Thermal modeling and real time overload capacity prediction of overhead power lines

Abstract

A widely and massively distributed power line sensor network (PLSN) has been proposed to monitor such a utility asset's status for enhancing line reliability and maximizing the existing power grid utilization. One of its important applications is to monitor and evaluate the real time dynamic overload current capacity of overhead power lines down to ‘per span’ level of granularity. How to predict the conductor temperature ahead of time subject to various conductor overload conditions is the most critical and challenging task to evaluate the line dynamic thermal rating. This paper proposes an Echo State Network (ESN) to adaptively identify the nonlinear overhead conductor thermal dynamics under different weather conditions, and to predict the conductor temperature. This method requires only temperatures and line current as inputs and its simplified calculation makes it an attractive and cost effective solution to real-time implementation. Furthermore, by continuously providing accurate real-time line thermal condition information, this method can assist in utilizing the power lines more effectively.