A novel ensemble method based on residual convolutional neural network with attention module for transient stability assessment considering operational variability
{"title":"A novel ensemble method based on residual convolutional neural network with attention module for transient stability assessment considering operational variability","authors":"Wensheng Liu, Song Han, Na Rong","doi":"10.1016/j.engappai.2024.109519","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197624016774","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
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.
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.