Identification and Analysis of Partial Discharge Origin using Xgboost Algorithm
Keywords:
Partial discharge, Xgboost, Google Colab, PythonAbstract
Partial Discharge (PD) patterns serve as a valuable diagnostic tool for assessing the condition of High Voltage (HV) insulation systems. Human experts are capable of identifying potential insulation flaws in different representations of PD data. Phase-resolved partial discharge (PRPD) Patterns are frequently employed as a means of conveying information. In order to determine the type of defect, it is necessary to establish a correlation between the statistical properties of partial discharges (PDs) and the characteristics of the defect in high-voltage (HV) equipment reliability. For instance, an empty space, a flat surface, or a luminous halo. Partial discharge is observable on the Graphical User Interface (GUI) of the model, which was developed using the Xgboost Method in Python on Google Colab.
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