IOT-Based Automatic Power Factor Correction with Real-Time Carbon Footprint Estimation

Abstract

A Low-cost IoT-enabled Automatic Power Factor Correction (APFC) system with realtime carbon footprint estimation is designed and implemented using a Raspberry Pi Pico W microcontroller. The system continuously monitors the power factor of an electrical load and automatically engages optimal capacitor banks to correct a lagging power factor, achieving a stable value of approximately 0.99. This correction reduces energy waste, minimizes electricity costs, and enhances grid stability. A key innovation of this project is the integration of real-time carbon footprint estimation, where the system calculates and displays the amount of CO emissions prevented due to the improved energy efficiency, promoting environmental awareness alongside economic benefits. All critical parameters, including power factor, active power, and estimated CO savings, are transmitted wirelessly to a ThingSpeak IoT dashboard, allowing remote real-time monitoring and analysis. Performance was validated through MATLAB Simulink simulations and hardware prototyping, confirming the system’s effectiveness. The completed ThingSpeak integration enables intuitive visualization of system performance, making this project a holistic, scalable, and sustainable solution for intelligent energy management.

Introduction

The increasing global demand for electricity has created pressure on conventional power systems, making energy efficiency essential in generation, transmission, and consumption. In AC systems with inductive loads such as motors and transformers, low lagging power factor (PF) is a major issue. A poor power factor leads to higher current flow, increased power losses, voltage drops, reduced system capacity, and financial penalties from utilities.

Traditional Automatic Power Factor Correction (APFC) systems improve PF by switching capacitor banks according to reactive power demand. However, these systems generally lack advanced capabilities such as remote monitoring, real-time analytics, and environmental impact evaluation.

To address these limitations, the project proposes an IoT-based APFC system using Raspberry Pi Pico W. The system automatically corrects the power factor close to unity while also estimating the reduction in CO? emissions resulting from improved energy efficiency. IoT technology enables real-time data monitoring, cloud-based analysis through ThingSpeak, and visualization via LCD and remote dashboards.

The system architecture includes current and voltage sensing using CT and PT, signal conditioning, phase detection with zero-crossing detectors and XOR logic, capacitor bank switching via relays, and IoT communication. A threshold-based control algorithm determines which capacitor bank should be activated depending on the measured PF.

Software is implemented in MicroPython, enabling sensor data acquisition, PF calculation, relay control, Wi-Fi communication, and carbon footprint estimation. The environmental module calculates avoided CO? emissions using load power, operating time, and a standard emission factor.

Simulation using MATLAB/Simulink validates the system under varying inductive loads (5–14 kW). Results show effective PF improvement, reduced reactive power, and measurable CO? emission savings. The integration with ThingSpeak allows cloud-based monitoring and visualization of PF and environmental benefits.

Overall, the proposed system combines power quality improvement, IoT monitoring, and environmental sustainability, providing both economic and ecological advantages compared to traditional APFC systems.

Conclusion

This project successfully designed, simulated, and validated an IoT-based Automatic Power Factor Correction (APFC) system integrated with real-time carbon footprint estimation, demonstrating effective power factor improvement from approximately 0.92 to 0.99 through automated capacitor bank switching and providing quantifiable environmental impact data by calculating CO emissions reduction using the established formula, thereby offering a comprehensive solution that addresses both energy efficiency optimization through reactive power compensation and environmental sustainability awareness via IoT-enabled monitoring and visualization capabilities. The system’s innovative approach bridges traditional electrical engineering practices with modern environmental accountability requirements, presenting significant potential for industrial adoption to reduce energy costs, minimize carbon footprint, and support global climate action initiatives while maintaining scalability for future enhancements such as integration with renewable energy sources, implementation of artificial intelligence for predictive maintenance, and expansion to three-phase power systems for larger industrial applications.

References

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Copyright

Copyright © 2026 Amal Roy, Govind Anilkumar, Muhammed Najil K, Swaraj C P. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.