Continuous enhancement of the performance of energy harvesters in recent years has broadened their arenas of applications. On the other hand, ample availability of IoT devices has made radio frequency (RF) a viable source of energy harvesting. Integration of a maximum power point tracking (MPPT) controller in RF energy harvester is a necessity that ensures maximum available power transfer with variable input power conditions. In this paper, FPGA implementation of a machine learning (ML) model for maximum power point tracking in RF energy harvesters is presented. A supervised learning-based ML model-feedforward neural network (FNN) has been designed which is capable of tracking maximum power point with optimal accuracy. The model was designed using stochastic gradient descent (SGD) optimizer and mean square error (MSE) loss function. Simulation results of the VHDL translated model demonstrated a good agreement between the expected and the obtained values. The proposed ML based MPPT controller was implemented in Artix-7 Field Programmable Gate Array (FPGA).
A High Performance Maximum Power Point Tracker for Photovoltaic Power System Using DC-DC Boost Converter
