Detection of Power Quality Disturbance using a Multidimensional Approach in an Embedded System

Mendes, Thais Martins; Faria, Euler Rodrigues Souza; Perle, Lucas Aparecido Vizioli; Ribeiro, Eduardo Godinho; Ferreira, Danton Diego; Barbosa, Bruno Henrique Groenner; Duque, Carlos A.

doi:10.1109/tla.2019.8931197

Cited by 2 publications

(1 citation statement)

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“…In order to overcome PQ challenges, it is necessary to monitor inputs and disturbances generated by nonlinear loads. The PQ monitoring can provide appropriate protection to equipment and can help to select suitable mitigation solutions that help to improve PQ in micro-grids [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Archives of Electrical Engineering

Khoa,

Dai,

Tung

et al. 2020

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This paper proposes an advanced Internet of Things (IoT) system for measuring, monitoring, and recording some power quality (PQ) parameters. The proposed system is designed and developed for both hardware and software. For the hardware unit, three PZEM-004T modules with non-invasive current transformer (CT) sensors are used to measure the PQ parameters and an Arduino WeMos D1 R1 ESP8266 microcontroller is used to receive data from the sensors and send this data to the server via the internet. For the software unit, an algorithm using Matlab software is developed to send measurement data to the ThingSpeak cloud. The proposed system can monitor and analyse the PQ parameters including frequency, root mean square (RMS) voltage, RMS current, active power, and the power factor of a low-voltage load in real-time. These PQ parameters can be stored on the ThingSpeak cloud during the monitoring period; hence the standard deviation in statistics of the voltage and frequency is applied to analyse and evaluate PQ at the monitoring point. The experimental tests are carried out on low-voltage networks 380/220 V. The obtained results show that the proposed system can be usefully applied for monitoring and analysing chosen PQ parameters in micro-grid solutions.

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Section: Introductionmentioning

confidence: 99%