  Accurate short-term prediction of overhead line (OHL) transmission ampacity
can directly affect the efficiency of power system operation and planning. Any
overestimation of the dynamic thermal line rating (DTLR) can lead to lifetime
degradation and failure of OHLs, safety hazards, etc. This paper presents a
secure yet sharp probabilistic prediction model for the hour-ahead forecasting
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 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 to state-of-the-art prediction models
using well-known evaluation metrics.
