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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:Sachin Thakur,Akhil Gupta,Kamal Kant Sharma.Optimizing Cascaded H-Bridge Multilevel Inverters for Solar PV Systems Using Machine Learning: A Comparative Study of 7-, 9-, and 11-Level Configurations with MPPT Control[J].Journal of Harbin Institute Of Technology(New Series),2026,33(3):55-63.DOI:10.11916/j.issn.1005-9113.2024121.
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Optimizing Cascaded H-Bridge Multilevel Inverters for Solar PV Systems Using Machine Learning: A Comparative Study of 7-, 9-, and 11-Level Configurations with MPPT Control
Author NameAffiliation
Sachin Thakur Electrical Engineering Department, Chandigarh University, Mohali 140413, Punjab, India 
Akhil Gupta Electrical Engineering Department, I.K.Gujral Punjab Technical University Main Campus, Kapurthala 144603, Punjab, India 
Kamal Kant Sharma Electrical Engineering Department, Chandigarh University, Mohali 140413, Punjab, India 
Abstract:
MultiLevel Inverters (MLIs) are vital for converting the DC output from Solar PhotoVoltaic (SPV) systems into high-quality Alternating Current (AC) waveforms for grid integration or load packages. This study examines the optimization of Cascaded H-Bridge (CHB) MLIs with 7, 9, and 11 voltage levels using the Support Vector Machines (SVM) technique. The goal is to evaluate the performance of these MLI configurations by estimating Total Harmonic Distortion (THD), with and without SVM optimization. The proposed system integrates the Maximum Power Point Tracking (MPPT) technique to extract maximum power from SPV systems. Detailed Simulink models were developed for each MLI configuration to simulate their operation with MPPT control. Findings indicate that SVM optimization is effective at reducing THD, with 11-level MLIs achieving the greatest decrease to less than 7%. The appearance of SVMs is effective in improving the performance of MLIs, providing information on the complexity-performance trade-off of inverters. This study leads to the optimization of SPV systems by combining superior device mastering techniques with a multilevel inverter.
Key words:  multilevel  harmonic  machine learning  optimization  photovoltaic  cascaded bridge
DOI:10.11916/j.issn.1005-9113.2024121
Clc Number:TM615
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