Abstract
The ampacity of overhead transmission lines play a key role in power system planning and control. Due to the volatility of the meteorological elements, the ampacity of an overhead line is timevarying. In order to fully utilize the transfer capability of overhead transmission lines, it is necessary to provide system operators with accurate probabilistic prediction results of the ampacity. In this paper, a method based on the Quantile Regression Neural Network (QRNN) is proposed to improve the performance of the probabilistic prediction of the ampacity. The QRNN-based method uses a nonlinear model to comprehensively model the impacts of historical meteorological data and historical ampacity data on the ampacity at predictive time period. Numerical simulations based on the actual meteorological data around an overhead line verify the effectiveness of the proposed method.
Highlights
Nowadays, the rapid development of renewable energy power generation technology and constant increase of electricity consumption bring great challenges to the transfer capability of the power grid [1]
The ampacity of an overhead line is calculated under conservative weather assumptions, which is called static thermal rating (STR) [3]
dynamic thermal rating (DTR) is an effective tool to enhance the absorption of renewable energy and exploit the transfer capability of existing power grid [5]
Summary
The rapid development of renewable energy power generation technology and constant increase of electricity consumption bring great challenges to the transfer capability of the power grid [1]. In order to deal with these challenges, constructing new transmission lines may be a good choice, but it is timeconsuming and uneconomical [2]. For this reason, system operators should find effective strategies to increase the transmission capacity of existing power grid. The ampacity of an overhead line is calculated under conservative weather assumptions, which is called static thermal rating (STR) [3]. Since conservative weather conditions are rare, the use of STR often causes under-utilization of the transmission capacity of an overhead line. DTR is an effective tool to enhance the absorption of renewable energy and exploit the transfer capability of existing power grid [5]
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