Operational-Condition-Independent Criteria Dedicated to Monitoring Wind Turbine Generators

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

PDF
Published Nov 1, 2020
DOI https://doi.org/10.36001/ijphm.2013.v4i3.2133
Wenxian Yang
School of Marine Science and Technology, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK
Shuangwen Sheng
National Renewable Energy Laboratory, Golden, CO, 80401, USA
Richard Court
National Renewable Energy Center, Blyth, Northumberland, NE24 1LZ, UK

Abstract

Condition monitoring is beneficial to the wind industry for both land-based and offshore plants. However, because of the variations in operational conditions, its potential has not been fully explored. There is a need to develop an operational-condition-independent condition monitoring technique, which has motivated the research presented here. In this paper, three operational-condition-independent criteria are developed. The criteria accomplish the condition monitoring by analyzing the wind turbine electrical signals in the time domain. Therefore, they are simple to calculate and ideal for online use. All proposed criteria were tested through both simulated and practical experiments, showing that these criteria not only provide a solution for detecting both mechanical and electrical faults that occur in wind turbine generators, but that they provide a potential tool for diagnosing generator winding faults.

Abstract 163 | PDF Downloads 173

##plugins.themes.bootstrap3.article.details##

Keywords

condition monitoring, Wind Turbine, wind turbine generators

References
BVG Associates (2011). A guide to an offshore wind farm.
Cardoso, A.J.M., & Saraiva, E.S. (1993). Computer-aided detection of airgap eccentricity in operating three-phase induction motors by Park’s vector approach. IEEE Transactions on Industry Applications, vol.29, no.5, pp.897901.
Feng, Y., Tavner, P., & Long, H. (2010). Early experiences with UK Round 1 offshore wind farms. Proceedings of the Institution of Civil Engineers: Energy, vol.163, no.4, pp.167181.
McMillan, D., & Ault, G.W. (2007). Quantification of condition monitoring benefit for offshore wind turbines. Wind Engineering, vol.31, no.4, pp.267285.
Nejjari, H., & Benbouzid, M.E.H. (2000). Monitoring and diagnosis of induction motors electrical faults using a current Park’s vector pattern learning approach. IEEE Transactions on Industry Applications, vol.36, no.3, pp.730735.
Tavner, P. (2008). Review of condition monitoring of rotating electrical machines. IET Electric Power Applications, vol.2, no.4, pp.215247.
Xiang, D., Ran, L., Tavner, P., & Yang, S. (2006). Control of doubly fed induction generator in a wind turbine during grid fault ride-through. IEEE Transactions on Energy Conversion, vol.21, no.3, pp.652662.
Yang, Wenxian, Tavner, P., Crabtree, C., & Wilkinson, M.R. (2010). Cost-effective condition monitoring for wind turbines. IEEE Transactions on Industrial Electronics, vol.57, no.1, pp.263271.
Yang, Wenxian, Court, R., Tavner, P., & Crabtree, C. (2011). Bivariate empirical mode decomposition and its contribution to wind turbine condition monitoring. Journal of Sound and Vibration, vol.330, no.15, pp.37663782.
Yang, Wenxian, Tavner, P., Crabtree, C., Feng, Y., & Qiu, Y. (2012). Wind turbine condition monitoring: Technical and commercial challenges. Wind Energy, In Press.
Yang, Wenxian, Tavner, P., & Court, R. (2013). An online technique for condition monitoring the induction generators used in wind and marine turbines. Mechanical Systems and Signal Processing, vol.38, no.1, pp.103-112.
Issue
Vol. 4 No. 3 (2013): IJPHM Special Issue on Wind Turbine PHM
Section
Technical Papers