Abstract
In this paper, a novel method for electric field intensity and magnetic flux density 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 flux density. The artificial neural networks are trained using the scaled conjugate gradient algorithm. Training datasets corresponds to different overhead transmission line configurations that are generated using an algorithm that is especially developed for this purpose. The target values for the electric field intensity and magnetic flux density 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 flux density 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 flux density 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 flux density estimation results for different overhead transmission line configurations.
Highlights
N UMEROUS epidemiological studies were conducted trying to find correlation between exposure to magnetic and electric fields, and human diseases
Analysis of data collected from several studies on the influence of low frequency magnetic field on the development of childhood leukemia showed that the existence of a small but not negligible risk of leukemia associated with exposure to fields above 0.3 μT [1]
Artificial neural networks (ANN) are trained for a fixed applied voltage and current intensity, the results show that the application of the proposed method is clearly not constrained to these particular applied voltage and current intensity values
Summary
N UMEROUS epidemiological studies were conducted trying to find correlation between exposure to magnetic and electric fields, and human diseases. For each overhead transmission line configuration, the target electric field intensity and magnetic induction values are generated for 81 points with different lateral displacement from the central vertical line. The target values for the magnetic induction dataset are evaluated using BS based law method under the assumption that 100 A current intensity flows trough phase conductors and that overhead transmission line is symmetrically loaded. The proposed neural network architecture has a sufficient number of free parameters to describe some very complex input-output relations, including the relationships between the various overhead transmission line configurations and electric field intensity/magnetic induction values at points lying within a considered range of lateral displacement from the central vertical line and at a height of 1 m above the ground surface. SCG performance is not significantly affected by any user-dependent parameters and for networks with a large number of free weights, the scaled conjugate gradient algorithm is shown to be efficient [27]
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