IOT-Based Three Phase Motor Protection System with Remote Monitoring

Abstract

Three-phase induction motors are critical components in industrial applications, yet they remain vulnerable to electrical faults such as over-voltage, under-voltage, overload, and phase failures. Traditional electromechanical protection systems lack precision and remote monitoring capabilities. This paper proposes an advanced IoT-based protection system utilizing the ESP32 microcontroller. The system continuously monitors voltage and current parameters using Potential Transformers (PT) and Current Transformers (CT). Upon detecting anomalies, it triggers a relay to disconnect the motor via a Direct On Line (DOL) starter, preventing catastrophic failure. Furthermore, the system integrates with a cloud platform (Blynk/ThingSpeak) to provide real-time data visualization and alert notifications to maintenance personnel via a mobile application. Experimental results demonstrate that the system effectively detects faults with a response time of less than 200ms, offering a cost-effective and reliable solution for industrial motor protection.

Introduction

Three-phase induction motors are widely used in industry, consuming 70–80% of industrial electrical energy due to their robustness, simplicity, and cost-effectiveness. However, they are prone to faults such as voltage fluctuations, overloading, and phase anomalies. Traditional protection using thermal relays and fuses is limited by slow response, low precision, and lack of real-time health monitoring. IoT integration enables proactive motor protection through real-time monitoring and cloud-based alerts.

Literature Review:

• Conventional relays are imprecise and slow; digital relays are expensive.

• Microcontroller-based systems (Arduino, PIC, GSM/Zigbee networks) improve monitoring but have limitations like high latency, complex setup, or no continuous data logging.

• The ESP32 microcontroller is ideal due to built-in Wi-Fi, dual-core processing, and high-resolution ADC, enabling low-cost, real-time monitoring and cloud connectivity.

System Architecture:

Hardware Components:

• ESP32 Microcontroller: 32-bit dual-core, 240MHz, Wi-Fi/Bluetooth, 12-bit ADC.

• Voltage Sensors (ZMPT101B): Step down phase voltages for ADC input.

• Current Sensors (ACS712-30A): Hall-effect sensors for current measurement.

• DOL Starter & Relay: Relay interrupts contactor coil to stop the motor upon faults.

Software Architecture:

• Developed in Arduino IDE.

• Continuously samples voltage and current to calculate RMS values.

• Compares measurements against thresholds and communicates via MQTT/HTTP to the cloud.

Protection Mechanisms:

1. Under Voltage (UV): Trips if voltage < 90% of rated for > 2s.

2. Over Voltage (OV): Trips if voltage > 110% of rated immediately.

3. Overload (OL): Trips after a delay if current exceeds FLC by 10%, instant trip at 200% current.

4. Single Phasing: Immediate trip if one phase voltage drops near zero.

Implementation:

• PCB assembly with calibrated sensors.

• ESP32 connects to Wi-Fi and pushes data to the Blynk IoT platform.

• Voltage readings mapped to virtual pins; smartphone notifications triggered on faults.

Results:

• Voltage Protection: Trip detected at 175V (phase voltage) under undervoltage with ~1.5s response.

• Overload Test: Motor tripped at 4.5A in 150ms; allowed temporary overload at 2.8A for 10s.

• IoT Performance: Blynk app showed ~1s data visualization latency; alerts received on smartphone within 3s.

Conclusion

This paper presented an IoT-based solution for three-phase motor protection. The system successfully integrates robust protection logic with modern communication capabilities. Compared to traditional bimetallic relays, the proposed system offers higher accuracy, faster response times for critical faults, and the significant advantage of remote data logging. This technology is highly applicable in agriculture and remote industrial sites where manual monitoring is difficult. Future work involves integrating Machine Learning algorithms on the cloud to predict bearing failures based on current signature analysis.

References

[1] K. Bose, \"Modern Power Electronics and AC Drives,\" Prentice Hall, 2001. [2] S. Gupta and R. Kumar, \"IoT Based Induction Motor Protection,\" in Proc. IEEE Int. Conf. Power Electron. Drives Energy Syst. (PEDES), Jaipur, India, 2020, pp. 1-6. [3] V. C. Gungor and G. P. Hancke, \"Industrial Wireless Sensor Networks: Challenges, Design Principles, and Technical Approaches,\" IEEE Trans. Ind. Electron., vol. 56, no. 10, pp. 4258-4265, Oct. 2009. [4] Espressif Systems, \"ESP32 Technical Reference Manual,\" Version 4.1, 2021. [5] M. A. Jabbar, \"Protection of AC Motors,\" in IEEE Industry Applications Magazine, vol. 12, no. 5, pp. 24-31, Sept. 2006. [6] Allegro MicroSystems, \"ACS712 Fully Integrated, Hall Effect-Based Linear Current Sensor Datasheet,\" 2018. [7] R. Ramasamy, \"Smart Protection System for Induction Motors using IoT,\" Int. J. Eng. Res. Technol., vol. 9, no. 5, May 2020. [8] T. Wildi, \"Electrical Machines, Drives, and Power Systems,\" Pearson Education, 6th Ed., 2006.

Copyright

Copyright © 2026 Musaib Juber Naikwadi , Aashutosh Aravind Mali, Rushikesh Shankar Gavade , Dipak Sanjay Gavali , Shraddha Pandurang Kakade. 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.