Fellner, D., Strasser, T., Wolfgang Kastner, Feizifar, B., & Abdulhadi, I. F. (2023). An Operational Data-Driven Malfunction Detection Framework for Enhanced Power Distribution System Monitoring – The DeMaDs Approach. In 27th International Conference on Electricity Distribution (CIRED 2023) (pp. 70–74). https://doi.org/10.1049/icp.2023.0244
E325-04 - Forschungsbereich Regelungstechnik und Prozessautomatisierung E191-03 - Forschungsbereich Automation Systems
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Erschienen in:
27th International Conference on Electricity Distribution (CIRED 2023)
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ISBN:
978-1-83953-855-1
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Datum (veröffentlicht):
2023
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Veranstaltungsname:
27th International Conference on Electricity Distribution (CIRED 2023)
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Veranstaltungszeitraum:
12-Jun-2023 - 15-Jun-2023
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Veranstaltungsort:
Rome, Italien
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Umfang:
5
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Keywords:
power distribution system; malfunction detection; framework
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Abstract:
The changes in the electric energy system toward a sustainable future are inevitable and already on the way today. This often entails a change of paradigm for the electric energy grid, for example, the switch from central to decentralized power generation which also has to provide grid-supporting functionalities. However, due to the scarcity of distributed sensors, new solutions for grid operators for monitoring these functionalities are needed. The framework presented in this work allows to apply and assess data-driven detection methods in order to implement such monitoring capabilities. Furthermore, an approach to a multi-stage detection of misconfigurations is introduced. Details on implementations of the single stages as well as their requirements are also presented. Furthermore, testing and validation results are discussed. Due to its feature of being seamlessly integrable into system operators{\textquoteright} current metering infrastructure, clear benefits of the proposed solution are pointed out.
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Projekt (extern):
Austrian Research Promotion Agency (FFG) European Community’s Horizon 2020 Program (H2020)
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Projektnummer:
879017 870620
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Forschungsschwerpunkte:
Mathematical and Algorithmic Foundations: 25% Information Systems Engineering: 25% Climate Neutral, Renewable and Conventional Energy Supply Systems: 50%