Serdar EKİNCİ, Davut İZCİ, Baran HEKİMOĞLU

Implementing the Henry Gas Solubility Optimization Algorithm for Optimal Power System Stabilizer Design

This study assesses the feasibility of the Henry gas solubility optimization (HGSO), a recent novel metaheuristic algorithm, to achieve optimal parametersfor a power system stabilizer (PSS) employed in a power system. To do so, the design problem was defined as an optimization problem and an integral oftime-multiplied absolute error objective function was adopted. This objective function was minimized to tune the PSS parameters and improve the dynamicperformance of the power system. The performance was evaluated using a single-machine infinite-bus system, and the obtained results were comparedwith atom search optimization and conventional based designed systems. The proposed HGSO approach for tuning PSS parameters has been shown tosuppress electromechanical oscillations and provide a better statistical performance and convergence ratio.

PDF

___

1. F. P. Demello, C. Concordia, “Concepts of Synchronous Machine Stability as Affected by Excitation Control,” IEEE Trans. Power Appar. Syst., vol. PAS-88, no. 4, pp. 316-329, Apr 1969. [Crossref]

2. P. W. Sauer, M. A. Pai, J. Chow, Power System Dynamics and Stability: With Synchrophasor Measurement and Power System Toolbox, 2nd ed. 2017. [Crossref]

3. S. Ekinci, A. Demiroren, B. Hekimoglu, “Parameter optimization of power system stabilizers via kidney-inspired algorithm,” Trans. Inst. Meas. Control, vol. 41, no. 5, pp. 1405-1417, Jun. 2019. [Crossref]

4. K. A. Hameed, S. Palani, “Robust Design of Power System Stabilizer using Harmony Search Algorithm,” Automatika, vol. 55, no. 2, pp. 162-169, Jan. 2014. [Crossref]

5. S. Ekinci, A. Demirören, “Modeling, simulation, and optimal design of power system stabilizers using ABC algorithm,” Turkish J. Electr. Eng. Comput. Sci., vol. 24, no. 3, pp. 1532-1546, Jan 2016. [Crossref]

6. S. M. Abd Elazim, E. S. Ali, “Optimal Power System Stabilizers design via Cuckoo Search algorithm,” Int. J. Electr. Power Energy Syst., vol. 75, pp. 99-107, Feb 2016. [Crossref]

7. G. Kasilingam, J. Pasupuleti, “BBO algorithm-based tuning of PID controller for speed control of synchronous machine,” Turkish J. Electr. Eng. Comput. Sci., vol. 24, no. 4, pp. 3274-3285, 2016. [Crossref]

8. L. Chaib, A. Choucha, S. Arif, “Optimal design and tuning of novel fractional order PID power system stabilizer using a new metaheuristic Bat algorithm,” Ain Shams Eng. J., vol. 8, no. 2, pp. 113- 125, Sep 2017. [Crossref]

9. S. Ekinci, B. Hekimoglu, “Parameter optimization of power system stabilizer via Salp Swarm algorithm,” in 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE), pp. 143-147, 2018 [Crossref]

10. M. Singh, R. N. Patel, D. D. Neema, “Robust tuning of excitation controller for stability enhancement using multi-objective metaheuristic Firefly algorithm,” Swarm Evol. Comput., vol. 44, no. 3, pp. 136-147, Feb 2019. [Crossref]

11. D. Butti, S. K. Mangipudi, S. R. Rayapudi, “An improved whale optimization algorithm for the design of multi-machine power system stabilizer,” Int. Trans. Electr. Energy Syst., vol. 30, no. 5, p. e12314, May 2020. [Crossref]

12. H. Verdejo, V. Pino, W. Kliemann, C. Becker, J. Delpiano, “Implementation of particle swarm optimization (PSO) algorithm for tuning of power system stabilizers in multimachine electric power systems,” Energies, vol. 13, no. 8. p. 2093, 2020. [Crossref]

13. A. Sabo, N. I. Abdul Wahab, M. L. Othman, M. Z. A. Mohd Jaffar, H. Beiranvand, “Optimal design of power system stabilizer for multimachine power system using farmland fertility algorithm,” Int. Trans. Electr. Energy Syst., p. e12657, Oct. 2020, https://doi. org/10.1002/2050-7038.12657. [Crossref]

14. F. A. Hashim, E. H. Houssein, M. S. Mabrouk, W. Al-Atabany, S. Mirjalili, “Henry gas solubility optimization: A novel physics-based algorithm,” Futur. Gener. Comput. Syst., vol. 101, pp. 646-667, Dec. 2019. [Crossref]

15. A. F. Mirza, M. Mansoor, Q. Ling, “A novel MPPT technique based on Henry gas solubility optimization,” Energy Convers. Manag., vol. 225, p. 113409, Dec 2020. [Crossref]

16. N. Neggaz, E. H. Houssein, K. Hussain, “An efficient henry gas solubility optimization for feature selection,” Expert Syst. Appl., vol. 152, p. 113364, Aug 2020. [Crossref]

17. W. Cao, X. Liu, J. Ni, “Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm,” IEEE Access, vol. 8, pp. 88633-88642, May 2020. [Crossref]

18. S. Ekinci, B. Hekimoğlu, D. Izci, “Opposition based Henry gas solubility optimization as a novel algorithm for PID control of DC motor,” Eng. Sci. Technol. an Int. J., vol. 24, no. 2, pp. 331-342. Apr 2021. [Crossref]

19. S. Ekinci, D. Izci, B. Hekimoğlu, “Henry Gas Solubility Optimization Algorithm Based FOPID Controller Design for Automatic Voltage Regulator,” in 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 2020, pp. 1-6, doi: 10.1109/ICECCE49384.2020.9179406. [Crossref]

20. D. Mondal, A. Chakrabarti, A. Sengupta, Power System Small Signal Stability Analysis and Control. Academic Press, 2020. [Crossref]

21. W. Zhao, L. Wang, Z. Zhang, “Atom search optimization and its application to solve a hydrogeologic parameter estimation problem,” Knowledge-Based Syst., vol. 163, pp. 283-304, Jan 2019. [Crossref]