A novel ensemble method based on residual convolutional neural network with attention module for transient stability assessment considering operational variability

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Engineering Applications of Artificial Intelligence Pub Date : 2024-10-30 DOI:10.1016/j.engappai.2024.109519
Wensheng Liu, Song Han, Na Rong
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Abstract

Data-driven methods have been extensively applied in the field of power system transient stability assessment (TSA) owing to their robust capabilities to excavate valuable features. However, TSA methods still face significant challenges in predictive accuracy and generalization ability under variable operation conditions with fluctuating loads or power generations. To address this, a data-driven ensemble TSA method which integrates convolutional block attention module (CBAM) with residual network (ResNet) is proposed to enhance the prediction accuracy. Meanwhile, the traditional cross entropy loss function is replaced by the focal loss function, aiming to reduce the misclassification of unstable samples. Moreover, a rapid updating strategy integrating active learning and fine turning techniques is suggested. It can renew the classifier quickly with limited labeled samples and less time when the network topology changes substantially and makes the pre-trained TSA model unavailable, thus ensuring optimal performance on the new topology. Finally, case studies conducted on the New England 10-machine 39-bus system and the Western Electricity Coordinating Council (WECC) 29-machine 179-bus system validate the effectiveness and robustness of the proposed TSA method. The accuracy of the proposed TSA method achieves 99.56% on 10-machine system and 99.47% on 29-machine system separately, demonstrating the superiority of the proposed TSA method.
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基于带关注模块的残差卷积神经网络的新型集合方法,用于考虑运行变异性的瞬态稳定性评估
由于数据驱动方法具有挖掘有价值特征的强大能力,因此已被广泛应用于电力系统暂态稳定性评估(TSA)领域。然而,在负载或发电量波动的多变运行条件下,TSA 方法在预测精度和泛化能力方面仍面临巨大挑战。针对这一问题,我们提出了一种数据驱动的集合 TSA 方法,该方法将卷积块注意模块(CBAM)与残差网络(ResNet)集成在一起,以提高预测精度。同时,用焦点损失函数取代了传统的交叉熵损失函数,以减少对不稳定样本的误分类。此外,还提出了一种融合了主动学习和微调技术的快速更新策略。当网络拓扑结构发生重大变化,导致预先训练好的 TSA 模型无法使用时,它可以在有限的标注样本下快速更新分类器,从而确保在新的拓扑结构下获得最佳性能。最后,在新英格兰 10 台机器 39 总线系统和西部电力协调委员会(WECC)29 台机器 179 总线系统上进行的案例研究验证了所提出的 TSA 方法的有效性和鲁棒性。建议的 TSA 方法在 10 机系统和 29 机系统上的准确率分别达到 99.56% 和 99.47%,证明了建议的 TSA 方法的优越性。
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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