COMPARATIVE STUDY OF MACHINE LEARNING TECHNIQUES FOR TRANSFORMER FAULT DETECTION / (Record no. 383225)
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000 -LEADER | |
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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 "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 - 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 |
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 |
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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 |