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 …
Adaptive crossover operator based multi-objective binary genetic algorithm for feature selection in classification
Feature selection is a key pre-processing technique for classification which aims at
removing irrelevant or redundant features from a given dataset. Generally speaking, feature …
removing irrelevant or redundant features from a given dataset. Generally speaking, feature …
Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review
Several factors affect existing electric power systems and negatively impact power quality
(PQ): the high penetration of renewable and distributed sources that are based on power …
(PQ): the high penetration of renewable and distributed sources that are based on power …
A new algorithm based on gray wolf optimizer and shuffled frog lea** algorithm to solve the multi-objective optimization problems
Multi-objective optimization is many important since most of the real world problems are in
multi-objective category. Looking at the literature, the algorithms proposed for the solution of …
multi-objective category. Looking at the literature, the algorithms proposed for the solution of …
Classification of power quality disturbances by 2D-Riesz Transform, multi-objective grey wolf optimizer and machine learning methods
In this study, a new method combined with two-dimensional Riesz Transform (RT) in feature
extraction stage and Multi-Objective Grey Wolf Optimizer (MOGWO) with k-Nearest Neighbor …
extraction stage and Multi-Objective Grey Wolf Optimizer (MOGWO) with k-Nearest Neighbor …
[HTML][HTML] An alzheimer's disease classification method using fusion of features from brain magnetic resonance image transforms and deep convolutional networks
Alzheimer's is a progressive and irreversible brain degenerative disorder, and presenting an
accurate early-stage diagnosis tool is vital for preventing disease progression. The previous …
accurate early-stage diagnosis tool is vital for preventing disease progression. The previous …
A self-adaptive multi-objective feature selection approach for classification problems
In classification tasks, feature selection (FS) can reduce the data dimensionality and may
also improve classification accuracy, both of which are commonly treated as the two …
also improve classification accuracy, both of which are commonly treated as the two …
[HTML][HTML] Recent advances in machine learning for maximal oxygen uptake (VO2 max) prediction: A review
Maximal oxygen uptake (VO 2 max) is the maximum amount of oxygen attainable by a
person during exercise. VO 2 max is used in different domains including sports and medical …
person during exercise. VO 2 max is used in different domains including sports and medical …
A new deep learning method for the classification of power quality disturbances in hybrid power system
With the advancement of technology, the demand for high quality and sustainable electrical
energy has been increased due to the widespread use of electrical devices in our daily lives …
energy has been increased due to the widespread use of electrical devices in our daily lives …
[HTML][HTML] Power quality daily predictions in smart off-grids using differential, deep and statistics machine learning models processing NWP-data
L Zjavka - Energy Strategy Reviews, 2023 - Elsevier
Microgrid autonomous networks need an effective plan and control of power supply, energy
storage, and retransmission. Prediction and monitoring of power quality (PQ) along with …
storage, and retransmission. Prediction and monitoring of power quality (PQ) along with …