Wind turbine fault diagnosis through temperature analysis: an artificial neural network approach

• Autorzy: Mana M. , Piccioni E. , Terzi L.
• Języki publikacji: EN

Abstrakty

EN

Wind turbines undergo dynamic loads along all the phases of transformation of wind kinetic energy into power output to be fed into the grid. Gearbox breakdowns are one of the most common and most severe causes of energy losses and it is therefore crucial to prevent and forecast them. Straightforward vibration analysis is very demanding by the point of view of technology, costs and complexity of signal denoising. A considerable keystone in fault diagnosis is the analysis of Supervisory Control And Data Acquisition (SCADA) systems. In particular, thermal behaviour of wind turbines fits well with the common time scale of SCADA data; heating trends are fairly responsive as a consequence of rotor vibration. Machine learning techniques applied to SCADA data are very powerful in reconstructing inputs - output dependency. On these grounds, in this work an Artificial Neural Network approach is proposed for early diagnosis of gearbox faults. The method is validated on the data of a wind farm operating in Italy. It is shown that the method is capable in recognizing incoming faults with a very manageable advance also with data on short time scales.

Słowa kluczowe

EN

artificial neural networks; wind turbine; fault diagnosis; wind energy; SCADA

PL

sztuczna sieć neuronowa; turbina wiatrowa; energia wiatru; diagnostyka uszkodzeń; SCADA

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2017
• Tom: Vol. 18, No. 1
• Strony: 9--16
• Opis fizyczny: Bibliogr. 41 poz., rys., wykr.

Twórcy

autor

Mana M.

• University of Perugia, Department of Engineering, Perugia, Italy, Via G. Duranti 93, 06125 Perugia, Italy & WindSim AS, Fjordgaten 15 N-3125 Tønsberg, Nowary

autor

Piccioni E.

• University of Perugia, Department of Engineering, Perugia, Italy, Via G. Duranti 93, 06125 Perugia, Italy

autor

Terzi L.

• Renvico srl, Via San Gregorio 34, Milano 20124, Italy

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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).