power quality monitoring
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2022 ◽  
Vol 119 (1) ◽  
pp. 359-369
Author(s):  
Qiang Yu ◽  
Xiankai Chen ◽  
Xiaoyue Li ◽  
Chaoqun Zhou ◽  
Zhichao Li

Author(s):  
Dmitry Petrov ◽  
Konstantin Kroschewski ◽  
Ibrahim Mwammenywa ◽  
G. Mark Kagarura ◽  
Ulrich Hilleringmann

2021 ◽  
Author(s):  
Olivia Florencias-Oliveros ◽  
Jose-Maria Sierra-Fernandez ◽  
Juan-Jose Gonzalez-de-la-Rosa ◽  
Manuel-Jesus Espinosa-Gavira ◽  
Agustin Aguera-Perez ◽  
...  

2021 ◽  
Vol 7 ◽  
pp. 230-239
Author(s):  
Jun Qu ◽  
Peng Fu ◽  
Yunxiang Tian ◽  
Jing Lu ◽  
Zhiwei Mao ◽  
...  

2021 ◽  
Vol 19 ◽  
pp. 211-216
Author(s):  
A. D. Gonzalez-Abreu ◽  
◽  
M. Delgado-Prieto ◽  
J.J. Saucedo-Dorantes ◽  
R.A. Osornio-Rios

Complex disturbance patterns take place over the corresponding power supply networks due to the increased complexity of electrical loads at industrial plants. Such complex patterns are the result of a combination of simpler standardized disturbances. However, their detection and identification represent a challenge to current power quality monitoring systems. The detection of disturbances and their identification would allow early and effective decision-making processes towards optimal power grid controls or maintenance and security operations of the grid. In this regard, this paper presents an evaluation of the four main techniques for novelty detection: k-Nearest Neighbor, Gaussian Mixture Models, One-Class Support Vector Machine, and Stacked Autoencoder. A set of synthetic signals have been considered to evaluate the performance and suitability of each technique as an anomaly detector applied to power quality disturbances. A set of statistical features have been considered to characterize the power line. The evaluation of the techniques is carried out throughout different scenarios considering combined and single disturbances. The obtained results show the complementary performance of the considered techniques in front of different scenarios due to their differences in the knowledge modelization.


Author(s):  
Abhishek Sur ◽  
Suman Lata Dhar

This Paper principally portrays that a Power Quality Monitoring system utilizing LabVIEW is created. Because of the higher utilization of power converters and other nonlinear burdens in industry and by customers all in all, it very well may be noticed an expanding crumbling of the power frameworks voltage and current waveforms [1]. The paper ‘Power Quality Monitoring using Virtual Instrumentation and LABVIEW’ basically depicts the monitoring of the quality of the power generated during the process with the help of a virtual environment that is LABVIEW. Now this paper is very much simple and is based on finding the power factor along with Active, Reactive and Apparent Power. In additional to that Active/Apparent and Reactive Energies is also found out and the nature of the graph is also found out accordingly. Here we are using Current and Voltage Transformers as the basic transducers and ideal values of voltage and current is taken and the graph is plotted with respect to voltage/current and the power/energies. The nature of both the graphs is quite different in case of power it is linearly increasing with the time, whereas in case of the energies the graph is linear in nature. Also the error and accuracy is found out with a fluke data which is based on the values of fluke instruments.


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