Fault Diagnosis of Wind Turbine Gearboxes Through Temperature and Vibration Data

Davide Astolfi, Lorenzo Scappaticci, Ludovico Terzi

Abstract


Gearbox faults are one of the most common and severe causes of energy losses in large wind turbine technology.  Further, degradation of gearboxes is an elusive phenomenon by the point of view of diagnostics. Yet, nowadays the widespread diffusion of Supervisory Control And Data Acquisition (SCADA) control systems is a keystone for fault prevention. It is desirable to conjugate accuracy of the outputs with intuitiveness and reasonable computational cost.  The present work deals with these issues: some methods are proposed for data mining of SCADA gearbox temperature and vibration measurements. In particular, a model based on Artificial Neural Networks (ANN) is proposed and its performances are compared against similar approaches in the literature. It arises that vibration analysis at the time scale of SCADA data isn’t effective for fault diagnosis, even if powered by the artificial intelligence of the ANN, while the proposed ANN model for gearbox temperatures is useful for early fault diagnosis. The method is tested on the data sets of a wind farm in southern Italy and it is shown that the method is capable of diagnosing incoming faults to three out of nine wind turbines of the site.


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Keywords


wind energy; wind turbines; Artificial Neural Network; gearboxes fault prevention; condition monitoring

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References


Condition Monitoring (2013) Exploring the innovations, challenges and potential of the products and services that keep wind turbines operating. Windpower Monthly, Haymarket Media Group, July 2013.

Wind Turbine Control Systems (2014) Exploring the capabilities of the latest systems, and the drivers and challenges for further development. Windpower Monthly, Haymarket Media Group, March 2014.

Bassett, K.; Carriveau, R.; Ting, D.S. Vibration analysis of 2.3 MW wind turbine operation using the discrete wavelet transform. Wind Engineering 2010, 34, 375–388.

Roshan-Ghias, A.; Shamsollahi, M.; Mobed, M.; Behzad, M. Estimation of modal parameters using bilinear joint time–frequency distributions. Mechanical Systems and Signal Processing 2007, 21, 2125–2136.

Lu, B.; Li, Y.; Wu, X.; Yang, Z. A review of recent advances in wind turbine condition monitoring and fault diagnosis. Power Electronics and Machines in Wind Applications, 2009. PEMWA 2009. IEEE. IEEE, 2009, pp. 1–7.

Jiang, Y.; Tang, B.; Qin, Y.; Liu, W. Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD. Renewable energy 2011, 36, 2146–2153.

Purarjomandlangrudi, A.; Nourbakhsh, G.; Esmalifalak, M.; Tan, A. Fault detection in wind turbine: a systematic literature review. Wind engineering 2013, 37, 535–547.

Zhang, Z.; Kusiak, A. Monitoring wind turbine vibration based on SCADA data. Journal of Solar Energy Engineering 2012, 134, 021004.

Siegel, D.; Zhao, W.; Lapira, E.; AbuAli, M.; Lee, J. A comparative study on vibration-based condition monitoring algorithms for wind turbine drive trains. Wind Energy 2014, 17, 695–714.

Sawalhi, N.; Randall, R.B. Gear parameter identification in a wind turbine gearbox using vibration signals. Mechanical Systems and Signal Processing 2014, 42, 368–376.

Tchakoua, P.; Wamkeue, R.; Ouhrouche, M.; Slaoui-Hasnaoui, F.; Tameghe, T.A.; Ekemb, G. Wind turbine condition monitoring: State-of-the-art review, new trends, and future challenges. Energies 2014, 7, 2595–2630.

Villa, L. F., Reñones, A., Perán, J. R., & De Miguel, L. J. (2011). Angular resampling for vibration analysis in wind turbines under non-linear speed fluctuation. Mechanical Systems and Signal Processing, 25(6), 2157-2168

Santos, P., Villa, L. F., Reñones, A., Bustillo, A., & Maudes, J. (2015). An SVM-based solution for fault detection in wind turbines. Sensors, 15(3), 5627-5648.

Castellani, F.; D’Elia, G.; Astolfi, D.; Mucchi, E.; Giorgio, D.; Terzi, L. Analyzing wind turbine flow interaction through vibration data. Journal of Physics: Conference Series. IOP Publishing, 2016, Vol. 753, p. 112008.

Tchakoua, P.; Wamkeue, R.; Ouhrouche, M.; Slaoui-Hasnaoui, F.; Tameghe, T.A.; Ekemb, G. Wind turbine condition monitoring: State-of-the-art review, new trends, and future challenges. Energies 2014, 7, 2595–2630.

Marvuglia, A.; Messineo, A. Monitoring of wind farms’ power curves using machine learning techniques. Applied Energy 2012, 98, 574–583.

Castellani, F.; Garinei, A.; Terzi, L.; Astolfi, D.; Moretti, M.; Lombardi, A. A new data mining approach for power performance verification of an on-shore wind farm. Diagnostyka 2013, 14.

Castellani, F.; Garinei, A.; Terzi, L.; Astolfi, D.; Gaudiosi, M. Improving windfarm operation practice through numerical modelling and supervisory control and data acquisition data analysis. IET Renewable Power Generation 2014, 8, 367–379.

Castellani, F.; Astolfi, D.; Terzi, L.; Hansen, K.S.; Rodrigo, J.S. Analysing wind farm efficiency on complex terrains. Journal of Physics: Conference Series. IOP Publishing, 2014, Vol. 524, p. 012142.

Astolfi, D.; Castellani, F.; Garinei, A.; Terzi, L. Data mining techniques for performance analysis of onshore wind farms. Applied Energy 2015, 148, 220–233.

Papatheou, E.; Dervilis, N.; Maguire, A.E.; Antoniadou, I.; Worden, K. A performance monitoring approach for the novel Lillgrund offshore wind farm. IEEE Transactions on Industrial Electronics 2015, 62, 6636–6644.

Astolfi, D.; Castellani, F.; Terzi, L. Mathematical methods for SCADA data mining of onshore wind farms: Performance evaluation and wake analysis. Wind Engineering 2016, p. 0309524X15624606.

Bartolini, N.; Scappaticci, L.; Garinei, A.; Becchetti, M.; Terzi, L. Analysing wind turbine state dynamics for fault diagnosis. Diagnostyka 2016, 17.

Schepers, G.; Barthelmie, R.; Rados, K.; Lange, B.; Schlez, W. Large off-shore windfarms: linking wake models with atmospheric boundary layer models. Wind Engineering 2001, 25, 307.

Rodrigo, J.; Gancarski, P.; Arroyo, R.; Moriarty, P.; Chuchfield, M.; Naughton, J.; Hansen, K.; Machefaux, E.; Koblitz, T.; Maguire, E.; others. IEA-Task 31 WAKEBENCH: Towards a protocol for wind farm flow model evaluation. Part 1: Flow-over-terrain models. Journal of Physics: Conference Series. IOP Publishing, 2014, Vol. 524, p. 012105.

Moriarty, P.; Rodrigo, J.S.; Gancarski, P.; Chuchfield, M.; Naughton, J.W.; Hansen, K.S.; Machefaux, E.; Maguire, E.; Castellani, F.; Terzi, L.; others. IEA-Task 31 WAKEBENCH: Towards a protocol for wind farm flow model evaluation. Part 2: Wind farm wake models. Journal of Physics: Conference Series. IOP Publishing, 2014, Vol. 524, p. 012185.

McKay, P.; Carriveau, R.; Ting, D.S.K. Wake impacts on downstream wind turbine performance and yaw alignment. Wind Energy 2013, 16, 221–234.

Crasto, G.; Castellani, F. Wakes calculation in a offshore wind farm. Wind Engineering 2013, 37, 269–280.

Porté-Agel, F.; Wu, Y.T.; Chen, C.H. A Numerical Study of the Effects of Wind Direction on Turbine Wakes and Power Losses in a Large Wind Farm. Energies 2013, 6, 5297–5313.

Barthelmie, R.; Hansen, K.; Pryor, S. Meteorological Controls on Wind Turbine Wakes. Proceedings of the IEEE 2013, 101, 1010–1019.

Barthelmie, R.; Pryor, S.; Frandsen, S.; Hansen, K.; Schepers, J.; Rados, K.; Schlez, W.; Neubert, A.; Jensen, L.; Neckelmann, S. Quantifying the Impact of Wind Turbine Wakes on Power Output at Offshore Wind Farms. Journal of Atmospheric and Oceanic Technology 2010, 27, 1302–1317.

Hansen, K.; Barthelmie, R.; Jensen, L.; Sommer, A. The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev wind farm. Wind Energy 2012, 15, 183–196.

Castellani, F.; Gravdahl, A.; Crasto, G.; Piccioni, E.; Vignaroli, A. A practical approach in the CFD simulation of off-shore wind farms through the actuator disc technique. Energy Procedia 2013, 35, 274–284.

Choudhry, A.; Mo, J.O.; Arjomandi, M.; Kelso, R. Effects of Wake Interaction on Downstream Wind Turbines. Wind Engineering 2014, 38, 535–547.

Marathe, N.; Swift, A.; Hirth, B.; Walker, R.; Schroeder, J. Characterizing power performance and wake of a wind turbine under yaw and blade pitch. Wind Energy 2015.

Zhong, H.; Du, P.; Tang, F.; Wang, L. Lagrangian dynamic large-eddy simulation of wind turbine near wakes combined with an actuator line method. Applied Energy 2015, 144, 224–233.

Castellani, F.; Astolfi, D.; Garinei, A.; Proietti, S.; Sdringola, P.; Terzi, L.; Desideri, U. How wind turbines alignment to wind direction affects efficiency? A case study through SCADA data mining. Energy Procedia 2015, 75, 697–703.

Moreno, P.; Gravdahl, A.R.; Romero, M. Wind flow over complex terrain: application of linear and CFD models. European wind energy conference and exhibition, 2003, pp. 16–19.

Lee, M.; Lee, S.H.; Hur, N.; Choi, C.K. A numerical simulation of flow field in a wind farm on complex terrain. Wind and Structures 2010, 13, 375.

Makridis, A.; Chick, J. Validation of a CFD model of wind turbine wakes with terrain effects. Journal of Wind Engineering and Industrial Aerodynamics 2013, 123, 12–29.

Castellani, F.; Astolfi, D.; Piccioni, E.; Terzi, L. Numerical and experimental methods for wake flow analysis in complex terrain. Journal of Physics: Conference Series. IOP Publishing, 2015, Vol. 625, p. 012042.

Castellani, F.; Astolfi, D.; Sdringola, P.; Proietti, S.; Terzi, L. Analyzing wind turbine directional behavior: SCADA data mining techniques for efficiency and power assessment. Applied Energy 2015.

Castellani, F.; Astolfi, D.; Burlando, M.; Terzi, L. Numerical modelling for wind farm operational assessment in complex terrain. Journal of Wind Engineering and Industrial Aerodynamics 2015, 147, 320–329.

Castellani, F.; Burlando, M.; Taghizadeh, S.; Astolfi, D.; Piccioni, E. Wind energy forecast in complex sites with a hybrid neural network and CFD based method. Energy Procedia 2014, 45, 188–197.

Castellani, F., Astolfi, D., Mana, M., Burlando, M., Meißner, C., & Piccioni, E. (2016, September). Wind power forecasting techniques in complex terrain: ANN vs. ANN-CFD hybrid approach. In Journal of Physics: Conference Series (Vol. 753, No. 8, p. 082002). IOP Publishing.

Watanabe, F.; Uchida, T. Micro-siting of Wind Turbine in Complex Terrain: Simplified Fatigue Life Prediction of Rotor bearing in Direct Drive Wind Turbines. Wind Engineering 2015, 39, 349–368.

Feng, Y.; Qiu, Y.; Crabtree, C.J.; Long, H.; Tavner, P.J. Monitoring wind turbine gearboxes. Wind Energy 2013, 16, 728–740.

Guo, P.; Infield, D.; Yang, X. Wind turbine generator condition-monitoring using temperature trend analysis. IEEE Transactions on sustainable energy 2012, 3, 124–133.

Zaher, A.; McArthur, S.; Infield, D.; Patel, Y. Online wind turbine fault detection through automated SCADA data analysis. Wind Energy 2009, 12, 574–593.

Kusiak, A.; Verma, A. Analyzing bearing faults in wind turbines: A data-mining approach. Renewable Energy 2012, 48, 110–116.

Hameed, Z.; Wang, K. Development of optimal maintenance strategies for offshore wind turbine by using artificial neural network. Wind Engineering 2012, 36, 353–364.

Chen, J., & Hao, G. (2011). Research on the fault diagnosis of wind turbine gearbox based on bayesian networks. In Practical Applications of Intelligent Systems (pp. 217-223). Springer Berlin Heidelberg.

Bangalore, P.; Tjernberg, L.B. An artificial neural network approach for early fault detection of gearbox bearings. IEEE Transactions on Smart Grid 2015, 6, 980–987.

Sun, P.; Li, J.; Wang, C.; Lei, X. A generalized model for wind turbine anomaly identification based on SCADA data. Applied Energy 2016, 168, 550–567.

Tautz-Weinert, J.; Watson, S.J. Comparison of different modelling approaches of drive train temperature for the purposes of wind turbine failure detection. Journal of Physics: Conference Series. IOP Publishing, 2016, Vol. 753, p. 072014.

Astolfi, D.; Castellani, F.; Terzi, L. Fault prevention and diagnosis through SCADA temperature data analysis of an onshore wind farm. Diagnostyka 2014, 15.

Ibrahim, R.K.; Tautz-Weinert, J.; Watson, S.J. Neural networks for wind turbine fault detection via current signature analysis 2016.


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