Work

Anomaly Detection for Charging Piles Based on Conditional Variational Autoencoder

Year: 2025

Type: article

Abstract: The dependability and robustness of electric vehicle (EV) charging infrastructure, which functions as a pivotal nexus between consumers and the electrical network, play a crucial role in enhancing the efficiency of EV usage and ensuring the safe management of the power grid. Therefore, it is highly urgent to develop an effective and robust anomaly detection system to provide early warnings of potential risks and address the issue of imbalanced data distribution. In this paper, a conditional variational autoencoder (CVAE) is employed to construct an anomaly detection model for charging pile data. In contrast to other anomaly detection methodologies, the method proposed in this study demonstrates more desirable performance. Furthermore, this paper extends the investigation by modifying the architecture of the CVAE to facilitate supervised learning. The reconfigured network structure yields enhanced detection accuracy, obtaining better anomaly detection performance when evaluated on the charging pile dataset.

Source: International Transactions on Electrical Energy Systems

DOI: 10.1155/etep/3531700

Authors: Chuanjun Wang, Yinyu Lu, Mingxin Wang, Ke Hu

Institutions: Nanjing Institute of Technology, Chongqing University of Posts and Telecommunications

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,350