Classification of multiple power quality events via compressed deep learning

[1]  Ivo Palu,et al.  Power Quality Issues Concerning Photovoltaic Generation in Distribution Grids , 2015 .

[2]  Pradipta Kishore Dash,et al.  Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier , 2013, Digit. Signal Process..

[3]  Yansheng Li,et al.  Unsupervised Spectral–Spatial Feature Learning With Stacked Sparse Autoencoder for Hyperspectral Imagery Classification , 2015, IEEE Geoscience and Remote Sensing Letters.

[4]  Maria Dolores Gil Montoya,et al.  Power quality techniques research worldwide: A review , 2016 .

[5]  U. Rajendra Acharya,et al.  An efficient compression of ECG signals using deep convolutional autoencoders , 2018, Cognitive Systems Research.

[6]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[7]  Juan-Carlos Montaño,et al.  Disturbance Ratio for Optimal Multi-Event Classification in Power Distribution Networks , 2016, IEEE Transactions on Industrial Electronics.

[8]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[9]  A. J. Jerri.  The Shannon sampling theorem—Its various extensions and applications: A tutorial review , 1977, Proceedings of the IEEE.

[10]  Witold Pedrycz,et al.  Superior solution guided particle swarm optimization combined with local search techniques , 2014, Expert Syst. Appl..

[11]  Q. Henry Wu,et al.  Detection and classification of power quality disturbances in time domain using probabilistic neural network , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[12]  Junwei Cao,et al.  Optimization of the power quality monitor number in Smart Grid , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[13]  Danfeng Xie,et al.  A Hierarchical Deep Neural Network for Fault Diagnosis on Tennessee-Eastman Process , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[14]  Zhigang Liu,et al.  A Classification Method for Complex Power Quality Disturbances Using EEMD and Rank Wavelet SVM , 2015, IEEE Transactions on Smart Grid.

[15]  Ali Enshaee,et al.  Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm , 2010 .

[16]  Danton Diego Ferreira,et al.  Classification of Multiple and Single Power Quality Disturbances Using a Decision Tree-Based Approach , 2013 .

[17]  Belkis Eristi,et al.  A new embedded power quality event classification system based on the wavelet transform , 2018 .

[18]  W. E. Reid.  Power quality issues-standards and guidelines , 1994 .

[19]  Ayşegül Uçar,et al.  Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines , 2010 .

[20]  Jun Zhang,et al.  Classification of Power Quality Disturbances via Deep Learning , 2017 .

[21]  Zahra Moravej,et al.  Detection and Classification of Power Quality Disturbances Using Wavelet Transform and Support Vector Machines , 2009 .

[22]  José G. M. S. Decanini,et al.  Detection and classification of voltage disturbances using a Fuzzy-ARTMAP-wavelet network , 2011 .

[23]  D. Böhning.  Multinomial logistic regression algorithm , 1992 .

[24]  Arun Kumar Puliyadi Kubendran,et al.  Detection and classification of complex power quality disturbances using S‐transform amplitude matrix–based decision tree for different noise levels , 2017 .

[25]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[26]  Hui Liu,et al.  Power Quality Disturbances Classification Using Compressive Sensing and Maximum Likelihood , 2018 .

[27]  Richard G. Baraniuk,et al.  Signal Processing With Compressive Measurements , 2010, IEEE Journal of Selected Topics in Signal Processing.

[28]  Om Prakash Mahela,et al.  A critical review of detection and classification of power quality events , 2015 .

[29]  Jianmin Li,et al.  Detection and Classification of Power Quality Disturbances Using Double Resolution S-Transform and DAG-SVMs , 2016, IEEE Transactions on Instrumentation and Measurement.

[30]  Prasanta Kundu,et al.  Power quality disturbance classification employing S-transform and three-module artificial neural network , 2014 .

[31]  Yonina C. Eldar,et al.  Sampling at the rate of innovation: theory and applications , 2012, Compressed Sensing.

[32]  Xiaojing Chen,et al.  Classification of power quality disturbances using dual strong tracking filters and rule‐based extreme learning machine , 2018 .

[33]  Arturo Garcia-Perez,et al.  Detection and Classification of Single and Combined Power Quality Disturbances Using Neural Networks , 2014, IEEE Transactions on Industrial Electronics.

[34]  M. Sabarimalai Manikandan,et al.  Detection and Classification of Power Quality Disturbances Using Sparse Signal Decomposition on Hybrid Dictionaries , 2015, IEEE Transactions on Instrumentation and Measurement.

[35]  Longbiao Wang,et al.  Deep neural network-based bottleneck feature and denoising autoencoder-based dereverberation for distant-talking speaker identification , 2015, EURASIP J. Audio Speech Music. Process..

[36]  N. Zareen,et al.  Automatic pattern recognition of single and multiple power quality disturbances , 2016 .

[37]  Shouxiang Wang,et al.  A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network , 2019, Applied Energy.

[38]  Rajiv Kapoor,et al.  Classification of power quality disturbances using non-linear dimension reduction , 2013 .

[39]  Aslam P. Memon,et al.  A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network , 2017 .

[40]  Ming Zhang,et al.  A Real-Time Power Quality Disturbances Classification Using Hybrid Method Based on S-Transform and Dynamics , 2013, IEEE Transactions on Instrumentation and Measurement.

[41]  Abbes Amira,et al.  Compressive Sensing-Based IoT Applications: A Review , 2018, J. Sens. Actuator Networks.

[42]  Manish Kumar Saini,et al.  Detection and classification of power quality disturbances in wind‐grid integrated system using fast time‐time transform and small residual‐extreme learning machine , 2018 .

[43]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[44]  Yue Shen,et al.  Complex power quality disturbances classification via curvelet transform and deep learning , 2018, Electric Power Systems Research.

[45]  Danton Diego Ferreira,et al.  Real-time system for automatic detection and classification of single and multiple power quality disturbances , 2018, Measurement.

[46]  Brahmadesam Viswanathan Krishna,et al.  Image pattern recognition technique for the classification of multiple power quality disturbances , 2013 .

[47]  M. Uyar,et al.  An effective wavelet-based feature extraction method for classification of power quality disturbance signals , 2008 .

[48]  Fan Ning,et al.  Power quality signal analysis for the smart grid using the Hilbert-Huang transform , 2013, 2013 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM).