Machine learning and deep learning in energy systems: A review
With population increases and a vital need for energy, energy systems play an important
and decisive role in all of the sectors of society. To accelerate the process and improve the …
and decisive role in all of the sectors of society. To accelerate the process and improve the …
Machine learning applications in power system fault diagnosis: Research advancements and perspectives
Newer generation sources and loads are posing new challenges to the conventional power
system protection schemes. Adaptive and intelligent protection methodology, based on …
system protection schemes. Adaptive and intelligent protection methodology, based on …
LSTM recurrent neural network classifier for high impedance fault detection in solar PV integrated power system
This paper presents the detection of High Impedance Fault (HIF) in solar Photovoltaic (PV)
integrated power system using recurrent neural network-based Long Short-Term Memory …
integrated power system using recurrent neural network-based Long Short-Term Memory …
A KNN based random subspace ensemble classifier for detection and discrimination of high impedance fault in PV integrated power network
This paper proposes an ensemble Random Subspace (RS) classifier for discrimination of
High Impedance Fault (HIF) in photovoltaic connected power network. The design and …
High Impedance Fault (HIF) in photovoltaic connected power network. The design and …
A review of distribution network applications based on smart meter data analytics
CL Athanasiadis, TA Papadopoulos… - … and Sustainable Energy …, 2024 - Elsevier
The large-scale roll-out of smart meters allows the collection of a vast amount of fine-grained
electricity consumption data. Once analyzed, such data can enable cutting-edge data-driven …
electricity consumption data. Once analyzed, such data can enable cutting-edge data-driven …
[HTML][HTML] A novel methodology to predict monthly municipal water demand based on weather variables scenario
This study provides a novel methodology to predict monthly water demand based on several
weather variables scenarios by using combined techniques including discrete wavelet …
weather variables scenarios by using combined techniques including discrete wavelet …
On the use of artificial intelligence for high impedance fault detection and electrical safety
S Wang, P Dehghanian - IEEE Transactions on Industry …, 2020 - ieeexplore.ieee.org
Accidents caused by faults on overhead power lines have been more frequently reported
under extreme weather conditions and may strongly threaten the safety and stability of the …
under extreme weather conditions and may strongly threaten the safety and stability of the …
Fault location for distribution smart grids: Literature overview, challenges, solutions, and future trends
Thanks to smart grids, more intelligent devices may now be integrated into the electric grid,
which increases the robustness and resilience of the system. The integration of distributed …
which increases the robustness and resilience of the system. The integration of distributed …
Intelligent anomaly identification in cyber-physical inverter-based systems
Modern cyber-physical systems have become more autonomous and distributed with the
inclusion of advanced control architectures and communication networks. Power electronics …
inclusion of advanced control architectures and communication networks. Power electronics …
The current state of the art in research on predictive maintenance in smart grid distribution network: Fault's types, causes, and prediction methods—A systematic …
MA Mahmoud, NR Md Nasir, M Gurunathan, P Raj… - Energies, 2021 - mdpi.com
With the exponential growth of science, Internet of Things (IoT) innovation, and expanding
significance in renewable energy, Smart Grid has become an advanced innovative thought …
significance in renewable energy, Smart Grid has become an advanced innovative thought …