The development of smart grids poses great challenges to the scientific and professional community. Increasingly strict requirements from regulators and consumers require appropriate actions from the Distribution System Operator (DSO), infrastructure development, and large investments in the modernization and digitalization of electrical distribution systems. The connection of a large number of electricity sources to the existing distribution grid causes problems that are reflected in unauthorized voltage changes or overloads in the network, as well as compromised power quality. Communication infrastructure, as well as the technologies themselves, are often not satisfactory for the requirements that arise in real networks, and the development of smart grids requires appropriate/advanced information and communication infrastructure. The development of smart grids requires an interdisciplinary approach, experts of different profiles, and clearly defined long-term strategies. This paper provides an overview of existing AI technologies which are proposed for application in power systems, as well as an overview of areas where AI can be implemented to support the operation of power systems in the future (such as maintenance, forecasting, optimization, protection, etc.). In a separate section, a simulation of the production of small PV systems connected to consumer households in weak low-voltage grids (LVG) is presented as an illustrative example. An overview of proposed AI applications in LVGs is provided, along with a discussion of possible improvements and overcoming issues that arise in existing grids with prosumers.

Energy poverty remains a significant issue in Bosnia and Herzegovina, characterized by limited access to affordable and sustainable energy sources. This paper examines the prevalence of energy poverty among 1500 retiree households and evaluates the potential of photovoltaic (PV) systems as a solution. The research highlights the multidimensional nature of energy poverty, incorporating variables such as income, energy expenditures, and heating methods. Using statistical methods, including factor analysis and regression models, the research developed an energy poverty index (EPI) to categorize households and identify key drivers of energy poverty. The findings reveal that 96.5% of households experience moderate to high energy poverty when transport costs are included, dropping to 84.3% when these costs are excluded. Households using wood for heating, with a combined rooftop area of 26,104 m2, could generate 7,831,200 kWh of solar energy annually, reducing CO2 emissions by 1,389,825 kg. The aggregated payback period for PV investments is approximately 9.3 years, demonstrating financial viability. The paper underscores the potential of energy communities in pooling resources, facilitating rooftop leasing for PV installations, and promoting policy reforms to promote renewable energy adoption. This research contributes to the understanding of energy poverty dynamics and provides actionable recommendations for integrating PV power plants, fostering energy equity, and reducing environmental impacts.

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.

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.

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.

Abstract Generation of photovoltaic power plants is growing rapidly in the last ten years in the world. One of the key factors for the construction of floating photovoltaic power plants is to provide space for their construction. This paper presents statistical indicators of installed capacities of floating photovoltaic power plants, as well as a detailed description of the components of these power plants. Approaches to construction and maintenance recommendations are described in more detail. The basic results of simulations are presented on a concrete example of a floating photovoltaic 1 MW power plant on Lake Modrac. The available areas of artificial lakes in Bosnia and Herzegovina were analysed, and it was shown that the installation of floating photovoltaic power plants on 5% of the surface of artificial lakes would provide around 10% of the total electricity consumption in Bosnia and Herzegovina.

Background: Epilepsy is a brain disorder characterised by unpredictable and excessive nerve cell activity that causes epileptic seizures. Epileptic seizures are more common in children and adolescents than in elderly population. Electroencephalography (EEG) is a diagram of electrical activity of the brain and it is used as a method of choice for diagnosing epilepsy. Despite the accurate EEG tracing of electrical activity in the brain, the disadvantage of this type of analysing is the doctor’s skill to read the EEG correctly. Objective: The aim of this study was ro represents further research presented in our pevious works with wavelet based EEG analysis after masuring a multiresolution as relation between time and frequency resolution. Methods: Signal database set consist of 51 patients: a) healthy patient; b) 50 patients with a diagnosis of epilepsy. Additional characteristics of the analysed data: a) 19 signals-channels of EEG, b) Duration – 20 s or 2688 samples and. Nowadays, we can find dozens of EEG signal analysis papers using mathematical approach and with a focus on identification of epilepsy. Results: This paper represents some results relating to the analysis of EEG in children using Wavelet Transform (WT). The signals was collected and analysed at the Department of neuropediatrics, Pediatric Clinic at the University Clinical Center, University of Sarajevo. Conclusion: Using this approach it is possible to clearly differentiate patients with a diagnosis of epilepsy from healthy ones.