Details
| Original language | English |
|---|---|
| Title of host publication | EMC Europe 2018 |
| Subtitle of host publication | 2018 International Symposium on Electromagnetic Compatibility |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 283-287 |
| Number of pages | 5 |
| ISBN (electronic) | 9781467396974 |
| Publication status | Published - 5 Oct 2018 |
| Event | 2018 International Symposium on Electromagnetic Compatibility: EMC Europe 2018 - Amsterdam, Netherlands Duration: 27 Aug 2018 → 30 Aug 2018 |
Publication series
| Name | IEEE International Symposium on Electromagnetic Compatibility |
|---|---|
| Volume | 2018-August |
| ISSN (Print) | 1077-4076 |
| ISSN (electronic) | 2158-1118 |
Abstract
In aviation, an aircraft determines its position by the use of electromagnetic signals from terrestrial antennas. Therefore, these signals should be disturbed as few as possible while propagating towards the aircraft. This can be ensured by a protection zone around the transmitters, in which no buildings are allowed. However, the location of the antennas is becoming increasingly interesting for operators of wind turbines. In order to be able to estimate the risk of wind turbines disturbing the antenna signals, many simulations and measurements have to be carried out. This paper demonstrates how artificial neural networks can be used to reduce the complexity of such an investigation by performing only a part of the simulations and predicting the results of the remaining ones.
Keywords
- artificial neural network, backpropagation, DVOR, prediction, risk analysis, wind turbines
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Condensed Matter Physics
- Engineering(all)
- Electrical and Electronic Engineering
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EMC Europe 2018: 2018 International Symposium on Electromagnetic Compatibility. Institute of Electrical and Electronics Engineers Inc., 2018. p. 283-287 8485040 (IEEE International Symposium on Electromagnetic Compatibility; Vol. 2018-August).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Simplifying Risk Analysis to Determine the Influence of Wind Turbines to the Electric Field of a DVOR Antenna Using Artificial Neural Networks
AU - Burghardt, Felix
AU - Sandmann, Sergei
AU - Garbe, Heyno
N1 - Publisher Copyright: © 2018 IEEE. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/10/5
Y1 - 2018/10/5
N2 - In aviation, an aircraft determines its position by the use of electromagnetic signals from terrestrial antennas. Therefore, these signals should be disturbed as few as possible while propagating towards the aircraft. This can be ensured by a protection zone around the transmitters, in which no buildings are allowed. However, the location of the antennas is becoming increasingly interesting for operators of wind turbines. In order to be able to estimate the risk of wind turbines disturbing the antenna signals, many simulations and measurements have to be carried out. This paper demonstrates how artificial neural networks can be used to reduce the complexity of such an investigation by performing only a part of the simulations and predicting the results of the remaining ones.
AB - In aviation, an aircraft determines its position by the use of electromagnetic signals from terrestrial antennas. Therefore, these signals should be disturbed as few as possible while propagating towards the aircraft. This can be ensured by a protection zone around the transmitters, in which no buildings are allowed. However, the location of the antennas is becoming increasingly interesting for operators of wind turbines. In order to be able to estimate the risk of wind turbines disturbing the antenna signals, many simulations and measurements have to be carried out. This paper demonstrates how artificial neural networks can be used to reduce the complexity of such an investigation by performing only a part of the simulations and predicting the results of the remaining ones.
KW - artificial neural network
KW - backpropagation
KW - DVOR
KW - prediction
KW - risk analysis
KW - wind turbines
UR - http://www.scopus.com/inward/record.url?scp=85056146687&partnerID=8YFLogxK
U2 - 10.1109/EMCEurope.2018.8485040
DO - 10.1109/EMCEurope.2018.8485040
M3 - Conference contribution
AN - SCOPUS:85056146687
T3 - IEEE International Symposium on Electromagnetic Compatibility
SP - 283
EP - 287
BT - EMC Europe 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Symposium on Electromagnetic Compatibility
Y2 - 27 August 2018 through 30 August 2018
ER -