scholarly journals Electrical Networks and Algebraic Graph Theory: Models, Properties, and Applications

2018 ◽  
Vol 106 (5) ◽  
pp. 977-1005 ◽  
Author(s):  
Florian Dorfler ◽  
John W. Simpson-Porco ◽  
Francesco Bullo
2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Yanrenthung Odyuo ◽  
Dipu Sarkar ◽  
Lilika Sumi

Abstract The development and planning of optimal network reconfiguration strategies for electrical networks is greatly improved with proper application of graph theory techniques. This paper investigates the application of Kruskal's maximal spanning tree algorithm in finding the optimal radial networks for different loading scenarios from an interconnected meshed electrical network integrated with distributed generation (DG). The work is done with an objective to assess the prowess of Kruskal's algorithm to compute, obtain or derive an optimal radial network (optimal maximal spanning tree) that gives improved voltage stability and highest loss minimization from among all the possible radial networks obtainable from the DG-integrated mesh network for different time-varying loading scenarios. The proposed technique has been demonstrated on a multiple test systems considering time-varying load levels to investigate the performance and effectiveness of the suggested method. For interconnected electrical networks with the presence of distributed generation, it was found that application of Kruskal's algorithm quickly computes optimal radial configurations that gives the least amount of power losses and better voltage stability even under varying load conditions. Article Highlights Investigated network reconfiguration strategies for electrical networks with the presence of Distributed Generation for time-varying loading conditions. Investigated the application of graph theory techniques in electrical networks for developing and planning reconfiguration strategies. Applied Kruskal’s maximal spanning tree algorithm to obtain the optimal radial electrical networks for different loading scenarios from DG-integrated meshed electrical network.


1973 ◽  
Vol 95 (2) ◽  
pp. 629-635 ◽  
Author(s):  
D. A. Smith ◽  
M. A. Chace ◽  
A. C. Rubens

This paper presents a detailed explanation of a technique for automatically generating a mathematical model for machinery systems. The process starts from a relatively small amount of input data and develops the information required to model a mechanical system with Lagrange’s equation. The technique uses elements of graph theory which were developed for electrical networks. The basic identifications required for mechanical systems are: paths from ground to mass centers, the independent loops of parts, if any, and paths associated with applied force effects. The techniques described in this paper have been used successfully in a generalized computer program, DAMN.


2018 ◽  
Vol 26 (3) ◽  
pp. 29-40
Author(s):  
S. U. Rehman ◽  
A. Q. Baig ◽  
M. Imran ◽  
Z. U. Khan

AbstractThe interplay between groups and graphs have been the most famous and productive area of algebraic graph theory. In this paper, we introduce and study the graphs whose vertex set is group G such that two distinct vertices a and b having di erent orders are adjacent provided that o(a) divides o(b) or o(b) divides o(a).


10.37236/2389 ◽  
2012 ◽  
Vol 19 (2) ◽  
Author(s):  
Murali Krishna Srinivasan

The number of spanning trees of a graph $G$ is called the complexity of $G$. A classical result in algebraic graph theory explicitly diagonalizes the Laplacian of the $n$-cube $C(n)$  and yields, using the Matrix-Tree theorem, an explicit formula for $c(C(n))$. In this paper we explicitly block diagonalize the Laplacian of the $q$-analog $C_q(n)$ of $C(n)$ and use this, along with the Matrix-Tree theorem, to give a positive combinatorial formula for $c(C_q(n))$. We also explain how setting $q=1$ in the formula for $c(C_q(n))$ recovers the formula for $c(C(n))$.


2002 ◽  
Vol 356 (1-3) ◽  
pp. 211-230 ◽  
Author(s):  
Pierre Hansen ◽  
Hadrien Mélot

2017 ◽  
Vol 40 (9) ◽  
pp. 2748-2755 ◽  
Author(s):  
Huanyu Zhao ◽  
Shumin Fei

This paper investigates the consensus problem for heterogeneous multi-agent systems consisting of third-order and first-order agents. The interaction topology includes both fixed and switching cases. First, by a model transformation, heterogeneous multi-agent systems are converted into equivalent error systems. Then we analyze the consensus problem of the multi-agent systems by analyzing the stability problem of the error systems. For a fixed topology, a sufficient condition for consensus of heterogeneous multi-agent systems is obtained based on algebraic graph theory and linear system theory. For a switching topology, a necessary and sufficient condition for mean-square consensus of multi-agent systems is obtained based on algebraic graph theory and Markovian jump system theory. Finally, we give some simulation examples.


Author(s):  
Max A. Alekseyev ◽  
Gérard P. Michon

Leonhard Euler (1707–1783) invented graph theory in 1735, by solving a puzzle of interest to the inhabitants of Königsberg. The city comprised three distinct land masses, connected by seven bridges. The residents sought a walk through the city that crossed each bridge exactly once but were consistently unable to find one. Euler showed that such a puzzle would have a solution if and only if every land mass was at the origin of an even number of bridges, with at most two exceptions—which could only be at the start or the end of the journey. Modern treatments of the problem capture Euler's reasoning by employing a diagram in which the land masses are represented by dots (called nodes), while the bridges are represented by line segments connecting the nodes (called edges). Such a diagram is referred to as a graph. This chapter uses algebraic graph theory to solve a number of counting problems.


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