Advanced Limited Search Strategy for Enhancing the Performance of MPPT Algorithms

Imran Pervez, Charalampos Antoniadis and Yehia Massoud
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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

Energies, 2022, vol. 15, issue 15, 1-19

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.

Keywords: metaheuristic algorithms; Advanced Limited Search Strategy (ALSS); Photovoltaic (PV); Partial Shading (PS); Maximum Power Point Tracking (MPPT); solution reduction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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