Load Profile Cycle Recognition for Industrial DC Microgrids with Energy Storage Systems

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Männel, Alexander; Müller, Kevin; Knöchelmann, Elias; Ortmaier, Tobias: Load Profile Cycle Recognition for Industrial DC Microgrids with Energy Storage Systems. In: 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE). Piscataway, NJ : IEEE, 2020, S.904-911. DOI: https://doi.org/10.1109/ISIE45063.2020.9152432

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/10381

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Sum total of downloads: 30




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Abstract: 
Many consumers in production plants like industrial robots or tool machines perform repetitive movements, which lead to a cyclic load demand. However, these load profiles can usually only be roughly estimated at the planning stage. Hence, a subsequent online adaptation of the energy distribution is useful for cases, such as balancing between the charging and discharging amount of energy storage systems to improve those lifetime and usage. This paper presents a novel method of online adaptation for the load distribution of production processes within industrial direct current (DC) microgrids. The online load profile cycle recognition was used to adapt the energy distribution among the sources and loads in the DC microgrid. These sources can be inverters, rectifiers, energy storage systems or decentralized power supply units, such as photo voltaic systems. The approach consists of three major points, the load profile cycle recognition, the load profile analysis and the online adaptation of the energy distribution. This solution was tested in simulation and in experiment with a test rig, that contains an inverter and an energy storage system. The results show, that the load profile will be recognized latest from the third cycle and that the imbalance between charging and discharging amounts of the energy storage is less than 0.6% for each cycle after adaptation.
License of this version: Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.
Document Type: conferenceObject
Publishing status: acceptedVersion
Issue Date: 2020
Appears in Collections:Fakultät für Maschinenbau

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pos. country downloads
total perc.
1 image of flag of Germany Germany 23 76.67%
2 image of flag of United States United States 3 10.00%
3 image of flag of No geo information available No geo information available 1 3.33%
4 image of flag of Poland Poland 1 3.33%
5 image of flag of France France 1 3.33%
6 image of flag of Czech Republic Czech Republic 1 3.33%

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