Artificial Neural Networks proved to be capable of finding internal representations of interdependencies within row data not explicitly given or even known by human experts. This typical characteristic together with the simplicity of building and training Neural Networks and their very short prediction delay time encourages researchers to apply neural networks to different tasks for power systems, such as load forecasting, fault diagnosis, alarm handling and state estimation. By combining more than one of these neural networks, better performance can be achieved with these new architectures.
