A stacking ensemble classification model for detection and classification of power quality disturbances in PV integrated power network

Measurement ◽  
2021 ◽  
Vol 175 ◽  
pp. 109025
Author(s):  
Padmavathi Radhakrishnan ◽  
Kalaivani Ramaiyan ◽  
Arangarajan Vinayagam ◽  
Veerapandiyan Veerasamy
Keyword(s):  
Power Quality ◽  
Ensemble Classification ◽  
Classification Model ◽  
Power Network ◽  
Power Quality Disturbances

An ensemble approach of classification model for detection and classification of power quality disturbances in PV integrated microgrid network

2021 ◽  
pp. 107294
Author(s):  
Arangarajan Vinayagam ◽  
Veerapandiyan Veerasamy ◽  
Padmavathi Radhakrishnan ◽  
Maheswari Sepperumal ◽  
Kalaivani Ramaiyan
Keyword(s):  
Power Quality ◽  
Classification Model ◽  
Ensemble Approach ◽  
Power Quality Disturbances

scholarly journals Classification of Power Quality Disturbances using Mahalanobis Distance Classifier and Stockwell Transformation

2018 ◽  
Vol 17 (1) ◽  
pp. 19-24
Author(s):  
Md. Jashim Uddin Bhuiyan ◽  
Mollah Rezaul Alam
Keyword(s):  
Power Quality ◽  
Mahalanobis Distance ◽  
Euclidean Distance ◽  
Classification Performance ◽  
Voltage Sags ◽  
Power Network ◽  
Voltage Signal ◽  
Power Quality Disturbances ◽  
Tools And Techniques

Detection and classification of PQ (Power Quality) disturbances in distribution/transmission systems are very important for protection of electricity network. Most of the disturbances of power network are non-stationary and momentary in nature, hence it requires advanced tools and techniques for the analysis and classification of PQ disturbances. This paper presents the detection and classification of PQ events or disturbances employing Stockwell-Transformation, known as S-Transformation, and Mahalanobis Distance (MD) based approach. The proposed method exploits only four features extracted through S-transformation of the voltage signal; then, using these four features, classification is conducted by MD based classifier. In this work, classification of several PQ disturbances, such as, voltage sags, swells, harmonics, notch, flicker, transient oscillation, momentary interruption, etc., are considered. The simulation results demonstrate that the proposed method is very effective and accurate in detecting and classifying PQ events. Validation of the proposed approach has also been conducted using real signal recorded in IEEE 1159.2 database. Moreover, comparative classification performance of MD based classifierwith MED (minimum Euclidean distance) and LVQ (learning vector quantization) reveals the superiority of the proposed approach.

Principal Component Analysis and CNN for Classification of Power Quality Disturbances

2021 ◽  
Author(s):  
Ananta Agarwalla ◽  
Diya Dileep ◽  
P. Jyothsana ◽  
Purnima Unnikrishnan ◽  
Karthik Thirumala
Keyword(s):  
Principal Component Analysis ◽  
Power Quality ◽  
Principal Component ◽  
Component Analysis ◽  
Power Quality Disturbances

A hybrid approach for detection and classification of power quality disturbances

2017 ◽  
Author(s):  
P. C. Chang ◽  
G. W. Chang ◽  
M. H. Shih ◽  
Y. Y. Chen ◽  
Y. H. Hong ◽  
...  
Keyword(s):  
Power Quality ◽  
Hybrid Approach ◽  
Power Quality Disturbances
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