In the modern generation, Electric Power has become one of the fundamental needs for humans to
survive. This is due to the dependence of continuous availability of power. However, for electric
power to be available to the society, it has to pass through a number of complex stages. Through
each stage power quality problems are experienced on the grid. Under-voltages and over-voltages
are the most common electric problems experienced on the grid, causing industries and business
firms losses of Billions of dollars each year. Researchers from different regions are attracted by an
idea that will overcome all the electrical issues experienced in the traditional grid using Artificial
Intelligence (AI). The idea is said to provide electric power that is sustainable, economical, reliable
and efficient to the society based on Evolutionary Algorithms (EAs). The idea is Smart Grid. The
research focused on Power Quality Optimization in Smart Grid based on improved Differential
Evolution (DE), with the objective functions to minimize voltage swells, counterbalance voltage sags
and eliminate voltage surges or spikes, while maximizing the power quality. During Differential
Evolution improvement research, elimination of stagnation, better and fast convergence speed
were achieved based on modification of DE’s mutation schemes and parameter control selection.
DE/Modi/2 and DE/Modi/3 modified mutation schemes proved to be the excellent improvement for
DE algorithm by achieving excellent optimization results with regards to convergence speed and
elimination of stagnation during simulations. The improved DE was used to optimize Power Quality
in smart grid in combination with the reconfigured and modified Dynamic Voltage Restorer (DVR).
Excellent convergence results of voltage swells and voltage sags minimization were achieved based
on application of multi-objective parallel operation strategy during simulations. MATLAB was used
to model the proposed solution and experimental simulations. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:uir.unisa.ac.za:10500/25884 |
| Date | 10 1900 |
| Creators | Saveca, John |
| Contributors | Wang, Zenghui |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | 1 online resource (xv, 98 leaves) : illustrations (chiefly color), graphs (chiefly color), application/pdf |
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