Modelling Solar PV Behavior Using the Interpolation Approach for Climatic Conditions of Eastern India

2014 ◽  
Vol 472 ◽  
pp. 206-210
Author(s):  
S. Basu Pal ◽  
S. Bijali ◽  
S.R. Bhadra Chaudhuri ◽  
D. Mukherjee
Keyword(s):  
Linear Interpolation ◽  
Climatic Conditions ◽  
Solar Pv ◽  
Eastern India ◽  
Interpolation Methods ◽  
Pv Modules ◽  
Indian Climatic ◽  
Photovoltaic Parameters

Linear Interpolation methods for predicting the I-V characteristics for c-Si PV modules in outdoor conditions have been used by various groups of researchers. This is essential for minimizing the uncertainty in predicting essential photovoltaic parameters of interpolated I-V characteristics. A near optimum value of empirical co-efficient used in Tsunos model has been investigated under typical Eastern Indian Climatic conditions.

scholarly journals The performance of solar PV modules with two glass types after 11 years of outdoor exposure under the mediterranean climatic conditions

2022 ◽  
Vol 49 ◽  
pp. 101771
Author(s):  
N. Belhaouas ◽  
F. Mehareb ◽  
E. Kouadri-Boudjelthia ◽  
H. Assem ◽  
S. Bensalem ◽  
...  
Keyword(s):  
Outdoor Exposure ◽  
Climatic Conditions ◽  
Solar Pv ◽  
Pv Modules ◽  
The Mediterranean

scholarly journals Techno-Economic Analysis and Performance Evaluation of 25 MW Solar PV Power Plant in Actual Environmental Condition in India

2019 ◽  
Vol 8 (6S) ◽  
pp. 659-669
Keyword(s):  
Outdoor Exposure ◽  
Climatic Conditions ◽  
Energy Yield ◽  
Solar Pv ◽  
Degradation Analysis ◽  
Pv Modules ◽  
And Performance ◽  
Utility Scale ◽  
Scale Grid ◽  
Moisture Ingression

This paper presents study, identification and evaluation of causes and impact of various degradation modes and environmental conditions on performance of a utility scale grid connected solar PV plant located in remote location in India. Degradation of solar PV modules results in considerable loss in energy yield of overall estimated plant generation. The research includes degradation analysis of 25 MW Roha Dyechem amorphous Si solar PV plant, Charanka, Patan, Gujarat under varying climatic conditions. Some of the well qualified modules were found to degrade in outdoor exposure for more than 7 years. Glass breakage, hot spots, backsheet puncture, micro-delamination, corrosion of cell edges, snail trails, Digital Process Control Board (DPCB) failure, moisture ingression, soiling losses etc. were among the main faults observed in fielded PV modules. A comparative analysis is presented between the simulated, computed and practically measured and recorded field data for drawing important conclusions.

scholarly journals A Simple Mismatch Mitigating Partial Power Processing Converter for Solar PV Modules

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2308
Author(s):  
Kamran Ali Khan Niazi ◽  
Yongheng Yang ◽  
Tamas Kerekes ◽  
Dezso Sera
Keyword(s):  
Experimental Tests ◽  
Energy Yield ◽  
Power Electronic ◽  
Solar Pv ◽  
Partial Shading ◽  
Loss Analysis ◽  
Pv Systems ◽  
Pv Module ◽  
Pv Modules ◽  
In Series

Partial shading affects the energy harvested from photovoltaic (PV) modules, leading to a mismatch in PV systems and causing energy losses. For this purpose, differential power processing (DPP) converters are the emerging power electronic-based topologies used to address the mismatch issues. Normally, PV modules are connected in series and DPP converters are used to extract the power from these PV modules by only processing the fraction of power called mismatched power. In this work, a switched-capacitor-inductor (SCL)-based DPP converter is presented, which mitigates the non-ideal conditions in solar PV systems. A proposed SCL-based DPP technique utilizes a simple control strategy to extract the maximum power from the partially shaded PV modules by only processing a fraction of the power. Furthermore, an operational principle and loss analysis for the proposed converter is presented. The proposed topology is examined and compared with the traditional bypass diode technique through simulations and experimental tests. The efficiency of the proposed DPP is validated by the experiment and simulation. The results demonstrate the performance in terms of higher energy yield without bypassing the low-producing PV module by using a simple control. The results indicate that achieved efficiency is higher than 98% under severe mismatch (higher than 50%).

Effect of Soiling on the Performance of Solar PV Modules: A Case Study of Aligarh

Smart Science ◽  
2021 ◽  
pp. 1-12
Author(s):  
Mohd Tariq ◽  
Mohsin Karim Ansari ◽  
Fazlur Rahman ◽  
Md Atiqur Rahman ◽  
Imtiaz Ashraf
Keyword(s):  
Solar Pv ◽  
Pv Modules

scholarly journals Efficient Cell Segmentation from Electroluminescent Images of Single-Crystalline Silicon Photovoltaic Modules and Cell-Based Defect Identification Using Deep Learning with Pseudo-Colorization

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4292
Author(s):  
Horng-Horng Lin ◽  
Harshad Kumar Dandage ◽  
Keh-Moh Lin ◽  
You-Teh Lin ◽  
Yeou-Jiunn Chen
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Crystalline Silicon ◽  
Cell Segmentation ◽  
Solar Pv ◽  
Single Crystalline Silicon ◽  
Defect Inspection ◽  
Defect Identification ◽  
Pv Modules ◽  
A Cell

Solar cells may possess defects during the manufacturing process in photovoltaic (PV) industries. To precisely evaluate the effectiveness of solar PV modules, manufacturing defects are required to be identified. Conventional defect inspection in industries mainly depends on manual defect inspection by highly skilled inspectors, which may still give inconsistent, subjective identification results. In order to automatize the visual defect inspection process, an automatic cell segmentation technique and a convolutional neural network (CNN)-based defect detection system with pseudo-colorization of defects is designed in this paper. High-resolution Electroluminescence (EL) images of single-crystalline silicon (sc-Si) solar PV modules are used in our study for the detection of defects and their quality inspection. Firstly, an automatic cell segmentation methodology is developed to extract cells from an EL image. Secondly, defect detection can be actualized by CNN-based defect detector and can be visualized with pseudo-colors. We used contour tracing to accurately localize the panel region and a probabilistic Hough transform to identify gridlines and busbars on the extracted panel region for cell segmentation. A cell-based defect identification system was developed using state-of-the-art deep learning in CNNs. The detected defects are imposed with pseudo-colors for enhancing defect visualization using K-means clustering. Our automatic cell segmentation methodology can segment cells from an EL image in about 2.71 s. The average segmentation errors along the x-direction and y-direction are only 1.6 pixels and 1.4 pixels, respectively. The defect detection approach on segmented cells achieves 99.8% accuracy. Along with defect detection, the defect regions on a cell are furnished with pseudo-colors to enhance the visualization.

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