Three-Random SVPWM Strategy Based on Markov Chain for NPC Three-Level Inverter

Permanent magnet synchronous motors (PMSMs), owing to the features of low maintenance costs, great efficiency, and high power density, are extensively utilized in applications such as rail transportation, industrial robots, and new energy electric vehicles. However, the application of space vector pulse width modulation (SVPWM) results in the motor phase current exhibiting clustered harmonic distributions at the integer multiples of the switching frequency, leading to motor noise and vibration issues. To address the issues, this paper proposes a three-random SVPWM (TRPWM) strategy based on a three-state Markov chain, integrating random pulse position, random switching frequency, and random small vector dwell time. By adhering to the principle of voltage-second balance, this strategy spreads the concentrated high-frequency harmonics over a wider frequency range, significantly reducing the magnitude of the concentrated harmonics in the phase current. Comparative experiments with conventional SVPWM, conventional dual-random SVPWM, and conventional three-random SVPWM strategies demonstrate that the proposed approach achieves the expansion of harmonics at integer multiples of the switching frequency in the phase current, effectively suppressing high-frequency vibrations in PMSMs.