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Current-Sensorless Method for Photovoltaic System Using Capacitor Charging Characteristics

Author

Listed:
  • Song-Do Ki

    (Department of Electrical Engineering, Sunchon National University, Suncheon-si 57922, Republic of Korea)

  • Cheol-Woong Choi

    (Department of Electrical Engineering, Sunchon National University, Suncheon-si 57922, Republic of Korea)

  • Jae-Sub Ko

    (Department of Electrical Engineering, Gangneung-Wonju National University, Wonju-si 26403, Republic of Korea)

  • Dae-Kyong Kim

    (Department of Electrical Engineering, Sunchon National University, Suncheon-si 57922, Republic of Korea)

Abstract

The installed capacity of photovoltaic (PV) systems has increased significantly over the past few decades, and related technologies have advanced significantly. The electrical characteristics of a PV system change nonlinearly based on irradiation and temperature, and the I–V characteristic curve, expressed in terms of the voltage and current, is used to verify these characteristics. The maximum power point tracking (MPPT) control method was applied to maximize the performance of the PV system. Voltage and current sensors are used to control the I–V characteristic curve and MPPT; however, current sensors have various disadvantages in terms of price and system configuration. Therefore, this study presents a method for calculating the current of a PV system using the charging characteristics of a capacitor. The method presented in this paper analyzes the I–V characteristic curve’s qualities through simulations and experiments under normal, shaded, and mismatched conditions of the PV module.

Suggested Citation

  • Song-Do Ki & Cheol-Woong Choi & Jae-Sub Ko & Dae-Kyong Kim, 2024. "Current-Sensorless Method for Photovoltaic System Using Capacitor Charging Characteristics," Energies, MDPI, vol. 17(19), pages 1-12, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4971-:d:1492375
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    References listed on IDEAS

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    1. Chen, Zhicong & Wu, Lijun & Cheng, Shuying & Lin, Peijie & Wu, Yue & Lin, Wencheng, 2017. "Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics," Applied Energy, Elsevier, vol. 204(C), pages 912-931.
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