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Velayati, M., ghaffarpour, R. (2015). Enhance the Passive Defense of Power System Networks Using Prediction Damping, Type and Location of Power System's Oscillations. Electronic and Cyber Defense, 6(1), 19-31.
mohammad Hossein Velayati; Reza ghaffarpour. "Enhance the Passive Defense of Power System Networks Using Prediction Damping, Type and Location of Power System's Oscillations". Electronic and Cyber Defense, 6, 1, 2015, 19-31.
Velayati, M., ghaffarpour, R. (2015). 'Enhance the Passive Defense of Power System Networks Using Prediction Damping, Type and Location of Power System's Oscillations', Electronic and Cyber Defense, 6(1), pp. 19-31.
Velayati, M., ghaffarpour, R. Enhance the Passive Defense of Power System Networks Using Prediction Damping, Type and Location of Power System's Oscillations. Electronic and Cyber Defense, 2015; 6(1): 19-31.

Enhance the Passive Defense of Power System Networks Using Prediction Damping, Type and Location of Power System's Oscillations

علوم و فناوریهای پدافند نوین
Article 3, Volume 6, Issue 1 - Serial Number 19, June 2015, Pages 19-31    PDF (576.7 K)
Authors
mohammad Hossein Velayati; Reza ghaffarpour*
Receive Date: 30 January 2019,  Revise Date: 26 July 2026,  Accept Date: 30 January 2019 
Abstract
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.
Keywords
Critical Eigenvalue; Inter-Area Oscillations; Local Oscillations; Participation Factor of Generator; Pδ Index; neural network
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