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Maiden Application of Hybrid Particle Swarm Optimization with Genetic Algorithm in AGC Studies Considering Optimized TIDN Controller

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Modeling, Simulation and Optimization

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 206))

Abstract

This paper presents an application of hybrid PSO-GA (HPSO-GA) in automatic generation control (AGC) studies of multi-area systems for controller gains and other parameters. A new ancillary controller named by tilt-integral-derivative with filter TIDN is proposed for the system. The system responses with the TIDN controller are compared with various controllers like PI and PID and outperforms over others. Moreover, the system performance is also analyzed by TIDN controller with optimization techniques named as genetic algorithm (GA), particles swarm optimization (PSO), and HPSO-GA, and it is observed that the system performance with HPSO-GA optimization technique provides better dynamics. Further, the sensitivity analysis is executed to check the controller robustness at nominal conditions and explored that the optimum gain and other parameters yield at nominal are robust with wide changes in loading.

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Author information

Authors and Affiliations

  1. Electrical Engineering Department, NIT Silchar, Silchar, India

    Sanjeev Kumar Bhagat, Lalit Chandra Saikia, Dhenuvakonda Koteswara Raju, Naladi Ram Babu, Satish Kumar Ramoji, Biswanath Dekaraja & Manoja Kumar Behra

Authors
  1. Sanjeev Kumar Bhagat
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  2. Lalit Chandra Saikia
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  3. Dhenuvakonda Koteswara Raju
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  4. Naladi Ram Babu
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  5. Satish Kumar Ramoji
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  6. Biswanath Dekaraja
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  7. Manoja Kumar Behra
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Corresponding author

Correspondence to Sanjeev Kumar Bhagat .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology, Silchar, Assam, India

    Biplab Das

  2. Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, Assam, India

    Ripon Patgiri

  3. National Institute of Technology Silchar, Silchar, India

    Sivaji Bandyopadhyay

  4. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

Appendix

Appendix

At nominal conditions:

  1. 1.

    Area number (i, j where i ≠ j) = 1, 2 and 3, frequency (F) = 60 Hz, Loading = 50%, Tie-power (Tij) = 0.086puMW/rad, Kpi = 120 Hz/pu, Ri = 2.4 Hz/pu, Bi = 0.425 MW/Hz, Tpi = 20 s, apfij = 0.5, Tgi = 0.08 s, Tti = 0.3 s, Kri = 0.5, Tri = 10 s, KHP2 = 1.25, TBP1 = 0.0415 s, KHP3 = 1.4, TBP2 = 0.041 s, KPC = 0.8.

  2. 2.

    Genetic algorithm (GA): Number population = 30, maximum number of iterations = 100, Parents (off springs) Ratio = 0.7, Mutants Population size Ratio = 0.2

  3. 3.

    Particles swarm optimization (PSO): Number population = 30, maximum number of sub-iterations = 100, PSO parameter C1 and C2 = 1.5, PSO momentum of inertia = 0.73.

  4. 4.

    Hybridization of PSO and GA (HPSO-GA): Number population = 30, maximum number of sub-iterations for PSO and GA = 20, Parents (off springs) Ratio = 0.7, Mutants Population size Ratio = 0.2, PSO parameter C1 and C2 = 1.5, PSO momentum or inertia = 0.73.

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Bhagat, S.K. et al. (2021). Maiden Application of Hybrid Particle Swarm Optimization with Genetic Algorithm in AGC Studies Considering Optimized TIDN Controller. In: Das, B., Patgiri, R., Bandyopadhyay, S., Balas, V.E. (eds) Modeling, Simulation and Optimization. Smart Innovation, Systems and Technologies, vol 206. Springer, Singapore. https://doi.org/10.1007/978-981-15-9829-6_26

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