Data Acquisition System for Monitoring JNEC Distribution Network

Abstract

Reliable electrical parameter monitoring is essential for managing institutional distribution networks; however, conventional power quality analysers remain cost-prohibitive for campus-wide deployment. The design and implementation of a low-cost, IoT-based Data Acquisition System (DAQ) for real-time monitoring of the JNEC campus distribution network is presented and its experimental validation is carried out. The system consists of a voltage sensor (ZMPT101B), a current sensor (ACS712), signal acquisition module (Arduino UNO), wireless module (ESP32), and cloud platform modules (Firebase, ThingSpeak). Validation was performed by comparing the multiparameter acquisition modes of the Multimeter with a Fluke 435 Series II Power Quality Analyser and a reference meter (MECO PM-VAC-5R) with resistive, capacitive and inductive loads. Under steady-state conditions, the system successfully reached the IEC Class 0.5 performance level in terms of Mean Absolute Error (MAE), with an error of less than 1.73% and Root Mean Square Error (RMSE), less than 2.06%, relative to the reference instruments for voltage and apparent power measurements under inductive loading. The voltage Total Harmonic Distortion (THD-V) and current Total Harmonic Distortion (THD-i) levels of the inductive load were calculated using MATLAB-based Fast Fourier Transform (FFT) analysis: The THD-V level was 15.38%, which was measured in the transient mode of the motor startup condition, and the THD-i level was 84.16%, which was also measured in transient mode of the motor startup condition, and both were within IEEE Std 519-2014 characterisation criteria. The proposed system shows that a scalable and inexpensive IoT DAQ platform can provide an applied engineering education with professionally relevant power quality data for electrical infrastructure management of a campus.