<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Although increasing the number of switches increases the switch losses, most designed controllers focus on controlling an inverter circuit with more than six switches. The paper aims to address this issue that arises in implementation of the voltage source inverter (VSI) for brushless DC (BLDC) motors. It optimises the sinusoidal pulse width modulation (PWM) controller, minimising total harmonic distortion (THD) while keeping the VSI’s circuit at six switches to avoid increased switching losses. This was achieved by applying an artificial neural network (ANN) to generate a signal, which combines with the already existing reference and carrier signals. The addition of the new signal to the existing signals contributed to generating more pulses compared with the conventional sinusoidal PWM. Simulink was used to design the system and analyse its performance with the conventional and neutral point clamped (NPC) VSI systems. Results indicated that the proposed system performs better when controlled with an LCC filter. Compared with the control experiments, its output waveform has the lowest THD value, which is 6.04%. The switching losses of all the systems were also computed. Results from the computation indicated that the proposed system is capable of reducing the switching losses by 0.6 kW compared with the NPC VSI brushless DC motor (BLDCM) system. BLDCM speed was tested across various conditions; the results are reported in Section 5.</p>
</abstract>

Although increasing the number of switches increases the switch losses, most designed controllers focus on controlling an inverter circuit with more than six switches. The paper aims to address this issue that arises in implementation of the voltage source inverter (VSI) for brushless DC (BLDC) motors. It optimises the sinusoidal pulse width modulation (PWM) controller, minimising total harmonic distortion (THD) while keeping the VSI’s circuit at six switches to avoid increased switching losses. This was achieved by applying an artificial neural network (ANN) to generate a signal, which combines with the already existing reference and carrier signals. The addition of the new signal to the existing signals contributed to generating more pulses compared with the conventional sinusoidal PWM. Simulink was used to design the system and analyse its performance with the conventional and neutral point clamped (NPC) VSI systems. Results indicated that the proposed system performs better when controlled with an LCC filter. Compared with the control experiments, its output waveform has the lowest THD value, which is 6.04%. The switching losses of all the systems were also computed. Results from the computation indicated that the proposed system is capable of reducing the switching losses by 0.6 kW compared with the NPC VSI brushless DC motor (BLDCM) system. BLDCM speed was tested across various conditions; the results are reported in Section 5.

Neural Network-Based Optimisation of Sinusoidal PWM Controller for VSI-Driven BLDC Motor

Department of Electrical and Electronic Engineering

Power Electronics and Drives

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Although increasing the number of switches increases the switch losses, most designed controllers focus on controlling an inverter circuit with more than six...

Neural Network-Based Optimisation of Sinusoidal PWM Controller for VSI-Driven BLDC Motor

Pubblicato online: 19 ago 2023

Pagine: 275 - 298

Ricevuto: 04 mag 2023

Accettato: 20 lug 2023

DOI: https://doi.org/10.2478/pead-2023-0018

Parole chiave
artificial neural network, harmonics, sinusoidal PWM, inverter, MATLAB/simulink

© 2023 George Owusu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Neural Network-Based Optimisation of Sinusoidal PWM Controller for VSI-Driven BLDC Motor

Pubblicato online: 19 ago 2023

Pagine: 275 - 298

Ricevuto: 04 mag 2023

Accettato: 20 lug 2023

DOI: https://doi.org/10.2478/pead-2023-0018

Parole chiaveartificial neural network, harmonics, sinusoidal PWM, inverter, MATLAB/simulink

© 2023 George Owusu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
artificial neural network, harmonics, sinusoidal PWM, inverter, MATLAB/simulink