Application of direct collocation method in short-term line ampacity calculation

Pavlinić, Alen; Komen, Vitomir; Uzelac, Marin

doi:10.1016/j.epsr.2017.10.018

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…Therefore, the current fluctuations and the resulting variations in the OHL temperature can be considered negligible, provided that the system does not require any abrupt or temporary switching [21]. Studies reveal that the maximum time required to reach the steady state due to a step change in current is approximately 30 minutes [35]. Based on these points, the transients in DTLR can be ignored, and DTLR can be estimated for every hour.…”

Section: A Dtlr Calculationmentioning

confidence: 99%

Secure Probabilistic Prediction of Dynamic Thermal Line Rating

Safari¹,

Mazhari

Chung

et al. 2022

Journal of Modern Power Systems and Clean Energy

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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 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.

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Section: A Dtlr Calculationmentioning

confidence: 99%