Asia Pacific University Library catalogue


COMPARATIVE STUDY OF MACHINE LEARNING TECHNIQUES FOR TRANSFORMER FAULT DETECTION / (Record no. 383225)

000 -LEADER
fixed length control field 01732nam a2200193 4500
003 - CONTROL NUMBER IDENTIFIER
control field APU
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200827013130.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200826b2019 xxu||||| |||| 00| 0 eng d
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number PG-22-0058
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name TSERING NAMGAIL (TP039581)
9 (RLIN) 46339
245 10 - TITLE STATEMENT
Title COMPARATIVE STUDY OF MACHINE LEARNING TECHNIQUES FOR TRANSFORMER FAULT DETECTION /
Statement of responsibility, etc TSERING NAMGAIL.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Kuala Lumpur :
Name of publisher, distributor, etc Asia Pacific University,
Date of publication, distribution, etc 2019.
300 ## - PHYSICAL DESCRIPTION
Extent xiii, 146 pages :
Other physical details illustrations ;
Dimensions 30 cm.
502 ## - DISSERTATION NOTE
Dissertation note A 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).<br/>
520 ## - SUMMARY, ETC.
Summary, etc Many 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 &quot;heart&quot; 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 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning
General subdivision Technological innovations.
9 (RLIN) 46340
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Ms. Jacqueline Lukose
Relator term Supervisor.
-- 46345
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type Undergraduate Theses
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Use restrictions Not for loan Collection code Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Copy number Koha item type
Not Withdrawn Available   Not Damaged Restricted access Not for loan Undergraduate Theses APU Library APU Library Reference Collection 26/08/2020   PG-22-0058 00018419 26/08/2020 1 Reference