To be considered for the project, candidates must:
- Possess an upper second (2.1) or above UK BEng Hons or MEng degree in a relevant engineering or physics related subject.
- Have strong skills and experience of at least two programming languages, including: Python, C/C++, Go, and R. Evidence must be provided, such as personal open source repositories.
- Have self-motivation and an ability to work autonomously, efficiently, and rigorously including the ability to manage own time and workload effectively.
- Possess good verbal communication skills and attitude, with an ability to convey technical information in clear and simple terms.
- Be able to prepare concise, accurate, high-quality documents and presentations, with excellent attention to detail.
- Be a UK or eligible EU national and adhere to Research Council (RCUK) eligibility criteria. International students may be considered, but additional funding could be required by the applicant (this will be assessed on a case-by-case basis).
Experience in signal processing, neural networks, machine learning, real-time systems, or time-series databases would also be preferable.
Applicants would be considered for the position from the following departments: EEE, Physics, Mechanical Engineering, Maths.
Eligibility for RCUK studentships
- Research Council (RC) fees and stipend can only be awarded to UK and EU students and not to EEA or International students.
- EU students are only eligible for RC stipend if they have been resident in the UK for 3 years, including for study purposes, immediately prior to starting their PhD.
- If an EU student cannot fulfil this condition then they are eligible for a fees only studentship.
- International students cannot be funded from RC funds unless they are ‘settled’ in the UK. ‘Settled’ means being ordinarily resident in the UK without any immigration restrictions on the length of stay in the UK. To be ‘settled’ a student must either have the Right to Abode or Indefinite leave to remain in the UK or have the right of permanent residence in the UK under EC law. If the student’s passport describes them as a British citizen they have the Right of Abode.
- Students with full Refugee status are eligible for fees and stipend.
The partial GB blackout on 9 August 2019, involving the failure of multiple grid elements following a lightning strike, highlights how our rapidly changing electricity grid requires new autonomous capabilities to detect and mitigate disturbances that threaten continuity of energy supply – which can lead to disastrous impacts on society and businesses.
As we progress to our “net-zero” 2050 target, electrical disturbances will have remarkably different characteristics in converter-dense systems, and this leads to drastically different patterns in voltage and current signals compared to conventional power systems. New approaches are needed to automatically recognise critical grid situations at an early stage to accelerate mitigating operations. Innovation is needed for processing signals at both the micro scale (individual measurement points) and at the macro scale (aggregated data over a wide geographical area). This is an important and timely industry need to provide electricity system operators with new, actionable information.
This project will create a power system model with high levels of converter-interfaced generation to realistically represent future grid scenarios. This model will be used to analyse the voltage and current waveform phenomena experienced during faults. New measurement and signal processing techniques must be established to cope with these non-conventional waveforms, to address fast-changing transients resulting from power electronic converters which contribute to faults, and to generate new types of “markers” for fault and non-fault conditions.
This project will access a unique Strathclyde real-time platform providing high-fidelity data from 64 simulated grid locations representing a wide geographical area, to generate data that represents a broad range of critical grid scenarios. The data will be analysed to identify the onset of events which may require immediate control or protection interventions.
An important focus of this work is to create methods that are very practical, and that are suitable for directly leveraging the unique distributed sensor platform developed by the industry partner. Therefore, a key novelty is that the processing and analysis must be developed to operate in real-time, and be resilient during probable system disturbances and other issues; this is important for providing useful and automated control and protection decisions for future grid operations.
Funding is provided for full tuition fees (for UK and EU applicants), along with a stipend and some support for laboratory equipment for the duration of the project. Industrial sponsor Synaptec will also provide access to equipment.
International students are welcome to apply, but additional funding could be required by the applicant (this will be assessed on a case-by-case basis).
The primary supervisor will be Dr Qiteng Hong, a Lecturer and Chancellor’s Fellow in Future Power Systems within the Institute for Energy and Environment. Dr Hong's research interest is the protection and control of future power systems with high penetration of renewables.
The secondary supervisor will be Dr Panagiotis Papadopoulos, a Senior Lecturer and UKRI Future Leaders Fellow in the Institute for Energy and Environment. Dr Papadopoulos’s research expertise is in power system stability and dynamics, and he is also interested in power system applications of machine learning.
The industry supervisor is Dr Steven Blair, who is the Head of Power Systems Technologies at Synaptec, UK.
Dr Qiteng Hong (email firstname.lastname@example.org)
How to apply
Candidates interested in applying may first email email@example.com for more information, or to arrange an informal discussion. Thereafter, they should submit their CV, academic transcript, and a covering letter outlining their suitability for the position, to Dr Hong. Following review of the application submissions, selected candidates will be invited for interview. Application submission deadline is 8 November 2020. The project is expected to start on 7 December 2020, or as soon as possible after this date.
Note that the work will be conducted remotely until government and university guidance permits safe on-campus working.