Model predictive control of a doubly fed induction generator.
| dc.contributor.advisor | Saha, Akshay Kumar. | |
| dc.contributor.author | Reddy, Kumeshan. | |
| dc.date.accessioned | 2021-11-18T07:18:44Z | |
| dc.date.available | 2021-11-18T07:18:44Z | |
| dc.date.created | 2020 | |
| dc.date.issued | 2020 | |
| dc.description | Masters Degree. University of KwaZulu- Natal, Durban. | en_US |
| dc.description.abstract | The world is currently is energy despair. For years, the world has relied on fossil fuels as the main energy source to produce electricity. At the start, this worked well as there was an abundance. However, due to the increase in population, urbanisation and the birth of many industries, this fuel source has been put under strain. Furthermore, the harmful emissions from the use of fossil fuels has been a great contributor to the destruction of our precious ozone layer. This in turn has gradually increased the harmful effects of global warming on Earth. The need for clean, reliable sources of energy has increased over time, and in a few years, it is expected to be the only source of energy utilized in the production of electrical energy. The research undertaken in this project involves the control of the doubly fed induction generator, which is used in wind energy conversion systems. Commonly termed DFIG, this generator has gained worldwide popularity and is used in majority of wind energy conversion systems. It provides direct grid connection and uses only a partially rated converter. However, the conventional control methods used in the control of the DFIG are either difficult to implement or inefficient. Some require complex tuning of proportional-integral controllers while some produce distorted results. The aim of this research was to investigate and evaluate the application of model predictive control to the control of the DFIG. There exist various different control strategies for the control of the DFIG. This research involved implementing all of the different control strategies via conventional methods and then via the use of model predictive control. Despite there being various methods to implement model predictive control, due to its simplicity and strong suitability, finite control set model predictive control was used in this research. Each of the control strategies implemented both conventionally and via model predictive control were thoroughly analysed in terms of the steady state response, dynamic response and quality of stator current. A comparison between the corresponding control methods is also presented. | en_US |
| dc.identifier.uri | https://researchspace.ukzn.ac.za/handle/10413/19906 | |
| dc.language.iso | en | en_US |
| dc.subject.other | Direct torque control. | en_US |
| dc.subject.other | Conversion systems. | en_US |
| dc.subject.other | Space vector pulse. | en_US |
| dc.subject.other | Rotor flux. | en_US |
| dc.subject.other | Electromagnetic torque. | en_US |
| dc.title | Model predictive control of a doubly fed induction generator. | en_US |
| dc.type | Thesis | en_US |