Low-cost educational prototype for IoT-based PV energy monitoring and MPPT control
TL;DR
This work contributes a reproducible, low-cost testbed that bridges embedded control, power electronics, and IoT-based monitoring, with potential applications in both education and rapid prototyping of smart PV systems.
Abstract
This paper presents a low-cost embedded platform integrating photovoltaic (PV) energy monitoring, maximum power point tracking (MPPT), and Internet of Things (IoT) communication for real-time control and performance evaluation. The proposed system is built around an ESP32 microcontroller with dual INA219 sensors and a custom-designed DC–DC boost converter operating at 20 kHz. Two MPPT strategies, perturb and observe (P&O) and fuzzy logic control (FLC), are implemented and experimentally compared under identical outdoor conditions. Experimental results obtained under natural irradiance demonstrate that the FLC approach achieves faster convergence and reduced steady-state oscillations compared to P&O. Specifically, FLC improves tracking efficiency from 82.5% to 84%, reduces duty cycle ripple, and increases average extracted power by approximately 1.5%. The system also enables real-time acquisition of I–V and P–V characteristics through duty-cycle sweeping. The proposed architecture integrates sensing, control, and IoT telemetry using MQTT and a ThingsBoard dashboard, enabling continuous monitoring with negligible communication latency relative to the MPPT update period. This work contributes a reproducible, low-cost testbed that bridges embedded control, power electronics, and IoT-based monitoring, with potential applications in both education and rapid prototyping of smart PV systems.