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Advanced Limited Search Strategy for Enhancing the Performance of MPPT Algorithms

Author

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  • Imran Pervez

    (Innovative Technologies Laboratories (ITL), Computer, Electrical and Mathematical Sciences & Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia)

  • Charalampos Antoniadis

    (Innovative Technologies Laboratories (ITL), Computer, Electrical and Mathematical Sciences & Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia)

  • Yehia Massoud

    (Innovative Technologies Laboratories (ITL), Computer, Electrical and Mathematical Sciences & Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia)

Abstract

Photovoltaic (PV) arrays are gaining popularity for electricity generation due to their simple and green energy production. However, the power transfer efficiency of PV varies depending on the load’s electrical properties, the PV panels’ temperature, and the insolation conditions. Maximum Power Point Tracking (MPPT) is a method formulated as an optimization problem that adjusts the PV output voltage to deliver maximum power to the load based on these criteria (maximum power in the P-V curve). MPPT is a convex optimization problem when the Sun’s rays completely cover the PV surface (full insolation). Several power points are formed in the Power vs. Voltage (P-V) curve, rendering MPPT as a non-convex problem during incomplete insolation (partial shadowing) on the PV surface due to barriers such as passing clouds or trees in the path of the Sun and the PV’s surface. Unfortunately, mathematical programming techniques, such as gradient ascent and momentum, are not good optimization candidate algorithms because they cannot distinguish between the local and global maximum of a function (the case of non-convex problems). On the other hand, metaheuristic algorithms have better search space exploration capability, making it easier to discern the P-V curve’s local and global power peaks. However, due to their pseudorandom search space exploration (random with some intuition), there is plenty of room for improving their performance. In this work, we elaborate on the Advanced Limited Search Strategy (ALSS), a technique we proposed in one of our previous works on MPPT. We prove its universal usefulness by applying it to other MPPT algorithms to enhance their performance. The ALSS first finds the direction where it is most probable to discover the MPP using the finite difference between two candidate duty cycles and then computes a duty cycle between two bounds designated by the previous direction. After that, the resulting duty cycle is further updated according to the metaheuristic update equation. Therefore, the single solution update is another advantage of ALSS that further improves the computational cost of the MPPT algorithms.

Suggested Citation

  • Imran Pervez & Charalampos Antoniadis & Yehia Massoud, 2022. "Advanced Limited Search Strategy for Enhancing the Performance of MPPT Algorithms," Energies, MDPI, vol. 15(15), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5650-:d:879960
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    References listed on IDEAS

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    1. Muhannad Alshareef & Zhengyu Lin & Mingyao Ma & Wenping Cao, 2019. "Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions," Energies, MDPI, vol. 12(4), pages 1-18, February.
    2. Eltamaly, Ali M. & Al-Saud, M.S. & Abokhalil, Ahmed G. & Farh, Hassan M.H., 2020. "Simulation and experimental validation of fast adaptive particle swarm optimization strategy for photovoltaic global peak tracker under dynamic partial shading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
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    1. Doaa Khodair & Saad Motahhir & Hazem H. Mostafa & Ahmed Shaker & Hossam Abd El Munim & Mohamed Abouelatta & Ahmed Saeed, 2023. "Modeling and Simulation of Modified MPPT Techniques under Varying Operating Climatic Conditions," Energies, MDPI, vol. 16(1), pages 1-20, January.

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