Open AccessMulti‐objective design of advanced power distribution networks using restricted‐population‐based multi‐objective seeker‐optimisation‐algorithm and fuzzy‐operator
Author(s) -
Kumar Deepak,
Samantaray Subhransu Ranjan,
Kamwa Innocent
Publication year - 2015
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2014.0757
Subject(s) - recloser , particle swarm optimization , sorting , mathematical optimization , computer science , fuzzy logic , pareto principle , reliability (semiconductor) , algorithm , electric power system , population , reliability engineering , power (physics) , engineering , mathematics , circuit breaker , artificial intelligence , physics , demography , quantum mechanics , sociology , electrical engineering
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.