Lightning Risk Assessment for Overhead Power Lines Using Enhanced PSO and SVL

Guang Yang
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Guang Yang: School of Mechanical and Electrical Engineering, Henan Industry and Trade Vocational College, Zhengzhou, China

International Journal of Swarm Intelligence Research (IJSIR), 2025, vol. 16, issue 1, 1-24

Abstract: This paper proposes an advanced framework that combines enhanced particle swarm optimization with least squares support vector machine learning (PSO-LSSVM) for intelligent lightning risk assessment in overhead power line systems. Our approach integrates comprehensive feature extraction methodologies capturing both temporal and spectral characteristics of lightning phenomena with an optimized classification system for rapid risk evaluation. The PSO algorithm is specifically adapted to determine optimal LSSVM parameters, improving classification accuracy and computational efficiency. Experimental validation using data from three climatically distinct regions - a 220kV tropical coastal line, a 345kV alpine mountain network, and a 400kV desert transmission system - demonstrates the framework's versatility and effectiveness. Results show that our PSO-LSSVM framework achieves 94.6%, 94.3%, and 94.9% classification accuracy in tropical, alpine, and desert regions, respectively, representing substantial improvements over baseline methods.

Date: 2025
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