Analytical Model and Topology Optimization of Doubly-Fed Induction Generator

Lu Sun;Haoyu Kang;Jin Wang;Zequan Li;Jianjun Liu;Yiming Ma;Libing Zhou
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Abstract

As the core component of energy conversion for large wind turbines, the output performance of doubly-fed induction generators (DFIGs) plays a decisive role in the power quality of wind turbines. To realize the fast and accurate design optimization of DFIGs, this paper proposes a novel hybrid-driven surrogate-assisted optimization method. It firstly establishes an accurate subdomain model of DFIGs to analytically predict performance indexes. Furthermore, taking the inexpensive analytical dataset produced by the subdomain model as the source domain and the expensive finite element analysis dataset as the target domain, a high-precision surrogate model is trained in a transfer learning way and used for the subsequent multi-objective optimization process. Based on this model, taking the total harmonic distortion of electromotive force, cogging torque, and iron loss as objectives, and the slot and inner/outer diameters as parameters for optimizing the topology, achieve a rapid and accurate electromagnetic design for DFIGs. Finally, experiments are carried out on a 3MW DFIG to validate the effectiveness of the proposed method.
双馈感应发电机的分析模型和拓扑优化
作为大型风力涡轮机能量转换的核心部件,双馈异步发电机(DFIG)的输出性能对风力涡轮机的电能质量起着决定性作用。为实现 DFIG 快速、准确的优化设计,本文提出了一种新颖的混合驱动代用辅助优化方法。它首先建立了精确的双馈变流器子域模型,通过分析预测性能指标。此外,以子域模型产生的廉价分析数据集为源域,以昂贵的有限元分析数据集为目标域,以迁移学习的方式训练出高精度的代用模型,并用于后续的多目标优化过程。基于该模型,以电动势总谐波畸变、齿槽转矩和铁损为目标,以槽孔和内外直径为参数优化拓扑结构,实现了快速、精确的双馈变流器电磁设计。最后,在 3MW DFIG 上进行了实验,验证了所提方法的有效性。
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