Performance estimation of photovoltaic module under partial shading based on explicit analytical model

Zhang, Yunpeng; Su, Jialei; Zhang, Chen; Lang, Zhe; Yang, Ming; Ting-kun, GU

doi:10.1016/j.solener.2021.06.019

Cited by 22 publications

(4 citation statements)

References 28 publications

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“…A PV module operating under 8 different shading scenarios from [7] is used to validate the developed model. The module consists of 3 strings with 24 solar cells in each.…”

Section: Validation Settingsmentioning

confidence: 99%

A High-Resolution Approach for Performance Estimation of Photovoltaics Under Partial Shaded Conditions

Fuxiang¹,

Li²,

Wu³

2022

Preprint

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Partial shaded conditions (PSC) significantly affect the performance of photovoltaic (PV) systems. Understanding and mitigating PSC effects require an accurate model with the ability to simulate the PV operating in both forward and reverse biased regions. Herein, a high-resolution approach is presented to address this issue. The reversed biased effects of PSC are fully considered at the cell level using an enhanced single diode model. Based on this, the electrical characteristic of the PV module can be accurately calculated. Good agreement is obtained between the simulation and measurement with a maximum relative error of 6.1%. This approach provides a useful tool for comprehensive modeling of the PV system.

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“…A PV module operating under 8 different shading scenarios from [7] is used to validate the developed model. The module consists of 3 strings with 24 solar cells in each.…”

Section: Validation Settingsmentioning

confidence: 99%

A High-Resolution Approach for Performance Estimation of Photovoltaics Under Partial Shaded Conditions

Fuxiang¹,

Li²,

Wu³

2022

Preprint

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“…1. Conventional algorithms are simple structured, easily implementable, and to code, and can efficiently track the MPP under Uniform Weather Condition (UWC), however, falls under Partial Shading Conditions (PSC) due to the formation of multiple peaks in the characteristic curve of a PV cell as shown in figure III [11]. Identification of the real or Global MPP (GMPP) among all the other local peaks is not possible for the conventional algorithms due to their searching strategies.…”

Section: Introductionmentioning

confidence: 99%

Strategic Perturb and Observe Algorithm for Partial Shading Conditions

Awan

2022

SSURJET

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Solar photovoltaic (PV) is the cheapest renewable resource of electrical energy. Its dependence on the connected load bounds its production capability. To deliver the determined power, it is required to operate at the maximum power point (MPP) in the characteristic curve of the PV array. Tracking the MPP requires electronic circuits govern by MPP tracking (MPPT) algorithms. The most simple, cheap, and softly implementable MPPT algorithm is “Perturb and Observe (P&O)”. It efficiently performs under uniform weather conditions (UWC) but fails under partial shading conditions (PSC) due to the formation of multiple peaks in the P-V characteristic curve of a PV cell. In this paper, weaknesses of the P&O algorithm are studied and overcome by structural modifications. The proposed structure is named the “strategic P&O algorithm”. Performance of the conventional and proposed algorithms is evaluated at the following standard benchmarks, tracking speed, tracking time, efficiency, and ability to track the GMPP under PSC. Results have shown that the proposed SP&O algorithm has outperformed the conventional P&O algorithm under numerous weather conditions. Simulations are performed in MATLAB/Simulink.

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“…In contrast, Moreira et al, (2021) represented the behavior of PV systems under PSC through an improved model with a superposition technique that considers the voltage drop caused by the bypass diodes. Kermadi et al, (2020) proposed an analytical approach to predict I-V characteristics under PSC that uses the DDM and requires only the information in the standard test condition (STC), while Zhang et al, (2021) developed an explicit analytical model based on SDM for PSC that requires lower computational cost when compared to NRM and FWL.…”

Section: Introductionmentioning

confidence: 99%

Bypass diode effect and photovoltaic parameter estimation under partial shading using a hill climbing neural network algorithm

et al. 2022

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In recent decades, population growth and industrial evolution have led to a significant increase in the need to produce electricity. Photovoltaic energy has assumed a key role in responding to this need, mainly due to its low cost and reduced environmental impact. Therefore, predicting and controlling photovoltaic power is an indispensable task nowadays. This paper studies how photovoltaic power can be affected under non-uniform irradiance conditions, i.e., when the photovoltaic energy production system is under partial shading. Concretely, the effect of bypass diodes on the current-voltage characteristic curve, according to the shaded area, was studied and the power loss under partial shading was quantified. In addition, electrical characteristics and the temperature distribution in the photovoltaic module were analyzed. Furthermore, we propose a hill climbing neural network algorithm to precisely estimate the parameters of the single-diode and double-diode models under partial shading conditions and, consequently, predict the photovoltaic power output. Different shading scenarios in an outdoor photovoltaic system were created to experimentally study how partial shading of a photovoltaic module affects the current-voltage characteristic curve. Six shading patterns of a single cell were examined, as well as three shading patterns of cells located in one or more strings. The hill climbing neural network algorithm was experimentally validated with standard datasets and different shading scenarios. The results show that the hill climbing neural network algorithm can find highly accurate solutions with low computational cost and high reliability. The statistical analysis of the results demonstrates that the proposed approach has an excellent performance and can be a promising method in estimating the photovoltaic model parameters under partial shading conditions.

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Performance estimation of photovoltaic module under partial shading based on explicit analytical model

Cited by 22 publications

References 28 publications

A High-Resolution Approach for Performance Estimation of Photovoltaics Under Partial Shaded Conditions

A High-Resolution Approach for Performance Estimation of Photovoltaics Under Partial Shaded Conditions

Strategic Perturb and Observe Algorithm for Partial Shading Conditions

Bypass diode effect and photovoltaic parameter estimation under partial shading using a hill climbing neural network algorithm

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