Pseudorandom Dynamic Test Power Signal Modeling and Electrical Energy Compressive Measurement Algorithm

Open access

Abstract

With the rapid construction of smart grid, many applications of the new generation and the large power dynamic loads are revolutionizing the electrical energy measurement of electricity meters. The dynamic measurement errors produced by electricity meters are intolerable. In order to solve the dynamic error measurement of electrical energy, firstly, this paper proposes a three-phase pseudorandom dynamic test power signal model to reflect the main characteristics of dynamic loads. Secondly, a compressive measurement algorithm is proposed by the means of steady-state optimization to accurately measure the electrical energy. The experimental results confirm the effectiveness of the three-phase pseudorandom dynamic test signal model, the maximum errors of compressive measurement algorithm are superior to 1×10-13, the high precision enables the algorithm to accurately measure the electrical energy under different dynamic conditions.

[1] Kukuča, P., Chrapčiak, I. (2016). From smart metering to smart grid. Measurement Science Review, 16 (3), 142-148.

[2] Lao, K.-W., Wong, M.-C., Dai, N., Wong, C.-K., Lam, C.-S. (2015). A systematic approach to hybrid railway power conditioner design with harmonic compensation for high-speed railway. IEEE Transactions on Industrial Electronics, 62 (2), 930-942.

[3] Bernieri, A., Betta, G., Ferrigno, L., Laracca, M., Moriello, R.S.L. (2013). Electrical energy metering: Some challenges of the European Directive on Measuring Instruments (MID). Measurement, 46 (9), 3347-3354.

[4] Artale, G., Cataliotti, A., Cosentino, V., Cara, D.D., Nuccio, S., Tine, G. (2017). Arc fault detection method based on CZT low-frequency harmonic current analysis. IEEE Transactions on Instrumentation and Measurement, 66 (5), 2232 -2239.

[5] Wang, X.W., Chen, J.X., Yuan, R.M., Jia, X.L., Zhu, M., Jiang, Z.Y. (2017). OOK power model based dynamic error testing for smart electricity meter. Measurement Science and Technology, 28 (2), 025015.

[6] OIML. (2006). Instruments for measuring electrical quantities, IR46.

[7] International Electrotechnical Commission (IEC). (2003). Electricity metering equipment (a.c.) - Particular requirements - Part 21: Static meters for active energy (classes 1 and 2). International Standard IEC 62053-21.

[8] Cataliotti, A., Cosentino, V., Lipari, A., Nuccio, S. (2009). Metrological characterization and operating principle identification of static meters for reactive energy: An experimental approach under nonsinusoidal test conditions. IEEE Transactions on Instrumentation and Measurement, 58 (5), 1427-1435.

[9] Georgakopoulos, D., Wright, P.S. (2007). Exercising the dynamic range of active power meters under nonsinusoidal conditions. IEEE Transactions on Instrumentation and Measurement, 56 (2), 369-372.

[10] IEEE. (2010). IEEE 1459-2010 IEEE Standard: Definitions for the measurement of electric power quantities under sinusoidal, nonsinusoidal, balanced, or unbalanced conditions.

[11] Cataliotti, A., Cosentino, V., Nuccio, S. (2008). A virtual instrument for the measurement of IEEE Std. 1459-2000 power quantities. IEEE Transactions on Instrumentation and Measurement, 57 (1), 85-94.

[12] Lu, Z.L., Li, M., Zhu, Z.Z., Zheng, J.Z., Wang, L., Eddy, S. (2012). Evaluation of the dynamic performance characteristic of electrical energy meters. In Conference on Precision Electromagnetic Measurements (CPEM), 1-6 July 2012, Washington, DC, USA.

[13] Huang, H.T., Lu, Z.L., Wang, L., Liu, L.J., Jia, Z.S., Huang, Q.L., Peng, X.J., Pan, Y. (2016). Dynamical waveforms and the dynamical source for electricity meter dynamical experiment. In Conference on Precision Electromagnetic Measurements (CPEM), 10-15 July 2016, Ottawa, Canada.

[14] Dix, C.H. (1982). Calculated performance of a digital sampling wattmeter using systematic sampling. IEE Proceedings A - Physical Science, Measurement and Instrumentation, Management and Education - Reviews, 129 (3), 172-175.

[15] Dai, X.Z., Tang, T., Gretsch, R. (1993). Quasisynchronous sampling algorithm and its applications I. Principle and measurement of 'average' values of periodic signal. In IEEE Instrumentation and Measurement Technology Conference, 18-20 May 1993. IEEE, 88-93.

[16] Voloshko, A.V., Kotsar, O.I., Malik, O.P. (1995). An approach to the design of digital algorithms for measuring power consumption characteristic. IEEE Transactions on Instrumentation and Measurement, 10 (2), 607-612.

[17] Daniel, B., Dario, P. (2016). Accuracy analysis of the sine-wave parameters estimation by means of the windowed three-parameter sine-fit algorithm. Digital Signal Processing, 50, 12-23.

[18] Donoho, D. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52 (4), 1289-1306.

[19] Candes, E.J., Romberg, J., Tao, T. (2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52 (2), 489-509.

[20] Yang, J.B ., Liao, X.J., Yuan, X., Llull, P., Brady, D.J., Sapiro, G., Carin, L. (2015). Compressive sensing by learning a Gaussian mixture model from measurements. IEEE Transactions on Image Processing, 24 (1), 106-119.

[21] Zahedi, R., Krakow, L.W., Chong, E.K.P., Pezeshki, A. (2013). Adaptive compressive measurement design using approximate dynamic programming. In American Control Conference (ACC), 17-19 June, 2013, Washington, DC, USA, 2442-2447.

[22] Bertocco, M., Frigo, G., Narduzzi, C., Tramarin, F. (2014). Resolution enhancement by compressive sensing in power quality and phasor measurement. IEEE Transactions on Instrumentation and Measurement, 63 (10), 2358-2367.

[23] Du, Z.H., Chen, X.F., Zhang, H., Miao, H.H., Guo, Y.J., Yang, B.Y. (2016). Feature identification with compressive measurements for machine fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 65 (5), 977-987.

[24] Yang, G., Tan, V.Y.F., Ho, C.K., Ting, S.H., Guan, Y.L. (2013). Wireless compressive sensing for energy harvesting sensor nodes. IEEE Transactions on Signal Processing, 61 (18), 4491-4505.

[25] Atia, G.K. (2015). Change detection with compressive measurements. IEEE Signal Processing Letters, 22 (2), 182-186.

[26] Mendelson, S., Pajor, A., Tomczak-Jaegermann, N. (2008). Uniform uncertainty principle for Bernoulli and subgaussian ensembles. Constructive Approximation, 28 (3), 277-289.

[27] Haupt, J., Bajwa, W., Raz, G., Nowak, R. (2010). Toeplitz compressed sensing matrices with applications to sparse channel estimation. IEEE Transactions on Information Theory, 56 (11), 5862-5875.

[28] Candes, E., Romberg, J. (2007). Sparsity and incoherence in compressive sampling. Inverse Problems, 23 (3), 969-985.

[29] Yan, W.J., Wang, Q., Shen, Y. (2014). Shrinkage- Based alternating projection algorithm for efficient measurement matrix construction in compressive sensing. IEEE Transactions on Instrumentation and Measurement, 63 (5), 1073-1084.

[30] Yu, Y., Petropulu, A.P., Poor, H.V. (2011). Measurement matrix design for compressive sensing- based MIMO radar. IEEE Transactions on Signal Processing, 59 (11), 5338-5352.

[31] Li, G., Zhu, Z.H., Yang, D.H., Chang, L.P., Bai, H. (2013). On projection matrix optimization for compressive sensing systems. IEEE Transactions on Signal Processing, 61 (11), 2887-2898.

[32] Davenport, M., Boufounos, P., Wakin, M., Baraniuk, R. (2010). Signal processing with compressive measurements. IEEE Journal of Selected Topics in Signal Processing, 4 (2), 445-460.

[33] Park, J.Y., Wakin, M., Gilbert, A. (2014). Modal analysis with compressive measurements. IEEE Transactions on Signal Processing, 62 (7), 1655-1670.

[34] Agrež, D. (2010). Estimation and tracking of the power quality disturbances in the frequency domain. Measurement Science Review, 10 (6), 189-194.

[35] Alizadeh, M., Scaglione, A., Applebaum, A., Kesidis, G., Levitt, K. (2015). Reduced-Order load models for large populations of flexible appliances. IEEE Transactions on Power Systems, 30 (4), 1758-1774.

[36] Kabalci, E., Kabalci, Y. (2013). A measurement and power line communication system design for renewable smart grids. Measurement Science Review, 13 (5), 248-252.

[37] Duy, T.N. (2015). Modeling load uncertainty in distribution network monitoring. IEEE Transactions on Power Systems, 30 (5), 2321-2328.

[38] Petersen, H.M., Koch, R.G., Swart, P.H., Heerden, R.V. (1995). Modeling arc furnace flicker and investigating compensation techniques. In Industry Applications Conference, 8-12 October, 1995. IEEE, 1733-1740.

[39] Yang, S.B., Wu, M.L., Yao, X., Jiang, J.C. (2015). Load modeling and identification based on ant colony algorithms for EV charging stations. IEEE Transactions on Power Systems, 30 (4), 1997-2003.

[40] Zepernick, H.J., Finger, A. (2005). Pseudo Random Signal Processing: Theory and Application. Wiley.

Measurement Science Review

The Journal of Institute of Measurement Science of Slovak Academy of Sciences

Journal Information


IMPACT FACTOR 2017: 1.345
5-year IMPACT FACTOR: 1.253



CiteScore 2017: 1.61

SCImago Journal Rank (SJR) 2017: 0.441
Source Normalized Impact per Paper (SNIP) 2017: 0.936

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 34 34 21
PDF Downloads 23 23 15