This paper introduces a novel method that leverages artificial neural networks to estimate magnetic flux density in the proximity of overhead transmission lines. The proposed method utilizes an artificial neural network to estimate the parameters of a mathematical model that describes the magnetic flux density distribution along the lateral profile for various configurations of overhead transmission lines. The training target data is acquired using the particle swarm optimization algorithm. A performance comparison between the proposed method and the Biot-Savart law-based method is conducted using an extensive test dataset. The resulting coefficient of determination and mean square error values demonstrate the successful application of the proposed method for a range of different spatial arrangements of phase conductors. Furthermore, the performance of the proposed method is thoroughly assessed on multiple test cases. The practical relevance of the proposed method is highlighted by contrasting its results with the field measurements obtained in the proximity of a 400 kV overhead transmission line.

Abstract The methodology for the evaluation of long-term exposure to the overhead line magnetic field is presented, in this paper. The developed methodology is based on the ambient temperature measurements and phase conductors’ height measurements to find a linear regression model to determine phase conductors’ height changes for different ambient temperatures. Based on the overhead transmission line geometry, and datasets about historical overhead line phase current intensity values and ambient temperatures long-term magnetic field exposure can be determined. For magnetic flux density determination, a method based on artificial neural networks is used. The methodology is applied to the case study of overhead line that connect substations Sarajevo 10 and Sarajevo 20. A period of one year is analyzed and magnetic flux density values are determined. The obtained results indicate that during the analyzed period for significant amounts of time magnetic flux density values surpass the recommended values for long-term exposure.

Abstract This paper presents an artificial neural network (ANN) based method for overhead lines magnetic flux density estimation. The considered method enables magnetic flux density estimation for arbitrary configurations and load conditions for single-circuit, multi-circuit, and also overhead lines that share a common corridor. The presented method is based on the ANN model that has been developed using the training dataset that is produced by a specifically designed algorithm. This paper aims to demonstrate a systematic and comprehensive ANN-based method for simple and effective overhead lines magnetic flux density estimation. The presented method is extensively validated by utilizing experimental field measurements as well as the most commonly used calculation method (Biot - Savart law based method). In order to facilitate extensive validation of the considered method, numerous magnetic flux density measurements are conducted in the vicinity of different overhead line configurations. The validation results demonstrate that the used method provides satisfactory results. Thus, it could be reliably used for new overhead lines’ design optimization, as well as for legally prescribed magnetic flux density level evaluation for existing overhead lines.

The decrease in overall inertia in power systems due to the shift from synchronous generator production to renewable energy sources (RESs) presents a significant challenge. This transition affects the system’s stable frequency response, making it highly sensitive to imbalances between production and consumption, particularly during large disturbances. To address this issue, this paper introduces a novel approach using Multivariate Empirical Mode Decomposition (MEMD) for the accurate estimation of power system inertia. This approach involves applying MEMD, a complex signal processing technique, to power system frequency signals. The study utilizes PMU (Phasor Measurement Unit) data and simulated disturbances in the IEEE 39 bus test system to conduct this analysis. MEMD offers substantial advantages in analyzing multivariate data and frequency signals during disturbances, providing accurate estimations of system inertia. This approach enhances the understanding of power system dynamics in the context of renewable energy integration. However, the complexity of this methodology and the requirement for precise data collection are challenges that need to be addressed. The results from this approach show high accuracy in estimating the rate of change of frequency (RoCoF) and system inertia, with minimal deviation from actual values. The findings highlight the significant impact of renewable energy integration on system inertia and emphasize the necessity of accurate inertia estimation in modern power systems.

Abstract Cathodic protection system is commonly employed approach for the protection of the metallic infrastructure placed in electrolyte against corrosion. Adequate design of the cathodic protection system requires the determination of electrical potential and current density distribution on the protected object surface that meets the defined criteria. In this paper, the application of the direct boundary element method in conjunction with Newton-Raphson method was considered for the calculation of the electric potential and current density distribution on the surface of the cathodically protected underground object. The considered method was applied on the sacrificial anode cathodic protection system of the underground pipeline. The non-linear boundary conditions of the electrode surfaces of the cathodic protection system are taken into account. The method was used to determine the current density and electric potential distribution on the external wall of the protected pipeline.

In order to decide the appropriate arrangements of fictitious charges in the charge simulation method, the use of the Monte Carlo method is proposed for the estimation of the probability density function of two variables, the radius ratio, and the angle ratio. Τhe scale and shape parameters of the Weibull's distribution are determined by the maximum likelihood estimator. The obtained results are used to calculate the electric field at arbitrary points in the neighborhood of high voltage transmission lines. The comparisons between the results computed by this method, the results calculated by the genetic algorithm, and those measured, confirm the effectiveness and accuracy of the proposed method.