Design and Experimental Verification of a TinyML-based MPPT Controller for Wind Energy Conversion Systems

The energy conversion efficiency of wind energy conversion systems (WECS) critically depends on the Maximum Power Point Tracking (MPPT) controller’s ability to maintain the turbine at its optimal power output under fluctuating wind conditions. Traditional control methods often struggle with providing both fast and stable responses. This paper presents a detailed process of designing, implementing, and experimentally verifying a breakthrough MPPT control strategy leveraging Tiny Machine Learning (TinyML). A lightweight artificial neural network (ANN) model is designed to directly infer the optimal duty cycle for the system’s DC-DC boost converter based on instantaneous electrical parameters (voltage and current), completely eliminating the need for mechanical sensors. The model is quantized to 8-bit integers and deployed on a low-cost STM32 microcontroller. Experimental results from a hardware prototype demonstrate that the TinyML controller achieves an exceptional tracking efficiency of 99.6% with a near-instantaneous dynamic response time of approximately 50 ms, significantly outperforming conventional algorithms. This work confirms the viability of TinyML as a powerful tool for creating next-generation, intelligent, and cost-effective renewable energy systems.