Optimizing DC-DC Converter Topologies for Enhanced Efficiency in Hybrid Renewable Energy Systems Using Hybrid Techniques
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Abstract
In hybrid renewable energy systems (HRES), the use of DC-DC converter topologies is fundamental for enabling efficient energy source utilization and significantly enhancing overall power efficiency. However, the complexity of designing and implementing various DC-DC converter topologies can lead to increased costs and maintenance challenges in HRES. This paper proposes a hybrid method for optimizing DC-DC converters in HRES to enhance power efficiency. The proposed hybrid method is the combined execution of Artificial Rabbits Optimization (ARO) and Pseudo-Hamiltonian Neural Networks (PHNN). Hence it is named as ARO-PHNN technique. The main objective of the propose method is to improve energy management and system efficiency. The ARO is used to optimize the converter design parameters. The PHNN is used to predict these optimized parameters. By then, the recommended method is implemented in MATLAB platform and evaluated their performance with various existing methods such as Jellyfish Search and Random Decision Forest (JS-RDF), Improved Non-Dominated Sorting Genetics (INSGA-II), Earthquake Optimization Algorithm (EA), Particle Swarm Optimization and Lightning Attachment Procedure Optimization (PSO-LAPO), Galactic Swarm Optimisation (GSO). The proposed ARO-PHNN method demonstrates exceptional performance with an efficiency of 98.1%. In terms of statistical analysis, it exhibits a mean value of 0.9421 and a median of 0.8612, reflecting its high accuracy. Additionally, the method has a low standard deviation of 0.0065, indicating not only superior performance but also enhanced stability and consistency compared to other techniques.
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