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

Bachelor Thesis (2024)
Author(s)

H. Yildiz (TU Delft - Electrical Engineering, Mathematics and Computer Science)

M. de Vries (TU Delft - Electrical Engineering, Mathematics and Computer Science)

E. Öztürkoglu (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

A. Lekić – Graduation committee member (TU Delft - Intelligent Electrical Power Grids)

P. Palensky – Graduation committee member (TU Delft - Electrical Sustainable Energy)

R.N. Koornneef – Graduation committee member (TU Delft - ESP LAB)

S. Renzaglia – Mentor (TU Delft - ESP LAB)

I.E. Lager – Graduation committee member (TU Delft - Electrical Engineering Education)

Faculty
Electrical Engineering, Mathematics and Computer Science
More Info
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Publication Year
2024
Language
English
Graduation Date
17-01-2024
Awarding Institution
Delft University of Technology
Project
['EE3L11 Bachelor graduation project Electrical Engineering', 'Low cost power quality measuring unit for household usage and small to enterprise scale installations']
Programme
['Electrical Engineering']
Faculty
Electrical Engineering, Mathematics and Computer Science
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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.

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