Probabilistic forecasting for the ampacity of overhead transmission lines using quantile regression method

Abstract

Dynamic thermal rating (DTR) is an effective technique to improve the ampacity of overhead power transmission lines based on the monitoring of real-time environmental conditions. With DTR technology being introduced, the thermal rating of transmission line is no longer constant but time-dependent. To fully utilize the transfer capability of transmission lines, it is necessary to provide operators with probabilistic forecasting information of DTR to make dispatch decision. In this paper, a new probabilistic forecast method based on the quantile regression analysis theory is proposed to realize the probabilistic prediction of DTR. The weather parameters which have significant influence on the real-time ampacity and historical line ratings are embedded in the quantile regression prediction model. The proposed method can not only give the point prediction and interval prediction result, but also can provide the forecasting results of probability distribution function. Finally, the validity of the proposed method is illustrated by case studies.