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Warianty tytułu
Sterowanie SVC przy wykorzystaniu algorytmu Poler Placement i sieci neuronowych
Języki publikacji
Abstrakty
In this study, an online Artificial Neural Networks based Pole Placement algorithm is proposed to stabilize the 2-bus system with nonlinear SVC and variable power. It uses pole placement method. Proposed algorithm are examined in point of improving the bus voltages which change under different demanding powers and the temporary state performance are compared with PID control based results. Simulation results demonstrate a good performance and robustness.
W artykule zaprezentowano online algorytm Pole Placement z wykorzystaniem sieci neuronowych do stabilizacji dwu-szynowego systemu z nieliniowym, SVC (Static Var Compensator) i zmienną mocą.
Wydawca
Czasopismo
Rocznik
Tom
Strony
33--37
Opis fizyczny
Bibliogr. 25 poz., schem., tab., wykr.
Twórcy
autor
autor
autor
- Department of Electronics and Computer Education, Faculty of Technical Education, Mersin University, 33480,Tarsus, Mersin,Turkey, ekose@mersin.edu.tr
Bibliografia
- [1] Anagquist, L., Lundin, B., and Samuelsson, J., Power oscillation damping using conrolled reactive power compensation, IEEE Trans, On Power System, (1993), vol.8,no.2,pp 687-700, 1993
- [2] Samir, B., Al, A., Design Of A Robust Svc Damping Controller Using Nonlinear H∞ Technique, The Arabian Journal For Science And Engineering, (2005), Vol.30, No.1b
- [3] Eslami, M., Shareef, H., Mohamed, A., Khujehzadeh, M., Particle swarm optimization for simultaneous tuning of static var compensator and power system stabilizer, Przeglad Elektrotechniczny (Electrical Review), (2011), 09a, pp 343-347
- [4] Jordan, AJ., Linearization of non-linear state equation, Bulletin of The Polish Academy of Sciences Technical Science, (2006), Vol.54, No.1
- [5] Wang, Y., Chen, H., Zhou, R., A nonlinear controller design for SVC to improve power system voltage stability, Electrical Power and Energy Systems 22(2000), 463–470
- [6] Conga, L., Wanga, Y., Hill, DJ., Transient stability and voltage regulation enhancement via coordinated control of generator excitation and SVC, Electrical Power and Energy Systems, 27(2005), 121–130
- [7] Aström, K.J., and Hagglund, T., Revisiting the Ziegler-Nichols step response method for PID control. Journal of Process Control 14(2004), 635–650
- [8] Aström, K.J., and Hagglund, T., Advanced PID Controlor, ISAInstrumentation, Systems, and Automation Society, (2006), USA
- [9] Chang, Y., Xu, Z., A novel SVC supplementary controllers based on wide area signals, Electr Power Syst, Res 77(2007), 1569–1574
- [10] Farrag, MEA., and Putrus, G.A., An on-line traning radial basis function neural network for optimum operation of the UPFC, European Transaction on Electrical Power , 21(2011), 27-39
- [11] Arvanitis, K.G., Transactions of ttie ASME, (1999), 668 / Vol. 121
- [12] Kumar, R., Khan, M., Journal of Vibration and Acoustics, (2007), Vol. 129 / 601
- [13] Shakir, M.S., Ahmad, M.N., and Amat, A., World Academy of Science, Engineering and Technology 50 (2009)
- [14] Hu, W.S., The Theory of Neural Network and Its Applications in Engineering, (2006), Beijing: SinoMaps Press
- [15] Yuan, C.R., Artificial Neural Network and Its Applications, (1999), Beijing: Tsinghua University Press
- [16]Wang, L.G., et al., Combined ANN prediction model for failure depth of coal seam floors, Mining Science and Technology 19(2009), 0684–0688
- [17] Said, İ.K., and Pirouti, M., Neural network-based load balancing and reactive power control by Static VAR compensator, International Journal of Computer and Electrical Engineering, (2009), Vol.1, No.1, pp.25-31
- [18] Al-Alawi, S.M., and Ellithy, K.A., Tuning of SVC damping controllers over a wide range of bad models using an artificial neural network. International Journal of Electrical Power & Energy Systems, (2000), Vol. 22, Issue 6, pp.405-420
- [19] Filik, U.B, Kurban, M., A new Approach for the short-Term Load Forecasting with Autoregressive and Artificial neural Network Models. International Journal of Computational Intelligence Research, (2007), Vol. 3, No.1,pp-66-71
- [20] Girish, JK., Artificial neural network application, I.A.R.I., (2007), New Delhi,110012
- [21] Köse, E., Abacı, K., Aksoy, S., and Yalçın, M.A., The Comparison of the improving Effects of ULTC and SVC on Dynamical Voltage Stability Using Neural Networks. IEEE Modern Electric Power Systems MEPS’10, (2010), Wroclaw, Poland
- [22] Ziegler, J.G., Nichols, N.B., and Rochester, N.Y., Optimum Settings for Automatic Controllers. Transactions of ASME, (1942), Vol.64, pp. 759-768
- [23] Aström, K.J., and Hagglund, T., Automatic Tuning of Simple Regulators with Specifications on Phase and Amplitude Margins. Automatica, (1984), Vol.20, No.5, pp. 645-651
- [24] Hang, C.C., Aström, K.J. and Ho, W.K., Refinements of the Ziegler-Nichols tuning Formula, IEE Proceeding-D, (1991), Vol.138, No.2
- [25] Kailath, T., Linear Systems, (1983), Prentice-Hall Inc., Tokyo
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPOH-0065-0007