Dual random optimized pulse width modulation controller for three-phase voltage source inverter driven brushless DC motor

Brushless DC (BLDC) motors are widely utilized in contemporary power electronic applications due to their inherent high efficiency and dynamic performance characteristics. However, the application of conventional pulse width modulation (PWM) techniques often results in the generation of concentrated harmonic components, which can lead to undesirable acoustic noise, increased torque ripple, and a reduction in overall inverter efficiency. This research proposes an artificial neural network–assisted dual random pulse width modulation (ANN-DRPWM) strategy designed to significantly enhance the output quality of a three-phase voltage source inverter that drives a BLDC motor. The methodology involves supervised ANN training to facilitate the dual randomization of both the carrier and modulation signals, thereby effectively

dispersing harmonic energy while simultaneously improving DC-link voltage utilization. To further mitigate residual harmonics and ensure compliance with relevant harmonic standards, a passive LC filter is subsequently integrated into the system. The proposed system is modeled and simulated within the MATLAB/Simulink environment, and its performance is rigorously evaluated against conventional sinusoidal PWM and flying capacitor multilevel inverter (FCMLI) techniques. The simulation results demonstrate that the ANN-DRPWM method achieves a post-filter total harmonic distortion (THD) of 2.17%, alongside a notable 6-9% improvement in inverter efficiency and a significant reduction in torque ripple. This strategy offers an efficient and intelligent modulation solution for high-performance BLDC motor drive systems.

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