Header Information

NPRP 5 - 044 - 2 - 016
NPRP 05
Qatar University
Award Closed
15 Nov 2012
Prof. Khaled Shaban
3 Year(s)
14 Aug 2016
New
Asset Management for Power Transformers in Smart Grid

Project Summary
Smart gird technology includes the application of automation and digital controls to power systems in general and to distribution systems in particular. Any smart system should increase the use of digital control and information technology with real-time availability, apply dynamic optimization related to grid operability, and increase the use of distribution automation and communications. Substation transformers represent extensive investment in any power delivery system. In many cases, unexpected transformer outages can be catastrophic due to load curtailment of large load sectors. Unexpected transformer outages cause both direct and indirect costs to be incurred by industrial, commercial, and residential sectors. Reduction of transformer’s cost of interruption is a very significant reason to grid reconfiguration. The main purpose of this research project is to develop a reliable health index for substation transformers. The proposed transformer health index will provide real-time assessment of the transformer health condition which will be useful for maintenance, dynamic distribution feeders’ reconfiguration, ensuring optimum transformer performance, increased efficiency and also increased expected life time. The unique design of this health unit will allow integrating the transformer health condition along with the distribution system reconfiguration and operation. This will ensure reliable and efficient operation which is in line with smart grid objectives.
The proposed research will yield substantial benefits to Qatar in several key areas. Foremost, this effort will establish a world-class technology R&D program that will deliver fundamental advances in the high-voltage power systems area. As this is a vital field, this work will have high relevance to power and energy sector. This effort will also complement ongoing QNRF projects on smart grid, and increase the potential for collaborations within Qatar. The project will also develop critical human talent in Qatar by training highly-qualified researchers with advanced expertise in new theories, practices, and tools. As there is a growing need for such personnel, this will accelerate the transition to a knowledge-based economywhich Qatar has as a strategic objective. The work will also build bridges between Qatar University and Canadian and American institutions. Hence this gateway will provide invaluable new opportunities for local students to progress to graduate studies at top-tier universities. This project will also yield many economic benefits for industry in Qatar. Most notably, the findings will allow Qatar main utility, Qatar General Electricity and Water Corporation, Kahramaa, to optimize and best manage its power network assets. Kahrama will be able to benefit from the solution and make educated decisions about various power grid problems.
Power transformers; asset management; condition monitoring and diagnostics; smart grid; performance optimization
Applied research
2. Engineering and Technology
2.11 Other Engineering and Technology
Other Engineering and Technologies
Yes
No

Institution
Qatar University
Qatar
Submitting Institution
University of Waterloo
Canada
Collaborative Institution
American University of Sharjah
United Arab Emirates
Collaborative Institution

Personnel
Lead PI
Prof. Khaled Shaban
Qatar University
Co-Lead PI
Prof. Khaled Shaban
Qatar University
PI
Dr. Magdy Salama
University of Waterloo
PI
Dr. Ayman El-Hag
University of Waterloo

Outputs/Outcomes
Conference Paper
Accurate partial discharge classification from acoustic emission signals
Mustafa Harbaji, Ayman El-Hag, Khaled Shaban
DOI:10.1109/EPECS.2013.6713000
Conference Paper
Cost Effective Assessment of Transformers Using Machine Learning Approach
Kamel Benhmed, Khaled Bashir Shaban, Ayman El-Hag
DOI:10.1109/ISGT-Asia.2014.6873812
Conference Paper
Histogram-based thresholding in discrete wavelet transform for partial discharge signal denoising
Ramy Hussein, Khaled Bashir Shaban, Ayman H. El-Hag
DOI:10.1109/ICCSPA.2015.7081289
Journal Paper
A Novel Bias Detection Technique for Partial Discharge Localization in Oil Insulation System
Wasim M. F. Al-Masri; Mamoun F. Abdel-Hafez ; Ayman H. El-Hag
ISSN:00189456
Journal Paper
Wavelet Transform With Histogram-Based Threshold Estimation for Online Partial Discharge Signal Denoising
Ramy Hussein; Khaled Bashir Shaban ; Ayman H. El-Hag
ISSN:00189456
Journal Paper
Classification of common partial discharge types in oil-paper insulation system using acoustic signals
Mustafa Harbaji; Khaled Shaban ; Ayman El-Hag
ISSN:10709878
Conference Paper
Acoustic partial discharge signal denoising using power spectral subtraction
Ramy Hussein ; Khaled Bashir Shaban ; Ayman H. El-Hag
DOI:10.1109/CEIDP.2015.7352003
Journal Paper
Prediction of Transformer Furan Levels
Khaled Shaban; Ayman El-Hag ; Kamel Benhamed
ISSN:08858977
Conference Paper
Transformer Health Index estimation using Orthogonal Wavelet Network
Mohamed Ahmed, Mohamed Elkhatib, Magdy Salama, Khaled Bashir Shaban
DOI:10.1109/EPEC.2015.7379937
Conference Paper
Design of hilbert fractal antenna for partial discharge detection and classification
Abdalmonam H. Zahed, Mustafa M. Harbaji, Shahed A. Habboub, Maryam A. AlMajidi, Mustafa J. Assaf, Ayman H. El-Hag, Nasser N. Qaddoumi
DOI:10.1109/EPECS.2015.7368499
Conference Paper
Comparison between Different De-noising Techniques for Acoustic Partial Discharge Signals
Mohsen Bakhshi Ashtiani, S. Mohammad Shahrtash, Ayman El-Hag, and Khaled Shaban
DOI:10.13140/RG.2.1.3062.4246
Journal Paper
Denoising of acoustic partial discharge signals corrupted with random noise
Ramy Hussein, Khaled BashirShaban, Ayman H. El-Hag
ISSN:10709878
Journal Paper
Robust Feature Extraction and Classification of Acoustic Partial Discharge Signals Corrupted with Noise
Ramy Hussein, Khaled Bashir Shaban, and Ayman H. El-Hag
ISSN:15579662
Conference Paper
Robust detection of acoustic partial discharge signals in noisy environments
Ramy Hussein-author-first, Khaled Bashir Shaban-author-additional, Ayman H. El-Hag-author-additional
DOI:10.1109/I2MTC.2017.7969684
Journal Paper
Robust Feature Extraction and Classification of Acoustic Partial Discharge Signals Corrupted With Noise
Ramy Hussein, Khaled Bashir Shaban, Ayman H. El-Hag
ISSN:00189456
Journal Paper
Comparison of different fourth order Hilbert fractal antennas for partial discharge measurement
Abd Almonam Zahed, Ayman H. El-Hag, Nasser Qaddoumi, Ramy Hussein, Khaled B. Shaban
ISSN:10709878