Designing Software for User-Friendly Class-S Power Quality Analyzer based on Raspberry Pi

Bachelor thesis (2024)

Authors

H. Yildiz Electrical Engineering, Mathematics and Computer Science
M. de Vries Electrical Engineering, Mathematics and Computer Science
E. Öztürkoglu Electrical Engineering, Mathematics and Computer Science

Contributors

A. Lekić Intelligent Electrical Power Grids - EEMCS (supervisor 2)
P. Palensky Electrical Sustainable Energy (supervisor 2)
R.N. Koornneef ESP LAB - EEMCS (supervisor 2)
S. Renzaglia ESP LAB - EEMCS (supervisor 1)
I.E. Lager Electrical Engineering Education - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2024 Hüsna Yildiz, Marten de Vries, Emirhan Öztürkoglu
More Info
expand_more

Published Date

17-01-2024

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This thesis describes the design of a low-cost class-S power quality analyzer based on a Raspberry Pi 4. Capable of detecting power frequency, magnitude, voltage dips and swells, harmonics, and total harmonic distortion, the project consists of four main components: the communication protocol, the interface, the algorithms for power quality parameters, and the database. Each with its subdivisions. The communication module, which uses the I2C protocol, is chosen for its high sampling rates and built-in acknowledgement system, ensuring robust and fast operation. The interface module features a secure login process with two-step verification, a graphical user interface for real-time monitoring, and integration with algorithms for power quality parameter calculation. The algorithm module includes Fast Fourier Transform for harmonic detection, zero crossing method for power factor and frequency, and peak detection for voltage dips and swells. The database, powered by MariaDB on Raspberry Pi 4, securely manages the received data with restricted access for increased security, allowing remote access only from specified IP addresses.