Novel algorithms of identifying types of Partial Discharges using electrical and non-contact methods / Mohammad Shukri Hapeez

Hapeez, Mohammad Shukri (2015) Novel algorithms of identifying types of Partial Discharges using electrical and non-contact methods / Mohammad Shukri Hapeez. In: The Doctoral Research Abstracts. IPSis Biannual Publication, 7 (7). Institute of Graduate Studies, UiTM, Shah Alam.

[img]
Preview
Text
ABS_MOHAMMAD SHUKRI HAPEEZ TDRA VOL 7 IGS 15.pdf

Download (1MB) | Preview

Abstract

Identifying types of Partial Discharge (PD) is very crucial in order to prepare and provide solutions before complete breakdown occurs. Before PD can be identified, detection of the PD is initially required and it can be made by ultrasonic and electrical methods. By using ultrasonic methods, the obtained PD data is conventionally identified using Neural Network (NN) models where it has several disadvantages. It can be said that NN suffer from several drawbacks such as black-box behaviour, inconsistencies in producing results, initialization issues and complex parameter setup. Similarly, electrical method, where PD is identified using PD circuit detectors, sensors and amplification circuits also presents drawbacks such as inconvenient system configuration as well as complex set up. Two novel algorithms are presented in this wok namely ‘Simple Partial Discharge Identifier (SPDI) and Fundamental Partial Discharge Identifier (FPDI) were developed to overcome the PD identification shortcoming…

Item Type: Book Section
Creators:
CreatorsEmail
Hapeez, Mohammad ShukriUNSPECIFIED
Subjects: L Education > LB Theory and practice of education > Higher Education > Dissertations, Academic. Preparation of theses > Malaysia
Q Science > QA Mathematics > Instruments and machines > Electronic computers. Computer science > Algorithms
Q Science > QA Mathematics > Instruments and machines > Electronic computers. Computer science > Algorithms
Divisions: Institut Pengajian Siswazah (IPSis) : Institute of Graduate Studies (IGS)
Series Name: IPSis Biannual Publication
Volume: 7
Number: 7
Item ID: 19233
Uncontrolled Keywords: Abstract; Abstract of thesis; Newsletter; Research information; Doctoral graduates; IPSis; IGS; UiTM; Novel algorithms
Last Modified: 12 Jun 2018 07:04
Depositing User: Staf Pendigitalan 5
URI: http://ir.uitm.edu.my/id/eprint/19233

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year