Design and Development of a Web-Based Electrical Disturbance Monitoring and Warning System

Design and Development of a Web-Monitored Integrated Electricity KWh Meter Using an IoT-Based Microcontroller

This study aims to develop an Internet of Things (IoT)-based electrical energy (kWh) monitoring system using an ESP32 microcontroller integrated with a web monitoring platform. The system employs ACS712 current and ZMPT101B voltage sensors to measure voltage, current, power, and electrical energy in real time. The research method used is Research and Development (R&D) with the 4D model, consisting of define, design, development, and dissemination stages. The test results indicate that the ZMPT101B sensor has an error rate of 0.49%–0.81%, while the ACS712 sensor has an error rate of 4.0%–14.2%. Measurement data were successfully transmitted and displayed on the monitoring website through an internet connection. The developed system is capable of providing real-time electrical energy monitoring with adequate accuracy and easy accessibility for users.

IoT-Based Automatic Environmental Monitoring and Network Device Electrical Protection System in a Server Room

The server room at the Class I Climatology Station of East Java requires reliable monitoring of temperature, humidity, flame detection, and electrical voltage; however, monitoring is still conducted manually without automatic electrical protection. This study aims to design and implement an Internet of Things (IoT)-based environmental condition monitoring and automatic electrical protection system using an ESP32 integrated with BME280, KY-026, and ZMPT101B sensors. The system applies a two-level threshold structure—warning and critical—to trigger notifications as well as automatic power disconnection via relays. The research methodology includes system design, sensor calibration of the BME280 and ZMPT101B using a comparative method, functional testing of the KY-026, and a 14-day field test. The test results demonstrated a temperature correction of -0.21 °C, a humidity correction of -0.80% RH, and an average voltage error of 1.05%. The system recorded two warning-status readings for humidity without reaching critical conditions and successfully transmitted and stored 114,903 data points during the testing period. The reading interval was designed to be 5 seconds, whereas the actual transmission interval was influenced by network conditions. The results indicate that the system can effectively support environmental monitoring and automatic electrical protection for server rooms.

Real-Time Monitoring and Fault Detection System for Solar Photovoltaic Installations

Industrial Parameters Monitoring 4.0

The proposed solution provides a low-cost, scalable, and efficient approach for improving industrial safety, minimizing manual intervention, and supporting predictive monitoring applications in smart manufacturing environments.

Design and Development of an Internet of Things (IoT) Based Automatic Transfer Switch (ATS) Monitoring System Using an Expert System Approach For Electrical Energy Backup

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.

ESP32 and MIT App Inventor-Based VRLA Battery Telemetry Monitoring System for Preventive Maintenance

Manual monitoring of Valve Regulated Lead Acid (VRLA) batteries in industrial electrical systems is considered inefficient and risks delayed fault detection. This study aims to design an Internet of Things (IoT)-based telemetry monitoring system to automate the process. The system comprises a PZEM-016 sensor for reading battery voltage, an ESP32 microcontroller as the data processor, Firebase as the cloud database, and an Android application built with MIT App Inventor as the user interface. Voltage data is sent in real-time from the sensor to the ESP32 via Modbus RTU communication through an RS485 to TTL converter, then forwarded to Firebase via Wi-Fi, and finally displayed on the application. Test results show a sensor reading accuracy of 99.60% with an average error of only 0.40%. The system successfully transformed the monitoring method from a manual inspection taking approximately 10 minutes per unit to real-time automatic monitoring. Therefore, the designed system is proven effective for remote monitoring, supports preventive maintenance strategies, and enhances the operational reliability of VRLA batteries in industrial environments.