Abstract
In this paper, an Artificial Intelligence-based (AI) system is proposed for an 11-level cascaded H-bridge multilevel inverter (MLI) with the aims of harmonic suppression and reliability enhancement. The system consists of three seamlessly integrated Neural Networks (NNs). First, a multilayer perceptron is used to generalize the optimal switching angles for selective harmonic elimination under non-equal DC voltages. Next, an autoencoder NN estimates the voltage sensor readings to address potential drifting. Finally, a perceptron NN detects inverter faults based solely on the output voltage of the MLI. Simulation scenarios were evaluated, and the results show that the proposed system provides a comprehensive solution for the robust operation of the MLI. The proposed solution is capable of minimizing the targeted harmonics orders with minimal impact on the fundamental voltage, even when the voltage sensor drifts. Furthermore, the inverter under fault conditions was successfully identified.
| Original language | English |
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| Title of host publication | 2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3413-3419 |
| Number of pages | 7 |
| ISBN (Electronic) | 9798350376067 |
| DOIs | |
| State | Published - 2024 |
| Event | 2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024 - Phoenix, United States Duration: Oct 20 2024 → Oct 24 2024 |
Publication series
| Name | 2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024 - Proceedings |
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Conference
| Conference | 2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024 |
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| Country/Territory | United States |
| City | Phoenix |
| Period | 10/20/24 → 10/24/24 |
Funding
This work was funded by the U.S. Department of Energy, Office of Electricity. This manuscript has been authored by UT-Battelle, LLC under Contract No. DEAC05- 00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Keywords
- Fault detection
- Harmonic elimination
- Multilevel Inverter
- Neural network
- Sensor estimation