Aug 2026· International Journal for Sciences and Technology· 0 citations· 26 references
TL;DR
Experimental validation demonstrates reliable phase detection, rapid relay response, and effective remote alerting, confirming the system's suitability for industrial automation, motor protection, and smart energy management applications.
Abstract
Three-phase induction motors are extensively utilized in industrial and commercial applications due to their robustness, efficiency, and capability to handle high-power loads. However, these motors are vulnerable to damage from phase failure conditions, including single-phasing, voltage imbalance, and phase loss, which can lead to overheating, reduced efficiency, and permanent equipment failure. This paper presents the design and development of a low-cost, intelligent three-phase monitoring and protection system integrated with Internet of Things (IoT) communication for real-time fault notification. The proposed system employs an Arduino Uno microcontroller as the central processing unit, continuously monitoring the availability of R, Y, and B phases through voltage sensor modules. Upon detection of any phase failure, the system automatically disconnects relay-controlled loads to prevent single-phasing damage. Local status indication is provided through an I2C LCD display and audible buzzer alerts, while remote monitoring is achieved via an ESP32 DevKit module that transmits fault notifications to a Telegram application through Wi-Fi connectivity. The system architecture combines embedded control, automated protection, and wireless communication into a single scalable platform. Experimental validation demonstrates reliable phase detection, rapid relay response, and effective remote alerting, confirming the system's suitability for industrial automation, motor protection, and smart energy management applications.
This research presents an internet of things (IoT-based) system for real-time monitoring and control of a three-phase induction motor that enables continuous monitoring, early fault detection, and predictive maintenance, thereby improving overall operational efficiency and reducing downtime.
Y. S. Pawar, Sandip Rahane, A. Thakare et al.· Bulletin of Electrical Engin...· 0 citations
The proposed framework provides a scalable smart distribution monitoring platform and a future enhancement path towards implementation with hardware, event logging, fault-location techniques and machine-learning based fault classification.
H. Ali, R. Asif, Muhammad Rizwan· Journal of Global Social Tra...· 0 citations
The novelty of this research lies in the integration of a responsive, cost-effective, multi-parameter control and monitoring platform equipped with automatic data logging within a single integrated HMI interface, which is ready to be applied for research as well as industrial automation laboratory practices.
Fahrul Marcello Rombon, Kevind Lefinro Rompas, N. Lombok et al.· Jambura Journal of Electrica...· 0 citations
IoT is one of the significant enabling technologies in the contemporary energy landscape. This work addresses intelligent load management under fault, under-load, and overload conditions. Electrical equipment requires automatic and rapid response to avoid damage and prevent service interruption. In contrast to traditional circuit breakers that disconnect the entire system, the proposed architecture employs ESP32-based intelligent control, together with ACS712 current sensors, to provide accurate per-load current measurements and load-specific overcurrent protection. The system is also capable of selective load isolation, meaning that it maintains the operation of healthy circuits and automatically disconnects only the faulty load without affecting other loads. Cloud-based monitoring is implemented via the ThingSpeak platform, enabling real-time remote system surveillance through any internet-connected device. The faulty section of the system is readily identified from the current readings visualised on the cloud dashboard and mobile interface. The proposed system is powered primarily by solar photovoltaic sources and can also operate from the utility grid, providing flexibility across renewable and conventional supply scenarios. Experimental testing demonstrates protection response times in the sub-200 ms range and high-accuracy current monitoring with a mean absolute error below 0.05 A. The key contributions include: selective load protection, real-time IoT-based energy analysis, and a cost-effective open-hardware architecture. The work advances the safety and efficiency of smart energy control systems in distributed renewable energy environments.
Jeevitha Kandasamy, Kalaivani C, Shashank S Bhagwat et al.· 2026 4th International Confe...· 0 citations
Reliable electrical power availability is essential for various applications, especially in facilities that rely on continuous power supply. To address power interruptions, this study developed an Internet of Things (IoT)-enabled Automatic Transfer Switch (ATS) monitoring system based on an expert system approach for electrical energy backup. The proposed system integrates an ESP32 microcontroller with a ZMPT101B voltage sensor, PZEM-004T power meter, DC voltage sensor, relay module, SIM900A GSM module, inverter, LCD, and a 12 V 35 Ah battery. The system automatically changes the power source from the utility supply (PLN) to the backup battery when a power failure occurs and restores the utility source after normal supply conditions are detected. System conditions and electrical parameters are monitored remotely in real time using the Blynk platform, which also provides system notifications. For decision-making, expert system principles are implemented through a Mamdani Fuzzy Inference System (FIS) developed in MATLAB R2018b, evaluating utility voltage, battery voltage, and load condition to determine the appropriate ATS power-source status. Experimental evaluation at the Instrumentation and Control Laboratory, Universitas Negeri Jakarta, confirmed the system functionality, with average measurement errors of 0.45% for the ZMPT101B sensor and 0.24% for the DC voltage sensor, while the PZEM-004T produced errors of 0.45% for voltage, 3.7% for current, and 0.91% for power measurements. The ATS completed the source transfer within 3 seconds, with the inverter maintaining an output voltage between 217 and 232 VAC. Under a 50 W load, the 12 V 35 Ah battery provided backup power for 3 hours and 30 minutes, and the MATLAB simulation generated a crisp output of 1.86, corresponding to the battery-supplied load condition. Beyond these technical validations, the scientific contribution of this research lies in the successful integration of a Mamdani FIS with IoT-based monitoring to enable adaptive, rule-based decision-making that overcomes the rigidity of conventional fixed-threshold ATS systems. Practically, this intelligent backup solution offers an affordable and reliable alternative for critical infrastructures, such as small-scale healthcare facilities and data centers, particularly in regions with unstable grid conditions. The modular system architecture also provides a scalable foundation for future integration with renewable energy sources and expansion to higher-capacity industrial applications.
Rafiuddin Syam, Ara Akdzal Al Tariq, Efri Sandi et al.· SPEKTRA Jurnal Fisika dan Ap...· 0 citations
The Sudden equipment failures are common in industrial systems, which means more downtime and higher maintenance costs. A Smart AI-Integrated Predictive Maintenance and Condition Monitoring System is proposed to solve this problem. It will allow for real-time monitoring and early fault detection in industrial machines. The system uses an Arduino Uno (ATmega328P) microcontroller that is connected to several sensors, such as voltage, current, temperature (LM35), vibration, proximity, and MPU6050 sensors, to collect important operational data. The ESP8266 NodeMCU Wi-Fi module sends the processed data to an IoT cloud platform so that it can be monitored and analyzed from afar. There is also an I2C display developed in for real-time viewing on position. The proposed system uses AI-based analysis to find problems and assume when equipment might break down, which enables maintenance be performed on time. By using a hardware prototype to evaluate shows that the system reliably monitor in real time and make accurate predictions, which cuts down on downtime and makes the system work better. The Integration of AI and IoT technologies makes predictive maintenance in modern industrial settings cheaper, more flexible, and smarter.
V. R. Kumar, P. Poornima, M. Abinesh et al.· International Conference on...· 0 citations
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