Adaptive Regulated Sparsity Promoting Approach for Data-Driven Modeling and Control of Grid-Connected Solar Photovoltaic Generation

TL;DR

This paper introduces an adaptive sparsity-promoting learning method for data-driven modeling and control of solar PV systems, improving efficiency and fault analysis capabilities compared to traditional techniques.

Contribution

The paper proposes the adaptive regulated sparse regression (ARSR) algorithm that adaptively tunes hyperparameters for better PV system modeling and control.

Findings

ARSR effectively models PV system dynamics from measurements.

The approach enables successful fault analysis in PV systems.

Validation through real-time simulations confirms its practical applicability.

Abstract

This paper aims to introduce a new statistical learning technique based on sparsity promoting for data-driven modeling and control of solar photovoltaic (PV) systems. Compared with conventional sparse regression techniques that might introduce computational complexities when the number of candidate functions increases, an innovative algorithm, named adaptive regulated sparse regression (ARSR) is proposed that adaptively regulates the hyperparameter weights of candidate functions to best represent the dynamics of PV systems. Utilizing this algorithm, open-loop and closed-loop models of single-stage and two-stage PV systems are obtained from measurements and are utilized for control design purposes. Moreover, it is demonstrated that the proposed data-driven approach can successfully be employed for fault analysis studies, which distinguishes its capabilities compared with other data-driven techniques. Finally, the proposed approach is validated through real-time simulations.