A critical and comprehensive review on power quality disturbance detection and classification
P Khetarpal, MM Tripathi - Sustainable Computing: Informatics and …, 2020 - Elsevier
With an elevating demand and use of power electronics equipment, green energy and the
development of smart grids, power quality disturbance detection and classification holds …
development of smart grids, power quality disturbance detection and classification holds …
Overview of signal processing and machine learning for smart grid condition monitoring
Nowadays, the main grid is facing several challenges related to the integration of renewable
energy resources, deployment of grid-level energy storage devices, deployment of new …
energy resources, deployment of grid-level energy storage devices, deployment of new …
Assessing the financial impact and mitigation methods for voltage sag in power grid
Voltage sags, as defined by the IEEE Standard, refer to sudden drops in voltage magnitude
that last for a duration greater than 0.5 cycles of the power frequency but less than or equal …
that last for a duration greater than 0.5 cycles of the power frequency but less than or equal …
An integrated of hydrogen fuel cell to distribution network system: Challenging and opportunity for D-STATCOM
The electric power industry sector has become increasingly aware of how counterproductive
voltage sag affects distribution network systems (DNS). The voltage sag backfires …
voltage sag affects distribution network systems (DNS). The voltage sag backfires …
Advances in the application of machine learning techniques for power system analytics: A survey
The recent advances in computing technologies and the increasing availability of large
amounts of data in smart grids and smart cities are generating new research opportunities in …
amounts of data in smart grids and smart cities are generating new research opportunities in …
Classification of power quality disturbances using Wigner-Ville distribution and deep convolutional neural networks
This paper proposes a hybrid approach combining Wigner-Ville distribution (WVD) with
convolutional neural network (CNN) for power quality disturbance (PQD) classification …
convolutional neural network (CNN) for power quality disturbance (PQD) classification …
Multi-stage voltage sag state estimation using event-deduction model corresponding to EF, EG, and EP
Y Wang, HS He, XY **ao, SY Li… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Knowledge of the disturbance of the concerned bus is essential to make decisions to
mitigate voltage sags. The most commonly used method is to estimate the state of voltage …
mitigate voltage sags. The most commonly used method is to estimate the state of voltage …
Real-time robust forecasting-aided state estimation of power system based on data-driven models
This paper presents a real-time robust power system forecasting-aided state estimation
method based on the Bayesian framework, deep learning, and Gaussian mixture model to …
method based on the Bayesian framework, deep learning, and Gaussian mixture model to …
Power quality event classification using optimized Bayesian convolutional neural networks
Management of the electrical grid has an importance on the sustainability and reliability of
the electrical energy supply. In the process, it is still crucial that power quality (PQ) is …
the electrical energy supply. In the process, it is still crucial that power quality (PQ) is …
A classification method for power-quality disturbances using Hilbert–Huang transform and LSTM recurrent neural networks
Power quality disturbances are one of the main problems in an electric power system, where
deviations in the voltage and current signals can be evidenced. These sudden changes are …
deviations in the voltage and current signals can be evidenced. These sudden changes are …