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.

In this paper approach for the experimental determination of the grounding system impulse impedance under the presence of the high-frequency electromagnetic interference is presented. The considered approach is based on the application of the discrete wavelet transform on the measured signals. Validation of the considered approach has been conducted in several experiments using a vertical grounding electrode. The experimental investigation has been performed using different impulse current peak values and different front rise times. On all measured current and voltage waveforms, high-frequency interferences were registered.

This paper considers calculation methods for the electric field intensity and magnetic flux density in the vicinity of the overhead transmission lines, as well as the calculation of alternating current (AC) corona onset electric field intensity. Calculations within this paper are made using the 2D algorithms of Charge Simulation Method (CSM) and Biot-Savart (BS) law based method. In order to obtain more accurate results, calculations are made by representing each overhead transmission line conductor with a large number of electric and magnetic field sources. By applying this approach, bundle conductors can be represented in a more realistic way and also singularity problems can be avoided when calculating electric field intensity. The presented methods are applied to a real overhead transmission line configuration, and obtained results are compared with field measurement results over the lateral profile. For considered overhead transmission line, AC corona onset electric field intensity is calculated and compared with calculated electric field intensity on the conductor’s surface. A comparison of calculated and measured results shows that considered calculation methods give satisfactory results.

This paper considers the method for the calculation of magnetic flux density in the vicinity of overhead distribution lines which takes into account the higher current harmonics. This method is based on the Biot–Savart law and the complex image method. The considered method calculates the values of the magnetic flux density for each harmonic component of the current separately at all points of interest (usually lateral profile). In this way, it is possible to determine the contributions of individual harmonic components of the current intensity to the total value of magnetic flux density. Based on the contributions of individual harmonic components, the total (resultant) value of the magnetic flux density at points of interest is determined. Validation of the computational method is carried out by comparison of the results obtained by the considered calculation method with measurement results. Furthermore, the application of the calculation method was demonstrated by calculating magnetic flux density harmonics in the vicinity of two overhead distribution lines of typical phase conductor arrangements.

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.