A comparative study of wind turbine-generator modeling techniques: Physical modeling, subspace identification, and dynamic neural networks. Issue 4 (August 2022)

• Record Type: Journal Article
• Authors: Qandil, Mo'ath; Mohamed, Omar; Abu Elhaija, Wejdan
• Is Part Of: Wind engineering. Volume 46:Issue 4(2022)
• Page Start: 1117
• Page End: 1132
• Publication Date: 2022-08
• Subjects: Wind energy -- physical modeling -- system identification -- artificial neural networks -- simulation; Wind-pressure -- Periodicals; Winds -- Periodicals; Wind power -- Periodicals; Engineering meteorology -- Periodicals; Pression du vent; Vents; Énergie éolienne; Météorologie appliquée; Engineering meteorology; Wind power; Wind-pressure; Winds; Periodicals; 621.4505
• DOI: 10.1177/0309524X211066623
• Languages: English
• ISSNs: 0309-524X

Abstract

The increase of the favorable impacts of wind energy on the environment and the global energy requires overall understanding of the modeling methods that are commonly used for time-based simulation of wind energy systems. This paper introduces a comprehensive comparison of three salient modeling techniques of wind energy conversion systems, which are: the physical modeling, subspace system identification, and Dynamic Neural Network (ANN). The models have been created with the different modeling philosophies with the aid of historical data-sets representing four apart days of operation. The real system incorporates (TWT-1.65) type Wind-Turbine intergated with Multi-Pole Synchronous Generators (MPSG). The compariosn provides some crucial answers to the concerns of which technique is suited for an application, consequently, the comparison includes quantitative and qualitative measures. This article can be considered as a brief guide for future researchers to have thorough understanding of the modeling concepts in the field of wind engineering.