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AloA Optimized RLNN Controller for LFC of Deregulated Two-Area Power System


Affiliations
1 Department of Electrical Engineering, Indian Maritime University, Kolkata Campus, Kolkata-700088, India
2 Department of Electrical Engineering, Kalyani Government Engineering College, Kalyani-741235, India
3 Department of Engineering & Technological Studies, University of Kalyani, Kalyani-741235, India
4 Department of Electrical Engineering, Govt. College of Engineering & Ceramic Technology, Kolkata-700010, India
     

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The 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.

Keywords

Reinforced Learning Neural Network (RLNN), Load Frequency Control (LFC), Area Control Error, Ant Lion Optimization Algorithm.
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  • AloA Optimized RLNN Controller for LFC of Deregulated Two-Area Power System

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Authors

Milton Kumar Das
Department of Electrical Engineering, Indian Maritime University, Kolkata Campus, Kolkata-700088, India
Parthasarathi Bera
Department of Electrical Engineering, Kalyani Government Engineering College, Kalyani-741235, India
Partha Pratim Sarkar
Department of Engineering & Technological Studies, University of Kalyani, Kalyani-741235, India
Krishnendu Chakrabarty
Department of Electrical Engineering, Govt. College of Engineering & Ceramic Technology, Kolkata-700010, India

Abstract


The 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.

Keywords


Reinforced Learning Neural Network (RLNN), Load Frequency Control (LFC), Area Control Error, Ant Lion Optimization Algorithm.

References





DOI: https://doi.org/10.24906/isc%2F2021%2Fv35%2Fi3%2F209195