Real-Time Monitoring of DC Microgrids of Solar Farms Using an MQTT-Based IIoT Device with Node-RedIntegration

This paper presents the design and development of a real-time monitoring system for DC microgrids in solar farms using an MQTT-based IIoT device integrated with Node-RED. The system incorporates voltage and current sensors connected to solar panels, with an ESP32 microcontroller reading data from the sensors to calculate power output. This data is transmitted to a Raspberry Pi server via a WiFi network, utilizing the MQTT protocol for efficient and reliable communication. Node-RED manages and visualizes the data flow, providing a user-friendly interface through its dashboard for monitoring real-time voltage, current, and power readings. This integration of IIoT devices with Node-RED streamlines data collection, processing, and visualization, enhancing solar farms' overall monitoring and control. The proposed system's scalability allows for the seamless addition of new sensors and microgrids without substantial modifications, addressing the need for flexible expansion in large-scale solar farms. Detailed flow diagrams and performance evaluations are included to demonstrate the system's effectiveness and reliability in real-time applications. Compared to existing monitoring solutions, this system offers lower costs, higher scalability, and enhanced real-time data processing capabilities, making it suitable for deployment in various renewable energy environments.

Authors

Institutions

Publication Details

Published
2025-02-28
DOI
https://doi.org/10.1109/ssdee64538.2025.10967828
Primary Topic
Energy Harvesting in Wireless Networks
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Real-Time Monitoring of DC Microgrids of Solar Farms Using an MQTT-Based IIoT Device with Node-RedIntegration

Tanmoy Maity, Soumyadip Banerjee, Priyanka Kumari
Energy Harvesting in Wireless Networks
article

Real-Time Monitoring of DC Microgrids of Solar Farms Using an MQTT-Based IIoT Device with Node-RedIntegration

Tanmoy Maity, Soumyadip Banerjee, Priyanka Kumari
article en

Abstract

This paper presents the design and development of a real-time monitoring system for DC microgrids in solar farms using an MQTT-based IIoT device integrated with Node-RED. The system incorporates voltage and current sensors connected to solar panels, with an ESP32 microcontroller reading data from the sensors to calculate power output. This data is transmitted to a Raspberry Pi server via a WiFi network, utilizing the MQTT protocol for efficient and reliable communication. Node-RED manages and visualizes the data flow, providing a user-friendly interface through its dashboard for monitoring real-time voltage, current, and power readings. This integration of IIoT devices with Node-RED streamlines data collection, processing, and visualization, enhancing solar farms' overall monitoring and control. The proposed system's scalability allows for the seamless addition of new sensors and microgrids without substantial modifications, addressing the need for flexible expansion in large-scale solar farms. Detailed flow diagrams and performance evaluations are included to demonstrate the system's effectiveness and reliability in real-time applications. Compared to existing monitoring solutions, this system offers lower costs, higher scalability, and enhanced real-time data processing capabilities, making it suitable for deployment in various renewable energy environments.

Indian Institute of Technology Dhanbad (IN)
Affordable and clean energy
Openalex Percentile: Top 93%
Energy Harvesting in Wireless Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.