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Open- and Short-Circuit Fault Identification for a Boost dc/dc Converter in PV MPPT Systems

Author

Listed:
  • Diego R. Espinoza Trejo

    (Coord. Acad. Región Altiplano, Universidad Autónoma de San Luis Potosí, 78700 Matehuala, S.L.P., Mexico)

  • Ernesto Bárcenas

    (Coord. Acad. Región Altiplano, Universidad Autónoma de San Luis Potosí, 78700 Matehuala, S.L.P., Mexico)

  • José E. Hernández Díez

    (Coord. Acad. Región Altiplano, Universidad Autónoma de San Luis Potosí, 78700 Matehuala, S.L.P., Mexico)

  • Guillermo Bossio

    (Applied Electronics Group, Universidad Nacional de Río Cuarto, Río Cuarto 5800, Córdoba, Argentina)

  • Gerardo Espinosa Pérez

    (Faculty of Engineering, Universidad Nacional Autónoma de México, México City 04510, Mexico)

Abstract

This paper proposes a fault identification system for short and open-circuit switch faults (SOCSF) for a dc/dc converter acting as a Maximum Power Point Tracker (MPPT) in Photovoltaic (PV) systems. A closed-loop operation is assumed for the boost dc/dc converter. A linearizing control plus a Proportional-Derivative (PD) controller is suggested for PV voltage regulation at the maximum power point (MPP). In this study, the SOCSF are modeled by using an additive fault representation and the fault identification (FI) system is synthesized departing from a Luenberger observer. Hence, an FI signal is obtained, which is insensitive to irradiance and load current changes, but affected by the SOCSF. For FI purposes, only the sensors used in the control system are needed. Finally, an experimental evaluation is presented by using a solar array simulator dc power supply and a boost dc/dc converter of 175 W in order to validate the ideas this study exposes.

Suggested Citation

  • Diego R. Espinoza Trejo & Ernesto Bárcenas & José E. Hernández Díez & Guillermo Bossio & Gerardo Espinosa Pérez, 2018. "Open- and Short-Circuit Fault Identification for a Boost dc/dc Converter in PV MPPT Systems," Energies, MDPI, vol. 11(3), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:616-:d:135615
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    References listed on IDEAS

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    1. Kuei-Hsiang Chao & Meng-Cheng Wu, 2016. "Global Maximum Power Point Tracking (MPPT) of a Photovoltaic Module Array Constructed through Improved Teaching-Learning-Based Optimization," Energies, MDPI, vol. 9(12), pages 1-18, November.
    2. Suliang Ma & Mingxuan Chen & Jianwen Wu & Wenlei Huo & Lian Huang, 2016. "Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems," Energies, MDPI, vol. 9(12), pages 1-24, November.
    3. Andrés Tobón & Julián Peláez-Restrepo & Juan P. Villegas-Ceballos & Sergio Ignacio Serna-Garcés & Jorge Herrera & Asier Ibeas, 2017. "Maximum Power Point Tracking of Photovoltaic Panels by Using Improved Pattern Search Methods," Energies, MDPI, vol. 10(9), pages 1-15, September.
    4. Carlos Robles Algarín & John Taborda Giraldo & Omar Rodríguez Álvarez, 2017. "Fuzzy Logic Based MPPT Controller for a PV System," Energies, MDPI, vol. 10(12), pages 1-18, December.
    5. Lyden, S. & Haque, M.E., 2015. "Maximum Power Point Tracking techniques for photovoltaic systems: A comprehensive review and comparative analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1504-1518.
    6. Canras Batunlu & Mohamad Alrweq & Alhussein Albarbar, 2016. "Effects of Power Tracking Algorithms on Lifetime of Power Electronic Devices Used in Solar Systems," Energies, MDPI, vol. 9(11), pages 1-23, October.
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    Cited by:

    1. Tarek Berghout & Mohamed Benbouzid & Toufik Bentrcia & Xiandong Ma & Siniša Djurović & Leïla-Hayet Mouss, 2021. "Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects," Energies, MDPI, vol. 14(19), pages 1-24, October.

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