Fault warning method based on extreme learning regression and fuzzy reasoning

Li Gang; Qiu Chen-guang; Cao Shuai; Wang Ya-ou

doi:10.1051/e3sconf/202123604008

All issues

Volume 236 (2021)

E3S Web Conf., 236 (2021) 04008

References

Open Access

Issue		E3S Web Conf. Volume 236, 2021 3^rd International Conference on Energy Resources and Sustainable Development (ICERSD 2020)


Article Number		04008
Number of page(s)		4
Section		Green Technology Innovation and Intelligent Application of Environmental Equipment
DOI		https://doi.org/10.1051/e3sconf/202123604008
Published online		09 February 2021

Tan Z X, Shi C R, Huang J, et al. Statistical analysis and improvement measures of thermal power unit outage in Jiangxi power grid unified dispatching in 2019[J]. Jiangxi electric power, 2020, 40(8): 39–46. [Google Scholar]
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Zhao H, Liu H, Hu W, et al. Anomaly detection and fault analysis of wind turbine components based on deep learning network[J]. Renewable energy, 2018, 127: 825–834. [Google Scholar]
Xiao L, Luo J, Ouyang C M. Research on coal mill fault prediction based on semi-supervised learning method [J]. Thermal Power Generation, 2019, 48(4): 121–127. [Google Scholar]
YANG Hu, Thomas Palme, Olga Fink. Fault Detection based on Signal Reconstruction with Auto-Associative Extreme Learning Machines[J]. Engineering Application of Artificial Intelligence, 2017, 57:105–117. [Google Scholar]

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[1] Tan Z X, Shi C R, Huang J, et al. Statistical analysis and improvement measures of thermal power unit outage in Jiangxi power grid unified dispatching in 2019[J]. Jiangxi electric power, 2020, 40(8): 39–46. [Google Scholar]

[2] Zhang Z Y, Meng Z B, Yuan H K. Zhang Zhenyu applied data mining to early warning of actual power station [J]. Power System Engineering, 2021, 37(01): 55–56. [Google Scholar]

[3] Fu W A, Zhang Z F, Huang W. Research on Fault Early Warning Algorithm of Reheater in Thermal Power Plant Based on Extreme Random [J]. Journal of Shanghai University of Electric Power, 2020, 36(5): 445–450. [Google Scholar]

[4] Alaei H K, Salahshoor K, Alaei H K. A new integrated on-line fuzzy clustering and segmentation methodology with adaptive PCA approach for process monitoring and fault detection and diagnosis[J]. soft computing, 2013, 17(3): 345–362. [Google Scholar]

[5] Zhao H, Liu H, Hu W, et al. Anomaly detection and fault analysis of wind turbine components based on deep learning network[J]. Renewable energy, 2018, 127: 825–834. [Google Scholar]

[6] Xiao L, Luo J, Ouyang C M. Research on coal mill fault prediction based on semi-supervised learning method [J]. Thermal Power Generation, 2019, 48(4): 121–127. [Google Scholar]

[7] YANG Hu, Thomas Palme, Olga Fink. Fault Detection based on Signal Reconstruction with Auto-Associative Extreme Learning Machines[J]. Engineering Application of Artificial Intelligence, 2017, 57:105–117. [Google Scholar]