Ant colony based method for reconfiguration of power distribution system to reduce losses

Pereira, Flávia Thomaz Veréchia; Vittori, Karla; Costa, Geraldo Roberto Martins da

doi:10.1109/tdc-la.2008.4641831

Cited by 18 publications

(6 citation statements)

References 10 publications

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“…To exemplify, a basic 5 buses and 7 branches system presented initially on [46] is used, and its meshes are represented on Figure 2 below: FIGURE 2. 5 buses and 7 branches system mesh composition.…”

Section: Buses and 7 Branches Illustrative Examplementioning

confidence: 99%

Distribution Network Reconfiguration Using Selective Firefly Algorithm and a Load Flow Analysis Criterion for Reducing the Search Space

et al. 2019

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This paper proposes an alternative to solve the distribution network reconfiguration (DNR) problem, aiming real power losses' minimization. For being a problem that has complexity for its solution, approximate techniques are adequate for solving it. Here, the proposition is a technique based on the firefly metaheuristic, named selective firefly algorithm, where the positioning of these insects is compressed in a selective range of values. The algorithm is applied to the DNR, and all its implementation and adequacy to the problem studied are presented. To define the search space, the methodology presented initially considers a set of candidate switches for opening based on the studied systems' mesh analysis. To reduce these possibilities, a refinement through a load flow analysis criterion (LFAC) is proposed. This LFAC considers the real power losses on each branch for a configuration with all switches closed, then, selecting possible switches to elimination from the set previously established. To demonstrate the behavior and the viability of the LFAC, it was initially applied on a 5 buses' and 7 branches' system. Also, to avoid getting stuck on results that may be considered not good, a disturbance resetting the population is set to occur every time a counter reaches a pre-defined number of times that the best solution does not change. Results found for simulations with 33, 70, and 84 buses are presented and comparisons with selective particle swarm optimization (SPSO) and selective bat algorithm (SBAT) are made.

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Section: Buses and 7 Branches Illustrative Examplementioning

confidence: 99%

Distribution Network Reconfiguration Using Selective Firefly Algorithm and a Load Flow Analysis Criterion for Reducing the Search Space

et al. 2019

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“…The problems of overloading and service restoration, unbalanced load on electrical distribution system can be solved through reconfiguration by using ant colony optimization and heuristic search technique [7], [8] respectively. F.S Pereira, K. Vittori et al [9], proposed meta heuristic methodology for reconfiguration of electrical distribution system to minimize power loss. In this paper two algorithms ant colony optimizationtravelling salesman and ant colony optimization-radial system reconfiguration are applied to one common standard five bus sample system.…”

Section: Introductionmentioning

confidence: 99%

AACO technique for solving multiobjectives in electrical distribution system

Babu¹,

Molughu²

2019

IJEECS

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The multi objectives in electrical distribution system are service restoration and minimization of Ohmic losses. These objectives can be achieved by artificial ant colony optimization through reconfiguration. Service restoration is also a major aspect for reliable service. If any fault occurs in the system, faulted zone is isolated from healthy system. This originates discontinuity in power supply. To maintain the continuity in power supply there is a necessity to provide service to these loads from adjacent healthy feeders through reconfiguration without infringement of radiality, load balance and critical parameters. In distribution system Ohmic losses are 30 to 40 percent. In order to minimize these losses, there is a need for reconfiguration of the system and perform the power flow analysis to get bus voltages, branch currents and power losses (i.e. real or Ohmic or copper loss and reactive power loss). A direct load flow analysis is adopted in the proposed work. In this paper four feeder test system used for service restoration and IEEE 33 bus, IEEE 69 bus test systems for minimization of Ohmic losses have been obtained.

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“…Also, genetic algorithms (GAs) were used in [14,15]. Ant colony search (ACS) optimisation techniques are applied in [16][17][18]. A method based on bacterial foraging optimisation algorithm is applied in [19] for distribution network reconfiguration, and the authors of [20] developed an algorithm based on simulated annealing.…”

Section: Introductionmentioning

confidence: 99%

Two‐stage heuristic methodology for optimal reconfiguration and Volt/VAr control in the operation of electrical distribution systems

Gutiérrez-Alcaraz¹,

Tovar-Hernández²

2017

IET Generation, Transmission & Distribution

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Ant colony based method for reconfiguration of power distribution system to reduce losses

Cited by 18 publications

References 10 publications

Distribution Network Reconfiguration Using Selective Firefly Algorithm and a Load Flow Analysis Criterion for Reducing the Search Space

Distribution Network Reconfiguration Using Selective Firefly Algorithm and a Load Flow Analysis Criterion for Reducing the Search Space

AACO technique for solving multiobjectives in electrical distribution system

Two‐stage heuristic methodology for optimal reconfiguration and Volt/VAr control in the operation of electrical distribution systems

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