A scaling law for monocrystalline PV/T modules with CCPC and comparison with triple junction PV cells

Li, W.; Paul, Manosh C.; Rolley, Matthew; Sweet, Tracy; Min, Gao; Siviter, Jonathan; Montecucco, Andrea; Knox, Andrew R.; Baig, Hasan; Mallick, Tapas K.; Férnández, Eduardo F.; Han, Guang; Gregory, Duncan H.; Azough, Feridoon; Freer, Robert

doi:10.1016/j.apenergy.2017.05.182

Cited by 12 publications

(4 citation statements)

References 32 publications

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“…Two categories of numerical methods have been illustrated: deterministic and meta‐heuristic. The deterministic techniques include the Newton–Raphson approach, 11 Lambert W‐functions, 12 and the iterative curve fitting 13 . Many model constraints, such as convexity and differentiability, must be met by these techniques.…”

Section: Introductionmentioning

confidence: 99%

Enhanced social network search algorithm with powerful exploitation strategy for PV parameters estimation

Shaheen

Elsayed

Ginidi

et al. 2022

Energy Science & Engineering

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In this paper, an enhanced social network search algorithm (ESNSA) has been proposed to model the solar photovoltaic (PV) modules accurately and efficiently. The proposed algorithm is introduced to minimize the least root-mean-square error (RMSE) between the calculated and experimental data for the single, double, and triple diode models of Kyocera KC200GT, STM6(40/36), and Photowatt-PWP201 modules. The original SNSA was inspired by users on social networks and their many moods, including imitation, conversation, disputation, and innovation mood. Two strategies are presented for the ESNSA. The first strategy is the powerful exploitation strategy (PES), which is intended to increase the SNSA's performance by boosting searching around the best view of all users. The second strategy is to suggest an adaptable parameter to aid in the exploitation of iterations in the end. Diverse comparisons and statistical analyses for validation purposes are carried out for mono-crystalline STM6(40/36), multicrystalline KC200GT, and polycrystalline photowatt-PWP201 modules. The comparative studies and statistical measures show the consistency and accurateness of the proposed ESNSA. As a numerical application, for the mono-crystalline STM6(40/36) PV module, the proposed ESNSA achieves the least RMSE of 1.751631E−3, 1.769953E−3, and 1.696504E−3, respectively for the three models. Also, it shows high robustness compared to the original SNSA as it acquires the least standard errors for the three models of 2.56E−18, 1.76E−6, and 1.24E−5, respectively. Moreover, the proposed ESNSA provides a higher convergence speed where it is approximately reached to the least RMSE in less than 60%, 50%, and 60% of the iterations for the three models, respectively. Nevertheless, the proposed ESNSA provides better performance than miscellaneous published approaches in minimizing the RMSE, with high robust indices.

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Section: Introductionmentioning

confidence: 99%

Enhanced social network search algorithm with powerful exploitation strategy for PV parameters estimation

Shaheen

Elsayed

Ginidi

et al. 2022

Energy Science & Engineering

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“…Meanwhile, deterministic approaches and meta-heuristic algorithms can handle PV parameter estimation with some reference points of given I-V curves. However, deterministic techniques, such as least squares (Newton-based method) (Li et al, 2017) and iterative curve fitting (Villalva et al, 2009) are extremely strict with model characteristics. Moreover, they are highly sensitive to initial operation conditions, while inherent high-nonlinearity and multi-modality of PV systems always leads to premature convergence.…”

Section: Introductionmentioning

confidence: 99%

A Powerful Bio-Inspired Optimization Algorithm Based PV Cells Diode Models Parameter Estimation

2021

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Accurate and reliable photovoltaic (PV) cell parameter identification is critical to simulation analysis, maximum output power harvest, and optimal control of PV systems. However, inherent high-nonlinear and multi-modal characteristics usually result in thorny obstacles to hinder conventional optimization methods to obtain a fast and satisfactory performance. In this study, a novel bio-inspired grouped beetle antennae search (GBAS) algorithm is devised to effectively identify unknown parameters of three different PV models, i.e., single diode model (SDM), double diode model (DDM), and triple diode model (TDM). Compared against beetle antennae search (BAS) algorithm, optimization efficiency of GBAS algorithm is markedly enhanced based on a cooperative searching group that consists of multiple individuals rather than a single beetle. Besides, a dynamic balance mechanism between local exploitation and global exploration is designed to increase the probability for a higher quality optimum. Comprehensive case studies demonstrate that GBAS algorithm can outperform other advanced meta-heuristic algorithms in both optimization precision and stability for estimating PV cell parameters, e.g., standard deviation (SD) of root mean square error (RMSE) obtained by GBAS algorithm is 64.34% smaller than the best value obtained by other algorithms in SDM, 61.86% smaller than that in DDM.

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“…Analytical methods are based on a series of mathematical calculations and a number of key points on the I-V curve, which owns a high degree of simplicity, but lacks high accuracy in different operating scenarios. Meanwhile, deterministic techniques, including iterative curve fitting (Villalva et al, 2009) and Newtonianbased methods (Li et al, 2017), can yield more accurate results, while they are extremely demanding in terms of model properties. Moreover, they are highly sensitive to initial operation conditions; thus, the inherent high nonlinearity and multimodality of PV systems always make them easy to be trapped at a local optimum.…”

Section: Introductionmentioning

confidence: 99%

PV Cell Parameter Extraction Using Data Prediction–Based Meta-Heuristic Algorithm via Extreme Learning Machine

Huang

Tan

2021

Front. Energy Res.

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To reliably evaluate the practical performance and to undertake optimal control of PV systems, a precise PV cell parameter extraction–based accurate modeling of PV cells is extremely crucial. However, its inherent high nonlinear and multimodal characteristics usually hinder conventional optimization methods to obtain a fast and satisfactory performance. Besides, insufficient current–voltage (I–V) data provided by manufacturers cannot guarantee high accuracy and flexibility of PV cell parameter extraction under various operation scenarios. Hence, this article proposes a novel parameter extraction strategy by data prediction–based meta-heuristic algorithm (DPMhA). An extreme learning machine (ELM) is adopted to predict output I–V data from measured data, which can provide a more reliable fitness function to meta-heuristic algorithms (MhAs). Consequently, MhAs can undertake a more stable search for optimal solution through extended I–V data; thus, PV cell parameters can be obtained with high accuracy and convergence rate. Its effectiveness is validated via three typical PV cell models, that is, single diode model (SDM), double diode model (DDM), and three diode model (TDM). Last, comprehensive case studies illustrate that the DPMhA can considerably enhance the accuracy and effectiveness compared with those without data prediction.

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A scaling law for monocrystalline PV/T modules with CCPC and comparison with triple junction PV cells

Cited by 12 publications

References 32 publications

Enhanced social network search algorithm with powerful exploitation strategy for PV parameters estimation

Enhanced social network search algorithm with powerful exploitation strategy for PV parameters estimation

A Powerful Bio-Inspired Optimization Algorithm Based PV Cells Diode Models Parameter Estimation

PV Cell Parameter Extraction Using Data Prediction–Based Meta-Heuristic Algorithm via Extreme Learning Machine

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