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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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  • V Donde, M A Pai, I A Hiskens, Simulation and optimization in an AGC system after deregulation, IEEE Trans on Power Syst, Vol 6, No 3, page 481-489, 2001.

  • W Tan, H Zhang, M Yu, Decentralized load frequency control in deregulated environments, Int J of Elec Power and Energy Syst, Vol 4, page 16-26, 2012.

  • A Demiroren, H L Zeynelgil, GA application to optimization of AGC in three-area power system after deregulation, Int J of Elec Power and Energy Syst, Vol 9, page 230-240, 2007.

  • M K Das, P Bera, P P Sarkar, Oppositional Krill Herd Algorithm-Based RLNN Controller for Discrete Mode AGC in Deregulated Hydrothermal Power System Using SMES, IJST Trans Elec Engg, Vol 42, No 3, page 309-325, 2018.

  • E S Ali, S M Abd-Elazim, Bacteria foraging optimization algorithm based load frequency controller for interconnected power system, Int J Elec Power and Energy Syst, Vol 33, No 3, page 633-638, 2011.

  • L C Saikia, J Nanda, S Mishra, Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system, Int J Elec Power and Energy Syst, Vol 33, No 3, page 394-401, 2011.

  • M Djukanovic, M Novicevic, D J Sobajic, Y P Pao, Conceptual development of optimal load frequency control using artificial neural networks and fuzzy set theory, Int J of Engg Intelligent Syst for Elec Engg and Comm, Vol 3, No 3, page 95-108, 1995.

  • F Beaufays, Y A Magid, B Widrow, Application of neural network to load frequency control in power systems, Int J of Neural Networks, Vol 7, No 1, page 183-194, 1994.

  • T P I Ahamed, P S N Rao, P S Sastry, A reinforcement learning approach to automatic generation control, Int J of Elec Power Syst, Vol 63, No 1, page 9-26, 2002.

  • T P I Ahamed, P S N Rao, P S Sastry, A neural network based automatic generation controller design through reinforcement learning, Int J of Emerging Elec Power Syst, Vol 6, page 11-31, 2006.

  • H L Zeynelgil, A Demiroren, N S Sengor, The application of ANN technique to Automatic Generation Control for multi-machine power system, Int J of Elec Power and Energy Syst, Vol 24, page 345-354, 2002.

  • L C Saikia, S Mishra, N Sinha, J Nanda, Automatic generation control of a multi area hydrothermal system using reinforced learning neural network controller, Int J of Elec Power and Energy Syst, Vol 33, page 1101-1108, 2011.

  • G T C Sekhar, R K Sahu, A K Baliarsingh, S Panda, Load frequency control of power system under deregulated environment using optimal firefly algorithm, Int J of Elec Power and Energy Syst, Vol 74, page 195-211, 2016.

  • M Raju, L C Saikia, N Sinha, Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller, Int J of Elec Power and Energy Syst, Vol 80, page 52-63, 2016.

  • S S Dhillon, J S Lather, S Marwaha, Multi objective load frequency control using hybrid bacterial foraging and particle swarm optimized PI controller, Int J of Elec Power and Energy Syst, Vol 79, page 196-209, 2016.

  • M L Kothari, J Nanda, D P Kothari, D Das, Discrete-Mode Automatic Generation Control of a Two-Area Reheat Thermal System with New area Control Error, IEEE Trans on Power Syst, Vol 4, No 2, page 730-738, 1989.

  • S Mirjalili, The ant lion optimizer, Int J of Advances in Engg Software, Vol 83, page 80-98, 2015.


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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