Study on Harmonic Impedance Estimation Based on Gaussian Mixture Regression Using Railway Power Supply Loads
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Jin, Huaiping & Shi, Lixian & Chen, Xiangguang & Qian, Bin & Yang, Biao & Jin, Huaikang, 2021. "Probabilistic wind power forecasting using selective ensemble of finite mixture Gaussian process regression models," Renewable Energy, Elsevier, vol. 174(C), pages 1-18.
- Xianyong Xiao & Xian Zheng & Ying Wang & Shuangting Xu & Zixuan Zheng, 2018. "A Method for Utility Harmonic Impedance Estimation Based on Constrained Complex Independent Component Analysis," Energies, MDPI, vol. 11(9), pages 1-15, August.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Oscar G. Duarte & Javier A. Rosero & María del Carmen Pegalajar, 2022. "Data Preparation and Visualization of Electricity Consumption for Load Profiling," Energies, MDPI, vol. 15(20), pages 1-30, October.
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.- Manisha Sawant & Rupali Patil & Tanmay Shikhare & Shreyas Nagle & Sakshi Chavan & Shivang Negi & Neeraj Dhanraj Bokde, 2022. "A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction," Energies, MDPI, vol. 15(21), pages 1-24, October.
- He, Yaoyao & Cao, Chaojin & Wang, Shuo & Fu, Hong, 2022. "Nonparametric probabilistic load forecasting based on quantile combination in electrical power systems," Applied Energy, Elsevier, vol. 322(C).
- Ogliari, Emanuele & Guilizzoni, Manfredo & Giglio, Alessandro & Pretto, Silvia, 2021. "Wind power 24-h ahead forecast by an artificial neural network and an hybrid model: Comparison of the predictive performance," Renewable Energy, Elsevier, vol. 178(C), pages 1466-1474.
- Wang, Yun & Xu, Houhua & Zou, Runmin & Zhang, Lingjun & Zhang, Fan, 2022. "A deep asymmetric Laplace neural network for deterministic and probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 196(C), pages 497-517.
- Du, Pei & Yang, Dongchuan & Li, Yanzhao & Wang, Jianzhou, 2024. "An innovative interpretable combined learning model for wind speed forecasting," Applied Energy, Elsevier, vol. 358(C).
- Márcio Arvelos Moraes & Vinícius Henrique Farias Brito & José Carlos de Oliveira, 2022. "An Approach for Determining Voltage Imbalance Contributions Based on Complex Independent Component Analysis," Energies, MDPI, vol. 15(19), pages 1-17, September.
- Sun, Shilin & Li, Qi & Hu, Wenyang & Liang, Zhongchao & Wang, Tianyang & Chu, Fulei, 2023. "Wind turbine blade breakage detection based on environment-adapted contrastive learning," Renewable Energy, Elsevier, vol. 219(P2).
- Bin Yang & Kangli Liu & Sen Zhang & Jianfeng Zhao, 2018. "Design and Implementation of Novel Multi-Converter-Based Unified Power Quality Conditioner for Low-Voltage High-Current Distribution System," Energies, MDPI, vol. 11(11), pages 1-21, November.
- John Boland & Sleiman Farah, 2021. "Probabilistic Forecasting of Wind and Solar Farm Output," Energies, MDPI, vol. 14(16), pages 1-15, August.
- Lin, Qingcheng & Cai, Huiling & Liu, Hanwei & Li, Xuefeng & Xiao, Hui, 2024. "A novel ultra-short-term wind power prediction model jointly driven by multiple algorithm optimization and adaptive selection," Energy, Elsevier, vol. 288(C).
- Zhang, Dongdong & Chen, Baian & Zhu, Hongyu & Goh, Hui Hwang & Dong, Yunxuan & Wu, Thomas, 2023. "Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model," Energy, Elsevier, vol. 285(C).
- Niu, Dongxiao & Sun, Lijie & Yu, Min & Wang, Keke, 2022. "Point and interval forecasting of ultra-short-term wind power based on a data-driven method and hybrid deep learning model," Energy, Elsevier, vol. 254(PA).
More about this item
Keywords
railway traction power supply loads; harmonic impedance; Gaussian mixture regression; uniform distribution; Gaussian distribution; robustness;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:15:y:2022:i:19:p:6952-:d:922329. 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.