scholarly journals Photovoltaic Module Fault Detection Based on a Convolutional Neural Network

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1635
Author(s):  
Shiue-Der Lu ◽  
Meng-Hui Wang ◽  
Shao-En Wei ◽  
Hwa-Dong Liu ◽  
Chia-Chun Wu
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Rapid Development ◽  
Recognition Rate ◽  
Photovoltaic System ◽  
Image Feature ◽  
Photovoltaic Module ◽  
Pv Module ◽  
Fault State

With the rapid development of solar energy, the photovoltaic (PV) module fault detection plays an important role in knowing how to enhance the reliability of the solar photovoltaic system and knowing the fault type when a system problem occurs. Therefore, this paper proposed the hybrid algorithm of chaos synchronization detection method (CSDM) with convolutional neural network (CNN) for studying PV module fault detection. Four common PV module states were discussed, including the normal PV module, module breakage, module contact defectiveness and module bypass diode failure. First of all, the defects in 16 pieces of 20W monocrystalline silicon PV modules were preprocessed, and there were four pieces of each fault state. When the signal generator delivered high frequency voltage to the PV module, the original signal was measured and captured by the NI PXI-5105 high-speed data acquisition system (DAS) and was calculated by CSDM, to establish the chaos dynamic error map as the image feature of fault diagnosis. Finally, the CNN was employed for diagnosing the fault state of the PV module. The findings show that after entering 400 random fault data (100 data for each fault) into the proposed method for recognition, the recognition accuracy rate of the proposed method was as high as 99.5%, which is better than the traditional ENN algorithm that had a recognition rate of 86.75%. In addition, the advantage of the proposed algorithm is that the mass original measured data can be reduced by CSDM, the subtle changes in the output signals are captured effectively and displayed in images, and the PV module fault state is accurately recognized by CNN.

Research on the Impact of Shadow on the Performance of Building Photovoltaic System

2014 ◽  
Vol 694 ◽  
pp. 163-168
Author(s):  
Liang Guo ◽  
Yun Liang ◽  
Xu Zhang ◽  
Xiao Tian Yang
Keyword(s):  
System Performance ◽  
Rapid Development ◽  
Photovoltaic System ◽  
Future Research ◽  
Photovoltaic Module ◽  
Pv Module ◽  
New Energy ◽  
Generation Capacity ◽  
The Impact ◽  
Support Cost

With the rapid development of world economy, the energy crisis has become one of the urgent problems to be solved. Photovoltaic technology is a green new energy industry, no pollution is widely used all over the world. Typically, for photovoltaic component installation, only considering the utilization of components support cost and area, and the arrangement of components have not given enough attention. Photovoltaic module in use process will inevitably encounter the shadow, the shadow changes to make appropriate adjustments to the PV module arrangement can enhance the power generation capacity. Effect of the shadow on the photovoltaic system performance can be effectively used for photovoltaic component to bring help, is of positive significance. This study analyzed the villa model typical, and the rectangular shadow is modeling, in order to analyze the influence on the photovoltaic component. Through the conclusion of this study can determine the horizontal and vertical components of photovoltaic components which caused little damage, and provide a reference for future research of shadow and photovoltaic system performance.

Carpal Bone Segmentation Using Fully Convolutional Neural Network

2019 ◽  
Vol 15 (10) ◽  
pp. 983-989
Author(s):  
Liang Kim Meng ◽  
Azira Khalil ◽  
Muhamad Hanif Ahmad Nizar ◽  
Maryam Kamarun Nisham ◽  
Belinda Pingguan-Murphy ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Bone Age ◽  
Image Feature ◽  
Qualitative Assessment ◽  
Carpal Bone ◽  
Radiographic Images ◽  
Accurate Quantitative Analysis

Background: Bone Age Assessment (BAA) refers to a clinical procedure that aims to identify a discrepancy between biological and chronological age of an individual by assessing the bone age growth. Currently, there are two main methods of executing BAA which are known as Greulich-Pyle and Tanner-Whitehouse techniques. Both techniques involve a manual and qualitative assessment of hand and wrist radiographs, resulting in intra and inter-operator variability accuracy and time-consuming. An automatic segmentation can be applied to the radiographs, providing the physician with more accurate delineation of the carpal bone and accurate quantitative analysis. Methods: In this study, we proposed an image feature extraction technique based on image segmentation with the fully convolutional neural network with eight stride pixel (FCN-8). A total of 290 radiographic images including both female and the male subject of age ranging from 0 to 18 were manually segmented and trained using FCN-8. Results and Conclusion: The results exhibit a high training accuracy value of 99.68% and a loss rate of 0.008619 for 50 epochs of training. The experiments compared 58 images against the gold standard ground truth images. The accuracy of our fully automated segmentation technique is 0.78 ± 0.06, 1.56 ±0.30 mm and 98.02% in terms of Dice Coefficient, Hausdorff Distance, and overall qualitative carpal recognition accuracy, respectively.

scholarly journals Comparison of cephalometric measurements between conventional and automatic cephalometric analysis using convolutional neural network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Sangmin Jeon ◽  
Kyungmin Clara Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Rapid Development ◽  
Cephalometric Analysis ◽  
Anatomical Landmarks ◽  
Limits Of Agreement ◽  
Mandibular Incisor ◽  
Lateral Cephalograms ◽  
Dental Measurements ◽  
Cephalometric Measurements

Abstract Objective The rapid development of artificial intelligence technologies for medical imaging has recently enabled automatic identification of anatomical landmarks on radiographs. The purpose of this study was to compare the results of an automatic cephalometric analysis using convolutional neural network with those obtained by a conventional cephalometric approach. Material and methods Cephalometric measurements of lateral cephalograms from 35 patients were obtained using an automatic program and a conventional program. Fifteen skeletal cephalometric measurements, nine dental cephalometric measurements, and two soft tissue cephalometric measurements obtained by the two methods were compared using paired t test and Bland-Altman plots. Results A comparison between the measurements from the automatic and conventional cephalometric analyses in terms of the paired t test confirmed that the saddle angle, linear measurements of maxillary incisor to NA line, and mandibular incisor to NB line showed statistically significant differences. All measurements were within the limits of agreement based on the Bland-Altman plots. The widths of limits of agreement were wider in dental measurements than those in the skeletal measurements. Conclusions Automatic cephalometric analyses based on convolutional neural network may offer clinically acceptable diagnostic performance. Careful consideration and additional manual adjustment are needed for dental measurements regarding tooth structures for higher accuracy and better performance.

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

ArcNet: Series AC Arc Fault Detection Based on Raw Current and Convolutional Neural Network

2021 ◽  
pp. 1-1
Author(s):  
Yao Wang ◽  
Linming Hou ◽  
Kamal Chandra Paul ◽  
Yunsheng Ban ◽  
Chen Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Ac Arc

scholarly journals A Study of the Electrical Output and Reliability Characteristics of the Crystalline Photovoltaic Module According to the Front Materials

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 163
Author(s):  
Jong Rok Lim ◽  
Woo Gyun Shin ◽  
Chung Geun Lee ◽  
Yong Gyu Lee ◽  
Young Chul Ju ◽  
...  
Keyword(s):  
Surface Film ◽  
Front Surface ◽  
Photovoltaic System ◽  
Photovoltaic Module ◽  
Reliability Test ◽  
Tempered Glass ◽  
Characteristic Analysis ◽  
Pv Module ◽  
Reliability Characteristics ◽  
Electrical Output

In recent years, various types of installations such as floating photovoltaic (PV) and agri-voltaic systems, and BIPV (building integrated photovoltaic system) have been implemented in PV systems and, accordingly, there is a growing demand for new PV designs and materials. In particular, in order to install a PV module in a building, it is important to reduce the weight of the module. The PV module in which low-iron, tempered glass is applied to the front surface, which is generally used, has excellent electrical output and reliability characteristics; however, it is heavy. In order to reduce the weight of the PV module, it is necessary to use a film or plastic-based material, as opposed to low-iron, tempered glass, on the front surface. However, if a material other than glass is used on the front of the PV module, various problems such as reduced electrical output and reduced reliability may occur. Therefore, in this paper, a PV module using a film instead of glass as the front surface was fabricated, and a characteristic analysis and reliability test were conducted. First, the transmittance and UV characteristics of each material were tested, and one-cell and 24-cell PV modules were fabricated and tested for electrical output and reliability. From the results, it was found that the transmittance and UV characteristics of the front material were excellent. In addition, the electrical output and reliability test results confirmed that the front-surface film was appropriate for use in a PV module.

scholarly journals Algorithm of Strawberry Disease Recognition Based on Deep Convolutional Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Ma ◽  
Xueliang Guo ◽  
Shuke Zhao ◽  
Doudou Yin ◽  
Yiyi Fu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Abiotic Factors ◽  
Convolution Neural Network ◽  
Image Feature ◽  
Identification Methods ◽  
Yield And Quality ◽  
Disease Identification ◽  
Deep Convolution Neural Network ◽  
And Control

The growth of strawberry will be stressed by biological or abiotic factors, which will cause a great threat to the yield and quality of strawberry, in which various strawberry diseased. However, the traditional identification methods have high misjudgment rate and poor real-time performance. In today's era of increasing demand for strawberry yield and quality, it is obvious that the traditional strawberry disease identification methods mainly rely on personal experience and naked eye observation and cannot meet the needs of people for strawberry disease identification and control. Therefore, it is necessary to find a more effective method to identify strawberry diseases efficiently and provide corresponding disease description and control methods. In this paper, based on the deep convolution neural network technology, the recognition of strawberry common diseases was studied, as well as a new method based on deep convolution neural network (DCNN) strawberry disease recognition algorithm, through the normal training of strawberry image feature representation in different scenes, and then through the application of transfer learning method, the strawberry disease image features are added to the training set, and finally the features are classified and recognized to achieve the goal of disease recognition. Moreover, attention mechanism and central damage function are introduced into the classical convolutional neural network to solve the problem that the information loss of key feature areas in the existing classification methods of convolutional neural network affects the classification effect, and further improves the accuracy of convolutional neural network in image classification.

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