基于定向对抗迁移的暂态稳定评估模型

符益华; 卢国强; 王怀远

doi:10.19912/j.0254-0096.tynxb.2023-1623

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (2) : 226-234. DOI: 10.19912/j.0254-0096.tynxb.2023-1623

基于定向对抗迁移的暂态稳定评估模型

符益华¹, 卢国强², 王怀远¹

作者信息 +

TRANSIENT STABILITY ASSESSMENT MODEL BASED ON DIRECTIONAL ADVERSARIAL TRANSFER LEARNING

Fu Yihua¹, Lu Guoqiang², Wang Huaiyuan¹

Author information +

文章历史 +

摘要

为解决电网实际故障样本与训练样本分布差异较大而使模型无法评估的问题,提出一种定向对抗迁移的评估模型。首先,建立以堆叠自编码器为基础的传统对抗迁移模型,通过训练样本和潜在样本间的对抗学习,使模型提取到样本的共同特征,提高了模型评估潜在故障的能力;然后,在传统对抗迁移模型的基础上加入一种定向对抗方法,有选择性地迁移训练样本,所提方法根据训练样本和潜在故障样本的相似度值更改不同训练样本在对抗训练中的权重,减小大差异样本对迁移训练的负面影响;在实际区域系统仿真算例中所提方法相较传统对抗迁移模型提高5.72%的准确率。测试结果表明所提方法能够有效提高模型的迁移能力和评估准确率。

Abstract

To address the problem that the model unable to evaluate accurately when the distribution difference between the actual fault samples and the training samples, an evalution model based on directional adversarial transfer is proposed. Firstly, a traditional adversarial transfer model based on stacked auto-encoder is built. Through adversarial learning between training samples and potential samples, the model extracts the common features of the samples, thus improving the ability of the model to evaluate potential faults. Then, a directional adversarial method is added to the traditional adversarial transfer model to selectively transfer the training samples. The proposed method changes the weights of different training samples in the adversarial training according to the similarity values of training samples and potential fault samples, thus reducing the negative impact of large difference samples on transfer training. The proposed method improves the accuracy by 5.72% compared to the traditional adversarial transfer model in the real system simulation examples. The test results show that the proposed method can effectively improve the transferability and evaluation accuracy of the model.

导出引用

符益华, 卢国强, 王怀远. 基于定向对抗迁移的暂态稳定评估模型[J]. 太阳能学报. 2025, 46(2): 226-234 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1623

Fu Yihua, Lu Guoqiang, Wang Huaiyuan. TRANSIENT STABILITY ASSESSMENT MODEL BASED ON DIRECTIONAL ADVERSARIAL TRANSFER LEARNING[J]. Acta Energiae Solaris Sinica. 2025, 46(2): 226-234 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1623

中图分类号： TM712

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