Journal
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
Volume 29, Issue 5, Pages 995-1009Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/0952813X.2016.1270359
Keywords
Wind power plant (WPP); distribution feeder reconfiguration (DFR); multi-objective distribution feeder reconfiguration (MDFR); fuzzy adaptive modified particle swarm optimization (FAMPSO); probability density function (PDF); wind turbine (WT)
Categories
Ask authors/readers for more resources
This article proposes a novel probabilistic Distribution Feeder Reconfiguration (DFR) based method to consider the uncertainty impacts into account with high accuracy. In order to achieve the set aim, different scenarios are generated to demonstrate the degree of uncertainty in the investigated elements which are known as the active and reactive load consumption and the active power generation of the wind power units. Notably, a normal Probability Density Function (PDF) based on the desired accuracy is divided into several class intervals for each uncertain parameter. Besides, the Weiball PDF is utilised for modelling wind generators and taking the variation impacts of the power production in wind generators. The proposed problem is solved based on Fuzzy Adaptive Modified Particle Swarm Optimisation to find the most optimal switching scheme during the Multi-objective DFR. Moreover, this paper holds two suggestions known as new mutation methods to adjust the inertia weight of PSO by the fuzzy rules to enhance its ability in global searching within the entire search space.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available