A Markovian approach to power generation capacity assessment of floating wave energy converters

Arzaghi, Ehsan; Abaei, Mohammad Mahdi; O’Reilly, Małgorzata M.; Garaniya, Vikram; Penesis, Irene

doi:10.1016/j.renene.2019.08.099

Cited by 12 publications

(9 citation statements)

References 16 publications

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“…The transition matrix is formed by the transition rate from one state to another, which describes the rate of degradation of the system with operating time . The Markov process describes the process whereby its future evolution is dependent on the state history and only uses the present state of the process for future prediction . Mathematically, ℙ ( X t + 1 = s | X t = s t , X t − 1 = s t − 1 , ⋯⋯, X 0 = s 0 ) = ℙ ( X t + 1 = s | X t = s t ). The basic property of the Markov chain is that only the most recent point in the trajectory affects what happens next.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is a very important tool for analyzing the probability distribution in possible states within the state space

S

. The recent application of the stochastic approach, Markov process, and neural network for offshore energy system analysis have shown the capacity of the Markovian process in dynamic system modeling. However, the application of the Markovian process in dynamic reliability prediction for the offshore wind turbine generators has not been demonstrated.…”

Section: Literature Reviewmentioning

confidence: 99%

“…15 The Markov process describes the process whereby its future evolution is dependent on the state history and only uses the present state of the process for future prediction. 16 Mathematically, P(…”

Section: Reliability Analysis Techniques For the Offshore Energy Systemmentioning

confidence: 99%

See 2 more Smart Citations

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: Literature Reviewmentioning

confidence: 99%

“…It is a very important tool for analyzing the probability distribution in possible states within the state space

S

Section: Literature Reviewmentioning

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 development in the marine and offshore energy sector has tremendously improved over the years. Several improvements in offshore renewable energy harvesting ranging from wave energy, 1 wind energy, 2 and ocean thermal energy 3 have increased over the past decades. In recent times, global energy contributions from offshore energy sources have improved and contributed to over 40% of world energy sources 4 .…”

Section: Introductionmentioning

confidence: 99%

“…The authors integrate the Markovian chain‐based decision process to capture the effect of the states’ transition on the offshore wind turbine's maintenance profile. Arzaghi et al 1 proposed the application of a continuous‐time homogeneous Markov process in wave energy converter long‐term energy generation. The proposed approach captures the system dynamic and the uncertainty through a state‐based performance period under variable sea state conditions.…”

Section: Introductionmentioning

confidence: 99%

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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Dynamic logistics disruption risk model for offshore supply vessel operations in Arctic waters

Adumene

Okwu

Yazdi

et al. 2021

Maritime Transport Research

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A Markovian approach to power generation capacity assessment of floating wave energy converters

Cited by 12 publications

References 16 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

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

Dynamic logistics disruption risk model for offshore supply vessel operations in Arctic waters

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