A transient stability assessment method using quantitative analysis of key influencing factors and HGNN
In current research on transient stability assessment， the absence of quantitative analysis of key influencing factors hampers the accuracy of assessment results. Thus， a transient stability assessment method for power systems using hierarchical graph neural network （HGNN） is proposed， concentrating...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
zhejiang electric power
2025-07-01
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| Series: | Zhejiang dianli |
| Subjects: | |
| Online Access: | https://zjdl.cbpt.cnki.net/portal/journal/portal/client/paper/79b2f9f123ea0aad38eaca8be66a0d15 |
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| Summary: | In current research on transient stability assessment， the absence of quantitative analysis of key influencing factors hampers the accuracy of assessment results. Thus， a transient stability assessment method for power systems using hierarchical graph neural network （HGNN） is proposed， concentrating on the quantitative analysis of two crucial factors： fault area and the proportion of renewable energy. Firstly， graph theory is employed to simplify and partition the fault areas， identifying key fault areas that significantly impact transient stability assessment. Secondly， with doubly-fed asynchronous wind turbine generators as typical renew energy generation devices and considering their equivalent characteristics， the influence of different renewable energy proportions on the input features of transient stability assessment is quantitatively analyzed. Then， the impact of key fault areas and renewable energy proportion on transient stability assessment is explored using the HGNN model. Finally， a case study is carried out on the IEEE 39-bus system. The results indicate that the performance indicators of the HGNN model outperform those of traditional models. Identifying key fault areas can enhance assessment accuracy， while an increase in the proportion of renewable energy will reduce assessment accuracy to a certain degree. |
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| ISSN: | 1007-1881 |