MPPT control without a prior learning phase: Combining Incremental Conductance and k-NN for robust and adaptive energy management

Abstract

Maximum Power Point Tracking (MPPT) control is a critical component in photovoltaic (PV) systems, ensuring optimal energy extraction under fluctuating environmental conditions. This study presents a ground-breaking artificial intelligence (AI)-driven MPPT control method that operates without the need for pre-training or extensive data collection. Departing from conventional techniques, this approach minimizes trial-and-error processes, significantly reducing system disruptions and energy losses. The proposed control mechanism dynamically adapts to real-time variations, including component degradation and hardware modifications, making it a versatile and resilient solution for diverse PV systems. This method removes the need for setup or calibration. It greatly cuts production costs. It also reduces system complexity. Plus, it lessens the reliance on extra electronic components. The system demonstrates exceptional responsiveness and operational simplicity, outperforming traditional intelligent control methods in terms of efficiency, reliability, and ease of implementation. These advantages position it as a highly promising solution for enhancing energy management in PV systems across a wide range of environments and configurations.