EPSRC Reference: |
GR/K82031/01 |
Title: |
CONDITION MONITORING FOR EHV TRANSMISSION LINES USING ARTIFICIAL INTELLIGENCE (ERCOS) |
Principal Investigator: |
Johns, Professor A |
Other Investigators: |
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Researcher Co-Investigators: |
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Project Partners: |
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Department: |
Electronic and Electrical Engineering |
Organisation: |
University of Bath |
Scheme: |
Standard Research (Pre-FEC) |
Starts: |
01 April 1996 |
Ends: |
31 May 1999 |
Value (£): |
163,256
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EPSRC Research Topic Classifications: |
Power Sys Man, Prot & Control |
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EPSRC Industrial Sector Classifications: |
Energy |
No relevance to Underpinning Sectors |
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Related Grants: |
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Panel History: |
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Summary on Grant Application Form |
This research project is a collaborative venture between the Uni Bath and the National Grid Co. which is one of the leading Electric Power Supply authorities all over the world. The main objective of the project is to research into on-line, practicable condition monitoring techniques for overhead transmission line that will reduce costs, improve reliability and quality of power supply globally, by reducing line outages and prolonging conductor life; this would be achieved through giving early warner signs of fault spacer and weak polluted insulators. The fundamental basis of the work will be to research into AI-based techniques, capable of dealing satifactorily with the partical discharge phenomena associated with faulty spacer and weak insulators. Finally, prototype hardware will be developed and its performance evaluated and proved through HV laboratory tests.
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Key Findings |
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Potential use in non-academic contexts |
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Impacts |
Description |
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Summary |
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Date Materialised |
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Sectors submitted by the Researcher |
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Project URL: |
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Further Information: |
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Organisation Website: |
http://www.bath.ac.uk |