Accurate short-term prediction of overhead line(OHL)transmission ampacity can directly affect the efficiency of power system operation and planning.Any overcstiniation of the dynamic thermal line rating(DTLR)can lead ...Accurate short-term prediction of overhead line(OHL)transmission ampacity can directly affect the efficiency of power system operation and planning.Any overcstiniation of the dynamic thermal line rating(DTLR)can lead to the lifetime degradation and failure of OHLs,safety hazards,etc.This paper presents a secure yet sharp probabilistic model for the hour-ahead prediction of the DTLR.The security of the proposed DTLR limits the frequency of DTLR prediction exceeding the actual DTLR.The model is based on an augmented deep learning architecture that makes use of a wide range of predictors,including historical climatology data and latent variables obtained during DTLR calculation.Furthermore,by introducing a customized cost function,the deep neural network is trained to consider the DTLR security based on the required probability of exceedance while minimizing the deviations of the predicted DTLRs from the actual values.The proposed probabilistic DTLR is developed and verified using recorded experimental data.The simulation results validate the superiority of the proposed DTLR compared with the state-of-the-art prediction models using well-known evaluation metrics.展开更多
文摘Accurate short-term prediction of overhead line(OHL)transmission ampacity can directly affect the efficiency of power system operation and planning.Any overcstiniation of the dynamic thermal line rating(DTLR)can lead to the lifetime degradation and failure of OHLs,safety hazards,etc.This paper presents a secure yet sharp probabilistic model for the hour-ahead prediction of the DTLR.The security of the proposed DTLR limits the frequency of DTLR prediction exceeding the actual DTLR.The model is based on an augmented deep learning architecture that makes use of a wide range of predictors,including historical climatology data and latent variables obtained during DTLR calculation.Furthermore,by introducing a customized cost function,the deep neural network is trained to consider the DTLR security based on the required probability of exceedance while minimizing the deviations of the predicted DTLRs from the actual values.The proposed probabilistic DTLR is developed and verified using recorded experimental data.The simulation results validate the superiority of the proposed DTLR compared with the state-of-the-art prediction models using well-known evaluation metrics.
