Projects per year
Abstract
We report on the design and construction of a low-cost, accurate optically-interrogated low voltage transducer (LVT) which, when combined with a Rogowski coil or conventional current transformer, forms an optically-interrogated current sensor (OCS). The LVT has been designed to be capable of being mass produced. The OCS requires no power supply and is interrogated remotely. Furthermore, it can be multiplexed in series or in parallel, allowing for multiple current measurements at different sites to be made by the same interrogator. The LVT and OCS have been tested against the accuracy requirements of the IEC 60044-8 standards for electronic current transformers (ECTs) both by direct signal injection, and by primary current injection when interfaced with a conventional PX class CT. The LVT and OCS are shown to meet the stringent 5P protection class requirements and also the additional requirements of the Transient Protection Electronic (TPE) class.
Original language | English |
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Title of host publication | 2016 IEEE International Workshop on Applied Measurements for Power Systems (AMPS) |
Place of Publication | Piscataway |
Publisher | IEEE |
Number of pages | 5 |
ISBN (Print) | 9781509023738 |
DOIs | |
Publication status | Published - 24 Oct 2016 |
Keywords
- optical fiber sensors
- optical fibres
- adaptive optics
- current transformers
- Fiber Bragg grating
- optical current sensor
- power system instrument
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Dive into the research topics of 'Development and testing of optically-interrogated current sensors'. Together they form a unique fingerprint.Projects
- 1 Finished
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Low-Cost Distributed Multi-Parameter Sensing for Energy Networks (InnovateUK Catalyst)
Niewczas, P. (Principal Investigator), Booth, C. (Co-investigator) & Fusiek, G. (Researcher)
EPSRC (Engineering and Physical Sciences Research Council)
1/04/15 → 31/03/16
Project: Research
Datasets
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Low-Cost Distributed Multi-Parameter Sensing for Energy Networks (InnovateUK Catalyst)
Nelson, J. (Creator), University of Strathclyde, 10 Mar 2017
DOI: 10.15129/5a62ac25-3033-438a-a4bd-e67c344ea8ef
Dataset