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Fast Algorithm for PQ Data Compression Using Integer DTCWT and Entropy Encoding

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Fast Algorithm for PQ Data Compression Using Integer DTCWT and Entropy Encoding International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 22 (2017) pp. 12219-12227 © Research India Publications. http://www.ripublication.com Fast Algorithm for PQ Data Compression using Integer DTCWT and Entropy Encoding Prathibha Ekanthaiah 1 Associate Professor, Department of Electrical and Electronics Engineering, Sri Krishna Institute of Technology, No 29, Chimney hills Chikkabanavara post, Bangalore-560090, Karnataka, India. Orcid Id: 0000-0003-3031-7263 Dr.A.Manjunath 2 Principal, Sri Krishna Institute of Technology, No 29, Chimney hills Chikkabanavara post, Bangalore-560090, Karnataka, India. Orcid Id: 0000-0003-0794-8542 Dr. Cyril Prasanna Raj 3 Dean & Research Head, Department of Electronics and communication Engineering, MS Engineering college , Navarathna Agrahara, Sadahalli P.O., Off Bengaluru International Airport,Bengaluru - 562 110, Karnataka, India. Orcid Id: 0000-0002-9143-7755 Abstract metering infrastructures (smart metering), integration of distributed power generation, renewable energy resources and Smart meters are an integral part of smart grid which in storage units as well as high power quality and reliability [1]. addition to energy management also performs data By using smart metering Infrastructure sustains the management. Power Quality (PQ) data from smart meters bidirectional data transfer and also decrease in the need to be compressed for both storage and transmission environmental effects. With this resilience and reliability of process either through wired or wireless medium. In this power utility network can be improved effectively. Work paper, PQ data compression is carried out by encoding highlights the need of development and technology significant features captured from Dual Tree Complex encroachment in smart grid communications [2]. Due to Wavelet Transform (DTCWT) sub bands. The DTCWT filter Continuous monitoring of PQ data based on data logging from coefficients are scaled to integer values and fixed point smart meter. This leads data will be in Giga byte of algorithm is developed to improve computation speed. The information [3] [4]. In [5], compression of power quality bands corresponding to PQ disturbances such as Swell, Sag, disturbance data in wavelet transform combined with adaptive Harmonics and Transients are captured accurately from arithmetic encoding, proposed demonstrating with 7.09% CR corresponding DTCWT sub bands. A novel thresholding and and mean square error by 1.42x10-3NMSE, compared with quantization algorithm is developed to convert the bands to result of wavelet coefficient threshold of 13.67%CR and packets by reducing data size with minimum loss. Run Length 1.88x10-3NMSE for voltage sag. In [6], compression Encoding (RLC) and Huffman Coding algorithm encodes the technique algorithm used based on Huffman coding to data further to achieve compression. The proposed algorithm improve compression ratio, different input samples are taken achieves PSNR of 42dB and an improvement is achieved. as input. In [7], input data’s are transformed into wavelet of Significant PQ disturbances signal are retained and sub bands to gain multi resolutions, so that the PQ redundancies in insignificant PQ data are minimized to disturbances are chosen and noise is eliminated to reach achieve compression ratio of 67%. advanced compressions. In [8], the techniques are based on Keywords: Power Quality, Data Compression, Complex different wavelet theory and also multi-resolution analysis. By Wavelets, Smart Grid. using of data compression technique, power quality disturbances are reconstructed. In [9], adaptive quantization technique is proposed to select considerable data from the PQ INTRODUCTION signals after Parks transform. The quantizers are considered to Information and Communication Technology for power predictive logic so that inverse process is carried out without generation, distribution and monitoring been used for Smart loss during reconstruction process. The limitations of wavelet grids. The advanced monitoring systems will upgrade the grid transform are the shift variance and loss of directional performance such as self-healing from power disturbances, selectivity. The power fluctuations can lead to time delays in energy management, and automation and highly developed PQ signals are being monitored and using DWT will lead to change in PQ signal metrics as DWT is shift variance. Dual 12219 International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 22 (2017) pp. 12219-12227 © Research India Publications. http://www.ripublication.com Tree Complex Wavelet Transform (DTCWT) will overcome Figure 1. The input signal are represented by X, that shift variance limitations as DTCWT has real and imaginary consisting of N samples are decomposed to 10-sub bands are filters that will generate wavelet coefficients that are shift represented of real and imaginary bands of DTCWT outputs invariant. In this paper, novel algorithm based on DTCWT is consisting of N/16 samples. The transform is twice expansive proposed to compress PQ signals and suitable encoding as it generates 2N DWT coefficients for N-point input signal. schemes are presented to compress PQ signals achieving Table 1 represents the filter coefficients used for real tree and advanced compression. Section 2 presents brief introduction imaginary tree for first level decomposition. There are 10 to DTCWT, Section 3 discusses the proposed PQ data coefficients are considered for each of low pass and high pass compression algorithm based on DTCWT, Section 4 presents filters of real and imaginary decomposition tree structures. the experimental setup and flow diagram for software The filter coefficients need minimum of 16-bits for implementation of proposed algorithm. Section 5 presents the representation and hence time consuming in terms of results and finally conclusion is presented in Section 6. performing arithmetic operations. In this work, the filter coefficients are scaled to nearest integers and fixed points arithmetic based algorithm and data DUAL TREE COMPLEX WAVELET TRANSFORM representation is proposed. The scaled filter coefficients are ALGORITHM presented in Table2 The input signal are decomposes to low pass and high pass sub bands in DTCWT, which is similar to DWT but generates imaginary sub bands in adding up to real sub bands. The Table 1: DTCWT filter coefficients for first stage wavelet filter coefficients for computation of real and DTCWT Filter DTCWT Filter imaginary sub bands are orthogonally shifted and which are Coefficients (Real) Coefficients associated by Hilbert transform. The complex wavelet (Imaginary) transform is represented by Eq. (1), LOW HIGH LOW HIGH PASS PASS PASS PASS Ψ(t) = Ψn(t) + j Ψg(t) ---------------------- (1) 0 0 0.0112267 0 9215254 Where, Ψg(t) is Hilbert transform of Ψn(t). - - 0.0112267 0 The input signal S(z) is decomposed into low frequency part 0.0883883 0.011226 9215254 4764832 7921525 Sl1(z) and high frequency part Sh1(z) and can be represented as 4 in Eq. (2), 0.0883883 0.011226 - - 4764832 7921525 0.0883883 0.088388 S (z) = Sl1 (z) + Sh1 (z) ---------------------- (2) 4 4764832 3476483 2 0.6958799 0.088388 0.0883883 - N/8 N/16 8903400 3476483 4764832 0.088388 4 4 Ca 2 3476483 H 0a N/4 2 3 H 0a 4 N/2 4 Da 0.6958799 0.088388 0.6958799 0.695879 LPR 2 H 1a H 0a 3 8903400 3476483 8903400 9890340 3 Da 1 H 1a H 0a 2 2 0 2 Da H 1a 0.0883883 - 0.6958799 - 1 1 4764832 0.695879 8903400 0.695879 H 1a Da 9890340 9890340 N 4 4 H 0b Cb HPR 0 0 3 H 0b 4 4 Db - 0.695879 0.0883883 0.088388 X 2 H 1b LPI H 0b 3 0.0883883 9890340 4764832 3476483 3 Db 1 H 1b 4764832 0 2 H 0b 2 2 1b Db H 0.0112267 - - 0.088388 1 9215254 0.088388 0.0883883 3476483 1 Db H 1b 3476483 4764832 2 HPI 2 0.0112267 - 0 0.011226 L1 L2 L3 L4 9215254 0.088388 7921525 3476483 4 2 Figure 1: DCTWT algorithm for four-level decomposition 0 0 0 - 0.011226 7921525 4 DCTWT algorithm for four-level decomposition is shown in 12220 International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 22 (2017) pp. 12219-12227 © Research India Publications. http://www.ripublication.com Table 2: Scaled filter coefficients. coefficients. The low pass coefficients contain the lowest band of PQ signal (pure sine wave) and the high pass band contains DTCWT Filter DTCWT Filter Coefficients the detail features such as PQ disturbances. Coefficients (Real) (Imaginary) LOW HIGH LOW PASS HIGH PASS PQ DATA COMPRESSION USING DTCWT BASED PASS PASS ALGORITHM 0 0 2 0 -22 -22 2 0 DTCWT based new PQ data compression algorithm is 22 2 -22 -22 presented in Figure 2. The input raw data’s are pre-processed 178 22 22 -22 to eliminate noise, later preprocessing operation. The noise 178 22 178 178 filtered PQ signal is processed by the DTCWT block, which is 22 -178 178 -178 to generate sub bands. The level select input denoted by N is -22 178 22 22 set to find out the number of levels required. The N is input, 2 -22 -22 22 set based on input sampling frequency. The sub bands 2 -22 0 2 coefficients are processed by the thresholder and quantizer 0 0 0 -2 unit by setting the threshold level and quantization level respectively. In this process the insignificant coefficients and redundant information in the sub bands are eliminated. The DTCWT algorithm of four levels decomposition generates ten entropy encoding schemes such as Run Length Coding (RLC 4 4 3 2 1 4 4 3 sub bands denoted by {Ca , Da , Da , Da , Da , Cb , Db , Db , and Huffman Coding process the quantized data to achieve 2 1 Db , and Db }. The subscripts are used as a and b represent compression. Later these compressed data are grouped into real tree and imaginary trees respectively. C represents number of packets and is prepared for storage or transmission approximation output, D represents detail output.
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