Neural Networks technique applicability for voltage stability of power systems

This paper demonstrates the use of the Artificial Neural Networks for voltage stability assessment of a sample power system. The neural network is trained with data containing a variety of loading factors by using scaling factor. The five-bus sample system is considered for application to neural network. Proportionally increase of total load demand is recorded with bus voltages. Comparison of actual value of different loading and corresponding voltage collapse index is done. Another comparison for voltage at test bus is done for actual solved by conventional methods results and test voltage values for corresponding obtained index. The multi-layer feed forward back propagation (L-M) method is used. With the input/output being known already, supervised learning is employed for training the network. The structure of the proposed neural network is also presented. Test results based on a simple power system are presented to illustrate the suitability of the proposed method.