Wavelet Based Signal Processing Technique for Classification of Power Quality Disturbances

2014 ◽  
Author(s):  
Madhura Tuljapurkar ◽  
A.A. Dharme
Keyword(s):  
Signal Processing ◽  
Power Quality ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
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.

scholarly journals A Remote Sensor System Based on TDLAS Technique for Ammonia Leakage Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2448
Author(s):  
Hongbin Lu ◽  
Chuantao Zheng ◽  
Lei Zhang ◽  
Zhiwei Liu ◽  
Fang Song ◽  
...  
Keyword(s):  
Signal Processing ◽  
Limit Of Detection ◽  
Processing Technique ◽  
Tunable Diode Laser ◽  
Sensor System ◽  
Signal Processing Technique ◽  
Suppression Effect ◽  
Output Wavelength ◽  
Deviation Analysis ◽  
Remote Sensor

The development of an efficient, portable, real-time, and high-precision ammonia (NH3) remote sensor system is of great significance for environmental protection and citizens’ health. We developed a NH3 remote sensor system based on tunable diode laser absorption spectroscopy (TDLAS) technique to measure the NH3 leakage. In order to eliminate the interference of water vapor on NH3 detection, the wavelength-locked wavelength modulation spectroscopy technique was adopted to stabilize the output wavelength of the laser at 6612.7 cm−1, which significantly increased the sampling frequency of the sensor system. To solve the problem in that the light intensity received by the detector keeps changing, the 2f/1f signal processing technique was adopted. The practical application results proved that the 2f/1f signal processing technique had a satisfactory suppression effect on the signal fluctuation caused by distance changing. Using Allan deviation analysis, we determined the stability and limit of detection (LoD). The system could reach a LoD of 16.6 ppm·m at an average time of 2.8 s, and a LoD of 0.5 ppm·m at an optimum averaging time of 778.4 s. Finally, the measurement result of simulated ammonia leakage verified that the ammonia remote sensor system could meet the need for ammonia leakage detection in the industrial production process.

scholarly journals Application of UAV in Search and Rescue Actions in Underground Mine—A Specific Sound Detection in Noisy Acoustic Signal

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3725
Author(s):  
Paweł Zimroz ◽  
Paweł Trybała ◽  
Adam Wróblewski ◽  
Mateusz Góralczyk ◽  
Jarosław Szrek ◽  
...  
Keyword(s):  
Signal Processing ◽  
Acoustic Signal ◽  
Processing Technique ◽  
Underground Mine ◽  
Search And Rescue ◽  
Reference Spectrum ◽  
Signal Processing Technique ◽  
Time Frequency ◽  
Communication Device ◽  
Frequency Representation

The possibility of the application of an unmanned aerial vehicle (UAV) in search and rescue activities in a deep underground mine has been investigated. In the presented case study, a UAV is searching for a lost or injured human who is able to call for help but is not able to move or use any communication device. A UAV capturing acoustic data while flying through underground corridors is used. The acoustic signal is very noisy since during the flight the UAV contributes high-energetic emission. The main goal of the paper is to present an automatic signal processing procedure for detection of a specific sound (supposed to contain voice activity) in presence of heavy, time-varying noise from UAV. The proposed acoustic signal processing technique is based on time-frequency representation and Euclidean distance measurement between reference spectrum (UAV noise only) and captured data. As both the UAV and “injured” person were equipped with synchronized microphones during the experiment, validation has been performed. Two experiments carried out in lab conditions, as well as one in an underground mine, provided very satisfactory results.

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