Oscillation Control in a Synchronous Machine using a Neural based PSS
DOI:
https://doi.org/10.17533/udea.redin.18118Keywords:
Power system stabilizer, neural nets, synchronous machineAbstract
This paper presents the methodological design and the laboratory test of neural net based power system stabilizer (PSS). The architecture of the proposed PSS uses two neural networks, one neural based controller which is used to generate a supplementary control signal to the excitation system, and an additional neural net used to improve the performance of the neural based controller. In order to guarantee the correct operation of the proposed PSS, it is trained by using data obtained from several machine operating conditions and a variety of disturbances. The effectiveness is demonstrated by testing the proposed approach in a real synchronous machine in a laboratory facility.
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K. S. Narendra, K. Parthasarathy. “Identification and control of dynamical systems using neural networks”. IEEE Trans. Neural Networks. Vol. 1. 1990. pp. 4-27. DOI: https://doi.org/10.1109/72.80202
P. Shamsollahi, O. P. Malik. “Direct Neural Adaptive Control to synchronous generator”. IEEE Trans. On energy conversion. Vol. 14. 1999. pp. 1341–1346. DOI: https://doi.org/10.1109/60.815070
P. Shamsollahi, O. P. Malik. “Application of neural adaptive power system stabilizer in a multi-machine power system”. IEEE Trans. On energy conversion. Vol 14. 1999. pp. 731–736. DOI: https://doi.org/10.1109/60.790943
P. Shamsollahi, O. P. Malik. “Real-time implemen¬tation and experimental studies of a neural adaptive power system stabilizer”. IEEE Trans. On energy con¬version. Vol. 14. 1999. pp. 737 – 742. DOI: https://doi.org/10.1109/60.790944
W. Liu, G. Venayagamoorthy, D. Wunsch. “Adaptive neural network based power system stabilizer design”. IEEE Trans. On energy conversion. 2003. pp. 2970–2975.
K. Prabha. Power System Stability and Control. New York: McGraw-Hill, 1994.
S. Pérez, J. Mora, G. Olguin. “Maintaining voltage profiles using an adaptive PSS”. Transmission & Dis-tribution Conference and Exposition: Latin America, 2006. TDC apos. 06. IEEE/PES Volume 1. 2006. pp. 1–5. DOI: https://doi.org/10.1109/TDCLA.2006.311429
P. Shamsollahi, O. P. Malik, “On-line identification of synchronous generator using neural networs,” in Proc. Can. Conf. Elect. Comput. Eng.1996. pp. 595-598.
R. Wankeue, I. Kanwa, X. Dai-Do, A. Keyhani. “Iteratively reweighted least square for maximum likeliho¬od identification of synchronous machine parameters from on line tests”. IEEE Trans. Energy Conversion. Vol. 14. 1999. pp.159-166. DOI: https://doi.org/10.1109/60.766971
G. Goodwin, K. Sang. Adaptive filtering predic¬tion and control. Prentice – Hall. New Jersey. 1984. pp.125-134.
S. Pérez. Control de Oscilaciones de la Máquina Síncrona utilizando un Estabilizador Neuronal. M.Sc thesis, Universidad Tecnológica de Pereira, Pereira, Risaralda, Colombia, 2005. pp. 12-86.
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