Representation of power system load dynamics with ANN for real-time applications
作者： D.M. Vilathgamuwa , H.M. Wijekoon
关键词: Voltage sag 、 Transient (oscillation) 、 Control engineering 、 Power (physics) 、 Artificial neural network 、 Engineering 、 Autoregressive–moving-average model 、 Electric power system 、 Black box 、 Control theory 、 Active filter
摘要: Among severe power system disturbances degrading quality are voltage sags and transient supply interruptions. Dynamic behaviour of loads under these types must be taken into account in the development mitigating devices such as dynamic restorer (DVR), active filters etc. This paper presents a representation load dynamics based on non-linear black box approach with artificial neural networks (ANN). Two models, network autoregressive moving average exogenous inputs (NNARMAX) have been developed. These models trained tested to predict dynamical especially at bulk point sag conditions. Off-line which included nevertheless exhibits random behaviour. A window has adopted real-time parameter updating proposed models.
sci-hub.se 参考文章(9)
C.W. Taylor, G.J. Rogers, W.W. Price, Standard load models for power flow and dynamic performance simulation IEEE Transactions on Power Systems. ,vol. 10, ,(1995)
Lennart Ljung, System Identification: Theory for the User ,(1987)
T. Hiyama, M. Tokieda, W. Hubbi, H. Andou, Artificial neural network based dynamic load modeling IEEE Transactions on Power Systems. ,vol. 12, pp. 1576- 1583 ,(1997) , 10.1109/59.627861
A.P. Alves da Silva, C. Ferreira, A.C. Zambroni de Souza, G. Lambert-Torres, A new constructive ANN and its application to electric load representation IEEE Transactions on Power Systems. ,vol. 12, pp. 1569- 1575 ,(1997) , 10.1109/59.627860
T. Kataoka, H. Uchida, T. Kai, T. Funabashi, A method for aggregation of a group of induction motor loads ieee international conference on power system technology. ,vol. 3, pp. 1683- 1688 ,(2000) , 10.1109/ICPST.2000.898233
K.S. Narendra, K. Parthasarathy, Identification and control of dynamical systems using neural networks IEEE Transactions on Neural Networks. ,vol. 1, pp. 4- 27 ,(1990) , 10.1109/72.80202
K.S. Narendra, K. Parthasarathy, Gradient methods for the optimization of dynamical systems containing neural networks IEEE Transactions on Neural Networks. ,vol. 2, pp. 252- 262 ,(1991) , 10.1109/72.80336
M. Bostanci, J. Koplowitz, C.W. Taylor, Identification of power system load dynamics using artificial neural networks IEEE Transactions on Power Systems. ,vol. 12, pp. 1468- 1473 ,(1997) , 10.1109/59.627843
T.Y.J. Lem, R.T.H. Alden, Comparison of experimental and aggregate induction motor responses IEEE Transactions on Power Systems. ,vol. 9, pp. 1895- 1900 ,(1994) , 10.1109/59.331447