Application of neural networks in numerical busbar protection systems (NBPS)

During the development of a (conventional) busbar protection algorithm which is able to cope with current signals distorted by current transducer saturation, the question came up, whether it would be possible to use a neural network for preprocessing the data and restoring the distorted signals. A training tool for neural networks and a set of typical distorted and undistorted current signals was selected for a verification of the idea. The test showed that the application of a neural network to this issue is possible in principal and that the signal quality is improved with respect to the needs of a busbar protection system, respectively. The ability of the neural networks to map an increasing number of input signals to reasonable output signals is investigated. Furthermore some studies were made for implementing the trained neural network in hardware