A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Adaptive control using neural networks and approximate models
IEEE Transactions on Neural Networks
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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 'per span' 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 thi s 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.