Design of Power System Stabilizer with Neuro-Fuzzy UPFC Controller

Authors: U. Ramesh Babu, V. Vijay Kumar Reddy, S. Tara Kalyani

Abstract:

The growth in the demand of electrical energy is leading to load on the Power system which increases the occurrence of frequent oscillations in the system. The reason for the oscillations is due to the lack of damping torque which is required to dominate the disturbances of Power system. By using FACT devices, such as Unified Power Flow Controller (UPFC) can control power flow, reduce sub-synchronous resonances and increase transient stability. Hence, UPFC is used to damp the oscillations occurred in Power system. This research focuses on adapting the neuro fuzzy controller for the UPFC design by connecting the infinite bus (SMIB - Single machine Infinite Bus) to a linearized model of synchronous machine (Heffron-Phillips) in the power system. This model gains the capability to improve the transient stability and to damp the oscillations of the system.

Keywords: Power System, UPFC, (ANFIS) Adaptive Neuro Fuzzy Inference System, transient, Low frequency oscillations.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1109786

References:

[1] Gyugyl, “The Umfled Power Flow Controller. A New Approach to Power Transmission Control”, IEEE Trmns on Power DehL wJy, Vol. 10, No.2, April 1995, pp.1085-1093.
[2] CD. Schauder, L. Gyugyl, M R Lund, D.M. Hamal, T R. Rletman. D. RTorgerson, and A Edns, “Operation of the Unified Power Flow Controller (UPFC) under Practical Constraints”, IEEE Trans. On Power Delivery, Vol 13, No.2, Aprd 1998, pp 630-639.
[3] S. Limyingchal-oen, U, D. Annakkage, and NC. Pahalawaththa, “Effects of Unified Power Flow Controllers on Transient Stability”, lEEE Proc - GenerTrfm.rrn Distrlb , Vol. 145, No.2, March 1998, pp 182-188.
[4] Y. Morloka. M. Kate, Y. Mlshlma, and Y. Nakachi, “Implementation of Undied Power Flow Controller and Verdicatlon for Transmm.lon Capablhty Impl-ovement”, IEEE Trans on Power Systems, Vol. 14, No.2, May 1999, pp.575-581.
[5] F.P. Demello and C. Concordia, “Concept of synchronous machine stability as affected by excitation control”, IEEE Trans. PAS Vol. 88, 1969, pp 316-329.
[6] E.V. Larsen and D.A. Swann, “ Applying Power System Stabilizers”, Part I- III, IEEE Trans. PAS Vol. 100, no 6, 1981, pp 3017-3046.
[7] P. Kundur, “Power System Stability and Control”, McGraw Hill Inc.1993.
[8] Cheng S, Malik O.P., Hope G.S., “Design of self-tuning PID stabilizer for a multi machine power system”, IEE Proceedings, Part C, 1986; 133(4), pp 176-185.
[9] Hsu Y.Y., Liou K.L., Hsu Y.Y, Cheng C.V., “Design of fuzzy power system stabilizers for multi machine power systems ”, IEE Proceedings, Part C,1990, 137(3).
[10] Y. Zhang, G.P. Chen, O.P. Malik and G.S. Hope, “An artificial neural based adaptive power system stabilizer ”, IEEE Transactions on Energy Conversion, Vol.8, No.1, March 1993, pp.71-77.
[11] Y.Zhang, O.P.Malik and G.P.Chen, “Artificial neural network power system stabilizers in multi-machine power system environment ” , IEEE Transactions on Energy Conversion, Vol. 10, No.1, March 1995, pp.147-154
[12] J. R. Jang, “ANFIS: Adaptive-network-Based Fuzzy Inference System”, IEEE Trans. Syst. Man Cybernetics, vol. 23, no.3, pp. 665-685, May 1993
[13] T. Takagi, and M. Sugeno, “Derivation of fuzzy control rules from human operator’s control actions”, IFAC Symp. Fuzzy Inf. Knowledge Repres. Dec. Analysis, pp. 55-60, 1983.
[14] E. M. Abdelrahim, and T. Yahagi, “A new transformed input-domain ANFIS for highly nonlinear system modeling and prediction”, Electrical and Computer Engineering, 2001. Canadian Conference on, vol. 1, pp. 655-660, 2001
[15] Jyh-Shing Roger Jang, Chuen-Tsai Sun, EijiMizutani, "Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence", Prentice-Hall, 1997
[16] Ravi Segala, Avdhesh Sharma, M.L. Kothari, “A self-tuning power system stabilizer based on artificial neural network”, Electrical Power and Energy Systems 26 (2004) 423-430