Transformer Substation Network Disconnection Prediction via Semantic Reasoning with Causal Modeling.
Saved in:
| Title: | Transformer Substation Network Disconnection Prediction via Semantic Reasoning with Causal Modeling. |
|---|---|
| Authors: | Jie, Ren1 rengj9@js.sgcc.com.cn, Xiaojun, Yao1 yaoxj1@js.sgcc.com.cn, Hong, Chen1 ch_jt_sz@js.sgcc.com.cn |
| Source: | Computer Science & Information Systems. Apr2026, Vol. 23 Issue 2, p707-727. 21p. |
| Subjects: | Causal models, Electric substations, Telecommunication systems, Counterfactuals (Logic), Semantics (Philosophy) |
| Abstract: | Reliable communication networks are indispensable for the stable operation of smart grids and substations. Currently, WAPI networks have been widely adopted in relevant scenarios. Nevertheless, WAPI networks are confronted with disconnection risks attributed to complex network topologies, dynamic traffic fluctuations, and external environmental disturbances. Most methods rely on correlation analysis and lack causal interpretability, which restricts their effectiveness in rootcause localization and preventive maintenance practices. To address the problem, we propose a disconnection prediction approach that integrates prompt-driven semantic reasoning with structured causal analysis. The approach constructs a causal event graph that models semantic, temporal, and topological dependencies across devices and alarm sequences after extracts heterogeneous information to unified event representation. Based on the established graph, an inference module combines causal path analysis, structural causal models, and counterfactual reasoning to assess the influence of events, predict emerging disconnection risks, and identify plausible root causes with coherent and interpretable justification. By tightly coupling semantic abstraction with causal reasoning, the proposed approach provides a proactive, explainable, and extensible mechanism for anticipating network disruptions and supporting informed maintenance decisions. Experiments demonstrate that the proposed approach improves prediction accuracy and interpretability, verifying its value for smart grid communication networks. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 193328683 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Transformer Substation Network Disconnection Prediction via Semantic Reasoning with Causal Modeling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jie%2C+Ren%22">Jie, Ren</searchLink><relatesTo>1</relatesTo><i> rengj9@js.sgcc.com.cn</i><br /><searchLink fieldCode="AR" term="%22Xiaojun%2C+Yao%22">Xiaojun, Yao</searchLink><relatesTo>1</relatesTo><i> yaoxj1@js.sgcc.com.cn</i><br /><searchLink fieldCode="AR" term="%22Hong%2C+Chen%22">Hong, Chen</searchLink><relatesTo>1</relatesTo><i> ch_jt_sz@js.sgcc.com.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Science+%26+Information+Systems%22">Computer Science & Information Systems</searchLink>. Apr2026, Vol. 23 Issue 2, p707-727. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Causal+models%22">Causal models</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+substations%22">Electric substations</searchLink><br /><searchLink fieldCode="DE" term="%22Telecommunication+systems%22">Telecommunication systems</searchLink><br /><searchLink fieldCode="DE" term="%22Counterfactuals+%28Logic%29%22">Counterfactuals (Logic)</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics+%28Philosophy%29%22">Semantics (Philosophy)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Reliable communication networks are indispensable for the stable operation of smart grids and substations. Currently, WAPI networks have been widely adopted in relevant scenarios. Nevertheless, WAPI networks are confronted with disconnection risks attributed to complex network topologies, dynamic traffic fluctuations, and external environmental disturbances. Most methods rely on correlation analysis and lack causal interpretability, which restricts their effectiveness in rootcause localization and preventive maintenance practices. To address the problem, we propose a disconnection prediction approach that integrates prompt-driven semantic reasoning with structured causal analysis. The approach constructs a causal event graph that models semantic, temporal, and topological dependencies across devices and alarm sequences after extracts heterogeneous information to unified event representation. Based on the established graph, an inference module combines causal path analysis, structural causal models, and counterfactual reasoning to assess the influence of events, predict emerging disconnection risks, and identify plausible root causes with coherent and interpretable justification. By tightly coupling semantic abstraction with causal reasoning, the proposed approach provides a proactive, explainable, and extensible mechanism for anticipating network disruptions and supporting informed maintenance decisions. Experiments demonstrate that the proposed approach improves prediction accuracy and interpretability, verifying its value for smart grid communication networks. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193328683 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2298/CSIS251022018R Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 707 Subjects: – SubjectFull: Causal models Type: general – SubjectFull: Electric substations Type: general – SubjectFull: Telecommunication systems Type: general – SubjectFull: Counterfactuals (Logic) Type: general – SubjectFull: Semantics (Philosophy) Type: general Titles: – TitleFull: Transformer Substation Network Disconnection Prediction via Semantic Reasoning with Causal Modeling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jie, Ren – PersonEntity: Name: NameFull: Xiaojun, Yao – PersonEntity: Name: NameFull: Hong, Chen IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18200214 Numbering: – Type: volume Value: 23 – Type: issue Value: 2 Titles: – TitleFull: Computer Science & Information Systems Type: main |
| ResultId | 1 |
