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Load Recognition in Hardware-Based Low Voltage Distribution Grids using Convolutional Neural Networks

Schlachter, Henning and Geißendörfer, Stefan and Maydell, Karsten von and Agert, Carsten (2023) Load Recognition in Hardware-Based Low Voltage Distribution Grids using Convolutional Neural Networks. IEEE Transactions on Smart Grid. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TSG.2023.3280326. ISSN 1949-3053.

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Official URL: https://ieeexplore.ieee.org/document/10136822


Due to climate targets of the German government, the share of renewable energy in the power grid will be increased and the number of grid participants connected to the low voltage level of the power grid will rise. This leads to new requirements in voltage control, especially in low voltage distribution grids. In order to achieve a stable power grid in future, further development of control strategies is necessary. In this paper, a load recognition concept, which was tested on simulative data in previous work, is further developed to reduce simulation effort. Additionally, the concept is adapted for real hardware influences and active grid participants complicating the recognition task. Thus, the main contribution of this study is the successful application of the methodology within a hardware-based test grid containing a charging electric vehicle. Using a convolutional neural network in a time series classification setting, the recognition rates in this use-case exceeded 99 % while benefiting from an asymmetric charging behavior. Due to these promising results, future voltage control strategies could be supported based on gained information through integration of the presented concept.

Item URL in elib:https://elib.dlr.de/196536/
Document Type:Article
Title:Load Recognition in Hardware-Based Low Voltage Distribution Grids using Convolutional Neural Networks
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Schlachter, HenningUNSPECIFIEDhttps://orcid.org/0000-0002-6356-9128UNSPECIFIED
Geißendörfer, StefanUNSPECIFIEDhttps://orcid.org/0000-0002-7496-8191UNSPECIFIED
Maydell, Karsten vonUNSPECIFIEDhttps://orcid.org/0000-0003-0966-5810UNSPECIFIED
Agert, CarstenUNSPECIFIEDhttps://orcid.org/0000-0003-4733-5257UNSPECIFIED
Date:26 May 2023
Journal or Publication Title:IEEE Transactions on Smart Grid
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:convolutional neural networks; deep learning; electric vehicles; load recognition; low voltage distribution grids; grid management
HGF - Research field:Energy
HGF - Program:Energy System Design
HGF - Program Themes:Digitalization and System Technology
DLR - Research area:Energy
DLR - Program:E SY - Energy System Technology and Analysis
DLR - Research theme (Project):E - Energy System Technology
Location: Oldenburg
Institutes and Institutions:Institute of Networked Energy Systems > Energy System Technology
Deposited By: Schlachter, Henning
Deposited On:11 Aug 2023 13:46
Last Modified:11 Aug 2023 13:46

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