Optimal Network Reconfiguration of Large-Scale Distribution System Using Harmony Search Algorithm

By R. Rao, S. Narasimham, Manyala Ramalinga Raju, A. Rao. IEEE Transactions on Power Systems, 2011

467 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8c8a81c3da7ce9348b011eecb17ebe5042e37b6c

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In Control and Systems Engineering

In Electrical and Electronic Engineering

Citations in the corpus

Reconfiguration of distribution network with DG using fuzzy multi-objective method (2012, unknown): “[16].”
Feature Selection With Harmony Search (2012, unknown): “[31], [49], [52], [59] and machine learning tasks [10], [38], [39], [45].”
Stochastic DG Placement for Conservation Voltage Reduction Based on Multiple Replications Procedure (2015, methodology): “A wide range of methods has been proposed for DG placement, which can be divided into three categories: 1) sensitivity analysis [13], [15]; 2) analytical approaches [16], [17]; and intelligence algorithms (IAs) [18]–[20].”
Distribution System Optimization Based on a Linear Power-Flow Formulation (2015, unknown): “colony [16], [17], harmony search algorithm [18], evolutionary algorithms [19]–[22], artificial neural networks (ANNs) [23], and particle swarm optimization [24].”
Multi-objective reconfiguration of radial distribution systems using reliability indices (2016, background): “In [9] a harmony search algorithm was proposed and was further extended in [10] to consider also the effect of optimal placement of distributed generation (DG) on active power losses.”
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”
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].”
Novel Linearized Power Flow and Linearized OPF Models for Active Distribution Networks With Application in Distribution LMP (2018, background): “In a large practical distribution system, the OPF, if fully modeled, is usually a nonlinear non-convex programming with a large amount of computation required [13].”