A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Secure Probabilistic Prediction of Dynamic Thermal Line Rating
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.
Secure Probabilistic Prediction of Dynamic Thermal Line Rating
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.
Secure Probabilistic Prediction of Dynamic Thermal Line Rating
N. Safari (author) / S. M. Mazhari (author) / C. Y. Chung (author) / S. B. Ko (author)
2022
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Dynamic line rating for wind power
| American Institute of Physics | 2016
PMU-based Cable Temperature Monitoring and Thermal Assessment for Dynamic Line Rating
| BASE | 2021
Comparative Probabilistic Initial Bridge Load Rating Model
| Online Contents | 2007
Comparative Probabilistic Initial Bridge Load Rating Model
| British Library Online Contents | 2007
Application of Integrated Instrumentation Systems in Dynamic Line Rating
| British Library Conference Proceedings | 1994