PhD LIVE Study Fair

Oxford | Edinburgh | Sheffield

Coventry University Featured PhD Programmes
Imperial College London Featured PhD Programmes
Sheffield Hallam University Featured PhD Programmes
University of Sheffield Featured PhD Programmes
University of Reading Featured PhD Programmes

Intelligent and nonlinear control of doubly-fed induction generators (DFIG)

This project is no longer listed on FindAPhD.com and may not be available.

Click here to search FindAPhD.com for PhD studentship opportunities
  • Full or part time
    Prof V Becerra
    Dr Nils Bausch
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Wind energy has many advantages, as it does not produce greenhouse gases and is an inexhaustible source of energy. The Double Fed Induction Generator (DFIG) is the most important machine used in large wind turbines. The connection of the DFIG rotor with the electric grid, through a back to back converter, allows the rotor speed to vary while synchronizing the stator directly to a fixed frequency, which is achieved by controlling the rotor magnetic field by means of rotor currents.

Different control techniques have been proposed for DFIG generators. Many of the previously proposed techniques are challenged by the
presence of uncertainties in the dynamic model of the generator. Neural Networks techniques have shown to be competitive methods for nonlinear/intelligent control of uncertain dynamical systems; their capacities for identification and control of non-linear systems has been investigated over several years.

In this project, a nonlinear controller will be designed for a DFIG generator based on neural networks and nonlinear control approaches such as feedback linearisation and sliding mode control. A neural network based modelling approach will be used to obtain an accurate model which is robust to disturbances and parameter variations. Computer simulation will be carried out and real-time experiments will be
performed on an experimental DFIG platform to confirm the validity and effectiveness of the proposed control approach. Matlab/Simulink and OPAL-RT real-time software and hardware will be used in this project.


Funding Notes

This project does not have any specific funding attached to it. It can be suitable for candidates who are self-funded, or those who are sponsored by a governmental institution or NGO.

Related Subjects



FindAPhD. Copyright 2005-2019
All rights reserved.