Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO Algorithm

Variations in generator parameters that occur during the operation of a doubly-fed induction wind turbine (DFIG) constitute a significant factor in the degradation of control performance. To address the problem of difficulty in identifying multiple parameters simultaneously in DFIG, a parameter iden...

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Published inEnergies (Basel) Vol. 16; no. 3; p. 1355
Main Authors Yang, Fanjie, Zeng, Yun, Qian, Jing, Li, Youtao, Xie, Shihao
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2023
Subjects
Accuracy
Adaptive algorithms
Air-turbines
Algorithms
Design and construction
doubly-fed induction wind turbine
Electric generators
Energy
Identification methods
ISIAGWO algorithm
Mathematical models
Mathematical optimization
Neural networks
Optimization
Parameter identification
Parameter sensitivity
Sensitivity analysis
Simulation
Trajectory analysis
trajectory sensitivity
Turbines
Velocity
Wind power
China
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Summary:Variations in generator parameters that occur during the operation of a doubly-fed induction wind turbine (DFIG) constitute a significant factor in the degradation of control performance. To address the problem of difficulty in identifying multiple parameters simultaneously in DFIG, a parameter identification method depending on the adaptive grey wolf algorithm with an information-sharing search strategy (ISIAGWO) is proposed to solve the problem of low accuracy and slow identification speed of multiple parameters in DFIG. The easily obtainable generator outlet current was selected as the observed quantity, and the trajectory sensitivity analysis was performed on the five electrical parameters of the DFIG to derive its discriminability. Finally, the parameter recognition of the DFIG was carried out using the ISIAGWO algorithm in the MATLAB/Simulink simulation software, applying the proposed calculation functions. The experimental results show that the ISIAGWO algorithm has better identification accuracy, stability, and convergence for DFIG’s generator parameter identification.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16031355