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.

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 An efficient method for evaluation of an optimal two-layer soil model from Wenner four-probe measuring method, which has been used during experimental investigations, is presented within this paper. A two-layer soil model is assumed, and this soil model is an adequate representation of nonhomogeneous soil for grounding system design. The application of optimization techniques is required to estimate the electrical parameters of the proposed soil model. In this paper, first the fast gradient-descent method to solve a given optimization problem is chosen, and then with the aim of faster calculation for accelerating the rate of convergence of an infinite sum, the application of Aitken’s δ2 method is proposed.

Abstract Underfrequency load shedding is a common technique for maintaining the stability of the power system by removing the overload in a certain part of the system after a disturbance. The purpose of underfrequency load shedding is to balance output and load when a particular event causes a significant frequency drop in the power system. In conventional underfrequency load shedding schemes, the frequency thresholds of frequency relays are constant, this way it is difficult and sometimes impossible to control the frequency in various disturbances in the system. In this paper, an adaptive underfrequency load shedding (AUFLS) algorithm that is independent of communication between relays is presented. The relays are tuned to reduce loads taking into account local parameters such as voltage and frequency to prevent the occurrence of a cascade failure that can ultimately lead to the breakdown of the entire power system. In this paper, the rate of change of frequency (ROCOF) is obtained by applying the Hilbert-Huang transformation. Numerical simulations conducted on the New England 39 bus test system in the DIgSILENT PowerFactory and MATLAB software packages confirm the effectiveness of the proposed approach.