Identification of low voltage ride-through parameters of DFIG based on improved particle swarm optimization algorithm

Abstract

To improve the accuracy of LVRT control parameter identification for DFIG, a method is proposed based on an improved particle swarm optimization (PSO) algorithm. Firstly, a single DFIG connected to an infinite bus system model is established using the MATLAB/Simulink simulation platform. Subsequently, logistic chaos mapping is employed to initialize the population based on the standard PSO algorithm. An adaptive parameter adjustment mechanism is introduced, along with the Lévy flight strategy integrated into the algorithm’s flow. Finally, based on the residual measured operating conditions, an analysis and verification of the identified parameter accuracy are conducted. Simulations demonstrate the effectiveness and accuracy of the proposed parameter identification method. The results indicate that the proposed approach can rapidly and accurately identify the LVRT control parameters of DFIG. This method utilizes advanced signal processing techniques and machine learning algorithms to analyze and model real-time data from the DFIG system, enabling the rapid and accurate identification of the DFIG low-voltage ride-through saturation control parameters. This provides researchers and engineers with an effective tool to better understand and optimize the performance of DFIG systems, thereby driving greater progress in research and practice in this field.