Identification of critical components of wind turbines using FTA over time

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Identification of critical components of wind turbines using FTA over time. / García Márquez, Fausto Pedro ; Pinar Pérez, Jesús María ; Marugán, Alberto Pliego; Papaelias, Mayorkinos.

In: Renewable Energy, Vol. 87, No. 2, 03.2016, p. 869-883.

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García Márquez, Fausto Pedro ; Pinar Pérez, Jesús María ; Marugán, Alberto Pliego ; Papaelias, Mayorkinos. / Identification of critical components of wind turbines using FTA over time. In: Renewable Energy. 2016 ; Vol. 87, No. 2. pp. 869-883.

Bibtex

@article{da9d7c0eaa6e4ef39f601d2b3db9b871,
title = "Identification of critical components of wind turbines using FTA over time",
abstract = "Wind energy is currently the most widely implemented renewable energy source in global scale. Complex industrial multi-MW wind turbines are continuously being installed both onshore and offshore. Projects involving utility-scale wind turbines require optimisation of reliability, availability, maintainability and safety, in order to guarantee the financial viability of large scale wind energy projects, particularly offshore, in the forthcoming years. For this reason, critical wind turbine components must be identified and monitored as cost-effectively, reliably and efficiently as possible. The condition of industrial wind turbines can be qualitatively evaluated through the Fault Tree Analysis (FTA). The quantitative analysis requires high computational cost. In this paper, the Binary Decision Diagram (BDD) method is proposed for reducing this computational cost. In order to optimise the BDD a set of ranking methods of events has been considered; Level, Top-Down-Left-Right, AND, Depth First Search and Breadth-First Search. A quantitative analysis approach in order to find a general solution of a Fault Tree (FT) is presented. An illustrative case study of a FT of a wind turbine based on different research studies has been developed. Finally, this FT has been solved dynamically through the BDD approach in order to highlight the identification of the critical components of the wind turbine under different conditions, employing the following heuristic methods: Birnbaum, Criticality, Structural and Fussell-Vesely. The results provided by this methodology allow the performance of novel maintenance planning from a quantitative point of view.",
keywords = "Fault tree analysis, Binary diagram decisions, Wind turbines, Condition monitoring, Maintenance management",
author = "{Garc{\'i}a M{\'a}rquez}, {Fausto Pedro} and {Pinar P{\'e}rez}, {Jes{\'u}s Mar{\'i}a} and Marug{\'a}n, {Alberto Pliego} and Mayorkinos Papaelias",
year = "2016",
month = mar,
doi = "10.1016/j.renene.2015.09.038",
language = "English",
volume = "87",
pages = "869--883",
journal = "Renewable Energy",
issn = "0960-1481",
publisher = "Elsevier Korea",
number = "2",

}

RIS

TY - JOUR

T1 - Identification of critical components of wind turbines using FTA over time

AU - García Márquez, Fausto Pedro

AU - Pinar Pérez, Jesús María

AU - Marugán, Alberto Pliego

AU - Papaelias, Mayorkinos

PY - 2016/3

Y1 - 2016/3

N2 - Wind energy is currently the most widely implemented renewable energy source in global scale. Complex industrial multi-MW wind turbines are continuously being installed both onshore and offshore. Projects involving utility-scale wind turbines require optimisation of reliability, availability, maintainability and safety, in order to guarantee the financial viability of large scale wind energy projects, particularly offshore, in the forthcoming years. For this reason, critical wind turbine components must be identified and monitored as cost-effectively, reliably and efficiently as possible. The condition of industrial wind turbines can be qualitatively evaluated through the Fault Tree Analysis (FTA). The quantitative analysis requires high computational cost. In this paper, the Binary Decision Diagram (BDD) method is proposed for reducing this computational cost. In order to optimise the BDD a set of ranking methods of events has been considered; Level, Top-Down-Left-Right, AND, Depth First Search and Breadth-First Search. A quantitative analysis approach in order to find a general solution of a Fault Tree (FT) is presented. An illustrative case study of a FT of a wind turbine based on different research studies has been developed. Finally, this FT has been solved dynamically through the BDD approach in order to highlight the identification of the critical components of the wind turbine under different conditions, employing the following heuristic methods: Birnbaum, Criticality, Structural and Fussell-Vesely. The results provided by this methodology allow the performance of novel maintenance planning from a quantitative point of view.

AB - Wind energy is currently the most widely implemented renewable energy source in global scale. Complex industrial multi-MW wind turbines are continuously being installed both onshore and offshore. Projects involving utility-scale wind turbines require optimisation of reliability, availability, maintainability and safety, in order to guarantee the financial viability of large scale wind energy projects, particularly offshore, in the forthcoming years. For this reason, critical wind turbine components must be identified and monitored as cost-effectively, reliably and efficiently as possible. The condition of industrial wind turbines can be qualitatively evaluated through the Fault Tree Analysis (FTA). The quantitative analysis requires high computational cost. In this paper, the Binary Decision Diagram (BDD) method is proposed for reducing this computational cost. In order to optimise the BDD a set of ranking methods of events has been considered; Level, Top-Down-Left-Right, AND, Depth First Search and Breadth-First Search. A quantitative analysis approach in order to find a general solution of a Fault Tree (FT) is presented. An illustrative case study of a FT of a wind turbine based on different research studies has been developed. Finally, this FT has been solved dynamically through the BDD approach in order to highlight the identification of the critical components of the wind turbine under different conditions, employing the following heuristic methods: Birnbaum, Criticality, Structural and Fussell-Vesely. The results provided by this methodology allow the performance of novel maintenance planning from a quantitative point of view.

KW - Fault tree analysis

KW - Binary diagram decisions

KW - Wind turbines

KW - Condition monitoring

KW - Maintenance management

U2 - 10.1016/j.renene.2015.09.038

DO - 10.1016/j.renene.2015.09.038

M3 - Article

VL - 87

SP - 869

EP - 883

JO - Renewable Energy

JF - Renewable Energy

SN - 0960-1481

IS - 2

ER -