Proper planning and operation of power system as a defense issue, is affected by several factors such as small signal rotor angle stability of generators. This type of stabilities usually studied in two oscillatory modes including local and inter-area oscillations. Since the study of these events by using conventional methods of stability studies is difficult, in this paper, index-based participation factor of generators in the dominant mode is introduced and then Type and location of the oscillations of power system are predicted by the neural network. In addition, the damping of the critical mode is predicted using neural network. Moreover, capability of the proposed method is evaluated by considering the effect of static load models and noisy data. The proposed method gives appropriate information of power system to the network operators in the normal and emergency conditions with high accuracy and speed.
Velayati, M. H., & ghaffarpour, R. (2015). Enhance the Passive Defense of Power System Networks Using Prediction Damping, Type and Location of Power System's Oscillations. Journal of Advanced Defense Science & Technology, 6(1), 19-31.
MLA
mohammad Hossein Velayati; Reza ghaffarpour. "Enhance the Passive Defense of Power System Networks Using Prediction Damping, Type and Location of Power System's Oscillations", Journal of Advanced Defense Science & Technology, 6, 1, 2015, 19-31.
HARVARD
Velayati, M. H., ghaffarpour, R. (2015). 'Enhance the Passive Defense of Power System Networks Using Prediction Damping, Type and Location of Power System's Oscillations', Journal of Advanced Defense Science & Technology, 6(1), pp. 19-31.
VANCOUVER
Velayati, M. H., ghaffarpour, R. Enhance the Passive Defense of Power System Networks Using Prediction Damping, Type and Location of Power System's Oscillations. Journal of Advanced Defense Science & Technology, 2015; 6(1): 19-31.
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