit that is of particular interest in patient monitoring systems is the integration of different software components. Because the MA is generic, it can be extended to support any modelling paradigm. This means that the development framework can handle all aspects in design of the system, e.g. instrument interface, user interface, signal processing and knowledge-based signal interpretation. To demonstrate this, the IMDE has been extended to include the Causal Probabilistic Network (CPN) modelling paradigm as an integral component. This facilitates the modelling of uncertaiilty. Using the techniques described in summary above, the benefits gained from applying the multigraph architecture to intelligent patient monitoring can be ascertained. This forms the bulk of the work to be described in this thesis

Fault Diagnosis and Estimation of Dynamical Systems with Application to Gas Turbines

This thesis contributes and provides solutions to the problem of fault diagnosis and estimation from three different perspectives which are i) fault diagnosis of nonlinear systems using nonlinear multiple model approach, ii) inversion-based fault estimation in linear systems, and iii) data-driven fault diagnosis and estimation in linear systems. The above contributions have been demonstrated to the gas turbines as one of the most important engineering systems in the power and aerospace industries. The proposed multiple model approach is essentially a hierarchy of nonlinear Kalman filters utilized as detection filters. A nonlinear mathematical model for a gas turbines is developed and verified. The fault vector is defined using the Gas Path Analysis approach. The nonlinear Kalman filters that correspond to the defined single or concurrent fault modes provide the conditional probabilities associated with each fault mode using the Bayes' law. The current fault mode is then determined based on the maximum probability criteria. The performance of both Extended Kalman Filters (EKF) and Unscented Kalman Filters (UKF) are investigated and compared which demonstrates that the UKF outperforms the EKF for this particular application. The problem of fault estimation is increasingly receiving more attention due to its practical importance. Fault estimation is closely related to the problem of linear systems inversion. This thesis includes two contributions for the stable inversion of non-minimum phase systems. First, a novel methodology is proposed for direct estimation of unknown inputs by using only measurements of either minimum or non-minimum phase systems as well as systems with transmission zeros on the unit circle. A dynamic filter is then identified whose poles coincide with the transmission zeros of the system. A feedback is then introduced to stabilize the above filter dynamics as well as provide an unbiased estimation of the unknown input. The methodology is then applied to the problem of fault estimation and has been shown that the proposed inversion filter is unbiased for certain categories of faults. Second, a solution for unbiased reconstruction of general inputs is proposed. It is based on designing an unknown input observer (UIO) that provides unbiased estimation of the minimum phase states of the system. The reconstructed minimum phase states serve then as inputs for reconstruction of the non-minimum phase states. The reconstruction error for non-minimum phase states exponentially decrease as the estimation delay is increased. Therefore, an almost perfect reconstruction can be achieved by selecting the delay to be sufficiently large. The proposed inversion scheme is then applied to the output-tracking control problem. An important practical challenge is the fact that engineers rarely have a detailed and accurate mathematical model of complex engineering systems such as gas turbines. Consequently, one can find a trend towards data-driven approaches in many disciplines, including fault diagnosis. In this thesis, explicit state-space based fault detection, isolation and estimation filters are proposed that are directly identified from only the system input-output (I/O) measurements and through the system Markov parameters. The proposed procedures do not involve a reduction step and do not require identification of the system extended observability matrix or its left null space. Therefore, the performance of the proposed filters is directly connected to and linearly dependent on the errors in the Markov parameters estimation process. The estimation error dynamics is then derived in terms of the Markov parameters identification errors and directly synthesized from the healthy system I/O data. Consequently, the estimation errors have been effectively compensated for. The proposed data-driven scheme requires the persistently exciting condition for healthy input data which is not practical for certain real life applications and in particular to gas turbine engines. To address this issue, a robust methodology for Markov parameters estimation using frequency response data is developed. Finally, the performance of the proposed data-driven approach is comprehensively evaluated for the fault diagnosis and estimation problems in the gas turbine engines

Measurements, Models, Systems and Design

531 s.