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The use of Petri computing networks for optimization of the structure of distribution networks to minimize power losses

Authors: Murat Asanov; Sergey Kokin; Salima Asanova; Kalmurza Satarkulov; Stepan Dmitriev; Murodbek Safaraliev;

The use of Petri computing networks for optimization of the structure of distribution networks to minimize power losses

Abstract

The paper suggests a self-organizing multi-component computational algorithm as a solution to the problem of optimizing the structure of distribution electrical networks to minimize the loss of power. The suggested algorithm is consistent with the method of branches and borders and uses the apparatus of the Petri computer networks (PCN) apparatus. The PCN apparatus has a universal computational capability to process symbolic-numeric data, which along with the solution of calculating problems, provides for the structural and logical analysis of the systems and processes under study. The structure of the PCN based algorithm is similar to the studied system, which provides for better visualization and convenience of interpretation, modification, and implementation of this algorithm on one or more computers by paralleling computational processes for better system performance. Computing modules within the general text of the algorithm can be arranged in any given order and solve the problem by organizing themselves in the process of functioning.

Country
Russian Federation
Keywords

Distribution electrical networks, LOGICAL ANALYSIS, OPTIMIZATION OF NETWORK STRUCTURE, SYSTEMS AND PROCESS, Graph, LOSS OF ELECTRICITY, COMPUTATIONAL MODULES, PETRI NETWORKS, Loss of electricity, COMPUTATIONAL ALGORITHM, STRUCTURAL OPTIMIZATION, Petri networks, ALGORITHM, ENERGY MANAGEMENT, COMPUTATIONAL PROCESS, Optimization of network structure, ELECTRICAL NETWORKS, ENERGY RESOURCES, TK1-9971, Algorithm, COMPUTATIONAL CAPABILITY, GRAPH, Electrical engineering. Electronics. Nuclear engineering, DISTRIBUTION ELECTRICAL NETWORKS, MULTICOMPONENTS, SYMBOLIC NUMERICS

  • BIP!
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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
Green
gold