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Partial discharge (PD) is a well-known indicator of the failure of insulators in electrical plant. Operators are pushing toward lower operating cost and higher reliability and this stimulates a demand for a diagnostic system capable of accurately locating PD sources especially in ageing electricity substations. Existing techniques used for PD source localisation can be prohibitively expensive. In this paper, a cost-effective radio fingerprinting technique is proposed. This technique uses the Received Signal Strength (RSS) extracted from PD measurements gathered using RF sensors. The proposed technique models the complex spatial characteristics of the radio environment, and uses this model for accurate PD localisation. Two models were developed and compared: k-nearest neighbour and a feed-forward neural network which uses regression as a form of function approximation. The results demonstrate that the neural network produced superior performance as a result of its robustness against noise.
|Title of host publication||2015 Loughborough Antennas and Propagation Conference, LAPC 2015|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 24 Dec 2015|
|Event||Loughborough Antennas & Propagation Conference - Loughborough, United Kingdom|
Duration: 2 Nov 2015 → 3 Nov 2015
https://communities.theiet.org/communities/events/item/21/39/9025 (Link to Conference Information)
|Conference||Loughborough Antennas & Propagation Conference|
|Abbreviated title||LAPC 2015|
|Period||2/11/15 → 3/11/15|
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- 1 Finished
Scalable Non-invasive Radiometric Wireless Sensor Network for Partial Discharge Monitoring in the Future Smart Grid
Glover, I., Atkinson, R., Soraghan, J., Judd, M. & Vieira, M. D. F. Q.
1/04/13 → 1/04/18