Power quality monitoring of a low voltage distribution network
Date
2022-05
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Abstract
A rising population creates a high demand for electricity which leads to network failures, the
Low Voltage (LV) network to be precise since is directly impacted by the consumer loads. As
the usage of LV networks continues to grow, LV monitoring needs to become more widespread
and detailed so that network operators can see accurate and real-time information. Research
has been done over the years to find efficient ways of monitoring and assessing the power
quality of LV networks. This project explored the use of software development tools for power
quality monitoring and analysis. Models were designed to create the disturbances of interest in
power systems. Simulated power systems were decomposed using the wavelet transform
toolbox in MATLAB to attain their energy densities. The obtained energy densities were input
into a trained neural network in MATLAB to detect and classify fault types with respect to the
parameter deviation in the power quality. An accuracy of 92.3% correct classification was
achieved for the neural network training process. In MATLAB Server Pages (MSP), once all
processes were completed, the results of the analysis were sent to a database. The results were
then made accessible via the internet by creating a login system for users.
Description
Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Electrical and Electronic Engineering, May 2022
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Capstone Project
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Keywords
power system performance