An Improved Particle Swarm Optimization Algorithm for Maximum Power Point Tracking Of Photovoltaic Cells in Normal and Under Partial Shading Conditions.(Dept.E)

Samir, Hend; Elksasy, Mohamed Shreif; Saraya, Sabry F.; Abdelsalam, Mohamed M.

doi:10.21608/bfemu.2021.146311

Cited by 5 publications

(1 citation statement)

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“…Particle swarm optimization is a population-based evolution algorithm search method based on the behavior of bird flocks [32]. This method maintains a swarm of individuals (referred to as particles), each of which represents a candidate solution.…”

Section: Conventional Pso Mppt Algorithmmentioning

confidence: 99%

Application of a Modified Particle Swarm Optimization for Maximum Power Point Tracking for Solar Photovoltaic Systems

Gedefaye,

Lakeou,

Tadiwos

et al. 2023

JERA

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The maximum power point extraction at any instant of time on photovoltaic (PV) systems has attracted attention. This study introduces a novel DC-DC converter-based power point tracking (PPT) algorithm for solar PV systems. The proposed optimization technique is a modified form of the standard particle swarm optimization (PSO), where the limitations of the standard PSO algorithm, like random number assignment of the acceleration factors and constant weight, are modified. The main goal of the suggested modified particle swarm optimization (MPSO) algorithm is to change the particle weight within a range of values and remove the random number from the acceleration factors. As a result, some of the contributions to this work are: First, when the weight is within some interval values, velocity restriction with a constant number improves. It offers the chance to expedite the search without limitation because of the constantly shifting environmental conditions. Second, the solution shows that the lack of acceleration constants predicts the particle's behavior. Thirdly, the algorithm's input parameters are incredibly minimal. The MATLAB/Simulink simulation of a modeled standalone 2.9 kW solar PV system in shading and non-shading conditions proved the proposed algorithm's performance. Thus, the average efficiency and time tracking of the global maximum power point (GMPP) is 99.45% and 6.285 s, respectively. Generally, the proposed MPPT method is more straightforward and adaptable than perturb and observe (P&O), the cuckoo search algorithm, and standard PSO.

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Section: Conventional Pso Mppt Algorithmmentioning

confidence: 99%