AloA Optimized RLNN controller for LFC of deregulated two-area power system /

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dc.campusChennai
dc.contributor.authorDas, Milton Kumar
dc.date.accessioned2025-02-25T10:27:51Z
dc.date.accessioned2025-03-31T17:00:10Z
dc.date.available2025-02-25T10:27:51Z
dc.date.issued2021-07-20
dc.description.abstractThe performance of the ant lion optimization algorithm (AloA) optimized RLNN controllers are analysed in this work for LFC of two-area deregulation power system. The comparisons are performed for time domain performances of AloA optimized RLNN controllers with traditional PID controllers. The input of PID and RLNN controllers are used area control error (ACE) and controllers’ gains are adjusted through online for RLNN controllers. The performance analyses are also studied to check the controllers’ robustness with variation in parameters of the system and loads. The analysis exposes that AloA optimized RLNN controllers significantly improve the time domain performances of the considered power system compared to PID controllers.
dc.identifier.urihttps://doi.org/10.24906/isc/2021/v35/i3/209195
dc.identifier.urihttps://dspacenew8-imu.refread.com/handle/123456789/2522
dc.language.isoen
dc.publisherInformatics Publishing Limited
dc.schoolSchool of Marine Engineering and Technology
dc.subjectReinforced Learning Neural Network (RLNN), Load Frequency Control (LFC), Area Control Error, Ant Lion Optimization Algorithm.
dc.titleAloA Optimized RLNN controller for LFC of deregulated two-area power system /
dc.typeArticle

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