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A Hybrid Artificial Ecosystem Optimizer and Incremental-Conductance Maximum-Power-Point-Tracking-Controlled Grid-Connected Photovoltaic System

Author

Listed:
  • Burhan U Din Abdullah

    (School of Engineering and Technology, Sharda University, Greater Noida 201301, Uttar Pradesh, India)

  • Suman Lata

    (School of Engineering and Technology, Sharda University, Greater Noida 201301, Uttar Pradesh, India)

  • Shiva Pujan Jaiswal

    (Virendra Happiness School, Mirzapur 231304, Uttar Pradesh, India)

  • Vikas Singh Bhadoria

    (Industry Integration Cell, Shri Vishwakarma Skill University, Palwal 121102, Haryana, India)

  • Georgios Fotis

    (Department of Electrical and Electronics Engineering Educators, ASPETE—School of Pedagogical and Technological Education, 14121 N. Heraklion, Greece)

  • Athanasios Santas

    (Smart Sustainable Social Innovations Single Member P.C., 96 Dimitriou Gounari Str., 15125 Athens, Greece)

  • Lambros Ekonomou

    (UBITECH Energy Sprl, 367 Avenue Louise, B-1050 Brussels, Belgium)

Abstract

When tracking the peak power point in PV systems, incremental conductance is the most common technique used. This approach preserves the first trap in the local peak power point, but it is unable to quickly keep up with the ever-changing peak power point under varying irradiance and temperature conditions. In this paper, the authors propose a hybrid algorithm, combining an artificial ecosystem optimizer and an incremental-conductance-based MPPT to solve these issues of traditional MPPT under varying irradiance and temperature conditions. The proposed hybrid algorithm has been applied to three scenarios, namely the constant irradiance condition, the varying irradiance condition, and the varying temperature condition. Under the constant irradiance condition, the PV array is maintained at a temperature of 25 °C and an irradiance of 1000 W / m 2 . The voltage of the DC link of the neutral-pointed-clamped inverter is maintained at 1000 V. Under the varying irradiance condition, the irradiance of the PV array is increased from 400 W / m 2 to 1000 W / m 2 with a step size of 0.2 s. The same step size is maintained while decreasing the irradiance level from 1000 W / m 2 to 400 W / m 2 , with a step change of 0.2 s. However, the temperature is maintained at 25 °C. Under the varying temperature condition, the temperature of the PV array varies from 35 °C, 25 °C, 15 °C, 10 °C, 15 °C, 25 °C, and 35 °C with a step size of 0.2 s, and the irradiance is maintained at 1000 W / m 2 . The DC link voltage in all three conditions is maintained at 1000 V, which confirms that the hybrid algorithm has been able to vary the duty cycle of the pulse wave modulation generator in such a manner that the variable DC voltage produced by the PV array has been changed by the flyback converter into a stable DC voltage. The simulation results show that the total harmonic distortion (THD) under all the simulated scenarios is within 5%, which agrees with IEEE standards. In the future, this algorithm may be compared with other types of available MPPTs under partial shading.

Suggested Citation

  • Burhan U Din Abdullah & Suman Lata & Shiva Pujan Jaiswal & Vikas Singh Bhadoria & Georgios Fotis & Athanasios Santas & Lambros Ekonomou, 2023. "A Hybrid Artificial Ecosystem Optimizer and Incremental-Conductance Maximum-Power-Point-Tracking-Controlled Grid-Connected Photovoltaic System," Energies, MDPI, vol. 16(14), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5384-:d:1194312
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    References listed on IDEAS

    as
    1. Nguyen Van Tan & Nguyen Binh Nam & Nguyen Huu Hieu & Le Kim Hung & Minh Quan Duong & Le Hong Lam, 2020. "A Proposal for an MPPT Algorithm Based on the Fluctuations of the PV Output Power, Output Voltage, and Control Duty Cycle for Improving the Performance of PV Systems in Microgrid," Energies, MDPI, vol. 13(17), pages 1-21, August.
    2. Kuo-Hua Huang & Kuei-Hsiang Chao & Ying-Piao Kuo & Hong-Han Chen, 2023. "Maximum Power Point Tracking of Photovoltaic Module Arrays Based on a Modified Gray Wolf Optimization Algorithm," Energies, MDPI, vol. 16(11), pages 1-21, May.
    3. Ibrahim Al-Wesabi & Zhijian Fang & Hassan M. Hussein Farh & Abdullrahman A. Al-Shamma’a & Abdullah M. Al-Shaalan & Tarek Kandil & Min Ding, 2022. "Cuckoo Search Combined with PID Controller for Maximum Power Extraction of Partially Shaded Photovoltaic System," Energies, MDPI, vol. 15(7), pages 1-26, March.
    4. Ingilala Jagadeesh & Vairavasundaram Indragandhi, 2022. "Comparative Study of DC-DC Converters for Solar PV with Microgrid Applications," Energies, MDPI, vol. 15(20), pages 1-21, October.
    5. Tamir Shaqarin, 2023. "Particle Swarm Optimization with Targeted Position-Mutated Elitism (PSO-TPME) for Partially Shaded PV Systems," Sustainability, MDPI, vol. 15(5), pages 1-23, February.
    6. Kuei-Hsiang Chao & Shu-Wei Zhang, 2023. "An Maximum Power Point Tracker of Photovoltaic Module Arrays Based on Improved Firefly Algorithm," Sustainability, MDPI, vol. 15(11), pages 1-28, May.
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