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

Mana M., Piccioni E., Terzi L.

Diagnostyka

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2017

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Vol. 18, No. 1

9--16

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.

2

Diagnosis of wind turbine misalignment through SCADA data

Astolfi D., Castellani F., Scappaticci L., Terzi L.

Diagnostyka

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2017

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Vol. 18, No. 1

17--24

EN

Optimal alignment of wind turbines to the wind direction is a crucial condition for the quality of power output and for the health of the turbines. Actually, bad alignment can cause degraded performances and dangerous loads that can affect, on the long run, the mechanical safety of the wind turbine. Supervisory Control And Data Acquisition (SCADA) systems are becoming widespread in modern wind energy technology because of the appreciable costs – benefits ratio. The common time scale of SCADA, yet, usually is not effective for misalignment diagnosis because the wind varies too rapidly. For this reason, misalignment is often diagnosed using ad hoc techniques as LIDAR-based or spinner anemometers. In the present work, it is shown that very useful indications for the diagnosis of misalignment can be obtained also from the SCADA data, without invoking expensive supplementary control techniques. The method is validated on the data set of a wind farm sited in Italy.

3

Analysing wind turbine state dynamics for fault diagnosis

Bartolini N., Scappaticci L., Garinei A., Becchetti M., Terzi L.

Diagnostyka

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2016

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Vol. 17, No. 4

19--25

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.

4

Fault prevention and diagnosis through scada temperature data analysis of an onshore wind farm

Astolfi D., Castellani F., Terzi L.

Diagnostyka

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2014

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Vol. 15, No. 2

71--78

EN

Wind turbines, due to the distribution of the source, are an energy conversion system having low density on the territory, whose operational behaviour and production on the short term strongly depends on the stochastic nature of wind. They therefore need accurate assessment prior installation and careful condition monitoring in the operative phase. In the present work, smart post processing of Supervisory Control And Data Acquisition (SCADA) control system data sets is employed for fault prevention and diagnosis through the analysis of the temperatures of the machines. Automatic routines are developed for monitoring the evolution of all the temperature SCADA channels against power production. The methods are tested on an onshore wind farm sited in southern Italy, where nine turbines with 2 MW rated power are installed. The tests are performed both ex post and in real time: it is shown that in the former case, a major mechanical problem is detected, and in the latter case a significant problem to the cooling system is identified before compromising turbine functionality.

5

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

Castellani F., Garinei A., Terzi L., Astolfi D., Moretti M., Lombardi A.

Diagnostyka

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2013

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Vol. 14, No. 4

35--42

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.