Increased Productivity Digital Power Converters Employing Machine Learning Control Algorithms and Adaptive Pulse Width Modulation
Basim Abdulkareem Farhan
Computer Techniques Engineering Department, Imam Al- Kadhim University College, Baghdad, Iraq
Muna M. Salman
Ministry of High Education and Scientific Research/ Scientific research commission
Ahmed Mazin Jalal
Computer Techniques Engineering Department, Imam Al- Kadhim University College, Baghdad, Iraq
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http://doi.org/10.37648/ijiest.v11i01.011
Abstract
In this work, we put forward a new approach to enhance digital power converter efficiency using machine learning control algorithms together with adaptive pulse width modulation (PWM) techniques. The proposed method enhances power conversion efficiency by 12.5%, while preserving better transient response pattern in comparison with classical controllers. The experimental results demonstrate that the total harmonic distortion (THD) is reduced to lower than 2.1% and the power efficiency single 96.2% at different loading conditions. The introduction of ANN for real-time parameter optimization produces promising results for next-generation power electronics oriented applications.
Keywords: power electronics; Neural networks; adaptive PWM; machine learning control; digital power conversion
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