{"title":"Power system transient stability assessment based on hierarchical graph pooling method considering missing data","authors":"Chenhao Zhao, Zaibin Jiao, Penghui Zhang, Linbo Zhang","doi":"10.1016/j.ijepes.2025.111194","DOIUrl":null,"url":null,"abstract":"<div><div>The transient stability assessment (TSA) model based on graph deep learning relies on complete system topology and its characteristics. However, electrical operation data may be missing during the measurement and transmission process, which can lead to a decline in model evaluation performance. To address this issue, this paper proposes a hierarchical graph pooling TSA method for power systems that accounts for missing data. First, the power system is modeled as a graph with topological connections, and the missing data is filled using the K-order neighborhood mean (KNM). Next, a masked graph autoencoder with jumping knowledge is developed to reconstruct the missing features. Finally, considering both the topological attributes of nodes and the temporal characteristics of electrical quantities, a well-designed graph pooling method is introduced. During hierarchical graph pooling, subgraph features of the power grid at different scales are extracted and fused to achieve accurate and reliable TSA. Test results on the IEEE 39-bus system and a provincial power system in China demonstrate that the proposed method can maintain high evaluation performance under various types of missing data, exhibiting strong robustness and practicality.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111194"},"PeriodicalIF":5.0000,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Electrical Power & Energy Systems","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0142061525007422","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The transient stability assessment (TSA) model based on graph deep learning relies on complete system topology and its characteristics. However, electrical operation data may be missing during the measurement and transmission process, which can lead to a decline in model evaluation performance. To address this issue, this paper proposes a hierarchical graph pooling TSA method for power systems that accounts for missing data. First, the power system is modeled as a graph with topological connections, and the missing data is filled using the K-order neighborhood mean (KNM). Next, a masked graph autoencoder with jumping knowledge is developed to reconstruct the missing features. Finally, considering both the topological attributes of nodes and the temporal characteristics of electrical quantities, a well-designed graph pooling method is introduced. During hierarchical graph pooling, subgraph features of the power grid at different scales are extracted and fused to achieve accurate and reliable TSA. Test results on the IEEE 39-bus system and a provincial power system in China demonstrate that the proposed method can maintain high evaluation performance under various types of missing data, exhibiting strong robustness and practicality.
期刊介绍:
The journal covers theoretical developments in electrical power and energy systems and their applications. The coverage embraces: generation and network planning; reliability; long and short term operation; expert systems; neural networks; object oriented systems; system control centres; database and information systems; stock and parameter estimation; system security and adequacy; network theory, modelling and computation; small and large system dynamics; dynamic model identification; on-line control including load and switching control; protection; distribution systems; energy economics; impact of non-conventional systems; and man-machine interfaces.
As well as original research papers, the journal publishes short contributions, book reviews and conference reports. All papers are peer-reviewed by at least two referees.