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

Prabhakaran, S.; Uthra, R. Annie; Preetharoselyn, J.

doi:10.32604/cmc.2023.032300

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…The work of S. Prabhakaran et al [16] focused on a CNN-based approach to damage classification. Their proposed architecture used a series of convolution and normalization operations to extract spatial features from the RGB images of the solar panel provided as an input.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning-Based Detection and Segmentation of Damage in Solar Panels

Shaik,

Balasundaram,

Kakarla

et al. 2024

Automation

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Renewable energy can lead to a sustainable future and solar energy is one the primary sources of renewable energy. Solar energy is harvested mainly by photovoltaic plants. Though there are a large number of solar panels, the economic efficiency of solar panels is not that high in comparison to energy production from coal or nuclear matter. The main risk involved in solar plants is the high maintenance cost involved in maintaining the plants. To help reduce this issue, automated solutions using Unmanned Aerial Vehicles (UAVs) and satellite imagery are proposed. In this research work, we propose a novel deep learning architecture for the segmentation of solar plant aerial images, which not only helps in automated solar plant maintenance, but can also be used for the area estimation and extraction of solar panels from an image. Along with this, we also propose a transfer learning-based model for the efficient classification of solar panel damage. Solar panel damage classification has a lot of applications. It can be integrated into monitoring systems, raising alerts when there is severe damage or damage of a certain type. The adaptive UNet model with Atrous Spatial Pyramid Pooling (ASPP) module that performed the dilated convolutions that we proposed achieved an overall accuracy of 98% with a Mean Intersection-Over-Union (IoU) Score of 95% and took under a second to process an image. Our classification model using Visual Geometry Group 19 (VGG19) as the backbone for feature extraction has achieved a classification accuracy of 98% with an F1 score of 99%, thus detecting the five classes of damage, including undamaged solar panels, in an efficient manner.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Learning-Based Detection and Segmentation of Damage in Solar Panels

Shaik,

Balasundaram,

Kakarla

et al. 2024

Automation

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“…In PV panel overlay detection technology based on image processing, feature extraction from images is mainly to distinguish normal and faulty PV panels, and to locate the location and type of faults [57]. Depending on the type of overlay (shadow or attachment), different feature extraction methods can be used.…”

Section: Overlay Detection Technology Based On Image Processingmentioning

confidence: 99%

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

Yang,

Sun,

Zou

et al. 2024

Energies

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Photovoltaic (PV) panels are prone to experiencing various overlays and faults that can affect their performance and efficiency. The detection of photovoltaic panel overlays and faults is crucial for enhancing the performance and durability of photovoltaic power generation systems. It can minimize energy losses, increase system reliability and lifetime, and lower maintenance costs. Furthermore, it can contribute to the sustainable development of photovoltaic power generation systems, which can reduce our reliance on conventional energy sources and mitigate environmental pollution and greenhouse gas emissions in line with the goals of sustainable energy and environmental protection. In this paper, we provide a comprehensive survey of the existing detection techniques for PV panel overlays and faults from two main aspects. The first aspect is the detection of PV panel overlays, which are mainly caused by dust, snow, or shading. We classify the existing PV panel overlay detection methods into two categories, including image processing and deep learning methods, and analyze their advantages, disadvantages, and influencing factors. We also discuss some other methods for overlay detection that do not process images to detect PV panel overlays. The second aspect is the detection of PV panel faults, which are mainly caused by cracks, hot spots, or partial shading. We categorize existing PV panel fault detection methods into three categories, including electrical parameter detection methods, detection methods based on image processing, and detection methods based on data mining and artificial intelligence, and discusses their advantages and disadvantages.

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“…This approach primarily relies on extensive historical output data and meteorological data to establish a mapping relationship between the conditions governing photovoltaic power prediction and the desired output volume using artificial intelligence algorithms [11,12]. Common machine learning prediction methods encompass back propagation (BP) neural networks and recurrent neural networks (RNN) [13][14][15][16][17]. While these neural network models have notably augmented the prediction accuracy of photovoltaic power output for photovoltaic power stations, the initial configuration parameters and weights of individual computational models are randomly assigned.…”

Section: Introductionmentioning

confidence: 99%

Short‐term power forecasting method for 5G photovoltaic base stations on non‐sunny days based on SDN‐integrated INGO‐BP and RGAN

Huang,

Guo,

Wei

et al. 2024

IET Renewable Power Gen

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In response to the suboptimal efficiency observed in the network configuration and administration of 5G photovoltaic base stations (PVBSs), as well as the inherent limitations in accurately forecasting photovoltaic power during inclement weather conditions, this research article introduces a concise and effective method for short‐term power prediction of PVBSs when subjected to non‐sunny weather conditions, leveraging the paradigm of software defined networking. This method ingeniously amalgamates the improved Northern Goshawk optimized back propagation neural network (INGO‐BP) and recurrent generative adversarial networks (RGAN). The investigation commences by delineating a comprehensive PVBS system framework underpinned by SDN principles. Subsequently, it employs the Pearson correlation coefficient technique and principal component analysis for the judicious selection of pertinent features from the PVBS photovoltaic dataset, thereby ameliorating the issue of multicollinearity within the predictive model. To culminate the research endeavour, the INGO algorithm is enlisted to optimize the weightings and bias parameters of the BP neural network, while RGAN is harnessed for the acquisition of distinctive distribution patterns in photovoltaic data across diverse meteorological conditions. This augmentation enhances the model's capacity to discern the intricate long‐term mappings between photovoltaic power and meteorological data. The empirical findings substantiate the elevated accuracy of the INGO‐BP prediction model across sunny, cloudy, and rainy weather conditions, with a discernible enhancement in predictive accuracy for PVBS performance under non‐sunny weather conditions facilitated by data augmentation via RGAN.

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

Cited by 3 publications

References 30 publications

Deep Learning-Based Detection and Segmentation of Damage in Solar Panels

Deep Learning-Based Detection and Segmentation of Damage in Solar Panels

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

Short‐term power forecasting method for 5G photovoltaic base stations on non‐sunny days based on SDN‐integrated INGO‐BP and RGAN

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