Optimal location and sizing of capacitor placement in radial distribution system using Bacterial Foraging Optimization Algorithm

Devabalaji, K.R.; Ravi, K.; Kothari, D.P.

doi:10.1016/j.ijepes.2015.03.008

Cited by 177 publications

(79 citation statements)

References 19 publications

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“…They have been already faced by using metaheuristic techniques. Some examples include the gravitational search algorithm [18,19], bacterial foraging [20,21], crow search algorithm [14], particle swarm optimization [22][23][24][25], genetic algorithm [26,27], differential evolution [28][29][30], tabu search [31] and firefly [32].…”

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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Section: Introductionmentioning

confidence: 99%

An Improved Crow Search Algorithm Applied to Energy Problems

et al. 2018

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“…Nevertheless, the reliance of the PSO algorithm on the altering factors and the trapping in modifying parameters' probability can reduce its accuracy and efficacy for various conditions. Other researches about reactive power placement, which employed AI procedures, are Bacterial Foreaging Optimization Algorithm (BFOA) (Devabalaji et al, 2015), artificial bee colony algorithm (ElFergany & Abdelaziz, 2014), search space of GA (Carpinelli et al, 2010), hybrid method of CODEQ (HCODEQ) (Chiou & Chang, 2015), teaching learning based optimization (Sultana & Roy, 2014), Gravitational Search Algorithm (GSA) (Shuaib et al, 2015), Clustering Based Optimization (CBO) (Vuletić & Todorovski, 2014) and bio-inspired optimization (Duque et al, 2015). The applications of AI in solving capacitor placement may have effectual operation and can handle complicated problems.…”

Section: Introductionmentioning

confidence: 99%

Analytical Method for Reactive Power Compensators Allocation

Arief¹,

Nappu²,

Antamil³

2018

IJTech

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This paper presents a novel analytical methodology to determine the placement of reactive power devices in power systems. The proposed method modifies the Modal analysis technique and, based on the inversed reduced Jacobian matrix, develops a new formulation to compute the Reactive Contribution Factor (RCF) of each load buses. The objective of this research is to achieve the most stable condition and to minimize network losses. The proposed method is implemented at the modified IEEE 30-bus Reliability Test System (RTS) and compared with a different placement. In order to assess the method, this work compares the voltage profile, eigenvalue and network losses. The simulation results show that the proposed method can provide a solution to the ideal shunt compensator placement by improving the system's voltage stability and by minimizing losses.

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“…Carpinelli et al (2012) have investigated the single objective probabilistic optimal allocation to solve the capacitor placement problem. Devabalaji et al (2015) have studied the optimal location and sizing of capacitors using the Bacterial Foraging (BF) optimization algorithm in radial distribution systems. Kannan et al (2011) have used fuzzy Differential Evolution (DE) and fuzzy Multi Agent PSO (MAPSO) methods for optimal capacitor placement in radial distribution feeders in order to reduce real power losses, to improve the voltage profile, and to achieve economic saving.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Symbiotic Organisms Search for Optimal Fuzzified Joint Reconfiguration and Capacitor Placement in Electric Distribution Systems

2017

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Network reconfiguration and capacitor allocation are two valuable tools to optimally manage distribution systems. In the present work, an efficient algorithm is proposed for the joint problem of reconfiguration and capacitor placement in distribution systems. The multiobjective problem is formulated to minimize objective functions of total network power losses, bus voltage violation, and branch loading deviations subject to different technical and operational constraints. To enhance exploration ability, the Symbiotic Organisms Search algorithm is strengthened with Particle Swarm Optimization capabilities to develop the Hybrid Symbiotic Organisms Search algorithm, which is applied to solve the proposed problem. Objective functions are fuzzified and aggregated by a geometric mean operator to have the same scales. The proposed joint optimization problem is solved with the introduced Hybrid Symbiotic Organisms Search algorithm in two different case studies of balanced and unbalanced test systems. According to obtained results that are compared with some well-known evolutionary algorithms, the superiority of the Hybrid Symbiotic Organisms Search algorithm is confirmed in terms of solution optimality and convergence speed.

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Optimal location and sizing of capacitor placement in radial distribution system using Bacterial Foraging Optimization Algorithm

Cited by 177 publications

References 19 publications

An Improved Crow Search Algorithm Applied to Energy Problems

An Improved Crow Search Algorithm Applied to Energy Problems

Analytical Method for Reactive Power Compensators Allocation

Hybrid Symbiotic Organisms Search for Optimal Fuzzified Joint Reconfiguration and Capacitor Placement in Electric Distribution Systems

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