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Horse Herd Optimized Intelligent Controller for Sustainable PV Interface Grid-Connected System: A Qualitative Approach

Author

Listed:
  • Anupama Ganguly

    (Department of Electrical and Electronics Engineering, National Institute of Technology Mizoram, Aizawl 796012, India)

  • Pabitra Kumar Biswas

    (Department of Electrical and Electronics Engineering, National Institute of Technology Mizoram, Aizawl 796012, India)

  • Chiranjit Sain

    (Electrical Engineering Department, Ghani Khan Choudhury Institute of Engineering and Technology, Malda 732141, India)

  • Ahmad Taher Azar

    (College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
    Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh 11586, Saudi Arabia
    Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt)

  • Ahmed Redha Mahlous

    (College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
    Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh 11586, Saudi Arabia)

  • Saim Ahmed

    (Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh 11586, Saudi Arabia)

Abstract

The need for energy is always increasing as civilization evolves. Renewable energy sources are crucial for meeting energy demands as conventional fuel resources are slowly running out. Researchers are working to extract the most amount of power possible from renewable resources. Numerous resources are in demand, including solar, wind, biomass, tidal, and geothermal resources. Solar energy outperformed all the aforementioned resources in terms of efficiency, cleanliness, and pollution freeness. Intermittency, however, is the resource’s main shortcoming. Maximum power point tracking algorithm (MPPT) integration is required for the system to achieve continuous optimum power by overcoming the feature of intermittency. However, generating electrical energy from solar energy has presented a significant problem in ensuring the output power’s quality within a reasonable range. Total harmonic distortion (THD), a phenomenon, may have an impact on the power quality. Depending on the properties of the load, variables like power factor, voltage sag/swell, frequency, and unbalancing may occur. The quality of power and its criterion exhibits a non-linear connection. The article’s primary objective is to analyze the PV interface grid-linked system’s qualitative and quantitative performance. With respect to varying solar irradiation conditions, partial shading conditions, and solar power quality within the acceptable dimension, a novel intelligent multiple-objective horse herd optimization (HHO)-based adaptive fractional order PID (HHO-AFOPID) controller is used to achieve this goal. Adaptive fractional order PID (AFOPID), conventional FOPID, and PID controllers were used to evaluate the performance of the suggested controller, which was then validated using a commercially available PV panel in MATLAB/Simulink by varying the productivity of non-conventional resources, the inverter’s level of uncertainty, and the potential at the grid’s end. In order to realize the features of the system, sensitivity examination is also carried out for solar energy’s sensitive parameters. The stability analysis of the proposed control topology is also carried out in terms of the integral absolute error (IAE) and integral time absolute error (ITAE). The examination of the sensitivity of variations in solar radiation in kilowatt per square meter per day is based on the total net present cost (TNPC) and levelized cost of energy (LCOE), as optimal dimension and energy cost are both aspects of priority. The suggested control methodology is an approach for the qualitative and quantitative performance analysis of a PV interface grid-oriented system.

Suggested Citation

  • Anupama Ganguly & Pabitra Kumar Biswas & Chiranjit Sain & Ahmad Taher Azar & Ahmed Redha Mahlous & Saim Ahmed, 2023. "Horse Herd Optimized Intelligent Controller for Sustainable PV Interface Grid-Connected System: A Qualitative Approach," Sustainability, MDPI, vol. 15(14), pages 1-26, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11160-:d:1196238
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    References listed on IDEAS

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    1. Rajbongshi, Rumi & Borgohain, Devashree & Mahapatra, Sadhan, 2017. "Optimization of PV-biomass-diesel and grid base hybrid energy systems for rural electrification by using HOMER," Energy, Elsevier, vol. 126(C), pages 461-474.
    2. Lalili, D. & Mellit, A. & Lourci, N. & Medjahed, B. & Berkouk, E.M., 2011. "Input output feedback linearization control and variable step size MPPT algorithm of a grid-connected photovoltaic inverter," Renewable Energy, Elsevier, vol. 36(12), pages 3282-3291.
    3. Dhimish, Mahmoud & Badran, Ghadeer, 2020. "Current limiter circuit to avoid photovoltaic mismatch conditions including hot-spots and shading," Renewable Energy, Elsevier, vol. 145(C), pages 2201-2216.
    4. Manoharan Premkumar & Umashankar Subramaniam & Thanikanti Sudhakar Babu & Rajvikram Madurai Elavarasan & Lucian Mihet-Popa, 2020. "Evaluation of Mathematical Model to Characterize the Performance of Conventional and Hybrid PV Array Topologies under Static and Dynamic Shading Patterns," Energies, MDPI, vol. 13(12), pages 1-37, June.
    5. Sajid Sarwar & Muhammad Annas Hafeez & Muhammad Yaqoob Javed & Aamer Bilal Asghar & Krzysztof Ejsmont, 2022. "A Horse Herd Optimization Algorithm (HOA)-Based MPPT Technique under Partial and Complex Partial Shading Conditions," Energies, MDPI, vol. 15(5), pages 1-22, March.
    6. Bahgat, A.B.G. & Helwa, N.H. & Ahmad, G.E. & El Shenawy, E.T., 2005. "Maximum power point traking controller for PV systems using neural networks," Renewable Energy, Elsevier, vol. 30(8), pages 1257-1268.
    7. Ghusn Abdul Redha Ibraheem & Ahmad Taher Azar & Ibraheem Kasim Ibraheem & Amjad J. Humaidi, 2020. "A Novel Design of a Neural Network-Based Fractional PID Controller for Mobile Robots Using Hybridized Fruit Fly and Particle Swarm Optimization," Complexity, Hindawi, vol. 2020, pages 1-18, April.
    Full references (including those not matched with items on IDEAS)

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