To reduce the stress of data transmission and storage for power quality (PQ) in smart distribution systems and help PQ analysis, a multichannel data compression based on iterative PCA (principal component analysis) algorithm is introduced. The proposed method uses PCA to reduce the redundancy of data to achieve the purpose of compressing data. In order to improve the calculating speed, an iterative method is proposed to compute the principal components of the covariance matrix. The correctness and feasibility of the proposed method are verified by field PQ data tests. Compared with discrete wavelet transform (DWT) method, the proposed method has good performance on compression ratio and reconstruction accuracy.
References
[1]
Heydt, G.T. (2010) The Next Generation of Power Distribution Systems. IEEETransactions on Smart Grid, 1, 225-335. https://doi.org/10.1109/TSG.2010.2080328
[2]
McBee, K.D. and Simoes, M.G. (2012) Utilizing a Smart Grid Monitoring System to Improve Voltage Quality of Customers. IEEE Transactions on Smart Grid, 3, 738-743. https://doi.org/10.1109/TSG.2012.2185857
[3]
Li, S. and Wang, X. (2015) Cooperative Change Detection for Voltage Quality Monitoring in Smart Grids. IEEE Transactions on Information Forensics and Security, 11, 86-99. https://doi.org/10.1109/TIFS.2015.2477796
[4]
Tcheou, M.P. and Lovisolo, L. (2014) The Compression of Electric Signal Waveforms for Smart Grid: State of the Art and Future Trend. IEEE Transactions on Smart Grid, 5, 291-304. https://doi.org/10.1109/TSG.2013.2293957
[5]
Cormane, J. and Astonishment, F. (2015) Spectral Shape Estimation in Data Compression for Smart Grid Monitoring. IEEE Transactions on Smart Grid, 7, 1214-1221. https://doi.org/10.1109/TSG.2015.2500359
[6]
Unterweger, A. and Engel, D. (2015) Resumable Load Data Compression in Smart Grids. IEEE Transactions on Smart Grid, 6, 919-929.
https://doi.org/10.1109/TSG.2014.2364686
[7]
Santo, S., Powers, E.J. and Grady, W.M. (1997) Power Quality Disturbance Data Compression Using Wavelet Transform Methods. IEEE Transactions on Power Delivery, 12, 1250-1256. https://doi.org/10.1109/61.637001
[8]
Meher, S.K., Pradhan, A.K. and Panda, G. (2004) An Integrated Data Compression Scheme for Power Quality Events Using Spline Wavelet and Neural Network. Electric Power System Research, 69, 213-220. https://doi.org/10.1016/j.epsr.2003.10.001
[9]
Norman, C.F.T. and John, Y.C.C. (2012) Real-Time Power-Quality Monitoring with Hybrid Sinusoidal and Lifting Wavelet Compression Algorithm. IEEE Transactions on Power Delivery, 27, 1718-1726. https://doi.org/10.1109/TPWRD.2012.2201510
[10]
Ning, J., Wang, J., Gao, W. and Liu, C. (2011) A Wavelet-Based Data Compression Technique for Smart Grid. IEEE Transactions on Smart Grid, 2, 212-218.
https://doi.org/10.1109/TSG.2010.2091291
[11]
Zhang, M., Li, K.C. and Hu, Y.S. (2011) A High Efficient Compression Method for Power Quality Applications. IEEE Trans-actions on Instrumentation and Measurement, 60, 1976-1985. https://doi.org/10.1109/TIM.2011.2115590
[12]
He, S.F., Zhang, M., Tian, W., Zhang, J. and Ding, F. (2015) A Parameterization Power Data Compress Using Strong Trace Filter and Dynamics. IEEE Transactions on Instrumentation and Measurement, 64, 2636-2645.
https://doi.org/10.1109/TIM.2015.2416451
[13]
Souza, J.C.S. de, Assis, T.M.L. and Pal, B.C. (2015) Data Compression in Smart Distribution Systems via Singular Value Decomposition. IEEE Transactions on Smart Grid, 6, 275-284.
[14]
Draper, B.A., Baek, K., Bartlett, M.S. and Beveridgea, J.R. (2003) Recognizing Faces with PCA and ICA. Computer Vision and Image Understanding, 91, 115-137.
https://doi.org/10.1016/S1077-3142(03)00077-8
[15]
Ding, Q. and Kolaczyk, E.D. (2010) A Compressed PCA Subspace Method for Anomaly Detection in High-Dimensional Data. IEEE Transactions on Information Theory, 59, 7419-7419. https://doi.org/10.1109/TIT.2013.2278017
[16]
Liu, Y. and Pados, D.A. (2016) Compressed-Sensed-Domain L1-PCA Video Surveillance. IEEE Transactions on Multimedia, 18, 351-363.
https://doi.org/10.1109/TMM.2016.2514848
[17]
Yi, Z, Ye, M., Lv, J.C. and Tan, K.K. (2005) Convergence Analysis of a Deterministic Discrete Time System of Oja’s PCA Learning Algorithm. IEEE Transactions on Neural Network, 16, 1318-1328. https://doi.org/10.1109/TNN.2005.852236
