Fault Detection of UHV Converter Valve Based on Optimized Cost-Sensitive Extreme Random Forest

Fuqiang Xiong, Chenhuan Cao, Mingzhu Tang (), Zhihong Wang, Jun Tang and Jiabiao Yi
Additional contact information
Fuqiang Xiong: State Grid Hunan Extra High Voltage Substation Company, Changsha 410004, China
Chenhuan Cao: School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China
Mingzhu Tang: School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China
Zhihong Wang: State Grid Hunan Extra High Voltage Substation Company, Changsha 410004, China
Jun Tang: School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China
Jiabiao Yi: School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China

Energies, 2022, vol. 15, issue 21, 1-17

Abstract: Aiming at the problem of unbalanced data categories of UHV converter valve fault data, a method for UHV converter valve fault detection based on optimization cost-sensitive extreme random forest is proposed. The misclassification cost gain is integrated into the extreme random forest decision tree as a splitting index, and the inertia weight and learning factor are improved to construct an improved particle swarm optimization algorithm. First, feature extraction and data cleaning are carried out to solve the problems of local data loss, large computational load, and low real-time performance of the model. Then, the classifier training based on the optimization cost-sensitive extreme random forest is used to construct a fault detection model, and the improved particle swarm optimization algorithm is used to output the optimal model parameters, achieving fast response of the model and high classification accuracy, good robustness, and generalization under unbalanced data. Finally, in order to verify its effectiveness, this model is compared with the existing optimization algorithms. The running speed is faster and the fault detection performance is higher, which can meet the actual needs.

Keywords: converter valve; cost-sensitive; extreme random forest; fault detection; particle swarm optimization algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/21/8059/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/21/8059/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:21:p:8059-:d:957724

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().