Electrical Harmonic Energy Measurement Based on Wavelet Packet Decomposition and Reconstruction Algorithm (This paper has been retracted. It has come to the publishers attention that plagiarism was involved.)
DOI:
https://doi.org/10.13052/dgaej2156-3306.37212Keywords:
Harmonic energy, wavelet packet decomposition and recon- struction, electric harmonicAbstract
In order to study the accurate measurement of electric energy in complex
industrial field, a method of harmonic electric energy measurement based
on wavelet packet decomposition and reconstruction algorithm, as well as
the calculation formula of harmonic power and the principle of harmonic
electric energy measurement are proposed. Using db42 wavelet function to
carry out harmonic energy metering simulation analysis, the results show
that: The fundamental frequency of the simulation signal is 50 Hz, two-layer
wavelet packet transform is adopted, the simulation input signals within 40
fundamental wave cycles are taken, and the sampling frequency fs is 800 Hz.
Conclusion: The three-phase harmonic energy metering device based on
virtual instrument technology has realized the measurement of each harmonic
active power and reactive power, and the accuracy reaches 0.2 s.
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References
Veitinger J, Purves R S, Sovilla B. Potential slab avalanche release area
identification from estimated winter terrain: a multi-scale, fuzzy logic
approach[J]. Natural Hazards and Earth System Sciences Discussions,
(10), pp. 6569–6614, 2015.
Zhao L Y, Wang L, Yan R Q. Rolling Bearing Fault Diagnosis
Based on Wavelet Packet Decomposition and Multi-Scale Permutation
Entropy[J]. Entropy, 17(9), pp. 6447–6461, 2015.
Lin Z, Jia Y. Transcale control for a class of discrete stochastic sys-
tems based on wavelet packet decomposition[J]. Information Sciences,
(1), pp. 25–41, 2015.
Wang, Ming-yue, Miao. Adaptive Bearing Fault Diagnosis based on
Wavelet Packet Decomposition and LMD Permutation Entropy[J]. Inter-
national Journal of Plant Engineering and Management, 04(v.47;No.229),
pp. 12–26, 2016.
GMR, Institute, of. Detection and power quality disturbance classifi-
cation based on wavelet packet decomposition and modified immune
optimization algorithm[J]. International Journal of Knowledge-based
and Intelligent Engineering Systems, 16(4), pp. 261–277, 2016.
Resalat S N, Saba V. A practical method for driver sleepiness detec-
tion by processing the EEG signals stimulated with external flickering
light[J]. Signal Image & Video Processing, 9(8), pp. 1751–1757, 2015.
Ye R, Guo Z, Liu R. Wind Speed and Wind Power Forecasting Method
Based on Wavelet Packet Decomposition and Improved Elman Neural
Network[J]. Diangong Jishu Xuebao/Transactions of China Electrotech-
nical Society, 32(21), pp. 103–111, 2017.
Zhang Y, Pan F, Shen J. Adaptive audio steganography algorithm
based on wavelet packet decomposition and matrix code[J]. Journal of
Computer Applications, 35(3), pp. 722–725, 2015.
Djerbouai S, Souag-Gamane D. Drought Forecasting Using Neural
Networks, Wavelet Neural Networks, and Stochastic Models: Case of
the Algerois Basin in North Algeria[J]. Water Resources Management,
(7), pp. 2445–2464, 2016.
Kim J. Discrete Wavelet Transform-Based Feature Extraction of Experi-
mental Voltage Signal for Li-Ion Cell Consistency[J]. IEEE Transactions
on Vehicular Technology, 65(3), pp. 1150–1161, 2016
