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.
• 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

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