Implementation and analysis of economic load dispatch using Krill Herd algorithm

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dc.contributor.authorUmar Zahid, Muhammad
dc.date.accessioned2017-12-13T03:51:25Z
dc.date.available2017-12-13T03:51:25Z
dc.date.issued2016
dc.descriptionSupervised by:Professor Dr. A. Aziz Bhattien_US
dc.description.abstractThe importance of economic load dispatch (ELD) problems in electric power operation emphasizes to be solved effectively so that an efficient plan for power generation could be developed for a given load demand. In real life scenarios, non-linearity and complexity of ELD cost function make it a hideous task to be solved effectively. In this research work, a nature inspired optimization algorithm (Krill Herd Algorithm (KHA)) is efficiently implemented for different ELD problems involving complex and convex functions. Different test systems of thermal power generating units involving active power limits, transmission loss, and valve point loading constraints are considered for the implementation of the optimization algorithm. A statistical comparison of the results obtained by the implemented algorithm with different optimization techniques already developed is also made to analyze the efficiency of the KHA. By comparing the outcomes, it is observed that the implemented KHA is far better in robustness and efficiency than other previously implemented algorithms. Fuel cost optimization response of KHA is also better than other algorithms in studies. Simulations of implemented algorithm for different test systems are obtained by MATLAB. Inclusion of different real time constraints involved in ELD problems makes this implementation an effective and reliable factor in practical field.en_US
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/2276
dc.language.isoenen_US
dc.publisherUniversity of Management and Technology Lahoreen_US
dc.subjectOptimization algorithmen_US
dc.subjectEmphasizesen_US
dc.subjectMS Thesisen_US
dc.titleImplementation and analysis of economic load dispatch using Krill Herd algorithmen_US
dc.typeThesisen_US
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