A review of capacitor placement techniques on distribution feeders

Carlisle, Joanna; El-Keib, A.A.; Boyd, D.M.; Nolan, K.

doi:10.1109/ssst.1997.581664

Cited by 39 publications

(17 citation statements)

References 56 publications

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“…Besides, the individual needs to satisfy constraint (2). In genetic algorithm, a constraint optimization problem is replaced by a series of unconstrained problems using the penalty method [19] and defining the penalty function [4].…”

Section: Defining Individual Fitness In Evolutionary Methodsmentioning

confidence: 99%

“…In genetic algorithm, a constraint optimization problem is replaced by a series of unconstrained problems using the penalty method [19] and defining the penalty function [4]. By testing the evolutionary method for different networks, it has been noticed that for some networks certain individuals have a low objective value (1) but do not satisfy the voltage constraint (2). On the other hand, some individuals satisfy the voltage constraint but have a considerable higher objective value.…”

Section: Defining Individual Fitness In Evolutionary Methodsmentioning

confidence: 99%

“…Considerable effort has been put into solving the optimization problem in current literature. In [1] and [2], a review of optimal capacitor placement methods sorted by methods and authors is given. Different soft computing methods such as: genetic algorithms simulated annealing, artificial neural networks, fuzzy based logic, heuristics based methods, and tabu search algorithms are widely used for recent solution of the optimization problem.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation

Barukčić¹,

Nikolovski²,

Jović³

2010

Journal of Electrical Engineering

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The issue of optimal allocation of capacitor banks concerning power losses minimization in distribution networks are considered in this paper. This optimization problem has been recently tackled by application of contemporary soft computing methods such as: genetic algorithms, neural networks, fuzzy logic, simulated annealing, ant colony methods, and hybrid methods. An evolutionaryheuristic method has been proposed for optimal capacitor allocation in radial distribution networks. An evolutionary method based on genetic algorithm is developed. The proposed method has a reduced number of parameters compared to the usual genetic algorithm. A heuristic stage is used for improving the optimal solution given by the evolutionary stage. A new cost-voltage node index is used in the heuristic stage in order to improve the quality of solution. The efficiency of the proposed two-stage method has been tested on different test networks. The quality of solution has been verified by comparison tests with other methods on the same test networks. The proposed method has given significantly better solutions for time dependent load in the 69-bus network than found in references.

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Section: Defining Individual Fitness In Evolutionary Methodsmentioning

confidence: 99%

Section: Defining Individual Fitness In Evolutionary Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation

Barukčić¹,

Nikolovski²,

Jović³

2010

Journal of Electrical Engineering

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“…Thus, this problem can be stated as the determination of the locations and the capacities of new sources of reactive power, searching simultaneously to accomplish the following goals: 1) A good bus tension profile: The quality of service is directly related to the difference between the effective and the nominal bus voltage; 2) Minimization of transmission losses: Active power transmission losses can be directly translated into monetary losses since they are the main component in the difference between the generated power and the consumed power; 3) Minimization of the amount of reactive compensation installed: Although shunt capacitor compensation generally provides the most economical reactive power source for voltage control, heavy use of these devices could lead to the reduction of stability margins and poor voltage regulation [6,7]. Traditionally, this problem is addressed as a Single Objective Optimization Problem (SOP) [8][9][10][11][12][13][14]. A SingleObjective Optimization Algorithms (SOA) usually provides a unique optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Particle Swarm Optimization Based Local Search for Reactive Power Compensation Problem

Mousa¹,

El-Shorbagy²

2012

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This paper presents an enhanced Particle Swarm Optimization (PSO) algorithm applied to the reactive power compensation (RPC) problem. It is based on the combination of Genetic Algorithm (GA) and PSO. Our approach integrates the merits of both genetic algorithms (GAs) and particle swarm optimization (PSO) and it has two characteristic features. Firstly, the algorithm is initialized by a set of a random particle which traveling through the search space, during this travel an evolution of these particles is performed by a hybrid PSO with GA to get approximate no dominated solution. Secondly, to improve the solution quality, dynamic version of pattern search technique is implemented as neighborhood search engine where it intends to explore the less-crowded area in the current archive to possibly obtain more nondominated solutions. The proposed approach is carried out on the standard IEEE 30-bus 6-generator test system. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective RPC.

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“…These problems include (a) non-clarity of the conduct of the feeders' loads, particularly domestic loads, (b) complexity of distribution networks, (c) uncertainty of electric distribution companies in returning the primary capital used for capacitor placement, and (d) the variety of the kinds of network loads. With due regard to these problems, in the many studies made so far of capacitor placement [1][2][3][4][5][6][7][8][9][10][11][12][13]15], criteria have been taken into consideration in order to solve the problem by a simpler method, which has not been appealing to distribution companies, so that the losses are still high in the network.…”

Section: Introductionmentioning

confidence: 99%

Application of a genetic algorithm in the reduction of loss and control of the voltage of distribution networks using the real optimization of fixed and switched capacitors

Hooshmand

2005

Euro. Trans. Electr. Power

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SUMMARYFor a long time, reduction of the loss and control of the voltage of electric distribution networks have been considered to be some of the most important problems of such networks. The importance of these two features arises from the fact that they are directly related to the loss of electric energy. One of the essential and effective methods in solving these two problems is the use of fixed and switched capacitors.In this paper, a new method has been presented on the basis of a genetic algorithm aimed at reducing the loss and controlling the network voltage by using fixed and switched capacitors in the form of real optimization. By real optimization in this method, we mean the use of the range of low-and medium-voltage capacitors available in the market with their real prices and by modelling the load at its different levels, the real model of the network is also taken into consideration. With due regard to the above facts and considering the large number of variables of optimization, the use of the genetic algorithm is an important factor in finding the real optimal method in the application of capacitors.

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A review of capacitor placement techniques on distribution feeders

Cited by 39 publications

References 56 publications

Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation

Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation

Enhanced Particle Swarm Optimization Based Local Search for Reactive Power Compensation Problem

Application of a genetic algorithm in the reduction of loss and control of the voltage of distribution networks using the real optimization of fixed and switched capacitors

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