结合生成对抗网络与支持向量回归提升发电厂励磁系统参数辨识的准确性

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摘要现有的发电厂励磁系统参数辨识方法通常存在辨识精度低、训练稳定性差等问题，导致系统参数辨识的准确性不足，影响发电厂励磁系统的动态调节与稳定性。因此，本文结合生成对抗网络（GAN）与支持向量回归（SVR）模型，旨在提升发电厂励磁系统参数辨识的准确性。首先，采用Wasserstein GAN结构，通过生成器与判别器的对抗训练，生成与实际励磁系统相似的特征数据。然后，通过主成分分析对生成数据进行特征提取与降维，将高维数据映射到低维空间，作为后续SVR模型的输入。SVR模型采用径向基核函数处理复杂的非线性关系，进而预测励磁系统的关键参数。最终，通过结合GAN与SVR的双重优化，仿真结果表明，GAN+SVR优化方法的方均误差普遍较低，最高为0.000 9，最低为0.000 1，表明模型能够有效地捕捉励磁电流的动态特征，并保持较高的准确性。

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	覃莹
	王保国
	项文雅
	蒋宗良
	黄荣峰

关键词 ：发电厂, 励磁系统, 生成对抗网络（GAN）, 支持向量回归（SVR）, 参数辨识

Abstract：The existing methods for parameter identification of the excitation system in power plants usually have problems such as low identification accuracy and poor training stability. These issues lead to insufficient accuracy of parameter identification in the system, which affects the dynamic regulation and stability of the excitation system in power plants. Therefore, this paper combines the generative adversarial network (GAN) and the support vector regression (SVR) model to improve the accuracy of parameter identification in the excitation system of power plants. Firstly, the Wasserstein GAN structure is adopted, and through the adversarial training of the generator and the discriminator, feature data similar to the actual excitation system is generated. Then, the principal component analysis is used to extract and reduce the features of the generated data, mapping the high-dimensional data to a low-dimensional space, providing input for the subsequent SVR model. The SVR model uses the radial basis function kernel function to handle complex nonlinear relationships, thereby accurately predicting the key parameters of the excitation system. Finally, through the dual optimization of combining GAN and SVR, the simulation results show that the mean square error of the GAN+SVR optimization method is generally low, with the highest being 0.000 9 and the lowest being 0.000 1, indicating that the model can effectively capture the dynamic characteristics of the excitation current and maintain high accuracy.

Key words： power plant excitation system generative adversarial network (GAN) support vector regression (SVR) parameter identification

收稿日期: 2025-08-25

作者简介: 覃莹（1991—），女，广西宾阳人，硕士，主要从事发电机励磁技术、控制理论、人工智能方面的工作。

引用本文:

覃莹, 王保国, 项文雅, 蒋宗良, 黄荣峰. 结合生成对抗网络与支持向量回归提升发电厂励磁系统参数辨识的准确性[J]. 电气技术, 2026, 27(2): 40-45. QIN Ying, WANG Baoguo, XIANG Wenya, JIANG Zongliang, HUANG Rongfeng. Improving the accuracy of excitation system parameter identification in power plants by combining generative adversarial network and support vector regression. Electrical Engineering, 2026, 27(2): 40-45.

链接本文:

https://dqjs.cesmedia.cn/CN/Y2026/V27/I2/40

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