Machine learning and deep learning in energy systems: A review

MM Forootan, I Larki, R Zahedi, A Ahmadi - Sustainability, 2022 - mdpi.com
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 …

Machine learning applications in power system fault diagnosis: Research advancements and perspectives

R Vaish, UD Dwivedi, S Tewari, SM Tripathi - Engineering Applications of …, 2021 - Elsevier
Newer generation sources and loads are posing new challenges to the conventional power
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

V Veerasamy, NIA Wahab, ML Othman… - IEEE …, 2021 - ieeexplore.ieee.org
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 …

A KNN based random subspace ensemble classifier for detection and discrimination of high impedance fault in PV integrated power network

KSV Swarna, A Vinayagam, MBJ Ananth, PV Kumar… - Measurement, 2022 - Elsevier
This paper proposes an ensemble Random Subspace (RS) classifier for discrimination of
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 …

[HTML][HTML] A novel methodology to predict monthly municipal water demand based on weather variables scenario

SL Zubaidi, K Hashim, S Ethaib, NSS Al-Bdairi… - Journal of King Saud …, 2022 - Elsevier
This study provides a novel methodology to predict monthly water demand based on several
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 …

Fault location for distribution smart grids: Literature overview, challenges, solutions, and future trends

J De La Cruz, E Gómez-Luna, M Ali, JC Vasquez… - Energies, 2023 - mdpi.com
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 …

Intelligent anomaly identification in cyber-physical inverter-based systems

AA Khan, OA Beg, M Alamaniotis, S Ahmed - Electric Power Systems …, 2021 - Elsevier
Modern cyber-physical systems have become more autonomous and distributed with the
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 …