Development of hybrid optimization approach combined with AI-based techniques for prediction of electrical fields in overhead transmission lines

Abstract Getting a precise estimate of electric fields around extra-high-voltage (EHV) transmission lines is essential for keeping the public safe, ensuring environmental compliance, and planning infrastructure effectively. Unfortunately, traditional numerical methods often struggle with accuracy and can be slow to converge, which makes them less suitable for large-scale projects. This study introduces a hybrid computational framework that combines the Charge Simulation Method (CSM) with the Firefly Algorithm (FA). This combination helps optimize the number, position, and strength of simulation charges, leading to better modeling accuracy and efficiency. Additionally, we have trained three artificial intelligence (AI) models: Multilayer Perceptron Neural Network (MLPNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Least Squares Support Vector Machine (LS-SVM) on real-world field data to reliably predict electric field values. Notably, LS-SVM is being used in this context for the first time and has shown to outperform the other models in accuracy, generalization, and speed. We evaluated the proposed CSM-FA hybrid model alongside AI predictions using metrics like Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination ( R 2 ), revealing significant improvements over traditional methods. Given the heavy computational demands of the optimization and learning phases, we utilized high-performance computing (HPC) resources for implementation. This work not only advances algorithmic innovation and AI-assisted simulation but also enhances HPC applications, providing a scalable and precise solution for real-time field monitoring and regulatory assessments. The methodology aligns well with the scientific goals of The Journal of Supercomputing and fosters advanced research in intelligent power system modeling.