Modeling of renewable energy systems on convolution codesusing interference patterns

Abstract

This study proposes a novel systems modeling approach based on interference patterns to characterize element interactions with energy variables through resulting projective patterns. The methodology encompasses three key phases: identification of distinct system stages, implementation of convolutional code modeling, and establishment of correlation indices between wave patterns to derive generalized parametric equations. Results demonstrate the development of a convolution element containing dual terms that quantify residual stored energy and energy polarized by the system's component elements. The model presents an innovative framework for physical systems analysis and renewable energy park optimization by comprehensively integrating all energy components into a unified mathematical representation. This approach enables more accurate prediction of system behavior under varying conditions and provides a foundation for improved efficiency in renewable energy applications.