Advanced digital signal processing for condition monitoring for rotative electrical machines

About the Project

The production of electric energy from wind technology has experience great advances in the later years. It is estimated that the potential of wind energy in the world is between 20.000 x 106 and 50.000 x 106 MWh/year, whereas the energy consumption is about 15.000 x 106 MWh/year. According to the International Energy Agency (IEA), a net wind capacity of 40.68 GW was added in 2014 by the IEA Wind member countries 39% more than the capacity added in 2013, with a total installed wind capacity of 314.72 GW. The IEA estimates that in 2050 12% of electric energy will be produced by the wind energy industry. The global electric car stock surpassed 2 million vehicles in 2016 with a growth of 60% with respect to 2010. It is expected a great increase of Electrical Vehicles (EVs) by 2030. All this data show the importance of the wind energy at the present time, not only form a quantitative aspect, but also since it is a renewable energy, local and clean, that improves the energy independence. Furthermore, both the wind energy and the electrical vehicle contribute to climate change mitigation. Problems such as the evaluation of the wind energy potential, location, wind turbines design, topology of the drive, integration of the wind energy in the grid, among others, caught the attention of a huge number of researchers.

This PhD opportunity focuses on the productivity of wind turbines and electrical vehicles by detecting faults at the early stages. Hence the downtimes and maintenance cost are reduced.

The main goal of this PhD is to develop new diagnostic techniques for the early diagnosis of failures in electrical machines fed by variable speed drives. The techniques which are intended to be developed are based on the monitoring of electric and magnetic magnitudes such as line current, electric power, magnetic flux, etc., which are easily accessible, for analysis by adequate signal processing tools.

It is intended to develop diagnostic methods optimised for wind turbines and electrical vehicles. Their working conditions imply that the speed and load regimes are always in transient state. Furthermore, both the wind turbine generators and electrical vehicle motors are fed by variable speed drives. It is also intended that the proposed methodologies can be integrated in the predictive maintenance system of wind farms and in the diagnosis system of electrical vehicles.

Academic qualifications

A first-class honours degree, or a distinction at master level, or equivalent achievements in Electrical Engineering

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:

Experience of fundamental electrical machines and their control techniques. Competent in use of Matlab, Matlab Simulink and signal processing and analysis.

Knowledge of electrical machines operation and their modelling, programming scripts in Matlab and practical skills.

Good written and oral communication skills. Strong motivation, with evidence of independent research skills relevant to the project.

Good time management.

Desirable attributes:

Advanced knowledge of induction machines, synchronous machines and DC machines. Knowledge of signal acquisition, processing and analysis

APPLICATION CHECKLIST

• Completed application form
• CV
• 2 academic references, using the Postgraduate Educational Reference Form (download)
• Research project outline of 2 pages (list of references excluded). The outline may provide details about
  1. Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
  2. Research questions or objectives.
  3. Methodology: types of data to be used, approach to data collection, and data analysis methods.
  4. List of references.
• The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.
• Statement no longer than 1 page describing your motivations and fit with the project.
• Evidence of proficiency in English (if appropriate)

To be considered, the application must use

• the advertised title as project title

For informal enquiries about this PhD project, please contact f.vedrenosantos@napier.ac.uk

Application Enquiries: https://www.napier.ac.uk/research-and-innovation/doctoral-college/application-guidance

Application link: https://evision.napier.ac.uk/si/sits.urd/run/siw_sso.go?ElOlarlItFiG37xnH5PRRBvv3d563wLdwX4JfhYskMa3bJWTuc