Abstract This paper presents a detailed model of low-frequency oscillations and their damping within the Electric Power System (EPS) of Bosnia and Herzegovina (B&H). The system is modeled using MATLAB software, analysing the steady state and dynamic responses. This research highlights the challenges and impacts of integrating renewable energy sources, such as wind farms, on grid stability and oscillation damping. The paper utilizes eigenvalue analysis to investigate the dynamic characteristics of the system, emphasizing the need for efficient damping strategies to maintain system stability. The methodology includes a comprehensive review of existing literature, the creation of a detailed EPS model of B&H, and the application of eigenvalue and oscillation amplitude analysis to determine damping ratios. The dynamic responses of hydro power plants, HPP Mostar and HPP Jablanica, to transient disturbances are analysed to validate the model and refine damping strategies. The results indicate that the B&H EPS is well-damped, with all eigenvalues possessing negative real parts, and demonstrate the system’s resilience to small disturbances. The results are compared with the technical report on the integration of the wind power plant WPP Podveležje. This comparative analysis shows consistent patterns between the modeled calculations and empirical data, confirming the robustness of the EPS model. This alignment underscores the effectiveness of current damping mechanisms and provides a foundational strategy for enhancing system stability with increasing renewable energy penetration. The findings highlight the importance of developing advanced control strategies to sustain system stability as the integration of variable renewable energy sources continues to grow.

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.

In this paper, a comparative analysis of different methods for magnetic induction estimation in the vicinity of overhead power lines is presented. The methods for determining magnetic induction, considered in the paper, include the recently proposed artificial neural network based method and the Biot-Savart law based method. In addition, the paper considers a method that employs the genetic algorithm to fit a considered mathematical model to the field measurements. The performance of various methods is evaluated on an actual 400 kV overhead power line. The method based on the artificial neural networks is able to accurately evaluate magnetic induction values along the lateral profile without relying on field measurements using only the description of power line conductor configuration and the current intensity value.

Due to the significant growth in the number of devices, the range of services it provides, and strict air conditioning requirements, the telecommunications infrastructure is becoming an increasingly important electricity consumer. The efficiency of the power supply system and the power quality are significant challenges in the design and maintenance of telecommunications infrastructure elements. In such systems, power electronic converters play an indispensable role. This paper discusses the results of power quality measurements for supply systems of telecommunications devices. The power supply systems of telecommunications devices with different power converters were analyzed. Also, the power supply of a mobile telephony base station at a remote location was considered, with special reference to the reaction of battery storage in the event of a power outage. Obtained results demonstrate that it is necessary to treat such consumers with special care and take measures to limit their emission of current harmonics.

The paper presents an algorithm for determining the optimal connection location and power of a photovoltaic plant in a distribution network. The proposed algorithm is based on the use of the fuzzy logic and power flow calculation method. The fuzzy logic is used for the selection of candidate buses for the photovoltaic plant connection, while load flow analysis is used for the verification of voltage conditions and power losses in the distribution network. For each of the candidate buses photovoltaic plant of a certain power range was considered. The practical application of the considered algorithm was demonstrated on a part of Sarajevo's 10 kV distribution network.

This paper presents the use of the Hilbert-Huang Transform (HHT) to identify low-frequency electromechanical oscillatory modes, their characteristics, and damping. As these oscillations can have varying features, locations, and impacts on power systems, identifying and monitoring them is crucial for the monitoring, protection, and control of modern power systems. The Hilbert-Huang transform (HHT) is a technique used to analyze nonlinear and non-stationary time series data. It involves breaking down the data into components using Empirical Mode Decomposition (EMD), which generates components with varying amplitudes and frequencies. The EMD process includes an inner loop called sifting, which produces an Intrinsic Mode Function (IMF) until the signal reaches a mean value of zero or a maximum number of iterations. The obtained IMF is a characteristic function of a fundamental oscillation that is symmetrical around the abscissa. The dominant oscillatory mode's frequency can be determined by applying the Hilbert transformation to the first IMF, and the damping ratio and damping can be calculated by fitting a least square line to the logarithmic instantaneous amplitude of the first IMF. To demonstrate the efficacy of the methodology, three case studies are examined. The first case involves generating a synthetic signal to simulate a load angle change with a defined frequency and damping. In the second case, a small disturbance in mechanical power change in the Single Machine System is simulated. The third case simulates a three-phase short circuit on the transmission line using the IEEE 39 bus test system. The results are compared to modal analysis conducted in DigSilent PowerFactory software. The application of HHT yielded satisfactory and promising results in identifying the dominant mode's oscillation frequency and damping.

This paper considers the application of artificial neural network (ANN) models for electric field intensity and magnetic flux density estimation in the proximity of overhead transmission lines. Specifically, two distinct ANN models are used to facilitate independent estimation of electric field intensity and magnetic flux density in the proximity of overhead transmission lines. The considered ANN approach is systematically evaluated under different scenarios. An example of an overhead transmission line with horizontal phase conductor configuration is used to enable a direct comparison of the electric field intensity and magnetic flux density estimates generated by the two ANN models to measurement results obtained over the lateral profile. Further investigation of ANN models involves an extensive study whereby 13 different overhead transmission lines of horizontal configurations are used as the basis for comparing measurement results to estimates provided by the ANN models. In this study, the performance analysis of the ANN models was evaluated using coefficient of determination and root mean square error. The obtained results demonstrate that the considered ANN approach can be used to estimate the electric field intensity and magnetic flux density in the proximity of overhead transmission lines.