Network reconfiguration of distribution systems using improved mixed-integer hybrid differential evolution

By C. Su, Chu-Sheng Lee. 2002

383 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/0e0d52a4bde06af67404f32b6828027fbe476aae

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In Control and Systems Engineering

In Electrical and Electronic Engineering

Citations in the corpus

Multi-objective reconfiguration of radial distribution systems using reliability indices (2016, methodology): “1) Network Data and Multi-Objective Optimization: to demonstrate the applicability of the proposed methodology on a practical DS, the DS of Taiwan Power Company (TPC) is examined [41].”
Feeder Reconfiguration in Distribution Networks Based on Convex Relaxation of OPF (2015, methodology): “Test network 1 is from Taiwan Power Company and the network data is taken from [24].”
Application of binary group search optimization to distribution network reconfiguration (2014, methodology): “In order to solve DNR problem, a mix-integer hybrid differential evolution is reported in [16] and variable scaling hybrid differential evolution in [17].”
Distribution System Reconfiguration for Annual Energy Loss Reduction Considering Variable Distributed Generation Profiles (2015, background): “The available literature investigating DSR for power-loss minimization usually considers a constant load scenario based on peak or average active and reactive power demand [2]–[17], [21], [22], which results in obtaining an optimum switching configuration which minimizes power loss at one instant in”
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.”
A Mixed-Integer Linear Programming Approach for the Computation of the Minimum-Losses Radial Configuration of Electrical Distribution Networks (2012, methodology): “In the configuration reported in [35] and in [39]–[41], line 41 is open instead of line 42, resulting in a network losses value”
Minimum Loss Network Reconfiguration Using Mixed-Integer Convex Programming (2012, methodology): “Some of the promising meta-heuristic or artificial intelligence techniques include simulated annealing [12], [13], genetic algorithms [14], [15], ant colony optimization [16], mixed-integer [17] and variable scaling [18] hybrid differential evolution, and harmony search [19].”