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This paper introduces a novel approach to simultaneously optimize the reliability and efficiency of a switching power supply (SPS) using the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Honey Bee Algorithm (HBA). The proposed method uses a Markov model to evaluate the reliability of the converter and maximizes objective functions, including efficiency and reliability, using optimization algorithms to identify the optimum values of the converter parameters. The optimization algorithm determines the optimal values for converter parameters, such as switching frequency, transformer turn ratio, and DC-link voltage. It also fine-tunes circuit components, such as the power factor correction inductor, DC-link capacitor, and output filter components. A sensitivity analysis is carried out to assess the impact of various parameters on the multi-objective functions of the converter. The converter’s reliability and mean time to failure (MTTF) are evaluated using the Markov reliability model, considering all components’ short and open circuit faults. The failure rates of converter components are calculated using the MIL-HDBK-217 methodology. The results obtained from the NSGA-II and HBA are compared to identify the superior algorithm with the best-optimized parameters. Simulation results indicate that the proposed optimization technique enhances the converter’s reliability performance while maintaining its efficiency. Furthermore, this paper presents comprehensive analyses, comparisons, and experimental findings to demonstrate the converter’s fault-tolerant capability. The multi-objective optimization approach offers a practical and effective strategy for optimizing switching power supplies for efficiency and reliability |
