MODEL-AGNOSTIC META-LEARNING-BASED UNIVERSAL MPPT CONTROL FOR SOLAR WATER PUMPING APPLICATIONS

Abstract

This paper introduces an innovative maximum power point tracking controller for off-grid solar water pumping systems using Model-Agnostic Meta-Learning (MAML). In contrast to traditional MPPT algorithms, meta-learned controller provided robust per- formance across a wide variety of DC-DC converter topologies via few-shot adaptation, without manual tuning.  The controller utilizes a two-layer feedforward neural network (32 hidden units, 193 parameters) trained with Reptile to reach optimal convergence in just 5 ms using a mere five gradient descent updates on recent data.  The architecture is validated on three high-gain DC-DC converter topologies, LUO, Boost, and SEPIC, under dynamic irradiance.  Simulation yields an MPPT tracking efficiency of more than 99.89% in all topologies with outstanding consistency.  The system achieves 60–65% to- tal efficiency from photovoltaic to hydraulic output with water flow variability less than 0.016% for all converter topologies.  Model simulation includes real-world converter loss mechanisms, motor-pump dynamics, and hydraulic performance characteristics.  A hybrid control approach fuses meta-learned predictions with incremental conductance for strong stability  at near maximum power point.   Its  computational  power  and  high  speed  of adaptation make deployment possible on low-power embedded microcontrollers in distant agricultural environments. Simulation experiments validate meta-learning as a universal- izable and effective approach for universal MPPT control over various power electronics architectures.