A Novel Hybrid AI-JAYA and Deep Neural Network-Based Optimization for Voltage Stability and Power Loss Reduction in Distribution Networks

This paper presents a novel Hybrid JAYA-DNN optimization algorithm designed to improve voltage stability and minimize power losses in power distribution networks. By integrating the JAYA algorithm with a Deep Neural Network (DNN), the proposed method leverages the predictive capabilities of DNNs to enhance the performance of the JAYA algorithm, ensuring faster convergence and more accurate optimization results. The algorithm was applied to the IEEE 39-bus system, where it achieved a 30% reduction in power losses and significantly improved voltage stability. Comparative analysis with traditional optimization methods, such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), demonstrates the superior efficiency and accuracy of the Hybrid JAYA-DNN, with faster convergence and better optimization outcomes. The results indicate that the proposed algorithm is well-suited for real-time power system applications, offering a robust and efficient solution for optimizing complex power distribution networks.

A Novel Hybrid AI-JAYA and Deep Neural Network-Based Optimization for Voltage Stability and Power Loss Reduction in Distribution Networks Keywords:

Hybrid AI-JAYA , Optimization , Voltage Stability , Power Loss Reduction , Power Distribution Networks , Deep Neural Networks (DNNs)