Implementation of Population Algorithms to Minimize Power Losses and Cable Cross-Section in Power Supply System

The article dues to the arrangement of the reactive power sources in the power grid to reduce the active power losses in transmission lines and minimize cable cross-sections of the lines. The optimal arrangement is considered from two points of view. In the first case, it is possible to minimize the active power losses only. In the second case, it is possible to change the cross-sections of the supply lines to minimize both the active power losses and the volume of the cable lines. The sum of the financial cost of the active power losses, the capital investment to install the deep reactive power compensation, and cost of the cable volume is introduced as the single optimization criterion. To reduce the losses, the deep compensation of reactive power sources in nodes of the grid are proposed. This optimization problem was solved by the Genetic algorithm and the Particle Swarm optimization algorithm. It was found out that the deep compensation allows minimizing active power losses the cable cross-section. The cost-effectiveness of the suggested method is shown. It was found out that optimal allocation of the reactive power sources allows increasing from 9% to 20% the financial expenses for the enterprise considered

Computational and experimental study of air-core HTS transformer electrothermal behaviour at current limiting mode

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

Application of swarm intelligence algorithms to energy management of prosumers with wind power plants

The paper considers the problem of optimal control of a prosumer with a wind power plant in smart grid. It is shown that control can be performed in non-deterministic conditions due to the impossibility of accurate forecasting of the generation from renewable plants. A control model based on a priority queue of logical rules with structural-parametric optimization is applied. The optimization problem is considered from a separate prosumer, not from the entire distributed system. The solution of the optimization problem is performed by three swarm intelligence algorithms. Computational experiments were carried out for models of wind energy systems on Russky Island and Popov Island (Far East). The results obtained showed the high effectiveness of the swarm intelligence algorithms that demonstrated reliable and fast convergence to the global extreme of the optimization problem under different scenarios and parameters of prosumers. Also, we analyzed the influence of accumulator capacity on the variability of prosumers. The variability, in turn, affects the increase of the prosumer benefits from the interaction with the external global power system and neighboring prosumers

Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units

The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out

Improving accuracy and generalization performance of small-size recurrent neural networks applied to short-term load forecasting

The load forecasting of a coal mining enterprise is a complicated problem due to the irregular technological process of mining. It is necessary to apply models that can distinguish both cyclic components and complex rules in the energy consumption data that reflect the highly volatile technological process. For such tasks, Artificial Neural Networks demonstrate advanced performance. In recent years, the effectiveness of Artificial Neural Networks has been significantly improved thanks to new state-of-the-art architectures, training methods and approaches to reduce overfitting. In this paper, the Recurrent Neural Network architecture with a small-size model was applied to the short-term load forecasting of a coal mining enterprise. A single recurrent model was developed and trained for the entire four-year operational period of the enterprise, with significant changes in the energy consumption pattern during the period. This task was challenging since it required high-level generalization performance from the model. It was shown that the accuracy and generalization properties of small-size recurrent models can be significantly improved by the proper selection of the hyper-parameters and training method. The effectiveness of the proposed approach was validated using a real-case dataset. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Data Mining Applied to Decision Support Systems for Power Transformers’ Health Diagnostics

This manuscript addresses the problem of technical state assessment of power transformers based on data preprocessing and machine learning. The initial dataset contains diagnostics results of the power transformers, which were collected from a variety of different data sources. It leads to dramatic degradation of the quality of the initial dataset, due to a substantial number of missing values. The problems of such real-life datasets are considered together with the performed efforts to find a balance between data quality and quantity. A data preprocessing method is proposed as a two-iteration data mining technology with simultaneous visualization of objects’ observability in a form of an image of the dataset represented by a data area diagram. The visualization improves the decision-making quality in the course of the data preprocessing procedure. On the dataset collected by the authors, the two-iteration data preprocessing technology increased the dataset filling degree from 75% to 94%, thus the number of gaps that had to be filled in with the synthetic values was reduced by 2.5 times. The processed dataset was used to build machine-learning models for power transformers’ technical state classification. A comparative analysis of different machine learning models was carried out. The outperforming efficiency of ensembles of decision trees was validated for the fleet of high-voltage power equipment taken under consideration. The resulting classification-quality metric, namely, F1-score, was estimated to be 83%. © 2022 by the authors.Ministry of Education and Science of the Russian Federation, MinobrnaukaThe research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority-2030 Program) is gratefully acknowledged

Improvement of rock crushing quality based on load specifications set for electrically-driven hydraulic drilling rigs

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

Current limitation by transformers with high temperature superconducting windings

The urgency of the discussed issue is caused by the widespread use prospect of superconductivity phenomenon in power devices including transformer for losses reduction. The main aim of the study is to investigate the influence of superconducting transformers on electromagnetic transients, to define the current limiting possibility by the superconducting transformers, to reveal the features of current limitation using superconducting transformers. The methods used in the study: calculations using the software package MatLab, ATP EMTP, the use of the superconductivity theory, mathematical modeling in power. The results: the mathematical model of electromagnetic and thermal transients during short-circuit current limiting is developed; the processes of superconducting transformer transition in the normal (nonsuperconducting) state and recovery to the superconducting state after the fault current clearance is simulated; the criterion of superconducting transformer recovery in the superconducting state after fault clearance under load is defined; the possibility of short-circuit current limiting from the viewpoint of providing the required resistance is determined

The technique for increasing lift force control capacitance under wind turbine power limiting conditions by plasma technology

The need to increase the control capacitance margin under wind turbine power limiting conditions allows developing favorable conditions of operational reliability. There are two traditional methods of controlling windwheel and generator output capacity: speed control by adjusting blade angle of attack and by blade profile with airflow breakdown. Currently such means of efficient control of wind turbine as plasma drives are developed. The use of plasma technology for extending the range of controlling the wind turbine applying the technique of developing surface dc corona discharge on a blade is of appropriate interest. Taking into account this fact it is necessary to carry out the mathematical modeling which gives an idea of the controlling range value of the studied model