Reliability Analysis on Crucial Subsystems of a Wind Turbine through FTA Approach

Katsavounis, Stefanos; Patsianis, N.; Botsaris, Pantelis N.; Konstantinidis, E.I.

doi:10.14195/978-972-8954-42-0_31

Cited by 9 publications

(11 citation statements)

References 2 publications

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“…For the purpose of this analysis, the basic elements represented in Figure , are extensions of the logical framework presented by Reference and the failure rate in Table is adapted from References 12,19, and 21.…”

Section: Multistate Markovian Process Methodology For Reliability Assmentioning

confidence: 99%

“…The checklist provides a set of questions for hazard identification (HAZID) for reliability engineer and system operators, as a means of gathering facts on critical contributing parameters. An example is presented in References 12 and 13. Several researchers have made tremendous progress in reliability modeling based on both qualitative and quantitative methods .…”

Section: Literature Reviewmentioning

confidence: 99%

“…The degradable elements transition intensity is assigned to simplify the computation. The assumptions for the transition intensity (failures rate) are made as follows for all the degradable elements: Bearing shaft matching failure; Bearing cap failure; Rollaway net corrosion; Shaft wear; Cooling system failure; 12 : 13 : 23 = 1 : 3 : 6; 1 : 2 : 4; 1 : 1.5 : 3. As reflected in Table 2, the prior distribution of the subsystem's element is assumed and given as.…”

Section: Estimation Of the State Transition Probability Matrixmentioning

confidence: 99%

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A Markovian reliability approach for offshore wind energy system analysis in harsh environments

Adumene

Okoro

2020

Engineering Reports

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For an effective monitoring strategy of offshore energy systems in harsh environments, it is vital the system reliability dynamics be fully understood. This article presents the application of a Markovian process for dynamic reliability prediction of an offshore energy system. In the proposed approach, a three‐state Markovian process is developed for the analysis of the subsystem states, which dynamically determines the overall system reliability performance, overcoming the static limitation of fault tree analysis. The multistate subsystems are discretized into normal, degraded, and failed states to demonstrate the performance dynamics of the system for condition monitoring. The proposed approach is tested on a case study and the most critical influencing elements are identified. The application of the methodology efficiently identifies the system vulnerability path so as to assist in system integrity management and to provide a guide to early remedial action against total failure and downtime in offshore energy generation.

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Section: Multistate Markovian Process Methodology For Reliability Assmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Estimation Of the State Transition Probability Matrixmentioning

confidence: 99%

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A Markovian reliability approach for offshore wind energy system analysis in harsh environments

Adumene

Okoro

2020

Engineering Reports

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“…The cluster with the highest density of information is the cluster that consists of the systems 25,29,32,28,26,37,33, and 36, all belonging either to electrical or control system. Within this cluster, there are cells with strong interactions among the systems and high values of failure criticality.…”

Section: Evaluation Of the Final Clustered Dsmmentioning

confidence: 99%

“…[45][46][47][48][49] The final clustered DSM (Figure 13) consists of clusters that already belong to one or even two main subsystems (except the last cluster that consists of systems that belong to four subsystems). The systems that belong to the first cluster (25,29,32,28,26,37,33,36)-which also belong to Electrical and Control Systems-interact with almost all the rest of the systems. As these systems (Controller, Electrical Protection System, Grounding System, etc) play a vital role, a system architecture that consists of smaller, individual, and independent units of these systems, it is in the direction of reducing the total failure impact.…”

Section: Proposed Maintenance Interventionsmentioning

confidence: 99%

Design structure matrix (DSM) method application to issue of modeling and analyzing the fault tree of a wind energy asset

2019

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The perpetual energy production of a wind farm could be accomplished (under proper weather conditions) if no failures occurred. But even the best possible design, manufacturing, and maintenance of a system cannot eliminate the failure possibility.In order to understand and minimize the system failures, the most crucial components of the wind turbines, which are prone to failures, should be identified. Moreover, it is essential to determine and classify the criticality of the system failures according to the impact of these failure events on wind turbine safety. The present study is processing the failure data from a wind farm and uses the Fault Tree Analysis as a baseline for applying the Design Structure Matrix technique to reveal the failure and risk interactions between wind turbine subsystems. Based on the analysis performed and by introducing new importance measures, the "readiness to fail" of a subsystem in conjunction with the "failure riskiness" can determine the "failure criticality." The value of the failure criticality can define the frame within which interventions could be done. The arising interventions could be applied either to the whole system or could be focused in specified pairs of wind turbine subsystems. In conclusion, the method analyzed in the present research can be effectively applied by the wind turbine manufacturers and the wind farm operators as an operation framework, which can lead to a limited (as possible) design-out maintenance cost, failures' minimization, and safety maximization for the whole wind turbine system. KEYWORDS design structure matrix, failures, fault tree analysis, safety impact, wind turbines | INTRODUCTIONWind is a clean and inexhaustible energy source and has become an important factor in the field of sustainable development. The global wind energy potential was estimated to be 72 TW, sufficient to supply the entire world's energy demand for all purposes. 1 Wind turbines (WTs) are installed either onshore (on land) or offshore (beyond the coast). Offshore wind farms seem to be advantageous because of the enormous energy potential associated with the large continuous areas and the stronger winds that imply greater power generation, even though they have higher investment, operation, and maintenance costs. The size of WTs is of great significance as a bigger WT produces more power in a more efficient way-resulting in greener produced electricity. 2The issue of WTs' failures is of particular interest. Failure is the loss of the ability of an item to perform a required function. 4 In WT, the unusually tough ambient conditions and the extremely high dynamic loading of the components often result to failures. These two factors are carefully considered in the design phase and materials' selection of a WT. Common causes of WTs failure can be components failure, high wind, waves,

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Dynamic failure analysis of renewable energy systems in the remote offshore environments

Nitonye

Adumene

Sigalo

et al. 2020

Quality & Reliability Eng

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For effective integrity management of marine renewable energy systems in the dynamic and uncertain ocean environments, understanding the failure dynamics is crucial. The cost of investment in marine/offshore renewable energy infrastructures and the associated cost due to failure and loss of energy production necessitate a predictive monitoring methodology that is dynamic and adaptive. This paper presents an integrated multi‐state pure‐birth‐pure‐death Markovian‐net profit value model for the offshore turbine subsystem failure analysis and its cost‐based consequences. The integrated model captures the offshore turbine subsystem's dynamic failure states and its economic implications due to the cost of energy loss and downtime for the period under consideration. The model applies a phase‐type exponential distribution to describe the monotonic state of failure. The methodology is demonstrated with an offshore wind turbine gearbox, and it captures the dynamic state of the system and its failure mechanisms. The cumulative effect of the subelements deterioration decreases the gearbox performance by over 35% within the first 2 years of operation.

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Reliability Analysis on Crucial Subsystems of a Wind Turbine through FTA Approach

Cited by 9 publications

References 2 publications

A Markovian reliability approach for offshore wind energy system analysis in harsh environments

A Markovian reliability approach for offshore wind energy system analysis in harsh environments

Design structure matrix (DSM) method application to issue of modeling and analyzing the fault tree of a wind energy asset

Dynamic failure analysis of renewable energy systems in the remote offshore environments

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