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Conditional monitoring and fault prognosis for renewable energy systems (Advert Reference: SF18/MPEE/GAO)

  • Full or part time
    Dr David Gao
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Renewable energy has contributed more and more proportions of electricity generations in the UK, Europe and around the world. Renewable energy equipments/systems such as wind turbines, photovoltaics and so forth would be subjected to faults or malfunctions due to the age or unexpected events. As a result, there is strong motivation to develop advanced monitoring and fault predictions algorithms which enable the supervisory level to take proper actions to avoid economic loss and potential damages/collapse of the whole system. The used methods are either data-mining based learning and training methods, time-domain, frequency-domain, and time-frequency signal processing techniques, or model-based approaches, or the integration of the methods above. The investigation would be based on highly validated wind turbine benchmark models and real data from wind or PV farms.

We are recruiting one or two PhD students to contribute to the project above to lead to new knowledge discovery, and bridge the gap between the monitoring and diagnosis algorithms and renewable industries. The candidates are expected to have solid knowledge in electrical engineering, computations and simulations. The successful candidates would carry out research in Power, Renewable Energy and Control groups/labs, which is one of the research strengths in E&E faculty at Northumbria University.

Please note eligibility requirement:

* Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
* Appropriate IELTS score, if required

For further details of how to apply, entry requirements and the application form, see

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF18/MPEE/GAO) will not be considered.

Start Date: 1 March 2019 or 1 June 2019 or 1 October 2019

Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University hold an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality and is a member of the Euraxess network, which delivers information and support to professional researchers.

Funding Notes

This is an unfunded research project.


1) Gao, Z.*, and Sheng, S. (2018): Real-time monitoring, prognosis, and resilient control for wind turbine systems. Renewable Energy 116, 1-4.
2) Shao, H., Gao, Z.*, Liu, X., and Busawon, K. (2018): Parameter-varying modelling and fault reconstruction for wind turbine systems. Renewable Energy 116, 145-152.
3) Gao, Z.*, Liu, X., and Chen, M. (2017): Takagi-Sugeno fuzzy model based fault estimation and signal compensation with application to wind turbines. IEEE Transactions on Industrial Electronics 64, 5678-5689.
4) Gao, R., and Gao, Z*. (2016): Pitch control for wind turbine systems using optimization, estimation and compensation. Renewable Energy 91, 501-515.
5) Gao, Z.*, Ding, S. and Cecati, C. (2015): Real-time fault diagnosis and fault-tolerant control. IEEE Transactions on Industrial Electronics 62, 3752 -3756.
6) Gao, Z.*, Cecati, C., and Ding, S. (2015): A survey of fault diagnosis and fault-tolerant techniques- Part I: fault diagnosis with model-based and signal-based approaches. IEEE Transactions on Industrial Electronics 62, 3757-3767 (ESI paper, highly cited).
7) Gao, Z.*, Cecati, C., and Ding, S. (2015): A survey of fault diagnosis and fault-tolerant techniques Part II: fault diagnosis with knowledge-based and hybrid/active approaches. IEEE Transactions on Industrial Electronics 62, 3768 -3774 (ESI paper, highly cited).
8) Dai, X., and Gao, Z*. (2013): From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis. IEEE Transactions on Industrial Informatics 9, 2226-2238 (ESI paper, highly cited).

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