New Evolutionary Technique for Optimization Shunt Capacitors in Distribution Networks

Elmaouhab, Ali; Boudour, Mohamed; Gueddouche, Rabah

doi:10.2478/v10187-011-0027-x

Cited by 17 publications

(5 citation statements)

References 9 publications

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“…Recombination is used mainly in evolutionary strategies. Contrary to k points crossover operators, which exchanges information between two parents, the recombination creates the descendants, by weighting many parents' components [27]. We define four weighting operators:

O p_{1} ()(, c h r) = \frac{P r_{1} ()(, c h r) + P r_{2} ()(, c h r)}{2},

O p_{2} ()(, c h r) = \max ()(, P r_{1} ()(, c h r), P r_{2} ()(, c h r)) + \frac{(||, P r_{1} ()(, c h r) - P r_{2} ()(, c h r))}{2},

O p_{3} ()(, c h r) = \min ()(, P r_{1} ()(, c h r), P r_{2} ()(, c h r)) - \frac{(||, P r_{1} ()(, c h r) - P r_{2} ()(, c h r))}{2},

N O p ()(, c h r) = P r ()(, c h r) - \frac{F i t ()(, \Pr ()(, c h r))}{F i t^{'} ()(, \Pr ()(, c h r))} .

With:

\begin{matrix} right leftthickmathspace.5em \\ F i t^{normal′} (P r (c h r)) \end{matrix}

…”

Section: Estimation Methodsmentioning

confidence: 99%

Intelligent non‐linear dynamic equivalent approach applied to Algerian power system

Gueddouche

Boudour

2019

IET Generation, Transmission & Distribution

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O p_{1} ()(, c h r) = \frac{P r_{1} ()(, c h r) + P r_{2} ()(, c h r)}{2},

O p_{2} ()(, c h r) = \max ()(, P r_{1} ()(, c h r), P r_{2} ()(, c h r)) + \frac{(||, P r_{1} ()(, c h r) - P r_{2} ()(, c h r))}{2},

O p_{3} ()(, c h r) = \min ()(, P r_{1} ()(, c h r), P r_{2} ()(, c h r)) - \frac{(||, P r_{1} ()(, c h r) - P r_{2} ()(, c h r))}{2},

N O p ()(, c h r) = P r ()(, c h r) - \frac{F i t ()(, \Pr ()(, c h r))}{F i t^{'} ()(, \Pr ()(, c h r))} .

With:

\begin{matrix} right leftthickmathspace.5em \\ F i t^{normal′} (P r (c h r)) \end{matrix}

…”

Section: Estimation Methodsmentioning

confidence: 99%

Intelligent non‐linear dynamic equivalent approach applied to Algerian power system

Gueddouche

Boudour

2019

IET Generation, Transmission & Distribution

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“…In this section, to confirm capability of proposed iDEA in solving OSSC problem, this algorithm has been tested on IEEE standard 10‐bus and 33‐bus radial distribution networks and compared with hybrid approach and a novel evolutionary technique (ET) which was suggested in 2009 and 2011, respectively . Then, to illustrate ability of iDEA to employ in practical cases, optimal capacitor placement has been done on practical Meshkin‐shahr City 36‐bus radial distribution network in North West of Iran.…”

Section: Case Studymentioning

confidence: 99%

Optimal sizing and siting of shunt capacitor banks by a new improved differential evolutionary algorithm

Hosseinzadehdehkordi

Nasab

Shayeghi

et al. 2013

Int. Trans. Electr. Energ. Syst.

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SUMMARY In this context, a novel structure is proposed for differential evolutionary algorithm (DEA) to compensate reactive power by optimal siting and sizing capacitor (OSSC) in radial distribution systems (RDSs). DEA is among the evolutionary algorithms (EAs) which uses crossover, mutation and selection operators to obtain optimal solution from initial population. In proposed improved DEA (iDEA), two scenarios for mutation operator is defined. Other improvement is self‐adapting crossover rate. Main goal of shunt capacitor in RDS is minimizing annual cost, while in this work, in addition to annual cost, five other parameters are studied and analyzed, which are: total installed capacitor banks and its related cost, power loss and related cost as well as minimum voltage. To verify the ability of proposed technique, iDEA is tested on standard RDSs (i.e. IEEE 10‐bus and 33‐bus) and compared with other well‐developed techniques. Also, to confirm robustness of iDEA to be utilized in practical cases, three load levels are defined and iDEA applied on practical RDS of Meshkin‐shahr City in North West of Iran. Copyright © 2013 John Wiley & Sons, Ltd.

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“…Elmaouhab et al used an evolutionary optimization technique for allocating OC in the distribution network for reducing annual costs related to real power loss. In Taher et al, the authors have used particle swarm optimization (PSO) for OC allocation in the presence of harmonics in distorted and standard distribution networks.…”

Section: Introductionmentioning

confidence: 99%

Optimal capacitor allocation in distribution networks for minimization of power loss and overall cost using water cycle algorithm and grey wolf optimizer

Sampangi

Jayabarathi

2020

Int Trans Electr Energ Syst

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Summary In recent years, reactive power compensation techniques are receiving more attention across the globe from both the distribution network operators and research groups due to its ability in the reduction of network power loss, voltage deviation, and enhancement of voltage stability. In this paper, two novel and robust optimization techniques implemented for solving optimal capacitor (OC) allocation problem in various distribution networks are water cycle algorithm (WCA) and grey wolf optimizer (GWO). To crisscross the feasibility, GWO and WCA techniques are tested on practical Indian 28‐bus, 47‐bus, and 52‐bus and standard 33‐bus, 69‐bus, and 85‐bus radial distribution networks with uniformly varying load levels. The simulation results obtained by WCA and GWO are compared with other techniques like inclusion and interchange variable (IIV), direct search algorithm (DSA), and teaching and learning–based optimization (TLBO) and found to be better. In addition, from the obtained results, the algorithm's efficiency is evaluated in terms of exploration and exploitation, and convergence characteristics and computational time are discussed.

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New Evolutionary Technique for Optimization Shunt Capacitors in Distribution Networks

Cited by 17 publications

References 9 publications

Intelligent non‐linear dynamic equivalent approach applied to Algerian power system

Intelligent non‐linear dynamic equivalent approach applied to Algerian power system

Optimal sizing and siting of shunt capacitor banks by a new improved differential evolutionary algorithm

Optimal capacitor allocation in distribution networks for minimization of power loss and overall cost using water cycle algorithm and grey wolf optimizer

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