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PhD Power Systems Dynamic Security Assessment using machine learning.

This 42-month full-time, fully-funded PhD, supported by EPSRC focusing on the area of power system stability and dynamics using powerful machine learning tools that enable informative and fast online dynamic security assessment.

Number of places



Home fee, Stipend


17 January 2018


31 August 2018


3.5 years


To be considered for the studentship, candidates must:

  • Hold, or be about to gain preferably a 1st class UK BEng Honours or MEng degree or MSc with Distinction in Electronic / Electrical Engineering, Mechanical Engineering, Computer and Information Sciences, Physics, Mathematics or a related subject. Candidates with 2.1 Uk Honours degree may be considered.
  • Be a UK or eligible EU national and adhere to Research Council (RCUK) eligibility criteria
  • Possess skills include programming, understanding of Matlab, PowerFactory/Digsilent software as well as knowledge and understanding of machine learning methods.

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.

Project Details

Most countries, including the UK, are promoting the increased penetration of renewable energy sources (RES) to reduce carbon emissions. Ambitious targets are being set for the years to come, mainly because of climate change but also for economic and technical reasons. The generation mix is changing with many large, thermal power stations closing or operating with lower output and more power coming from sources connected via power electronic converters such as wind generators and Photo-Voltaics, raising questions about system security. These changes lead to significantly different dynamic behaviour of the power system that may vary in a both temporal and spatial manner due to the intermittent nature of many of the power electronic interfaced sources. Both the possible pre-disturbance operating conditions and the post-disturbance behaviour of power systems can be significantly affected. These developments, may lead to operation closer to the stability limit and increase the risk of widespread events, that might even lead to blackouts if not acted upon.

With the extensive installation of measurement devices in modern power systems, abundant data are available that hold valuable information about potential patterns of instability. Measurement data can also help in the fast prediction of imminent cascading events and enable automated control actions, all faster than with human operators.

This PhD project will deal with Dynamic Security Assessment (DSA) which focuses on the security of system dynamics in various timescales, from a few up to several seconds. DSA usually requires performing computationally intensive time domain simulations (especially for large power systems). This fact coupled with the increasing temporal and spatial variation introduced by RES as well as the huge number of possible combinations of equipment failure renders the challenge of identifying and predicting situations that might lead to cascading failures highly complex, and calls for the need of novel tools and methodologies.

Data analytics and advances in artificial intelligence (AI) and deep learning have made huge steps in recent years, providing powerful tools that can model the behaviour of complex, highly non-linear systems, such as power systems, with very high accuracy.

The project will commence on 1 October 2018.

Funding Details

Funding is provided for full tuition fees (UK and eligible EU applicants only), along with a generous stipend for the duration of the project.  The studenship also provide a budget of £1.5K per year to usse of student support activities.

Contact us

How to apply

Candidates interested in applying should email Dr Panagiotis Papadopoulos with their CV, academic transcript, and a covering letter outlining their suitability for the position.

Following review of the application submissions, selected candidates will be invited for interview.

Application submission deadline is 31 August 2018.

The project will start on 1 October 2018.