000			01732nam a2200193 4500
999			_c383225 _d383225
003			APU
005			20200827013130.0
008			200826b2019 xxu||||| |||| 00| 0 eng d
050			_aPG-22-0058
100	0		_aTSERING NAMGAIL (TP039581) _946339
245	1	0	_aCOMPARATIVE STUDY OF MACHINE LEARNING TECHNIQUES FOR TRANSFORMER FAULT DETECTION / _cTSERING NAMGAIL.
260			_aKuala Lumpur : _bAsia Pacific University, _c2019.
300			_axiii, 146 pages : _billustrations ; _c30 cm.
502			_aA project submitted in partial fulfillment of the requirement of Asia Pacific University of Technology and Innovation for the Degree of B.Eng (Hons) in Electrical and Electronic Engineering (UC4F1811EEE).
520			_aMany large structures, due to their large estimated load, are usually equipped with their own transformers due to the higher voltage supplied from the utility. These transformers form the "heart" of the entire structure when the transformer is out of order, essentially the entire structure can no longer function. Due to this, maintenance of transformers is given pre-eminence in any maintenance planning. However, conventionally, these works are carried out based on a fixed time and mostly the detection system is done manually. This is not an accurate measure of maintenance needs as these may vary with the usage of the transformer and the environment it is operated in. This could lead to wastage of resources. In this project, the performance of three machine learning (ML) techniques are compared in detecting transformers fault.
650		0	_aMachine learning _x Technological innovations. _946340
700	0		_aMs. Jacqueline Lukose _eSupervisor. _946345
942			_2lcc _cUndergraduate Theses