Work

Parameter identification and verification of doubly fed induction generator controllers based on Arctic puffin optimization

Year: 2025

Type: article

Abstract: In view of challenges faced by the traditional parameter identification methods of complex, nonlinear, multiparameter, and strongly coupled doubly fed induction generators (DFIGs), this paper proposes a parameter identification method based on the Arctic puffin optimization algorithm. The system structure and model of the rotor-side converter of a DFIG is introduced, and a mathematical model of the relationship between the power outer loop of the rotor-side converter and the rotor current is derived. The accuracy of parameter identification is verified via a mutual information (MI) analysis based on waveform data. Finally, measured data of the AC/DC hybrid microgrid system is used for parameter identification, further confirming the applicability and reliability of the algorithm in engineering applications.

Source: Electric Power Systems Research

DOI: 10.1016/j.epsr.2025.111768

Authors: Yanling Lv, Xiang Zhao, Zexin Mou

Institution: Harbin University of Science and Technology

Language: English

Topic: Wind Turbine Control Systems

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,810