Professor Linfei Yin PhD

Prof. Dr. Linfei Yin is a highly-ranked Associate Professor known for his innovative contributions at the intersection of Electrical Engineering and Artificial Intelligence (AI). His research primarily focuses on improving the efficiency, stability, and control of modern Power Systems and Microgrids. He develops advanced Deep Learning and Optimization Algorithms for tasks such as short-term load forecasting, economic dispatch, and automatic generation control. Recognized as a top global scientist, his work is crucial for integrating renewable energy sources and enabling smart, large-scale interconnected power systems.
 

Education Details:

• PhD / Doctor Of Engineering (Power Systems And Automation): South China University Of Technology, China (2018).
• M.Sc. (Control Engineering And Control Theory): Nanchang Hangkong University, China (2015).
• B.Sc. (Automation): Nanchang Hangkong University, China (2012).

Professional Background:

• Associate Professor (Current) - School Of Electrical Engineering, Guangxi University, China.
• Assistant Professor (2018–Present) - College Of Electrical Engineering, Guangxi University.
   

Current Research Projects:

• Quantum-Inspired AI: Developing quantum-inspired optimization and deep reinforcement learning algorithms for power system control.
• Deep Learning For Dispatch: Creating deep learning-based approaches for accelerated and distributed economic dispatch in IoT-integrated power systems.

Academic Profiles Of Prof. Dr. Linfei Yin

Explore his academic and professional presence across trusted platforms:

• Google Scholar
• ResearchGate

Prof. Dr. Yin's publications demonstrate an advanced application of deep learning, reinforcement learning, and quantum-inspired methods to complex control and optimization problems in large-scale power systems.

The list of recent publications of Prof. Dr. Linfei Yin is listed below:

• Yin, L., Ye, Y., & Zhang, X. (2025). Deep Learning-based Approach for Accelerated Economic Dispatch in Hierarchical Distributed Power Systems with Internet of Things. IEEE Internet Of Things Journal. DOI: 10.1109/JIOT.2025.3614203.
• Lin, Y., & Yin, L. (2025). Quantum Parallel Transformer Model for Detecting Multiclass Insulator Faults. IEEE Transactions On Instrumentation And Measurement. DOI: 10.1109/TIM.2025.3591852.
• Gao, F., Hu, R., Yin, L., Cao, H., Yu, J., & Shuang, F. (2024). Quantum Grover Search-Inspired Global Maximum Power Point Tracking for Photovoltaic Systems Under Partial Shading Conditions. IEEE Transactions On Sustainable Energy. DOI: 10.1109/TSTE.2024.3361483.
• Lu, Q., Ye, W., & Yin, L. (2024). Parallel Multiple CNNs With Temporal Predictions for Wind Turbine Blade Cracking Early Fault Detection. IEEE Transactions On Instrumentation And Measurement. DOI: 10.1109/TIM.2024.3370786.
• Lu, Q., Ju, W., & Yin, L. (2024). Parallel GhostNet classification prediction method for supercapacitor remaining useful life prediction. Advanced Engineering Informatics. DOI: 10.1016/j.aei.2024.102916.

Last Updated on October 09, 2025