Dual Stage Speed Control of BLDC Motor Using Hybrid QPSO-CSO Technique

Abstract

Systems for driving motors are frequently employed with renewable energy sources. By using cutting-edge methods and algorithms, the power converter controlling technology boosts performance. The switching state of the converter from the solar module’s generation unit is where the QPSO (Quantum Particle Swarm Optimization) technique is applied in the currently available work. The duty cycle depends on the swarm velocities, which indirectly depends on the QPSO parameters. Hence, the QPSO parameters inertia (W), and learning coefficients C1 and C2, were determined using the Cuckoo search algorithm (CSO). This approach helps to extract the most powerful solar panel output and continuously operate a BLDC motor performance increasing the power converter controlling technology by utilizing state-of-the-art techniques and algorithms. Every module of the suggested boost converter was examined by the experimental findings. Based on generation and load, a boost converter module is obtained. The Models for single-ended primary inductors (SEPIC) and zeta converters are contrasted with the suggested logic. The overall design model is done by using MATLAB/Simulink 2021a.