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Analysing wind turbine state dynamics for fault diagnosis

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Supervisory Control And Data Acquisition (SCADA) systems have recently become ubiquitous in wind energy technology. SCADA data analysis actually can provide considerable performance improvement at low cost. This also boosts wind energy exploitation, because it enlarges short and long term economic sustainability of investments. Nevertheless, SCADA data analysis poses several scientific and technological challenges, mostly related to the vastness of the data sets required for significant analysis. Separating the signal from the noise is therefore a complex task. In the present work, this issue is tackled by the point of view of state dynamics of wind turbines. SCADA control systems often record superabundant and ambiguous information. Therefore, in this work it is shown that hierarchical classification of information and time discretization of the continuous motion of states are powerful tools. The time-discretized state dynamics is processed in the formulation of several indices for performance evaluation and fault diagnosis. The method is tested on the data set of a wind farm owned by Renvico s.r.l. and sited in Italy.
Czasopismo
Rocznik
Strony
19--25
Opis fizyczny
Bibliogr. 33 poz., rys., tab., wykr.
Twórcy
autor
  • University of Perugia, Department of Engineering, Perugia, Italy
  • DSI, University Guglielmo Marconi, Roma, Italy
autor
  • DSI, University Guglielmo Marconi, Roma, Italy
autor
  • University of Perugia, Department of Engineering, Perugia, Italy
autor
  • Renvico srl, Via San Gregorio 34, Milano 20124, Italy
Bibliografia
  • [1] Rifkin J. The zero marginal cost society: the internet of things, the collaborative commons and the eclipse of capitalism. Macmillan 2014.
  • [2] Castellani F, Garinei A, Terzi L, Astolfi D. Applied statistics for extreme wind estimate. Wind Energy 2015; 18 (4): 613-624.
  • [3] 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.
  • [4] Castellani F, Astolfi D, Terzi L, Hansen KS, Rodrigo JS. Analysing wind farm efficiency on complex terrains. Journal of Physics: Conference Series 524, 1.
  • [5] Rodrigo JS, Gancarski P, Arroyo RC, Moriarty P, Chuchfield M, Naughton JW, Hansen KS, Machefaux E, Koblitz T, Maguire E, et al. Iea-task 31 wakebench: Towards a protocol for wind farm flow model evaluation. part 1: Flow- over-terrain models. Journal of Physics: Conference Series 2014.
  • [6] Moriarty P, Rodrigo JS, Gancarski P, Chuchfield M, Naughton JW, Hansen KS, Machefaux E, Maguire E, Castellani F, Terzi L, et al. Iea-task 31 wakebench: Towards a protocol for wind farm flow model evaluation. part 2: Wind farm wake models. Journal of Physics: Conference Series 2014; 524.
  • [7] Castellani F, Burlando M., Taghizaded 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.
  • [8] Kusiak A, Zhang Z, Verma A. Prediction, operations and condition monitoring in wind energy. Energy 2013; 60: 1-12.
  • [9] Bin Lu, Yaoyu Li, Xin Wu, Yang Z. A review in recent advances in wind turbine condition monitoring and fault diagnosis. Power Electronics and Machines in Wind Applications 2009. PEMWA 2009 IEEE, 1-7.
  • [10] Tchakoua P, Wamkeue R, Ouhrouche M, Slaoui-Hasnaoui F, Tameghe TA, Ekemb G. Wind turbine condition monitoring: State-of-the-art review, new trends, and future challenges. Energies 2014; 7(4): 2595-2630.
  • [11] Astolfi D, Castellani F, Terzi L. Fault prevention and diagnosis through SCADA temperature data analysis of an onshore wind farm. Diagnostyka 2014; 15(2): 71:78.
  • [12] Feng Y, Qiu Y, Crabtree CJ, Long H, Tavner PJ. Monitoring wind turbine gearboxes. Wind Energy 2013; 16(5): 728-740.
  • [13] Kusiak A, Verma A. Analyzing bearing faults in wind turbines: A data-mining approach. Renewable Energy 2012; 48: 110-116.
  • [14] Wilkinson M, Darnell B. van Delft T, Harman K. Comparison of methods for wind turbine condition monitoring with SCADA data. Renewable Power Generation 2014; 8(4): 390-397.
  • [15] Zhang ZY, Wang KS. Wind turbine fault detection based on scada data analysis using ann. Advances in Manufacturing 2014; 2(1):70-78.
  • [16] Mc Kay P, Carriveau R, Ting DSK.:Wake impacts on downstream wind turbine performance and yaw alignment. Wind Energy 2013; 16: 221-223.
  • [17] Hansen K., Barthelmie R., Jensen J., Sommer A.: The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev offshore wind farm. Wind Energy 2012; 15(1): 183-196.
  • [18] Barthelmie R, Pryor S, Frandsen S, Hansen K, Schepers J, Rados K, Schlez W, Neubert A, Jensel 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(8): 1302-1317.
  • [19] Barthelmie R, Hansen K, Pryor S. Meteorological controls on wind turbine wakes. Proceeding of the IEEE 2013; 10(4): 1010-1019.
  • [20] Porté - Agel F, Wu YT, Chen CH. A numerical study of the effects of wind direction on turbine wakes and power losses in a large wind farm. Energies 2013; 6(10): 5297-5313.
  • [21] 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.
  • [22] 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.
  • [23] 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.
  • [24] Castellani F, Astolfi D, Piccioni E, Terzi L. Numerical and experimental methods for wake flow analysis in complex terrain. Journal of Physics: Conference Series, 625. IOP Publishing (2015).
  • [25] 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. Renewable Power Generation 2014; 8 (4): 367-379.
  • [26] Gaumond M, Réthoré PE, Ott S, Pena A, Bechmann A, Hansen KS. Evaluation of the wind direction uncertainty and its impact on wake modeling at the horns rev offshore wind farm. Wind Energy 2014; 17(8): 1169-1178.
  • [27] Bastankah M, Porté - Agel F. A new analytical model for wind-turbine wakes. Renewable Energy 2014; 70: 116-123.
  • [28] Wu YT, Porté - Agel F. Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm. Renewable Energy 2015; 75: 945-955.
  • [29] 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(4): 35-42.
  • [30] Astolfi D, Castellani F, Garinei A, Terzi L. Data mining techniques for performance analysis of onshore wind farms. Applied Energy 2015; 148: 220-233.
  • [31] Astolfi D, Castellani F, Terzi L. Mathematical methods for SCADA data mining of onshore wind farms: Performance evaluation and wake analysis. Wind Engineering 2016; 40(1): 69-85.
  • [32] Castellani F, Astolfi D, Mana M, Burlando M, Meißner C, Piccioni E. Wind power forecasting techniques in complex terrain: ANN vs. ANN-CFD hybrid approach. Journal of Physics: Conference Series 2016; 753(8): 082002.
  • [33] Castellani F, D'Elia G, Astolfi D, Mucchi E, Dalpiaz G, Terzi L. Analyzing wind turbine flow interaction through vibration data. Journal of Physics: Conference Series 2016; 753(11): 112008.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b717c2e8-cf8f-4609-9459-b3581fc19182
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