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Lecturer in Electrical and Electronic Engineering
Dr Shuyue Lin joined the Department of Engineering at the University of Hull in September, 2021. Before that, she was an Assistant Professor at Fuzhou University, China.
She obtained her PhD in Engineering from the University of Warwick in 2019. Before that, she was an electrical engineer at PowerChina Fujian Electrical Power Engineering Co., Ltd., China. She obtained her MSc (Distinction) in Control Systems from Imperial College London in 2013. In 2012, she obtained her BEng degree (First-Class Honours) in Electrical Power Engineering from the University of Bath (UK) and the BEng degree in Electrical Power Engineering & Automation from North China Electric Power University (China).
661959 Smart grids
700982 Power distribution, energy storage and control
Wind Turbine Fault-Tolerant Control via Incremental Model-Based Reinforcement Learning
Xie, J., Dong, H., Zhao, X., & Lin, S. (in press). Wind Turbine Fault-Tolerant Control via Incremental Model-Based Reinforcement Learning. IEEE transactions on Automation Science and Engineering, https://doi.org/10.1109/TASE.2024.3372713
Advancing high impedance fault localization via adaptive transient process calibration and multiscale correlation analysis in active distribution networks
Gao, J., Guo, M., Lin, S., & Chen, D. (in press). Advancing high impedance fault localization via adaptive transient process calibration and multiscale correlation analysis in active distribution networks. Measurement, Article 114431. https://doi.org/10.1016/j.measurement.2024.114431
An incremental high impedance fault detection method under non-stationary environments in distribution networks
Guo, M. F., Yao, M., Gao, J. H., Liu, W. L., & Lin, S. (2024). An incremental high impedance fault detection method under non-stationary environments in distribution networks. International Journal of Electrical Power & Energy Systems, 156, Article 109705. https://doi.org/10.1016/j.ijepes.2023.109705
Semantic segmentation-based intelligent threshold-free feeder detection method for single-phase ground fault in distribution networks
Hong, C., Qiu, H., Gao, J., Lin, S., & Guo, M. (in press). Semantic segmentation-based intelligent threshold-free feeder detection method for single-phase ground fault in distribution networks. IEEE Transactions on Instrumentation and Measurement, https://doi.org/10.1109/TIM.2023.3335520
control systems, renewable energy, power system
Project
Funder
Grant
Started
Status
EPSRC Engineering & Physical Sciences Research Council
£1,634.00
1 December 2021
Complete
Dr. Lin welcome PhD applications in
control systems
renewable energy
electrical power system.
Co-opted member of IMechE Yorkshire Automotive Division Centre
2022
Fellow of the Higher Education Academy
2023
Electrical and Electronic Engineering
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