COMPARATIVE STUDY OF MACHINE LEARNING TECHNIQUES FOR TRANSFORMER FAULT DETECTION / TSERING NAMGAIL.
Material type:
TextPublication details: Kuala Lumpur : Asia Pacific University, 2019Description: xiii, 146 pages : illustrations ; 30 cmSubject(s): Machine learning -- Technological innovationsLOC classification: PG-22-0058Dissertation 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).
Summary: 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.
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
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APU Library Reference Collection | Undergraduate Theses | PG-22-0058 (Browse shelf (Opens below)) | 1 | Not for loan (Restricted access) | 00018419 |
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).
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.
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