Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC

Eltamaly, Ali M.; Farh, Hassan M. H.

doi:10.1016/j.solener.2018.11.028

Cited by 186 publications

(116 citation statements)

References 19 publications

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“…Metaheuristic techniques have the ability to track the GP in uniformly distributed irradiance as well as in case of PSCs. Many metaheuristic techniques have been used as MPPT, including particle swarm optimization (PSO) [4], bat algorithm (BA) [5], cuckoo search (CS) [6], grey wolf optimization (GWO) [7], crow search algorithm (CSA) [8], ant colony optimization (ACO) [9], colony of flashing fireflies [10], artificial bee colony (ABC) [11], gravitational search algorithm (GSA) [12], Cauchy and Gaussian sine cosine optimization [13], moth-flame optimization (MFO) [14], etc. Some of these techniques have been integrated with one of the conventional techniques to gain the benefits of both of these techniques.…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement of PV Systems’ Maximum Power Point Tracker Based on a Scanning PSO Particle Strategy

2020

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Partial shading of PV systems generates many peaks in the P–V curve. These peaks have one global peak (GP), the remaining being local peaks (LPs). Metaheuristic techniques such as PSO have proven superiority in capturing the GP and avoiding entrapment in an LP in comparison to conventional techniques. In case of partial shading conditions (PSC), the GP may change its position and value in the P–V curve and the PSO is unable to capture the GP unless they reinitialize. Reinitialization of PSO particles spends a long time for convergence; and it may cause premature convergence. This paper proposes a novel strategy for scanning the new position of the GP in case of PSC changes without a need for reinitialization. The proposed strategy sends a particle to the anticipated places of peaks to search for any peak with power greater than the current GP and when it locates this new GP it will move the PSO particles directly to the new GP. This strategy reduced the reinitialization time by 650% as compared to the time required for the random reinitialization of the conventional PSO technique. Moreover; this proposed strategy completely avoids the premature convergence associated with conventional PSO techniques.

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Section: Introductionmentioning

confidence: 99%

Performance Improvement of PV Systems’ Maximum Power Point Tracker Based on a Scanning PSO Particle Strategy

2020

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“…Under uniform radiations, power-voltage (P-V) characteristics contain a unique peak and conventional MPPT techniques are efficient in tracking this unique peak [3][4][5]. P-V characteristics contain multiple maximum power peaks; unique Global Maximum Power (GMP) and many Local Maximum Powers (LMPs) under partial shading conditions (PSCs), which cannot be tracked by conventional MPPT techniques [1,6,7]. As a result, numerous modern, efficient, and accurate bio-inspired MPPT techniques are proposed to track the GMP instead of LMPs.…”

Section: Introductionmentioning

confidence: 99%

Impact of PSO Reinitialization on the Accuracy of Dynamic Global Maximum Power Detection of Variant Partially Shaded PV Systems

2019

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The Global Maximum Power (GMP) of photovoltaic (PV) systems changes its location on the power–voltage (P–V) curve as the shading pattern (SP) changes over time. Although the original Particle Swarm Optimization (PSO) technique can catch the GMP easily under the same SP, once it changes its location, it cannot catch the new GMP because the particles search around the first GMP caught. Therefore, conventional PSO is a time-invariant GMP tracker that cannot follow the dynamic GMP under variant SP. The novelty in this study is the modification of the conventional PSO technique to become a time-variant GMP technique. This has been achieved through dispersing the particles based on two new reinitialization methodologies for searching for the variant GMP. The first methodology depends on dispersing the PSO particles at a certain predefined time (PDT) in order to look for the new GMP of the new SP. The latter depends on continually monitoring any changes in the SP to disperse the particles to follow the new GMP. A detailed comparison between the improved PSO with two new reinitialization methodologies and the conventional PSO is introduced. The improved PSO with SP change reinitialization methodology tracked the dynamic GMP efficiently and accurately compared the conventional PSO and the improved PSO with PDT reinitialization. Also, no hardware modification in the existing PV system is required, which makes it an excellent option to improve the performance of new and existing PV systems.

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“…Therefore, the selection of the most suitable technique, to track the unique peak under uniform conditions or to track the global peak under partial shading conditions (PSCs), is compulsory and is considered a hot research area. This will contribute in improving the PV system performance, efficiency, power quality, and reliability …”

Section: Introductionmentioning

confidence: 99%

“…This will contribute in improving the PV system performance, efficiency, power quality, and reliability. [1][2][3][4][5][6][7] Numerous studies have been introduced for conventional MPPT techniques, such as hill climbing (HC), perturb and observe (P&O), and incremental conductance (IC), which are simple and acceptable to track the unique peak on the P-V curve under uniform conditions. [8][9][10][11][12] Under PSCs, many peaks (one global maximum peak [GMP] and many local maximum peaks [LMPs]) in the P-V curve are generated.…”

Section: Introductionmentioning

confidence: 99%

Dynamic global power extraction from partially shaded photovoltaic using deep recurrent neural network and improved PSO techniques

Farh

Eltamaly

Ibrahim

et al. 2019

Int Trans Electr Energ Syst

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Summary Under partial shading (PS) condition, the P‐V curve becomes more complex where many peaks (one global maximum peak [GMP] and many other local maximum peaks [LMPs]) are generated. This GMP changes with time under a time‐variant PS; this is called dynamic GMP. Conventional particle swarm optimization (PSO) can track the GMP under the same PS effectively. Nevertheless, it cannot track the dynamic GMP because all particles will be concentrated at the first GMP caught. In addition, using PSO as a maximum power point tracker (MPPT) technique suffers from obvious power oscillations in the steady state. In this paper, the PSO technique is improved to make it able to follow the dynamic GMP under time‐invariant PS. In addition, a novel deep recurrent neural network (DRNN) is introduced to track the dynamic GMP under time‐variant PS. A detailed comparison between DRNN and improved PSO is introduced, analyzed, and discussed. DRNN performs well compared with the improved PSO in terms of dynamic GMP tracking with almost zero steady‐state oscillation, tracking speed, accuracy, and efficiency.

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Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC

Cited by 186 publications

References 19 publications

Performance Improvement of PV Systems’ Maximum Power Point Tracker Based on a Scanning PSO Particle Strategy

Performance Improvement of PV Systems’ Maximum Power Point Tracker Based on a Scanning PSO Particle Strategy

Impact of PSO Reinitialization on the Accuracy of Dynamic Global Maximum Power Detection of Variant Partially Shaded PV Systems

Dynamic global power extraction from partially shaded photovoltaic using deep recurrent neural network and improved PSO techniques

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