Multi-objective design of advanced power distribution networks using restricted-population-based multi-objective seeker-optimisation-algorithm and fuzzy-operator

This study proposes a method for designing advanced power distribution system (PDS) including distributed generations, using a combination of fundamental loop generator and multi-objective seeker-optimisation algorithm (MOSOA). The proposed approach reduces the searching space using fundamental loop...

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Bibliographic Details
Published in:IET generation, transmission & distribution Vol. 9; no. 11; pp. 1195 - 1215
Main Authors: Kumar, Deepak, Samantaray, Subhransu Ranjan, Kamwa, Innocent
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 06.08.2015
Subjects:
100‐bus distribution system
54‐bus distribution system
advanced power distribution system
Algorithms
automatic recloser
Automation
contingency‐load‐loss index
Cost analysis
Design analysis
Design engineering
distributed generation
distributed power generation
distribution automation device
economic costing
failure rate estimation
fault diagnosis
fault repair duration
feeder branch
fundamental loop generator technique
fuzzy operation
fuzzy set theory
Generators
immunised particle‐swarm‐optimisation
large‐scale PDS planning
maintenance engineering
MOSOA
nondominated sorting genetic algorithm‐II
Pareto optimisation
Pareto‐front
Pareto‐optimality‐based trade‐off analysis
Power distribution
power distribution economics
power distribution faults
power distribution planning
power distribution reliability
reliability evaluation
restricted‐population‐based multiobjective seeker‐optimisation‐algorithm
Searching
fault diagnosis
Pareto-front
fuzzy set theory
power distribution planning
maintenance engineering
fault repair duration
power distribution faults
100-bus distribution system
restricted-population-based multiobjective seeker-optimisation-algorithm
distributed power generation
contingency-load-loss index
advanced power distribution system
immunised particle-swarm-optimisation
fuzzy operation
Pareto-optimality-based trade-off analysis
nondominated sorting genetic algorithm-II
distribution automation device
Pareto optimisation
MOSOA
fundamental loop generator technique
feeder branch
automatic recloser
power distribution economics
RA
power distribution reliability
distributed generation
reliability evaluation
large-scale PDS planning
economic costing
failure rate estimation
54-bus distribution system
ISSN:1751-8687, 1751-8695
Online Access:Get full text
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Summary:This study proposes a method for designing advanced power distribution system (PDS) including distributed generations, using a combination of fundamental loop generator and multi-objective seeker-optimisation algorithm (MOSOA). The proposed approach reduces the searching space using fundamental loop generator technique to obtain initial feasible solutions which is further improved by SOA to generate new set of solutions with improved aptitude. The proposed methodology uses a contingency-load-loss-index for reliability evaluation, which is independent of the estimation of failure rate and fault repair duration of feeder branches. This planning strategy includes distribution automation devices such as automatic reclosers (RAs) to enhance the reliability of PDS. The proposed algorithm generates a set of non-dominated solution by simultaneous optimisation of two conflicting objectives (economic cost and system reliability) using Pareto-optimality-based trade-off analysis including a fuzzy-operation to automatically select the most suitable solution over the Pareto-front. The performance of the proposed approach is assessed and illustrated on 54-bus and 100-bus PDS, considering realtime design practices. Extensive comparisons are made against some well-known and efficient MO algorithms such as fast non-dominated sorting genetic algorithm-II, MO particle-swarm-optimisation and MO immunised-particleswarm-optimisation. Simulation results show that the proposed approach is accurate and efficient, and a potential candidate for large-scale PDS planning.
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ISSN:1751-8687
1751-8695
DOI:10.1049/iet-gtd.2014.0757