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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-4fa4c02a-e9a7-4c53-a24e-1e8c0511e2c6

Czasopismo

Diagnostyka

Tytuł artykułu

A new data mining approach for power performance verification of an on-shore wind farm

Autorzy Castellani, F.  Garinei, A.  Terzi, L.  Astolfi, D.  Moretti, M.  Lombardi, A. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
Abstrakty
EN Monitoring wind energy production is fundamental to improve the performances of a wind farm during the operational phase. In order to perform reliable operational analysis, data mining of all available information spreading out from turbine control systems is required. In this work a SCADA (Supervisory Control And Data Acquisition) data analysis was performed on a small wind farm and new post-processing methods are proposed for condition monitoring of the aerogenerators. Indicators are defined to detect the malfunctioning of a wind turbine and to select meaningful data to investigate the causes of the anomalous behaviour of a turbine. The operating state database is used to collect information about the proper power production of a wind turbine and a number map has been codified for converting the performance analysis problem into a purely numerical one. Statistical analysis on the number map clearly helps in detecting operational anomalies, providing diagnosis for their reasons. The most operationally stressed turbines are systematically detected through the proposal of two Malfunctioning Indices. Results demonstrate that a proper selection of the SCADA data can be very useful to measure the real performances of a wind farm and thus to define optimal repair/replacement and preventive maintenance policies that play a major role in case of energy production.
Słowa kluczowe
EN wind energy   renewable energy   wind turbine performance   data mining   SCADA database   control systems   fault diagnosis   performance optimization   wind turbine power output  
Wydawca Polskie Towarzystwo Diagnostyki Technicznej
Czasopismo Diagnostyka
Rocznik 2013
Tom Vol. 14, No. 4
Strony 35--42
Opis fizyczny Bibliogr. 15 poz., rys., tab., wykr.
Twórcy
autor Castellani, F.
  • University of Perugia, Department of Industrial Engineering, Perugia, Italy
autor Garinei, A.
  • DMII, Università degli Studi Guglielmo Marconi, Roma, 00193, Italy
autor Terzi, L.
  • Sorgenia Green srl, Via Viviani 12, Milano, 20124, Italy
autor Astolfi, D.
  • University of Perugia, Department of Industrial Engineering, Perugia, Italy
autor Moretti, M.
  • University of Perugia, Department of Industrial Engineering, Perugia, Italy
autor Lombardi, A.
  • Sorgenia Green srl, Via Viviani 12, Milano, 20124, Italy
Bibliografia
[1] Catmull S.: Self-Organising Map Based Condition Monitoring of Wind Turbines. EWEA 2011 - 14-17 March 2011, Brussels, Belgium.
[2] Lapira E., Brisset D., Davari Ardakani H., Siegel D., Lee J.: Wind turbine performance assessment using multi-regime modeling approach. Renewable Energy 45 (2012) 86-95.
[3] Kusiak A., Li W.: The prediction and diagnosis of wind turbine faults. Renewable Energy 36 (2011) 16.
[4] Carvalho H., Gaião M., Guedes R.: Wind Farm Power Performance Test, in the scope of the IEC 61400-12.3. EWEC 2010 European Wind Energy Conference & Exhibition Proceedings - Tuesday 20 - Friday 23 April 2010, Warsaw, Poland.
[5] Gill S., Stephen B. and Galloway S.: Wind Turbine Condition Assessment Through Power Curve Copula Modeling. Ieee Transactions On Sustainable Energy, Vol. 3, No. 1, January 2012.
[6] Paiva L. T., Veiga Rodrigues C., Palma J.M.L.M.: Determining wind turbine power curves based on operating conditions. Wind Energy, August 2013, DOI: 10.1002/we.1651.
[7] Kusiak A., Verna A.: Monitoring wind farms with performance curves. IEEE Transactions On Sustainable Energy Vol. 4, No. 1, January 2012.
[8] Gallardo-Calles J. M., Colmenar-Santos A., Ontanon-Ruiz J., Castro-Gil M.: Wind control centres: State of the art. Renewable Energy Volume 51 March 2013.
[9] Schlechtingen M., Ferreira Santos I., Achiche S.: Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description. Applied Soft Computing Volume 13 January 2013.
[10] Elijorde F.I., Moon D., Ahn S., Kim S., Lee J.: Wind turbine performance monitoring based on hybrid clustering method. Future Information Communication Technology and Applications, Lecture Notes in Electrical Engineering Volume 235, 2013, pp 317-32.
[11] Schlechtingen M., Ferreira Santos I., Achiche S.: Using Data-Mining Approaches for Wind Turbine Power Curve Monitoring: A Comparative Study. Sustainable Energy, February 2013, Volume PP, Issue 99, pp. 1-9, DOI:10.1109/TSTE.2013.2241797.
[12] Bangalore P., Bertling L.: An approach for self evolving neural network based algorithm for fault prognosis in wind turbine- IEEE Powertech Grenoble 2013 Proceedings.
[13] Borchersen A.B.: Predicting faults in wind turbines using SCADA data. 51st Aiaa Aerospace Sciences Meeting Including The New Horizons Forum And Aerospace Exposition, January 2013. DOI: 10.2514/6.2013-313.
[14] Yang W.,Court R., Jiang J.: Wind turbine condition monitoring by the approach of SCADA data analysis. Renewable Energy, May 2013, Volume 53, pp. 365-376, DOI:10.1016/j.renene.2012.11.030.
[15] Castellani F. , Vignaroli A.: An application of the actuator disc model for wind turbine wakes calculations. Applied Energy - Elsevier - ISSN 0306-2619 - 2012 - 10.1016/j.apenergy.2012.04.039.
Kolekcja BazTech
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