Effect of partial shading patterns and degrees of shading on Total Cross-Tied (TCT) photovoltaic array configuration

Tubniyom, Chayut; Jaideaw, Watcharin; Chatthaworn, Rongrit; Suksri, Amnart; Wongwuttanasatian, Tanakorn

doi:10.1016/j.egypro.2018.10.028

Cited by 37 publications

(11 citation statements)

References 5 publications

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“…Therefore, the solar PV reconfigurations with a non-ideal switch from SP to BL and TCT were made for other shading cases. It was found that, when the shaded area of the solar PV was higher than 50%, a reconfiguration cannot increase the output power in Cases 7 and 8 because of the shading of all solar PV panels in one string (Case 7) or the shading of every row of the solar PV array (Case 8) [28]. The simulation results are shown in Tables 3 and 4.…”

Section: Comparative Results Between the Ideal Switch And The Non-idementioning

confidence: 98%

Minimization of Losses in Solar Photovoltaic Modules by Reconfiguration under Various Patterns of Partial Shading

et al. 2018

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Configurations of photovoltaic (PV) modules, such as series-parallel (SP), bridge-linked (BL), and total cross-tied (TCT) configurations, always utilize a number of connecting switches. In a simulation, the ideal switch with no loss is used to optimize the reconfiguration method for a solar PV array. However, in practice, the switches are non-ideal, causing losses and resulting in a decrease in the total output power of the PV array. In this work, MATLAB/Simulink (R2016a) was employed to simulate nine PV modules linked in a 3 × 3 array, and they were reconfigured using series-parallel (SP), bridge-linked (BL), and total cross-tied (TCT) configurations for both ideal and non-ideal switch cases. It was not surprising that non-ideal switches deteriorated the output power compared with ideal cases. Then, the minimization of losses (ML) configuration was proposed by minimizing the number of switches to give the highest output power. A 5% higher power output was set as the criterion to reconfigure the PV modules when partial shading occurred. The results showed that if 50% or more of the area was partially shaded, reconfiguration was unnecessary. On the other hand, when the shaded area was less than 50%, reconfiguration gave a significant increase in power. Finally, the ML method had different configurations for various shading patterns, and provided better results than those of the TCT method.

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Section: Comparative Results Between the Ideal Switch And The Non-idementioning

confidence: 98%

Minimization of Losses in Solar Photovoltaic Modules by Reconfiguration under Various Patterns of Partial Shading

et al. 2018

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“…In the array level, Tubniyom et al (2018) studied the shading impact for three array configurations (3 × 3 array) under partial shading conditions using Matlab/Simulink: series-parallel (SP), bridge-linked (BL) and total cross-tied (TCT). They verified that the configurations BL and TCT were more suitable when the shaded area of module is less than 50%.…”

Section: Introductionmentioning

confidence: 99%

A methodology for prediction and assessment of shading on PV systems

Chepp

Krenzinger

2021

Solar Energy

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Frequently, PV systems are affected by dirt, dust or shadowing from surrounding elements, such as trees and buildings. The shadows can cause partial shading conditions, when the irradiance is not uniform on PV installation, which lead to losses of power. Thus, the shadow prediction is primordial for a better output power estimation and to minimize its losses. The aim of this article is to propose a methodology using intuitive and available tools for shading prediction and losses assessment on PV installations. A study about the shadow pattern and module orientation (portrait and landscape) influence and an analysis of the shading losses on a PV plant were performed in order to demonstrate the applicability of the methodology. The shading effect is drastic when the shading is parallel to the short edge, whereas the effect is lower when it is parallel to the long edge. On the other hand, the losses due to shading and the difference in the position of the modules (landscape or portrait) have a lower impact on the photovoltaic plant case. It was concluded that the methodology is feasible and applicable for shading impact evaluation, despite limitations for large systems due to the simulation time.

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“…Standard deviation genetic algorithm, SDGA, has been introduced by Rajan et al [36], it determined the connection matrix of the PV array configuration to mitigate the shadow effects. Three configurations of the shaded PV array have been investigated in [37]. Pachauri et al [38] introduced a control scheme-based, Arduino, for rearranging the PV panels in shaded array to enhance the output power.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Grey Wolf Optimizer for Optimal Design of Switching Matrix for Shaded PV array Dynamic Reconfiguration

et al. 2020

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One of the worst negative phenomena faced by photovoltaic (PV) array is the operation under the shadow phenomenon, which significantly affects the generated power. Multiple local maximum power point (MPP) and unique global MPP are generated from the shaded array. Therefore, regular dispersion of the shadow falling on the PV array surface is a vital issue to extract the GMP via reconfiguration of the shaded modules in the array. This paper proposes a recent approach based on Multi-objective grey wolf optimizer (MOGWO) to reconfigure the shaded PV array optimally. The main objective of the proposed MOGWO is providing the optimal structure for the switching matrix to minimize the row current difference and maximize the output power. The benefits of the proposed strategy is performing a dynamic reconfiguration process which closes to the reality. The proposed method is validated across 9 × 9 PV array with six shade patterns. MOGWO schemes results are compared with TCT and modified SuDoKu based on several statistical metrics. The comparison reveals the superiority of MOGWO in tackling the multi-peak issue in the P-V characteristics with harvesting the highest power levels. INDEX TERMS Multi-objective grey wolf optimizer; grey wolf optimizer; Total-cross-tied; PV reconfiguration, partial shading .

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Effect of partial shading patterns and degrees of shading on Total Cross-Tied (TCT) photovoltaic array configuration

Cited by 37 publications

References 5 publications

Minimization of Losses in Solar Photovoltaic Modules by Reconfiguration under Various Patterns of Partial Shading

Minimization of Losses in Solar Photovoltaic Modules by Reconfiguration under Various Patterns of Partial Shading

A methodology for prediction and assessment of shading on PV systems

Multi-Objective Grey Wolf Optimizer for Optimal Design of Switching Matrix for Shaded PV array Dynamic Reconfiguration

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