Associate Professors

Ph.D., Associate Professor

E-mail: xiaoshengpeng@hust.edu.cn


ü Artificial intelligent and its application in power system

ü Big data and deep learning based condition monitoring of power apparatuses

ü Partial discharge detection and pattern recognition of high voltage cables

ü Wind power prediction of wind farm and wind farm clusters based on data mining


ü Doctor (2009/2-2012/2) — Glasgow Caledonian University (UK),

ü Master (2006/9-2009/2) — Huazhong University of Science and Technology (China),

ü Bachelor (2002/9-2006/7) — Huazhong University of Science and Technology (China).


ü 2013/9-2019/9 Huazhong University of Science & Technology, School of Electrical and Electronic Engineering, Lecturer, Associate Professor

ü 2012/2-2013/9 Glasgow Caledonian University, Postdoctoral Research Fellow



1. Xiaosheng Peng, Jinshu Li, etal, Random Forest Based Optimal Feature Selection for Partial Discharge Pattern Recognition in HV Cables, IEEE Transactions on Power Delivery, 34(4):1715-1724, 2019.

2. Xiaosheng Peng, Fan Yang, etal, A Convolutional Neural Network Based Deep Learning Methodology for Recognition of Partial Discharge Patterns from High Voltage Cables, IEEE Transactions on Power Delivery, 34(4):1460-1469, 2019.

3. Xiaosheng Peng, Jinyu Wen, etal, SDMF based Interference Rejection and PD Interpretation for Simulated Defects in HV Cable Diagnostics, IEEE Transactions on Dielectrics and Electrical Insulation, 24(1): 83-91, 2017.

4. Xiaosheng Peng, Jinyu Wen, etal, Rough Set Theory Applied to Pattern Recognition of Partial Discharge in Noise Affected Cable Data, IEEE Transactions on Dielectrics and Electrical Insulation, 24(1):147-156, 2017.

5. Xiaosheng Peng, Chengke Zhou, Donald M. Hepburn, Martin D. Judd, W. H. Siew, Application of K-Means Method to Pattern Recognition in on-line Cable Partial Discharge Monitoring, IEEE Transactions on Dielectrics and Electrical Insulation, 20(3): 754-761, 2013.


ü 2009-2012: EPSRC(UK) research project: Knowledge Discovery from On-line Cable Condition Monitoring Systems--Insulation Degradation and Aging Diagnostics

ü 2011-2013: EDF Energy research project: Development and Provision of a Bespoke, Portable, Partial Discharge Based Cable Condition Monitoring System for EDF Energy

ü 2017-2018: Rolls-Royce research project: High Voltage Electrical Tests of Isothermally Aged Enamel Wires Used in Marine Electrical Machines

ü 2018-2020: China Southern Grid research project: Multi-points Synchronization Partial Discharge Detection Technology of High Voltage Cables Based on Built-in Sensor

ü 2018-2021: National Key R&D Program of China: Technology and application of wind power / photovoltaic power prediction for promoting renewable energy consumption (2018YFB0904200).


ü 2017, Frontrunner 5000: Top Articles in Outstanding S&T Journals of China

ü 2016, Best Paper Awards for IEEE PES CICED 2016

ü 2012, EDF Energy Research Fellowship

ü 2012, EPSRC Research Fellowship

ü 2011, EDF Energy Scholarship

ü 2010, British Energy Scholarship

ü 2009, EPSRC Full Scholarship (3 Years)


ü Member of IEEE

ü Member of IET

ü Member of IEC Working Group TC 8/SC 8A WG2

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