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• W1509871821 abstract "In this study, a stochastic multiobjective framework is proposed for distribution feeder reconfiguration (DFR). The proposed multiobjective framework can concurrently optimise competing objective functions including total power losses, voltage deviation and total cost. Moreover, system uncertainties including wind power generation and active and reactive load uncertainty are explicitly considered in the stochastic DFR problem. The solution methodology consists of two stages, which firstly, employs roulette wheel mechanism in conjunction with Weibull/Gaussian probability distribution function of wind/load forecast variations for random scenario generation wherein the stochastic multiobjective DFR problem is converted into its respective deterministic equivalents (scenarios). In the second stage, for each deterministic scenario, a multiobjective formulation based on the adaptive modified particle swarm optimisation (AMPSO) is implemented for each deterministic scenario of the first stage. Utilisation of the stochastic framework would capture more uncertainty spectrum of the investigated multiobjective DFR problem rather than that of the deterministic framework. Consequently, the results of the stochastic framework are more realistic and dependable. Moreover, the new adaptive optimisation algorithm (AMPSO) has much more ability than the other well-known algorithms in the area of optimisation applications." @default.
• W1509871821 created "2016-06-24" @default.
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• W1509871821 date "2012-01-01" @default.
• W1509871821 modified "2023-10-04" @default.
• W1509871821 title "Scenario-based multiobjective distribution feeder reconfiguration considering wind power using adaptive modified particle swarm optimisation" @default.
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• W1509871821 doi "https://doi.org/10.1049/iet-rpg.2011.0256" @default.
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