Swarm intelligence algorithms for the problem of the optimal placement and operation control of reactive power sources into power grids

Deep reactive power compensation allows for reduction of active power losses in transmission lines of power supply systems. The efficiency of the compensation depends on the allocation of reactive power compensation units (RPCUs) at the nodes of a network. In general, investigations devoted to the study of optimal allocation of the compensation units have revealed that it is a static and deterministic optimization problem that can be solved by heuristic methods. However, in real systems, it is reasonable to consider such optimization problems, taking into account the dynamic and stochastic properties of the problems. These properties are the result of equipment failures and operational changes in technical systems. In addition, optimizing the allocation of the compensation units is the NP-hard multifactor problem. Under these circumstances, it is advisable to use the swarm intelligence algorithms. Swarm intelligence is a relatively new approach to solving the optimization problem, which takes inspiration from the behaviour of ants, birds, and other animals. Advantages of swarm algorithms are most evident if problems involve the dynamic or stochastic nature of the objective function and constraints. Contrary to a number of similar studies, this research considers the problem of the optimal allocation of compensation units as a dynamic problem, taking into account the possible random failures of the compensation equipment. The optimization problem has been solved by two Swarm Intelligence algorithms (the Particle Swarm optimization and the Artificial Bee Colony optimization) and Genetic algorithms. It has been aimed at comparing the effectiveness of the algorithms for solving such problems. It was found that swarm algorithms could be successfully applied in the operation control of compensation units in real-time. © 2017 WIT Press

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

Swarm algorithms in dynamic optimization problem of reactive power compensation units control

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

Sintering oxide ceramics based on AI[2]O[3] and ZrO[2], activated by MgO, TiO[2] and SiO[2] additives

The positive effect of the addition of MgO and TiO[2] in an amount of no more than 1 wt. % on sintering and physico-mechanical properties of alumina ceramics is established. Addition of 5% of SiO[2] to A1[2]O[3] provides the mechanism of liquid phase sintering of ceramics, which leads to increase in its density and strength up to 480 MPa. In ceramic system A1[2]O[3] - ZrO[2] - Y[2]O[3] highest level of physical and mechanical properties of the composition had a hypereutectic composition 16.6% A1[2]O[3] - 76% Z1O[2] - 7.4% Y[2]O[3]. In this composition two mechanisms of hardening are realized simultaneously, such as transformational hardening by t-m -ZrO[2] transition and dispersion strengthening with high-modulus particles of [alpha]- A1[2]O[3]

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

Extraordinary Burial of the Great Migration Period from Karban-I Necropolis (Northern Altai)

The article presents the results of the materials study from kurgan 11 of the Karban-I site, excavated in 1989 by the expedition of the Barnaul State Pedagogical Institute. This archaeological complex is located on the left bank of the Katun river, 1.7 km northwest of the Kuyus village of Chemal district of the Altai Republic. The key characteristics of the fixed structures (mound with an oval-shaped crepe-laying; a shallow grave pit, a burial chamber in the form of a stone box) and the method of inhumation (single position of the corpse on the back; orientation of the deceased with his head to the western sector of the horizon; the absence of an accompanying burial of a horse) indicate that that this object belongs to the Karban tradition of ritual practice of the population of Altai of the 2nd century BC – 5th century AD. Analysis of the discovered inventory (horn onlays for a bow, combat knife, typesetting belt, bone arrowheads, awl, pendants and braids made of non-ferrous metal, beads) and its comparison with materials from synchronous complexes in adjacent territories became the basis for determining the chronology of the burial within the Early Xianbei period (2nd – early 3nd centuries AD). It was established that the set of objects included items that were typical for both female and male “standard” rite of the Altai nomads of this period. It is concluded that the buried individual, most likely a male, was a representative of the prosperous stratum of the ordinary population. Judging by the availability of means of long-range and close combat including numerous equipment, he was part of a group of professional warriors. At the same time, the deceased during his lifetime occupied a rather high position in a small group of pastoralists who left the Karban-I necropolis

Activation of consolidation processes of alumina ceramics

The methods for activating sintering ceramics based on Al[2]O[3] by mechanical activation in the planetary mill, by adding in the mixture of nanopowders (NP) Al, Al[2]O[3], and submicron powder TiO[2], and by applying the technology of spark plasma sintering (SPS) are developed. It has been shown that adding the nanopowder up to 20 wt. % Al2O3 in a coarse powder [alpha]-Al[2]O[3] activates the sintering process resulting in increased density and hardness of the sintered alumina ceramics. Substantial effect of increasing density of alumina ceramics due to adding the submicron powder TiO[2] in the compound of initial powder mixtures has been established