Parameter identification method for low voltage ride-through control of direct-drive wind turbines based on GRU-ISSA

In order to improve the accuracy of low voltage ride-through (LVRT) control parameter identification for direct-drive wind turbines, a identification method based on the GRU-ISSA algorithm is proposed. Firstly, the key parameters of LVRT are identified through theoretical analysis; Secondly, the GRU model is trained across various operating conditions to obtain identified control parameter values without requiring an optimization range, simplifying the identification process. Finally, the Improved Sparrow Algorithm (ISSA) is applied to refine the control parameters and enhance identification accuracy. The experimental results show that the error between the GRU-ISSA identification data and the measured data is only 0.27%, and the maximum error is 0.8%, which verifies the effectiveness and feasibility of the method.

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Publication Details

Published
2024-12-06
DOI
https://doi.org/10.1109/icips64173.2024.10900075
Primary Topic
Wind Turbine Control Systems
Type
article
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article

Parameter identification method for low voltage ride-through control of direct-drive wind turbines based on GRU-ISSA

Huibiao Yang, Hengshan Xu, Xutao Li, Hongqiang Li
Wind Turbine Control Systems
article

Parameter identification method for low voltage ride-through control of direct-drive wind turbines based on GRU-ISSA

Huibiao Yang, Hengshan Xu, Xutao Li, Hongqiang Li
article en

Abstract

In order to improve the accuracy of low voltage ride-through (LVRT) control parameter identification for direct-drive wind turbines, a identification method based on the GRU-ISSA algorithm is proposed. Firstly, the key parameters of LVRT are identified through theoretical analysis; Secondly, the GRU model is trained across various operating conditions to obtain identified control parameter values without requiring an optimization range, simplifying the identification process. Finally, the Improved Sparrow Algorithm (ISSA) is applied to refine the control parameters and enhance identification accuracy. The experimental results show that the error between the GRU-ISSA identification data and the measured data is only 0.27%, and the maximum error is 0.8%, which verifies the effectiveness and feasibility of the method.

China Three Gorges University (CN)
Affordable and clean energy
Openalex Percentile: Top 74%
Wind Turbine Control Systems
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Parameter identification method for low voltage ride-through control of direct-drive wind turbines based on GRU-ISSA — Huibiao Yang, Hengshan Xu, et al. · (2024) | TGRS Research Map | TGRS