scholarly journals A Virtual Instrument for Electrical Power Quality Analysis using Wavelet Technology in Real-time

2014 ◽  
pp. 91-96
Author(s):  
Debasis Tripathy ◽  
Amar Kumar Barik ◽  
B. Srinivas Rao ◽  
Ranjita Rout
Keyword(s):  
Power System ◽  
Real Time ◽  
Power Quality ◽  
Input Signal ◽  
Virtual Instrument ◽  
Wavelet Decomposition ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Decomposition Tree ◽  
Power Quality Disturbances

This paper discusses the design of a virtual instrument for detection and analysis of power quality disturbances in Power System using Wavelet Packet Transform with the help of LabVIEW® algorithm. The virtual instrument designed can operate in different working modes depending on the type of power quality disturbances to be detected and analyzed. Different wavelet analysis (discrete or wavelet-packet transform), with different mother wavelet, decomposition tree and different sampling rate is performed on the input signal either in real-time or off-line. The instrument also permits the partial implementation of a wavelet decomposition tree when we are only interested in a specific frequency band in the input signal. The real signals from chroma programming are used in LabVIEW® algorithm by Data Acquisition (DAQ) card to acquire and digitize the input line signal to obtain the results. The results obtained in simulation using real signals demonstrate good performance of the instrument developed for the detection and analysis of different power quality disturbances with proper time information of the signal. This helps us to analyze the power quality problems to improve the supply quality of the power system effectively by taking proper preventive measures

Power Quality Disturbance Classification Using Wavelet Packet Transform and Back Propagation Algorithm

2016 ◽  
Vol 13 (10) ◽  
pp. 7099-7109
Author(s):  
M. K Elango ◽  
A Jagadeesan ◽  
K. Mohana Sundaram
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Virtual Instrument ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Wavelet Packet Transform ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Power Quality Disturbances

This paper develops a real time solution for detecting the Power Quality events. Fourteen events are generated through experimental setup and the signals are acquired through a voltage Data Acquisition Card, NI DAQ-9225, controlled by a Virtual Instrument software package. The features extracted from the Wavelet Transformation are fed into the Back Propagation Neural Network for training. By the virtue of a Neural Network property, it gets self-adapted and self-learned aiding in automatic classification of Power Quality Events. A combination of Wavelet Transform technique and Neural Networks are employed to detect and characterize the Power Quality Disturbances. The result obtained shows the effectiveness of the Wavelet Packet Transform based Back Propagation algorithm in classifying the Power Quality Disturbances. The results produced by the proposed methodology based Back Propagation Algorithm is verified with the Power Quality Analyser.

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