通过自动编码器监测电动汽车供电设备并及早发现故障

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Sustainable Energy Grids & Networks Pub Date : 2024-08-13 DOI:10.1016/j.segan.2024.101497
Maciej Sakwa , Alfredo Nespoli , Silvana Matrone , Sonia Leva , Alice Guerini , Andrea Demartini , Emanuele Ogliari
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引用次数: 0

摘要

本文介绍了一种利用自动编码器与 LSTM 技术相结合检测电动汽车充电装置功率曲线异常的新方法。本研究介绍了一种结合两种机器学习技术的稳健方法,用于在实际案例研究中进行早期故障估计。通过对异常趋势进行更全面的分析,所提出的方法与现有方法相比具有显著优势。为了验证所提方法的有效性,作者对意大利配电系统运营商提供的真实电动汽车充电功率曲线进行了测试,该曲线记录在历史数据库中,并与传统异常检测技术的性能进行了比较。在电动汽车供电设备或充电站上进行的测试结果表明,所提出的方法在检测电动汽车充电曲线的异常趋势方面非常有效。
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Electric vehicle supply equipment monitoring and early fault detection through autoencoders

This paper presents a novel approach to detecting anomalies in Electric Vehicle charging unit power profiles using a combination of Autoencoders with LSTM techniques. This study presents a robust methodology, combining the two Machine Learning techniques, for early fault estimation in a real-world case study. The proposed methodology offers significant advantages over existing methods by providing a more comprehensive analysis of anomalous trends. To validate the effectiveness of the proposed methodology, the authors tested it on real Electric Vehicles charging power curves provided by an Italian Distribution System Operator recorded on a historical database and compared the performances with the ones of a traditional anomaly detection technique. The results of the study, tested on Electric Vehicles Supply Equipment or charging stations, demonstrate that the proposed approach is highly effective in detecting anomalous trends in Electric Vehicles charging profiles.

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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
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