Artificial neural network based photovoltaic module diagnosis by current–voltage curve classification

Laurino, Marica; Piliougine, Michel; Spagnuolo, Giovanni

doi:10.1016/j.solener.2022.02.039

Cited by 18 publications

(8 citation statements)

References 36 publications

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“…In order to improve the accuracy, some authors also included an additional dependence of the series resistance R s on the device temperature. This is the case in Laurino et al [54], where a direct linear dependence is assumed by means of the temperature coefficient κ of the series resistance R s , only introduced in their own IEC 60891:2021 [3] (procedures 1 and 2). In fact, (Equation ( 57)) is also proposed for use with (Equation ( 76)), (Equations ( 84) and ( 85)) to translate the intrinsic coefficients.…”

Section: C08: Methods Of Laurinomentioning

confidence: 99%

Comparative of IEC 60891 and Other Procedures for Temperature and Irradiance Corrections to Measured I–V Characteristics of Photovoltaic Devices

Piliougine,

Sánchez-Friera,

Spagnuolo

2024

Energies

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The photovoltaic literature contains a wide range of methods for translating the I–V curves of a solar device to other conditions of irradiance and cell temperature, different from those under which the measurements were performed. Some of these translation methods are included as part of the International Standard IEC 60891. In this paper, these techniques are classified, reviewed, and implemented to perform a deep comparative analysis between them and to discuss their suitability for converting the I–V curves of photovoltaic modules under different scenarios of irradiance and temperature. From the analysis conducted, it can be seen that the interpolation method proposed in IEC 60891 achieves accurate results when it is applied to correct small and medium irradiance and temperature gaps. If no interpolation is possible and for large irradiance corrections, other procedures described in IEC 60891 can be applied. However, certain explicit methods based on the single-diode model or on the double-diode model can overcome the most well-known approaches proposed by the standard.

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Section: C08: Methods Of Laurinomentioning

confidence: 99%

Comparative of IEC 60891 and Other Procedures for Temperature and Irradiance Corrections to Measured I–V Characteristics of Photovoltaic Devices

Piliougine,

Sánchez-Friera,

Spagnuolo

2024

Energies

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“…Temperature defects such as hot spots and other defects are identified by capturing the individual PV modules through infrared (IR) images and RGB images. Then quantitative mathematical methods are applied to identify the defective panels [9,10,17]. The PV modules are identified using a layout coordinate with conditions based on the fusion of computer vision algorithms, such as U-Blox NEO-M8 N, and RTK Global Navigation Satellite System (GNSS) [11].…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction and Classification of Photovoltaic Panels Based on Convolutional Neural Network

Prabhakaran¹,

Uthra²,

Preetharoselyn³

2023

Computers, Materials &Amp; Continua

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Photovoltaic (PV) boards are a perfect way to create eco-friendly power from daylight. The defects in the PV panels are caused by various conditions; such defective PV panels need continuous monitoring. The recent development of PV panel monitoring systems provides a modest and viable approach to monitoring and managing the condition of the PV plants. In general, conventional procedures are used to identify the faulty modules earlier and to avoid declines in power generation. The existing deep learning architectures provide the required output to predict the faulty PV panels with less accuracy and a more time-consuming process. To increase the accuracy and to reduce the processing time, a new Convolutional Neural Network (CNN) architecture is required. Hence, in the present work, a new Real-time Multi Variant Deep learning Model (RMVDM) architecture is proposed, and it extracts the image features and classifies the defects in PV panels quickly with high accuracy. The defects that arise in the PV panels are identified by the CNN based RMVDM using RGB images. The biggest difference between CNN and its predecessors is that CNN automatically extracts the image features without any help from a person. The technique is quantitatively assessed and compared with existing faulty PV board identification approaches on the large real-time dataset. The results show that 98% of the accuracy and recall values in the fault detection and classification process.

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“…In methods based on artificial intelligence, neural networks are usually used to model or identify the solar panel. Neural networks, although in topics such as fault detection [8], production energy prediction [9], estimation of some parameters such as solar cell temperature [10], amount of radiation on cloudy days [11], and losses due to dirt [12] perform well, the error of modeling the characteristics of solar panels is significant compared to other methods [13,14]. In some research [14,15], to improve accuracy, in addition to atmospheric conditions, voltage is also included as input and the output is only current or power.…”

Section: Introductionmentioning

confidence: 99%

Modeling Based on Daily Data of PV Power Plants

Reza Reisi

2024

Advances in Solar Photovoltaic Energy Systems

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Modeling performance characteristics is essential for the design and optimal operation of solar power plants. However, due to the influence of various factors on the performance of solar panels and data changes over time, determining an exact relationship between output power and weather conditions is still challenging. In this chapter, a hybrid method based on genetic programming will be presented for accurate modeling of solar power plant characteristics, which includes two steps. First, three points of open-circuit voltage, maximum power point, and short-circuit current are modeled as functions of atmospheric conditions. For this purpose, by using the modeling process based on genetic programming, relationships with high fit will be obtained for these three points in terms of cell temperature and radiation. Then, with the help of these equations, the voltage–current characteristics are modeled based on the circuit analysis methods and without the need for factory data. To evaluate the modeling for a 3 kW solar power plant, and based on the results, the effectiveness of the proposed method will be shown.

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Artificial neural network based photovoltaic module diagnosis by current–voltage curve classification

Cited by 18 publications

References 36 publications

Comparative of IEC 60891 and Other Procedures for Temperature and Irradiance Corrections to Measured I–V Characteristics of Photovoltaic Devices

Comparative of IEC 60891 and Other Procedures for Temperature and Irradiance Corrections to Measured I–V Characteristics of Photovoltaic Devices

Feature Extraction and Classification of Photovoltaic Panels Based on Convolutional Neural Network

Modeling Based on Daily Data of PV Power Plants

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