In this paper, a novel method for the magnetic flux density estimation in the vicinity of multi-circuit overhead transmission lines is proposed. The proposed method is based on a fully connected feed-forward artificial neural network model that is trained to estimate the magnetic flux density vector components for a range of single-circuit overhead transmission lines. The proposed algorithm is able to simplify estimation process in instances when there are two or more geometrically identical circuits present in the multi-circuit overhead transmission line. In such instances, artificial neural network model is employed to estimate the magnetic flux density distribution over a considered lateral profile for only one of such circuits. The magnetic flux density estimates of the other geometrically identical circuits are derived from these results. The proposed methodology defines the resultant magnetic flux density for the multi-circuit overhead transmission line in terms of the contributions made by individual circuits. The application of the proposed magnetic flux density estimation method is demonstrated on several multi-circuit configurations of overhead transmission lines. The performance of the proposed method is compared with the Biot-Savart law based method calculation results as well as with field measurement results.

This paper considers the application of machine learning models to electric field intensity and magnetic flux density estimation in the proximity of the overhead transmission lines. The machine learning models are applied on two horizontal overhead transmission line configurations at different rated voltages, at height 1 m above ground surface. The obtained results are compared with the results obtained by charge simulation method and Biot-Savart law based method as well as with the field measurement results.

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.

The use of renewable energy sources increases the energy self-sustainability of cities, enabling citizens to reduce energy costs, which results in an increase in their standard of living. However, solar energy penetration in Bosnia and Herzegovina, and its capital Sarajevo, is not in line with the possibilities. Furthermore, the Sarajevo Canton is extremely polluted during the winter months because of the use of unacceptable heating fuel. The aim of this paper is to introduce photovoltaic power systems use in heating electrification system. In this paper AQI is calculated based on historical data and the hybrid model EMD-SARIMA for air pollution and a solar production forecast is presented. The methodology was tested in the Sarajevo Canton, taking into account 35,000 households. In order to ensure clean air, renewable electric energy use for household heating should be implemented. The widespread use of inefficient individual heating systems characterized by inefficient and expensive use of firewood and the use of coal in individual furnaces in populated areas are the main problems of internal and urban air pollution in Sarajevo Canton. In order to reduce energy poverty in Sarajevo Canton, the use of a floating photovoltaic power plant located on Lake Jablanica with a capacity of 30 MW and the solar prosumers with capacity of 115 MW to provide the 196 GWh necessary for heating electrification of 35,000 households is implemented in this paper. Finally, based on correlation between AQI forecast and solar production it was calculated that the values of the AQI, considering the application of solar energy during 150 days (five months) in one heating season, have significantly decreased. Also renewable energy sources have a very important role in reducing carbon dioxide (CO2) emissions into the atmosphere and reducing urban pollution. With this approach, households would be heated by renewable electricity, which would make Sarajevo a cleaner, smarter city.

Abstract This paper specifies and compares the ancillary services used by transmission system operators in selected countries within ENTSO-E (European Network of Transmission System Operators-Electricity) for operational needs of their transmission systems. The comparison of the ancillary services’ unit prices is also given. In addition, analyses of various experiences gained by ENTSO-E countries within their electric power systems in relation to initiatives supporting the power generation from renewable energy sources (RES) and the methods of their integration into the electric power system were made. Namely, depending on the total amount of RES, depending on the incentive system for electricity production from RES and the development of an organized electricity market, different balancing responsibility models for electricity producers from RES were established.

Abstract This paper introduces and compares the various techniques for identification and analysis of low frequency oscillations in a power system. Inter-area electromechanical oscillations are the focus of this paper. After multiresolution decomposition of characteristic signals, physical characteristics of system oscillations in signal components are identified and presented using the Fourier transform, Prony’s method, Matrix Pencil Analysis Method, S-transform, Global Wavelet Spectrum and Hilbert Huang transform (Hilbert Marginal Spectrum) in time-frequency domain representation. The analyses were performed on real frequency signals obtained from FNET/GridEye system during the earthquake that triggered the shutdown of the North Anna Nuclear Generating Station in the east coast of the United States. In addition, according to the obtained results the proposed methods have proven to be reliable for identification of the model parameters of low-frequency oscillation in power systems. The relevant analyses are carried out in MATLAB coding environment.

This study interprets results of multiparametric measurements obtained on a pipeline experiencing dynamic stray currents caused by DC train traction. Measurements included three 24 h recordings of pipe-to-soil and rail-to-soil potentials, pipe-to-rail voltages and drainage currents in cases without mitigation measures, with polarized drainage and with forced drainage. Time domain analysis included construction of frequency diagrams and linear correlation between pipe-to-soil potentials and pipe-to-rail voltages. Wavelet space analysis pinpoints time intervals with harmful stray current periods within them and resolves between the dominant potential shifts due to the nearby passing trains and distant trains in the transport network.

Cathodic protection is defined as a method for slowing down or complete elimination of corrosion processes on underground or underwater, insulated or uninsulated metal structures. Protection by cathodic protection system is achieved by polarizing protected object to more negative value, with respect to its equilibrium potential. Design of the cathodic protection system implies determination of the electric potential and current density on the electrode surfaces after installation of the cathodic protection system. Most efficient way for determination of the electric potential and current density in the cathodic protection system is by applying numerical techniques. When modeling cathodic protection systems by numerical techniques, electrochemical reactions that occur on electrode surfaces are taken into account by polarization characteristics. Because of nature of the electrochemical reactions, polarization characteristics are nonlinear and under certain conditions can be time – varying (dynamic nonlinear polarization characteristics). This paper deals with numerical modeling of the cathodic protection system with dynamic nonlinear polarization characteristics. Numerical model presented in this paper is divided in the two parts. First part, which is based on the direct boundary element method, is used for the calculation of the distribution of electric potential and current density on the electrode surfaces in the spatial domain. Second part of the model is based on the finite difference time domain method and is used for the calculation of the electric potential and current density change over time. The use of presented numerical model is demonstrated on two simple geometrically examples.

In this paper impact of the tower arrangements on the mitigation of the increased values of the electric field intensity at high-voltage transmission line conductors was investigated. Several configurations of high-voltage towers (horizontal, delta, reverse delta, vertical and split-phase configurations) were analyzed. It assumed that the same height of the lowest phase conductors above the ground and the same value of the connected 400 kV voltage. The calculations were carried out for the values of the electric field intensity on the surface of the conductor and its immediate vicinity. A charge simulation method was used to calculate the electric field intensity on the surface of the stranded conductors and their immediate vicinity.

Ferroresonance is a nonlinear dynamic phenomenon that appears in the electric power system between a non-linear inductances and system capacities excited by a sinusoidal voltage source. The most common non-linear elements of the electric power system are the iron core inductances of power and measuring transformers, reactors, etc. in which saturation of non-linear elements can occur. As a consequence of ferroresonance there may be a significant increase in the voltage value and under the certain conditions an excessive increase of the current value across the transformer terminals. This phenomenon can lead to damage of the measuring transformers and other equipment used in the network. Ferroresonance is one of the most common low-frequency electromagnetic transient phenomena that appear in the isolated power networks. Therefore, special attention must be on analyzing the probability of ferroresonance occurrence in electric power systems. In this paper, ferroresonance measurement results will be given. The waveform of the phase to ground voltage and wave form of the open delta voltage is obtained. Also, the Fourier transformation is performed on these signals. Proposed