STEPWISE IDENTIFICATION METHOD OF CONTROL PARAMETERS FOR DFIG BASED ON COMPOSITE OPTIMIZATION ALGORITHM

Xu Hengshan; Li Yanru; Li Wenhao; Xue Fei; Wang Wei

doi:10.19912/j.0254-0096.tynxb.2022-1964

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (4) : 247-256. DOI: 10.19912/j.0254-0096.tynxb.2022-1964

STEPWISE IDENTIFICATION METHOD OF CONTROL PARAMETERS FOR DFIG BASED ON COMPOSITE OPTIMIZATION ALGORITHM

Xu Hengshan¹, Li Yanru¹, Li Wenhao¹, Xue Fei², Wang Wei³

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Abstract

To precisely obtain the control parameters of doubly fed induction generator （DFIG） and improve the accuracy of electromechanical/electromagnetic simulation for the power system, a composite optimization algorithm with combining long short-term memory （LSTM） neutral network and improved particle swarm （IPSO） algorithm is used to identify the control parameters of DFIG. Firstly, the response data of real DFIG controller is obtained by hardware-in-loop （HIL） based on RT-LAB platform. Secondly, the observable quantities that have high relevance of DFIG are extracted through maximum information coefficient to avoid the effect of irrelevant features on the predicting results of LSTM model. On the above basis, in order to improve the optimization speed of the algorithm, LSTM is used to preliminary optimize the initial value and search range of DFIG control parameters. Finally, IPSO algorithm is used to precisely identify the control parameters of DFIG, which improves the searching efficiency and accuracy. The HIL testing results verify that LSTM-IPSO identification method has well adaptability and high identification accuracy to the control parameters under the 20%-80% low voltage ride through （LVRT） conditions.

Key words

wind turbines / long short-term memory network / particle swarm optimization / parameter identification / maximum information coefficient

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Xu Hengshan, Li Yanru, Li Wenhao, Xue Fei, Wang Wei. STEPWISE IDENTIFICATION METHOD OF CONTROL PARAMETERS FOR DFIG BASED ON COMPOSITE OPTIMIZATION ALGORITHM[J]. Acta Energiae Solaris Sinica. 2024, 45(4): 247-256 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1964

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