Application of Arithmetic Coding for Electric Power Disturbance Data Compression With Wavelet Packet Enhancement

Huang, Shyh-Jier; Jou, Ming-Jong

doi:10.1109/tpwrs.2004.825899

Cited by 49 publications

(31 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is important to note that a disadvantage of this system is the large amount of data which has to be managed during the monitoring process. Even though several researchers have focused their attention on compacting the electrical information [3][4][5], a more detailed investigation on this state of the art process highlights that the compression of the signal waveform is not as mature as for speech, image, and video compression [6]. The complexity depends on the characteristics of the electrical signals of the modern equipment, which are related to the different loads (static, rotating, power electronics-based, and so on) and generators (deterministic-based, stochastic-based).…”

Section: Introductionmentioning

confidence: 99%

Fourier, Wavelet, and Hilbert-Huang Transforms for Studying Electrical Users in the Time and Frequency Domain

2017

View full text Add to dashboard Cite

Abstract:The analysis of electrical signals is a pressing requirement for the optimal design of power distribution. In this context, this paper illustrates how to use a variety of numerical tools, such as the Fourier, wavelet, and Hilbert-Huang transforms, to obtain information relating to the active and reactive power absorbed by different types of users. In particular, the Fourier spectrum gives the most important frequency components of the electrical signals, and the wavelet analysis highlights the non-stationarity of those frequency contributions, whereas the Hilbert-Huang transform, by means of the Empirical Mode Decomposition, provides a more complete spectrum of frequencies.

show abstract

Section: Introductionmentioning

confidence: 99%

Fourier, Wavelet, and Hilbert-Huang Transforms for Studying Electrical Users in the Time and Frequency Domain

2017

View full text Add to dashboard Cite

show abstract

“…Generally, the use of transient features requires the implementation of signal processing such as DWT or S-transform at the high sampling rate to capture the transient effects. In electrical transient analyses, these wavelet multi-resolution analysis (WMRA) techniques are useful to monitor power system small signal oscillations, power quality (PQ), and electric power disturbance [16][17][18][19]. For example, Energies 2017, 10, 611 3 of 20 the authors [19] employed harmonic voltages and wavelet coefficients as PQ features for placement of power quality measurement facilities to identify PQ problems.…”

Section: Introductionmentioning

confidence: 99%

Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

Chang

Linh

2017

Energies

View full text Add to dashboard Cite

This paper proposes statistical feature extraction methods combined with artificial intelligence (AI) approaches for fault locations in non-intrusive single-line-to-ground fault (SLGF) detection of low voltage distribution systems. The input features of the AI algorithms are extracted using statistical moment transformation for reducing the dimensions of the power signature inputs measured by using non-intrusive fault monitoring (NIFM) techniques. The data required to develop the network are generated by simulating SLGF using the Electromagnetic Transient Program (EMTP) in a test system. To enhance the identification accuracy, these features after normalization are given to AI algorithms for presenting and evaluating in this paper. Different AI techniques are then utilized to compare which identification algorithms are suitable to diagnose the SLGF for various power signatures in a NIFM system. The simulation results show that the proposed method is effective and can identify the fault locations by using non-intrusive monitoring techniques for low voltage distribution systems.

show abstract

“…Many PQ disturbance data compression methods were reported based on lossless and lossy coding techniques [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. In [12,14], three lossless coding methods based on the Huffman, LZ78 and WINZIP algorithms were developed for compressing PQ signals.…”

Section: Existing Pq Disturbance Compression Methodsmentioning

confidence: 99%

“…In [12,14], three lossless coding methods based on the Huffman, LZ78 and WINZIP algorithms were developed for compressing PQ signals.…”

Section: Existing Pq Disturbance Compression Methodsmentioning

confidence: 99%

Simultaneous denoising and compression of power system disturbances using sparse representation on overcomplete hybrid dictionaries

Manikandan

Samantaray

Kamwa

2015

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

This study introduces a novel unified framework for simultaneous denoising and compression of electric power system disturbance signals using sparse signal decomposition and reconstruction on overcomplete hybrid dictionary (OHD) matrix. In the proposed method, the power quality signal is first decomposed into deterministic sinusoidal components and non-deterministic components using the OHD matrix, including discrete impulse dictionary (I), cosine dictionary (C), sine dictionary (S) and the ℓ 1 -norm optimisation algorithm. Then, the hard-thresholding, uniform threshold dead-zone quantisation, modified index coding and Huffman coding techniques are used for compression of significant detail signal samples and approximation coefficients. To justify the selection of OHD matrix, four compression methods are implemented using the decomposition techniques based on the dictionaries Ψ = [I C S] and Ψ = [I C], the wavelet transform (WT) and the discrete cosine transform (DCT). The performance of each method is tested and validated using a wide variety of typical power quality disturbance (PQD) signals taken from the IEEE-1159-PQE and GIM-PQE databases and generated using the Microgrid model. The results show that the method with dictionary Ψ = [I C S] is capable of effectively compressing the PQD signals as well as suppressing the noise components in the signals.

show abstract

Application of Arithmetic Coding for Electric Power Disturbance Data Compression With Wavelet Packet Enhancement

Cited by 49 publications

References 24 publications

Fourier, Wavelet, and Hilbert-Huang Transforms for Studying Electrical Users in the Time and Frequency Domain

Fourier, Wavelet, and Hilbert-Huang Transforms for Studying Electrical Users in the Time and Frequency Domain

Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

Simultaneous denoising and compression of power system disturbances using sparse representation on overcomplete hybrid dictionaries

Contact Info

Product

Resources

About