Detection and classification of islanding and power quality disturbances in microgrid using hybrid signal processing and data mining techniques

2018 ◽  
Vol 12 (1) ◽  
pp. 82-94 ◽  
Author(s):  
Tatiana Chakravorti ◽  
Rajesh Kumar Patnaik ◽  
Pradipta Kishore Dash
Keyword(s):  
Data Mining ◽  
Signal Processing ◽  
Power Quality ◽  
Data Mining Techniques ◽  
Power Quality Disturbances

scholarly journals Power Quality Detection, Classification and Monitoring Using LABVIEW

2019 ◽  
Vol 4 (2) ◽  
pp. 101-111
Author(s):  
Fatma Zohra DEKHANDJI ◽  
Salim TALHAOUI ◽  
Youcef ARKAB
Keyword(s):  
Signal Processing ◽  
Power Quality ◽  
Wavelet Transforms ◽  
Support Vector ◽  
Labview Software ◽  
Quality Detection ◽  
Power Quality Disturbances ◽  
Processing Techniques ◽  
Signal Processing Techniques

In recent years, Power Quality becomes increasingly a major concern for both electric utilities and end users. Accordingly, the electrical engineering community has to deal with the analysis, diagnosis and solution of PQ issues using system approach rather than handling these issues as individual problems. This paper describes the analysis of PQ using advanced signal processing tools represented in Hilbert & Wavelet Transforms (HT-WT) and artificial intelligence tools represented in Artificial Neural Network & Support Vector Machine (ANN-SVM) for detection and classification of power quality disturbances respectively. These techniques were successfully simulated using LABVIEW software capabilities. The results of simulation indicate that the signal processing techniques are effective mechanisms to detect and classify power quality disturbances. At the end, the combination of WT as a tool of detection and features extraction with SVM as a classifier tool resulted as the best combination for PQ monitoring system.

A Comparative Study of Power Quality Monitoring Using Various Techniques

2021 ◽  
pp. 259-288
Author(s):  
Fatma Zohra Dekhandji ◽  
Mohamed Cherif Rais
Keyword(s):  
Signal Processing ◽  
Power Quality ◽  
Wavelet Transforms ◽  
Support Vector ◽  
Labview Software ◽  
Power Quality Monitoring ◽  
Power Quality Disturbances ◽  
Processing Techniques ◽  
Signal Processing Techniques

In recent years, power quality (PQ) has become an increasingly major concern for both electric utilities and the end users. Accordingly, the electrical engineering community has to deal with the analysis, diagnosis, and solution of PQ issues using system approach rather than handling these issues as individual problems. This chapter describes the analysis of PQ using advanced signal processing tools represented in Hilbert and wavelet transforms (HT-WT) and artificial intelligence tools represented in artificial neural network and support vector machine (ANN-SVM) for detection and classification of power quality disturbances, respectively. These techniques were successfully simulated using LABVIEW software capabilities. The results of simulation indicate that the signal processing techniques are effective mechanisms to detect and classify power quality disturbances. At the end, the combination of WT as a tool of detection and features extraction with SVM as a classifier tool resulted as the best combination for PQ monitoring system.

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