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Voltage-Based Load Recognition in Low Voltage Distribution Grids with Deep Learning

Schlachter, Henning and Geißendörfer, Stefan and von Maydell, Karsten and Agert, Carsten (2021) Voltage-Based Load Recognition in Low Voltage Distribution Grids with Deep Learning. Energies, 15 (1). Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/en15010104. ISSN 1996-1073.

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Official URL: https://www.mdpi.com/1996-1073/15/1/104


Due to the increasing penetration of renewable energies in lower voltage level, there is a need to develop new control strategies to stabilize the grid voltage. For this, an approach using deep learning to recognize electric loads in voltage profiles is presented. This is based on the idea to classify loads in the local grid environment of an inverter’s grid connection point to provide information for adaptive control strategies. The proposed concept uses power profiles to systematically generate training data. During hyper-parameter optimizations, multi-layer perceptron (MLP) and convolutional neural networks (CNN) are trained, validated, and evaluated to determine the best task configurations. The approach is demonstrated on the example recognition of two electric vehicles. Finally, the influence of the distance in a test grid from the transformer and the active load to the measurement point, respectively, onto the recognition accuracy is investigated. A larger distance between the inverter and the transformer improved the recognition, while a larger distance between the inverter and active loads decreased the accuracy. The developed concept shows promising results in the simulation environment for adaptive voltage control.

Item URL in elib:https://elib.dlr.de/147904/
Document Type:Article
Title:Voltage-Based Load Recognition in Low Voltage Distribution Grids with Deep Learning
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
von Maydell, KarstenUNSPECIFIEDhttps://orcid.org/0000-0003-0966-5810UNSPECIFIED
Agert, CarstenUNSPECIFIEDhttps://orcid.org/0000-0003-4733-5257UNSPECIFIED
Date:23 December 2021
Journal or Publication Title:Energies
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:deep learning; load recognition; low voltage grid; grid management; electric vehicles
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:05 Jan 2022 14:43
Last Modified:10 Jan 2022 08:17

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