Abstract
The Unified Power Flow Controller (UPFC) is regarded as one of the most versatile devices in the FACTS device family which has the ability to control the power flow in the transmission line, improve the transient stability, mitigate system oscillation, and provide voltage support. In this book chapter, the problem of UPFC based damping controller is formulated as an optimization problem which is solved using classic and Quantum-behaved Particle Swarm Optimization technique (QPSO). Two different objective functions are proposed in this work for the UPFC based damping controller design problem. The first objective function is the eigenvalues based comprising the damping factor, and the damping ratio of the lightly damped electromechanical modes, while the second is the time domain-based multi-objective function. The performance of the proposed controllers under different disturbances and loading conditions is investigated for a single machine infinite bus and multi-machine power systems. The results of the proposed controllers are demonstrated through eigenvalue analysis and nonlinear time domain simulation.
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References
Shayeghi H, Shayanfar HA, Jalilzadeh S, Safari A (2009) Design of output feedback UPFC controllers for damping of electromechanical oscillations using PSO. Energy Conver Manage 50:2554–2561
Anderson PM, Fouad AA (1977) Power system control and stability. Iowa State University Press, Ames
Keri AJF, Lombard X, Edris AA (1999) Unified power flow controller: modeling and analysis. IEEE Trans Power Deliv 14(2):648–654
Hingorani NG, Gyugyi L (1999) Understanding FACTS: concepts and technology of flexible AC transmission systems. Wiley-IEEE Press, New York
Gyugyi L (1992) Unified power-flow control concept for flexible ac transmission systems. IEE Proc Gen Transm Distrib 139 (4): 323–331
Song YH, Johns AT (1999) Flexible ac transmission systems (FACTS). IEE Press, London
Nabavi-Niaki A, Iravani MR (1996) Steady-state and dynamic models of unified power flow controller (UPFC) for power system studies. IEEE Trans Power Syst 11(4):1937–1943
Wang HF (1999) Damping function of unified power flow controller. IEE Proc Gen Transm Dist 146(1):81–87
Wang HF (1999) Application of modeling UPFC into multi-machine power systems. IEE Proc Gen Transm Dist 146(3):306–312
Wang HF (2000) A unified model for the analysis of FACTS devices in damping power system oscillations - Part III: unified power flow controller’. IEEE Trans Power Deliv 15(3):978–983
Rouco L (2001) Coordinated design of multiple controllers for damping power system oscillations. Elect Power Energy Syst 23:517–530
Dash PK, Mishra S, Panda G (2000) A radial basis function neural network controller for UPFC. IEEE Trans Power Syst 15(4):1293–1299
Kazemi A, Vakili Sohrforouzani M (2006) Power system damping controlled facts devices. Elect Power Energy Syst 28:349–357
Kundur P (1994) Power system stability and control. McGrew Hill, New York
Khon L, Lo KL (2006) Hybrid micro-GA based FLCs for TCSC and UPFC in a multi machine environment. Elec Power Syst Res 76:832–843
Mok TK, Liu H, Ni Y, Wu FF, Hui R (2005) Tuning the fuzzy damping controller for UPFC through genetic algorithm with comparison to the gradient descent training. Elect Power Energy Syst 27:275–283
Tripathy M, Mishra S, Venayagamoorthy GK, Bacteria foraging: a new tool for simultaneous robust design of UPFC controllers. In: Proceedings of the international joint conference on neural networks, pp 2274–2280
Shayeghi H, Jalili A, Shayanfar HA (2008) Multi-stage fuzzy load frequency control using PSO. Energy Conver Manage 49:2570–2580
Eberhart R, Kennedy J (1995). A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, pp 39–43
Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evolutionary Comput 6(1):58–73
Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization: an overview. Swarm Intell 1:33–57
Shayeghi H, Shayanfar HA, Jalilzadeh S, Safari A (2010) Tuning of damping controller for UPFC using quantum particle swarm optimizer. Energy Conver Manage 51:2299–2306
Coelho LS (2008) A quantum particle swarm optimizer with chaotic mutation operator. Chaos Soliton Fractals 37:1409–1418
Sun J, Fang W, Chen W, Xu W (2008) Design of two dimensional IIR digital filters using an improved quantum-behaved particle swarm optimization algorithm. In: Proceedings of the American control conference, pp 2603–2608
Sun J, Fang W, Chen W, Xu W (2005) Adaptive parameter control for quantum-behaved particle swarm optimization on individual level. In: Proceedings of the IEEE international conference on systems, man and cybernetics, Big Island, USA, pp 3049–3054
Noroozian M, Angquist L, Ghandhari M, Andersson G (1997) Improving power system dynamics by series connected FACTS devices. IEEE Trans Power Deliv 12(4):1635–1641
Sadikovic R (2006) Use of FACTS devices for power flow control and damping of oscillations in power systems. Ph. D. dissertation, University of Tuzla, Zurich, Swiss
Mishra S (2006) Neural network based adaptive UPFC for improving transient stability performance of power system. IEEE Trans Neural Netw 17(2):461–470
Abdel-Magid YL, Abido MA (2003) Optimal multiobjective design of robust power system stabilizers using genetic algorithms. IEEE Trans Power Syst 18(3):1125–1132
Nguyen TT, Gianto R (2007) Optimisation based control coordination of PSSs and FACTS devices for optimal oscillations damping in multi-machine power system. IET Proc Gener Transm Distrib 1(4):564–573
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Shayeghi, H., Safari, A. (2013). Optimal Design of UPFC Based Damping Controller Using PSO and QPSO . In: Bizon, N., Shayeghi, H., Mahdavi Tabatabaei, N. (eds) Analysis, Control and Optimal Operations in Hybrid Power Systems. Green Energy and Technology. Springer, London. https://doi.org/10.1007/978-1-4471-5538-6_5
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DOI: https://doi.org/10.1007/978-1-4471-5538-6_5
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