Tuning of automatic voltage regulator and power system stabilizer using particle swarm optimization
DOI:
https://doi.org/10.21754/tecnia.v33i1.1075Keywords:
Particle Swarm Optimization, Automatic Voltage Regulator, Power System StabilizerAbstract
This paper presents a new optimization technique for simultaneous tuning of the parameters into the controllers associated with the control voltage oscillation and damping in synchronous generators. The parameters of the Automatic Voltage Regulator - AVR and the Power System Stabilizer – PSS are optimized thought a modification proposed in the Modified Particle Swarm Optimization technique (called MPSO - Modified Particle Swarm Optimization). To analyze the performance of the controllers, an objective function was used, based on system time response to a variation in the reference voltage of the machine. The parameters obtained for AVR and PSS are compared with the values calculated by the conventional adjustment technique and by the MPSO technique. The results of the simulation show that the technique proposed was efficient in tuning the AVR and PSS parameters, highlighting its simplicity, low computational strength and good convergence characteristics.
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[29] P. M. Anderson, A.-A. A. Fouad, y I. of E. y E. Engineers, Power system control and stability. IEEE Press, 2003.
[2] A. M. El-Zonkoly, “Optimal tuning of power systems stabilizers and AVR gains using particle swarm optimization”, Expert Systems with Applications, vol. 31, no. 3, pp. 551–557, oct. 2006, doi: 10.1016/j.eswa.2005.09.061.
[3] H. Bourles, S. Peres, T. Margotin, y M. P. Houry, “Analysis and design of a robust coordinated AVR/PSS”, IEEE Transactions on Power Systems, vol. 13, no. 2, pp. 568–575, may. 1998, doi: 10.1109/59.667384.
[4] P. S. Rao y I. Sen, “Robust pole placement stabilizer design using linear matrix inequalities”, IEEE Transactions on Power Systems, vol. 15, no. 1, pp. 313–319, feb. 2000, doi: 10.1109/59.852138.
[5] M. A. Abido, “Pole placement technique for PSS and TCSC-based stabilizer design using simulated annealing”, International Journal of Electrical Power & Energy Systems, vol. 22, no. 8, pp. 543–554, nov. 2000, doi: 10.1016/S0142-0615(00)00027-2.
[6] G. Gurrala y I. Sen, “Power System Stabilizers Design for Interconnected Power Systems”, IEEE Transactions on Power Systems, vol. 25, no. 2, pp. 1042–1051, may 2010, doi: 10.1109/TPWRS.2009.2036778.
[7] D. P. Ke, C. Y. Chung, y Y. Xue, “An Eigenstructure-Based Performance Index and Its Application to Control Design for Damping Inter-Area Oscillations in Power Systems”, IEEE Transactions on Power Systems, vol. 26, no. 4, pp. 2371–2380, nov. 2011, doi: 10.1109/TPWRS.2011.2123119.
[8] S. Panda y N. P. Padhy, “Optimal location and controller design of STATCOM for power system stability improvement using PSO”, Journal of the Franklin Institute, vol. 345, no. 2, pp. 166–181, mar. 2008, doi: 10.1016/j.jfranklin.2007.08.002.
[9] B. Selvabala y D. Devaraj, “Co-ordinated Design of AVR-PSS Using Multi Objective Genetic Algorithm”, en Swarm, Evolutionary, and Memetic Computing, Berlin, Heidelberg, 2010, pp. 481–493, doi: 10.1007/978-3-642-17563-3_57.
[10] A. Kahouli, T. Guesmi, H. Hadj Abdallah, y A. Ouali, “A genetic algorithm PSS and AVR controller for electrical power system stability”, en 2009 6th International Multi-Conference on Systems, Signals and Devices, mar. 2009, pp. 1–6, doi: 10.1109/SSD.2009.4956736.
[11] J. Usman, M. W. Mustafa, G. Aliyu, y B. U. Musa, “Coordinated AVR-PSS for Transient Stability Using Modified Particle Swarm Optimization”, Jurnal Teknologi, vol. 67, no. 3, mar. 2014, doi: 10.11113/jt.v67.2758.
[12] A. D. Falehi, M. Rostami, y H. Mehrjadi, “Transient Stability Analysis of Power System by Coordinated PSS-AVR Design Based on PSO Technique”, Engineering, vol. 3, no. 5, pp. 720–726, jun. 2011, doi: 10.4236/eng.2011.35055.
[13] M. A. Abido y Y. L. Abdel-Magid, “Robust design of multimachine power system stabilisers using tabu search algorithm”, Transmission and Distribution IEE Proceedings - Generation, vol. 147, no. 6, pp. 387–394, nov. 2000, doi: 10.1049/ip-gtd:20000717.
[14] S. S. S. Farahani, M. Nikzad, M. G. Naraghi, M. B. Tabar, y A. Javadian, “Power System Stabilizer Parameters Tuning Based On Tabu Search in a Multi Machine Power System”, p. 7, 2011.
[15] B. Selvabala y D. Devaraj, “Co-ordinated tuning of AVR-PSS using differential evolution algorithm”, en 2010 Conference Proceedings IPEC, oct. 2010, pp. 439–444, doi: 10.1109/IPECON.2010.5697036.
[16] S. S. S. Farahani, M. Nikzad, M. B. Tabar, y M. G. N. and A. Javadian, “Multi-machine power system stabilizer adjustment using Simulated Annealing”, Indian Journal of Science and Technology, no. 8, ago. 08, 2011.
[17] A. M. El-Zonkoly, A. A. Khalil, y N. M. Ahmied, “Optimal tunning of lead-lag and fuzzy logic power system stabilizers using particle swarm optimization”, Expert Systems with Applications, vol. 36, no. 2, Part 1, pp. 2097–2106, mar. 2009, doi: 10.1016/j.eswa.2007.12.069.
[18] M. Shafiullah, M. J. Rana, y M. A. Abido, “Power system stability enhancement through optimal design of PSS employing PSO”, en 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), set. 2017, pp. 26–31, doi: 10.1109/ICAEE.2017.8255321.
[19] W. S. Mota, Simulação de transitórios eletromecânicos em sistemas de potência. Campina Grande: EPGRAF, 2006.
[20] “IEEE Recommended Practice for Excitation System Models for Power System Stability Studies”, IEEE Std 421.5-2016 (Revision of IEEE Std 421.5-2005), pp. 1–207, ago. 2016, doi: 10.1109/IEEESTD.2016.7553421.
[21] K. Ogata, INGENIERÍA DE CONTROL MODERNA. Madrid, 2010.
[22] Zwe-Lee Gaing, “A particle swarm optimization approach for optimum design of PID controller in AVR system”, IEEE Transactions on Energy Conversion, vol. 19, no. 2, pp. 384–391, jun. 2004, doi: 10.1109/TEC.2003.821821.
[23] Kennedy, J., Eberhart, R. C., y Shi, Y, Swarm Intelligence. Elsevier, 2001.
[24] Y. Shi y R. Eberhart, “A modified particle swarm optimizer”, in 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), may 1998, pp. 69–73, doi: 10.1109/ICEC.1998.699146.
[25] R. C. Eberhart y Yuhui Shi, “Tracking and optimizing dynamic systems with particle swarms”, en Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), may. 2001, vol. 1, pp. 94–100 vol. 1, doi: 10.1109/CEC.2001.934376.
[26] J. Xin, G. Chen, y Y. Hai, “A Particle Swarm Optimizer with Multi-stage Linearly-Decreasing Inertia Weight”, in 2009 International Joint Conference on Computational Sciences and Optimization, abr. 2009, vol. 1, pp. 505–508, doi: 10.1109/CSO.2009.420.
[27] Guimin Chen, Xinbo Huang, Jianyuan Jia, y Zhengfeng Min, “Natural Exponential Inertia Weight Strategy in Particle Swarm Optimization”, en 2006 6th World Congress on Intelligent Control and Automation, jun. 2006, vol. 1, pp. 3672–3675, doi: 10.1109/WCICA.2006.1713055.
[28] J. N. R. da Silva Junior, “Sintonia ótima de regulador automático de tensão e estabilizador de sistema de potência utilizando algoritmo de otimização por enxame de partículas”, 2012.
[29] P. M. Anderson, A.-A. A. Fouad, y I. of E. y E. Engineers, Power system control and stability. IEEE Press, 2003.
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Published
2023-06-01
How to Cite
[1]
Z. Ñaupari Huatuco, F. W. Rodrigues, and Y. P. Molina Rodriguez, “Tuning of automatic voltage regulator and power system stabilizer using particle swarm optimization”, TEC, vol. 33, no. 1, pp. 1–9, Jun. 2023.
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Renewable energy, electrical engineering and / or power systems
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