Adaptive Selective Harmonic Minimization Based on ANNs for Cascade Multilevel Inverters With Varying DC Sources

Journal Article · Tue Jan 01 00:00:00 EST 2013 · IEEE Transactions on Industrial Electronics

DOI:https://doi.org/10.1109/TIE.2012.2224072· OSTI ID:1093757

Filho, Faete ^[1]; Maia, Helder Z ^[2]; Mateus, Tiago Henrique D ^[1]; Ozpineci, Burak ^[1]; Tolbert, Leon M ^[1]; Pinto, Joao Onofre P ^[1]

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1. ORNL
2. UFMS, Department of Electrical Engineering

A new approach for modulation of an 11-level cascade multilevel inverter using selective harmonic elimination is presented in this paper. The dc sources feeding the multilevel inverter are considered to be varying in time, and the switching angles are adapted to the dc source variation. This method uses genetic algorithms to obtain switching angles offline for different dc source values. Then, artificial neural networks are used to determine the switching angles that correspond to the real-time values of the dc sources for each phase. This implies that each one of the dc sources of this topology can have different values at any time, but the output fundamental voltage will stay constant and the harmonic content will still meet the specifications. The modulating switching angles are updated at each cycle of the output fundamental voltage. This paper gives details on the method in addition to simulation and experimental results.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Power Electronics and Electric Machinery Research Facility
• Sponsoring Organization: USDOE
• DOE Contract Number: DE-AC05-00OR22725
• OSTI ID: 1093757
• Journal Information: IEEE Transactions on Industrial Electronics, Vol. 60, Issue 5; ISSN 0278--0046
• Country of Publication: United States
• Language: English