Loss Reduction from Capacitors Installed on Primary Feeders [includes discussion]

Neagle, N. M.; Samson, D. R.

doi:10.1109/aieepas.1956.4499390

Cited by 73 publications

(35 citation statements)

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“…Analytical method in conjunction with heuristics for capacitor placement was introduced by Neagle and Samson [2] and subsequently by cook [3]. A pioneering work which determines the capacitor sizes as discrete variables using dynamic programming technique was reported by Duran [4].…”

Section: Introductionmentioning

confidence: 99%

“…Grainger and Lee [5] formulated this problem as a non-linear programming problem by treating the capacitors locations and sizes as continuous variables. Fawzi et al [6] incorporated the released substation kva and the voltage rise at light-load level into a model developed by Neagle and Samson [2]. Ponnavaikko and Prakasa Rao [7] proposed a model, which considered the load growth and the discrete nature of capacitor size, apart from those considered in [5] and used a local optimization technique.…”

Section: Introductionmentioning

confidence: 99%

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Optimal allocation of capacitors in distribution systems using particle swarm optimization

Singh

Rao

2012

International Journal of Electrical Power & Energy Systems

120

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal allocation of capacitors in distribution systems using particle swarm optimization

Singh

Rao

2012

International Journal of Electrical Power & Energy Systems

120

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“…Neagle and Samson [4] considered loss reduction by one capacitor bank placed along the feeder by considering uniformly distributed loads, uniformly decreasing loads and equally distributed loads along the feeder. Cork [5] considered the effect of fi xed capacitors a radial distribution network with distributed loads and considered the reduction in energy loss.…”

Section: Introductionmentioning

confidence: 99%

Optimal placement of capacitor in radial distribution system using PSO

Sattianadan¹,

Sudhkaran²,

Vijayakumar³

et al. 2011

International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2011)

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The problem of capacitor allocation in Electric Distribution Systems involves maximizing energy and peak power loss reductions by means of capacitor installations. This paper presents a novel approach using approximate reasoning to determine system candidate nodes in a distribution system for capacitor placement. The solution methodology has two parts: in part one the loss sensitivity factors are used to select the candidate locations for the capacitor placement and in part two the Particle Swarm Optimization Technique is used to identify the sizes of the capacitor for minimizing the energy loss cost and capacitor cost. The proposed method is applied to 15-bus and 33-bus radial distribution system.

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“…The problem of banks allocation can be formulated as a combinatorial optimization problem where the number of capacitors, their sizes and location have to be optimally selected for satisfying the system restrictions. Many techniques have been proposed to solve this combinatorial optimization problem, which can be classified in four main methods; analytical [5,7,8], numerical [9,10], heuristic [11][12][13], and based on artificial intelligence [14,15]. A detailed study about the methods are described in [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

An Improved Crow Search Algorithm Applied to Energy Problems

et al. 2018

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Abstract:The efficient use of energy in electrical systems has become a relevant topic due to its environmental impact. Parameter identification in induction motors and capacitor allocation in distribution networks are two representative problems that have strong implications in the massive use of energy. From an optimization perspective, both problems are considered extremely complex due to their non-linearity, discontinuity, and high multi-modality. These characteristics make difficult to solve them by using standard optimization techniques. On the other hand, metaheuristic methods have been widely used as alternative optimization algorithms to solve complex engineering problems. The Crow Search Algorithm (CSA) is a recent metaheuristic method based on the intelligent group behavior of crows. Although CSA presents interesting characteristics, its search strategy presents great difficulties when it faces high multi-modal formulations. In this paper, an improved version of the CSA method is presented to solve complex optimization problems of energy. In the new algorithm, two features of the original CSA are modified: (I) the awareness probability (AP) and (II) the random perturbation. With such adaptations, the new approach preserves solution diversity and improves the convergence to difficult high multi-modal optima. In order to evaluate its performance, the proposed algorithm has been tested in a set of four optimization problems which involve induction motors and distribution networks. The results demonstrate the high performance of the proposed method when it is compared with other popular approaches.

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Loss Reduction from Capacitors Installed on Primary Feeders [includes discussion]

Cited by 73 publications

References 0 publications

Optimal allocation of capacitors in distribution systems using particle swarm optimization

Optimal allocation of capacitors in distribution systems using particle swarm optimization

Optimal placement of capacitor in radial distribution system using PSO

An Improved Crow Search Algorithm Applied to Energy Problems

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