| EPSRC Reference: |
GR/J50590/01 |
| Title: |
LEARNING SYSTEMS FOR POWER TRANSMISSION NETWORKS (ERCOS) |
| Principal Investigator: |
Warwick, Professor K |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Cybernetics |
| Organisation: |
University of Reading |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
01 October 1993 |
Ends: |
30 September 1996 |
Value (£): |
123,754
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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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| Panel History: |
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Summary on Grant Application Form |
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Learning systems have already been applied in a number of ways to various problems in the power systems domain. The main investigator on this proposal has been concerned with an on-going project funded by the National Grid over the last 4 years, the project being concerned with neural computing systems for alarm analysis on the Power Transmission Network.This proposal is aimed at research which looks at more fundamental issues in terms of the type of learning systems of use within the power systems domain. It is therefore intended to investigate the use particularly of hybrid learning systems involving Kohonen networks; back propagation networks and genetic algorithms; the most fruitful approach being taken forward for further study.The research will also look into potential application areas within power systems domain although work will concentrate on the load forecasting problem. The learning systems will be used to represent such as temperature-load variations and to classify load curve shapes.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.rdg.ac.uk |