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This paper presents a new approach to improve the performance of Zeta converters, which are commonly used in cost-sensitive circuits to manage unregulated power supply. The converters are designed to produce positive output voltages based on input voltages, and they use a buck controller to power a PMOS-based FET for high-side control. Compared to other converters, such as SEPIC, Zeta converters are smaller and more scalable for micro applications due to the use of coupled inductor circuits. The performance of Zeta converters is heavily influenced by the ratings of their passive components. To optimize component rating choices, researchers have developed several pattern analysis models. However, these models often require context-specific ratings and lack a parameter selection method for continual reconfigurations, making them difficult to deploy in practice for different use cases. To address these limitations, the authors propose a hybrid soft computing methodology for passive component selection in multiple load Zeta converters. The proposed approach combines Particle Swarm Optimization (PSO) to determine initial component ratings and Grey Wolf Optimization (GWO) to improve conversion efficiency, output gain, and Total Harmonic Distortion (THD). This is achieved by modeling a fitness function that incorporates output metrics and optimizes them incrementally for real-time deployments. The results show that the suggested methodology can reduce THD by 6.5 %, increase conversion efficiency by 3.4 %, and maintain a gain improvement of 1.5 % across numerous use cases. These improvements make the model suitable for real-time use applications. Overall, the proposed approach provides a promising solution to the challenges of passive component selection in Zeta converters, which can lead to more efficient and cost-effective power management in various circuits.

Design of a novel hybrid soft computing model for passive components selection in multiple load Zeta converter topologies of solar PV energy system