Power System Stabilizer Design Using Adaptive FOPID Controller based on Self-Learning Wavelet Neural Networks

Document Type : Original Article

Authors

Assistant Professor, Department of Electrical Engineering, Technical and Vocational University (TVU), Tehran, Iran.

Abstract

Several methods were proposed for the design of power system stabilizer, PSS, based on PI, PID, and FOPID controllers. In these controllers, the degree of freedom increases from two to three and five, respectively. Although increasing the degree of freedom can enhance the convergence rate and the robustness of the controller, it does come with more challenges when it comes to tuning the control parameters. For instance, it is no longer possible to adjust FOPID parameters using trial and error. One of the conventional methods is to use optimization algorithms, but it should be noted that the power system is highly non-linear. This research aimed to propose an algorithm to design the PSS controller based on FOPID, in which the controller coefficients were adjusted based on the system conditions. For this purpose, the controller coefficients were defined based on the gradient of the power system, so that the coefficients were adjusted at any moment by the adaptive-indirect gradient method in such a way that the cost function of the controller was minimized, and as a result, the rate of oscillation damping increased. In the proposed algorithm, an identifier based on self-tuning wavelet neural network with online learning was used to estimate the gradient of the power system. Finally, the proposed adaptive controller was designed for a two-zone, two-machine power system including FACTs devices, SSSC-type, and its performance was evaluated in comparison with other methods. The results confirm the effectiveness of the proposed method.

Keywords

Main Subjects

References

[1] Saadatmand, M., Gharehpetian, G. B., Kamwa, I., Siano, P., Guerrero, J. M., & Haes Alhelou, H. (2021). A Survey on FOPID Controllers for LFO Damping in Power Systems Using Synchronous Generators, FACTS Devices and Inverter-Based Power Plants. Energies, 14(18), 1-26. https://doi.org/10.3390/en14185983
[2] Machowski, J., Lubosny, Z., Bialek, J. W., & Bumby, J. R. (2020). Power system dynamics: stability and control (3 ed.). John Wiley & Sons. https://www.wiley.com/en-us/Power+System+Dynamics%3A+Stability+and+Control%2C+3rd+Edition-p- 978111 9526360
[3] Delavari, H., & Flahzadeh, K. (2019, 30 April-2 May ). Robust Fractional Order Adaptive Power System Stabilizer for a Multi-Machine System. 27th Iranian Conference on Electrical Engineering, Yazd, Iran.
[4] Ghany, M. A., & Shamseldin, M. A. (2020). Model reference self-tuning fractional order PID control based on for a power system stabilizer. International Journal of Power Electronics and Drive System, 11(3), 1333-1434. https://doi.org/10.11591/ijpeds.v 11.i3.pp1333-1343
[5] Paital, S. R., Ray, P. K., Mohanty, S. R., & Mohanty, A. (2021). An adaptive fractional fuzzy sliding mode controlled PSS for transient stability improvement under different system uncertainties. Institution of Engineering and Technology Smart Grid, 4(1), 61-75. https://doi.org/10.1049/stg2.12002
[6] Alizadeh, M., Ganjefar, S., & Alizadeh, M. (2013). Wavelet neural adaptive proportional plus conventional integral-derivative controller design of SSSC for transient stability improvement. Engineering Applications of Artificial Intelligence, 26(9), 2227-2242. https://doi.org/10.1016/j.engappai.2013.06.018
[7] Saadatmand, M., Mozafari, B., Gharehpetian, G. B., & Soleymani, S. (2020). Optimal PID controller of large-scale PV farms for power systems LFO damping. International Transactions on Electrical Energy Systems, 30(6), 1-14. https://doi.org/10.1002/205 0-7038.12372
[8] Tapin, L., & Mehta, R. K. (2014). Low Frequency Oscillations in Power Systems: A Review. Seventh Sense Research Group International Journal of Electrical and Electronics Engineering, 1(4), 6-17. https://doi.org/10.14445/23488379/IJEEE-V1I4P102
[9] Varma, R. K., & Akbari, M. (2020). Simultaneous Fast Frequency Control and Power Oscillation Damping by Utilizing PV Solar System as PV-STATCOM. IEEE Transactions on Sustainable Energy, 11(1), 415-425. https://doi.org/10.1109/TSTE. 2019.2892943
[10] Yao, W., Jiang, L., Wen, J., Wu, Q. H., & Cheng, S. (2014). Wide-Area Damping Controller of FACTS Devices for Inter-Area Oscillations Considering Communication Time Delays. IEEE Transactions on Power Systems, 29(1), 318-329. https://doi.org/10.1109/TPWRS.2013.2280216
[11] Abido, M. A., & Abdel-Magid, Y. L. (2003). Coordinated design of a PSS and an SVC-based controller to enhance power system stability. International Journal of Electrical Power & Energy Systems, 25(9), 695-704. https://doi.org/10.1016/S014 2-0615(02)00124-2
[12] Shayeghi, H., Safari, A., & Shayanfar, H. A. (2010). PSS and TCSC damping controller coordinated design using PSO in multi-machine power system. Energy Conversion and Management, 51(12), 2930-2937. https://doi.org/10.1016/j.enconman.2010.06.034
[13] Hingoranl, N. G., & Gyugyi, L. (2000). Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems. Wiley-IEEE Press. https://ieeexplore.ieee.org/ book/5264253
[14] Abdolhosseini, M., Abdollahi, R., & Rajaee, M. (2021). Designing of PIλDδ controller for PMBLDC motor using metaheuristic algorithms. Karafan Quarterly Scientific Journal, 17(4), 149-165. https://doi.org/10.48301/kssa.2021.128401
[15] Åström, K. J., Hägglund, T., Hang, C. C., & Ho, W. K. (1993). Automatic tuning and adaptation for PID controllers - a survey. Control Engineering Practice, 1(4), 699-714. https://doi.org/10.1016/0967-0661(93)91394-C
[16] Ganjefar, S., & Alizadeh, M. (2013). On-line self-learning PID controller design of SSSC using self-recurrent wavelet neural networks. Turkish Journal of Electrical Engineering and Computer Sciences, 21(4), 980-1001. https://doi.org/10.3906/elk-1112-49
[17] Cominos, P., & Munro, N. (2002). PID controllers: recent tuning methods and design to specification. Institution of Electrical Engineers Proceedings - Control Theory and Applications, 149(1), 46-53. https://doi.org/10.1049/ip-cta:20020103
[18] Ho, S. J., Li-Sun, S., & Shinn-Ying, H. (2006). Optimizing fuzzy neural networks for tuning PID controllers using an orthogonal simulated annealing algorithm OSA. Institute of Electrical and Electronics Engineers Transactions on Fuzzy Systems, 14(3), 421-434. https://doi.org/10.1109/TFUZZ.2006.876985
[19] Skogestad, S. (2003). Simple analytic rules for model reduction and PID controller tuning. Journal of Process Control, 13(4), 291-309. https://doi.org/10.1016/S0959-1524(02)00062-8
[20] Sabri, M. (2017). Stabilization and control of the power system using meta-heuristic algorithms. Karafan Quarterly Scientific Journal, 14(2), 33-55. https://karafan.tvu. ac.ir/article_100504.html?lang=en
[21] Micev, M., Ćalasan, M., & Oliva, D. (2020). Fractional Order PID Controller Design for an AVR System Using Chaotic Yellow Saddle Goatfish Algorithm. Mathematics, 8(7), 1-21. https://doi.org/10.3390/math8071182
[22] Ray, P. K., Paital, S. R., Mohanty, A., Foo, Y. S. E., Krishnan, A., Gooi, H. B., & Amaratunga, G. A. J. (2019). A Hybrid Firefly-Swarm Optimized Fractional Order Interval Type-2 Fuzzy PID-PSS for Transient Stability Improvement. IEEE Transactions on Industry Applications, 55(6), 6486-6498. https://doi.org/10.1109/TIA.2019.2938473
[23] Saadatmand, M., Gharehpetian, G. B., Siano, P., & Alhelou, H. H. (2021). PMU-Based FOPID Controller of Large-Scale Wind-PV Farms for LFO Damping in Smart Grid. Institute of Electrical and Electronics Engineers Access, 9, 94953-94969. https://doi.org/10.1109/ACCESS.2021.3094170
[24] Shah, P., & Agashe, S. (2016). Review of fractional PID controller. Review of fractional PID controller, Mechatronics, 38(7), 29-41. https://doi.org/10.1016/j.mechatronics. 2016.06.005
[25] Pirasteh-Moghadam, M., Saryazdi, M. G., Loghman, E., E, A. K., & Bakhtiari-Nejad, F. (2020). Development of neural fractional order PID controller with emulator. International Society of Automation Transactions, 106, 293-302. https://doi.org/10. 1016/j.isatra.2020.06.014
[26] Hou, R., Wang, L., Gao, Q., Hou, Y., & Wang, C. (2017). Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system. International Society of Automation Transactions, 70, 298-307. https://doi.org/10. 1016/j.isatra.2017.04.010
[27] Yoo, S. J., Park, J. B., & Choi, Y. H. (2007). Indirect adaptive control of nonlinear dynamic systems using self recurrent wavelet neural networks via adaptive learning rates. Information Sciences, 177(15), 3074-3098. https://doi.org/10.1016/j.ins.2007.02.009
[28] Valerio, D., & Da Costa, J. S. (2004). Ninteger: a non-integer control toolbox for MatLab. Proceedings of fractional differentiation and its applications, Bordeaux, 1-6. https://www.researchgate.net/publication/228993622_Ninteger_a_non-integer _control _toolbox_for_MatLab
[29] Oustaloup, A., Levron, F., Mathieu, B., & Nanot, F. M. (2000). Frequency-band complex noninteger differentiator: characterization and synthesis. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 47(1), 25-39. https://doi.org/10.1109/81.817385
[30] Wan, J., He, B., Wang, D., Yan, T., & Shen, Y. (2019). Fractional-Order PID Motion Control for AUV Using Cloud-Model-Based Quantum Genetic Algorithm. Institute of Electrical and Electronics Engineers Access, 7, 124828-124843. https://doi.org/ 10.1109/ACCESS.2019.2937978
[31] Zamani, A.-A., Tavakoli, S., & Etedali, S. (2017). Fractional order PID control design for semi-active control of smart base-isolated structures: A multi-objective cuckoo search approach. International Society of Automation Transactions, 67, 222-232. https://doi.org/10.1016/j.isatra.2017.01.012
Karafan Quarterly Scientific Journal
Volume 19, Issue 3 - Serial Number 59
Technical and Engineering
December 2022
Pages 247-277

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History

  • Receive Date: 23 February 2022
  • Revise Date: 27 August 2022
  • Accept Date: 06 November 2022

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