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Improved Performance of M-Class PMUs Based on a Magnitude Compensation Model for Wide Frequency Deviations

Author

Listed:
  • Jose Roberto Razo-Hernandez

    (ENAP-Research Group, CA-Fuentes Alternas y Calidad de la Energía Eléctrica, Departamento de Ingeniería Electromecánica, Tecnológico Nacional de México, Instituto Tecnológico Superior de Irapuato (ITESI), Carr. Irapuato-Silao km 12.5, Colonia El Copal 36821, Irapuato, Guanajuato C. P. 36821, Mexico)

  • Ismael Urbina-Salas

    (ENAP-Research Group, Departamento de Ingeniería Mecatrónica, Tecnológico Nacional de México, Instituto Tecnológico Superior de Guanajuato (ITESG), Carretera Guanajuato a Puentecillas km 10.5, Puentecillas 36262, Guanajuato, Guanajuato C. P. 36262, Mexico)

  • Guillermo Tapia-Tinoco

    (ENAP-Research Group, Departamento de Ingeniería Electrónica, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago, km 3.5 + 1.8, Salamanca, Guanajuato C. P. 36885, Mexico)

  • Juan Pablo Amezquita-Sanchez

    (ENAP-Research Group, CA-Sistemas Dinámicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, San Juan del Río, Querétaro C. P. 76807, Mexico)

  • Martin Valtierra-Rodriguez

    (ENAP-Research Group, CA-Sistemas Dinámicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, San Juan del Río, Querétaro C. P. 76807, Mexico)

  • David Granados-Lieberman

    (ENAP-Research Group, CA-Fuentes Alternas y Calidad de la Energía Eléctrica, Departamento de Ingeniería Electromecánica, Tecnológico Nacional de México, Instituto Tecnológico Superior de Irapuato (ITESI), Carr. Irapuato-Silao km 12.5, Colonia El Copal 36821, Irapuato, Guanajuato C. P. 36821, Mexico)

Abstract

Phasor measurement units (PMUs) are important elements in power systems to monitor and know the real network condition. In order to regulate the performance of PMUs, the IEEE Std. C37.118.1 stablishes two classes—P and M, where the phasor estimation is carried out using a quadrature oscillator and a low-pass (LP) filter for modulation and demodulation, respectively. The LP filter plays the most important role since it determines the accuracy, response time and rejection capability of both harmonics and aliased signals. In this regard and by considering that the M-class filters are used for more accurate measurements, the IEEE Std. presents different M-class filters for different reporting rates (when a result is given). However, they can degrade their performance under frequency deviations if the LP frequency response is not properly considered. In this work, a unified model for magnitude compensation under frequency deviations for all the M-class filters is proposed, providing the necessary values of compensation to improve their performance. The model considers the magnitude response of the M-class filters for different reporting rates, a normalized frequency range based on frequency dilation and a fitted two-variable function. The effectiveness of the proposal is verified using both static and dynamic conditions for frequency deviations. Besides that, a real-time simulator to generate test signals is also used to validate the proposed methodology.

Suggested Citation

  • Jose Roberto Razo-Hernandez & Ismael Urbina-Salas & Guillermo Tapia-Tinoco & Juan Pablo Amezquita-Sanchez & Martin Valtierra-Rodriguez & David Granados-Lieberman, 2020. "Improved Performance of M-Class PMUs Based on a Magnitude Compensation Model for Wide Frequency Deviations," Mathematics, MDPI, vol. 8(8), pages 1-21, August.
  • Handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1361-:d:398859
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    References listed on IDEAS

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    1. H. Lee & Tushar & B. Cui & A. Mallikeswaran & P. Banerjee & A. K. Srivastava, 2017. "A review of synchrophasor applications in smart electric grid," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 6(3), May.
    2. Yekui Chang & Rao Liu & Yu Ba & Weidong Li, 2018. "A New Control Logic for a Wind-Area on the Balancing Authority Area Control Error Limit Standard for Load Frequency Control," Energies, MDPI, vol. 11(1), pages 1-20, January.
    3. Howlader, Abdul Motin & Sadoyama, Staci & Roose, Leon R. & Chen, Yan, 2020. "Active power control to mitigate voltage and frequency deviations for the smart grid using smart PV inverters," Applied Energy, Elsevier, vol. 258(C).
    4. David Granados-Lieberman, 2020. "Global Harmonic Parameters for Estimation of Power Quality Indices: An Approach for PMUs," Energies, MDPI, vol. 13(9), pages 1-17, May.
    5. Georgilakis, Pavlos S., 2008. "Technical challenges associated with the integration of wind power into power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(3), pages 852-863, April.
    6. Sa-ngawong, Nattapol & Ngamroo, Issarachai, 2015. "Intelligent photovoltaic farms for robust frequency stabilization in multi-area interconnected power system based on PSO-based optimal Sugeno fuzzy logic control," Renewable Energy, Elsevier, vol. 74(C), pages 555-567.
    7. Andrés Bravo Cuesta & Francisco Javier Gomez-Gil & Juan Vicente Martín Fraile & Jesús Ausín Rodríguez & Justo Ruiz Calvo & Jesús Peláez Vara, 2013. "Feasibility of a Simple Small Wind Turbine with Variable-Speed Regulation Made of Commercial Components," Energies, MDPI, vol. 6(7), pages 1-19, July.
    8. Soon-Ryul Nam & Seung-Hwa Kang & Sang-Hee Kang, 2014. "Real-Time Estimation of Power System Frequency Using a Three-Level Discrete Fourier Transform Method," Energies, MDPI, vol. 8(1), pages 1-15, December.
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