Modelling smart grid technologies in optimisation problems for electricity grids
The decarbonisation of the electricity grid is expected to create new electricity flows. As a
result, it may require that network planners make a significant amount of investments in the …
result, it may require that network planners make a significant amount of investments in the …
Deep learning-based cyber resilient dynamic line rating forecasting
Increased integration of renewable energy resources into the grid may create new difficulties
for ensuring a sustainable power grid which drives electric utilities to use a number of cost …
for ensuring a sustainable power grid which drives electric utilities to use a number of cost …
Optimizing Grid With Dynamic Line Rating of Conductors: A Comprehensive Review
As the world is pledged towards net zero carbon by 2050, the need for clean and efficient
energy transitions is more critical than ever. Optimizing the power grid transfer capacity is …
energy transitions is more critical than ever. Optimizing the power grid transfer capacity is …
Machine learning models for forecasting and estimation of business operations
SF Ahamed, A Vijayasankar, M Thenmozhi… - The Journal of High …, 2023 - Elsevier
Abstract Machine Learning (ML) systems are built to shift through large amounts of data.
Applying ML in production settings allows for the collection of additional data that can be …
Applying ML in production settings allows for the collection of additional data that can be …
A global cyber-resilient model for dynamic line rating forecasting based on deep federated learning
H Moayyed, A Moradzadeh… - IEEE Systems …, 2023 - ieeexplore.ieee.org
Uncertainty in renewable energy generation and energy demand reduces generation
flexibility, which should be compensated by increasing flexibility on the supply grid and …
flexibility, which should be compensated by increasing flexibility on the supply grid and …
Extreme learning machine: A comprehensive survey of theories & algorithms
The Extreme Learning Machine (ELM), a quick and effective approach for training single-
hidden-layer feedforward neural networks, is thoroughly reviewed in this paper. The …
hidden-layer feedforward neural networks, is thoroughly reviewed in this paper. The …
Evaluating power transfer of transmission lines and proposing power transfer reduction actions to enhance small-signal stability
H Panahi, M Sanaye-Pasand… - IEEE Systems Journal, 2022 - ieeexplore.ieee.org
Small-signal stability (SSS) is one of the limiting factors for determining the power transfer
capability (PTC) of transmission lines. Control centers try to optimally operate the system …
capability (PTC) of transmission lines. Control centers try to optimally operate the system …
Performance Analysis of a Dynamic Line Rating System Based on Project Experiences
This paper aims to demonstrate the performance and reliability analysis of a dynamic line
rating (DLR) system at the Bulgarian demonstration site of the FLEXITRANSTORE project …
rating (DLR) system at the Bulgarian demonstration site of the FLEXITRANSTORE project …
A comparative study of different dynamic line rating standards considering transient heat balance
Nowadays, dynamic line rating (DLR) has attracted much attention. DLR leads to cost
reduction and congestion management. Studies have shown that the maximum extractable …
reduction and congestion management. Studies have shown that the maximum extractable …
Image processing-based data integrity attack detection in dynamic line rating forecasting applications
A Moradzadeh, H Moayyed… - … on Smart Grid …, 2022 - ieeexplore.ieee.org
Dynamic line rating (DLR) is considered a key concept in transmission lines that can
guarantee the variable nature of renewable energy sources with minimal economic …
guarantee the variable nature of renewable energy sources with minimal economic …