Performance Analysis of Neural Network Based Bridgeless Converters for Switched Mode Power Supplies
Switched mode power supplies (SMPS) play a vital role in digital processors and personal computer based control applications. The performance of the SMPS depends upon the choice of control strategy. In this paper, a neural network based control strategy is proposed for the bridgeless converter based MOSMPS with buck-boost and cuk converter topologies for output voltage regulation and power quality improvement at the input ac side. The proposed topologies have reduced conduction losses and thermal stress across the switches due to the absence of input diode bridge rectifier. The bridgeless converter is operated in discontinuous conduction mode to offer inherent power factor correction (PFC) for wide variation in input voltages and is cascaded to an isolated dc-dc converter with a High Frequency Transformer (HFT) to achieve multiple dc voltages such as ±12V and ± 5V applicable for PC power supply. The proposed converter topologies are simulated using MATLAB/Simulink software and their performances are analyzed in terms of output voltage regulation, Total Harmonic Distortion of source current and input power factor under varying source conditions. The performance of the proposed controller is also compared with that of conventional controller. The simulation results are validated using the prototype model developed.
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