SUBSUELO Local SSB Garaje
3.13. SEGURIDAD EN OBRA - PREVENCIONISTA
The application of a torque transducer to a wind turbine would be prohibitively expensive, however, its global monitoring capability is attractive, but the analysis of electrical terminal signals, such as current and power, would be a viable alternative. These signals, particularly current, have been shown [14, 16] to be effective for the condition monitoring and diagnosis of electrical and mechanical faults in electrical machinery. However, research on their effectiveness for diagnosing mechanical faults in machinery connected to an electric machine demonstrates that they work [16] but are not proven in the practical environment and not yet applied to wind turbines.
It is easy to understand that mechanical drive train faults will lead to abnormal changes of torque Tpm, although according to (2) and (10), once Tpm changes, the phase current Ia of the
generator will respond accordingly. Moreover, because of the linear relationship between Tpm and Ia, the characteristic
frequency of a mechanical fault will also be present in the current signal. Thus, in theory, a drive train mechanical fault can be detected by investigating the generator current. In the following section, this prediction will be verified by analysing the current signal shown in Fig. 12 with the aid of a CWT and the results are shown inFig. 15.
FromFig. 15a, it is shown that the amplitude-modulation phenomenon presented in Fig. 13is observed in the CWT map of the current signal, as predicted above. Moreover, the characteristic frequency 300 Hz also appears, regardless of the connection state of the coils. These phenomena confirm the effectiveness of using the time-frequency characteristics of generator electrical signals to detect mechanical faults.
However, the power signal has been shown in[14, 17]to be a more generic condition monitoring signal than current Figure 14 Diagram of the gearbox used in the drive train of
the wind turbine
Figure 15 CWT maps of the electrical signals
a Current signal b Power signal
and is already monitored in the wind turbine for energy production purposes. The CWT map in Fig. 15b shows the amplitude-modulation phenomenon presented in
Fig. 13.
The experimental results ofFig. 15suggest that the CWT is a promising tool for accomplishing this task but it is computationally intensive.
4
Conclusions
The application of WTs to the condition monitoring and fault diagnosis of a synchronous wind turbine generator drive train has been investigated. Wind turbine signals contain harmonic-rich, instantaneous information because of the variable, stochastic aerodynamic forces on the machine. The effectiveness of the proposed approach has been validated by both theoretical analysis and experimental results from a test rig on signals subject to these stochastic variations. The following conclusions have been reached.
† The condition monitoring criterion proposed, based on the torque-speed characteristic of the synchronous machine, was tested on the wind turbine condition monitoring test rig and was shown to be effective for detecting electrical faults in a direct drive generator wind turbine drive train. † The criterion overcomes the stochastic effects of the highly variable signals in the assessment of wind turbine condition. † The use of DWT reduced the noise in the highly variable shaft torque and speed signals, and enhanced the effectiveness of the proposed condition monitoring criterion. † The CWT can extract time-frequency features correctly from the highly variable wind turbine signals.
† The experiment proves that it is also feasible to detect drive train mechanical faults by analysing electrical signals from the generator and, in particular, the power signal has been shown to be valid. This will be simpler and cheaper to obtain from a wind turbine generator than torque, vibration or proximeter measurements.
† The remaining problem will be how to analyse the power signal correctly using a valid signal processing technique. The work presented in this paper suggests that the CWT is a promising and effective technique for this purpose. Therefore the WT will play a role in solving the condition monitoring and fault diagnosis problems in wind turbines.
† The condition monitoring test rig has been demonstrated to be an effective way of testing condition monitoring algorithms.
5
Acknowledgments
Wenxian Yang was funded by the EPSRC Supergen Wind Energy Technologies Consortium, EP/D034566/1.
Michael Wilkinson was funded under the EPSRC Engineering Doctorate Scheme, GR/R99737/01, on Power Electronics Machines and Drives at Newcastle University but supervised at Durham University.
The Wind Turbine Condition Monitoring Test Rig was funded by the New and Renewable Energy Centre (NaREC), Blyth, Northumberland for which support the authors are grateful.
The authors are grateful for the advice and collaboration
of, Drs Simon Watson and Xiang Jianping of
Loughborough University.
Durham and Loughborough Universities, Garrad Hassan and NaREC are partners in the Supergen Wind Energy Technologies Consortium funded by the EPSRC.
6
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Published in IET Electric Power Applications Received on 23rd June 2007
Revised on 13th November 2007 doi: 10.1049/iet-epa:20070280
ISSN 1751-8660