Allocation of capacitor banks in distribution systems through a modified monkey search optimization technique

Duque, Felipe Gomes; Oliveira, Leonardo W. de; Oliveira, Edimar J. de; Marcato, André L. M.; Silva, Ivo Chaves da

doi:10.1016/j.ijepes.2015.05.034

Cited by 34 publications

(15 citation statements)

References 30 publications

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“…fixed and/or switched capacitor banks are placed by nature-observed heuristic algo rithms, usually known as AI methods. These optimization methods are used for deciding optimal location and size of SCB; eg, fuzzy logic, [19][20][21][22] genetic algorithm, [23][24][25][26][27] fuzzy GA, [28][29][30][31] particle swarm optimization, [32][33][34] plant growth simulation algorithm, 35,36 teaching learning-based optimization, 37 gravitational search algorithm, 38 ant colony optimization, 39,40 artificial bee colony algorithm, 41,42 cuckoo search algorithm, 43 evolutionary tech nique, 44,45 bacterial foraging algorithm, 46 bioinspired algorithm, 47 flower pollination algorithm, 48 monkey search technique, 49 and improved harmony algorithm 50 etc. A simple analytical approach 51 and differential evolution 52 are also proposed for optimal capacitor placement in radial DS.…”

Section: Literature Reviewmentioning

confidence: 99%

Framework for multipoint optimal reactive power compensation in radial distribution system with high distributed generation penetration

Bharti

2019

Int Trans Electr Energ Syst

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Summary Reactive power management and voltage control are essential for improved operational performance of distribution system (DS). With increasing distributed generation (DG) penetration in DS, reactive power compensation (RPC) becomes more significant, as most of the renewable‐based DGs are not able to supply reactive power. This paper presents new graph‐theoretic method for placement of reactive power compensator in radial DS using “average electrical centrality index.” The proposed method is efficient with less compute intensive because appropriate location for the compensator is decided by centrality measures, unlike the existing methods that use optimization techniques to determine location. The proposed method is tested in a modified 69‐bus radial system to demonstrate its applicability in practice. The robustness of the proposed method is demonstrated in case of renewable generation uncertainty for modified 69‐bus radial DS. The performance of the proposed method is compared with existing multipoint RPC methods also.

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Section: Literature Reviewmentioning

confidence: 99%

Framework for multipoint optimal reactive power compensation in radial distribution system with high distributed generation penetration

Bharti

2019

Int Trans Electr Energ Syst

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“…is soared into the logarithmic spiral path as shown in Fig. 2(a) to make a deep search around the corresponding artificial light source x i , which is chosen on the basis of the probability P i using (18). The new position of th prospector moth, can be expressed mathematically as follows:…”

Section: 33mentioning

confidence: 99%

“…Therefore, several optimization algorithms have been proposed in recent years to solve the optimal of DG resources and shunt capacitor placement and sizing problems in radial and ring distribution systems for maximizing their benefits such as Flower pollination algorithm (FPA) [9], particle swarm optimization (PSO) [10,11], discrete particle swarm optimization (DPSO) [12], genetic algorithm (GA) [13], teaching-learning-based optimization (TLBO) [14], artificial bee colony (ABC) [15], cuckoo search algorithm (CSA) [16], gravitational search algorithm (GSA) [17], modified monkey search (MMS) [18], whale optimization algorithm (WOA) [19], improved harmony algorithm (IHA) [20], moth swarm algorithm (MSA) [21], direct search algorithm (DSA) [22], differential evolution algorithm (DEA) [23], simulated annealing (SA) [24], plant growth simulation algorithm (PGSA) [25], fuzzy reasoning (FRB) [26], improved binary particle swarm optimization (IBPSO) [27], and fuzzy-GA [28] have been presented to deal with the problem of the DG and capacitor allocation. However, some of these algorithms are not highly effective as the power losses still have high values.…”

Section: Introductionmentioning

confidence: 99%

Hybrid GMSA for Optimal Placement and Sizing of Distributed Generation and Shunt Capacitors

Mohamed¹,

Mohamed²,

Mitani³

2018

JESTR

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This work presents a hybrid approach based on the genetic algorithm (GA) and moth swarm algorithm (MSA), namely genetic moth swarm algorithm (GMSA). Minimizing the electrical power loss in radial distribution systems (RDN) within the framework of system operation and under system constraints is the main objective of this study. In GMSA, the global search ability has been regulated by the incorporation of GA operations by the adaptive mutation operator on the reconnaissance phase using genetic pathfinder moths. In addition, the selection of artificial light sources has been expanded over the swarm. The representation of individuals within the three phases of MSA has been modified in term of quality and ratio. Elite individuals have been used to play different roles in order to reduce the design space and thus increase the exploitation ability. GMSA and other optimization methods have been carried out on the IEEE 33 and 69-bus power systems. The reduction of power loss and total system cost in addition to the improvement of the minimum bus voltage are simulated for the competitive algorithms under several power system constraints and conditions. The computational results proved the superiority of the GMSA compared with other techniques.

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“…In Modified Monkey Search (MMS) [17] optimization, the Monkey search optimization technique [1] is modified with a better representation of the problem. Modified monkey search is inspired by the behavior of a monkey searching for food in a jungle.…”

Section: Modified Monkey Searchmentioning

confidence: 99%

Monkey Behavior Based Algorithms - A Survey

Devi¹,

Sathya²

2017

IJISA

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Abstract-Swarm intelligence algorithms (SIA) are bioinspired techniques based on the intelligent behavior of various animals, birds, and insects. SIA are problemindependent and are efficient in solving real world complex optimization problems to arrive at the optimal solutions. Monkey behavior based algorithms are one among the SIAs first proposed in 2007. Since then, several variants such as Monkey search, Monkey algorithm, and Spider Monkey optimization algorithms have been proposed. These algorithms are based on the tree or mountain climbing and food searching behavior of monkeys either individually or in groups. They were designed with various representations, covering different behaviors of monkeys and hybridizing with the efficient operators and features of other SIAs and Genetic algorithm. They were explored for applications in several fields including bioinformatics, civil engineering, electrical engineering, networking, data mining etc. In this survey, we provide a comprehensive overview of monkey behavior based algorithms and their related literatures and discuss useful research directions to provide better insights for swarm intelligence researchers.

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Allocation of capacitor banks in distribution systems through a modified monkey search optimization technique

Cited by 34 publications

References 30 publications

Framework for multipoint optimal reactive power compensation in radial distribution system with high distributed generation penetration

Framework for multipoint optimal reactive power compensation in radial distribution system with high distributed generation penetration

Hybrid GMSA for Optimal Placement and Sizing of Distributed Generation and Shunt Capacitors

Monkey Behavior Based Algorithms - A Survey

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