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An application of backtracking search algorithm in designing power system stabilizers for large multi-machine system

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dc.citedby50
dc.contributor.authorIslam N.N.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorShareef H.en_US
dc.contributor.authorMohamed A.en_US
dc.contributor.authorid56119162900en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57189691198en_US
dc.contributor.authorid57195440511en_US
dc.date.accessioned2023-05-29T06:38:36Z
dc.date.available2023-05-29T06:38:36Z
dc.date.issued2017
dc.descriptionDamping; Eigenvalues and eigenfunctions; Electric power systems; Learning algorithms; Optimization; Particle swarm optimization (PSO); Problem solving; State space methods; Test facilities; Backtracking search algorithms; Multi machine power system; Power system damping; Power system oscillations; Power system stability; Power System Stabilizer; System stability; algorithm; Article; backtracking search algorithm; bacterial foraging optimization algorithm; machine; mathematical analysis; mathematical computing; mathematical parameters; particle swarm optimization; power supply; power system stabilizer; process optimization; statistical modelen_US
dc.description.abstractThis paper deals with the backtracking search algorithm (BSA) optimization technique to solve the design problems of multi-machine power system stabilizers (PSSs) in large power system. Power system stability problem is formulated by an optimization problem using the LTI state space model of the power system. To conduct a comprehensive analysis, two test systems (2-AREA and 5-AREA) are considered to explain the variation of design performance with increase in system size. Additionally, two metaheuristic algorithms, namely bacterial foraging optimization algorithm (BFOA) and particle swarm optimization (PSO) are accounted to evaluate the overall design assessment. The obtained results show that BSA is superior to find consistent solution than BFOA and PSO regardless of system size. The damping performance that achieved from both test systems are sufficient to achieve fast system stability. System stability in linearized model is ensured in terms of eigenvalue shifting towards stability regions. On the other hand, damping performance in the non-linear model is evaluated in terms of overshoot and setting times. The obtained damping in both test systems are stable for BSA based design. However, BFOA and PSO based design perform worst in case of large power system. It is also found that the performance of BSA is not affected for large numbers of parameter optimization compared to PSO, and BFOA optimization techniques. This unique feature encourages recommending the developed backtracking search algorithm for PSS design of large multi-machine power system. � 2017en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.neucom.2016.10.022
dc.identifier.epage184
dc.identifier.scopus2-s2.0-85008704831
dc.identifier.spage175
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85008704831&doi=10.1016%2fj.neucom.2016.10.022&partnerID=40&md5=c767fe8d76945e48f67ef699908d9c7b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23229
dc.identifier.volume237
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleNeurocomputing
dc.titleAn application of backtracking search algorithm in designing power system stabilizers for large multi-machine systemen_US
dc.typeArticleen_US
dspace.entity.typePublication
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