Fuzzy Based Intelligent Approach to AGC of Multi-Area Multi-Unit Power System with SMES under Random Load Disturbances

Abstract:

In reality, load variations in power systems are random in nature. Therefore, the automatic generation control (AGC) performance of the power system needs to be investigated under random load disturbances so as to have a realistic evaluation of the control strategy. This paper reports results for one such investigation. The intelligent control strategy, based on fuzzy gain scheduling of a proportional-integral (PI) controller, is developed and implemented for a multi-area multi-unit thermal power system with reheat nonlinearity. The paper also investigates the effect of superconducting magnetic energy storage (SMES) system on the AGC performance. For the sake of comparison, the behavior of the system for the same load disturbance is also investigated with a conventional PI controller. Simulation studies indicate that the proposed intelligent control strategy is very effective under random load disturbances and provides significant improvement over the conventional PI controller.

• Periodical: Applied Mechanics and Materials (Volume 492)
• Pages: 431-438
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.492.431
• Online since: January 2014
• Authors: Sathans Suhag*, A. Swarup
• Keywords: Automatic Generation Control, Fuzzy Gain Scheduling, Power System, Random Load Disturbance, SMES

References

[1] P. Kundur, Power System Stability and Control, McGraw Hill, New York, (1994).

[2] Ibraheem, P. Kumar and D. P. Kothari, Recent Philosophies of Automatic Generation Control Strategies in Power System. IEEE Trans. on Power Systems, vol. 20, no. 1, pp.346-357, (2005).

DOI: 10.1109/tpwrs.2004.840438

[3] C. S. Indulkar and B. Raj, "Application of Fuzzy Controller to Automatic Generation Contro. Electric Machines and Power Systems, vol. 23, no. 2, p.209–220, (1995).

DOI: 10.1080/07313569508955618

[4] C. S. Chang and W. Fu, "Area Load-Frequency Control using Fuzzy Gain Scheduling of PI Controllers. Electric Power Systems Research, vol. 42, no. 2, pp.145-152, (1997).

DOI: 10.1016/s0378-7796(96)01199-6

[5] J. Talaq and F. Al-Basri: Adaptive Fuzzy Gain Scheduling for Load-Frequency Control. IEEE Trans. Power Syst., vol. 14, no. 1, p.145–150, (1999).

DOI: 10.1109/59.744505

[6] I. Kocaarslan and E. Cam, "Fuzzy Logic Controller in Interconnected Electric Power Systems for Load-Frequency Control. Electrical Power and Energy Systems, vol. 27, no. 8, pp.542-549, (2005).

DOI: 10.1016/j.ijepes.2005.06.003

[7] R. A. Shoureshi, B. Hoffner, Z. Hu and R. A. Kramer, Neural-Based Generation Control for Highly Varying and Uncertain Loads. IEEE Porto Power Tech conference, Sept. 10-13, 2001, Porto, Portugal.

DOI: 10.1109/ptc.2001.964799

[8] K. Sabahi, M. A. Nekoui, M. Teshnehlab, M. Aliyari and M. Mansouri, Load Frequency Control in Interconnected Power System Using Modified Dynamic Neural Networks, Mediterranean Conference on Control and Automation, July 27-29, 2007, Athens, Greece.

DOI: 10.1109/med.2007.4433651

[9] S. C. Tripathy, R. Balasubramanian and P. S. C. Nair, Adaptive Automatic Generation Control with Superconducting Magnetic Energy Storage in Power Systems, IEEE Transactions on Energy Conversions, vol. 7, no. 3, p.434–441, (1992).

DOI: 10.1109/60.148563