Hybrid Event Classification Scheme for Converter-Based DG with Improved Power Quality

2020 ◽  
pp. 207-238 ◽  
Author(s):  
Om Hari Gupta ◽  
Manoj Tripathy ◽  
Vijay K. Sood
Keyword(s):  
Power Quality ◽  
Classification Scheme ◽  
Event Classification ◽  
Hybrid Event

A new embedded power quality event classification system based on the wavelet transform

2018 ◽  
Vol 28 (9) ◽  
pp. e2597 ◽  
Author(s):  
Belkis Eristi ◽  
Ozal Yildirim ◽  
Huseyin Eristi ◽  
Yakup Demir
Keyword(s):  
Wavelet Transform ◽  
Power Quality ◽  
Classification System ◽  
Event Classification ◽  
Embedded Power

Power Quality Event Classification Using Transfer Learning on Images

2019 ◽  
Author(s):  
Santhosh K G Manikonda ◽  
Joe Santhosh ◽  
Sanjayan Pradeep Kumar Sreekala ◽  
Siddharth Gangwani ◽  
Dattatraya N Gaonkar
Keyword(s):  
Power Quality ◽  
Transfer Learning ◽  
Event Classification

Power Quality Event Classification Using Convolutional Neural Networks on Images

2019 ◽  
Author(s):  
Santhosh K G Manikonda ◽  
Siddharth Gangwani ◽  
Sanjayan Pradeep Kumar Sreckala ◽  
Joe Santhosh ◽  
Dattatraya N Gaonkar
Keyword(s):  
Neural Networks ◽  
Power Quality ◽  
Convolutional Neural Networks ◽  
Event Classification

scholarly journals Power quality event classification using complex wavelets phasor models and customized convolution neural network

2022 ◽  
Vol 12 (1) ◽  
pp. 22
Author(s):  
Likhitha Ramalingappa ◽  
Aswathnarayan Manjunatha
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Wavelet Transforms ◽  
Input Voltage ◽  
Convolution Neural Network ◽  
Training Time ◽  
Event Classification ◽  
Low Pass ◽  
Space Phasor

Origin and triggers of power quality (PQ) events must be identified in prior, in order to take preventive steps to enhance power quality. However it is important to identify, localize and classify the PQ events to determine the causes and origins of PQ disturbances. In this paper a novel algorithm is presented to classify voltage variations into six different PQ events considering the space phasor model (SPM) diagrams, dual tree complex wavelet transforms (DTCWT) sub bands and the convolution neural network (CNN) model. The input voltage data is converted into SPM data, the SPM data is transformed using 2D DTCWT into low pass and high pass sub bands which are simultaneously processed by the 2D CNN model to perform classification of PQ events. In the proposed method CNN model based on Google Net is trained to perform classification of PQ events with default configuration as in deep neural network designer in MATLAB environment. The proposed algorithm achieve higher accuracy with reduced training time in classification of events than compared with reported PQ event classification methods.

Power Quality Event Classification Using Long Short-Term Memory Networks

2019 ◽  
Author(s):  
Santhosh K G Manikonda ◽  
Joe Santhosh ◽  
Sanjayan Pradeep Kumar Sreekala ◽  
Siddharth Gangwani ◽  
Dattatraya N Gaonkar
Keyword(s):  
Power Quality ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Event Classification ◽  
Long Short Term Memory
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