Wah Hoon Siew, SMIEEE; MIEE; C.Eng

Prof

Electronic And Electrical Engineering

https://orcid.org/0000-0003-4000-6856

Phone01415482890
Emailwh.siew@strath.ac.uk

United Kingdom

Accepting PhD Students

Willing to speak to media

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Personal profile

Research Interests

Electromagnetic Compatibility with emphasis on transients emissions and immunity arising from switching; lightning; electromagnetic pulse; electrostatic discharges; and system harmonics.
Lightning Protection for aircraft and grounded structures, including wind turbines and large solar photovoltaic systems.
Cable diagnostics including partial discharge and cable fault location.
Wireless Sensor Network
Photovoltaic

Personal Statement

I am a professional engineer (registered in the UK) with more than 30 years of teaching, research and consultancy experience in Electronic & Electrical Engineering. During this period, I have worked with UK and overseas organisations on various projects related to lightning protection; large systems electromagnetic compatibility; and electrical plant monitoring and diagnostics. I am also very active in the activities of CIGRE and was the Convener of C4.208 and C4.30

Teaching Interests

I have taught electromagnetics at level 3 of a 4 level degree programme.

I have also taught power systems electromagnetic compatibility at level 5 of a 5-year degree programme and at MSc level.

Education/Academic qualification

Master of Business Administration, UNIVERSITY OF STRATHCLYDE

Doctor of Philosophy, UNIVERSITY OF STRATHCLYDE

Bachelor of Science in Engineering, UNIVERSITY OF STRATHCLYDE

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Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Projects 2009 2020

1 Not started
2 Active
13 Finished

Lightning protection of wind turbines

Siew, W. H.

1/01/16 → 31/12/20

Project: Research

Partial discharge in HVDC cables

Siew, W. H.

Scottish Power UK PLC

1/11/15 → 31/10/20

Project: Research - Studentship › Research Studentship

Research Output 2001 2019

51 Paper
47 Article
31 Conference contribution book
13 Poster
10 More

A convolutional neural network based deep learning methodology for recognition of partial discharge patterns from high voltage cables

Peng, X., Yang, F., Wang, G., Wu, Y., Lee, L., Li, Z., Ahmed Bhatti, A., Zhou, C., Hepburn, D. M., Reid, A. J., Judd, M. & Siew, W. H., 1 Aug 2019, In : IEEE Transactions on Power Delivery. 34, 4, p. 1460-1469 10 p.

Research output: Contribution to journal › Article

Open Access

File

Partial discharges

Cables

Neural networks

Electric potential

Pattern recognition