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Dr Zhiwei Gao

Associate Professor

Department: Mathematics, Physics and Electrical Engineering

Dr David (Zhiwei) Gao

Reader. Department: Mathematics, Physics and Electrical Engineering

Dr. Gao joined Northumbria University in 2011, and he is the Head of Electrical Power and Control Systems Research Group. Before joining Northumbira University, he held research and academic positions respectively in Newcaslte University, University of Liverpool, University of Leicester, University of Manchester, University of Duisburg-Essen, and Tianjin University. 

Dr. Gao is the senior member of IEEE, and HEA fellow. He was the recipient of the Alexander von Humboldt Reserach Fellowship in 2004.

Dr. Gao is currently the Associate Editor of IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Industrial Informatics, IEEE Transactions on Industral Electronics, IEEE Transactions on Automatic Control and the editorial board member of Renewable Energy (Elsevier). 

The following special issues are open for the submission:

The special issue ``Real-time monitoring, fault prediction and health management for offshore wind turbine systems'' in Renewable Energy is open for the submission. 

The special issue ``Pattern Recognition for Cyber-Physical-Social Services'' in Pattern Recognition Letter is open for the submission. 

The focused session ``Real-time monitoring, diagnosis, and prognosis and health management for electric vehicles'' in IEEE/ASME Transactions on Mechatronics is open for the submission.  

For the publication information, please see the Google Shcolar.

 

 

Zhiwei Gao

Research Themes and Scholarly Interests

Condition monitoring and fault diagnosis, fault tolerant design and control, machine learning and data-driven approaches, and their applications in wind turbine systems, power converters, power systems, onshore and offshore renewable energy systems. 

Key Publications

  • Please visit the Pure Research Information Portal for further information
  • A deep learning-based fault diagnosis of leader-following systems, Liu, X., Lu, X., Gao, Z. 2022, In: IEEE Access
  • Cloud-Based Event-Triggered Predictive Control for Heterogeneous NMASs Under Both DoS Attacks and Transmission Delays, Yin, X., Gao, Z., Yue, D., Hu, S. 28 Mar 2022, In: IEEE Transactions on Systems, Man, and Cybernetics: Systems
  • Convolutional neural network fault classification based on time-series analysis for benchmark wind turbine machine, Rahimilarki, R., Gao, Z., Jin, N., Zhang, A. 1 Feb 2022, In: Renewable Energy
  • Coordinated Electric Vehicle Active and Reactive Power Control for Active Distribution Networks, Wang, Y., Qiu, D., Strbac, G., Gao, Z. 25 Apr 2022, In: IEEE Transactions on Industrial Informatics
  • Guest Editorial: Digital Twinning: Integrating AI-ML and Big Data Analytics for Virtual Representation, Gao, Z., Paul, A., Wang, X. 1 Feb 2022, In: IEEE Transactions on Industrial Informatics
  • Material Removal Optimization Strategy of 3D Block Cutting Based on Geometric Computation Method, Shao, H., Liu, Q., Gao, Z. 2 Apr 2022, In: Processes
  • Mean‐square strong stability and stabilization of discrete‐time stochastic systems with multiplicative noises, Yan, Z., Su, F., Gao, Z. 25 Apr 2022, In: International Journal of Robust and Nonlinear Control
  • Nonexistence of the Asymptotic Flocking in the Cucker−Smale Model With Short Range Communication Weights, Yin, X., Gao, Z., Chen, Z., Fu, Y. Feb 2022, In: IEEE Transactions on Automatic Control
  • Non-flocking and flocking for the Cucker-Smale model with distributed time delays, Zhang, Z., Yin, X., Gao, Z. 2 Apr 2022, In: Journal of the Franklin Institute

Current and Recent Projects

PGR Supervision

  • Xiaoxu Liu Fault estimation and fault tolerant control with application to wind turbine systems Start Date: 01/10/2014 End Date: 26/03/2018
  • Sarah Odofin Robust Fault Diagnosis by GA Optimisation with Applications to Wind Turbine Systems and Induction Motors Start Date: 31/01/2013 End Date: 27/02/2017
  • Yichuan Fu Data-Driven Fault Classification for Wind Turbine Systems Start Date: 01/02/2016 End Date: 21/04/2021
  • Reihane Rahimilarki Fault Diagnosis for Wind Turbine Systems by Using Neural Network and Deep Learning Techniques Start Date: 01/10/2017 End Date: 19/07/2021

Qualifications

  • Engineering PhD April 14 1996
  • Fellow (FHEA) Higher Education Academy (HEA) 2015
  • Senior member Institute of Electrical & Electronic Engineers (IEEE) 2009


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