Model predictive control of a doubly fed induction generator.

Masters Degree. University of KwaZulu- Natal, Durban.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

An investigation into the utilization of swarm intellingence for the control of the doubly fed induction generator under the influence of symmetrical and assymmetrical voltage dips.

Doctoral Degree. University of KwaZulu-Natal, Durban.The rapid depletion of fossil, fuels, increase in population, and birth of various industries has put a severe strain on conventional electrical power generation systems. It is because of this, that Wind Energy Conversion Systems has recently come under intense investigation. Among all topologies, the Doubly Fed Induction Generator is the preferred choice, owing to its direct grid connection, and variable speed nature. However, this connection has disadvantages. Wind turbines are generally placed in areas where the national grid is weak. In the case of asymmetrical voltage dips, which is a common occurrence near wind farms, the operation of the DFIG is negatively affected. Further, in the case of symmetrical voltage dips, as in the case of a three-phase short circuit, this direct grid connection poses a severe threat to the health and subsequent operation of the machine. Owing to these risks, there has been various approaches which are utilized to mitigate the effect of such occurrences. Considering asymmetrical voltage dips, symmetrical component theory allows for decomposition and subsequent elimination of negative sequence components. The proportional resonant controller, which introduces an infinite gain at synchronous frequency, is another viable option. When approached with the case of symmetrical voltage dips, the crowbar is an established method to expedite the rate of decay of the rotor current and dc link voltage. However, this requires the DFIG to be disconnected from the grid, which is against the rules of recently grid codes. To overcome such, the Linear Quadratic Regulator may be utilized. As evident, there has been various approaches to these issues. However, they all require obtaining of optimized gain values. Whilst these controllers work well, poor optimization of gain quantities may result in sub-optimal performance of the controllers. This work provides an investigation into the utilization of metaheuristic optimization techniques for these purposes. This research focuses on swarm-intelligence, which have proven to provide good results. Various swarm techniques from across the timeline spectrum, beginning from the well-known Particle Swarm Optimization, to the recently proposed African Vultures Optimization Algorithm, have been applied and analysed

An Investigation into the Utilization of Swarm Intelligence for the Design of Dual Vector and Proportional–Resonant Controllers for Regulation of Doubly Fed Induction Generators Subject to Unbalanced Grid Voltages

This work presents an investigation into the use of swarm intelligence techniques for the control of the doubly fed induction generator under unbalanced grid voltages. Swarm intelligence is a concept that was introduced in the late 20th century but has since undergone constant evolution and modifications. Similarly, the doubly fed induction generator has recently come under intense investigation. Owing to the direct grid connection of the DFIG, an unbalanced grid voltage harshly impacts its output power. Established mitigation measures include the use of the dual vector and proportional–resonant control methods. This work investigates the effectiveness of utilizing swarm intelligence for the purpose of controller gain optimization. A comparison of the application of swarm intelligence to the dual vector and proportional–resonant controllers was carried out. Three swarm intelligence techniques from across the timeline were utilized including particle swarm optimization, the bat algorithm, and the gorilla troops optimization algorithm. The system was subject to single-phase voltage dips of 5% and 10%. The results indicate that modern swarm intelligence techniques are effective at optimizing controller gains. This shows that as swarm intelligence techniques evolve, they may be suitable for use in the optimization of controller gains for numerous applications

A Heuristic Approach to Optimal Crowbar Setting and Low Voltage Ride through of a Doubly Fed Induction Generator

In this paper, a heuristic approach to doubly fed induction generator (DFIG) protection and low voltage ride through (LVRT) is carried out. DFIG-based wind systems are rapidly penetrating the power generation section. Despite their advantages, their direct coupling grid makes them highly sensitive to symmetrical faults. A well-known solution to this is the crowbar method of DFIG protection. This paper provides a method to determine the optimal crowbar resistance value, to ensure a strong trade-off between the rotor current and DC voltage transients. Further, since the crowbar method requires disconnection from the grid, the linear quadratic regulator (LQR) is applied to the system. This is to ensure fault ride through compliance with recent grid code requirements. The well-known PSO, as well as the recently developed African vultures optimization algorithm (AVOA), was applied to the problem. The first set of results show that for severe symmetrical voltage dips, the AVOA provides a good option for crowbar magnitude optimization, whereas PSO performed better for moderately severe dips. Secondly, when the LQR was optimized via the AVOA, it exhibited superiority over the conventional PSO-based PI controller. This superiority was in terms of rotor current transient magnitude, DC voltage transient magnitude, and reactive power steady-state ripple and were in the order of 67.5%, 20.35%, and 37.55%, respectively. When comparing the crowbar method and the LQR, it was observed that despite the LQR exhibiting superiority in terms of transient performance, the crowbar method offered a unanimously superior settling time