Enhancing Wind Farm Reliability: A Field of View Enhanced Convolutional Neural Network-Based Model for Fault Diagnosis and Prevention

Guojian Li; Jian Wang; Yingwu Qin; Xuefeng Bai; Yuhan Jiang; Yi Deng; Zhiyuan Ma; Mengnan Cao

doi:10.15837/ijccc.2024.3.6609

Authors

Guojian Li SPIC Nei Mongol Energy CO., LTD., Tongliao, Inner Mongolia, 028000, China
Jian Wang SPIC Nei Mongol Energy CO., LTD., Tongliao, Inner Mongolia, 028000, China
Yingwu Qin Mengdong Concord New Energy Company, Tongliao, Inner Mongolia, 028000, China
Xuefeng Bai SPIC Nei Mongol Energy CO., LTD., Tongliao, Inner Mongolia, 028000, China
Yuhan Jiang SPIC Nei Mongol Energy CO., LTD., Tongliao, Inner Mongolia, 028000, China
Yi Deng Shanghai Energy Technology Development CO., LTD., Shanghai, 200233, China
Zhiyuan Ma Shanghai Energy Technology Development CO., LTD., Shanghai, 200233, China
Mengnan Cao Shanghai Energy Technology Development CO., LTD., Shanghai, 200233, China

DOI:

https://doi.org/10.15837/ijccc.2024.3.6609

Keywords:

Enhanced visual field; Convolutional neural network; Wind farms; Fault diagnosis; Prevention model

Abstract

Wind farms play a crucial role in renewable energy generation, but their reliability is often compromised by complex environmental and equipment conditions. This study proposes a field of view enhanced convolutional neural network (CNN) model for fault diagnosis and prevention in wind farms. The model is developed by collecting and processing wind farm fault data and compared with support vector machine (SVM) and k-nearest neighbor (KNN) models. The results showed that the proposed CNN model outperformed the other models in terms of convergence speed (17 iterations to reach the minimum loss), fault diagnosis accuracy (99.3% and 99.2% for inner and outer circle faults, respectively), and stable power output improvement. The model’s application to maintenance scheduling and economic benefit analysis in a real wind farm case demonstrated its high consistency and accuracy in fault prediction and maintenance optimization. The proposed approach has the potential to enhance wind farm reliability, efficiency, and economy by enabling accurate fault diagnosis, early warning, and preventive maintenance.

References

Xiao, J.; Heng, N.; Chen, Z. & Bo, P. (2022). Optimal Power Coordinated Control Strategy for DFIG-Based Wind Farm to Increase Transmission Capacity of the LCC-HVDC System Considering Commutation Failure, IEEE journal of emerging and selected topics in power electronics, 10(3), 3129-3139.

https://doi.org/10.1109/JESTPE.2021.3124794

Md, I. H. T. & Bhaba, R. S. (2022). Spare parts control strategies for offshore wind farms: A critical review and comparative study, Wind Engineering, 46(5), 1629-1656.

https://doi.org/10.1177/0309524X221095258

Yan, Y.; Zhao, Y.; Zhao K.;Trinchero,R.;Stievano,IS.; Li,H. (2023). A high-efficiency portable system for insulation condition assessment of wind farm inter-array cables with double-sided partial discharge detection and localisation, IET generation, transmission & distribution, 11(17):2523- 2534.

https://doi.org/10.1049/gtd2.12834

Jonathan, R. & Mark, C. (2022). Methodology to assess wind turbine blade throw risk to vehicles on nearby roads, Wind Engineering, 46(4), 1187-1202.

https://doi.org/10.1177/0309524X211072869

Lu, W.; Zheng, Q.; Hamidreza, Z. & Fanghong, Z. (2022). Comprehensive aging assessment of pitch systems combining SCADA and failure data, IET renewable power generation, 16(1), 198-210.

https://doi.org/10.1049/rpg2.12281

ui-Hung, L.; Kathleen, P. & Rong-Mao, L. (2022). Development of Auxiliary SCADA System for Wind Farm Operation Based on Open Platform Communication, Journal of the Chinese Society of Mechanical Engineers, Series C: Transactions of the Chinese Society of Mechanical Engineers, 43(1), 21-27.

Yang,Y.; Chen, Z.; Qiu,Y.;Huang, Y.; Wei,Q.;Yang, S. (2022). Analysis on Lightning Strike Cause of Directly Buried Optical Cable in Wind Farm and Its Prevention Methods, Meteorological and Environmental Research, 3(13):67-70.

Athena, Z.; Tim, B. & Lesley, W. (2022). Modeling Epistemic Uncertainty in Offshore Wind Farm Production Capacity to Reduce Risk, Risk analysis, 42(7), 1524-1540.

https://doi.org/10.1111/risa.13846

Tchertchian, N. & Millet, D. (2023). Which eco-maintenance for renewable energy? A simulation model for optimising the choice of offshore wind farm maintenance vessel, Journal of Marine Engineering and Technology, 22(1/2), 1-11.

https://doi.org/10.1080/20464177.2022.2044584

Mengfei, Q.; Wei, S.; Wei, C.; Xing, F.; Lin, L. & Xin, L. (2023). Extreme structural response prediction and fatigue damage evaluation for large-scale monopile offshore wind turbines subject to typhoon conditions, Renewable energy, 208(5), 450-464.

https://doi.org/10.1016/j.renene.2023.03.066

Anderson, F.; McMillan, D.; Dawid, R. & Garcia, C. D. (2023). A Bayesian hierarchical assessment of night shift working for offshore wind farms, Wind energy, 26(4), 402-421.

https://doi.org/10.1002/we.2806

Congshan, L.; Zikai, Z.; Ping, H.; Yan, L. & Pu, Z. (2023). Frequency Coordinated Control of Wind Power Flexible Direct System Based on Voltage Sourced Converter Based Multi-Terminal High Voltage Direct Current, Recent advances in electrical & electronic engineering, 16(1), 56-65.

https://doi.org/10.2174/2352096515666221012090511

Shilin, S.; Tianyang, W. & Fulei, C. (2023). A multi-learner neural network approach to wind turbine fault diagnosis with imbalanced data, Renewable energy, 208(5), 420-430.

https://doi.org/10.1016/j.renene.2023.03.097

Wenliao, D.; Pengjie, H.; Hongchao, W.; Xiaoyun, G. & Shuangyuan, W. (2023). Fault Diagnosis of Rotating Machinery Based on 1D-2D Joint Convolution Neural Network, IEEE Transactions on Industrial Electronics,

Zhijie, X.; Di, Y.; Changshu, Z.; Qiancheng, Z.; Junxiang, W.; Jiuqing, L. & Jiaxiu, L. (2023). Ball screw fault diagnosis based on continuous wavelet transform and two-dimensional convolution neural network, Measurement and Control: Journal of the Institute of Measurement and Control, 56(3/4), 518-528.

https://doi.org/10.1177/00202940221107620

Jian, C.; Zhihao, C.; Yinbo, W. & Zhuohong, Y. (2023). Intelligent machine fault diagnosis based on deep transfer convolutional neural network and extreme learning machine. Proceedings of the Institution of Mechanical Engineers, Part C, Journal of mechanical engineering science, 237(9), 2201-2212.

https://doi.org/10.1177/09544062221136490

Hongpeng, L. (2023). Application of industrial Internet of things technology in fault diagnosis of food machinery equipment based on neural network, Soft computing: A fusion of foundations, methodologies and applications, 27(13), 9001-9018.

https://doi.org/10.1007/s00500-023-08412-5

Qi, Z. & Linfeng, D. (2023). An Intelligent Fault Diagnosis Method of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network, Journal of Failure Analysis and Preventio, 23(2), 795-811.

https://doi.org/10.1007/s11668-023-01616-9

Mohammadreza, G.; Mohammadreza, K.; Mohammad, T.; H. B. & Amin, R. (2021). Spatial graph convolutional neural network via structured subdomain adaptation and domain adversarial learning for bearing fault diagnosis, Neurocomputing, 517(1), 44-61.

https://doi.org/10.1016/j.neucom.2022.10.057

Pang, M.;Ouyang, J.; Yu, J.;Chen, J.;Ye, J.; Diao, Y.; Xiao, C. (2023). Interruption method for commutation failure caused cascading reaction of HVDC with wind farm integration under grid fault, International journal of electrical power and energy systems, 2(148):1-10.

https://doi.org/10.1016/j.ijepes.2023.108975

Anderson, F.;Mcmillan, D.; Dawid,R.;Cava, DG . (2023). A Bayesian hierarchical assessment of night shift working for offshore wind farms, Wind energy, 4(26):402-421.

https://doi.org/10.1002/we.2806

Liu,X.;Zhang,P.; Deng,X.;Sun,D. (2023). Hierarchical overvoltage predictive control scheme for a DFIG-based wind farm, Electric Power Systems Research, 4(217):1-10.

https://doi.org/10.1016/j.epsr.2023.109172

Xia, B.; Han, D.; Yin, X.; Gao, N. (2022). RICNN: A ResNet&Inception Convolutional Neural Network for Intrusion Detection of Abnormal Traffic, Computer Science and Information Systems, Vol.19, No. 1, 309-326.

https://doi.org/10.2298/CSIS210617055X

Oke, O.; Ozgonenel, O.; Thomas, D.W.P. & Ataseven, M.S. (2019). Probabilistic Load Flow of Unbalanced Distribution Systems with Wind Farm, Tehnički vjesnik, 26 (5), 1260-1266.

https://doi.org/10.17559/TV-20180213180751

Park, J.; Yoo, T. Y.; Lee, S. J. & Kim, T. Y. (2023). Urban Noise Analysis and Emergency Detection System using Lightweight End-to-End Convolutional Neural Network, INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, 18(5), 1-19.

https://doi.org/10.15837/ijccc.2023.5.5814

Sun, H. (2023). Optimizing Manufacturing Scheduling with Genetic Algorithm and LSTM Neural Networks, Int. Journal of Simulation Modelling, 2(3), 508-519.

https://doi.org/10.2507/IJSIMM22-3-CO13