Hardware-in-the-Loop Assessment of Neural MPPT Control in Photovoltaic Systems with Two-Phase Boost Conversion

Abstract

Photovoltaic power conversion systems require maximum power point tracking (MPPT) strategies capable of fast dynamic response with low computational burden, while remaining reliable under variable environmental conditions. While neural-network-based methods have been widely investigated, their practical deployment is often limited by the availability of representative training data and by the gap between offline algorithm development and real-time converter-level validation. This paper presents a reproducible hardware-in-the-loop workflow for the development and assessment of a lightweight neural MPPT controller applied to a photovoltaic system with a two-phase interleaved boost converter. The proposed approach generates a large synthetic training dataset using the single-diode photovoltaic model, leveraging only measurable quantities (PV voltage, PV current, and module temperature) as neural network inputs. The trained network estimates the voltage and current corresponding to the maximum power point, while a proportional-integral controller drives the converter toward the predicted operating point. The trained network is deployed on an STM32 microcontroller interfaced with the Typhoon HIL platform, allowing its real-time behavior to be tested against the emulated system. The measured neural MPPT execution time on the microcontroller is around 65 μs, with an overall CPU occupancy of nearly 4%, considering the PI controller stage. The implemented setup reproduces the photovoltaic generator, converter dynamics, switching behavior, and realistic irradiance and temperature profiles under repeatable real-time conditions. The interleaved boost architecture also reduces input current ripple and distributes current stress, making the setup suitable for medium-power photovoltaic applications. The main contribution of this work lies in the integrated modeling, training, control, and hardware-in-the-loop validation procedure, supporting the implementation of neural MPPT strategies.