Gengda Li
Gengda Li has a total of 24 co-authors. Here are the top co-authors by number of shared publications:
- Baowei Chen
Southern Marine Science and Engineering Guangdong Laboratory, School of Marine Sciences, Sun Yat-Sen University, Zhuhai, 519082, China
1 shared publication - Baowei Chen
National Key Laboratory of Underwater Acoustic Technology, the Key Laboratory of Marine Information Acquisition and Security, Ministry of Industry and Information Technology, and the College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin, China
1 shared publication Cheng Peng
Hebei Key Laboratory of Low-carbon and Efficient Power generation Technology, North China Electric Power University, Baoding, China
1 shared publication- Deliang Zeng
School of Control and Computer Engineering, North China Electric Power University, Beijing, P. R. China
1 shared publication Gongqing Liu
School of Control and Computer Science, North China Electric Power University, Beijing
1 shared publicationHanqiang Liu
Guodian New Energy Technology Research Institute, Beijing Key Laboratory of Power Generation System Functional Material, Changping District, Beijing 102209, China
1 shared publication
Latest Publications
- 22 August 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Dielectrics and Electrical Insulation
- Vol. 31 (5) , 2564-2571
Dielectric Spectroscopy of Aqueous Insulating Oil Based on Maxwell-Wagner Modified Model
The dielectric change law of aqueous insulating oil has an important impact on the safe operation of transformers. In this paper, the integration method is used to modify the Maxwell-wanger non-uniform dielectric model to make it suitable for aqueous insulating oil, and the reliability of the new model is verified. The experimental results show that when the water content is 20 ppm, the maximum error of the real part of the complex dielectric constant of the insulating oil does not exceed 0.0541%, and the maximum error of the imaginary part does not exceed 1.076%. Finally, based on this model, the dielectric spectrum of the aqueous insulating oil is obtained, the variation law of the real part and the imaginary part of the aqueous insulating oil under different conditions are revealed, and the internal reasons for the dielectric properties of the aqueous insulating oil with temperature, frequency, and moisture content are analyzed, which provides an important reference data for the evaluation and online monitoring of the insulation performance of the aqueous insulating oil.
- 22 August 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Dielectrics and Electrical Insulation
- Vol. 31 (5) , 2564-2571
Dielectric Spectroscopy of Aqueous Insulating Oil Based on Maxwell-Wagner Modified Model
The dielectric change law of aqueous insulating oil has an important impact on the safe operation of transformers. In this paper, the integration method is used to modify the Maxwell-wanger non-uniform dielectric model to make it suitable for aqueous insulating oil, and the reliability of the new model is verified. The experimental results show that when the water content is 20 ppm, the maximum error of the real part of the complex dielectric constant of the insulating oil does not exceed 0.0541%, and the maximum error of the imaginary part does not exceed 1.076%. Finally, based on this model, the dielectric spectrum of the aqueous insulating oil is obtained, the variation law of the real part and the imaginary part of the aqueous insulating oil under different conditions are revealed, and the internal reasons for the dielectric properties of the aqueous insulating oil with temperature, frequency, and moisture content are analyzed, which provides an important reference data for the evaluation and online monitoring of the insulation performance of the aqueous insulating oil.
- Open Access
- 1 November 2021
- Published by Elsevier in Energy Reports
- Vol. 7, 2484-2492
Status evaluation method for arrays in large-scale photovoltaic power stations based on extreme learning machine and k-means
- 1 November 2021
- Published by Elsevier in Energy Reports
- Vol. 7, 2484-2492
Open AccessStatus evaluation method for arrays in large-scale photovoltaic power stations based on extreme learning machine and k-means
- Open Access
- 13 August 2016
- Published by Atlantis Press SARL
Review of Monitoring Methods for Submicronsized Particulates Emission in Coal-fired Power Plants
- 13 August 2016
- Published by Atlantis Press SARL
Open AccessReview of Monitoring Methods for Submicronsized Particulates Emission in Coal-fired Power Plants