Muhammad Ansari

About

Muhammad Abdul Hafeez Ansari received his B.Eng. degree in electrical engineering from NED University of Engineering & Technology, Karachi, Pakistan in 2009. He completed his M.Sc. degree from King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia in 2014. Currently, he is working in Saudi Electricity Company (SEC) as an Electrical Engineer in Engineering & Design Department. His employment experience includes being a Design Engineer with Alstom Grid, Technical trainings with Siemens and Eni.

His research interests include power system controls and operations, power system analysis, power system protection, smart grids, vehicle-to-grid and optimization techniques.

Employment

Employment history is unavailable.

Education

• The University of Queensland PhD
  2017 - 2021
• King Fahd University of Petroleum and Minerals Master of Science
  2011 - 2014
• NED University of Engineering & Technology B.E.
  2004 - 2008

Projects & Funding

Projects & funding information is unavailable.

Publications (8)

• Condition monitoring of transformers using fibre optics sensors
  2021 DOI: 10.14264/8e46ff2
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• Investigation of Distributed Moisture and Temperature Measurements in Transformers Using Fiber Optics Sensors
  IEEE Transactions on Power Delivery 2019 DOI: 10.1109/tpwrd.2019.2924271
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• Performance Evaluation of Fiber Optic Sensors to Measure Moisture in Transformer Insulation
  2019 IEEE Power & Energy Society General Meeting (PESGM) 2019 DOI: 10.1109/pesgm40551.2019.8973552
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• Feasibility of Optical Fibre Sensors for Moisture Diagnosis in Transformer Insulation
  2018 Condition Monitoring and Diagnosis (CMD) 2018 DOI: 10.1109/cmd.2018.8535766
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• Preliminary assessment of using optical fibre sensors to measure moisture in transformers
  2017 DOI: 10.1109/AUPEC.2017.8282498
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• Stochastic Dynamic Economic Dispatch for Grids with Significant Wind Using Mixed Gaussian Distribution
  Arabian Journal for Science and Engineering 2016 DOI: 10.1007/s13369-015-1696-8
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• Coordinated Bidding of Ancillary Services for Vehicle-to-Grid Using Fuzzy Optimization
  IEEE Trans. Smart Grid 2015 DOI: 10.1109/TSG.2014.2341625
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• Optimal charging strategies for unidirectional vehicle-to-grid using fuzzy uncertainties
  2014 IEEE PES T&D Conference and Exposition 2014 DOI: 10.1109/tdc.2014.6863314
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