- By:
- Rezende Da Costa Reis Kimpara, Renata ; Magri Kimpara, Marcio Luiz ; Ribeiro, Pedro Eugenio M; Pereira Pinto, Joao Onofre ; Ozpineci, Burak
- Page Number:
- 3413-3419
- Book Title:
- 2024 IEEE Energy Conversion Congress and Exposition
- Publication Date:
- August 27, 2025
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- 2024 IEEE Energy Conversion Congress and Exposition (ECCE)
- Conference Location:
- Phoenix, Arizona, United States of America
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/ECCE55643.2024.10861526
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.
