An Intelligent Decision Tree Framework For Power System Fault Classification And Localization
Keywords:
Decision tree, Conventional machine learning, phase faults, AI based systemsAbstract
This paper is majorly directed towards exploring the newly emerging idea in the field of machine learning, which is termed as decision tree (DT). In this thesis, we have discussed about how they are being designed using mathematical formulae, and incorporate the basic principle of the classification of the input data. The DT is quite simple to design when compared to other available choices. They are easy to train, and give efficient performance for practical purposes. Since the DT is used in various diverse applications, they are also tweaked to make them usable for a particular operation by using modified DT, or Kernels. The DT are now being in usage for various industrial purposes too and are doing better than the conventional machine learning, AI based systems. As discussed a power system can be encountered with the faults named as AG, BG, CG, AB, BC, CA, ABG, BCG, CAG, ABC and ABCG phase fault. Hence it should be equipped suitably to tackle these faults in the most appropriate manner. Tackling these faults means to classify and finding out its location and graveness. In past on occurrence of fault, current is measured from either ends of the line which were then used in the algorithm to classify them. It could be the raise in magnitude of current magnitude as the data in algorithm. Since each fault react differently i.e. different characteristics of current when it occurs
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