Artificial neural networks based steady state equivalents of power systems

The authors propose a new method for artificial neural networks (ANNs) based steady state equivalents of power systems. Because the multilayer Perceptron network (MPN) is a typical ANN and its training algorithm is quite effective, the authors use this network. When the studied power system is divided into three parts, which are internal system (IS), external system (ES) and boundary system (BS). Some tests show that the method has advantages of high accuracy, powerful suitability and high recognition speed.<>