MOGOA Based RLNN controller for LFC of three area deregulated HDG power system /

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dc.campusChennai
dc.contributor.authorDas, Milton Kumar
dc.date.accessioned2025-02-25T10:50:13Z
dc.date.accessioned2025-03-31T16:57:55Z
dc.date.available2025-02-25T10:50:13Z
dc.date.issued2021-09-24
dc.description.abstractThis paper presents the multi-objective grasshopper optimization algorithm (MOGOA) based reinforced learning neural network controller (RLNN) controllers in the load frequency control (LFC) problems for three area deregulated hybrid distributed generation (HDG) power system. The controller parameters and gains are optimized by MOGOA and its performance is compared with PID controllers. Sensitivity analyses are performed to investigate robustness of the considered MOGOA-RLNN controllers expose to change of inertia constant and loading conditions and also analysis exposes that MOGOA-RLNN controllers is superior performances than PID controllers.
dc.identifier.urihttps://doi.org/10.1109/gucon50781.2021.9573671
dc.identifier.urihttps://dspacenew8-imu.refread.com/handle/123456789/2433
dc.language.isoen
dc.publisherIEEE
dc.schoolSchool of Marine Engineering and Technology
dc.subjectload frequency control
dc.subjectreinforced learning neural network
dc.subjectgrasshopper optimization algorithm
dc.subjectrobustness
dc.titleMOGOA Based RLNN controller for LFC of three area deregulated HDG power system /
dc.typeArticle

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