<![CDATA[The use of solar power plants (SPP) requires a monitoring system capable of displaying operating parameters and identifying malfunctions early on. This study aims to design and build an Internet of Things (IoT)-based monitoring and early fault detection system for solar panels using the ESP32 and Blynk communication. The system uses an INA219 sensor to measure voltage, current, and power; a BH1750 sensor to measure light intensity; and a DS3231 RTC for time logging. Data is processed using the Rule-Based Fault Detection method to identify NORMAL, SHADOW, and CABLE FAULT conditions, displayed in real-time on Blynk, and saved to Google Spreadsheet at five-minute intervals. Testing included sensor validation, 24-hour monitoring, and fault simulations. Validation results showed an average error of 0.94% for the INA219 current, 0.50% for voltage, and 0.35% for the BH1750 light intensity, with accuracies of 99.06%, 99.50%, and 99.65%, respectively. During the monitoring test, the maximum power during charging reached 33.841 W, while the minimum power during discharging was recorded at 0 W. The system identified normal conditions at a voltage of 14.38 V, a current of 2.16 A, a power of 30.62 W, and a light intensity of 54,612 lux without issuing a notification. Under SHADOW conditions, a light intensity of 67.5 lux, a current of 0.0002 A, and a power of 0.002 W triggered a fault status and a notification. Under CABLE FAULT conditions, a light intensity of 1,496.67 lux with a current of 0 A and a power of 0 W was also detected and generated a notification. These results demonstrate that the system can monitor solar panel parameters and distinguish between three operating conditions based on the applied rules.]]>
