# Optimal Maintenance Management of Offshore Wind Farms

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

- -
- The wind power captured by wind turbines (WTs) is more than onshore.
- -
- The size of offshore wind farms can be larger than onshore.
- -
- The environmental impact for offshore is less than in onshore.

- -
- It is more complex to evaluate the wind characteristics.
- -
- Larger investment costs. The offshore installation cost is 1.44 million €/MW, where the onshore is 0.78 million €/MW [3].
- -
- Operation and maintenance (O & M) tasks are more complex and expensive than onshore. The offshore O & M costs tasks are from 18% to 23% of the total system costs, being 12% for onshore wind farms [4].

## 2. CM Applied to WT

**Figure 1.**Components of the wind turbine (WT) where: 1—pitch system; 2—hub; 3—main bearing; 4—low speed shaft; 5—gearbox; 6—high speed shaft; 7—brake system; 8—generator; 9—yaw system; 10—bedplate; 11—converter; 12—tower; 13—meteorological unit.

## 3. FTA and BDD

**p**represents the failure probabilities of the basic events q

_{i}, i ∈ {1, …, n}, being n the total number of events [20,21].

_{sys}can be obtained via FTA according to

**q**:

_{sys}[21]. The determination of these minimal cut sets can be a large and time-consuming process, even on modern high speed computers. When the FT has many minimal cut sets, the determination of the exact failure probability of the top event also requires a high calculation costs. For many complex FTs, this requirement may be beyond the capability of the available computers. Therefore, some approximation techniques have been introduced with a loss of accuracy.

**V**,

**N**), with vertex set

**V**and index set

**N**, of a Boolean function where equivalent Boolean sub-expressions are uniquely represented [29]. A directed acyclic graph is a directed graph, i.e., to each vertex v there is no possible directed path that starts and finishes in v. It is composed of some interconnected nodes with two vertices. Each vertex is possible to be a terminal or non-terminal vertex. Each single variable has two branches: 0-branch corresponds to the cases where the variable has not occur and it is graphically represented by a dashed line (Figure 2); on the other hand, 1-branch cases are those where the event is being carried out and corresponds to the occurrence of the variable, and it is represented by a solid line (Figure 2). It allows to obtain an analytical expression depending on the probability of failure of the basic events and the topology of the FT. Paths starting from the top event to a terminal 1 provide a state in which the top event will occur. These paths are named cut sets.

_{1}, Else f

_{2}” [31]. The solid line always belongs to the ones and the dashed lines to the zeros, explained above.

## 4. FTA for WTs

Foundation and Tower Failure | Structural fault [17,38,42,43,44,45] | |

Yaw system failure [46] | ||

Critical Rotor Failure | Blade failure | Structural failure [17,34,47,48,49,50,51,52,53] |

Pitch system failure [54,55] | ||

Hydraulic system fault [50,56] | ||

Meteorological unit failure [50,57] | ||

Rotor system failure | Rotor hub [42,46] | |

Bearings [45,46,47] | ||

Power Train Failure | Low speed train failure [17,46,48] | |

Critical gearbox failure [7,46,53,58,59,60,61,62] | ||

High speed train failure | Shaft [6,46,58] | |

Critical brake failure [6,56] | ||

Electrical Components Failure | Critical generator failure [6,46,58,60,63,64,65] | |

Power electronics and electric controls failure [17,56,58,60] |

- -
- g001 corresponds to a “Foundation and Tower Failure” described in Section 4.1.
- -
- g002 corresponds to a “Critical Rotor Failure” depicted in Section 4.2.
- -
- g003 corresponds to a “Power Train Failure” showed in Section 4.4.
- -
- g004 corresponds to a “Electrical Components Failure” presented in Section 4.3.

#### 4.1. Foundation and Tower

#### 4.2. Blade System

#### 4.3. Generator, Electrical and Electronic Components

#### 4.4. Power Train

## 5. Maintenance Management Approach

## 6. Case Study

- Constant probabilityIn this model the probability of the event is constant over the time:$$q\left(t\right)=K,(K\in \mathrm{\mathbb{R}}/0\le K\le 1)$$
- Exponential increasing probabilityIn this model, the probability function assigned is:$$q\left(t\right)=1-{e}^{-\mathsf{\lambda}t},(\mathsf{\lambda}\in \mathbb{R}/\mathsf{\lambda}\ge 0)$$
- Linear increasing probabilityIn this model, the probability function is:$$q\left(t\right)=\{\begin{array}{c}mtmt1\\ 1mt\ge 1\end{array};\forall m1$$
- Periodic probabilityThis model represents those components that need to be replaced, repaired, and zeroed in a periodical way. In this model, the events have a periodic behavior following the next expression:$$q\left(t\right)=1-{e}^{-\mathsf{\lambda}\left(t-n\alpha \right)},n=1,2,3$$

## 7. Results

**Figure 9.**Boxplot of the fault probability of the offshore wind farm for WT operated at the same time.

## 8. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Appendix 1. FT for a Wind Turbine

## Appendix 2. Events and Probabilistic Models

Fault Tree 1 Foundation and Tower Failure | Probabilistic Model Assignment | |||

Intermediate Event | Code | Final Event | Code | |

Yaw System Failure | g005 | Yaw motor fault | e001 | Constant |

Critical Structural Failure | g006 | Abnormal Vibration I | e002 | Linear Increasing |

yaw motor failure | g007 | Abnormal Vibration H | e003 | Linear Increasing |

Wrong Yaw Angle | g008 | Cracks in concrete base | e004 | Constant |

Structural Failure (Foundation and tower) | g009 | Welding damage | e005 | Constant |

No electric power for yaw motor | g010 | Corrosion | e006 | Linear Increasing |

Metereologhical Unit Failure | g011 | Loosen studs in joining foundation and first section | e007 | Linear Increasing |

Structural Fault (Foundation and tower) | g012 | Loosen bolts in joining different sections | e008 | Linear Increasing |

Gaps in the foundation section | e009 | Exponential Increasing | ||

Vane damage | e010 | Exponential Increasing | ||

Anemometer damage | e011 | Exponential Increasing | ||

High wind speed | e012 | Periodic | ||

No power supply from generator | e013 | Constant | ||

No power supply from grid | e014 | Constant | ||

Fault Tree 2 Critical Rotor Failure | Probabilistic Model Assignment | |||

Intermediate Event | Code | Final Event | Code | |

Critical blade failure | g013 | High wind speed | e015 | Periodic |

Blade Failure | g014 | Blade Angle asymmetry | e016 | Exponential Increasing |

Pitch System Failure | g015 | Abnormal Vibration A | e017 | Exponential Increasing |

Critical structural Failure (Blades) | g016 | Motor failure | e018 | Exponential Increasing |

Hydraulic system Failure | g017 | Leakages | e019 | Constant |

Wrong Blade Angle | g018 | Over pressure | e020 | Constant |

Hydraulic system Fault | g019 | Corrosion | e021 | Exponential Increasing |

Metereologhical Unit Failure | g020 | Vane damage | e022 | Constant |

Structural Failure (Blades) | g021 | Anemometer damage | e023 | Constant |

Leading and traililling edges | g022 | Abnormal Vibration B | e024 | Constant |

Shell | g023 | Root Cracks | e025 | Constant |

Tip | g024 | Cracks | e026 | Constant |

Rotor System Failure | g025 | Erosion | e027 | Exponential Increasing |

Rotor System Fault | g026 | Delamination in leading edges of blades | e028 | Exponential Increasing |

Bearings (Rotor) | g027 | Delamination in trailing edges of blades | e029 | Exponential Increasing |

Rotor Hub | g028 | Debonding in edges of blades | e030 | Exponential Increasing |

Wear | g029 | Delamination in shell | e031 | Exponential Increasing |

Imbalance | g030 | Crack with structural damage | e032 | Constant |

Crack on the beam-shell joint | e033 | Constant | ||

Open tip | e034 | Constant | ||

Lightning strike | e035 | Periodic | ||

Abnormal Vibration C | e036 | Constant | ||

Cracks | e037 | Constant | ||

Corrosion of Pins | e038 | Exponential Increasing | ||

Abrasive Wear | e039 | Exponential Increasing | ||

Pitting | e040 | Linear Increasing | ||

Deformation of face & rolling element | e041 | Linear Increasing | ||

Lubrication Fault | e042 | Linear Increasing | ||

Clearance loosening at root | e043 | Exponential Increasing | ||

Cracks | e044 | Constant | ||

Surface Roughness | e045 | Constant | ||

Mass Imbalance | e046 | Exponential Increasing | ||

Fault in Pitch adjustment | e047 | Exponential Increasing | ||

Fault Tree 3 Electrical Components Failure | Probabilistic Model Assignment | |||

Intermediate Event | Code | Final Event | Code | |

Critical Generator Failure | g031 | Abnormal Vibration G | e048 | Exponential Increasing |

Power Electronics and Electric Controls Failure | g032 | Cracks | e049 | Constant |

Mechanical Failure (Generator) | g033 | Imbalance | e050 | Exponential Increasing |

Electrical Failure (Generator) | g034 | Asymmetry | e051 | Exponential Increasing |

Bearing Generator Failure | g035 | Air-Gap eccentricities | e052 | Linear Increasing |

Rotor and Stator Failure | g036 | Broken bars | e053 | Linear Increasing |

Bearing Generator Fault | g037 | Dynamic eccentricity | e054 | Linear Increasing |

Rotor and Stator Fault | g038 | Sensor T error | e055 | constant |

Abnormal Signals A | g039 | T above limit | e056 | Periodic |

Overwarming generator | g040 | Short Circuit (Gen) | e057 | Constant |

Electrical Fault (PE) | g041 | Open Circuit (Gen) | e058 | Constant |

Mechanical Fault (PE) | g042 | Short Circuit | e059 | Constant |

Open Circuit | e060 | Constant | ||

Gate drive circuit | e061 | linear increasing | ||

Corrosion | e062 | Periodic | ||

Dirt | e063 | Periodic | ||

Terminals damage | e064 | linear increasing | ||

Fault Tree 4 Power Train Failure | Probabilistic Model Assignment | |||

Intermediate Event | Code | Final Event | Code | |

Low speed train Failure | g043 | Abnormal Vibration D | e065 | Constant |

Critical Gearbox Failure | g044 | Cracks in main bearing | e066 | Constant |

High speed train Failure | g045 | Spalling | e067 | Linear Increasing |

Main Bearing failure | g046 | Corrosion of Pins | e068 | Linear Increasing |

Low speed shaft failure | g047 | Abrasive Wear | e069 | Constant |

Main Bearing fault | g048 | Deformation of face & rolling element | e070 | Linear Increasing |

Wear main bearing | g049 | Pitting | e071 | exponential increasing |

Low speed shaft fault | g050 | Imbalance | e072 | Constant |

Wear low shaft | g051 | Cracks in l.s. shaft | e073 | Linear Increasing |

Gearbox Fault | g052 | Spalling | e074 | Constant |

Bearings failure(Gearbox) | g053 | Abrasive Wear | e075 | Constant |

Lubrication fault | g054 | Pitting | e076 | Constant |

Gear Failure | g055 | Abnormal Vibration F | e077 | Linear Increasing |

Wear bearing gearbox | g056 | Corrosion of Pins | e078 | Exponential Increasing |

Gear Fault | g057 | Abrasive Wear | e079 | Linear Increasing |

Tooth Wear | g058 | Pitting | e080 | Constant |

Offset | g059 | Deformation of face & rolling element | e081 | Linear Increasing |

High speed shaft Failure | g060 | Oil Filtration | e082 | Constant |

Critical Brake Failure | g061 | Particle Contamination | e083 | Exponential Increasing |

High speed structural damage | g062 | Overwarming gearbox | e084 | Linear Increasing |

Wear high shaft | g063 | Abnormal Vibration E | e085 | Periodic |

Brake Fault | g064 | Eccentricity | e086 | Constant |

Abnormal Signals B | g065 | Pitting | e087 | Linear Increasing |

Hydraulic brake system Fault | g066 | Cracks in gears | e088 | Exponential Increasing |

Abnormal Signals C | g067 | Gear tooth deterioration | e089 | Exponential Increasing |

Overwarming brake | g068 | Poor design | e090 | Periodic |

Tooth surface defects | e091 | Constant | ||

Abnormal Vibration J | e092 | Constant | ||

Cracks in h.s. shaft | e093 | Linear Increasing | ||

Imbalance | e094 | Periodic | ||

Overwarming | e095 | Exponential Increasing | ||

Spalling | e096 | Constant | ||

Abrasive Wear | e097 | Linear Increasing | ||

Pitting | e098 | Constant | ||

Cracks in brake disk | e099 | Exponential Increasing | ||

Motor brake fault | e100 | Constant | ||

Oil Leakage | e101 | Linear Increasing | ||

Over pressure | e102 | Constant | ||

Abnormal speed | e103 | Linear Increasing | ||

T sensor error | e104 | Periodic | ||

T above limit | e105 | Periodic |

## References

- Márquez, F.P.G.; Tobias, A.M.; Pérez, J.M.P.; Papaelias, M. Condition monitoring of wind turbines: Techniques and methods. Renew. Energy
**2012**, 46, 169–178. [Google Scholar] [CrossRef] - Esteban, M.D.; Diez, J.J.; López, J.S.; Negro, V. Why offshore wind energy? Renew. Energy
**2011**, 36, 444–450. [Google Scholar] [CrossRef] [Green Version] - Guidelines for the Certification of Condition Monitoring Systems for Wind Turbines; Germanisher LLoyd: Hamburg, Germany, 2007.
- Tavner, P. Offshore Wind Turbines Reliability, Availability and Maintenance; The Institution of Engineering and Technology: London, UK, 2012. [Google Scholar]
- Novaes Pires, G.; Alencar, E.; Kraj, A. Remote Conditioning Monitoring System for a Hybrid Wind Diesel System-Application at Fernando de Naronha Island. Brasil. Available online: http://www.globalislands.net/userfiles/_brazil_FdNpdf2.pdf (accessed on 10 July 2015).
- Tsai, C.-S.; Hsieh, C.-T.; Huang, S.-J. Enhancement of damage-detection of wind turbine blades via CWT-based approaches. IEEE Trans. Energy Convers.
**2006**, 21, 776–781. [Google Scholar] [CrossRef] - Guo, P.; Bai, N. Wind turbine gearbox condition monitoring with AAKR and moving window statistic methods. Energies
**2011**, 4, 2077–2093. [Google Scholar] [CrossRef] - Chen, Z.; Guerrero, J.M.; Blaabjerg, F. A review of the state of the art of power electronics for wind turbines. IEEE Trans. Power Electronics
**2009**, 24, 1859–1875. [Google Scholar] [CrossRef] - Jiang, W.; Fan, Q.; Gong, J. Optimization of welding joint between tower and bottom flange based on residual stress considerations in a wind turbine. Energy
**2010**, 35, 461–467. [Google Scholar] [CrossRef] - Pérez, J.M.P.; Márquez, F.P.G.; Tobias, A.; Papaelias, M. Wind turbine reliability analysis. Renew. Sustain. Energy Rev.
**2013**, 23, 463–472. [Google Scholar] [CrossRef] - Soua, S.; van Lieshout, P.; Perera, A.; Gan, T.-H.; Bridge, B. Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring. Renew. Energy
**2013**, 51, 175–181. [Google Scholar] [CrossRef] - Chacon, J.L.F.; Andicoberry, E.A.; Kappatos, V.; Asfis, G.; Gan, T.-H.; Balachandran, W. Shaft angular misalignment detection using acoustic emission. Appl. Acoust.
**2014**, 85, 12–22. [Google Scholar] [CrossRef] - Park, S.; Inman, D.J.; Yun, C.-B. An outlier analysis of MFC-based impedance sensing data for wireless structural health monitoring of railroad tracks. Eng. Struct.
**2008**, 30, 2792–2799. [Google Scholar] [CrossRef] - De la Hermosa González, R.R.; Márquez, F.P.G.; Dimlaye, V.; Ruiz-Hernández, D. Pattern recognition by wavelet transforms using macro fibre composites transducers. Mech. Syst. Signal Proc.
**2014**, 48, 339–350. [Google Scholar] [CrossRef] - Nie, M.; Wang, L. Review of condition monitoring and fault diagnosis technologies for wind turbine gearbox. Procedia CIRP
**2013**, 11, 287–290. [Google Scholar] [CrossRef] - Zeng, Z.; Tao, N.; Feng, L.; Li, Y.; Ma, Y.; Zhang, C. Breakpoint detection of heating wire in wind blade moulds using infrared thermography. Infrared Phys. Technol.
**2014**, 64, 73–78. [Google Scholar] [CrossRef] - García Márquez, F.P.; Pinar Pérez, J.M.; Pliego Marugán, A.; Papaelias, M. Identification of critical components of wind turbines using FTA over the time. Renew. Energy
**2016**, 87, 869–883. [Google Scholar] [CrossRef] - Lambert, H.E. Measures of Importance of Events and Cut Sets. Reliability and Fault Tree Analysis; SIAM: Philadelphia, PA, USA, 1975; pp. 77–100. [Google Scholar]
- Pliego Marugán, A.; García, F.P. A novel approach to diagnostic and prognostic evaluations applied to railways: A real case study. J. Rail Rapid Transit
**2015**. [Google Scholar] [CrossRef] - Sinnamon, R.M.; Andrews, J.D. Fault tree analysis and binary decision diagrams. In Proceedings of the Reliability and Maintainability Symposium, Las Vegas, NV, USA, 22–25 January 1996; pp. 215–222.[Green Version]
- Jinglun, Z.; Quan, S. Reliability analysis based on binary decision diagrams. J. Qual. Maint. Eng.
**1998**, 4, 150–161. [Google Scholar] [CrossRef] - Bryant, R.E. Graph-based algorithms for Boolean function manipulation. IEEE Trans. Comput.
**1986**, 100, 677–691. [Google Scholar] [CrossRef] - Remenyte, R.; Andrews, J.D. Qualitative analysis of complex modularized fault trees using binary decision diagrams. Proc. Inst. Mech. Eng. O
**2006**, 220, 45–53. [Google Scholar] [CrossRef] [Green Version] - Prescott, D.R.; Remenyte-Prescott, R.; Reed, S.; Andrews, J.; Downes, C. A reliability analysis method using binary decision diagrams in phased mission planning. Proc. Inst. Mech. Eng. Part O
**2009**, 223, 133–143. [Google Scholar] [CrossRef] [Green Version] - Moret, B.M. Decision trees and diagrams. ACM Comput. Surv.
**1982**, 14, 593–623. [Google Scholar] [CrossRef] - Lee, C.-Y. Representation of switching circuits by binary-decision programs. Bell Syst. Technol. J.
**1959**, 38, 985–999. [Google Scholar] [CrossRef] - Akers, S.B. Binary decision diagrams. IEEE Trans. Comput.
**1978**, 100, 509–516. [Google Scholar] [CrossRef] - Pliego Marugán, A.; García Márquez, F.P.; Lorente, J. Decision making process via binary decision diagram. Int. J. Manag. Sci. Eng. Manag.
**2015**, 10, 3–8. [Google Scholar] [CrossRef] - Fujita, M.; Fujisawa, H.; Kawato, N. Evaluation and improvements of Boolean comparison method based on binary decision diagrams. In Proceedings of the Computer-Aided Design IEEE International Conference (ICCAD-88), Santa Clara, CA, USA, 7–10 November 1988; pp. 2–5.
- Márquez, F.P.G.; Mangurán, A.P.; Zaman, N. For information systems design. Softw. Dev. Technol. Constr. Inf. Syst. Des.
**2013**, 1, 308–318. [Google Scholar] - Brace, K.S.; Rudell, R.L.; Bryant, R.E. Efficient implementation of a BDD package. In Proceedings of the 27th ACM/IEEE Design Automation Conference, Orlando, FL, USA, 24–28 June 1991; pp. 40–45.
- Liu, Q.; Homma, T. A new computational method of a moment-independent uncertainty importance measure. Reliab. Eng. Syst. Saf.
**2009**, 94, 1205–1211. [Google Scholar] [CrossRef] - Cheok, M.C.; Parry, G.W.; Sherry, R.R. Use of importance measures in risk-informed regulatory applications. Reliab. Eng. Syst. Saf.
**1998**, 60, 213–226. [Google Scholar] [CrossRef] - Birnbaum, Z.W. On the Importance of Different Components in a Multicomponent System; Washington University Seattle Lab of Statistical Research: Washington, DC, USA, 1968. [Google Scholar]
- Arabian-Hoseynabadi, H.; Oraee, H.; Tavner, P. Failure modes and effects analysis (FMEA) for wind turbines. Int. J. Electr. Power Energy Syst.
**2010**, 32, 817–824. [Google Scholar] [CrossRef] [Green Version] - RELIAWIND Project. European Union’s Seventh Framework Programme for RTD (FP7). Available online: http://www.reliawind.eu/ (accessed on 22 January 2014).
- Lotsberg, I. Structural mechanics for design of grouted connections in monopile wind turbine structures. Mar. Struct.
**2013**, 32, 113–135. [Google Scholar] [CrossRef] - Chou, J.-S.; Tu, W.-T. Failure analysis and risk management of a collapsed large wind turbine tower. Eng. Fail. Anal.
**2011**, 18, 295–313. [Google Scholar] [CrossRef] - International Electrotechnical Commission. Wind Turbine—Part 1: Design Requirements, IEC 61400-1; International Electrotechnical Commission: Geneva, Switzerland, 2005. [Google Scholar]
- Development and Demonstration of a Novel Integrated Condition Monitoring System for Wind Turbines, NIMO Project. (NIMO, Ref.:FP7-ENERGY-2008-TREN-1: 239462). Available online: http://www.nimoproject.eu (accessed on 30 January 2012).
- Demonstration of Methods and Tools for the Optimisation of Operational Reliability of Large-Scale Industrial Wind Turbines, OPTIMUS Project. (OPTIMUS, Ref.: FP-7-Energy-2012-TREN-1: 322430). Available online: http://www.optimusproject.eu (accessed on 25 February 2014).
- Ciang, C.C.; Lee, J.-R.; Bang, H.-J. Structural health monitoring for a wind turbine system: A review of damage detection methods. Meas. Sci. Technol.
**2008**, 19. [Google Scholar] [CrossRef] - Stol, K.A. Disturbance tracking control and blade load mitigation for variable-speed wind turbines. J. Sol. Energy Eng.
**2003**, 125, 396–401. [Google Scholar] [CrossRef] - Caithness Windfarm Information Forum. Available online: http://www.caithnesswindfarms.co.uk/ (accessed on 30 January 2012).
- Cotton, I.; Jenkins, N.; Pandiaraj, K. Lightning protection for wind turbine blades and bearings. Wind Energy
**2001**, 4, 23–37. [Google Scholar] [CrossRef] - Hameed, Z.; Hong, Y.; Cho, Y.; Ahn, S.; Song, C. Condition monitoring and fault detection of wind turbines and related algorithms: A review. Renew. Sustain. Energy Rev.
**2009**, 13, 1–39. [Google Scholar] [CrossRef] - Padgett, W. A multiplicative damage model for strength of fibrous composite materials. IEEE Trans. Reliab.
**1998**, 47, 46–52. [Google Scholar] [CrossRef] - Jørgensen, E.R.; Borum, K.K.; McGugan, M.; Thomsen, C.; Jensen, F.M.; Debel, C.; Sørensen, B.F. Full Scale Testing of Wind Turbine Blade to Failure-Flapwise Loading; RISØ National Laboratory: Copenhagen, Denmark, 2004. [Google Scholar]
- Jensen, F.M.; Falzon, B.; Ankersen, J.; Stang, H. Structural testing and numerical simulation of a 34m composite wind turbine blade. Compos. Struct.
**2006**, 76, 52–61. [Google Scholar] [CrossRef] - Borum, K.K.; McGugan, M.; Brondsted, P. Condition monitoring of wind turbine blades. In Proceedings of the 27th Riso International Symposium on Materials Science: Polymer Composite Materials for Wind Power Turbines, Denmark, 4–7 September 2006; pp. 139–145.
- Van Leeuwen, H.; van Delft, D.; Heijdra, J.; Braam, H.; Jo̸rgensen, E.; Lekou, D.; Vionis, P. Comparing Fatigue Strength from Full Scale Blade Tests with Coupon-Based Predictions; American Society of Mechanical Engineers: New York, NY, USA, 2002; pp. 1–9. [Google Scholar]
- Griffin, D.A.; Zuteck, M.D. Scaling of composite wind turbine blades for rotors of 80 to 120 meter diameter. J. Sol. Energy Eng.
**2001**, 123, 310–318. [Google Scholar] [CrossRef] - Herbert, G.J.; Iniyan, S.; Sreevalsan, E.; Rajapandian, S. A review of wind energy technologies. Renew. Sustain. Energy Rev.
**2007**, 11, 1117–1145. [Google Scholar] [CrossRef] - Gray, C.S.; Watson, S.J. Physics of failure approach to wind turbine condition based maintenance. Wind Energy
**2010**, 13, 395. [Google Scholar] [CrossRef] - Maughan, J.R. Technology and reliability improvements in GE’s 1.5 MW WT fleet. In Proceedings of the 2nd WT Reliability Workshop, Albuquerque, NM, USA, 17–18 September 2007.
- Liu, W.; Tang, B.; Jiang, Y. Status and problems of wind turbine structural health monitoring techniques in china. Renew. Energy
**2010**, 35, 1414–1418. [Google Scholar] [CrossRef] - Parent, O.; Ilinca, A. Anti-icing and de-icing techniques for wind turbines: Critical review. Cold Reg. Sci. Technol.
**2011**, 65, 88–96. [Google Scholar] [CrossRef] - Lu, B.; Li, Y.; Wu, X.; Yang, Z. A review of recent advances in wind turbine condition monitoring and fault diagnosis. In Proceedings of the Power Electronics and Machines in Wind Applications (PEMWA), Lincoln, NM, USA, 24–26 June 2009; pp. 1–7.
- Ribrant, J. Reliability Performance and Maintenance—A Survey of Failures in Wind Power Systems. Ph.D. Thesis, KTH School of Electrical Engineering, Stockholm, Sweden, 2006. [Google Scholar]
- Fischer, K.; Besnard, F.; Bertling, L. A limited-scope reliability-centred maintenance analysis of wind turbines. In Proceedings of the European Wind Energy Conference and Exhibition EWEA 2011, Brussels, Belgium, 14–17 March 2011; pp. 89–93.
- Feng, Y.; Qiu, Y.; Crabtree, C.J.; Long, H.; Tavner, P.J. Use of SCADA and CMS signals for failure detection and diagnosis of a wind turbine gearbox. In Proceedings of the European Wind Energy Conference and Exhibition 2011, Sheffield, UK, 2011; pp. 17–19.
- Entezami, M.; Hillmansen, S.; Weston, P.; Papaelias, M. Fault detection and diagnosis within a WT mechanical braking system. In Proceedings of the International Conference on Condition Monitoring and Machinery Failure Prevention Technologies (CM 2012 and MFPT 2011), Cardiff, UK, 20–22 June 2011.
- Popa, L.M.; Jensen, B.-B.; Ritchie, E.; Boldea, I. Condition monitoring of wind generators. In Proceedings of the Industry Applications Conference (38th IAS Annual Meeting), Salt Lake City, UT, USA, 12–16 October 2003; pp. 1839–1846.
- Douglas, H.; Pillay, P.; Ziarani, A. Broken rotor bar detection in induction machines with transient operating speeds. IEEE Trans. Energy Convers.
**2005**, 20, 135–141. [Google Scholar] [CrossRef] - Hansen, A.D.; Michalke, G. Fault ride-through capability of DFIG wind turbines. Renew. Energy
**2007**, 32, 1594–1610. [Google Scholar] [CrossRef] - Bazeos, N.; Hatzigeorgiou, G.; Hondros, I.; Karamaneas, H.; Karabalis, D.; Beskos, D. Static, seismic and stability analyses of a prototype wind turbine steel tower. Eng. Struct.
**2002**, 24, 1015–1025. [Google Scholar] [CrossRef] - Scottishpower SP Transmission Ltd. Black Law Wind Farm Extension Grid Connection Environmental Statement. Available online: http://www.spenergynetworks.co.uk/userfiles/file/Black_Law_Environmental_Statement_Windfarm_Extension_Grid_Connection.pdf (accessed on 20 July 2015).
- Van Bussel, G.; Zaaijer, M. Estimation of Turbine Reliability Figures within the DOWEC Project; DOWEC Report Nr. 10048; The Netherlands; Issue 4, October 2003. [Google Scholar]
- García, F.P.; Pedregal, D.J.; Roberts, C. Time series methods applied to failure prediction and detection. Reliab. Eng. Syst. Saf.
**2010**, 95, 698–703. [Google Scholar] [CrossRef] - Márquez, F.P.; Chacón Muñoz, J.M.; Tobias, A.M. B-spline approach for failure detection and diagnosis on railway point mechanisms case study. Qual. Eng.
**2015**, 27, 177–185. [Google Scholar] [CrossRef] - Tavner, P.; Qiu, Y.; Korogiannos, A.; Feng, Y. The Correlation between Wind Turbine Turbulence and Pitch Failure. In Proceedings of European Wind Energy Conference & Exhibition, Brussels, Belgium, 14–17 March 2011.
- Wu, A.P.; Chapman, P.L. Simple expressions for optimal current waveforms for permanent-magnet synchronous machine drives. IEEE Trans. Energy Conver.
**2005**, 20, 151–157. [Google Scholar] [CrossRef] - Spinato, F.; Tavner, P.J.; van Bussel, G.J.W.; Koutoulakos, E. IET Reliability of WT subassemblies. Renew. Power Gener.
**2009**, 3, 387–401. [Google Scholar] [CrossRef] - De la Hermosa González, R.R.; Márquez, F.P.G.; Dimlaye, V. Maintenance management of wind turbines structures via mfcs and wavelet transforms. Renew. Sustain. Energy Rev.
**2015**, 48, 472–482. [Google Scholar] [CrossRef] - Marquez, F.P.G. An approach to remote condition monitoring systems management. In Proceedings of the Institution of Engineering and Technology International Conference on Railway Condition Monitoring, Birmingham, UK, 29–30 November 2006; pp. 156–160.
- Márquez, F.P.G.; Pedregal, D.J.; Roberts, C. New methods for the condition monitoring of level crossings. Int. J. Syst. Sci.
**2015**, 46, 878–884. [Google Scholar] [CrossRef] - Vasquez, T. Weather Forecasting Handbook; Weather Graphics Technologies: Garland, TX, USA, 2002; ISBN 0970684029. [Google Scholar]
- Sørensen, J.D. Framework for risk-based planning of operation and maintenance for offshore wind turbines. Wind Energy
**2009**, 12, 493–506. [Google Scholar] [CrossRef] - Rothkopf, M.H.; McCarron, J.K.; Fromovitz, S. A weather model for simulating offshore construction alternatives. Manag. Sci.
**1974**, 20, 1345–1349. [Google Scholar] [CrossRef] - Yasseri, S.; Bahai, H.; Bazargan, H.; Aminzadeh, A. Prediction of safe sea-state using finite element method and artificial neural networks. Ocean Eng.
**2010**, 37, 200–207. [Google Scholar] [CrossRef] - Härdle, W.; Horowitz, J.; Kreiss, J.P. Bootstrap methods for time series. Int. Stat. Rev.
**2003**, 71, 435–459. [Google Scholar] [CrossRef]

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Pliego Marugán, A.; García Márquez, F.P.; Pinar Pérez, J.M.
Optimal Maintenance Management of Offshore Wind Farms. *Energies* **2016**, *9*, 46.
https://doi.org/10.3390/en9010046

**AMA Style**

Pliego Marugán A, García Márquez FP, Pinar Pérez JM.
Optimal Maintenance Management of Offshore Wind Farms. *Energies*. 2016; 9(1):46.
https://doi.org/10.3390/en9010046

**Chicago/Turabian Style**

Pliego Marugán, Alberto, Fausto Pedro García Márquez, and Jesús María Pinar Pérez.
2016. "Optimal Maintenance Management of Offshore Wind Farms" *Energies* 9, no. 1: 46.
https://doi.org/10.3390/en9010046