Application of Shannon Wavelet Entropy and Shannon Wavelet Packet Entropy in Analysis of Power System Transient Signals

To reduce the computing complexity of Shannon wavelet entropy(WE), Tsallis WE algorithm was proposed and implemented by combining Tsallis entropy with lifting wavelet transform(LWT), which provided a new method to extract features of transient signals in transmission system. By adjusting the nonextension index, the property of Tsallis entropy was analyzed, and the relations between Tsallis entropy and Shannon entropy were discussed. Taking for instance Tsallis wavelet energy entropy(WEE), the computing complexity of Tsallis WE was analyzed and compared with Shannon WE. In order to verify the practicality of the new algorithm, the paper carried out not only the simulation test for transient faults in transmission system model, but also the processing of practical harmonics and lighting signal based on DSP, which showed that in comparison with Shannon WE the new algorithm can ensure the accuracy of feature extraction for transient signals , but its runtime has been partially reduced.

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Fault Data Compression of Power System with Wavelet Neural Network Based on Wavelet Entropy

Advances in Neural Networks - ISNN 2006 - Lecture Notes in Computer Science ◽

10.1007/11760023_202 ◽

2006 ◽

pp. 1402-1408

Author(s):

Zhigang Liu ◽

Dabo Zhang

Keyword(s):

Neural Network ◽

Power System ◽

Data Compression ◽

Wavelet Neural Network ◽

Wavelet Entropy

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Wavelet Entropy Definition and Its Application in Detection and Identification of Power Systems’ Transient Signals

Wavelet Analysis and Transient Signal Processing Applications for Power Systems ◽

10.1002/9781118977019.ch12 ◽

2016 ◽

pp. 207-242

Keyword(s):

Power Systems ◽

Transient Signals ◽

Wavelet Entropy ◽

Detection And Identification

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Fault detection on a ring-main type power system network using artificial neural network and wavelet entropy method

International Conference on Computing, Communication & Automation ◽

10.1109/ccaa.2015.7148528 ◽

2015 ◽

Cited By ~ 5

Author(s):

Sragdhara Bhattacharya

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fault Detection ◽

Power System ◽

Main Type ◽

Entropy Method ◽

Wavelet Entropy ◽

Artificial Neural ◽

Type Power

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Power Quality Disturbance Research of Grid-Connected Wind Generation Based on Fractional Fourier Transform with Wavelet Entropy

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.130-134.1600 ◽

2011 ◽

Vol 130-134 ◽

pp. 1600-1604

Author(s):

Hua Li Chen ◽

Yun Lian Sun

Keyword(s):

Fourier Transform ◽

Power System ◽

Power Quality ◽

Fractional Fourier Transform ◽

Wind Generation ◽

Wavelet Entropy ◽

Time Frequency ◽

System A ◽

Power Quality Disturbance ◽

Simulation Results

In the process of connected wind generation with the power grid, the classification and recognition of power quality disturbance signals which are researched by the scholars at home and abroad are always the hot issues in Power System. A new method using Fractional Fourier Transform (FRFT) with Wavelet Entropy is presented for recognizing the signals of PQ disturbance based on the characteristics of power quality (PQ) signals. FRFT have better time-frequency aggregation and can choose better domain instead of and make PQ disturbance recognize more accurately with wavelet entropy. Simulation results demonstrate its effectiveness.

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