Keywords
distortion of electric energy quality parameters
wavelet analysis якість електричної енергії
спотворення параметрів якості електричної енергії
вейвлет-аналіз
How to Cite
Abstract
Development of measures to ensure the quality of electricity is possible only after assessing the actual state of electricity quality in all nodes of the electricity network. Therefore, the system of ensuring the required quality of electricity should be based on its monitoring system. An approach to building a real-time electricity quality monitoring system by developing a generalized identifier for the presence of electricity quality distortion regardless of its type is presented, time of occurrence and duration based on the construction of spatio-temporal distribution of the information signal and subsequent orthogonal analysis of frequency-temporal changes of its spectral components. This allows the creation of a system of real-time monitoring of electricity quality in contrast to existing methods, when using which the sequential processing of the measuring signal is carried out to determine certain indicators of the quality of electric energy, which makes it impossible to conduct it in real time. References 10, figures 2.
References
1. EN 50160:2010&A1:2015&A2:2019&A3:2019 Voltage characteristics of electricity supplied by public electricity networks. (NSAI), 2019. 36 p.
Zwe-Lee Gaing. Wavelet-based neural network for Power Disturbance recognition and classification. IEEE Trans. on Power Delivery. 2004. Vol. 19. No 4. Pp. 1560-1567. DOI: https://doi.org/10.1109/TPWRD.2004.835281
Emmanouil S., Bollen M.H.J., Gu I.Y.H. Expert system for classification and analysis of Power system event’s. IEEE Trans. On Power Delivery. 2002. Vol. 17. No 2. Pp. 423-428. DOI: https://doi.org/10.1109/61.997911
Bizjak B., Planinsic P. Classification of Power Disturbances using Fuzzy Logic. 12th International Power Electronics and Motion Control Conference. Portoroz, 30 August–1 September 2006. Pp. 1356-1360. DOI: https://doi.org/10.1109/EPEPEMC.2006.283352
Axelberg, P., Gu I.Y.-H., Bollen M. H. Support Vector Machine for Classification of Voltage Disturbances IEEE Trans. on Power Delivery. 2007. Vol. 22. No 3. Pp. 1297-1303. . DOI: https://doi.org/10.1109/TPWRD.2007.900065
Janic P. Automated classification of Power-quality disturbances using SVM and RBF network. IEEE Trans. on Power Delivery. 2006. Vol. 21. No 3. Pp. 1663-1669. DOI: https://doi.org/10.1109/TPWRD.2006.874114
Grouse M.S., Nowak R.D., Baraniuk R.G. Wavelet-based statistical signal processing using hidden Markov models. IEEE Trans. Signal Processing. 1998. Vol. 46. No 4. Pp. 886-902. DOI: https://doi.org/10.1109/78.668544
Dash P.K., Mishra K.S., Salama M.M.A. Classification of Power Disturbances using a Fuzzy expert system and a Fourier linear combiner. IEEE Trans. on Power Delivery. 2000. Vol. 15. No 2. Pp. 472-477. DOI: https://doi.org/10.1109/61.852971
Mallat S.A. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Machine Intell. 1989. Vol. 11. Pp. 674-693. DOI: https://doi.org/10.1109/34.192463
Coh H.H., Liao L., Zhang D., Dai W., Lim C.S. Denoising Transient Power Quality Using an Improved Adaptive Wavelet Threshold Method Based on Energy Optimization. Energies. 2022. No 15. Pp. 1-21. DOI: https://doi.org/10.3390/en15093081.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright (c) 2022 Tekhnichna Elektrodynamika