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Hybrid Wind-Solar Power System with a Battery-Assisted Quasi-Z-Source Inverter: Optimal Power Generation by Deploying Minimum Sensors

Author

Listed:
  • Matija Bubalo

    (Department of Power Engineering, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia)

  • Mateo Bašić

    (Department of Power Engineering, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia)

  • Dinko Vukadinović

    (Department of Power Engineering, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia)

  • Ivan Grgić

    (Department of Power Engineering, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia)

Abstract

This paper presents a hybrid renewable energy system (RES) including wind and photovoltaic (PV) power sources. The wind energy subsystem (WES) consists of a squirrel-cage induction generator (SCIG) driven by a variable-speed wind turbine (WT) and corresponding power electronic converter, by means of which a speed-sensorless indirect-rotor-field-oriented control of the SCIG is implemented. The outputs of both the WES and PV power source rated 1.5 kW and 3.5 kW, respectively, are connected to the DC bus, with the quasi-Z-source inverter (qZSI) acting as an interlinking converter between the DC bus and the AC grid/load. An advanced pulse-width-modulation scheme is applied to reduce the qZSI switching losses. The considered RES can operate both in grid-tie and island operation, whereas the battery storage system—integrated within the qZSI impedance network—enables more efficient energy management. The proposed control scheme includes successively executed algorithms for the optimization of the WES and PV power outputs under varying atmospheric conditions. A perturb-and-observe PV optimization algorithm is executed first due to the significantly faster dynamics and higher-rated power of the PV source compared to the WES. The WES optimization algorithm includes two distinct fuzzy logic optimizations: one for extraction of the maximum wind power and the other for minimization of the SCIG losses. To reduce the number of the required sensors, all three MPPT algorithms utilize the same input variable—the qZSI’s input power—thus increasing the system’s reliability and reducing the cost of implementation. The performance of the proposed hybrid RES was experimentally evaluated over wide ranges of simulated atmospheric conditions in both the island and grid-tie operation.

Suggested Citation

  • Matija Bubalo & Mateo Bašić & Dinko Vukadinović & Ivan Grgić, 2023. "Hybrid Wind-Solar Power System with a Battery-Assisted Quasi-Z-Source Inverter: Optimal Power Generation by Deploying Minimum Sensors," Energies, MDPI, vol. 16(3), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1488-:d:1055745
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    References listed on IDEAS

    as
    1. Matija Bubalo & Mateo Bašić & Dinko Vukadinović & Ivan Grgić, 2021. "Experimental Investigation of a Standalone Wind Energy System with a Battery-Assisted Quasi-Z-Source Inverter," Energies, MDPI, vol. 14(6), pages 1-17, March.
    2. Senjyu, Tomonobu & Ochi, Yasutaka & Kikunaga, Yasuaki & Tokudome, Motoki & Yona, Atsushi & Muhando, Endusa Billy & Urasaki, Naomitsu & Funabashi, Toshihisa, 2009. "Sensor-less maximum power point tracking control for wind generation system with squirrel cage induction generator," Renewable Energy, Elsevier, vol. 34(4), pages 994-999.
    3. Lluís Monjo & Luis Sainz & Juan José Mesas & Joaquín Pedra, 2021. "State-Space Model of Quasi-Z-Source Inverter-PV Systems for Transient Dynamics Studies and Network Stability Assessment," Energies, MDPI, vol. 14(14), pages 1-15, July.
    4. Lluís Monjo & Luis Sainz & Juan José Mesas & Joaquín Pedra, 2021. "Quasi-Z-Source Inverter-Based Photovoltaic Power System Modeling for Grid Stability Studies," Energies, MDPI, vol. 14(2), pages 1-16, January.
    5. Eltawil, Mohamed A. & Zhao, Zhengming, 2013. "MPPT techniques for photovoltaic applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 793-813.
    6. Ashwin Kumar Devarakonda & Natarajan Karuppiah & Tamilselvi Selvaraj & Praveen Kumar Balachandran & Ravivarman Shanmugasundaram & Tomonobu Senjyu, 2022. "A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems," Energies, MDPI, vol. 15(22), pages 1-30, November.
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    Cited by:

    1. Muhammed Y. Worku & Mohamed A. Hassan & Luqman S. Maraaba & Md Shafiullah & Mohamed R. Elkadeem & Md Ismail Hossain & Mohamed A. Abido, 2023. "A Comprehensive Review of Recent Maximum Power Point Tracking Techniques for Photovoltaic Systems under Partial Shading," Sustainability, MDPI, vol. 15(14), pages 1-28, July.

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