Prediction of overhead transmission line ampacity based on extreme learning machine

Abstract

Abstract Caused by the fluctuation of weather conditions, the transmission line ampacity varies with time. For power system operators, the accurate forecast results of overhead transmission lines ampacity are very important and helpful in making planning and control decisions. To assist the system operators in making better use of the transfer capability of transmission lines, it is urgent to improve the ampacity forecast results. In this paper, a novel method based on the extreme learning machine (ELM) is proposed to predict the ampacity. Taking the historical weather and ampacity data as the input data, an ELM-based method can predict the ampacity rapidly and accurately. Numerical simulations based on the recorded actual weather data around a transmission line validate the efficiency of the ELM-based ampacity forecast method.