A Grid Fault Diagnosis Method Based on Stacking Algorithm

Power grid dispatching is developing towards digitalization and intellectualization. Using the data generated in power grid operations to diagnose faults through artificial intelligence technology directly has become the development trend of the power grid. However, traditional machine learning algo...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of physics. Conference series Jg. 2477; H. 1; S. 12067 - 12072
Hauptverfasser: Du, Mingxuan, Wang, Zirui, Zhang, Ziqi, Zhang, Xu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.04.2023
Schlagworte:
Algorithms
Artificial intelligence
Digitization
Fault diagnosis
Machine learning
Physics
Stacking
ISSN:1742-6588, 1742-6596
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Power grid dispatching is developing towards digitalization and intellectualization. Using the data generated in power grid operations to diagnose faults through artificial intelligence technology directly has become the development trend of the power grid. However, traditional machine learning algorithms often need help to achieve good diagnostic results in practical applications. Therefore, a fusion model based on the Stacking integration algorithm is proposed, and four commonly used machine learning algorithms are selected for fusion. Furthermore, the model is trained using the fault alarm information features extracted from the SCADA system. Then, by comparing and analyzing the diagnostic effect of four single algorithms and the Stacking model for various fault types, the conclusion that the Stacking model has a better overall effect than other algorithms in the diagnosis of various fault types is obtained, and the advantage of the stacking fusion algorithm is proved.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2477/1/012067