Particle Swarm Optimization Based Solar PV Array Reconfiguration of the Maximum Power Extraction Under Partial Shading Conditions

Babu, Thanikanti Sudhakar; Ram, J. Prasanth; Dragičević, Tomislav; Miyatake, Masafumi; Blaabjerg, Frede; Rajasekar, N.

doi:10.1109/tste.2017.2714905

Cited by 268 publications

(152 citation statements)

References 19 publications

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“…To further explore the proposed methods in the literature, first, their proposed functions are examined. The fitness function is defined as follows [12,18]:…”

Section: Optimization Problemmentioning

confidence: 99%

“…Generally, there are three solutions for alleviating the effects of partial shading consisting of physical relocation, electrical rewiring, and electrical array reconfiguration. Among such solutions, the electrical array reconfiguration is the most effective method in spite of the complexity [12]. Physical relocation-based algorithms, such as Su Do Ku [13] or competence square method [14] have a drawback of a laborious task.…”

Section: Introductionmentioning

confidence: 99%

“…Physical relocation-based algorithms, such as Su Do Ku [13] or competence square method [14] have a drawback of a laborious task. Although, the electrical rewiring-based algorithms introduced by Rao et al [15] does not have such drawback, they need additional rewiring [12].…”

Section: Introductionmentioning

confidence: 99%

“…In such cases, the problem becomes more complex, and the metaheuristic algorithms are the best candidate for the solution method. Babu et al [12] and Deshkar et al [18], solved the problem of optimal electrical array reconfiguration by using particle swarm optimization (PSO) and genetic algorithm, respectively. The process of determining an optimum arrangement is similar in both of these two, but PSO has the advantage of faster convergence in comparison with the genetic algorithm, resulting in less time.…”

Section: Introductionmentioning

confidence: 99%

“…The process of determining an optimum arrangement is similar in both of these two, but PSO has the advantage of faster convergence in comparison with the genetic algorithm, resulting in less time. However, the objective functions reported in literature [12,18] included terms that may caused the variables to be trapped in local optimal points and did not reach to the main goal of reconfiguration, maximum power output. Additionally, the problem of time and accuracy of results have always been under discussion for such algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimal Reconfiguration of Solar Photovoltaic Arrays Using a Fast Parallelized Particle Swarm Optimization in Confront of Partial Shading

Heidari

Hagh

2019

IJE

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A B S T R A C TPartial shading reduces the power output of solar modules, generates several peak points in P-V and I-V curves and shortens the expected life cycle of inverters and solar panels. Electrical array reconfiguration of PV arrays that is based on changing the electrical connections with switching devices, can be used as a practical solution to prevent such problems. Valuable studies have been performed to justify the electrical array reconfiguration efficiency. However, there are some problems such as algorithms complexity, simulations runtime and the inability of objective functions to detect the best array. In this paper, the photovoltaic (PV) array reconfiguration problem is solved by using a parallelized Particle Swarm Optimization (PSO) algorithm, which searches for reducing the rows current difference. The proposed algorithm is implemented in MATLAB/Simulink and is numerically compared with some related works. Results show the simplicity and higher power outputs of the proposed algorithm compared to published papers while ensuring less simulation runtime. Depending on the shading pattern, the power enhancement is different. The maximum power increase is 26.5 percent of the total array output power.

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“…To further explore the proposed methods in the literature, first, their proposed functions are examined. The fitness function is defined as follows [12,18]:…”

Section: Optimization Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal Reconfiguration of Solar Photovoltaic Arrays Using a Fast Parallelized Particle Swarm Optimization in Confront of Partial Shading

Heidari

Hagh

2019

IJE

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A detailed comparative analysis of different photovoltaic array configurations under partial shading conditions

Jha

Triar

2019

Int Trans Electr Energ Syst

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Summary The objective of this paper is to compare the performances of different photovoltaic (PV) array configurations (series, parallel, series‐parallel, total‐cross‐tied, bridge‐linked, and honey comb) and proposed hybrid configurations (series parallel–total cross tied, bridge linked–total cross tied, honey comb–total cross tied, and bridge linked–honey comb) under artificial and realistic partial shading conditions. The performances of all these configurations have been compared on the basis of their maximum powers, fill factors, thermal voltages, and relative power losses. Different PV array configurations have also been compared on the basis of other significant factors (faults, limitations of specific parameters of the PV system, and cost). The single‐diode model of PV module is considered in this paper for modelling of PV module. The obtained results of different PV array configurations give beneficial knowledge regarding the performances of these configurations for several shading patterns and assist to choose the most satisfactory configuration for a particular shading pattern.

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Analysis on inverter selection for domestic rooftop solar photovoltaic system deployment

Ali

Khan

2020

Int Trans Electr Energ Syst

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Summary Partial shading is a common occurrence in residential and commercial photovoltaic (PV) installations. It causes mismatch losses, particularly in string and central inverter‐based systems, leading to output power loss and in turn lower performance ratio under partial shading conditions. The choice of string configuration itself is critical in lowering mismatch losses and the levelized energy cost. While a range of commercial inverters with different string configurations and prices are available, many standard string topologies are not optimum for common settings under various shading scenarios. Therefore, in this article, we evaluate various string configurations attached to state‐of‐the‐art commercial inverters widely used in the industry from both mismatch loss and cost perspective. We quantify the impact of various shading scenarios on multiple inverter configurations to ascertain the relative performance of PV systems under symmetrical shading as well as random shading in field settings. For multiple PV system configurations (on a single rooftop), the mismatch loss varied up to 2.3% and 6% under symmetrical shading and field settings, respectively. The levelized cost of electricity also varies from 0.062$/kWh to 0.041$/kWh and is dependent on the type of inverter and string configurations.

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Particle Swarm Optimization Based Solar PV Array Reconfiguration of the Maximum Power Extraction Under Partial Shading Conditions

Cited by 268 publications

References 19 publications

Optimal Reconfiguration of Solar Photovoltaic Arrays Using a Fast Parallelized Particle Swarm Optimization in Confront of Partial Shading

Optimal Reconfiguration of Solar Photovoltaic Arrays Using a Fast Parallelized Particle Swarm Optimization in Confront of Partial Shading

A detailed comparative analysis of different photovoltaic array configurations under partial shading conditions

Analysis on inverter selection for domestic rooftop solar photovoltaic system deployment

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