Improved particle swarm optimization for global maximum power point tracking of partially shaded PV array

Ibrahim, Аhmed; Aboelsaud, Raef; Обухов, С. Г.

doi:10.1007/s00202-019-00794-w

Cited by 45 publications

(19 citation statements)

References 28 publications

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“…[26] Therefore, random search and information sharing mathematical models of swarms are developed to significantly improve the probability of detecting GM. [27] Recently, intelligent techniques are applied as MPPT control for PV systems, such as grey wolf optimization (GWO), [28] spline guided Jaya (S-Jaya) algorithm, ant colony optimization (ACO), artificial bee colony (ABC), improved PSO (IPSO), [29] Harris hawk optimization (HHO), [30] cuckoo search algorithm (CSA), grasshopper optimization (GHO), [31] Henry gas optimization (HGO), salp swarm optimization (SSO), [32] overall distribution (OD) algorithm, [33] and DC power optimizer (DCPO). [34] These intelligent MPPT techniques have their limitations.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Tunicate Swarm Algorithm Based MPPT Control Strategy for Multiple Configurations of PV Systems Under Partial Shading Conditions

Mansoor

Mirza

Feng³

et al. 2021

Advcd Theory and Sims

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Solar energy is the most techno-economically viable renewable source of energy and can be effectively converted into electrical power by Photovoltaic (PV) systems. As partial shading (PS) may reduce the harvested energy of PV systems, this paper proposes a novel tunicate swarm algorithm (TSA) based MPPT (maximum power point tracking) strategy to tackle the PS issue. More specifically, the simple and effective modeling of TSA with a search and skipping (SAS) scheme is utilized to minimize the tracking time and search area. The SAS scheme can discard the voltage range lacking global maximum power point (GMPP) and significantly reduce computation time. Consequently, power tracking, tracking time, and robustness can be greatly enhanced. The performance of the proposed TSA strategy is comprehensively analyzed against state-of-the-art techniques, including incremental conductance (InC), improved particle swarm optimization (IPSO), grey wolf optimization (GWO), and cuckoo search algorithm (CSA), through detailed case studies, which include standard array configurations, PS conditions, varying irradiance patterns, fast-changing temperature, and the field atmospheric data. TSA is further validated on a low-cost hardware setup, confirming its superior performance. The results provide insightful validation of the practicality of the proposed TSA strategy in the real-world applications.

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Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Tunicate Swarm Algorithm Based MPPT Control Strategy for Multiple Configurations of PV Systems Under Partial Shading Conditions

Mansoor

Mirza

Feng³

et al. 2021

Advcd Theory and Sims

View full text Add to dashboard Cite

show abstract

“…2 [7]. The mathematical equation that expresses the output current of the PV cell is given as follows [16]: Normally, a PV array comprises N P parallel branches, each with N S solar cells in series. The total current from these modules can be described as [7]:…”

Section: Mathematical Models Of the Components Of A Photovoltaic System A Modeling Of A Pv Cellmentioning

confidence: 99%

“…Particle swarm optimization (PSO) has proven itself as the most effective method for determining GMPP of the PV arrays under the PSC. Moreover, it has been considered simple in implementation and faster to extract the optimal solution in many engineering problems [16], [19], [21]. Based on literature studies [18]- [29], the main problem in utilizing the PSO algorithm to detect the GMPP of the PV systems is the selection of its parameters.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Performance of Dynamic Particle Swarm Optimization Based Maximum Power Trackers for Stand-Alone PV System Under Partial Shading Conditions

et al. 2020

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One of the important tasks for increasing the efficiency of photovoltaic (PV) system is the development and improvement of the maximum power point tracking algorithms (MPPT). These MPPT algorithms lead to the ability to catch efficiently the global maximum power point of the partially shaded PV array. One of these trackers is the particle swarm optimization (PSO) algorithm which is one of the Soft computing techniques. The conventional PSO based trackers have many advantages such as the simplicity of hardware implementation and independence from the installed system. The actual problem of the practical application of PSO is the determination of its parameters to ensure high effectiveness of extracting the global MPP. Analysis of scientific papers devoted to the PSO algorithm has shown that there is currently no methodology for the optimal parameters' selection of PSO algorithm based maximum power trackers for the PV system. This paper aims to create a convenient and reasonable method for choosing the optimal parameters of the PSO algorithm, taking into account the topology and parameters of the DC-DC converter and the configuration of solar panels. A new method for selecting the parameters of a buck converter connected to a battery has been presented. The optimal value of the sampling time for the digital MPP controllers, providing their maximum performance; has been determined based on a new methodology. Matlab/Simulink software package is used as the main research tool. The prominent outcomes identify that the modified PSO and its designed parameters best meet the requirements of the MPPT controller for the PV system.

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“…In order to achieve an optimal power transfer, from the generator to load, it is imperative to maintain both the PV and wind generators and the load at their respective optimum operating conditions (i.e., maximum power point tracking (MPPT) operations) [29,30]. Therefore, it should be observed that for ensuring the energy balance in the system, the amount of power generated WT P and PV P are considered unmanaged variables.…”

Section: Description Of the Proposed Architecture Of Construction Andmentioning

confidence: 99%

Power Balance Management of an Autonomous Hybrid Energy System Based on the Dual-Energy Storage

et al. 2019

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The urgent task of modern energy is to ensure reliable and efficient power supply to consumers, even those located in remote, far end places. A hybrid energy system with renewable energy sources is a promising way to ensure such a process. A characteristic feature of the modes of such systems, especially with high penetration levels of renewable energy sources, is the presence of ripples in the charge–discharge currents of the batteries used as energy storage devices. Batteries operation with such current fluctuations leads to rapid degradation of its characteristics as well as a reduction in its lifetime. Furthermore, it leads to a decrease in the reliability of the power supply system and an increase in the cost of generated electricity. A significant drawback of hybrid systems built according to well-known standard schemes is the inefficient use of the primary renewable energy, which is especially critical for energy systems located geographically in areas with severe climatic conditions. This article proposes a new construction method and an algorithm for controlling the modes of hybrid energy systems based on a dual-circuit energy storage device, which increases their reliability and energy efficiency. The prominent outcomes of operating modes of a hybrid power plant with a high penetration of renewable sources are presented, which proves that the proposed method of construction and the proposed control algorithm provide reliable and efficient control of the power balance of the hybrid power system in all possible operating conditions. In addition, the overall efficiency of the proposed renewable energy system is increased from 28% to 60% compared to standard hybrid power plants.

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Improved particle swarm optimization for global maximum power point tracking of partially shaded PV array

Cited by 45 publications

References 28 publications

An Intelligent Tunicate Swarm Algorithm Based MPPT Control Strategy for Multiple Configurations of PV Systems Under Partial Shading Conditions

An Intelligent Tunicate Swarm Algorithm Based MPPT Control Strategy for Multiple Configurations of PV Systems Under Partial Shading Conditions

Optimal Performance of Dynamic Particle Swarm Optimization Based Maximum Power Trackers for Stand-Alone PV System Under Partial Shading Conditions

Power Balance Management of an Autonomous Hybrid Energy System Based on the Dual-Energy Storage

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