충전데이터를 이용한 이상감지 제어시스템

In this paper, we implement a system that detects abnormalities in the charging data transmitted from the charger during the charging process of electric vehicles and controls them remotely. Using classification algorithms such as logistic regression, KNN, SVM, and decision trees, to do this, an ana...

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Bibliographic Details
Published in한국정보통신학회논문지 Vol. 26; no. 2; pp. 313 - 316
Main Author Moon, Sang-Ho
Format Journal Article
LanguageKorean
Published 한국정보통신학회 2022
Subjects
전자/정보통신공학
Electronic vehicle charging
Classification model
Charger
Clustering
Anomaly detection
Online AccessGet full text
ISSN2234-4772
2288-4165
DOI10.6109/jkiice.2022.26.2.313

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Summary:In this paper, we implement a system that detects abnormalities in the charging data transmitted from the charger during the charging process of electric vehicles and controls them remotely. Using classification algorithms such as logistic regression, KNN, SVM, and decision trees, to do this, an analysis model is created that judges the data received from the charger as normal and abnormal. In addition, a model is created to determine the cause of the abnormality using the existing charging data based on the analysis of the type of charger abnormality. Finally, it is solved using unsupervised learning method to find new patterns of abnormal data.
Bibliography:KISTI1.1003/JNL.JAKO202209833895350
http://jkiice.org
ISSN:2234-4772
2288-4165
DOI:10.6109/jkiice.2022.26.2.313