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Regenerative and combinatorial random variable based particle swarm optimization towards optimal transmission switching
S. Pal, S. Sen, , S. Sengupta
Published in Elsevier Ltd
2020
Volume: 95
   
Abstract
This paper describes the development of a meta-heuristic based algorithm to solve the optimal transmission switching (OTS) problem. The approach solves the mixed-integer non-linear problem considering simultaneous optimization of transmission topology and generation dispatch. The algorithm is the first ever application of particle swarm optimization (PSO) to model OTS and operates using combined real and binary (CRB) variables to solve a weighted sum of interdependent multiple objective functions. The unique stochastic generation principle of combinatorial variables is based on a blend of both uniform and biased Gaussian probability distribution functions. The required binary values of swarms are generated from the continuous values of the random Gaussian distribution with the application of the Heaviside function. All the transmission lines are considered as potential switch variables the operations of which are limited by different aspects. The algorithm can tackle the complex optimization problem with many constraints of varying difficulty. The randomness in CRB variables and unpredictability in switching may generate infeasible particle(s) resulting in island formation. The algorithm also proposes regeneration of these infeasible particle(s) by modifying them to a feasible one to avoid this islanding as well as to have stable switching possibilities. The improvement in the computational efficiency of the algorithm is proposed with the adoption of Micro-PSO for small load deviations. A wide range of unique solutions are obtained based on the preferences of the system operator. This OTS algorithm is tested using the IEEE 57 bus and the IEEE 118 bus system and encouraging results are obtained. © 2020 Elsevier B.V.
About the journal
JournalData powered by TypesetApplied Soft Computing Journal
PublisherData powered by TypesetElsevier Ltd
ISSN15684946