A Piecewise Solution to the Reconfiguration Problem by a Minimal Spanning Tree Algorithm

2014 ◽  
Vol 15 (5) ◽  
pp. 419-427
Author(s):  
Juan M. Ramirez ◽  
Diana P. Montoya
Keyword(s):  
Graph Theory ◽  
Combinatorial Optimization ◽  
Radial Distribution ◽  
Spanning Tree ◽  
Distribution Systems ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Minimal Spanning Tree ◽  
Power Losses ◽  
Tree Algorithm

Abstract This paper proposes a minimal spanning tree (MST) algorithm to solve the networks’ reconfiguration problem in radial distribution systems (RDS). The paper focuses on power losses’ reduction by selecting the best radial configuration. The reconfiguration problem is a non-differentiable and highly combinatorial optimization problem. The proposed methodology is a deterministic Kruskal’s algorithm based on graph theory, which is appropriate for this application generating only a feasible radial topology. The proposed MST algorithm has been tested on an actual RDS, which has been split into subsystems.

A Fast Restoration Strategy in Distribution Systems Using an Enhanced Differential Evolution Approach

2010 ◽  
Vol 11 (3) ◽  
Author(s):  
Chao-Ming Huang ◽  
Cheng-Tao Hsieh ◽  
Yann-Chang Huang ◽  
Yung-Shan Wang
Keyword(s):  
Combinatorial Optimization ◽  
Differential Evolution ◽  
Distribution System ◽  
Distribution Systems ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Convergence Time ◽  
Ant System ◽  
Power Company ◽  
Typical Distribution

This paper proposes a fast restoration strategy of distribution systems using an enhanced differential evolution (EDE) approach. Service restoration of distribution systems is an emergent task that must be performed rapidly by the system operators. Basically, it is a complicated combinatorial optimization problem, often having many candidate solutions to be evaluated by the operators. To improve the efficiency of restoration and reduce the burden on the operators, this paper proposes an EDE method combining variable scaling differential evolution (VSDE) algorithm and ant system (AS) to solve the combinatorial optimization problem. To verify the effectiveness of the proposed method, a typical distribution system of the Taiwan Power Company (TPC) was tested and compared with the existing methods. The results show the proposed method was superior to the existing methods in terms of convergence time and the obtained restoration plan.

An upper bound for the probability of a union

1976 ◽  
Vol 13 (03) ◽  
pp. 597-603 ◽  
Author(s):  
David Hunter
Keyword(s):  
Graph Theory ◽  
Upper Bound ◽  
Spanning Tree ◽  
Upper Bounds ◽  
Minimal Spanning Tree ◽  
Multivariate Normal ◽  
Tail Probabilities ◽  
Tree Algorithm ◽  
New Family ◽  
The Family

The problem of bounding P(∪ Ai ) given P(A i) and P(A i A j) for i ≠ j = 1, …, k goes back to Boole (1854) and Bonferroni (1936). In this paper a new family of upper bounds is derived using results in graph theory. This family contains the bound of Kounias (1968), and the smallest upper bound in the family for a given application is easily derivable via the minimal spanning tree algorithm of Kruskal (1956). The properties of the algorithm and of the multivariate normal and t distributions are shown to provide considerable simplifications when approximating tail probabilities of maxima from these distributions.

An upper bound for the probability of a union

1976 ◽  
Vol 13 (3) ◽  
pp. 597-603 ◽  
Author(s):  
David Hunter
Keyword(s):  
Graph Theory ◽  
Upper Bound ◽  
Spanning Tree ◽  
Upper Bounds ◽  
Minimal Spanning Tree ◽  
Multivariate Normal ◽  
Tail Probabilities ◽  
Tree Algorithm ◽  
New Family ◽  
The Family

The problem of bounding P(∪ Ai) given P(Ai) and P(AiAj) for i ≠ j = 1, …, k goes back to Boole (1854) and Bonferroni (1936). In this paper a new family of upper bounds is derived using results in graph theory. This family contains the bound of Kounias (1968), and the smallest upper bound in the family for a given application is easily derivable via the minimal spanning tree algorithm of Kruskal (1956). The properties of the algorithm and of the multivariate normal and t distributions are shown to provide considerable simplifications when approximating tail probabilities of maxima from these distributions.

Inverse version of the kth maximization combinatorial optimization problem

2018 ◽  
Vol 54(5) ◽  
pp. 72
Author(s):  
Quoc, H.D. ◽  
Kien, N.T. ◽  
Thuy, T.T.C. ◽  
Hai, L.H. ◽  
Thanh, V.N.
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem

scholarly journals Optimal feeder reconfiguration in distributed generation environment under time-varying loading condition

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Yanrenthung Odyuo ◽  
Dipu Sarkar ◽  
Lilika Sumi
Keyword(s):  
Graph Theory ◽  
Distributed Generation ◽  
Spanning Tree ◽  
Voltage Stability ◽  
Electrical Network ◽  
List Type ◽  
Network Reconfiguration ◽  
Time Varying ◽  
Electrical Networks ◽  
Tree Algorithm

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.

scholarly journals Distributed Generation Placement in Radial Distribution Networks using a Bat-inspired Algorithm

DYNA ◽  
2015 ◽  
Vol 82 (192) ◽  
pp. 60-67 ◽  
Author(s):  
John Edwin Candelo-Becerra ◽  
Helman Hernández-Riaño
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Reactive Power ◽  
Distribution Networks ◽  
Power Losses ◽  
Number Of Generators ◽  
Metaheuristic Techniques ◽  
Distributed Generation Placement ◽  
Made In

<p>Distributed generation (DG) is an important issue for distribution networks due to the improvement in power losses, but the location and size of generators could be a difficult task for exact techniques. The metaheuristic techniques have become a better option to determine good solutions and in this paper the application of a bat-inspired algorithm (BA) to a problem of location and size of distributed generation in radial distribution systems is presented. A comparison between particle swarm optimization (PSO) and BA was made in the 33-node and 69-node test feeders, using as scenarios the change in active and reactive power, and the number of generators. PSO and BA found good results for small number and capacities of generators, but BA obtained better results for difficult problems and converged faster for all scenarios. The maximum active power injections to reduce power losses in the distribution networks were found for the five scenarios.</p>

Sign in / Sign up

Export Citation Format

Share Document