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Machine-learning based control of bi-modular multilevel PWM inverter for high power applications

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  • Ravi Teja Srungaram
  • Kishore Yadlapati

Abstract

This paper presents the topology and machine learning-based intelligent control of high-power PV inverter for maximum power extraction and optimal energy utilization. Modular converters with reduced components economic and reliable for high power applications. The proposed integrated intelligent machine learning based control delivers power conversion control with maximum power extraction and supervisory control for optimal load demand control. The topology of the inverter, operating modes, power control and supervisory control aspects are presented. Simulation is carried out in MATLAB/SIMULINK to verify the feasibility of the proposed inverter and control algorithm. The experimental study is presented to validate the simulation results. The operational performance of the proposed topology is evaluated in terms of operational parameters such as regulation of output power, and load relay control and is compared to existing topologies. The economic performance is also evaluated in terms of power switch sizing and reliability in power delivery concerning switch or power sources failure.

Suggested Citation

  • Ravi Teja Srungaram & Kishore Yadlapati, 2024. "Machine-learning based control of bi-modular multilevel PWM inverter for high power applications," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-23, July.
    • Handle: RePEc:plo:pone00:0305759
    DOI: 10.1371/journal.pone.0305759
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