Fibonacci series-based local search in spider monkey optimisation for transmission expansion planning Online publication date: Mon, 06-Nov-2017
by Ajay Sharma; Harish Sharma; Annapurna Bhargava; Nirmala Sharma
International Journal of Swarm Intelligence (IJSI), Vol. 3, No. 2/3, 2017
Abstract: The power system is a complex interconnected network which consists of four components: generation, distribution, transmission, and load. The loads may be varying in nature. For supplying these loads, with an aim of minimum losses in transmission and distribution, the additional transmission lines are required to be added for expansion. To find out the optimum locations of these additional lines are a complex and challenging task. Swarm intelligence motivated algorithms have been proved to be efficient to deal this type of optimisation problem. Therefore, this work applies a recent swarm intelligence motivated algorithm namely, spider monkey optimisation (SMO) to identify the optimum locations for the additional lines in the system. Further, to augment the solution search capacity of SMO algorithm, a Fibonacci series-based local search (FLS) is proposed and incorporated with SMO. The modified SMO is named as Fibonacci inspired spider monkey optimisation algorithm (FSMO). The authenticity of the suggested FSMO is analysed through statistical analysis over 20 benchmark functions. Further, both the algorithms, FSMO and SMO are applied to solve the transmission expansion planning (TEP) problem for IEEE-24 bus system. The reported results are judged against other state-of-art algorithms for solving TEP issues.
Online publication date: Mon, 06-Nov-2017
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