Abstract
STATCOM is one of the FACTS devices that are used in power systems. The algorithms used to control the STATCOM often use PID controller. However, there are a lot of elements in the network and have complex configurations and their dynamic model is highly non-linear, and convention PID controller are not robust for their stability control. In this paper, we propose the intelligent controllers for STATCOM based on dynamic model of the system and two control schemes have been developed: (i) Fuzzy-PID self-tuning controller (Hybrid F-PID); (ii) Adaptive neuro-fuzzy inference system – PID (ANFIS-PID) controller. The operating performance of the studied system is using the popular benchmark three-machine nine-bus system. The two-axis four-order model of synchronous generator (SG) is used. Time-domain scheme based on a nonlinear system model subject to a three-phase short-circuit fault at the load connected bus is utilized to examine the effectiveness of the proposed control schemes. It can be concluded from the simulation results that ANFIS has provide the best results for controlling STATCOM to enhance power quality in power system as compared to the conventional control strategies under large disturbance.
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References
Pokharel, B., Gao, W.: Mitigation of disturbances in DFIG-based wind farm connected to weak distribution system using STATCOM. In: Proceedings North American Power Symposium (NAPS), pp. 1–7, 26–28 September 2010
Ganesh, A., Dahiya, R., Singh, G.K.: Development of simple technique for STATCOM for voltage regulation and power quality improvement. In: 2016 IEEE International Conference on Power Electronics, PEDES, Trivandrum, India, pp. 1–6 (2016)
Selwa, F., Djamel, L., Imen, L., Hassiba, S.: Impact of PSS and STATCOM on transient stability of multi-machine power system connected to PV generation. In: 2015 International Conference on Renewable Energy Research and Applications, Palermo, pp. 1416–1421 (2015)
Eskander, M.N., Amer, S.I.: Mitigation of voltage dips and swells in grid-connected wind energy conversion systems. In: Proceedings ICCAS-SICE (2009)
Chhor, J., Tourou, P., Sourkounis, C.: Evaluation of state-based controlled STATCOM for DFIG-based WECS during voltage sags. In: 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA), Birmingham, pp. 463–471 (2016)
Mak, L.O., Ni, Y.X., Shen, C.M.: STATCOM with fuzzy controllers for interconnected power systems. Electr. Power Syst. Res. 55(2), 87–95 (2000)
Uddin, M.N., Rebeiro, R.S.: Improved dynamic and steady state performance of a hybrid speed controller based IPMSM drive. In: Proceedings IEEE Industry Applications Society Annual Meeting (IAS), pp. 1–8, 9–13 October 2011
Varshney, A., Garg, R.: Comparison of different topologies of fuzzy logic controller to control D-STATCOM. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, pp. 2492–2497 (2016)
Anderson, P.M., Fouad, A.A.: Power System Control and Stability, 2nd ed. IEEE Press (2003)
Tian, L.: Intelligent self-tuning of PID control for the robotic testing system for human musculoskeletal joints test. Ann. Biomed. Eng. 32(6) (2004)
Adhyaru, D., Patel, J., Gianchandani, R.: Adaptive neurofuzzy inference system based control of robotic manipulators. In: Proceedings 2010 IEEE International Conference on Mechanical and Electrical Technology, pp. 353–358 (2010)
Bachir, O., Zoubir, A.F.: Adaptive neuro-fuzzy inference system based control of puma 600 robot manipulator. Int. J. Electr. Comput. Eng. 2(1), 90–97 (2012)
Molinas, M., Suul, J.A., Undeland, T.: LVRT of wind farms with cage generators: STATCOM versus SVC. IEEE Trans. Power Electron. 23(3), 1104–1117 (2008)
Jang, J., Sun, C., Mizutani, E.: Neuro-Fuzzy and Soft Computing. Prentice Hall, Upper Saddle River (1997)
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Nguyen, H.V., Nguyen, H., Le, K.H. (2020). ANFIS and Fuzzy Tuning of PID Controller for STATCOM to Enhance Power Quality in Multi-machine System Under Large Disturbance. In: Zelinka, I., Brandstetter, P., Trong Dao, T., Hoang Duy, V., Kim, S. (eds) AETA 2018 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application. AETA 2018. Lecture Notes in Electrical Engineering, vol 554. Springer, Cham. https://doi.org/10.1007/978-3-030-14907-9_4
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DOI: https://doi.org/10.1007/978-3-030-14907-9_4
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