PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

PQ data compression algorithm with modified quantizer and adaptive band logic using DTCWT

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Abstract:With growing demand for energy, power generated in renewable sources at various locations are distributed throughout the power grid. The power grid known as the smart grid needs to monitor power generation and its smart distribution. Smart meters provide solutions for monitoring power over smart grids. Smart meters need to continuously log data and at every source there is a large amount of data generated that needs to be compressed for both storage and transmission over the smart grid. In this paper, a novel algorithm for PQ data compression is proposed that uses the Dual Tree Complex Wavelet Transform (DTCWT) for sub-band computation and a modified quantizer is designed to reduce subband coefficient limits to less than 4 bits. The Run Length Encoding (RLC) and Huffman Coding algorithm encode the data further to achieve compression. The performance metrics such as a peak-signal-to-noise ratio (PSNR) and compression ratio (CR) are used for evaluation and it is found that the modified DTCWT (MDTCWT) improves PSNR by a factor of 3% and the mean squared error (MSE) by a factor of 16% as compared with the DTCWT based PQ compression algorithm.
Rocznik
Strony
207--223
Opis fizyczny
Bibliogr. 15 poz., rys., tab., wz.
Twórcy
autor
  • Sri krishna Institute of Technology No 29, Chimney hills Chikkabanavara post, Bangalore-560090, Karnataka, India
  • Sri krishna Institute of Technology No 29, Chimney hills Chikkabanavara post, Bangalore-560090, Karnataka, India
autor
  • MS Engineering College Navarathna Agrahara Sadahalli Post, Bengaluru - 562 110, Karnataka, India
Bibliografia
  • [1] http://ibm.com/software/data/industry/energy.html, accessed May 2012.
  • [2] http://netl.doe.gov/moderngrid, accessed January 2007.
  • [3] Norman C.F., Chan Y.C., Lau W-H., Real-Time Power-Quality Monitoring with Hybrid Sinusoidal and Lifting Wavelet Compression Algorithm, IEEE Transactions on power Delivery, vol. 27, iss. 4, pp. 1718–1726 (2012).
  • [4] Bingham R.P., Kreiss D., Santoso S., Advances in data reduction techniques for power quality Instrumentation, Proceeding of 3rd European Power Quality Conference, Bremen, Germany, pp. 47–55 (1995).
  • [5] Wang J., Wang C., Compression of Power Quality Disturbance Data Based on Energy Threshold and Adaptive Arithmetic Encoding, TENCON-2005, IEEE region10 Conference, Melbourne, Qld, Australia, pp. 1–4 (2005).
  • [6] Zhang D., Bi Y., Zhao J., A new data compression algorithm for power quality online monitoring, Proceeding of International Conference on Sustainable Power Generation and Supply, China, pp. 1–4 (2009).
  • [7] Ning J.,Wang J., Gao W., Liu C., A wavelet-based data compression technique for smart grid, IEEE Transactions on Smart Grid, vol. 2, no. 1, pp. 212–218 (2011).
  • [8] Parseh R., Acevedo S.S., Kansanen K., Molinas M., Ramstad T.A., Real-time compression of Measurements in distribution grids, Proceeding of 3rd International conference, Smart Grid Communication, Tainan City, Taiwan, pp. 223–228 (2012).
  • [9] Dash P.K., Panigrahi B.K., Sahoo D.K., Panda G., Power Quality Disturbance Data Compression, Detection, and Classification Using Integrated Spline Wavelet and S-Transform, IEEE Transactions on Power Delivery, vol. 18, no. 2, pp. 595–600 (2003).
  • [10] Mohammadzadeh S., Seifossadat S., Ahmadzadeh M., Power Quality Disturbance Data Compression Using WaveletsTransform, International Conference on Computer, Systems and Electronics Engineering, South Africa, Johannesburg, pp. 78–82 (2014).
  • [11] Zhao Hongtu, Xi Dongmei, Compression and Realization of Power Quality Disturbance Data Based on Wavelet Analysis, International Conference on Uncertainty Reasoning and Knowledge Engineering, Bali, Indonesia, pp. 217–219 (2011).
  • [12] Yi Zhong, Cheng Chen, Hang Su, Measurement and Analysis for Power Quality Using Compressed Sensing, Journal of Applied Science and Engineering, vol. 17, no. 3, pp. 305–318 (2014).
  • [13] Santoso S., Powers E.J., Grady W.M., Power quality disturbance data compression using wavelet transform methods, IEEE Transactions on Power Delivery, vol. 12, no. 3, pp. 1250–1257 (1997).
  • [14] Albu M.M., Neurohr R., Apetrei D., Silvas I., Monitoring voltage and frequency in smart distribution grids. A case study on data compression and accessibility, Published in Power and Energy Society General Meeting, 2010 IEEE, Providence, RI, USA, pp. 1–6 (2010).
  • [15] Zhang Li, Ma Shiqiang, An Improved Method Based onWavelet for Power Quality Compression, 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), Hefei, China, pp. 1750–1753 (2016).
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0d19f5fc-ad95-40a8-84ee-8c26f5dcf83d
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.