Classification of overcurrent and inrush current for power system reliability using Slantlet transform and artificial neural network

If a fault occurs in the distribution system, for protectivity and reliability, the fault should be immediately cleared and the system should be re-energized. It is mandatory to ensure that upon re-energization, overcurrent, if encountered, may be due to inrush of current and not for the persistent fault. Hence, it could be impossible to protect the distribution system unless the fault current can be distinguished from the inrush current. Different measures are to be taken to handle the two different events of fault and inrush. Hence, the authors pose the problem as a classification problem, where, the fault current can be deterministically separated from inrush current. The concept of classification, in general, depends on some characteristic features of the events, which are the key components in which the events differ. For automated classification, these distinguishing features of the events are to be judiciously extracted first. This work proposes a novel scheme for automated feature extraction, using Slantlet Transform (ST) and subsequently an automated classification mechanism based on Artificial Neural Network (ANN). ST has been regarded as a contemporary development in the field of multiresolution analysis, which is proposed as an improvement over the discrete wavelet transform (DWT). For each candidate inrush or fault current waveform, suitable features are extracted by employing ST. Then, a successfully trained ANN based classifier, developed utilizing inputs comprising the features extracted from a training set of waveforms, is implemented for a testing set of sample waveforms. The proposed scheme could achieve 100% classification accuracy in the testing phase.