1. Proceedings
  2. I3M
  3. 2020
  4. EMSS
  5. 10.46354/i3m.2020.emss.004

Digital algorithm for determining the root-mean-square signal

  • a Oleg Chernoyarov , 
  • b Vladimir Litvinenko , 
  • c Boris Matveev , 
  • d Serguei Dachian , 
  • e Kirill Melnikov 
  • a,e National Research University “Moscow Power Engineering Institute”, Krasnokazarmennaya st. 14, Moscow, 111250, Russia
  • a,e National Research Tomsk State University, Lenin Avenue 36, Tomsk, 634050, Russia
  • a Maikop State Technological University, Pervomayskaya st. 191, Maikop, 385000, Russia
  • b,c Voronezh State Technical University, Moscow Avenue 14, Voronezh, 394026, Russia
  • d University of Lille, 42 rue Paul Duez, Lille, 59000, France
Cite as
Chernoyarov O., Litvinenko V., Matveev B., Dachian S., Melnikov K. (2020). Digital algorithm for determining the root-mean-square signal. Proceedings of the 32nd European Modeling & Simulation Symposium (EMSS 2020), pp. 22-27. DOI: https://doi.org/10.46354/i3m.2020.emss.004

Abstract

The need to determine the root-mean-square values of alternating signals often arises during the circuit simulation of electronic devices. In this paper, there is introduced a digital algorithm for the direct estimation (measurement) of the root-mean-square value of deterministic and random signals of arbitrary shape for the current signal sampling over the set time interval. It requires the minimum number of simple arithmetic operations while generating the result and ensures a high degree of estimation accuracy. Simulation is then carried out demonstrating the high efficiency of the proposed algorithm. There are analyzed the characteristics of the resulting estimate within a wide frequency range of the measured signals. It is shown that the algorithm can be software-implemented and then it will be a part of an application package, and it also can be hardware-implemented and then one uses the microprocessor system or the field programmable gate arrays.

References

  1. Bird, J. (2007). Engineering mathematics (5th ed.). Burlington, MA: Newnes.
  2. Chernoyarov, O.V., Glushkov, A.N., Litvinenko, V.P., Faulgaber, A.N., and Salnikova, A.V. (2019). The hardware implementation of the multi-position signal digital demodulators. Proceedings of the European Modeling and Simulation Symposium (EMSS), pp. 54-58, September 18-20, Lisbon (Portugal).
  3. Chernoyarov, O.V., and Goloborodko, P.A. (2008). Algorithms of nonlinear functions representation in orthogonal polynomial basis. Proceedings of the 10th Digital signal processing and its applications (DSPA), pp. 201-204, March 26-28, Moscow (Russia).
  4. Chernoyarov, O.V., Salnikova, A.V. , Litvinenko, V.P., Litvinenko, Y.V., Matveev, B.V., and Pchelintsev, E.A. (2018). Digital integrator. Patent for the invention № 2670389, Russia, IPC G06F7/00.
  5. Crow, E.L., and Shimizu, K. (1988). Lognormal distributions: Theory and applications. New York, NY: Marcel Dekker.
  6. Herniter, M.E. (2003). Schematic capture with electronics workbench MultiSIM. Saddle River, NJ:
    Prentice Hall.
  7. Kasatkin, A.S., and Nemtsov, M.V. (1986). Electrical engineering (2nd ed.). Moscow, Russia:
    Energoatomizdat. 
  8. Korn, G.A., and Korn, T.M. (2000). Mathematical handbook for scientists and engineers: Definitions, theorems, and formulas for reference and review. Mineola, NY: Dover Publications.
  9. Northrop, R.B. (2005). Introduction to instrumentation and measurements. Boca Raton, FL: CRC Press. 
  10. Poularikas, A.D. (Ed.). (2000). The transforms and applications handbook (2nd ed.). Boca Raton, FL: CRC Press.
  11. Sklar, B. (2017). Digital communications: Fundamentals and applications (2nd ed.). Saddle River, NJ: Prentice Hall.
  12. Texas Instruments (2008, August 14). Getting Started with TINA-TI. Retrieved from
    https://www.ti.com/lit/ug/sbou052a/sbou052a.pdf