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It is important to note that the generated power of renewable sources depends on the natural conditions at a particular geographic point, the level of wind flow speeds and solar radiation. The patterns characterizing these parameters depend on the time of year, locality and are purely probabilistic in nature. Taking into account the above-mentioned conditions for the effective implementation of “green” objects in the power supply system, the purpose of this work is to build forecasting models that are more likely to be able to determine what part of the load can be covered by the power supply system based on wind power and solar installations. This purpose was achieved by constructing and training artificial neural networks with data on the speed of wind flow and solar radiation obtained from real renewable energy facilities. The most significant result is the identification of the necessary forecasting horizon, taking into account the preservation of a relatively good quality of metrics, as well as understanding what additional data is required to improve this quality. The significance of the results obtained lies in the fact that they make it possible to determine what reserve capacity is required to be included in the project.

Modern energy strategies aimed at the development of energy industry presuppose a significant change in the structure of process of formation, transmission, consumption of electrical energy and increasing energy efficiency by introducing modern technologies at all stages. The growth of capacities of industrial enterprises in the conditions of wholesale market of electrical energy and capacity in the modern energy system determines the need for development technologies of predictive control of power consumption process of these enterprises. The introduction of such technologies at the control rooms of the operational management of enterprises will allow to reduce the number of human errors, the number of emergency stops of technological process, increase the reliability of power system mode, rationally manage the process of power consumption of enterprises. In this regard, forecasting the load demand and consumption is an important stage in the functioning and planning of modern power systems. An accurate, correctly compiled forecast is the key to effective management of energy consumption process and reliable operation of the enterprise. Forecasting errors lead to imbalanced supply-demand, which negatively affects operating costs, reliability and efficiency.

Existing models for accounting for the “skin effect” in conductors have been examined and analyzed. A mathematical model has been developed that features increased accuracy and allows for the determination of the coefficient of increase in active resistance in relation to direct current resistance k ν ( f ) for conductors of various cross sections, including steel–aluminum wires of the AC brand. A model based on piecewise linear approximation of dependence k ν ( f ) has been developed, taking into account the cross section of the conductors. This model allows for the consideration of additional active power losses due to higher harmonics when the frequency is increased up to 1000 Hz.

Optimization of a power supply system is one of the main directions in power engineering research. The reactive power compensation reduces active power losses in transmission lines. In general, researches devoted to allocation and control of the compensation units consider this issue as a static optimization problem. However, it is dynamic and stochastic optimization problem that requires a real-time solution. To solve the dynamic optimization NP-hard problem, it is advisable to use Swarm Intelligence. This research deals with the problem of the compensation units power control as a dynamic optimization problem, considering the possible stochastic failures of the compensation units. The Particle Swarm Optimization and the Bees Algorithm were applied to solve it to compare the effectiveness of these algorithms in the dynamic optimization of a power supply system.

Assessment of the current technical condition is an important task, so the state of electrical equipment depends on its further operability. Thanks to modern computing devices, it is possible to implement actively artificial intelligence and computer-assisted learning methods that allow achieving high efficiency in data processing. A study was conducted and an algorithm for diagnosing the technical condition based on an artificial neural network was developed. A model based on a multilayer perceptron is proposed, which allows evaluating the technical condition of a high-voltage power transformer. The result of the technical diagnostics of the model is the assignment of the condition to one of the five classes, proposed by the guidelines presented by the International Council on Large Electrical Systems. The methodology is presented on the example of a 250 MVA transformer with a certain defect history, which allowed us to show the reliability and validity of the obtained results. It is shown that the use of the proposed model makes it possible to achieve accuracy in determining the technical condition of 0.95. The introduction of this model into an automated monitoring and diagnostics system will allow assessing the technical condition of electrical equipment in real time with sufficient accuracy.

The priority directions of the prospective development of the energy sector involve improving the quality of generated electricity, reducing the consumption of fuel resources, and implementing modern technologies and methodologies. These strategies are considerably aimed at enterprises of the fuel and energy sector having individual features of electricity consumption. Predictive control of technological processes of these objects is a relevant problem that, in turn, requires the use of up-to-date methods of data analysis and processing. One of these methods is the wavelet transform having several advantages over known methods, such as the Fourier transform. The paper presents a comparative analysis of the wavelet transform of active power consumption for various excavators of a mining enterprise in the Siberian region. The obtained results illustrate the statistics of operation of each excavator and allow making conclusions about their operation for a chosen time interval and presenting them in the form of a 3D model. Mathematical instruments of wavelet analysis can be introduced into existing complexes of automated control systems for the production process of enterprises. The proposed method indicates the possibility of developing the predictive control in the electricity consumption process, planning price categories and optimizing the load schedule with cost reduction and an increase of the financial profit of the enterprise.

The paper provides the results of the experimental and computational study of the processes occurring in high temperature superconducting transformer windings while secondary winding is short-circuited. The obtained mathematical simulation matches closely with the experimental results. The temperature variation curves for superconducting windings were analysed, and conclusions were made on the necessity of changes in HTS transformer design, namely the necessity of windings heat-insulation from each other and adding a high-resistance coating material for HTS wire in HTS transformer primary winding.

The paper considers a forecast model of electricity consumption of a coal industry enterprise based on three forecast methods, namely the wavelet transform, the vector method, and the recurrent neutral network. For preprocessing the data for forecasting by vector and recurrent methods, the Singular Spectrum Analysis method was chosen. The structure of the model allows taking into account individual features of the operating cycle of the production process and smoothing the noise components and outliers caused by these features. The results of a short-term hourly forecast for one day ahead are presented with the comparison of the obtained values. The results of short-term electricity consumption forecast were verified based on the actual data of the coal industry enterprise in order to assess the adequacy of the model to the actual values.

The urgency and expediency of additional passenger transportation by urban railway as an extension of the transport infrastructure of megacities has been proved. The fact that the urban railway uses AC 2x25 kV, which is uses to out of the three phases of city's power supply system negatively impacts. Thus, it creates a significant asymmetry. In this paper, a comparative analysis on the negative sequence component of the voltage unbalance, which occurs in the nodes of connection of traction substations through both a three-phase transformer or a Scott transformer are been carried out. A mathematical method was created for determining the negative sequence voltage unbalance factor is based on the phase coordinates and symmetrical components methods. The results show that the Scott transformer provides a more symmetrical load distribution to the three-phase electric grid, when compared to the three-phase transformer and was verified using a simulated model in the Matlab program. Consequently, the use of a Scott transformer has better symmetrising abilities than a three-phase transformer, which gives it a significant advantage in the traction power systems.

The analysis was completed on the main production factors which determine energy characteristics of an electrically-driven hydraulic roller-bit drilling rig during drilling operations under the condition of the Far North deposit. In addition, the correlation ratio between the parameters and electric energy consumption was analyzed for the DM-H drilling rig. The equations expressing the relation of the drilling rig load to the drilling speed have been identified. In order to improve the quality of rock crushing, we have modified the method of determining the properties and condition of the rock mass in order to correct the connection layout of the blasting circuit, the activation system, the change in the type of an explosive agent, as well as the composition and weight of the blasthole charge. The proposed approach allows reducing the cost of drilling and blasting operations by 6 % through the improvement in the accuracy of the designed physical and mechanical properties in terms of both the stratum depth and the strike of the mining block.