Volume 27, Nº 4 (2023)
Mechanical Engineering
On developing structural and technical solutions to ensure the vibratory displacement of the working medium
Resumo
636–644
Calculation of force parameters of workpiece machining process with end mill cutters
Resumo
645-654
Particle swarm optimization support vector machine-based coal and rock cutting tool load spectrum identification method
Resumo
The goal of this research is to achieve safe and efficient excavation of coal and rock tunnels with complex geological structures, and to enhance the self-sensing ability of coal and rock cutting equipment and tools. Particle swarm optimization support vector machine is used to identify the cutting state of disc cutting tools. EDEM finite element analysis software is used to analyze cutting process characteristics of the disc cutting tool when used to cut through coal and rock with different compressive strengths. Empirical mode decomposition is used to decompose the load spectrum characteristics; for this purpose, the first-order and seventh-order intrinsic mode functions containing all the feature information of the original signal of the load spectrum are selected. The sample entropy is calculated as the feature input vector. The extracted feature vector is input into the trained support vector machine model and the particle swarm optimization support vector machine model. By extracting the sample entropy of the load spectrum of the disc cutter as the feature vector, the particle swarm optimization support vector model is used to identify the cutting state of the coal and rock. The recognition accuracy of the support vector machine model before and after the improvement is compared and analyzed. The results show that compared to the unoptimized support vector machine, the support vector machine optimized by particle swarm optimization can identify the load spectrum of the coal more quickly and accurately. The recognition accuracy is 96,82%, which verifies the effectiveness of the particle swarm optimization support vector machine model in identifying the load spectrum of the coal and rock disc cutter.
655-663
Automated assembly of products by a robot-manipulator with dynamometric control of screw joint tightening
Resumo
664-681
Developing the method ensuring stable braking via advanced design of braking devices
Resumo
682-693
Power Engineering
Applicability of multi-agent control for virtual inertia modes in a wind power plant
Resumo
694-726
Allocation of power losses and energy in the distribution network
Resumo
The goal is to determine methods for calculating power losses in a three-phase four-wire low voltage distribution network using measurements of a balance smart meter and consumer smart meters, and to establish the factors influencing the power losses and their allocation among individual network wires, loads, and consumers. The study involved examining three methods for determining power losses for current measurement snapshot. The first method suggests calculating losses as the difference between the power supplied to the network and the total power consumed. The second method calculates power losses using the contribution method. The third method, which in addition to measurement information requires knowledge of the topology and parameters of the network components, determines power losses based on the results of the state estimation method. The research proposes an algorithm for transition from a four-wire distribution network modeling to a three-wire one. The algorithm allocates power losses of the neutral wire among the phase wires. The findings indicate that the negative losses in the network with unbalanced phase loads are caused by the presence at the nodes of the least loaded phase of higher voltage than the voltage at the power supply node. The reason for higher losses in phases with minimal load is the uneven allocation of loads in the phases. In addition, the study reveals that the power loss values obtained by the contribution method, i.e. directly from the measurements of smart meters, are closer to the losses determined from the readings of the balance meter and consumer meters, compared to the losses found from the state estimation results. The considered methods for calculation and allocation of power losses are illustrated by an example of a real-world distribution network equipped with smart meters. The paper demonstrates the examples of allocating total power losses between phase wires and a neutral wire, among phase wires only, and between total loads at phase nodes and individual consumers in phases.
727-736
Operation of a solar power plant with dual-axis solar tracker
Resumo
737-748
Selection of power supply scheme for controlled excitation converters in traction electric motors of single-phase DC electric locomotives
Resumo
749-759
Optimization of normal operation mode of an electric system with renewable energy sources in Mongolia
Resumo
760-772
Review of methods for modeling and control of cyber-physical systems in multi-energy microgrids
Resumo
773-789
Metallurgy
Low-temperature sintering of bauxite raw material with alkali as an alternative to the parallel Bayer sintering process
Resumo
790-799
Reducing the environmental impact of aluminum production through the use of petroleum pitch
Resumo
800-808
Comparison of methods for enhancing gold recovery from double refractory concentrates using the technology of autoclave oxidation
Resumo
809-820
Heap sulphuric-thiocyanate leaching of gold and uranium
Resumo
821-828




