郑瀚林,温步瀛,王怀远
ZHENG Hanlin,WEN Buying,WANG Huaiyuan

• 1:福州大学电气工程与自动化学院

摘要(Abstract)：

针对暂态电压稳定评估模型因实际故障样本与训练样本的分布存在偏差而导致的精度下降问题，提出一种融合样本加权迁移学习的暂态电压稳定评估模型。首先，采用长短期记忆网络进行特征提取，借助特征提取器与域判别器的对抗训练机制，实现源域与目标域样本特征空间的对齐。其次，基于源域训练样本与目标域故障样本的相似度指标，在对抗训练过程中为源域样本动态分配权重，以强化关键样本在迁移过程中的作用。最后，在IEEE 39节点系统中进行仿真实验，结果表明，所提模型能够有效提升评估精度与泛化性能。
To address the issue of accuracy degradation in transient voltage stability assessment models caused by the distribution discrepancy between actual fault samples and training samples, this paper proposes a novel assessment model that integrates sample-weighted transfer learning. Firstly, long short-term memory(LSTM) networks are employed for feature extraction. An adversarial training mechanism between the feature extractor and a domain discriminator is utilized to align the feature spaces of samples from the source and target domains. Secondly, based on similarity metrics between the source domain training samples and the target domain fault samples, dynamic weights are assigned to the source domain samples during the adversarial training process. This reinforces the contribution of critical samples to the transfer learning. Finally, simulation experiments conducted on the IEEE 39-bus system demonstrate that the proposed model effectively enhances assessment accuracy and generalization performance.

关键词(KeyWords)： 暂态电压稳定评估;样本加权;迁移学习;长短期记忆网络
transient voltage stability assessment;sample weighting;transfer learning;LSTM networks

Abstract:

Keywords:

基金项目(Foundation): 福建省自然科学基金(2022J01113)

作者(Author): 郑瀚林,温步瀛,王怀远
ZHENG Hanlin,WEN Buying,WANG Huaiyuan

DOI: 10.19585/j.zjdl.202602004

参考文献(References)：

• [1]韩肖清，李廷钧，张东霞，等.双碳目标下的新型电力系统规划新问题及关键技术[J].高电压技术，2021,47(9):3036-3046.HAN Xiaoqing,LI Tingjun,ZHANG Dongxia,et al.New issues and key technologies of new power system planning under double carbon goals[J].High Voltage Engineering,2021,47(9):3036-3046.
• [2]周孝信，陈树勇，鲁宗相，等.能源转型中我国新一代电力系统的技术特征[J].中国电机工程学报，2018,38(7):1893-1904.ZHOU Xiaoxin,CHEN Shuyong,LU Zongxiang,et al.Technical characteristics of China’s new generation power system in energy transformation[J]. Proceedings of the CSEE,2018,38(7):1893-1904.
• [3]张智刚，康重庆.碳中和目标下构建新型电力系统的挑战与展望[J].中国电机工程学报，2022,42(8):2806-2819.ZHANG Zhigang,KANG Chongqing. Challenges and prospects for constructing the new-type power system towards a carbon neutrality future[J]. Proceedings of the CSEE,2022,42(8):2806-2819.
• [4]汤奕，崔晗，李峰，等.人工智能在电力系统暂态问题中的应用综述[J].中国电机工程学报，2019,39(1):2-13.TANG Yi,CUI Han,LI Feng,et al.Review on artificial intelligence in power system transient stability analysis[J].Proceedings of the CSEE,2019,39(1):2-13.
• [5]STOTT B. Power system dynamic response calculations[J].Proceedings of the IEEE,1979,67(2):219-241.
• [6]FOUAD A A,KRUEMPEL K C,VITTAL V,et al.Transient stability program output analysis[J]. IEEE Power Engineering Review,1986,PER-6(2):24-25.
• [7]GLESS G E. Direct method of liapunov applied to transient power system stability[J]. IEEE Transactions on Power Apparatus and Systems,1966,PAS-85(2):159-168.
• [8]张哲，秦博宇，高鑫，等.融合注意力机制和卷积神经网络的电网暂态电压稳定评估及可解释性分析[J].电网技术，2024,48(11):4648-4657.ZHANG Zhe,QIN Boyu,GAO Xin,et al.Transient voltage stability assessment and interpretability analysis of power grids based on the fusion of attention mechanism and convolutional neural network[J].Power System Technology,2024,48(11):4648-4657.
• [9]庄福振，罗平，何清，等.迁移学习研究进展[J].软件学报，2015,26(1):26-39.ZHUANG Fuzhen,LUO Ping,HE Qing,et al.Survey on transfer learning research[J].Journal of Software,2015,26(1):26-39.
• [10]卓振宇，张宁，谢小荣，等.高比例可再生能源电力系统关键技术及发展挑战[J].电力系统自动化，2021,45(9):171-191.ZHUO Zhenyu,ZHANG Ning,XIE Xiaorong,et al.Key technologies and developing challenges of power system with high proportion of renewable energy[J].Automation of Electric Power Systems,2021,45(9):171-191.
• [11]王渝红，何其多，郑宗生，等.基于条件生成对抗网络与迁移学习的暂态电压稳定超前判别[J].电力自动化设备，2025,45(2):159-166.WANG Yuhong,HE Qiduo,ZHENG Zongsheng,et al.Lead discrimination of transient voltage stability based on conditional generation adversarial network and transfer learning[J].Electric Power Automation Equipment,2025,45(2):159-166.
• [12]李晶晶，孟利超，张可，等.领域自适应研究综述[J].计算机工程，2021,47(6):1-13.LI Jingjing,MENG Lichao,ZHANG Ke,et al.Review of studies on domain adaptation[J].Computer Engineering,2021,47(6):1-13.
• [13]HAN H G,MENG Y,WU X L,et al.A transfer learningbased multimodal feature fusion model for bearing fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement,2025,74:3530413.
• [14]LI B Q,WU J Y.Adaptive assessment of power system transient stability based on active transfer learning with deep belief network[J]. IEEE Transactions on Automation Science and Engineering,2023,20(2):1047-1058.
• [15]吴俊勇，史法顺，李栌苏，等.基于MRSE-CNN的电力系统多任务暂态稳定自适应评估[J].电力自动化设备，2025,45(2):167-175.WU Junyong,SHI Fashun,LI Lusu,et al.Multi-task transient stability adaptive assessment of power system based on MRSE-CNN[J]. Electric Power Automation Equipment,2025,45(2):167-175.
• [16]李楠，张帅，胡禹先，等.一种基于深度自适应网络迁移的暂稳评估模型更新框架[J].电力系统保护与控制，2024,52(14):25-35.LI Nan,ZHANG Shuai,HU Yuxian,et al. An updating framework of a model for transient stability assessment based on a deep adaptive network transfer[J].Power System Protection and Control,2024,52(14):25-35.
• [17]ZHANG W,DENG L F,ZHANG L,et al.A survey on negative transfer[J]. IEEE/CAA Journal of Automatica Sinica,2023,10(2):305-329.
• [18]申锦鹏，杨军，李蕊，等.基于改进域对抗迁移学习的电力系统暂态稳定自适应评估[J].电力系统自动化，2022,46(23):67-75.SHEN Jinpeng,YANG Jun,LI Rui,et al. Self-adaptive transient stability assessment of power system based on improved domain adversarial transfer learning[J].Automation of Electric Power Systems,2022,46(23):67-75.
• [19]王博文，王龙飞，董炜，等.受端电网暂态失稳场景识别及控制措施优化[J].浙江电力，2023,42(3):71-78.WANG Bowen,WANG Longfei,DONG Wei,et al.Recognition of transient instability scenarios of the receivingend grid and control strategy optimization[J]. Zhejiang Electric Power,2023,42(3):71-78.
• [20]李保罗，孙华东，张恒旭，等.基于两阶段迁移学习的电力系统暂态稳定评估框架[J].电力系统自动化，2022,46(17):176-185.LI Baoluo,SUN Huadong,ZHANG Hengxu,et al.Transient stability assessment framework of power system based on two-stage transfer learning[J]. Automation of Electric Power Systems,2022,46(17):176-185.
• [21]GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial networks[J].Communications of the ACM,2020,63(11):139-144.
• [22]GANIN Y,USTINOVA E,AJAKAN H,et al.Domainadversarial training of neural networks[M]//Domain Adaptation in Computer Vision Applications.Cham:Springer International Publishing,2017:189-209.
• [23]SMOLA A,GRETTON A,SONG L,et al. A Hilbert space embedding for distributions[C]//Algorithmic Learning Theory.Berlin,Heidelberg:Springer,2007:13-31.
• [24]王艳新，闫静，王建华，等.基于域对抗迁移卷积神经网络的小样本GIS绝缘缺陷智能诊断方法[J].电工技术学报，2022,37(9):2150-2160.WANG Yanxin,YAN Jing,WANG Jianhua,et al.Intelligent diagnosis for GIS with small samples using a novel adversarial transfer learning in convolutional neural network[J].Transactions of China Electrotechnical Society,2022,37(9):2150-2160.
• [25]薛安成，周健，刘瑞煌，等.采用多二元表判据的实用暂态电压稳定裕度指标研究[J].中国电机工程学报，2018,38(14):4117-4125.XUE Ancheng,ZHOU Jian,LIU Ruihuang,et al. Study on practical transient voltage stability margin index using multi-binary table criterion[J].Proceedings of the CSEE,2018,38(14):4117-4125.