• Placement Reference #: EQU/2019/045
  • Careerwise Placement
  • Glasgow
  • 29th March 2019

University of Strathclyde

The University of Strathclyde is constantly looking to improve its research output and working environments. A key aspect of this is ensuring we build teams made up of diverse groups of thinkers and problem solvers, collaborating in supportive and inclusive environments. We know that the best results are achieved when different voices and different points of view are all brought together. In order to foster the next generation of game changing researchers, we have therefore established this internship program to demystify research and ensure the possibilities of academia are open to all.


This program offers fully funded 6 week internships, in areas of renewable energy research, to people between first year undergraduate and masters degree level studies. Each project sees a candidate joining a Strathclyde research team and working alongside them throughout. Interns will have a chance to see, experience and contribute to real life research, gaining valuable insight into PhD studies and academic life. They will also learn valuable skills which can aid them in their studies and future projects they might undertake. Most importantly, these projects will help bring the world of research out from behind closed doors, exposing new minds to a world of possibilities where their skills and insight can provide huge contributions in the years to come.


Job title: Building visualisations of wind turbine performance data

Duration:  6 weeks

Salary:    £2000

Location:   Glasgow

Placement Ref:  EQU/2019/045


Background and motivation

One of the ways in which costs associated with wind energy can be reduced is through efficient wind farm asset management. Wind turbines now come equipped with a variety of systems which can monitor wind turbine performance, for example by measuring power, shaft speeds and temperature distributions throughout the nacelle to name a few. More sophisticated condition monitoring systems now go beyond that to measure vibration as well as oil debris and particles. With all this data available to wind farm operators and owners the problem now becomes how to best use it to make operation and maintenance more efficient.

What will you do?

 Throughout the 6 weeks you will be placed in a small research team at the University of Strathclyde looking to understand how big data analytics and machine learning can work alongside current techniques in performance engineering and vibration analysis to predict failure and remaining useful life of wind turbine components. This all starts with good data visualisation, and this is where you come in. We are looking for a motivated student with some knowledge of data visualisation and graphical user interface design. Working closely with the research team, you will help build a user friendly environment to visualise wind turbine performance data in which predictive models can eventually be introduced.

This project offers an opportunity to test your programming skills in an innovative environment, where you will have the ability to really influence an engineering tool’s initial design. By working closely supported by the research team, you will also build up knowledge and understanding of wind turbine design and operation and maintenance, as well as future trends in predictive maintenance.

Required skills and experience

Candidates should be studying a degree in an engineering/computer-science/analytical subject and have strong programming skills, ideally in Python. Some experience with GUI development would also be beneficial. They should also have an interest in data visualisation, renewable energy and machine learning. Candidates should have good communication skills and enjoy working as part of a team.

This placement asks for a Personal Statement and a CV.