Hybrid Power Generating System Sources and Load Synchronization Using Adaptive-Neural-Fuzzy-Inference-System and Pelican Optimization Algorithm Technique

Abstract

The integration of renewable energy sources (RESs) into current power networks addresses rising global energy demand and mitigates greenhouse gas emissions. This transition shifts the role of frequency regulation from synchronous generators to power converters, serving as interfaces between the grid and RESs. This paper proposes a hybrid method for optimizing power flow in hybrid power-generating systems. The proposed hybrid technique is the combined execution of an Adaptive-Neural-Fuzzy-Inference-System (ANFIS) and Pelican Optimization Algorithm. Hence it is named as ANFIS-POA technique. POA optimizes current and voltage controllers, while ANFIS predicts optimal system parameters. Implemented in MATLAB, the proposed strategy achieves an impressive efficiency of 96%, outperforming existing strategies like genetic algorithm (GA) and particle swarm optimization (PSO). With a faster settling time (0.56 s), a shorter rising time (1.32 s), and a reduced peak time (5.78 s), the proposed approach performs better than existing methods. Additionally, it significantly lessens system costs (Rs. 99,450) and demonstrates superior statistical performance in terms of lower error rates and improved key metrics like accuracy, precision, and recall compared to existing methods.