Emerging trends and global challenges to predict drop in thermal performance of WTG gearbox

Abstract

The assessment of performance is the key role factor for the gearboxes in the field of wind turbine industry. The thermal performance depends upon the viscous forces of the oil; bearing with stand capacity of the gearboxes and unnecessary irrotational forces or movements caused during the rotation of the gears at intermediate stage and high speed stage. The generation of the power starts from 15 m/s to 25 m/s with the starting rpm of 15 rpm to 1150 rpm; from initial stage to high speed stage of the gearbox. Hence the reduction of torque at higher revolutions may tends to complete reduction in power; owing to the thermal performance drop occurred due to the reduction of oil viscosities; improper maintenance during the high load conditions. This may lead to cause higher maintenance costs for the investors who is coming in front to invest huge amount of money. This present experimental work deals with latest sensors utilization to analyse the data from master gear box to slave gear box. From the results it is observed that the implementation of latest technology sensors tends to improves the maintenance costs by 20% as compared to conventional sensors. Hence it is adviced to implement the latest technology sensors which is capable to measure the wind speed loads of 20 m/s to 45 m/s. This gives range of resolution for downloading the past data and predicting the futurized data for evaluating the thermal performance drop; leads to save the maintanence costs 20% as compared to conventional methods.

Keywords

• Breakdown Costs
• PAG Oil
• Rpm
• Sensors
• Thermal Performance Drop
• WTGS

References

1. [1] Zhongshan H, Ling T, Dong X, Yaozhong WEI. Prediction of oil temperature variations in a wind turbine gearbox based on PCA and an SPC-dynamic neural network hybrid. J Tsinghua Univ Sci Technol 2018;58:539–546.
2. [2] Kumar KS, Kalos PS, Akhtar MN, Shaik S, Sundara V, Fayaz H, et al. Experimental and theoretical analysis of exhaust manifold by uncoated and coated ceramics (Al2O3, TiO2 and ZrO2). Case Stud Therm Engineer 2023;50:103465. [CrossRef]
3. [3] Lin Y, Tu L, Liu H, Li W. Fault analysis of wind turbines in China. Renew Sustain Energy Rev 2016;55:482–490. [CrossRef]
4. [4] Babu JM, Kumar KS, Kumar RR, Ağbulut Ü, Razak A, Thakur D, et al. Production of HHO gas in the water-electrolysis unit and the influences of its introduction to CI engine along with diesel-biodiesel blends at varying injection pressures. Int J Hydrogen Energy 2024;52:865885. [CrossRef]
5. [5] Kurugundla SK, Muniamuthu S, Raja P, Mohan KR. Measurement of temperature flow analysis by condition monitoring system for WTG gearbox to evaluate the thermal performance associated with plant load factor. J Therm Engineer 2023;9:970−978. [CrossRef]
6. [6] Kumar KS, Babu JM, Prakash PJ, Nagappan M. Modal analysis of natural rubber-enhanced suspension system for vibration reduction. AIP Conf Proc 2023;2715:020019. [CrossRef]
7. [7] Nagappan M, Babu JM, Jayaprakash P, Kumar KS. The effects of additives to biodiesel and ethanol for enhancement of combustion characteristics and pollution reduction: A review. AIP Conf Proc 2023;2715:020017. [CrossRef]
8. [8] Kannan R, Palani S, Kurugundla SK. Numerical investigation on swirl flow through burner with the effect of rotation. AIP Conf Proc 2023;2766:020023. [CrossRef]

9. [9] Srimanickam B, Kumar S. Drying investigation of coriander seeds in a photovoltaic thermal collector with solar dryer. J Therm Engineer 2023;9:659−668. [CrossRef]
10. [10] Kumar KS, Bishnoi D. Pressure exertion and heat dissipation analysis on uncoated and ceramic (Al2O3, TiO2 and ZrO2) coated braking pads. Mater Today Proc 2023;74:774787. [CrossRef]
11. [11] Kumar KS, Babu JM, Venu H. Performance, combustion and emission characteristics of a single- cylinder DI diesel engine fuelled with lotus biodiesel-diesel-n-butanol blends. Int J Ambient Energy 2022;43:79417951. [CrossRef]
12. [12] Kumar KS, Babu JM, Venu H, Muthuraja A. Waste plastic as a source of biofuel for stationary diesel engine: a critical review. Int J Ambient Energy 2022;43:85778591. [CrossRef]
13. [13] Kumar SK, Muniamuthu S, Mohan A, Amirthalingam P, Anbu Muthuraja M. Effect of charging and discharging process of PCM with paraffin and Al2O3 additive subjected to three point temperature locations. J Ecol Eng 2022;23:3442. [CrossRef]
14. [14] Kumar S, Muniamuthu S, Tharanisrisakthi BT. An investigation to estimate the maximum yielding capability of power for mini venturi wind turbine. Ecol Eng Environ Technol 2022;23:72–78. [CrossRef]
15. [15] Muniamuthu S, Kumar KS, Raja K, Rupesh PL. Dynamic characterization of hybrid composite based on flax/E-glass epoxy composite plates. Mater Today Proc 2022;59:1786–1791. [CrossRef]
16. [16] Rupesh PL, Raja K, Kumar KS, Vijaydharan S, Reddy AMM, Kumar PD. Experimental evaluation of thermal stress on the surface of butterfly specimen through irreversible colour change of thermal paint. Mater Today Proc 2022;59:1768–1775. [CrossRef]
17. [17] Babu JM, Sarath Chandra M, Ravi Chandra Ganesh P, Jayaprakash P, Kumar KS, Nagappan M. Experimental evaluation of direct injection diesel engine performance and emissions with acacia biodiesel. Int J Ambient Energy 2022;43:7038–7045. [CrossRef]
18. [18] Vivekananthan V, Vignesh R, Vasanthaseelan S, Joel E, Kumar KS. Concrete bridge crack detection by image processing technique by using the improved OTSU method. Mater Today Proc 2022;74:10021007. [CrossRef]
19. [19] Karthickeyan NK, Arun S, Mohan GS, Kumar S. Structural analysis of exhaust manifold for 1500 Hp engine. Int J Mech Eng Technol 2017;8:379–387.
20. [20] Arun S, Nagoorvali SK, Kumar KS, Mohan GS. Automation of main bearing bolt and cap loosening machine for automobile crankshaft. Int J Mech Eng Technol 2017;8:41–49.
21. [21] Kumar KS, Palanisamy R, Aravindh S, Mohan GS. Design and analysis of windmill blades for domestic applications. Int J Mech Eng Technol 2017;8:25–36.
22. [22] Kumar KS, Muniamuthu DS, Arun S, Mohan A. Identification experimental analysis of noise and vibration reduction in windmill gear box for 5mw wind turbine. Int J Mech Eng Technol 2016;7:7685.
23. [23] Muniamuthu S, Raju NL, Sathishkumar S, Kumar KS. Investigation on mechanical properties of Al 7075-Al2O3 metal matrix composite. Int J Mech Eng Technol 2016;7:474–482.
24. [24] Sequeira C, Pacheco A, Galego P, Gorbeña E. Analysis of the efficiency of wind turbine gearboxes using the temperature variable. Renew Energy 2019;135:465–472. [CrossRef]
25. [25] Astolfi D, Scappaticci L, Terzi L. Fault diagnosis of wind turbine gearboxes through temperature and vibration data. Int J Renew Energy Res 2017;7:965–976.
26. [26] Fu J, Chu J, Guo P, Chen Z. Condition monitoring of wind turbine gearbox bearing based on deep learning model. IEEE Access 2019;7:5707857087. [CrossRef]
27. [27] Dhiman HS, Deb D, Carroll J, Muresan V, Unguresan ML. Wind turbine gearbox condition monitoring based on class of support vector regression models and residual analysis. Sensors 2020;20:6742. [CrossRef]
28. [28] Aafif Y, Chelbi A, Mifdal L, Dellagi S, Majdouline I. Optimal preventive maintenance strategies for a wind turbine gearbox. Energy Rep 2022;8:803814. [CrossRef]
29. [29] Zhang X, Zhong J, Li W, Bocian M. Nonlinear dynamic analysis of high-speed gear pair with wear fault and tooth contact temperature for a wind turbine gearbox. Mech Mach Theory 2022;173:104840. [CrossRef]
30. [30] Liu H, Yu C, Yu C. A new hybrid model based on secondary decomposition, reinforcement learning and SRU network for wind turbine gearbox oil temperature forecasting. Meas 2021;178:109347. [CrossRef]
31. [31] Kumar KS, Raju DBN, Arulmani J, Amirthalingam P. Design and structural analysis of liquefied cryogenic tank under seismic and operating loading. Int J Mech Eng Technol 2016;7:345–366.
32. [32] Guo P, Fu J, Yang X. Condition monitoring and fault diagnosis of wind turbines gearbox bearing temperature based on Kolmogorov-Smirnov test and convolutional neural network model. Energies 2018;11:2248. [CrossRef]
33. [33] Bangalore P, Letzgus S, Karlsson D, Patriksson M. An artificial neural network-based condition monitoring method for wind turbines, with application to the monitoring of the gearbox. Wind Energy 2017;20:14211438. [CrossRef]
34. [34] Papatzimos AK, Dawood T, Thies PR. Data insights from an offshore wind turbine gearbox replacement. J Phys Conf Ser 2018;1104:012003. [CrossRef]
35. [35] Carroll J, Koukoura S, McDonald A, Charalambous A, Weiss S, McArthur S. Wind turbine gearbox failure and remaining useful life prediction using machine learning techniques. Wind Energy 2019;22:360375. [CrossRef]
36. [36] Orozco R, Sheng S, Phillips C. Diagnostic models for wind turbine gearbox components using SCADA time series data. In: 2018 IEEE International Conference on Prognostics and Health Management (ICPHM), Jun 2018. pp. 19. [CrossRef]
37. [37] Wang H, Zhao X, Wang W. Fault diagnosis and prediction of wind turbine gearbox based on a new hybrid model. Environ Sci Pollut Res 2023;30:2450624520. [CrossRef]
38. [38] Zhao Q, Bao K, Wang J, Han Y, Wang J. An online hybrid model for temperature prediction of wind turbine gearbox components. Energies 2019;12:3920. [CrossRef]
39. [39] Tautz-Weinert J, Watson SJ. Using SCADA data for wind turbine condition monitoring–a review. IET Renew Power Gener 2017;11:382394. [CrossRef]
40. [40] Arabian-Hoseynabadi H, Oraee H, Tavner PJ. Failure modes and effects analysis (FMEA) for wind turbines. Int J Electr Power Energy Syst 2010;32:817824. [CrossRef]