Statistical analysis of I–V curve parameters from photovoltaic modules

Gasparin, Fabiano Perin; Bühler, Alexandre José; Rampinelli, Giuliano Arns; Krenzinger, Arno

doi:10.1016/j.solener.2016.01.061

Cited by 36 publications

(27 citation statements)

References 11 publications

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“…Statistical distribution of the leading electrical parameters is required to evaluate mismatch losses using simulation software [18]. Furthermore, it is also essential to verify if a module produced by the manufacturer satisfies the requirements.…”

Section: Statistical Features Of the IV And Pv Curve Datamentioning

confidence: 99%

Electrical Characteristic Classification of the Pv’s Using Support Vector Machines

Serttaş

Hocaoğlu

2021

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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Photovoltaics(PVs) have more attraction day by day due to increasing technologies and decreasing prices. However, in practice, the applicants encounter fundamental problems such as shadow effects and degradation. Due to these problems, the amount of produced annual electricity decreases considerably. Moreover, it is not easy to understand if the company's PV satisfies the requirements or is appropriate for the application. In order to solve such problems, it is instructed to examine the electrical characteristics of the modules. An initial task should be the classification of PV modules according to performance results. Here in this study, it is aimed to classify different PV modules, including different output characteristics. It is aimed to show whether it is possible to classify different PV modules including the same output power or not? To find the answer to this question, a test platform is built. 4 different panels are tested on the platform. While the test, the panels produced by different companies split into two groups, each has the same output power. Under a different insulation condition, the test is performed, and the cells' current-voltage curves are constructed. Different statistics are extracted by using this information. Different variations of these statistics are presented to multi-Support Vector Machine (Multi-SVM). Finally, accurate classification results are obtained.

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Section: Statistical Features Of the IV And Pv Curve Datamentioning

confidence: 99%

Electrical Characteristic Classification of the Pv’s Using Support Vector Machines

Serttaş

Hocaoğlu

2021

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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“…The I-V curve of a PV cell, module or array under determined operating conditions provides information related to its electrical behaviour (normal or anomalous) and about its energy generation capacity [9]. It is the most important and widely used metric to describe the electrical output of a PV device [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Low-Cost I–V Tracer for PV Modules under Real Operating Conditions

et al. 2020

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Solar photovoltaic technologies have undergone significant scientific development. To ensure the transfer of knowledge through the training of qualified personnel, didactic tools that can be acquired or built at a reasonable price are needed. Most training and research centres have restrictions on acquiring specific equipment due to its high cost. With this in mind, this article presents the development and transfer of a low-cost I–V curve tracer acquisition system. The device is made up of embedded systems with all the necessary hardware and software for its operation. The hardware and software presented are open source and have a low cost, i.e., the estimated material cost of the system is less than 200 euros. For its development, four institutions from three different countries participated in the project. Three photovoltaic technologies were used to measure the uncertainties related to the equipment developed. In addition, the system can be transferred for use as an academic or research tool, as long as the measurement does not need to be certified. Two accredited laboratories have certified the low uncertainties in the measurement of the proposed system.

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“…Among them, MML due to manufacturing tolerance is one of the significant factors. MML is caused by the I-V characteristics mismatch of PV modules, which is a common phenomenon in a PV array [16].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Mismatch Power Loss Minimization Techniques in Series-Parallel PV Array Configurations

2019

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The mismatch in current-voltage (I-V) characteristics of photovoltaic (PV) modules causes significant power loss in a large PV array, which is known as mismatch power loss (MML). The PV array output power generation can be improved by minimizing MML using different techniques. This paper investigates the performance of different module arrangement techniques to minimize MML both for long series string (LSS) and long parallel branch (LPB) in series-parallel (SP) array configurations at uniform irradiance condition. To investigate the significance of MML LSS-SP configuration with dimensions: 1 × 40, 2 × 20, 4 × 10, 5 × 8 and LPB-SP configuration with dimensions: 40 × 1, 20 × 2, 10 × 4, 8 × 5 were used. A comparative analysis is made to find the effectiveness of MML reduction techniques on PV arrays with three different power ratings. Simulation results show that the PV modules arrangement obtained by the genetic algorithm (GA) and current based arrangement (Im) performed better than the arrangements obtained by all other techniques in terms of PV array output power and MML minimization. The performance of the proposed technique was analyzed for both LSS-SP and LPB-SP array configurations in 400 W, 3400 W, and 9880 W arrays. To substantiate the simulation results experiment was performed using a 400 W PV array in outdoor weather condition and obtained similar results. It was also observed that the percentage of recoverable energy (%RE) obtained by arranging the modules using the GA method was higher than Im based method for both LSS-SP and LPB-SP array configurations. A maximum %RE of 4.159 % was recorded for a 5 × 8 LSS-SP array configuration by applying the GA based MML reduction method.

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Statistical analysis of I–V curve parameters from photovoltaic modules

Cited by 36 publications

References 11 publications

Electrical Characteristic Classification of the Pv’s Using Support Vector Machines

Electrical Characteristic Classification of the Pv’s Using Support Vector Machines

Low-Cost I–V Tracer for PV Modules under Real Operating Conditions

Performance Comparison of Mismatch Power Loss Minimization Techniques in Series-Parallel PV Array Configurations

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