Transient Stability Assessment Framework of Power System Based on Two-stage Transfer Learning

被引：0

作者：

Li B. ^{[1
]}

Sun H. ^{[2
]}

Zhang H. ^{[1
]}

Gao L. ^{[2
]}

Xu S. ^{[2
]}

Huang Y. ^{[2
]}

机构：

[1] Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan

[2] China Electric Power Research Institute Co., Ltd., Beijing

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2022年 / 46卷 / 17期

关键词：

assessment; data-driven; deep transfer learning; transient stability;

D O I：

10.7500/AEPS20211207005

中图分类号：

学科分类号：

摘要：

In order to improve the adaptability of the data-driven transient stability assessment model to the power grid, deep transfer learning is introduced into the updating process, and a transient stability assessment framework based on two-stage transfer learning is proposed. The proposed framework is divided into two stages according to the time scale. In the first stage, the deep subdomain adaptive network is used to mine unlabeled data information, and the evaluation performance of the model is rapidly improved to a relatively reliable level. The obtained transfer model is combined with the time domain simulation method for comprehensive stability judgment, which improves the usability of the model in the initial stage of power grid change. In the second stage, the transfer model is used to select the high-value sample set, and the second update is carried out by combining the sample transfer and fine-tuning technology to restore the evaluation performance to a higher level and reduce the updating time cost. The test results in the models of IEEE 39-bus system and a provincial power grid in China show that the proposed framework has completeness and can make the model quickly respond to the changes in the power grid. © 2022 Automation of Electric Power Systems Press. All rights reserved.

引用

页码：176 / 185

页数：9

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