An improved TS algorithm for loss-minimum reconfiguration in large-scale distribution systems

By Dong Zhang, Z. Fu, Liu-chun Zhang. 2007

632 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/5edc884121f0e46455b175d2387a7c56b4981df2

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In Control and Systems Engineering

In Electrical and Electronic Engineering

Citations in the corpus

Linear Current Flow Equations With Application to Distribution Systems Reconfiguration (2015, methodology): “Some good examples of these methods are genetic algorithms [18], plant growth algorithms [19], tabu search algorithms [20], harmony search algorithms [21], evolutionary algorithms [22], artificial neural networks [23], simulated annealing algorithms [24], ant colony algorithms [25], and particle swa”
Application of binary group search optimization to distribution network reconfiguration (2014, methodology): “The tabu search (TS) methodology and its modifications are reported in [18–20] as an efficient tool in order to solve the DNR problem.”
Imposing Radiality Constraints in Distribution System Optimization Problems (2012, methodology): “For the 119-node system, the B&B algorithm obtained a solution better than the one presented in [31], while the results obtained for the 33-, 84-, and 136-node systems are equal to those presented in [10].”
An efficient approach for the siting and sizing problem of distributed generation (2015, background): “The second system has been a 118-bus radial distribution test system [16], whose total power loads are 22.”
Distribution System Optimization Based on a Linear Power-Flow Formulation (2015, neutral): “Six widely-used test systems, that is, the 14-node [6], 33-node [2], 70-node [35], 84-node [19], 119-node [36], 136-node [37], and 880-node [38] systems are obtained from corresponding references in which the system data is provided.”
Optimal Network Reconfiguration of Large-Scale Distribution System Using Harmony Search Algorithm (2011, methodology): “It can be observed that 24.85% of average loss reduction is achieved by HSA comparing with 18.01% by the GA, 18.65% by the RGA, and 19.34% by the ITS as shown in Table III.”