SR
S. Renzaglia
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2 records found
1
Low cost power quality measuring unit for household usage and small to enterprise scale installations
Designing a Low Cost Power Quality Analyzer
Bachelor thesis
(2024)
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E. Bouma, L.L.E. Damoiseaux, J.D. Groeneveld, A. Lekić, P. Palensky, R.N. Koornneef, S. Renzaglia, I.E. Lager
The increase in non-linear loads of modern electronics raises concerns over power quality. Additionally, existing power-quality analyzers are expensive and not intended for household use. This thesis aims to develop a single-phase, user-friendly power-quality analyzer using a Raspberry Pi 4 Model B with an emphasis on low cost and class S specifications. The design was split into modules consisting of analog to digital conversion, voltage sensing, and current sensing. Sub-modules were added for circuit protection and PCB. Various approaches are discussed before circuit design, simulation, and testing occur. A functioning prototype was assembled on a dedicated PCB while not exceeding the set budget of €250.00. However, it could not be determined whether the class S specifications were achieved due to insufficient testing. A variety of improvements have been suggested.
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The increase in non-linear loads of modern electronics raises concerns over power quality. Additionally, existing power-quality analyzers are expensive and not intended for household use. This thesis aims to develop a single-phase, user-friendly power-quality analyzer using a Raspberry Pi 4 Model B with an emphasis on low cost and class S specifications. The design was split into modules consisting of analog to digital conversion, voltage sensing, and current sensing. Sub-modules were added for circuit protection and PCB. Various approaches are discussed before circuit design, simulation, and testing occur. A functioning prototype was assembled on a dedicated PCB while not exceeding the set budget of €250.00. However, it could not be determined whether the class S specifications were achieved due to insufficient testing. A variety of improvements have been suggested.
Bachelor thesis
(2024)
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H. Yildiz, M. de Vries, E. Öztürkoglu, A. Lekić, P. Palensky, R.N. Koornneef, S. Renzaglia, I.E. Lager
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.
...
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.