An Identification Method for Sequence Impedance Model of DFIG Based on the Joint of Knowledge Driving and Data Driving

2023 IEEE 6th International Electrical and Energy Conference (CIEEC) Pub Date : 2023-05-12 DOI:10.1109/CIEEC58067.2023.10166079

Wang Chenxu, Li Han, L. Chengyu, Xiang Zhongming, Ni Qiulong, Nian Heng, Ye Lin, Yang Ying, Ma Junchao

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Abstract

Impedance based stability analysis is an effective tool to study broadband oscillation in power system. However, the analytical modeling process of impedance of renewable energy equipment is complex, and the impedance acquisition method based on data measurement has the defects of time-consuming and inconvenience for online measurement. In order to solve the above problems, an impedance identification method based on the joint of knowledge driving and data driving for Doubly-Fed Induction Generator (DFIG) is proposed. First, the small-signal model of DFIG is established based on knowledge-driven, acquiring the variables with nonlinear relationship with sequence impedance. Then, the Extreme Gradient Boosting (XGBoost) model is trained based on data driving to realize the identification of DFIG's sequence impedance under multiple operating conditions. Finally, the experiment on CHIL (Control-hardware-in-loop) is carried out to verify the accuracy of the XGBoost model.

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基于知识驱动和数据驱动联合的DFIG序列阻抗模型识别方法

基于阻抗的稳定性分析是研究电力系统宽带振荡的有效工具。然而，可再生能源设备阻抗的分析建模过程复杂，基于数据测量的阻抗采集方法存在耗时和不方便在线测量的缺陷。为解决上述问题，提出了一种基于知识驱动和数据驱动相结合的双馈感应发电机阻抗识别方法。首先，基于知识驱动建立DFIG的小信号模型，获取与序列阻抗具有非线性关系的变量;然后，基于数据驱动训练极限梯度增强(XGBoost)模型，实现多工况下DFIG序列阻抗的识别。最后，在CHIL(控制硬件在环)上进行了实验，验证了XGBoost模型的准确性。

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来源期刊

2023 IEEE 6th International Electrical and Energy Conference (CIEEC)

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