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

• Autorzy: Owusu George , Annan John Kojo , Nunoo Solomon

Identyfikatory

DOI

10.2478/pead-2023-0018

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

artificial neural network; harmonics; sinusoidal PWM; inverter; Matlab/Simulink

• Wydawca: De Gruyter
• Czasopismo: Power Electronics and Drives
• Rocznik: 2023
• Tom: Vol. 8 (43)
• Strony: 275--298
• Opis fizyczny: Bibliogr. 17 poz., rys., tab.

Twórcy

autor

Owusu George

• Department of Electrical and Electronic Engineering, University of Mines and Technology, Tarkwa, Ghana

• https://orcid.org/0000-0001-9447-0158

autor

Annan John Kojo

• Department of Electrical and Electronic Engineering, University of Mines and Technology, Tarkwa, Ghana

autor

Nunoo Solomon

• Department of Electrical and Electronic Engineering, University of Mines and Technology, Tarkwa, Ghana

Bibliografia

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Uwagi

Special Section - Artificial Intelligent Based Designs and Applications for the Control of Electrical Drives

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).