Title :
Reduce Common-Mode Voltage in Cascaded Multilevel Inverter Based on Artificial Neural Network
Author :
Pinggang Song ; Eryong Guan ; Zhiming Lin
Author_Institution :
Dept. of Electr. & Electron. Eng., East China Jiaotong Univ., Jiangxi
Abstract :
Common-mode voltage due to different modulation in power converters has introduced numerous problems in electrical systems. This paper reviewed a space vector PWM (SVM), which can get rid of the common-mode voltage in multilevel inverters with fundamental switching frequency. But for difficulty of implement SVM algorithm, which requires complex and time-consuming online computation, a feedforward artificial neural network (ANN) with four-layer configuration is proposed, which receives angle command and modulation index as input and generate desirable PWM pattern for the three-phase cascaded multilevel inverter. The data derived from simulating of SVM-based scheme is employed to train the proposed ANN with MATLAB/Neural Network Toolbox offline. The performance of the trained ANN-based modulator is investigated in the 11-level cascaded inverters prototype, and the results of the experiment appear to be wonderful that common-mode voltage is reduced drastically and the power semiconductors operate with fundamental switching frequency
Keywords :
PWM invertors; feedforward neural nets; power engineering computing; switching convertors; ANN; MATLAB; Neural Network Toolbox; cascaded multilevel inverter; common-mode voltage reduction; feedforward artificial neural network; fundamental switching frequency; modulation index; power converters; power semiconductors; space vector PWM; Artificial neural networks; Computational modeling; Computer networks; MATLAB; Prototypes; Pulse width modulation inverters; Space vector pulse width modulation; Support vector machines; Switching frequency; Voltage;
Conference_Titel :
Power Electronics Specialists Conference, 2006. PESC '06. 37th IEEE
Conference_Location :
Jeju
Print_ISBN :
0-7803-9716-9
DOI :
10.1109/PESC.2006.1711813