Abstract
A new method for spectral analysis, based on ADALINE artificial neural networks (ANNs), is proposed. The network is able to calculate accurately the fundamental frequency and the harmonic content of the input signal. This method is especially useful in high precision digital measurement systems in which periodical signals are involved, i.e. digital watt meters. Most of these system use spectrum analysis algorithms for the computation of the magnitudes of interest. The traditional spectrum analysis methods require synchronous sampling, which introduce limitations to the sampling circuitry. Sine-fitting multiharmonics algorithms resolve the hardware limitations concerning the synchronous sampling but have some limitations with regard to the phase of the array of samples. The new implementation of sine-fitting multiharmonics algorithms based in ANN, eliminates these limitations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Svensson, S.: Power Measurement techniques for nonsinusoidal conditions, Doctoral Thesis, Chalmers University of Technology, Sweden (1999)
Wright, P.S.: Traceability in power/reactive power measurements and assessment tests for ‘IEC555 power analyzers’, using the NPL Mk.III digital sampling wattmeter. In: IEE Colloquium on Low Frequency Power Measurement and Analysis (Digest No. 1994/203), pp. 1/1-1/6 (1994)
Pogliano, U.: Use of integrative analog-to-digital converters for high-precision measurement of electrical power. IEEE Trans. on Instrumentation and Measurement 50(5), 1315–1318 (2001)
Salinas, J.R., et al.: Versatile Digital System for High Accuracy Power Measurements. In: CPEM 2006 Digest, pp. 98–99 (July 2006)
IEEE standard 1241: Waveform Measurement and Analysis Technical committee. IEEE Standard for Terminology and Test Methods for Analog-to-Digital Converters (2001)
Ramos, P.M., Silva, M.F., Serra, A.C.: Low frequency impedance measurement using sine-fitting. Measurement 35(1), 89–96 (2004)
Tóth, E.: Power and energy reference system, applying dual-channel sampling. IEEE Transactions on Instrumentation and Measurement 54(1), 404–408 (2005)
Oldham, N., Nelson, T., Zhan, N.F., Liu, H.K.: Final Report CCEM-K5 Comparison of 50/60 Hz Power. Internal Report National Institute of Standards and Technology, USA (2002)
Salinas, J.R., et al.: Sampling Strategies Comparison for High Accuracy Power Measurements. In: Instrumentation and Measurement Technological Conference, IMTC (2007)
Silva, M.F., Serra, A.C.: New methods to improve convergence of sine fitting algorithms. Journal of Computer Standards and Interfaces 25(1), 23–31 (2003)
Dash, P.K., Swain, D.P., Liew, A.C., Rahman, S.: An Adaptive Linear combiner For On-Line Tracking of Power System Harmonics. IEEE Transactions on Power Systems 11(4) (1996)
Elshatshat, R., Kazerani, M., Salama, M.M.A.: Modular Approach to Active Power-Line Harmonic Filtering. In: Proceedings of IEEE Power Electronics Specialists Conference (PESC 98), Japan, pp. 223–228 (1998)
Abdeslam, D.O., Mercklé, J., Wira, P.: ADALINE-based estimation of Power Harmonics. In: European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, April 27-29 (2005)
El Shathat, R., Kazerani, M., Salama, M.M.A.: On-Line Tracking and Mitigation of Power System Harmonics Using ADALINE-Based Active Power Filter System. In: Canadian Conference on Electrical and Computer Engineering (CCECE), vol. 4, 2-5 May 2004, pp. 2119–2124 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Salinas, J.R., Garcia-Lagos, F., Joya, G., Sandoval, F. (2007). Sine Fitting Multiharmonic Algorithms Implemented by Artificial Neural Networks. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_75
Download citation
DOI: https://doi.org/10.1007/978-3-540-73007-1_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73006-4
Online ISBN: 978-3-540-73007-1
eBook Packages: Computer ScienceComputer Science (R0)