2004
DOI: 10.1109/tpwrs.2003.821447
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Online Reconfiguration Considering Variability Demand: Applications to Real Networks

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Cited by 212 publications
(111 citation statements)
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References 24 publications
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“…The population based heuristic algorithm is based on the law of gravity and mass interactions. The algorithm is comprised of collection of searcher agents that interact with each other through the gravity force [1]. The agents are considered as objects and their performance is measured by their masses.…”
Section: Gravitational Search Algorithmmentioning
confidence: 99%
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“…The population based heuristic algorithm is based on the law of gravity and mass interactions. The algorithm is comprised of collection of searcher agents that interact with each other through the gravity force [1]. The agents are considered as objects and their performance is measured by their masses.…”
Section: Gravitational Search Algorithmmentioning
confidence: 99%
“…Equation (1) shows that in the Newton law of gravity, the gravitational force between two objects is directly proportional to the product of their masses and inversely proportional to the square of the distance between the objects. While for Equation (2), Newton's second law shows that when a force, F, is applied to an object, its acceleration, a, depends on the force and its mass, M.…”
Section: Gravitational Search Algorithmmentioning
confidence: 99%
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“…Goswami and Basu introduced a power-flow-minimum heuristic algorithm for distribution feeder reconfiguration [10]. Vanderson Gomes et al proposed a heuristic strategy for reconfiguration of distribution systems [11].Lopez presented an approach for online reconfiguration [12]. Das proposed a fuzzy multi-objective approach to solve the network reconfiguration problem [13].…”
mentioning
confidence: 99%
“…The demand of the system under the study is assumed to follow a 24-h daily commercial load curve [125] as shown in Figure 6.1. The time-varying commercial load model as defined in Section 3.2 is also considered in this work.…”
Section: Load and Generation Modelling 721 Load Modellingmentioning
confidence: 99%