An Enhanced MPPT Algorithm Based on Adaptive Linear Regression with Contextual Weight Memory (ALR-CWM) for PV Systems

Abstract

This paper presents an Adaptive Linear Regression With Contextual Weight Memory (ALR-CWM) model to use for Maximum Power Point Tracking (MPPT) control in photovoltaic (PV) systems. The proposed approach employs a linear regression model with a similarity-based memory mechanism to use irradiance and temperature measurements to yield predictions of Vmp, enabling direct estimation of operating voltage without extensive training. On a realistic dataset over three years, the ALR-CWM model under various irradiance levels leads to a voltage prediction mean absolute error (MAE) of 0.00124 V and a tracking efficiency above 97%. MATLAB/Simulink simulations reveal the system model's correctness, fast convergence, and advantages in embedded MPPT applications. ALR-CWM realizes an interpretable and computationally efficient option for dynamic energy optimization of PV systems relative to conventional and machine learning-based methods.