Faculty Publications
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Item Some aspects of location identification of PD source using AE signals by an iterative method(2012) Punekar, G.S.; Jadhav, P.; Bhavani, S.T.; Nagamani, H.N.An acoustic Partial Discharge (PD) location problem modeled mathematically, gives system of sphere equations, which are non-linear. These equations are formed with known acoustic emission (AE) sensors co-ordinates, with PD locations co-ordinates as unknowns. Newton's method is implemented to locate the PD activity using the AE signals. This is an iterative method and the convergence depends on the initial guess. Different aspects such as initial guess, location of sensors (sensor co-ordinates) and tank orientation in space are studied in this paper by numerical experiments on the algorithm implemented using the experimental data published (available) in a literature. The published data considered for the study here uses 8 number of sensors (4 on the front and 4 on the back wall of the transformer tank; laboratory model). The method of locating acoustic emission partial discharge (AEPD) requires at most 4 sensors (three to identify the coordinates of the location and one for arrival time of AE signal). Hence, results of such 70 combinations (i.e. 8C4) are studied using the algorithm implemented. The numerical test runs indicate that some combinations either do not lead to convergence or yield results with high errors. At least such 10 combinations (out of 70) are identified and analyzed. © 2012 IEEE.Item Improvements in AEPD location identification by removing outliers and post processing(Institute of Electrical and Electronics Engineers Inc., 2016) Antony, D.; Punekar, G.S.The mathematical model of an Acoustic Emission Partial Discharge (AEPD) system is solved in the literature using Newton's method with redundant number of sensors (more than 4; eight in this case). The system for numerical experiments consists of eight sensors. The algorithm is implemented using three different initial guesses. For the calculated PD source coordinates, histograms are plotted. After finding the mean and standard deviation, coordinate values which are lying outside different fractions of sigma are removed. The average of remaining set is calculated and it is found that, the accuracy of location identification can be greatly improved. © 2015 IEEE.