Review of electric vehicles' charging data anomaly detection based on deep learning-Reference-Cited by-同舟云学术

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Review of electric vehicles' charging data anomaly detection based on deep learning

Published:2022-08 Issue: Volume: Page:
ISSN:
Container-title:2022 Power System and Green Energy Conference (PSGEC)
language:
Short-container-title:

Author:

Feng Qi¹,Li Hengjie¹,Zhou Yun²,Feng Donghan²,Wang Yun³,Su Yun³

Affiliation:

1. School of Electrical and Information Engineering, Lanzhou University of Technology,Lanzhou,China

2. Shanghai Jiao Tong University,Dept. of Electrical Engineering,Shanghai,China

3. Electric Power Research Institute, State Grid Shanghai Electric Power Company,Shanghai,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9880926/9880914/09881073.pdf?arnumber=9881073

Reference33 articles.

1. Fault Diagnosis System for Electric Vehicle Charging Devices Based on Fault Tree Analysis

2. A SVM-based detection method for electricity stealing behavior of charging pile

3. Research on Detection and Fault Diagnosis Technology of Electric Vehicle Charging Facilities

4. Detecting Anomalies in Intelligent Vehicle Charging and Station Power Supply Systems With Multi-Head Attention Models

5. Using Imbalanced Triangle Synthetic Data for Machine Learning Anomaly Detection

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2. A Combined Model for Ultra-Short-Term PV Forecasting Based on SOM Clustering;2023 IEEE 14th International Symposium on Power Electronics for Distributed Generation Systems (PEDG);2023-06-09

3. Performance Curve Profiling and Gated Recurrent Unit Based State Detection for Cloud Native Microservices;Communications in Computer and Information Science;2023

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