Optimal placement of capacitors in radial distribution system using shark smell optimization algorithm

Gnanasekaran, N; Chandramohan, S.; Kumar, Pradeep; Imran, Arif

doi:10.1016/j.asej.2016.01.006

Cited by 75 publications

(35 citation statements)

References 21 publications

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“…Gnanasekaran et al employ the shark smell optimization (SSO) algorithm. The method uses a gradient search and a rotational movement‐based local search.…”

Section: Introductionmentioning

confidence: 99%

Capacitor placement by variables' inclusion and interchange improved algorithm

Abril

2020

Int Trans Electr Energ Syst

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Summary Very diverse optimization methods have been used to determining the optimal capacitor placement in distribution systems. Most of these methods analyze the use of fixed capacitor banks on circuits with only one load state. Other contributions consider the compensation with capacitors switched at different load levels. Almost all previous works represent the load as constant power with respect to voltage variations. This work improves the variables' inclusion and interchange algorithm with a new voltage‐dependent model that considers the variation of active and reactive load with the increase of voltage due to the capacitor placement. The load model used in this work can represent the load by a generalized function of the voltage, in which the constant power load, the constant current load, or the constant impedance load are particular cases. This voltage‐dependent model is the fundamental novelty of the method. The presented algorithm maximizes the annual cost saving in a circuit with several load levels by determining the locations, sizes, and control in time of the needed fixed and switched capacitors. The optimization is constrained with the bounds of minimum and maximum voltages. The capacitor placement solutions proposed by the developed method for various examples of four known circuits are compared with several of the most recently published solutions for these examples. The comparison shows the effectiveness of this method. The solutions obtained for different models of loads show the importance of considering, in the optimization, the load dependence with voltage.

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“…Gnanasekaran et al employ the shark smell optimization (SSO) algorithm. The method uses a gradient search and a rotational movement‐based local search.…”

Section: Introductionmentioning

confidence: 99%

Capacitor placement by variables' inclusion and interchange improved algorithm

Abril

2020

Int Trans Electr Energ Syst

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“…The general formula used in previous research to find the optimal value of capacitor or DG size and placement is to minimize system power loss 14 . The power loss reduction factor index is stated as:…”

Section: Power Loss Indexmentioning

confidence: 99%

Evaluating the effect placement capacitor and distributed photovoltaic generation for power system losses minimization in radial distribution system

Rahman

Manjang

Yusran

et al. 2018

AIP Conference Proceedings

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“…Because of the uncertainties in wind speed and poor reactive power compensation, the fitness cost function in the study was nonlinear, and appropriate mathematical tools were applied. The Whale Optimization Algorithm (WOA) [21] and the Shark Smell Optimization (SSO) algorithm [22] are two additional recently developed metaheuristic optimization techniques that have been used to solve the optimal capacitor problem in distribution networks.…”

Section: Introductionmentioning

confidence: 99%

Optimal Capacitor Placement in Wind Farms by Considering Harmonics Using Discrete Lightning Search Algorithm

Sirjani¹

2017

Sustainability

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Currently, many wind farms exist throughout the world and, in some cases, supply a significant portion of energy to networks. However, numerous uncertainties remain with respect to the amount of energy generated by wind turbines and other sophisticated operational aspects, such as voltage and reactive power management, which requires further development and consideration. To fix the problem of poor reactive power compensation in wind farms, optimal capacitor placement has been proposed in existing wind farms as a simple and relatively inexpensive method. However, the use of induction generators, transformers, and additional capacitors represent potential problems for the harmonics of a system and therefore must be taken into account at wind farms. The optimal location and size of capacitors at buses of an 80-MW wind farm were determined according to modelled wind speed, system equivalent circuits, and harmonics in order to minimize energy losses, optimize reactive power and reduce the management costs. The discrete version of the lightning search algorithm (DLSA) is a powerful and flexible nature-inspired optimization technique that was developed and implemented herein for optimal capacitor placement in wind farms. The obtained results are compared with the results of the genetic algorithm (GA) and the discrete harmony search algorithm (DHSA).

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Optimal placement of capacitors in radial distribution system using shark smell optimization algorithm

Cited by 75 publications

References 21 publications

Capacitor placement by variables' inclusion and interchange improved algorithm

Capacitor placement by variables' inclusion and interchange improved algorithm

Evaluating the effect placement capacitor and distributed photovoltaic generation for power system losses minimization in radial distribution system

Optimal Capacitor Placement in Wind Farms by Considering Harmonics Using Discrete Lightning Search Algorithm

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