TY  - JOUR
A1  - Gitzel, Ralf
A1  - Hoffmann, Martin
A1  - Heiden, Philipp zur
A1  - Skolik, Alexander
A1  - Kaltenpoth, Sascha
A1  - Müller, Oliver
A1  - Kanak, Cansu
A1  - Kandiah, Kajan
A1  - Stroh, Max-Ferdinand
A1  - Boos, Wolfgang
A1  - Zajadatz, Maurizio
A1  - Suriyah, Michael
A1  - Leibfried, Thomas
A1  - Singhal, Dhruv Suresh
A1  - Bürger, Moritz
A1  - Hunting, Dennis
A1  - Rehmer, Alexander
A1  - Boyaci, Aydin
T1  - Towards Cognitive Assistance and Prognosis Systems in Power Distribution Grids
T2  - IEEE Access
N2  - In recent times, both geopolitical challenges and the need to counteract climate change have led to an increase in generated renewable energy as well as an increased demand for clean electrical energy. The resulting variability of electricity production and demand as well as an overall demand increase, put additional stress on the existing grid infrastructure. This leads to strongly increased maintenance demands for distribution system operators (DSOs). Today, condition monitoring is used to address these challenges. Researchers have already explored solutions for monitoring critical assets like switchgear and circuit breakers. However, with a shrinking knowledgeable technical workforce and increasing maintenance requirements, mere monitoring is insufficient. Already today, DSOs ask for actionable recommendations, optimization strategies, and prioritization methods to manage the growing task backlog effectively. In this paper we propose a vision of a grid-level cognitive assistance system that translates the outcome of diagnosis and prognosis systems into actionable work tasks for the grid operator. The solution is highly interdisciplinary and based on empirical studies of real-world requirements. We also describe the related work relevant to the multi-disciplinary aspects and summarize the research gaps that need to be closed over the next years.
KW  - Renewable energy
KW  - Condition-Monitoring
KW  - Künstliche Intelligenz
KW  - Electrical grid infrastructure
KW  - Maintenance planning
KW  - AI
KW  - Artificial intelligence
KW  - Explainable AI
KW  - KI
KW  - Large language models
KW  - Cognitive assistance system
KW  - Service engineering
KW  - LLM
KW  - 03
Y1  - 2024
UR  - https://epub.fir.de/frontdoor/index/index/docId/3535
SN  - 2169-3536
N1  - Dieser Beitrag gehört zum Projekt AProSys.
Laufzeit:
01.01.2023 – 31.12.2025
Förderkennzeichen:
03EI6090B
Zuwendungsgeber:
Bundesministerium für Wirtschaft und Klimaschutz (BMWK)
Projektträger:
Projektträger Jülich (PtJ) – Forschungszentrum Jülich GmbH
VL  - 12
PB  - Institute of Electrical and Electronics Engineers (IEEE)
ER  -