Spatiotemporal Adversarial Domain Generalization for Locating Subsynchronous Oscillation Sources Under Unseen Conditions in Large-Scale Renewable Power Systems

IF 8.6 1区 工程技术 Q1 ENERGY & FUELS IEEE Transactions on Sustainable Energy Pub Date : 2024-09-25 DOI:10.1109/TSTE.2024.3468151
Xin Dong;Wenjuan Du;Qiang Fu;Haifeng Wang
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Abstract

Subsynchronous oscillations (SSOs) in renewable power systems have emerged as a major challenge, jeopardizing the stability and safety of power system operations. Thus, it is essential to accurately and timely locate SSO sources. Artificial intelligence (AI)-based methods for locating SSO sources have become increasingly popular, existing AI-based methods usually fail in practical applications due to unavailable or insufficient real-world SSO data for model training, and significant distribution gaps in samples under different operational conditions. They also fail to fully utilize the temporal characteristics of oscillations and the spatial topology of the system. Moreover, these methods only focus on locating either negative-damping-SSO or forced-SSO sources. To overcome these limitations, we introduce a novel strategy termed Spatiotemporal-Adversarial-Domain-Generalization (STADG) to locate oscillation sources in both SSO scenarios of real power systems. This method allows the model to train on multi-source domains (simplified-simulation power systems) with sufficient labeled samples, and to be directly applied to an unseen test target domain (real power system) under unknow operating conditions. The proposed approach employs a graph-attention network and a long-short-term-memory network to fully leverage spatial and temporal features of SSOs. Extensive experiments on the modified IEEE-39 and WECC-179 bus systems confirm the effectiveness of the proposed approach.
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在大规模可再生电力系统的未知条件下定位次同步振荡源的时空对抗域泛化技术
可再生能源系统的次同步振荡问题已成为电力系统运行稳定和安全的重大挑战。因此,准确、及时地定位SSO源非常重要。基于人工智能(AI)的单点登录源定位方法越来越受欢迎,现有的基于人工智能的方法通常在实际应用中失败,因为无法获得或缺乏用于模型训练的真实单点登录数据,以及不同操作条件下样本的分布差距很大。它们也不能充分利用振荡的时间特性和系统的空间拓扑结构。此外,这些方法只关注于定位负阻尼sso或强制sso源。为了克服这些限制,我们引入了一种称为时空对抗域泛化(STADG)的新策略来定位实际电力系统中两种SSO场景中的振荡源。该方法允许模型在具有足够标记样本的多源域(简化仿真电力系统)上进行训练,并直接应用于未知运行条件下的未知测试目标域(真实电力系统)。该方法采用图形注意网络和长短期记忆网络,充分利用了sso的时空特征。在改进的IEEE-39和WECC-179总线系统上进行的大量实验证实了所提出方法的有效性。
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来源期刊
IEEE Transactions on Sustainable Energy
IEEE Transactions on Sustainable Energy ENERGY & FUELS-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
21.40
自引率
5.70%
发文量
215
审稿时长
5 months
期刊介绍: The IEEE Transactions on Sustainable Energy serves as a pivotal platform for sharing groundbreaking research findings on sustainable energy systems, with a focus on their seamless integration into power transmission and/or distribution grids. The journal showcases original research spanning the design, implementation, grid-integration, and control of sustainable energy technologies and systems. Additionally, the Transactions warmly welcomes manuscripts addressing the design, implementation, and evaluation of power systems influenced by sustainable energy systems and devices.
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