Related references
Note: Only part of the references are listed.Modified methods for voltage-sag source detection using transient periods
Younes Mohammadi et al.
ELECTRIC POWER SYSTEMS RESEARCH (2022)
Distributed evidential clustering toward time series with big data issue
Chaoyu Gong et al.
EXPERT SYSTEMS WITH APPLICATIONS (2022)
Voltage-sag source detection: Developing supervised methods and proposing a new unsupervised learning
Younes Mohammadi et al.
SUSTAINABLE ENERGY GRIDS & NETWORKS (2022)
An unsupervised learning schema for seeking patterns in rms voltage variations at the sub-10-minute time scale
Younes Mohammadi et al.
SUSTAINABLE ENERGY GRIDS & NETWORKS (2022)
Comprehensive strategy for classification of voltage sags source location using optimal feature selection applied to support vector machine and ensemble techniques
Younes Mohammadi et al.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2021)
Deep Learning Method With Manual Post-Processing for Identification of Spectral Patterns of Waveform Distortion in PV Installations
Roger Alves de Oliveira et al.
IEEE TRANSACTIONS ON SMART GRID (2021)
Unsupervised deep learning and analysis of harmonic variation patterns using big data from multiple locations
Chenjie Ge et al.
ELECTRIC POWER SYSTEMS RESEARCH (2021)
Variations in harmonic voltage at the sub-10-minute time scale
Aurora Gil-de-Castro et al.
ELECTRIC POWER SYSTEMS RESEARCH (2021)
Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey
Seyed Mahdi Miraftabzadeh et al.
ENERGIES (2021)
Rapid Voltage Change Detection: Limits of the IEC Standard Approach and Possible Solutions
David Macii et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)
A new approach for voltage sag source relative location in active distribution systems with the presence of inverter-based distributed generations
Younes Mohammadi et al.
ELECTRIC POWER SYSTEMS RESEARCH (2020)
An Automatic Identification Framework for Complex Power Quality Disturbances Based on Multifusion Convolutional Neural Network
Wei Qiu et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)
Improved DR and CBM methods for finding relative location of voltage sag source at the PCC of distributed energy resources
Younes Mohammadi et al.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2020)
Characterization methods and typical levels of variations in rms voltage at the time scale between 1 second and 10 minutes
Math Bollen et al.
ELECTRIC POWER SYSTEMS RESEARCH (2020)
Characterization of the impact of PV and EV induced voltage variations on LED lamps in a low voltage installation
Vineetha Ravindran et al.
ELECTRIC POWER SYSTEMS RESEARCH (2020)
A time-series clustering methodology for knowledge extraction in energy consumption data
L. G. B. Ruiz et al.
EXPERT SYSTEMS WITH APPLICATIONS (2020)
Advance short-term wind energy quality assessment based on instantaneous standard deviation and variogram of wind speed by a hybrid method
Guangbiao Liu et al.
APPLIED ENERGY (2019)
A shape-based clustering method for pattern recognition of residential electricity consumption
Lulu Wen et al.
JOURNAL OF CLEANER PRODUCTION (2019)
A new approach for probabilistic harmonic load flow in distribution systems based on data clustering
Sadjad Galvani et al.
ELECTRIC POWER SYSTEMS RESEARCH (2019)
Clustering as a tool to support the assessment of power quality in electrical power networks with distributed generation in the mining industry
Michal Jasinski et al.
ELECTRIC POWER SYSTEMS RESEARCH (2019)
Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural Networks
Kewei Cai et al.
IEEE ACCESS (2019)
Employing instantaneous positive sequence symmetrical components for voltage sag source relative location
Younes Mohammadi et al.
ELECTRIC POWER SYSTEMS RESEARCH (2017)
A novel method for voltage-sag source location using a robust machine learning approach
Younes Mohammadi et al.
ELECTRIC POWER SYSTEMS RESEARCH (2017)
Fuzzy clustering of time series data using dynamic time warping distance
Hesam Izakian et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2015)
An effective Power Quality classifier using Wavelet Transform and Support Vector Machines
D. De Yong et al.
EXPERT SYSTEMS WITH APPLICATIONS (2015)
Variability assessment and forecasting of renewables: A review for solar, wind, wave and tidal resources
Joakim Widen et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2015)
Detection and Classification of Single and Combined Power Quality Disturbances Using Neural Networks
Martin Valtierra-Rodriguez et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2014)
A randomized algorithm for the decomposition of matrices
Per-Gunnar Martinsson et al.
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS (2011)
Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions
N. Halko et al.
SIAM REVIEW (2011)
Detection and classification of power quality disturbances using S-transform and probabilistic neural network
S. Mishra et al.
IEEE TRANSACTIONS ON POWER DELIVERY (2008)
Support vector machine for classification of voltage disturbances
Peter G. V. Axelberg et al.
IEEE TRANSACTIONS ON POWER DELIVERY (2007)
A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine
LJ Cao et al.
NEUROCOMPUTING (2003)