Application of New Fast, Efficient-Self adjusting PSO-Search Algorithms in Power Systems' Studies

Adel M Sharaf, Hani Mavalizadeh, Abdollah Ahmadi, Foad Heidari Gandoman, Omid Homaee, Heidar Ali Shayanfar

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

7 Citations (Scopus)

Abstract

In this chapter, we present the validated particle swarm optimization (PSO) search and optimization technique developed by the First Author, which is based on dynamic error scaling of the weightings Omega and Delta in speed and position equations. The dynamic scaled/corrective action is based on a judicious selection of the error and its. This ensures efficient search and fast convergence to optimality condition with a reduced computational burden. The primary challenge is to find an optimal scheme to relate the error in each iteration, to the direction of particles. The primary goal is to find a scheme that moves particles toward the optimal solution. Several algorithms are presented for this purpose. The presented algorithms are compared to the conventional PSO method for multiple case studies. The algorithms are also used to solve a reactive market clearing problem. A new index is also introduced in this chapter which considers the convergence speed and the accuracy and compares the performance of the presented algorithms. Experimental results from case studies in which these proposed algorithms were tested show good performance from both an accuracy and convergence speed point of view.
Original languageEnglish
Title of host publicationClassical and Recent Aspects of Power System Optimization
PublisherElsevier
Chapter3
Pages33-61
Number of pages29
Volume1
Edition1
ISBN (Electronic)9780128124420
ISBN (Print)978-0-12-812441-3
DOIs
Publication statusPublished - 20 Jun 2018

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