Secure Probabilistic Prediction of Dynamic Thermal Line Rating

N. Safari, S. M. Mazhari, C. Y. Chung, S. B. Ko

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)

Abstract

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 life-time 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.

Original languageEnglish
Pages (from-to)378-387
Number of pages10
JournalJournal of Modern Power Systems and Clean Energy
Volume10
Issue number2
DOIs
Publication statusPublished - 1 Mar 2022
Externally publishedYes

Keywords

  • Deep neural network
  • dynamic thermal line rating
  • overhead line
  • prediction
  • recurrent neural network

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology

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