Improving the Stability of a Power System Including SVC Based on Energy Function Minimization in a Multi-Model Optimal Coordinated Control Structure

Subject Areas : Power Engineering

Elaheh  Pagard ¹ (Department of Electrical Engineering Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran)
Shahrokh  Shojaeian ² (Department of Electrical Engineering Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran)
Mohammad Mahdi  Rezaei ³ (Department of Electrical Engineering Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran)

Received: 2023-07-10 Accepted : 2023-09-26 Published : 2024-02-20

Keywords: Multi-model controller, Linear optimal controller, particle swarm optimization algorithm, Low frequency oscillations, power system stability,

Abstract :

In this paper, the improvement of low frequency oscillation (LFO) damping in a power system including SVC is investigated. To achieve this goal, a new control strategy has been presented in which the multi-model controller is optimized using the linear optimal controller (LOC) and the particle swarm algorithm (PSO). The control bank in the multi-model controller includes three LOC controllers that generate optimal signals through the linearization of the nonlinear equations of the system and the minimization of an energy function to be combined by the Bayes recursive algorithm simultaneously to the generator excitation system and SVC. In order to generate an optimal linear signal, Riccati's equation must be solved; Riccati's equation includes two weight matrices Rric and Qric. These matrices elements are optimized by PSO algorithm. The PSO algorithm has calculated the optimal Rric and Qric with two different objective functions of maximizing the eigenvalues and minimizing the area under the speed curve. To evaluate the MMC-LOC-PSO control strategy, the symmetrical three-phase error is applied to the worst bus and the results of these two objective functions are compared. The simulation of the single machine power system has been done by MATLAB. The proposed control strategy, while maintaining stability, also effectively damps the LFOs, in addition, the permanent rotor speed and rotor angle error have also been favorably pushed to zero.

References:

1. K. S. Shim, S. J. Ahn, and J. H. Choi, “Synchronization of low-frequency oscillation in power systems,” Energies, vol. 10, no. 4, p. 558, 2017, doi: 10.3390/en10040558.


2. Y. Sang and M. Sahraei-Ardakani, “Economic benefit comparison of d-Facts and Facts in transmission networks with uncertainties,” in IEEE Power & Energy Society General Meeting (PESGM), Portland, OR, USA, 2018, pp. 1-5, doi: 10.1109/PESGM.2018.8585939, doi: 10.1109/PESGM.2018.8585939.


3. B. Dasu, M. Sivakumar, and R. Srinivasarao, “Interconnected multi-machine power system stabilizer design using whale optimization algorithm,” Protection and Control of Modern Power Systems, vol. 4, pp. 1–11, 2019, doi: 10.1186/s41601-19-0116-6.


4. T. Guesmi, B. M. Alshammari, Y. Almalaq, A. Alateeq, and K. Alqunun, “New coordinated tuning of SVC and PSSs in multimachine power system using coyote optimization algorithm,” Sustainability, vol. 13, no. 6, p. 3131, 2021, doi: 10.3390/su13063131.


5. J. Morsali and T. G. Bolandi, “Proposing FOPID-based PSS2B stabilizer using MGSO to improve damping of electromechanical oscillations in a multi-machine power system,” in 27th Iranian Conference on Electrical Engineering (ICEE), pp. 742–747, IEEE, 2019, doi: 10.1109/IranianCEE.2019.8786726.


6. S. Jalali and G. Shahgholian, “Designing of power system stabilizer based on the root locus method with lead-lag controller and comparing it with PI controller in multi-machine power system,” Journal of Power Technologies, vol. 98, no. 1, pp. 45-56, 2018.


7. D. Acharya, D. K. Das and A. Rai, Particle Swarm Optimization (PSO) based 2-DoF-PID power system stabilizer design for damping out low frequency oscillations in power systems," in 2nd International Conference on Innovations in Electronics, Signal Processing and Communication (IESC), Shillong, India, 2019, pp. 148-153, doi: 10.1109/IESPC.2019.8902378.


8. W. Du, W. Dong, Y. Wang, and H. Wang, “A method to design power system stabilizers in a multi-machine power system based on singlemachine infinite-bus system model,” IEEE Transactions on Power Systems, vol. 36, no. 4, pp. 3475–3486, 2020, doi: 10.1109/TPWRS.2020.3041037.


9. C. Abdelghani, L. Chaib, and S. Arif, "Robust control design of PSS for dynamic stability enhancement of power system," Journal of Electrical Systems, vol. 13, pp. 376-386, 06/01 2017.


10. S. Shojaeian and J. Soltani, “Low frequency oscillations damping of power system including unified power flow controller based on adaptive backstepping control,” Rev. Roum. Sci. Techn.–Électrotechn. et Énerg, vol. 58, no. 2, pp. 193–204, 2013.


11. C. Liu, G. Cai, J. Gao, and D. Yang, “Design of nonlinear robust damping controller for power oscillations suppressing based on backstepping fractional order sliding mode,” Energies, vol. 10, no. 5, p. 676, 2017, doi: 10.3390/en10050676.


12. E. Sharifi and A. Mazinan, “On transient stability of multi-machine power systems through takagi–sugeno fuzzy-based sliding mode control approach,” Complex & Intelligent Systems, vol. 4, pp. 171–179, 2018, doi: 10.1007/s40747-017-0063-7.


13. S. Huang, L. Xiong, J. Wang, P. Li, Z. Wang, and M. Ma, “Fixed-time fractional-order sliding mode controller for multimachine power systems,” IEEE Transactions on Power Systems, vol. 36, no. 4, pp. 2866–2876, 2020, doi: 10.1109/TPWRS.2020.3043891.


14. S. Tummala, R. Inapakurthi, and P. Ramanarao, “Observer based sliding mode frequency control for multi-machine power systems with high renewable energy,” Journal of Modern Power Systems and Clean Energy, vol. 6, pp. 473–481, 2018, doi: 10.1007/s40565-017-0363-3.


15. Z. Liu, S. Liu, Z. Li, and I. A. Tasiu, “A novel approach based on extended state observer sliding mode control to suppress voltage low frequency oscillation of traction network,” IEEE Access, vol. 7, pp. 52440–52454, 2019, doi: 10.1109/ACCESS.2019.2912219.


16. S. Shojaeian, J. Soltani, and G. A. Markadeh, “Damping of low frequency oscillations of multi-machine multi-upfc power systems, based on adaptive input-output feedback linearization control,” IEEE Transactions on power systems, vol. 27, no. 4, pp. 1831–1840, 2012, doi: 10.1109/TPWRS.2012.2194313.


17. B. Chaudhuri, R. Majumder, and B. C. Pal, “Application of multiple-model adaptive control strategy for robust damping of interarea oscillations in power system,” IEEE transactions on control systems technology, vol. 12, no. 5, pp. 727–736, 2004, doi: 10.1109/TCST.2004.833409.


18. L. Zhou, X. Yu, B. Li, C. Zheng, J. Liu, Q. Liu, and K. Guo, “Damping inter-area oscillations with large-scale pv plant by modified multiple model adaptive control strategy,” IEEE Transactions on Sustainable Energy, vol. 8, no. 4, pp. 1629–1636, 2017, doi: 10.1109/TSTE.2017.2697905.


19. Mostajabi, S. Shojaeian, and M. Lotfi, “Improving low frequency oscillation damping of a multi-area power system using multi-model adaptive control approach,” International Journal of Mechatronics, Electrical and Computer Technology, vol. 4, no. 11, pp. 628–646, 2014.


20. M. Sayidi, M. A. Nekoui and N. S. Boghrabidi, "Adaptive Optimal Control for a One-Machine Infinite-Bus Power System," in International Conference on Computational Intelligence for Modelling Control & Automation, Vienna, Austria, 2008, pp. 202-207, doi: 10.1109/CIMCA.2008.164.


21. E. Pagard, S. Shojaeian, M. M. Rezaei, “Damping of low-frequency oscillations in a power system, based on multiple-model optimal control strategy,” International Transactions on Electrical Energy Systems, vol. 2023, doi: 10.1155/2023/3992158.


22. H. Abniki, A. Asadi, P. Khajavi and M. T. Nabavi-Razavi, "A novel optimization technique for Linear Optimal Control and Power System Stabilizer in multi-machine power systems," in 10th International Conference on Environment and Electrical Engineering, Rome, Italy, 2011, pp. 1-4, doi: 10.1109/EEEIC.2011.5874718.


23. M. Jazaeri and H. F. Wang, "Multi-mode genetic algorithm based linear optimal control design for power systems," in International Conference on Sustainable Power Generation and Supply, Nanjing, China, 2009, pp. 1-6, doi: 10.1109/SUPERGEN.2009.5347920.


24. M. Z. Youssef, P. K. Jain and E. A. Mohamed, "A robust system stabilizer configuration using artificial neural network based on linear optimal control (student paper competition)," in Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH37436), Montreal, QC, Canada, 2003, pp. 569-573 vol.1, doi: 10.1109/CCECE.2003.1226460.


25. H. Abniki, A. Asadi, P. Khajavi, and M. Nabavi-Razavi, “A novel optimization technique for linear optimal control and power system stabilizer in multi-machine power systems,” in 10th International Conference on Environment and Electrical Engineering, pp. 1–4, IEEE, 2011, doi: 10.1109/EEEIC.2011.5874718.


26. R. Fazal and M. Choudhry, “Design of non-linear static var compensator based on synergetic control theory,” Electric Power Systems Research, vol. 151, pp. 243–250, 2017, doi: 10.1016/j.epsr.2017.05.014.


27. P. Krause, O. Wasynczuk, and S. D. Sudhoff., et al. Analysis of electric machinery and drive systems. Vol. 2., New York: IEEE press, 2002.


28. R. Badar, M. Z. Khan, and M. A. Javed, “Mimo adaptive bspline-based wavelet neuro fuzzy control for multi-type facts,” IEEE Access, vol. 8, pp. 28109–28122, 2020, doi: 10.1109/ACCESS.2020.2969387.


29. A. Gupta, A., Sharma, P., 2013. “Fuzzy based Svc auxiliary controller for damping low frequency oscillations in a power system,” in The Next Generation Information Technology Summit (4th International Conference). IET, pp. 87–91, doi: 10.1049/cp.2013.2298.