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Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review
Meteorological changes urge engineering communities to look for sustainable and clean
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting
In recent years, renewable and sustainable energy sources have attracted the attention of
various investors and stakeholders, such as energy sector agents and even consumers. It is …
various investors and stakeholders, such as energy sector agents and even consumers. It is …
Faults in smart grid systems: Monitoring, detection and classification
AEL Rivas, T Abrao - Electric Power Systems Research, 2020 - Elsevier
Smart Grid (SG) is a multidisciplinary concept related to the power system update and
improvement. SG implies real-time information with specific communication requirements …
improvement. SG implies real-time information with specific communication requirements …
Real-time prediction of grid voltage and frequency using artificial neural networks: An experimental validation
N Chettibi, AM Pavan, A Mellit, AJ Forsyth… - Sustainable Energy, Grids …, 2021 - Elsevier
Abstract In grid-connected Distributed Generation (DG) systems, with high-penetrations of
renewable and energy storage assets, the prediction of grid voltage and frequency plays an …
renewable and energy storage assets, the prediction of grid voltage and frequency plays an …
Learning reactive power control polices in distribution networks using conditional value-at-risk and artificial neural networks
Scalable coordination of photovoltaic (PV) inverters, considering the uncertainty in PV and
load in distribution networks (DNs), is challenging due to the lack of real-time …
load in distribution networks (DNs), is challenging due to the lack of real-time …
Data-driven analytics for reliability in the buildings-to-grid integrated system framework: A systematic text-mining-assisted literature review and trend analysis
Data-driven machine learning-based methods have provided immense capabilities,
revolutionizing sectors like the Buildings-to-grid (B2G) integrated system. Since the …
revolutionizing sectors like the Buildings-to-grid (B2G) integrated system. Since the …
Intensive data-driven model for real-time observability in low-voltage radial DSO grids
Increasing levels of distributed generation (DG), as well as changes in electricity
consumption behavior, are resha** power distribution systems. These changes might …
consumption behavior, are resha** power distribution systems. These changes might …
On state estimation modeling of smart distribution networks: a technical review
J Xu, Y **, T Zheng, G Meng - Energies, 2023 - mdpi.com
State estimation (SE) is regarded as an essential tool for achieving the secure and efficient
operation of distribution networks, and extensive research on SE has been conducted over …
operation of distribution networks, and extensive research on SE has been conducted over …
[HTML][HTML] Uncertainty quantification in LV state estimation under high shares of flexible resources
The ongoing electrification introduces new challenges to distribution system operators
(DSOs). Controllable resources may simultaneously react to price signals, potentially …
(DSOs). Controllable resources may simultaneously react to price signals, potentially …
New analytical method for analysing the effectiveness of infrastructure reinforcement in electric power distribution systems
TSD Ferreira, FCL Trindade, YG Pinto… - Electric Power Systems …, 2020 - Elsevier
Technical problems are emerging in electric power distribution systems caused by the
recent widespread deployment of new technologies of load, as the plug-in electric vehicles …
recent widespread deployment of new technologies of load, as the plug-in electric vehicles …