TRANSIENT STABILITY ASSESSMENT MODEL BASED ON DIRECTIONAL ADVERSARIAL TRANSFER LEARNING

Fu Yihua; Lu Guoqiang; Wang Huaiyuan

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

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (2) : 226-234. DOI: 10.19912/j.0254-0096.tynxb.2023-1623

TRANSIENT STABILITY ASSESSMENT MODEL BASED ON DIRECTIONAL ADVERSARIAL TRANSFER LEARNING

Fu Yihua¹, Lu Guoqiang², Wang Huaiyuan¹

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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.

Key words

transient stability / transfer learning / adversarial machine learning / sample similarity measure / stacked auto-encoder

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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

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