Electric and Magnetic Field Estimation under Overhead Transmission Lines using Artificial Neural Networks

Ajdin Alihodzic, Adnan Mujezinovic, Emir Turajlic
2021 IEEE Access  
In this paper, a novel method for electric field intensity and magnetic induction estimation in the vicinity of the high voltage overhead transmission lines is proposed. The proposed method is based on two fully connected feed-forward neural networks to independently estimate electric field intensity and magnetic induction. The artificial neural networks are trained using the scaled conjugate gradient algorithm. Training datasets corresponds to different overhead transmission line
more » ... that are generated using an algorithm that is especially developed for this purpose. The target values for the electric field intensity and magnetic induction datasets are calculated using the charge simulation method and Biot-Savart law based method, respectively. This data is generated for fixed applied voltage and current intensity values. In instances when the applied voltage and current intensity values differ from those used in the artificial neural network training, the electric field intensity and magnetic induction results are appropriately scaled. In order to verify the validity of the proposed method, a comparative analysis of the proposed method with the charge simulation method for electric field intensity calculation and Biot-Savart law-based method for magnetic induction calculation is presented. Furthermore, the results of the proposed method are compared to measurement results obtained in the vicinity of two 400 kV transmission lines. The performance analysis results showed that proposed method can produce accurate electric field intensity and magnetic induction estimation results for different overhead transmission line configurations. INDEX TERMS Artificial neural networks (ANN), Biot-Savart (BS) law based method, Charge simulation method (CSM), Electric field intensity, Magnetic induction, Scaled Conjugated Gradient (SCG)
doi:10.1109/access.2021.3099760 fatcat:hknhzx7ckzf4jcqsrogqvjwkwy