Pseudorandom Dynamic Test Power Signal Modeling and Electrical Energy Compressive Measurement Algorithm
Affiliation:
1. Institute of Information Science and Technology, Beijing University of Chemical Technology, North Third Ring Road, Beijing , China
Abstract
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.
Publisher
Walter de Gruyter GmbH
Subject
Instrumentation,Biomedical Engineering,Control and Systems Engineering
Reference40 articles.
1. [1] Kukuča, P., Chrapčiak, I. (2016). From smart metering to smart grid. Measurement Science Review, 16 (3), 142-148. 2. [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. [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. [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. [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.
|
|