Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Condition Assessment for Wind Turbines with Doubly Fed Induction Generators Based on SCADA Data

  • Sun, Peng (State Grid Henan Electrical Power Research Institute) ;
  • Li, Jian (State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University) ;
  • Wang, Caisheng (Department of Electrical and Computer Engineering, Wayne State University) ;
  • Yan, Yonglong (State Grid Wulumuqi Electric Supply Company)
  • Received : 2016.04.19
  • Accepted : 2016.10.28
  • Published : 2017.03.01
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Abstract

This paper presents an effective approach for wind turbine (WT) condition assessment based on the data collected from wind farm supervisory control and data acquisition (SCADA) system. Three types of assessment indices are determined based on the monitoring parameters obtained from the SCADA system. Neural Networks (NNs) are used to establish prediction models for the assessment indices that are dependent on environmental conditions such as ambient temperature and wind speed. An abnormal level index (ALI) is defined to quantify the abnormal level of the proposed indices. Prediction errors of the prediction models follow a normal distribution. Thus, the ALIs can be calculated based on the probability density function of normal distribution. For other assessment indices, the ALIs are calculated by the nonparametric estimation based cumulative probability density function. A Back-Propagation NN (BPNN) algorithm is used for the overall WT condition assessment. The inputs to the BPNN are the ALIs of the proposed indices. The network structure and the number of nodes in the hidden layer are carefully chosen when the BPNN model is being trained. The condition assessment method has been used for real 1.5 MW WTs with doubly fed induction generators. Results show that the proposed assessment method could effectively predict the change of operating conditions prior to fault occurrences and provide early alarming of the developing faults of WTs.

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References

  1. Chehouri A and Younes R: "Review of performance optimization techniques applied to wind turbines", Applied Energy, vol. 142, pp. 361-388, 2015. https://doi.org/10.1016/j.apenergy.2014.12.043
  2. Yuan XM: "Overview of problems in large-scale wind integrations", Journal of Modern Power Systems and Clean Energy, vol. 1, no. 1, pp. 22-25, 2013. https://doi.org/10.1007/s40565-013-0010-6
  3. Gil MDP, Gomis-bellmunt O, and Sumper A: "Technical and economic assessment of offshore wind power plants based on variable frequency operation of clusters with a single power converter", Applied Energy, vol. 125, no. 21, pp. 218-229, 2014. https://doi.org/10.1016/j.apenergy.2014.03.031
  4. Yang WX, Tavner PJ, Crabtree CJ, Feng Y, and Qiu Y: "Wind turbine condition monitoring: technical and commercial challenges", Wind Energy, vol. 17, no. 5, pp. 673-693, 2014. https://doi.org/10.1002/we.1508
  5. Johan R and Margareta BL: "Survey of failures in wind power systems with focus on Swedish wind power plants during 1997-2005", IEEE Transactions on Energy Conversion, vol. 22, no. 1, pp. 167-173, 2007. https://doi.org/10.1109/TEC.2006.889614
  6. Milborrow D: "Operation and maintenance costs compared and revealed", Wind Stats, vol. 19, no. 3, pp. 3, 2006.
  7. Caselitz P, Bussel GW, and Spinato F: "Rotor condition monitoring for improved operational safety of offshore wind energy converters", Journal of Solar Energy Engineering, vol. 127, no. 2, pp. 253-261, 2005. https://doi.org/10.1115/1.1850485
  8. Soua S, Lieshout PV, Perera A, Gan TH, and Bridge B: "Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a prerequisite for effective condition monitoring", Renew Energy, vol. 51, no. 2, pp. 175-181, 2013. https://doi.org/10.1016/j.renene.2012.07.004
  9. Becker E and Posta P: "Keeping the blades tunning: Condition monitoring of wind turbine gearbox", Refocus, vol. 7, no. 2, pp. 26-32, 2013.
  10. Verbruggen TW: "Wind turbine operation and maintence based on condition monitoring", WT-${\Omega}$. Final Report, ECN-C-03-047, Energy Research Center, Netherlands, 2003.
  11. Schlechtingen M, Santos IF, and Achiche S: "Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description", Applied Soft Computing Journal, vol. 13, no. 1, pp. 259-270, 2013. https://doi.org/10.1016/j.asoc.2012.08.033
  12. Kusiak A and Verma A: "A data-mining approach to monitoring wind turbines", IEEE Transactions on Sustainable Energy, vol. 3, no. 1, pp. 150-157, 2012. https://doi.org/10.1109/TSTE.2011.2163177
  13. Nilsson J, Bertling L: "Maintenance management of wind power systems using condition monitoring systems - Life cycle cost analysis for two case studies", IEEE Transactions on Energy Conversion, vol. 22, no. 1, pp. 223-229, 2007. https://doi.org/10.1109/TEC.2006.889623
  14. Kusiak A, Verma A and Wei XP: "Wind turbine capacity frontier from SCADA", Wind Systems Magazine, vol. 3, no. 9, pp. 36-39, 2012.
  15. Hameed Z, Hong YS, Cho YM, Ahn SH, and Song CK. "Condition monitoring and fault detection of wind turbines and related algorithms: A review", Wind Energy, vol. 13, no. 1, pp. 1-39, 2009. https://doi.org/10.1002/we.330
  16. Lapira E, Brisset D, Ardakani HD, Siegel D, and Lee J: "Wind turbine performance assessment using multi-regime modeling approach", Renew Energy, vol. 45, no. 3, pp. 86-95, 2012. https://doi.org/10.1016/j.renene.2012.02.018
  17. Schlechtingen M, Santos IF, and Achiche S: "Using data-mining approaches for wind turbine power curve monitoring: A comparative study", IEEE Transactions on Sustainable Energy, vol. 4, no. 3, pp. 671-679, 2013. https://doi.org/10.1109/TSTE.2013.2241797
  18. Kusiak A, Zheng HY, and Song Z: "Models for monitoring wind farm power", Renewable Energy, vol. 34, no. 3, pp. 583-590, 2009. https://doi.org/10.1016/j.renene.2008.05.032
  19. Marvuglia A, and Messineo A: "Monitoring of wind farms power curves using machine learning techniques", Applied Energy, vol. 98, pp. 574-583, 2012. https://doi.org/10.1016/j.apenergy.2012.04.037
  20. Jia XD, Jin C, Buzza M, Wang W, Lee J: "Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves", Renewable Energy, vol. 99, pp. 1191-1201, 2016. https://doi.org/10.1016/j.renene.2016.08.018
  21. Kusiak A and Verma A: "The prediction and diagnosis of wind turbine faults", Renewable Energy, vol. 36, no. 1, pp. 16-23, 2011. https://doi.org/10.1016/j.renene.2010.05.014
  22. Lydia M, Kumar SS, Selvakumar AI, and Kumer GEP: "A comprehensive review on wind turbine power curve modeling techniques", Renewable and Sustainable Energy Reviews, vol. 30, no. 2, pp. 452-460, 2014. https://doi.org/10.1016/j.rser.2013.10.030
  23. Yang WX, Court R, and Jiang JS: "Wind turbine condition monitoring by the approach of SCADA data analysis", Renewable Energy, vol. 53, no. 9, pp. 365-376, 2013. https://doi.org/10.1016/j.renene.2012.11.030
  24. Ata R: "Artificial neural networks applications in wind energy systems: a review", Renewable and Sustainable Energy Reviews, vol. 49, no. 534-562, 2015. https://doi.org/10.1016/j.rser.2015.04.166
  25. Schlechtingen M and Santos IF: "Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection", Mechanical Systems & Signal Processing, vol. 25, no. 5, pp. 1849-1875, 2011. https://doi.org/10.1016/j.ymssp.2010.12.007
  26. Zaher A, McArthur SDJ, and Infield DG: "Online wind turbine fault detection through automated SCADA data analysis", Wind Energy, vol. 12, no. 6, pp. 574-593, 2009. https://doi.org/10.1002/we.319
  27. Liu YQ, Shi J, Yang YP, Lee WJ: "Short-term windpower prediction based on wavelet transform-support vector machine and statistic-characteristics analysis", IEEE Transactions on Industry Applications, vol. 48, no. 4, pp. 1136-1141, 2012. https://doi.org/10.1109/TIA.2012.2199449
  28. Chen B, Peter CM, and Peter JT: "Automated on-line fault prognosis for wind turbine pitch systems using supervisory control and data acquisition", IET Renewable Power Generation, vol. 9, 503-513, 2015. https://doi.org/10.1049/iet-rpg.2014.0181
  29. Wang L, Zhang ZJ, Long H, Xu J, Liu RH: "Wind turbine gearbox failure identification with deep neural networks", IEEE Transactions on Industrial Informatics, to be published.
  30. Guo P, Infield D, and Yang XY: "Wind turbine generator condition-monitoring using temperature trend analysis", IEEE Transactions on Sustainable Energy, vol. 3, no. 1, pp. 124-133, 2012. https://doi.org/10.1109/TSTE.2011.2163430
  31. Kusiak A and Verma A: "Monitoring wind farms with performance curves", IEEE Transactions on Sustainable Energy, vol. 4, no. 1, pp. 192-199, 2013. https://doi.org/10.1109/TSTE.2012.2212470
  32. Cross P and Ma XD: "Nonlinear system identification for model-based condition monitoring of wind turbines", Renewable Energy, vol. 71, no. 11, pp. 166-175, 2014. https://doi.org/10.1016/j.renene.2014.05.035
  33. Kusiak A, and Verma A: "Analyzing bearing faults in wind turbines: A data-mining approach", Renewable Energy, vol. 48, no. 6, pp. 110-116, 2012. https://doi.org/10.1016/j.renene.2012.04.020
  34. Schlechtingen M, and Santos IF: "Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection", Mechanical Systems & Signal Processing, vol. 25, no. 5, pp. 1849-1875, 2011. https://doi.org/10.1016/j.ymssp.2010.12.007
  35. Liao RJ, Zheng HB, and Grzybowski S: "An integrated decision-making model for condition assessment of power transformers using fuzzy approach and evidential reasoning", IEEE Transactions on Power Delivery, vol. 26, no. 2, pp. 1111-1118, 2011. https://doi.org/10.1109/TPWRD.2010.2096482
  36. Qian Z and Yan Y: "Fuzzy synthetic method for life assessment of power transformer", IEE Proceedings - Science Measurement and Technology, vol. 151, no. 3, pp. 175-180, 2004. https://doi.org/10.1049/ip-smt:20040239
  37. Li H, Hu YG, Chen Z, Ji HT, and Zhan B: "An improved fuzzy synthetic condition assessment of a wind turbine generator system", International Journal of Electrical Power and Energy Systems, vol. 45, no. 1, pp. 468-476, 2013. https://doi.org/10.1016/j.ijepes.2012.09.014
  38. Yampikulsakul N, Byon E, Huang S, Sheng S, and You M: "Condition monitoring of wind power system with nonparametric regression analysis", IEEE Transactions on Energy Conversion, vol. 29, no. 2, pp. 288-299, 2014. https://doi.org/10.1109/TEC.2013.2295301
  39. Xiang DW, Ran L, Tavner P and Bryant A: "Monitoring Solder Fatigue in a Power Module Using Case-Above-Ambient Temperature Rise", IEEE Transactions on Industry Applications, vol. 47, no. 6, pp. 2578-2591, 2012. https://doi.org/10.1109/TIA.2011.2168556
  40. Spera DA: "Wind turbine technology: fundamental concepts of wind turbine engineering", New York, ASME, USA (1994)
  41. Parzen E: "On the estimation of a probability density function and the mode", The Annals of Mathematical Statistics, vol. 33, no. 3, pp. 1065-1076, 1962. https://doi.org/10.1214/aoms/1177704472
  42. Silverman BW: "Density Estimation for Statistics and Data Analysis", New York, Chapman and Hall, USA, 1986.
  43. Parzen E: "On the estimation of a probability density function and the mode", The Annals of Mathematical Statistics, vol. 33, no. 3, pp. 1065-1076, 1962. https://doi.org/10.1214/aoms/1177704472
  44. Yin Z and Zhang JH: "Operator functional state classification using least-square support vector machine based recursive feature elimination technique", Computer Methods and Programs in Biomedicine, vol. 113, no. 1, pp. 101-115, 2014. https://doi.org/10.1016/j.cmpb.2013.09.007
  45. Zheng HB, Liao RJ, Grzybowski S, and Yang LJ: "Fault diagnosis of power transformers using multiclass least square support vector machines classifiers with particle swarm optimization" IET Electric Power Applications, vol. 5, no. 9, pp. 691-696, 2011. https://doi.org/10.1049/iet-epa.2010.0298