Overhead conductor thermal dynamics identification by using Echo State Networks

Dramatic reductions in sensor, computing and communications costs, coupled with significant performance enhancements has increased the possibility of realizing widely and massively distributed power line sensor networks (PLSNs) to monitor utility asset status for enhancing line reliability and utili...

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Bibliographic Details
Published in2009 International Joint Conference on Neural Networks pp. 3436 - 3443
Main Authors Yi Yang, Harley, R.G., Divan, D., Habetler, T.G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2009
Subjects
Computer networks
Conductors
Costs
Distributed computing
Dynamic thermal rating
dynamical system identification
echo state network
Monitoring
overhead conductors
power grid monitoring
Predictive models
sensor networks
Telecommunication network reliability
Temperature
Thermal conductivity
Weather forecasting
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ISBN142443548X
9781424435487
ISSN2161-4393
DOI10.1109/IJCNN.2009.5179006

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Summary:Dramatic reductions in sensor, computing and communications costs, coupled with significant performance enhancements has increased the possibility of realizing widely and massively distributed power line sensor networks (PLSNs) to monitor utility asset status for enhancing line reliability and utilization. One of the important applications of such a PLSN is to evaluate the overhead power line dynamic current capacity down to dasiaper spanpsila level of granularity. Due to the inherent non-linearity of overhead conductor thermal behavior, it is usually quite complex to directly calculate the conductor temperature. Therefore the prediction for the conductor dynamic thermal behavior becomes difficult. In this work, an echo state network (ESN) is proposed to identify the overhead conductor thermal dynamics in real-time. The well trained ESN model is used to predict the dynamic thermal behavior, and thus to evaluate the dynamic current capacity of the line under current ambient weather conditions. This paper addresses the design and implementation issues for such an ESN for this specific application. Simulation results reveal that the ESN model is very effective to predict the conductor temperature and to identify the conductor thermal dynamics subject to wide variations in line current and ambient weather conditions.
ISBN:142443548X
9781424435487
ISSN:2161-4393
DOI:10.1109/IJCNN.2009.5179006