Abstract
Background: Compared with traditional power generation systems, wind turbines have more units and work in a more harsh environment, and thus have a relatively high failure rate. Among blade faults, the faults of high-strength bolts are often difficult to detect and need to be analyzed with high-precision sensors and other equipment. However, there is still little research on blade faults.
Methods: The improved complete ensemble empirical mode decomposition (ICEEMD) model is used to extract the fault features from the time series data, and then combined with the support vector machine optimized by sparrow search algorithm (SSA-SVM) to diagnose the bolt faults of different degrees, so as to achieve the purpose of early warning.
Results: The results show that the ICEEMD model used in this paper can extract the bolt fault signals well, and the SSA-SVM model has a shorter optimization time and more accurate classification compared with models such as PSO-SVM.
Conclusion: The hybrid model proposed in this paper is important for bolt fault diagnosis of operation monitoring class.
Graphical Abstract
[1]
B. Xu, and B. Lin, "Do we really understand the development of China’s new energy industry?", Energy Econ., vol. 74, pp. 733-745, 2018.
[http://dx.doi.org/10.1016/j.eneco.2018.07.024]
[http://dx.doi.org/10.1016/j.eneco.2018.07.024]
[2]
Y. Pan, and F. Dong, "Dynamic evolution and driving factors of new energy development: Fresh evidence from China", Technol. Forecast. Soc. Change, vol. 176, p. 121475, 2022.
[http://dx.doi.org/10.1016/j.techfore.2022.121475]
[http://dx.doi.org/10.1016/j.techfore.2022.121475]
[3]
C. Thellbro, T. Bjärstig, J. Svensson, W. Neumann, and A. Zachrisson, "Readiness and planning for more wind power: Municipalities as key actors implementing national strategies", Cleaner Energy Systems, vol. 3, p. 100040, 2022.
[http://dx.doi.org/10.1016/j.cles.2022.100040]
[http://dx.doi.org/10.1016/j.cles.2022.100040]
[4]
J.Y. He, Q.S. Li, P.W. Chan, and X.D. Zhao, "Assessment of future wind resources under climate change using a multi-model and multi-method ensemble approach", Appl. Energy, vol. 329, p. 120290, 2023.
[http://dx.doi.org/10.1016/j.apenergy.2022.120290]
[http://dx.doi.org/10.1016/j.apenergy.2022.120290]
[5]
K. Xu, J. Chang, W. Zhou, S. Li, Z. Shi, H. Zhu, Y. Chen, and K. Guo, "A comprehensive estimate of life cycle greenhouse gas emissions from onshore wind energy in China", J. Clean. Prod., vol. 338, p. 130683, 2022.
[http://dx.doi.org/10.1016/j.jclepro.2022.130683]
[http://dx.doi.org/10.1016/j.jclepro.2022.130683]
[6]
F. Dong, and W. Li, "Research on the coupling coordination degree of “upstream-midstream-downstream” of China’s wind power industry chain", J. Clean. Prod., vol. 283, p. 124633, 2021.
[http://dx.doi.org/10.1016/j.jclepro.2020.124633]
[http://dx.doi.org/10.1016/j.jclepro.2020.124633]
[7]
F. Li, X. Li, B. Liu, D. Shang, and H. Ma, "Dynamic modeling and vibration analysis of offshore wind turbine rotor system with insulated bearing under inclined shaft current damage", Ocean Eng., vol. 280, p. 114654, 2023.
[http://dx.doi.org/10.1016/j.oceaneng.2023.114654]
[http://dx.doi.org/10.1016/j.oceaneng.2023.114654]
[8]
H. Li, and C. Guedes Soares, "Assessment of failure rates and reliability of floating offshore wind turbines", Reliab. Eng. Syst. Saf., vol. 228, p. 108777, 2022.
[http://dx.doi.org/10.1016/j.ress.2022.108777]
[http://dx.doi.org/10.1016/j.ress.2022.108777]
[9]
N. Sarma, P.M. Tuohy, O. Özgönenel, and S. Djurović, "Early life failure modes and downtime analysis of onshore type-III wind turbines in Turkey", Electr. Power Syst. Res., vol. 216, p. 108956, 2023.
[http://dx.doi.org/10.1016/j.epsr.2022.108956]
[http://dx.doi.org/10.1016/j.epsr.2022.108956]
[10]
A. Bakhshi, and A. Alfi, "Robust LMI-based active fault tolerant pitch control of a wind turbine using a fuzzy model", CEAI, vol. 22, no. 4, pp. 34-42, 2020.
[11]
L. Zhang, Y. Guo, J. Wang, X. Huang, X. Wei, and W. Liu, "Structural failure test of a 52.5 m wind turbine blade under combined loading", Eng. Fail. Anal., vol. 103, pp. 286-293, 2019.
[http://dx.doi.org/10.1016/j.engfailanal.2019.04.069]
[http://dx.doi.org/10.1016/j.engfailanal.2019.04.069]
[12]
Q. Zhao, Y. Yuan, W. Sun, X. Fan, P. Fan, and Z. Ma, "Reliability analysis of wind turbine blades based on non-Gaussian wind load impact competition failure model", Measurement, vol. 164, p. 107950, 2020.
[http://dx.doi.org/10.1016/j.measurement.2020.107950]
[http://dx.doi.org/10.1016/j.measurement.2020.107950]
[13]
S. Li, and L. Cai, "Fan blade crack fault diagnosis based on the analysis of pneumatic signals", J Vibrat Shock, vol. 36, no. 19, pp. 227-231, 2017.
[http://dx.doi.org/10.13465/j.cnki.jvs.2017.19.034]
[http://dx.doi.org/10.13465/j.cnki.jvs.2017.19.034]
[14]
P. Pourazarm, L. Caracoglia, M. Lackner, and Y. Modarres-Sadeghi, "Perturbation methods for the reliability analysis of wind-turbine blade failure due to flutter", J. Wind Eng. Ind. Aerodyn., vol. 156, pp. 159-171, 2016.
[http://dx.doi.org/10.1016/j.jweia.2016.07.011]
[http://dx.doi.org/10.1016/j.jweia.2016.07.011]
[15]
B. Badrkhani Ajaei, and S. Soyoz, "Effects of preload deficiency on fatigue demands of wind turbine tower bolts", J. Construct. Steel Res., vol. 166, p. 105933, 2020.
[http://dx.doi.org/10.1016/j.jcsr.2020.105933]
[http://dx.doi.org/10.1016/j.jcsr.2020.105933]
[16]
J. Zhang, X. Du, C. Qian, and H-M. Tai, "A quasi-online condition monitoring technique for the wind power converter", Int. J. Electr. Power Energy Syst., vol. 130, p. 106971, 2021.
[http://dx.doi.org/10.1016/j.ijepes.2021.106971]
[http://dx.doi.org/10.1016/j.ijepes.2021.106971]
[17]
Q. Wang, C. Su, and Z. Wen, "Multi-condition monitoring and fault diagnosis of wind turbines based on cointegration analysis", China Mech Eng, vol. 33, no. 13, pp. 1596-1603, 2022.
[http://dx.doi.org/10.3969/j.issn.1004-132X.2022.13.010]
[http://dx.doi.org/10.3969/j.issn.1004-132X.2022.13.010]
[18]
D. Astolfi, R. Pandit, L. Celesti, A. Lombardi, and L. Terzi, "SCADA data analysis for long-term wind turbine performance assessment: A case study", Sustain. Energy Technol. Assess., vol. 52, p. 102357, 2022.
[http://dx.doi.org/10.1016/j.seta.2022.102357]
[http://dx.doi.org/10.1016/j.seta.2022.102357]
[19]
A. Wan, and Z. Gong, "XGBoost-KDE-based main bearing temperature prediction and fault warning method for wind turbines", Thermal Power Generation, no. 12, pp. 164-171, 2022.
[http://dx.doi.org/10.19666/j.rlfd.202207179]
[http://dx.doi.org/10.19666/j.rlfd.202207179]
[20]
T.A. Mankhi, "J.H. AL-Bedhany, and S. Legutko, “Investigation of subsurface microcracks causing premature failure in wind turbine gearbox bearings”", Results Eng., vol. 16, p. 100667, 2022.
[http://dx.doi.org/10.1016/j.rineng.2022.100667]
[http://dx.doi.org/10.1016/j.rineng.2022.100667]
[21]
J. Dong, Y. Liu, and W. Tneg, "A fault early warning method for wind turbine gearbox oil temperature based on data statistical analysis in operational condition division", Renewable Energy Resources, vol. 39, no. 4, pp. 501-506, 2021.
[http://dx.doi.org/10.13941/j.cnki.21-1469/tk.2021.04.011]
[http://dx.doi.org/10.13941/j.cnki.21-1469/tk.2021.04.011]
[22]
Q. Li, S. He, C. Qin, W. Feng, W. He, Z. Yang, and H. Huang, "Failure analysis on converter cooling system blocking of wind turbine", Eng. Fail. Anal., vol. 140, p. 106490, 2022.
[http://dx.doi.org/10.1016/j.engfailanal.2022.106490]
[http://dx.doi.org/10.1016/j.engfailanal.2022.106490]
[23]
A. Lv, and L. Wei, "Research Progress on Fault Detection Technology of Wind Turbine Blade Based on Fiber Optic Sensor", High Voltage Apparatus, vol. 58, no. 7, pp. 83-92, 2022.
[http://dx.doi.org/10.13296/j.1001-1609.hva.2022.07.011]
[http://dx.doi.org/10.13296/j.1001-1609.hva.2022.07.011]
[24]
Y. Hong, S. Hui, and Y. Song, "Risk Assessment of Wind Turbine High Voltage Trip-off in Wind Power DC Delivery System", High Voltage Apparatus, vol. 58, no. 9, pp. 102-111, 2022.
[http://dx.doi.org/10.13296/j.1001-1609.hva.2022.09.013]
[http://dx.doi.org/10.13296/j.1001-1609.hva.2022.09.013]
[25]
Q. Liu, H. Ma, and X. Chu, "Performance assessment and anomaly detection of wind turbine based on long short time memory-auto encoder neural network", Jisuanji Jicheng Zhizao Xitong, vol. 25, no. 12, pp. 3209-3219, 2019.
[http://dx.doi.org/10.13196/j.cims.2019.12.022]
[http://dx.doi.org/10.13196/j.cims.2019.12.022]
[26]
Y. Qi, F. Liu, and Y. Li, "Compound fault diagnosis of wind turbine rolling bearing based on mk-momeda and teager energy operator", Taiyang Neng Xuebao, vol. 42, no. 7, pp. 297-307, 2021.
[http://dx.doi.org/10.19912/j.0254-0096.tynxb.2019-0276]
[http://dx.doi.org/10.19912/j.0254-0096.tynxb.2019-0276]
[27]
H. Shokri-Ghaleh, A. Alfi, S. Ebadollahi, A. Mohammad Shahri, and S. Ranjbaran, "Unequal limit cuckoo optimization algorithm applied for optimal design of nonlinear field calibration problem of a triaxial accelerometer", Measurement, vol. 164, p. 107963, 2020.
[http://dx.doi.org/10.1016/j.measurement.2020.107963]
[http://dx.doi.org/10.1016/j.measurement.2020.107963]
[28]
J. Zhang, and H. Jiang, Detection method of wind turbine blade based on acoustic characteristics and BAS-SVM., China Meas. Test, 2022, pp. 1-6.
[29]
J. Jiang, J. Bao, and Q. Shao, "Fault feature extraction method for wind turbine gearbox based on VMD-FHT", Machine Tool Hydraul, vol. 48, no. 23, pp. 202-207, 2022.
[30]
Z. Han, W. Zhao, and X. Zhu, "Research on fault diagnosis method of fan drive system based on KL-CEEMD", China Measur Test, vol. 48, no. 5, pp. 88-95, 2022.
[31]
J. Peng, A. Kimmig, Z. Niu, J. Wang, X. Liu, D. Wang, and J. Ovtcharova, "Wind turbine failure prediction and health assessment based on adaptive maximum mean discrepancy", Int. J. Electr. Power Energy Syst., vol. 134, p. 107391, 2022.
[http://dx.doi.org/10.1016/j.ijepes.2021.107391]
[http://dx.doi.org/10.1016/j.ijepes.2021.107391]
[32]
J. Dong, and Y. Liu, "Wind turbine blade ice detection based on BP_Adaboost algorithm", Renewable Energy Resources, vol. 39, no. 05, pp. 632-636, 2021.
[http://dx.doi.org/10.13941/j.cnki.21-1469/tk.2021.05.001]
[http://dx.doi.org/10.13941/j.cnki.21-1469/tk.2021.05.001]
[33]
P.B. Dao, "On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines", Appl. Energy, vol. 318, p. 119209, 2022.
[http://dx.doi.org/10.1016/j.apenergy.2022.119209]
[http://dx.doi.org/10.1016/j.apenergy.2022.119209]
[34]
J. Liu, X. Wang, S. Wu, L. Wan, and F. Xie, "Wind turbine fault detection based on deep residual networks", Expert Syst. Appl., vol. 213, p. 119102, 2023.
[http://dx.doi.org/10.1016/j.eswa.2022.119102]
[http://dx.doi.org/10.1016/j.eswa.2022.119102]
[35]
P.B. Dao, "Condition monitoring and fault diagnosis of wind turbines based on structural break detection in SCADA data", Renew. Energy, vol. 185, pp. 641-654, 2022.
[http://dx.doi.org/10.1016/j.renene.2021.12.051]
[http://dx.doi.org/10.1016/j.renene.2021.12.051]
[36]
Y. Zuo, J. Montesano, and C.V. Singh, "Assessing progressive failure in long wind turbine blades under quasi-static and cyclic loads", Renew. Energy, vol. 119, pp. 754-766, 2018.
[http://dx.doi.org/10.1016/j.renene.2017.10.103]
[http://dx.doi.org/10.1016/j.renene.2017.10.103]
[37]
B. Yeter, Y. Garbatov, and C. Guedes Soares, "Risk-based life-cycle assessment of offshore wind turbine support structures accounting for economic constraints", Struct. Saf., vol. 81, p. 101867, 2019.
[http://dx.doi.org/10.1016/j.strusafe.2019.06.001]
[http://dx.doi.org/10.1016/j.strusafe.2019.06.001]
[38]
J. Lian, G. Hou, O. Cai, and K. Xu, "Assessing the life cycle risks of offshore wind turbines with suction bucket foundations", J. Clean. Prod., vol. 362, p. 132366, 2022.
[http://dx.doi.org/10.1016/j.jclepro.2022.132366]
[http://dx.doi.org/10.1016/j.jclepro.2022.132366]
[39]
A. Rincón-Casado, J.M. Juliá-Lerma, D. García-Vallejo, and J. Domínguez, "Experimental estimation of the residual fatigue life of in-service wind turbine bolts", Eng. Fail. Anal., vol. 141, p. 106658, 2022.
[http://dx.doi.org/10.1016/j.engfailanal.2022.106658]
[http://dx.doi.org/10.1016/j.engfailanal.2022.106658]
[40]
C. Han, C. Mo, L. Tao, Y. Ma, and X. Bai, "An efficient fatigue assessment model of offshore wind turbine using a half coupling analysis", Ocean Eng., vol. 263, p. 112318, 2022.
[http://dx.doi.org/10.1016/j.oceaneng.2022.112318]
[http://dx.doi.org/10.1016/j.oceaneng.2022.112318]
[41]
Y. Chen, D. Wu, H. Li, and W. Gao, "Quantifying the fatigue life of wind turbines in cyclone-prone regions", Appl. Math. Model., vol. 110, pp. 455-474, 2022.
[http://dx.doi.org/10.1016/j.apm.2022.06.001]
[http://dx.doi.org/10.1016/j.apm.2022.06.001]
[42]
W. Liu, G. Guo, F. Chen, and Y. Chen, "Meteorological pattern analysis assisted daily PM2.5 grades prediction using SVM optimized by PSO algorithm", Atmos. Pollut. Res., vol. 10, no. 5, pp. 1482-1491, 2019.
[http://dx.doi.org/10.1016/j.apr.2019.04.005]
[http://dx.doi.org/10.1016/j.apr.2019.04.005]
[43]
H. Liu, H. Wu, and Y. Li, "Smart wind speed forecasting using EWT decomposition, GWO evolutionary optimization, RELM learning and IEWT reconstruction", Energy Convers. Manage., vol. 161, pp. 266-283, 2018.
[http://dx.doi.org/10.1016/j.enconman.2018.02.006]
[http://dx.doi.org/10.1016/j.enconman.2018.02.006]
[44]
S.M. Abedi Pahnehkolaei, A. Alfi, and J.A.T. Machado, "Convergence boundaries of complex-order particle swarm optimization algorithm with weak stagnation: dynamical analysis", Nonlinear Dyn., vol. 106, no. 1, pp. 725-743, 2021.
[http://dx.doi.org/10.1007/s11071-021-06862-w]
[http://dx.doi.org/10.1007/s11071-021-06862-w]
[45]
Z. Ji, D. Niu, M. Li, W. Li, L. Sun, and Y. Zhu, "A three-stage framework for vertical carbon price interval forecast based on decomposition–integration method", Appl. Soft Comput., vol. 116, p. 108204, 2022.
[http://dx.doi.org/10.1016/j.asoc.2021.108204]
[http://dx.doi.org/10.1016/j.asoc.2021.108204]
Related Journals
Recent Patents on Engineering
Related Books
Voltammetry for Sensing Applications
