Affiliations: [a] Department of EEE, SRM Institute of Science and Technology, Chennai, India
| [b] Department of EEE, Vel tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, India
| [c] Lead Data Scientist, Briteyellow pvt. Ltd, Cranefield Research Center, UK
| [d] EEE KPR Institute of Engineering and Technology, Coimbatore
Correspondence:
[*]
Corresponding author. K. Selvakumar, Department of EEE, SRM Institute of Science and Technology, Chennai,
India. E-mail: [email protected].
Abstract: An Artificial Neural Network (ANN) based Space Vector Pulse Width Modulation (SVPWM) for five level cascaded H-bridge inverter (CHBI) fed grid connected photovoltaic (PV) system. The multilevel inverter topologies are offers better performance compare conventional two level inverter like reduced total harmonic distortion, less electromagnetic interferences and voltage stresses across switches are low. The ANN based SVPWM generates the switching pulses for cascaded H-bridge inverter; it improves the accuracy in reference vectors tuning and identification, which leads to improve the inverter output voltage, better utilization of dc-link voltage and controlled output current. The ANN control makes the implementation of SVPWM becomes simple and minimizes the intricacy in tracking reference vector and calculation of switching time; it is suitable for any type of non-linear systems. This proposed system is energized using PV system and it is boosted using dc-dc boost converter, and the output of CHBI is synchronized with grid connected system using coupled inductor. The simulation and experimental results of ANN based SVPWM for CHBI is verified using simulink-matlab and DSP processor.
