REVIEW OF THE LITERATURE

Priyanka M. Kothoke et al(2017) study was done on Artificial neural network, Partial Discharge (PD) patterns are an important tool for the diagnosis of High Voltage (HV) insulation systems. Human experts can discover possible insulation defects in various representations of the PD data. One of the most widely used representations is phase-resolved PD (PRPD) patterns. In order to ensure reliable operation of HV equipment, it is vital to relate the observable statistical characteristics of PDs to the properties of the defect and ultimately to determine the type of the defect. In this work, they had detected PD source using Artificial Neural Network (ANN) tool in Matlab software. From statistical parameters, the PD source cannot be concluded accurately so it needs to be applied to other classification methods such as neural network, Fuzzy logic etc. as a pre-processing parameters for getting accurate PD source. The ANN on execution produce the dual results for data2 and data2011 indicating the need for more input samples in training data set. They had observed that PD source can be classified using the phase resolved pattern as the input. It is also possible to detect multiple sources of breakdown those exist in insulation along with determining exact percentage of that source ANN. The use of back-propagation algorithm is better choice for training ANN as it performs optimization of the weights by first propagating the input signal forward, then propagating the calculated error backwards to make adjustment to the initial weights[10][11][12][36].