Artículos
Metodología de diagnóstico de perturbaciones en la calidad de potencia usando la Transformada S
Harrynson Ramírez-Murillo- Carlos Andrés Torres-Pinzón
- Edwin Forero-García
Publicado 2021-02-17
Palabras clave
- análisis tiempo-frecuencia,
- calidad de energía,
- espectro local,
- sistemas eléctricos de potencia,
- transformada
Cómo citar
Ramírez-Murillo, H., Torres-Pinzón, C. A., Forero-García, E., & Álzate-Gómez, A. (2021). Metodología de diagnóstico de perturbaciones en la calidad de potencia usando la Transformada S. Revista UIS Ingenierías, 20(2), 109–124. https://doi.org/10.18273/revuin.v20n2-2021010
Resumen
Este trabajo presenta una metodología conocida como la Transformada S para el análisis tiempo-frecuencia de diferentes distorsiones en sistemas eléctricos de potencia, las cuales, en su mayoría, son no estacionarias y de corta duración, debido a la contribución de las impedancias de la red y los tipos de cargas conectadas por los usuarios. La habilidad de identificar todos los tipos de distorsiones de calidad de energía encriptados en las señales de corriente y tensión es de gran importancia para el análisis de las causas de fallos y funcionamientos erróneos de dispositivos de medición, protección y control en las redes eléctricas. Una característica clave de la Transformada S consiste en que combina una frecuencia dependiente de la resolución del espacio tiempo-frecuencia con información totalmente relacionada a la fase local. Esto permite definir el término de fase en un espectro local. Adicionalmente exhibe una respuesta de amplitud invariante en la frecuencia, en contraste con transformadas tales como la Wavelet y Fourier de Tiempo Corto. Serán simulados mediante el software PSCAD los siguientes casos de estudio: arranque y salida de motores, fallo línea-tierra, armónicos de secuencia cero en una carga trifásica balanceada, carga trifásica desbalanceada, señal en presencia de muescas y componentes de alta frecuencia.
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Referencias
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[23] N. Liu, J. Gao, B. Zhang, F. Li, Q. Wang, “Time–Frequency Analysis of Seismic Data Using a Three Parameters S Transform”, en IEEE Geoscience and Remote Sensing Letters, 2018, vol. 15, no. 1, pp. 142-146, doi: 10.1109/LGRS.2017.2778045
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[3] S. Elphick, P. Ciufo, G. Drury, V. Smith, S. Perera, V. Gosbell, “Large Scale Proactive Power-Quality Monitoring: An Example From Australia”, en IEEE Transactions on Power Delivery, 2017, vol. 32, no. 2, pp. 881-889, doi: 10.1109/TPWRD.2016.2562680
[4] Y. H. Gu, M. H. J. Bollen, “Time-frequency and time-scale domain analysis of voltage disturbances”, en IEEE Transactions on Power Delivery, 2000, vol. 15, no. 4, pp. 1279-1284, doi: 10.1109/61.891515
[5] D. Kumar, F. Zare, “Harmonic Analysis of Grid Connected Power Electronic Systems in Low Voltage Distribution Networks”, en IEEE Journal of Emerging and Selected Topics in Power Electronics, 2016, vol. 4, no. 1, pp. 70-79, doi: 10.1109/JESTPE.2015.2454537
[6] G. T. Heydt, P. S. Fjeld, C. C. Liu, D. Pierce, L. Tu, G. Hensley, “Applications of the windowed FFT to electric power quality assessment”, en IEEE Transactions on Power Delivery, 1999, vol. 14, no. 4, pp. 1411-1416, doi: 10.1109/61.796235
[7] P. A. Karafotis, P. S. Georgilakis, “Power quality monitoring and evaluation in power systems under non-stationary conditions using wavelet packet transform”, en High Voltage, 2019, vol. 4, no. 3, pp. 186-196, doi: 10.1049/hve.2019.0062
[8] T. Tarasiuk, Y. Zunino, M. Bueno-Lopez, F. Silvestro, A. Pilatis, M. Molinas, “Frequency Fluctuations in Marine Microgrids: Origins and Identification Tools”, en IEEE Electrification Magazine, 2020, vol. 8, no. 3, pp. 40-46, doi: 10.1109/MELE.2020.3005698
[9] D. S. Laila, A. R. Messina, B. C. Pal, “A Refined Hilbert–Huang Transform With Applications to Interarea Oscillation Monitoring”, en IEEE Transactions on Power Systems, 2009, vol. 24, no. 2, pp. 610-620, doi: 10.1109/TPWRS.2009.2016478
[10] O. Poisson, P. Rioual, M. Meunier, “Detection and measurement of power quality disturbances using wavelet transform”, en IEEE Transactions on Power Delivery, 2000, vol. 15, no. 3, pp. 1039-1044, doi: 10.1109/61.871372
[11] F. Zhao, R. Yang, “Power-Quality Disturbance Recognition Using S-Transform”, en IEEE Transactions on Power Delivery, 2007, vol. 22, no. 2, pp. 944-950, doi: 10.1109/TPWRD.2006.881575
[12] Li, Jianmin, et al. “Detection and classification of power quality disturbances using double resolution S-transform and DAG-SVMs”, en IEEE Transactions on Instrumentation and Measurement, 2016, vol. 65, no. 10, pp. 2302-2312, doi: 10.1109/TIM.2016.2578518
[13] Zhong, Tie, et al. “Power quality disturbance recognition based on multiresolution s-transform and decision tree”, en IEEE Access, 2019, vol. 7, pp. 88380-88392, doi: 10.1109/ACCESS.2019.2924918
[14] O. P. Mahela, A. Gafoor Shaik, “Recognition of power quality disturbances using S-transform based ruled decision tree and fuzzy C-means clustering classifiers”, Applied Soft Computing, 2017, vol. 59, pp. 243-257, doi: 10.1016/j.asoc.2017.05.061
[15] N. H. T. Huda, A. R. Abdullah, M. H. Jopri, “Power quality signals detection using S-transform”, en IEEE 7th International Power Engineering and Optimization Conference (PEOCO), 2013, pp. 552-557, doi: 10.1109/PEOCO.2013.6564609
[16] F. Zhao, R. Yang, “Power quality disturbance recognition using S-transform”, en IEEE Power Engineering Society General Meeting, 2006, doi: 10.1109/PES.2006.1709411
[17] P. K. Dash, B. K. Panigrahi, G. Panda, “Power quality analysis using S-transform”, en IEEE transactions on power delivery, 2003, vol. 18, no. 2, pp. 406-411, doi: 10.1109/TPWRD.2003.809616
[18] A. J. E. M. Janssen, “Optimality property of the Gaussian window spectrogram”, en IEEE Transactions on Signal Processing, 1991, vol. 39, no. 1, pp. 202-204, doi: 10.1109/78.80783
[19] R. Wilson, A. D. Calway, E. R. S. Pearson, “A generalized wavelet transform for Fourier analysis: the multiresolution Fourier transform and its application to image and audio signal analysis”, en IEEE Transactions on Information Theory, 1992, vol. 38, no. 2, pp. 674-690, doi: 10.1109/18.119730
[20] I. Daubechies, “The wavelet transform, time-frequency localization and signal analysis”, en IEEE Transactions on Information Theory,1990, vol. 36, no. 5, pp. 961-1005, doi: 10.1109 / 18.57199
[21] M. Varanini et al., “Spectral analysis of cardiovascular time series by the S-transform”, Computers in Cardiology, pp. 383-386, 1997, doi: 10.1109/CIC.1997.647913
[22] P. C. Chu, “The s-transform for obtaining localized spectral”, Journal of Marine Technological Society, vol. 29, no. 4, pp. 28-38, 1996.
[23] N. Liu, J. Gao, B. Zhang, F. Li, Q. Wang, “Time–Frequency Analysis of Seismic Data Using a Three Parameters S Transform”, en IEEE Geoscience and Remote Sensing Letters, 2018, vol. 15, no. 1, pp. 142-146, doi: 10.1109/LGRS.2017.2778045
[24] P. K. Dash, M. Nayak, M. R. Senapati, I.W.C. Lee, “Mining for similarities in time series data using wavelet-based feature vectors and neural networks”, Engineering Applications of Artificial Intelligence, vol. 20, no. 2, pp. 185-201, 2007, doi: 10.1016/j.engappai.2006.06.018