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Abstract
This paper introduces physics-informed neural network (PINN) for control of grid connected converter by fusing its underlying equations into the training process, thereby reducing the requirement of qualitative training data. In comparison to the traditional data-driven methods, which either incur a significant computational burden, or use overly conservative surrogate models, it is explored that PINN can be easily optimized as per the performance requirements and is significantly superior in terms of computation time, data requirements (trained using only 3000 datapoints), and prediction accuracy (an average of 98.76%). As a result, PINN unravels new modeling orientation for power electronics, and is well-suited for commercial applications. Finally, its robustness under various grid disturbances has been validated under experimental conditions.98.76%). As a result, PINN unravels new modeling orientation for power electronics, and is well-suited for commercial applications. Finally, its robustness under various grid disturbances has been validated under experimental conditions.
Original language | English |
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Title of host publication | Proceedings of the 2023 IEEE Applied Power Electronics Conference and Exposition (APEC) |
Number of pages | 6 |
Publisher | IEEE |
Publication date | Mar 2023 |
Pages | 2801-2806 |
Article number | 10131654 |
ISBN (Print) | 978-1-6654-7540-2 |
ISBN (Electronic) | 978-1-6654-7539-6 |
DOIs | |
Publication status | Published - Mar 2023 |
Event | 38th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2023 - Orlando, United States Duration: 19 Mar 2023 → 23 Mar 2023 |
Conference
Conference | 38th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2023 |
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Country/Territory | United States |
City | Orlando |
Period | 19/03/2023 → 23/03/2023 |
Sponsor | IEEE Industry Applications Society (IAS), IEEE Power Electronics Society (PELS), Power Sources Manufacturers Association (PSMA) |
Series | I E E E Applied Power Electronics Conference and Exposition. Conference Proceedings |
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ISSN | 1048-2334 |
Keywords
- Active front end rectifier
- Artificial Intelligence
- Control
- Grid connected applications
- Grid connected converter
- Machine learning
- Neural Networks
- Physics-informed machine learning
- Physics informed neural network
- Power Electronics
- Grid-tied inverter
Fingerprint
Dive into the research topics of 'Data-Driven Controllability of Power Electronics Under Boundary Conditions: A Physics-Informed Neural Network Based Approach'. Together they form a unique fingerprint.Projects
- 2 Active
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AI-Power: Artificial Intelligence for Next-Generation Power Electronics
Blaabjerg, F., Wang, H., Sahoo, S., Zhao, S., Zhang, Y., Novak, M. & Frøstrup, S.
01/09/2022 → 31/08/2027
Project: Research
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