Browsing by Author "Reddy, Kumeshan."

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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.
(2022) Reddy, Kumeshan.; Saha, Akshay Kumar.
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.
Model predictive control of a doubly fed induction generator.
(2020) Reddy, Kumeshan.; Saha, Akshay Kumar.
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.