SCADA Data-Based Working Condition Classification for Condition Assessment of Wind Turbine Main Transmission System
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Dai, Juchuan & Tan, Yayi & Shen, Xiangbin, 2019. "Investigation of energy output in mountain wind farm using multiple-units SCADA data," Applied Energy, Elsevier, vol. 239(C), pages 225-238.
- Hong Wang & Hongbin Wang & Guoqian Jiang & Jimeng Li & Yueling Wang, 2019. "Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling," Energies, MDPI, vol. 12(6), pages 1-22, March.
- Zhu, Xu & Zhang, Shuai & Jin, Xinqiao & Du, Zhimin, 2020. "Deep learning based reference model for operational risk evaluation of screw chillers for energy efficiency," Energy, Elsevier, vol. 213(C).
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Davide Astolfi & Francesco Castellani, 2022. "Editorial on the Special Issue “Wind Turbine Monitoring through Operation Data Analysis”," Energies, MDPI, vol. 15(10), pages 1-4, May.
- Chengming Zuo & Juchuan Dai & Guo Li & Mimi Li & Fan Zhang, 2023. "Investigation of Data Pre-Processing Algorithms for Power Curve Modeling of Wind Turbines Based on ECC," Energies, MDPI, vol. 16(6), pages 1-24, March.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Liu, Hui & Duan, Zhu & Chen, Chao, 2020. "Wind speed big data forecasting using time-variant multi-resolution ensemble model with clustering auto-encoder," Applied Energy, Elsevier, vol. 280(C).
- Wang, Anqi & Pei, Yan & Qian, Zheng & Zareipour, Hamidreza & Jing, Bo & An, Jiayi, 2022. "A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification," Applied Energy, Elsevier, vol. 321(C).
- Liang, Xinbin & Liu, Zhuoxuan & Wang, Jie & Jin, Xinqiao & Du, Zhimin, 2023. "Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem," Applied Energy, Elsevier, vol. 337(C).
- Liang, Xinbin & Zhu, Xu & Chen, Siliang & Jin, Xinqiao & Xiao, Fu & Du, Zhimin, 2023. "Physics-constrained cooperative learning-based reference models for smart management of chillers considering extrapolation scenarios," Applied Energy, Elsevier, vol. 349(C).
- Zhao Zhang & Xiao He, 2020. "Fault-Structure-Based Active Fault Diagnosis: A Geometric Observer Approach," Energies, MDPI, vol. 13(17), pages 1-17, August.
- Huifan Zeng & Juchuan Dai & Chengming Zuo & Huanguo Chen & Mimi Li & Fan Zhang, 2022. "Correlation Investigation of Wind Turbine Multiple Operating Parameters Based on SCADA Data," Energies, MDPI, vol. 15(14), pages 1-24, July.
- Liang, Xinbin & Zhu, Xu & Chen, Kang & Chen, Siliang & Jin, Xinqiao & Du, Zhimin, 2023. "Endowing data-driven models with rejection ability: Out-of-distribution detection and confidence estimation for black-box models of building energy systems," Energy, Elsevier, vol. 263(PC).
- Lin, Zi & Liu, Xiaolei, 2020. "Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network," Energy, Elsevier, vol. 201(C).
- Chen, Jianli & Zhang, Liang & Li, Yanfei & Shi, Yifu & Gao, Xinghua & Hu, Yuqing, 2022. "A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
- Du, Zhimin & Liang, Xinbin & Chen, Siliang & Zhu, Xu & Chen, Kang & Jin, Xinqiao, 2023. "Knowledge-infused deep learning diagnosis model with self-assessment for smart management in HVAC systems," Energy, Elsevier, vol. 263(PD).
- Yuanyuan Yang & Md Muhie Menul Haque & Dongling Bai & Wei Tang, 2021. "Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review," Energies, MDPI, vol. 14(21), pages 1-26, October.
- Carta, José A. & Díaz, Santiago & Castañeda, Alberto, 2020. "A global sensitivity analysis method applied to wind farm power output estimation models," Applied Energy, Elsevier, vol. 280(C).
- Dai, Juchuan & Li, Mimi & Chen, Huanguo & He, Tao & Zhang, Fan, 2022. "Progress and challenges on blade load research of large-scale wind turbines," Renewable Energy, Elsevier, vol. 196(C), pages 482-496.
- Du, Zhimin & Liang, Xinbin & Chen, Siliang & Li, Pengcheng & Zhu, Xu & Chen, Kang & Jin, Xinqiao, 2023. "Domain adaptation deep learning and its T-S diagnosis networks for the cross-control and cross-condition scenarios in data center HVAC systems," Energy, Elsevier, vol. 280(C).
- Jinxin Wang & Zhongwei Wang & Xiuzhen Ma & Guojin Feng & Chi Zhang, 2020. "Locating Sensors in Complex Engineering Systems for Fault Isolation Using Population-Based Incremental Learning," Energies, MDPI, vol. 13(2), pages 1-14, January.
- Wang, Anqi & Pei, Yan & Zhu, Yunyi & Qian, Zheng, 2023. "Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern," Renewable Energy, Elsevier, vol. 211(C), pages 918-937.
- Yolanda Vidal, 2023. "Artificial Intelligence for Wind Turbine Condition Monitoring," Energies, MDPI, vol. 16(4), pages 1-4, February.
- Wu, Yueqi & Ma, Xiandong, 2022. "A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines," Renewable Energy, Elsevier, vol. 181(C), pages 554-566.
- Tongke Yuan & Zhifeng Sun & Shihao Ma, 2019. "Gearbox Fault Prediction of Wind Turbines Based on a Stacking Model and Change-Point Detection," Energies, MDPI, vol. 12(22), pages 1-20, November.
- Dai, Juchuan & He, Tao & Li, Mimi & Long, Xin, 2021. "Performance study of multi-source driving yaw system for aiding yaw control of wind turbines," Renewable Energy, Elsevier, vol. 163(C), pages 154-171.
More about this item
Keywords
main transmission system; working conditions classification; wind turbine working characteristics; alarm threshold; k -means clustering; SCADA data;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7043-:d:666418. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.