Design of coreless PCB transformers for power and signal isolation in a modular ADC system for power quality data acquisition

Since most of our industries use induction motors, it is essential to develop condition monitoring systems. Nowadays, industries have power quality issues such as sag, swell, harmonics, and transients. Thus, a condition monitoring system should have the ability to detect various faults, even in the presence of power quality issues. Most of the fault diagnosis and condition monitoring methods proposed earlier misidentified the faults and caused the condition monitoring system to fail because of misclassification due to power quality. The proposed method uses power quality data along with starting current data to identify the broken rotor bar and bearing fault in induction motors. The discrete wavelet transform (DWT) is used to decompose the current waveform, and then different features such as mean, standard deviation, entropy, and norm are calculated. The neural network (NN) classifier is used for classifying the faults and for analyzing the classification accuracy for various cases. The classification accuracy is 96.7% while considering power quality issues, whereas in a typical case, it is 93.3%. The proposed methodology is suitable for hardware implementation, which merges mean, standard deviation, entropy, and norm with the consideration of power quality issues, and the trained NN proves stable in the detection of the rotor and bearing faults.

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Non-intrusive discriminant analysis of loads based on power quality data

2019 IEEE Workshop on Power Electronics and Power Quality Applications (PEPQA) ◽

10.1109/pepqa.2019.8851568 ◽

2019 ◽

Author(s):

Yulieth Jimenez ◽

Jose Cortes ◽

Cesar Duarte ◽

Johann Petit ◽

Gilberto Carrillo

Keyword(s):

Discriminant Analysis ◽

Power Quality ◽

Quality Data

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Combined Correlation and Cluster Analysis for Long-Term Power Quality Data from Virtual Power Plant

Electronics ◽

10.3390/electronics10060641 ◽

2021 ◽

Vol 10 (6) ◽

pp. 641

Author(s):

Michał Jasiński

Keyword(s):

Cluster Analysis ◽

Power Plant ◽

Power Quality ◽

Quality Data ◽

Virtual Power ◽

Distributed Resources ◽

Virtual Power Plant ◽

And Cluster Analysis ◽

Global Indicators

Analysis of the connection between different units that operate in the same area assures always interesting results. During this investigation, the concerned area was a virtual power plant (VPP) that operates in Poland. The main distributed resources included in the VPP are a 1.25 MW hydropower plant and an associated 0.5 MW energy storage system. The mentioned VPP was a source of synchronic, long-term, multipoint power quality (PQ) data. Then, for five related measurement points, the conclusion about the relation in point of PQ was performed using correlation analysis, the global index approach, and cluster analysis. Global indicators were applied in place of PQ parameters to reduce the amount of analyzed data and to check the correlation between phase values. For such a big dataset, the occurrence of outliers is certain, and outliers may affect the correlation results. Thus, to find and exclude them, cluster analysis (k-means algorithm, Chebyshev distance) was applied. Finally, the correlation between PQ global indicators of different measurement points was performed. It assured general information about VPP units’ relation in point of PQ. Under the investigation, both Pearson’s and Spearman’s rank correlation coefficients were considered.

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Design of Power Quality Management Software Based on Virtual Instrument and Oracle

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.2441 ◽

2013 ◽

Vol 427-429 ◽

pp. 2441-2444

Author(s):

Wei Chen ◽

Long Chen ◽

Ming Li

Keyword(s):

Power Quality ◽

Software Design ◽

Virtual Instrument ◽

Quality Analysis ◽

Quality Data ◽

Data Handling ◽

Data Display ◽

Report Generation ◽

Management Capacity ◽

Power Quality Analysis

This paper presents a software design useful for power quality analysis and data management. The software was programmed in LabVIEW and Oracle, running on Windows in a regular PC. LabVIEW acquires data continuously from the lower machine via TCP/IP. Using its database connection toolkit, LabVIEW accesses to Oracle to stores and retrieve the power quality data according to different indicators. A friendly GUI was built for data display and user operation, taking advantage of the powerful data-handling capacity of LabVIEW and its rich controls. Moreover, Excel reports can be exported using report generation toolkit in LabVIEW. The software greatly improves the data analysis and management capacity.

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