Nonlinear Model Reduction by Moment-Matching for a Point Absorber Wave Energy Conversion System

Abstract

This paper presents a data-driven model reduction by moment-matching approach to construct control-oriented models for a point absorber device. The methodology chosen and developed generates models which are input-to-state linear, with any nonlinear behaviour confined to the output map. Such a map is the result of a data-driven approximation procedure, where the so-called moment of the point absorber system is estimated via a least-squares procedure. The resulting control-oriented model can inherently preserve steady-state properties of the target WEC system for a user-defined class of input signals of interest, with the computation only dependent upon a suitably defined set of input-output data.

https://www.academia.edu/93557562/Predicting_Power_and_Hydrogen_Generation_of_a_Renewable_Energy_Converter_Utilizing_Data_Driven_methods_A_Sustainable_Smart_Grid_Case_Study

“…By designing an artificial neural network, they could make accurate predictions about the purpose of their study. Through the designed algorithm, they established a relationship between wave height and electricity production, and by analyzing the errors, they expressed a relationship between energy absorption efficiency and other parameters [31]. Forbush and colleagues in machine learning methods use the latest and most up-to-date methods to introduce new software to achieve the article's goal of predicting the output using data combination [32].…”