Identification of critical components of wind turbines using FTA over the time

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.

[1]  Manuela M. Veloso,et al.  OBDD-based Universal Planning for Synchronized Agents in Non-Deterministic Domains , 2000, J. Artif. Intell. Res..

[2]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[3]  Lina Bertling,et al.  A Limited-Scope Reliability-Centred Maintenance Analysis of Wind Turbines , 2011 .

[4]  Randal E. Bryant,et al.  Graph-Based Algorithms for Boolean Function Manipulation , 1986, IEEE Transactions on Computers.

[5]  Z W Birnbaum,et al.  ON THE IMPORTANCE OF DIFFERENT COMPONENTS IN A MULTICOMPONENT SYSTEM , 1968 .

[6]  H. Douglas,et al.  Broken rotor bar detection in induction machines with transient operating speeds , 2005, IEEE Transactions on Energy Conversion.

[7]  María Isabel Blanco.  The economics of wind energy , 2009 .

[8]  W. J. Padgett.  A multiplicative damage model for strength of fibrous composite materials , 1998 .

[9]  Michael D. Zuteck,et al.  Scaling of Composite Wind Turbine Blades for Rotors of 80 to 120 Meter Diameter , 2001 .

[10]  Albert R. Wang,et al.  Logic verification using binary decision diagrams in a logic synthesis environment , 1988, [1988] IEEE International Conference on Computer-Aided Design (ICCAD-89) Digest of Technical Papers.

[11]  Peter Tavner,et al.  Reliability of wind turbine subassemblies , 2009 .

[12]  Diego J. Pedregal,et al.  Time series methods applied to failure prediction and detection , 2010, Reliab. Eng. Syst. Saf..

[13]  Diego J. Pedregal,et al.  An algorithmic approach for maintenance management based on advanced state space systems and harmonic regressions , 2009, Ann. Oper. Res..

[14]  Peter Tavner,et al.  Failure Modes and Effects Analysis (FMEA) for wind turbines. , 2010 .

[15]  P. Vionis,et al.  Comparing Fatigue Strength From Full Scale Blade Tests With Coupon-Based Predictions , 2002 .

[16]  Anca Daniela Hansen,et al.  Fault ride-through capability of DFIG wind turbines , 2007 .

[17]  L. M. Bartlett.  Progression of the binary decision diagram conversion methods , 2003 .

[18]  Wenyi Liu,et al.  Status and problems of wind turbine structural health monitoring techniques in China , 2010 .

[19]  Jui-Sheng Chou,et al.  Failure analysis and risk management of a collapsed large wind turbine tower , 2011 .

[20]  F. M. Jensen,et al.  Structural testing and numerical simulation of a 34 m composite wind turbine blade , 2006 .

[21]  Nan Bai,et al.  Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods , 2011 .

[22]  Nicholas Jenkins,et al.  Lightning protection for wind turbine blades and bearings , 2001 .

[23]  J. B. Fussell,et al.  How to Hand-Calculate System Reliability and Safety Characteristics , 1975, IEEE Transactions on Reliability.

[24]  Fausto Pedro García Márquez.  An Approach to Remote Condition Monitoring Systems Management , 2006 .

[25]  George D. Hatzigeorgiou,et al.  Static, seismic and stability analyses of a prototype wind turbine steel tower , 2002 .

[26]  S. Iniyan,et al.  A review of wind energy technologies , 2007 .

[27]  Jung-Ryul Lee,et al.  Structural health monitoring for a wind turbine system: a review of damage detection methods , 2008 .

[28]  Olivier Parent,et al.  Anti-icing and de-icing techniques for wind turbines: Critical review , 2011 .

[29]  Mayorkinos Papaelias,et al.  Condition monitoring of wind turbines: Techniques and methods , 2012 .

[30]  Karl Stol.  Disturbance Tracking Control and Blade Load Mitigation for Variable-Speed Wind Turbines , 2003 .

[31]  Sheldon B. Akers,et al.  Binary Decision Diagrams , 1978, IEEE Transactions on Computers.

[32]  Simon J. Watson,et al.  Physics of Failure approach to wind turbine condition based maintenance , 2009 .

[33]  C. Y. Lee.  Representation of switching circuits by binary-decision programs , 1959 .

[34]  Min Xie,et al.  Optimum prioritisation and resource allocation based on fault tree analysis , 2000 .

[35]  C Roberts,et al.  Railway point mechanisms: Condition monitoring and fault detection , 2010 .

[36]  Bernard M. E. Moret,et al.  Decision Trees and Diagrams , 1982, CSUR.

[37]  P.L. Chapman,et al.  Simple expressions for optimal current waveforms for permanent-magnet synchronous machine drives , 2005, IEEE Transactions on Energy Conversion.

[38]  Sung-Hoon Ahn,et al.  Condition monitoring and fault detection of wind turbines and related algorithms: A review , 2009 .

[39]  Ion Boldea,et al.  Condition monitoring of wind generators , 2003, 38th IAS Annual Meeting on Conference Record of the Industry Applications Conference, 2003..