Wide-area system of registration and processing of power quality data in power grid with distributed generation: Part II. Localization and tracking of the sources of disturbances

2014 ◽  
Author(s):  
Zbigniew Leonowicz ◽  
Jacek Rezmer ◽  
Tomasz Sikorski ◽  
Jaroslaw Szymanda ◽  
Pawel Kostyla
Keyword(s):  
Power Quality ◽  
Distributed Generation ◽  
Power Grid ◽  
Wide Area ◽  
Quality Data ◽  
Localization And Tracking

Modified Suggestions for Distribution Automation Standards to Coping with DG Connected to the Power Grid

2014 ◽  
Vol 672-674 ◽  
pp. 1281-1287
Author(s):  
Xiao Rui Jing ◽  
Qiang Fu ◽  
Bo Yang
Keyword(s):  
Power Quality ◽  
Distributed Generation ◽  
Low Voltage ◽  
Power Grid ◽  
Rapid Development ◽  
Distribution Network ◽  
Distribution Automation ◽  
Solar Photovoltaic ◽  
Voltage Distribution ◽  
Power Sources

With the rapid development of energy-saving and environmentally friendly power sources, such as solar photovoltaic and biomass power, the demand of distributed generation (DG) systems connected to the low-voltage distribution network is increasing. Distributed generation systems connected to the low-voltage distribution network will impact power quality, security and stability operation of power grid. So power companies should strengthen the supervision of DG. Based on the research of domestic and foreign DG monitoring standards, this paper sorts out the DG relevant provisions in existing distribution automation standards, and proposes some modified suggestions in the existing distribution automation standards to cope with DG connected to the distribution network.

scholarly journals Combined Cluster Analysis and Global Power Quality Indices for the Qualitative Assessment of the Time-Varying Condition of Power Quality in an Electrical Power Network with Distributed Generation

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2050 ◽  
Author(s):  
Michał Jasiński ◽  
Tomasz Sikorski ◽  
Paweł Kostyła ◽  
Zbigniew Leonowicz ◽  
Klaudiusz Borkowski
Keyword(s):  
Cluster Analysis ◽  
Power Quality ◽  
Distributed Generation ◽  
Working Conditions ◽  
Electrical Power ◽  
Quality Data ◽  
Power Network ◽  
Quality Indices ◽  
Aggregated Data ◽  
Global Power

This paper presents the idea of a combined analysis of long-term power quality data using cluster analysis (CA) and global power quality indices (GPQIs). The aim of the proposed method is to obtain a solution for the automatic identification and assessment of different power quality condition levels that may be caused by different working conditions of an observed electrical power network (EPN). CA is used for identifying the period when the power quality data represents a different level. GPQIs are proposed to calculate a simplified assessment of the power quality condition of the data collected using CA. Two proposed global power quality indices have been introduced for this purpose, one for 10-min aggregated data and the other for events—the aggregated data index (ADI) and the flagged data index (FDI), respectively. In order to investigate the advantages and disadvantages of the proposed method, several investigations were performed, using real measurements in an electrical power network with distributed generation (DG) supplying the copper mining industry. The investigations assessed the proposed method, examining whether it could identify the impact of DG and other network working conditions on power quality level conditions. The obtained results indicate that the proposed method is a suitable tool for quick comparison between data collected in the identified clusters. Additionally, the proposed method is implemented for the data collected from many measurement points belonging to the observed area of an EPN in a simultaneous and synchronous way. Thus, the proposed method can also be considered for power quality assessment and is an alternative approach to the classic multiparameter analysis of power quality data addressed to particular measurement points.

scholarly journals Intelligent Starting Current-Based Fault Identification of an Induction Motor Operating under Various Power Quality Issues

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 304
Author(s):  
Sakthivel Ganesan ◽  
Prince Winston David ◽  
Praveen Kumar Balachandran ◽  
Devakirubakaran Samithas
Keyword(s):  
Standard Deviation ◽  
Power Quality ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Classification Accuracy ◽  
Induction Motors ◽  
Quality Data ◽  
Starting Current ◽  
Quality Issues ◽  
Condition Monitoring System

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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