A neural networks approach to voltage security monitoring and control

Voltage collapse evaluation methods require elaborate computations to determine the existence of feasible load flow solutions in power systems. The time-consuming process of solving the stiff nonlinear system equations in these evaluation methods makes them inefficient for on-line monitoring of voltage collapse. The authors introduce an artificial neural network approach to voltage security monitoring and control. The neural network uses its association mechanism to approximate the complicated mathematical formulation of the voltage collapse phenomenon. The inherent parallel information processing nature of the neural network, which provides the capability of fast computation, enables the neural network approach to meet the rigorous demands of real-time monitoring and control. The IEEE 57 busbar system is used to demonstrate the applicability of the artificial neural network approach to the problem of voltage security monitoring and control in power systems