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Ann-based voltage collapse proximity prediction
S. Chauhan,
Published in
Volume: 17
Issue: 2
Pages: 132 - 136
In the present work, an artificial neural network technique is applied to determine the distance from system operating point to voltage collapse. The distance is measured in terms of the existing loading/generating scenario. The critical/bifurcation point is approached in steps while moving along the nose curve. Generation resheduling is done at each step to ensure economical operation of the system. The homotopy continuation-based Newton Raphson load flow method takes care of numerical instabilities associated with the singularity of Jacobian while approaching critical point. Proximity to critical loading of present operating point is a complex function of operating point attributes and loading/generating pattern followed to approach voltage collapse. The high adaptation capabilities of artificial neural networks make it feasible to synthesize the function that maps system state attributes (bus power injections and tap settings of transformers) to distance from, voltage collapse for uniform dispatch strategy. A three-layer (one hidden layer) feed-forward artificial neural network (ANN) is trained to predict the nearness of current operating point to voltage collapse in terms of existing loading condition. It has been demonstrated here that the predicted values are well in tune with their actual ones. The technique is tested on a Ward-Hale 6-bus system and an IEEE 14-bus system.
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