<![CDATA[This study presents a low-cost and scalable multi-channel power data acquisition system for real-time solar photovoltaic (PV) panel monitoring, addressing the limitations of conventional single-channel approaches that provide only aggregate system measurements. The proposed system enables simultaneous per-panel measurement to support detailed performance analysis and improved fault localization. The system is implemented using an ESP32 microcontroller integrated with multiple calibrated INA219 sensors, which are connected via the I²C protocol to measure voltage, current, and electric power. A modular hardware design supports three independent PV channels, while data handling is achieved through dual-mode operation, consisting of local microSD card storage and wireless data transmission to the ThingSpeak IoT platform for real-time visualization. Calibration results demonstrate high measurement accuracy, with average errors below 1%, a voltage root mean square error (RMSE) of less than 0.07 V, and a current RMSE of less than 7 mA. Field testing conducted over two consecutive days confirms stable and uninterrupted operation, achieving 100% data acquisition reliability. The recorded data clearly reveal per-panel performance differences under real operating conditions, enabling effective identification of mismatch behavior among panels. The proposed system provides an affordable, reliable, and scalable solution for distributed PV monitoring, making it suitable for multi-panel and remote photovoltaic installations. Future improvements will involve temperature-based efficiency analysis and the integration of thermal management strategies to enhance photovoltaic performance.]]>
Copyrights © 2026
