Wavelet and Signal Analyzer Based High- Frequency Ripple Extraction in the Context of MPPT Algorithm in Solar PV Systems

Abstract

This paper addresses the challenge of improving the efficiency of solar photovoltaic systems, which often suffer from intermittent energy harvesting and suboptimal power extraction methods. Solar energy, a promising renewable source, can replace fossil fuels, but its efficiency is hindered by fluctuating power output. To tackle this issue, the study introduces a novel approach utilizing wavelet-based ripple extraction. By employing a ten-level wavelet decomposition process, this method aims to obtain smooth, ripple-free power from solar canopies. The research examines the tracking efficiency of maximum power point tracking (MPPT) and the power oscillation issue. Notably, the study compares the Perturb and Observe method with the Artificial Neural Network MPPT approach, assessing ripple power during wavelet decomposition stages. Results reveal that the proposed wavelet and signal analyzer-based technique achieves a remarkable tracking efficiency of nearly 99.9% while minimizing power fluctuations, surpassing prior studies. This innovative approach enhances the performance and efficiency of solar PV systems, ultimately contributing to the broader adoption of solar energy. Real-time validation through OPAL-RT further validates its effectiveness.

Keywords

• Solar photovoltaic (PV)
• maximum power point tracker (MPPT)
• high-frequency ripple
• perturb and observe (PO)
• artificial neural network (ANN)
• wavelet analyzer