[HTML][HTML] A systematic review of machine learning applications in the operation of smart distribution systems

T Matijašević, T Antić, T Capuder - Energy reports, 2022 - Elsevier
Due to climate changes happening in the past few years, the necessity for the integration of
renewable energy sources and other low-carbon technologies is ever-growing. With the …

Secure and resilient demand side management engine using machine learning for IoT-enabled smart grid

M Babar, MU Tariq, MA Jan - Sustainable Cities and Society, 2020 - Elsevier
The national security, economy, and healthcare heavily rely on the reliable distribution of
electricity. The incorporation of communication technologies and sensors in the power …

[HTML][HTML] Advances in power quality analysis techniques for electrical machines and drives: A review

AD Gonzalez-Abreu, RA Osornio-Rios… - Energies, 2022 - mdpi.com
The electric machines are the elements most used at an industry level, and they represent
the major power consumption of the productive processes. Particularly speaking, among all …

A machine learning decision-support system improves the internet of things' smart meter operations

J Siryani, B Tanju, TJ Eveleigh - IEEE Internet of Things Journal, 2017 - ieeexplore.ieee.org
An Internet of Things'(IoT) connected society and system represents a tremendous paradigm
shift. We present a framework for a decision-support system (DSS) that operates within the …

Syncretic use of smart meters for power quality monitoring in emerging networks

MM Albu, M Sănduleac… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
Operation of distribution networks undergoes dramatic changes in the era of smart grid
deployment, due to the higher penetration of distributed generation (usually from intermittent …

A two-stage deep convolutional model for demand response energy management system in IoT-enabled smart grid

HS Shreenidhi, NS Ramaiah - Sustainable Energy, Grids and Networks, 2022 - Elsevier
Abstract Home Energy Management System (HEMS) plays an integral role in SG that
optimally schedules the appliances to achieve energy savings, cost reduction, Peak …

Data-driven multi-hidden markov model-based power quality disturbance prediction that incorporates weather conditions

F Xiao, Q Ai - IEEE Transactions on Power Systems, 2018 - ieeexplore.ieee.org
Power quality (PQ) disturbance in power systems has been a concern for operators and
customers. The purpose is to locate and forecast the presence of PQ disturbances to …

An optimizing and differentially private clustering algorithm for mixed data in SDN-based smart grid

Z Lv, L Wang, Z Guan, J Wu, X Du, H Zhao… - IEEE …, 2019 - ieeexplore.ieee.org
Software-defined network (SDN) is widely used in smart grid for monitoring and managing
the communication network. Big data analytics for SDN-based smart grid has got increasing …

[HTML][HTML] Machine learning-based management of electric vehicles charging: Towards highly-dispersed fast chargers

M Shibl, L Ismail, A Massoud - Energies, 2020 - mdpi.com
Coordinated charging of electric vehicles (EVs) improves the overall efficiency of the power
grid as it avoids distribution system overloads, increases power quality, and decreases …

[HTML][HTML] Advanced distribution measurement technologies and data applications for smart grids: A review

AE Saldaña-González, A Sumper, M Aragüés-Peñalba… - Energies, 2020 - mdpi.com
The integration of advanced measuring technologies in distribution systems allows
distribution system operators to have better observability of dynamic and transient events. In …