Improved identification method of doubly-fed induction generator based on trajectory sensitivity analysis

The drive train and generator parameters of doubly-fed induction generator (DFIG) may vary with the operation situation, which is significant for the stability of the DFIG. Previously used identification methods that have more measurements and experience difficulty in operation can easily fall into local optimum, and furthermore, there have been no studies on variable parameters. Therefore, in this study, we propose a novel identification method based on the improved particle swarm optimization (PSO) algorithm and trajectory sensitivity analysis. Initially, the equivalent model of DFIG was established. Selecting active power as a measurement based on trajectory sensitivity analysis can improve the accuracy of the identification method. Thereafter, an improved PSO algorithm compared with genetic algorithm (GA), PSO, and GA-PSO are verified by the classic test functions. Finally, a novel identification method is presented, which only requires one measurement and with no data processing of measurements. This method is suited for both constant and variable parameters. The results of parameter identification demonstrate that the proposed method can improve the accuracy and adaptability of the variable parameters. Besides, the proposed method can be used not only in doubly-fed wind turbines but also in other studies of new energy power generation.

Automated summary

This study introduces a novel identification method for doubly-fed induction generator (DFIG) parameters, which enhances accuracy and adaptability through an improved PSO algorithm and trajectory sensitivity analysis. The method only requires one measurement and no data processing, suitable for both constant and variable parameters.