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Satheesh Kumar, P.
- A Single Phase Seven Level Inverter for Grid Connected Photovoltaic System by Employing PID Controller
Abstract Views :146 |
PDF Views:3
Authors
Affiliations
1 Mailam Engineering College, Villupuram, IN
1 Mailam Engineering College, Villupuram, IN
Source
Programmable Device Circuits and Systems, Vol 4, No 10 (2012), Pagination: 480-483Abstract
This paper presents a single phase seven level photovoltaic (PV) inverter topology for grid connected PV systems with a novel Pulse Width Modulated (PWM) control scheme. Three reference signals identical to each other with an offset equivalent to the amplitude of the triangular carrier signal were used to generate PWM signals for the switches. A digital Proportional-Integral derivative (PID) current control algorithm is implemented in MICROCONTROLLER PIC16C7F88 to keep the current injected into the grid sinusoidal and to have high dynamic performance with rapidly changing atmospheric conditions. The inverter offers much less total harmonic distortion and can operate at near-unit power factor. The proposed system is verified through simulation and is implemented in a prototype. Experimental results are compared with the conventional single phase five level grid connected PWM inverter.Keywords
Grid Connected Photovoltaic System, Single Phase Seven Level Inverter, Maximum Power Point Tracking System and Proportional-Integral Derivative (PID) Controller.
- In-vitro Anti Oxidant and Antimicrobial Activities of Ethyl Acetate Extract of Evodia lunu-Ankenda (Gaertn) Merr. Bark
Abstract Views :168 |
PDF Views:0
Authors
T. Venkatachalam
1,
V. Kishor Kumar
1,
P. Satheesh Kumar
1,
P. Kalaiselvi
1,
M. Chitra
1,
N. Senthil Kumar
1
Affiliations
1 Department of Pharmaceutical Chemistry, JKKMMRF College of Pharmacy, B. Komarapalayam, Tamil Nadu, IN
1 Department of Pharmaceutical Chemistry, JKKMMRF College of Pharmacy, B. Komarapalayam, Tamil Nadu, IN
Source
Research Journal of Pharmacognosy and Phytochemistry, Vol 1, No 3 (2009), Pagination: 201-203Abstract
In this paper, in vitro antioxidant and antimicrobial activities of ethyl acetate extract of Evodia lunu-ankenda (Gaertn) Merr. bark was determined by four methods Diphenylpicrylhydrazyl (DPPH), Nitric Oxide, Super oxide disumatase, Hydrogen peroxide (H2O2) and by agar disc diffusion methods. The crude ethyl acetate extract of Evodia lunuankenda bark inhibited the growth of both gram positive bacteria (Bacillus substilis, Staphylococcus aureus and micrococcus luters) and gram negative bacteria (Escharichia coil, Pseudomonas aeruglinosa and Salmonella typhlmurium) and also the crude ethyl acetate extract of Evodia lunuankenda bark inhibited the growth of fungi (candida albicans and Aspergillus niger). The ethyl acetate extract of Evodia lunuankenda bark showed dose dependent increase in reducing anti oxidant power that was comparable to standards. Antibacterial activity, highest concentration of ethyl acetate extract (5mg/ml) having good activity against gram positive and gram negative organisms as compared with the reference Ciprofloxacin. Antifungal activity, highest concentration of extract (5mg/ml) having good activity as compared with the reference Amphotericin-B. It can be concluded that the plant posses potent antioxidant and antimicrobial activity, responsible of the secondary metabolites like flavonoids, saponins, are likely observed for the plants.
Keywords
Antioxidant Activity, Antimicrobial Activity, Agar Disc Diffusion Method, Evodia lunu-Ankenda (Gaertn) Merr. Bark.- Pre-Sowing Seed Hardening Enhancement Treatment on Seed Quality and Seed Yield in Rice ADT 36
Abstract Views :248 |
PDF Views:0
Authors
Affiliations
1 Department of Genetics and Plant Breeding, Annamalai University, Annamalainagar, Chidambaram (T.N.), IN
1 Department of Genetics and Plant Breeding, Annamalai University, Annamalainagar, Chidambaram (T.N.), IN
Source
International Journal of Plant Sciences, Vol 13, No 1 (2018), Pagination: 135-140Abstract
Rice is one of the main staple food of man and is grown in almost all the tropical and subtropical regions of the world. An experiment was carried out to investigate the effect of pre-sowing seed hardening treatment with different chemicals such as 1% CaCl2, 1% KCl, 1% KNO3 and 1% NaCl and organics such as 10% cow dung and 3% panchagavya on seed quality and seed yield in rice cv. ADT 36. In general seed hardening treatment of rice before sowing significantly increased the seed quality characteristics and yield attributing characters when compared to untreated seeds. From the result, it was observed that 1% CaCl2 seed hardening treatment improved the seed quality characters such as germination percentage, speed of germination, shoot length, ischolar_main length, seedling length, dry matter production, vigour index and yield attributing characters such as number of productive tillers per plant, number of seeds per panicle and seed yield per plant. Hence, rice seeds hardened with 1% CaCl2 may be recommended to get higher seed yield and seed quality.Keywords
Rice, Seed Hardening, CaCl2, Seed Quality, Seed Yield.References
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- Ganesh, K.S., Sundaramoorthy, P., Baskaran, L., Rajesh, M. and Rajasekaran, S. (2013). Effect of pre-sowing hardening treatments using various plant growth hormones on two varieties of green gram germination and seedling establishment. Internat. J. Modern Biol. Med., 3(2): 78-87.
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- Mulsanti, I.W. and Wahyuni, S. (2011). The use of salt solution as invigoration media for increasing rice seed germination and vigor. In: Suprihatno, B., Daradjat, A.A. Satoto, Baehaki, Sudir (eds.), Variability and climate change: Its effects on National food self-sufficiency: Proceedings of the Seminar on National Rice Research Results, Sukamandi, 24 November 2010. Indonesian Center for Rice Research. pp. 197-205.
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- Susmitha, S. (2006). Studies on the influence of seed quality enhancement techniques for drought tolerance in rice (Oryza sativa L.) cv. PMK(R) 3 and IR 50. M.Sc. (Ag). Thesis, Tamil Nadu Agricultural University, Coimbatore (T.N.) India.
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- Image Processing and CNN Based Manufacturing Defect Detection and Classification of Faults in Photovoltaic Cells
Abstract Views :8 |
PDF Views:0
Authors
Affiliations
1 Department of Electrical and Electronics Engineering, PSG College of Technology, IN
2 Department of Electrical and Electronics Engineering, Nachimuthu Polytechnic College, IN
1 Department of Electrical and Electronics Engineering, PSG College of Technology, IN
2 Department of Electrical and Electronics Engineering, Nachimuthu Polytechnic College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 3 (2024), Pagination: 3231-3236Abstract
Renewable energy resources such as solar energy, biomass, tidal, geothermal, and hydroelectric energy are becoming increasingly important due to their potential to mitigate the negative impacts of climate change and reduce our dependence on finite and polluting fossil fuels. Solar power can provide a clean, sustainable, and reliable source of renewable energy. Important component of solar power generation is the silicon panel and its surface quality is highly related to its robustness and power generation efficiency. Cell breakages resulting from micro-cracks, degradation and shunted areas on cells are proven to cause major issues and these affect the photovoltaic module efficiency and performance. Solar cell defect identification is important because defects in solar cells significantly reduce their efficiency, which in turn affects their power output and lifespan. By identifying and classifying defects during the production of these cells, engineers and researchers can improve the quality control of solar cells, leading to more reliable and efficient solar energy systems. The proposed method in this research paper, utilizes image processing operations such as adaptive Gaussian thresholding, horizontal and vertical line extraction morphological operations, Canny edge detection, K- Means clustering and VGG16 convolutional neural network to identify the defects in solar cells and classify them as defective or non-defective during the manufacturing process itself. Once the defects are classified, the classification data is exported to Excel file and the results are visually represented as labelled images. OpenCV and Keras modules in Python are used to perform the image processing operations which contributes to cost-effective, reduced computation and high-precision solution.Keywords
Black Core Fault, Broken Gate Fault, Crack Fault, Shunt Fault, Image Segmentation, Adaptive Gaussian Thresholding, K-Means Clustering, Convolutional Neural Network, VGG16.References
- Ying Yi Hong and Rolando A. Pula, “Methods of Photovoltaic Fault Detection and Classification”, A Review, Energy Reports, Vol. 8, pp. 5898-5929, 2022.
- B. Su, H. Chen, P. Chen, G. Bian, K. Liu and W. Liu, “Deep Learning-Based Solar-Cell Manufacturing Defect Detection with Complementary Attention Network,” IEEE Transactions on Industrial Informatics, Vol. 17, No. 6, pp. 4084-4095, 2021.
- Haiyong Chen, “Solar Cell Surface Defect Inspection based on Multispectral Convolutional Neural Network”, Journal of Intelligent Manufacturing, Vol. 31, No. 2, pp. 453-468, 2020.
- A.V. Patel, L. McLauchlan and M. Mehrubeoglu, “Defect Detection in PV Arrays Using Image Processing”, Proceedings of International Conference on Computational Science and Computational Intelligence, pp. 1653-1657, 2020.
- Z. Meng and Y. Zhao, “Defect Object Detection Algorithm for Electroluminescence Image Defects of Photovoltaic Modules based on Deep Learning”, Energy Science and Engineering, Vol. 2022, pp. 810-813, 2022.
- T.G. Amaral and A.J. Pires, “Fault Detection in PV Tracking Systems using an Image Processing Algorithm Based on PCA”, Energies, Vol. 14, No. 1, pp. 7278-7287, 2021.
- Arockia Dhanraj and P. Anitha, “An Effective Evaluation on Fault Detection in Solar Panels”, Energies, Vol. 14, pp. 7770-7782, 2021.
- Yifeng Zhang and Biao Zhao, “Research on Image Defect Detection of Silicon Panel Based on Prewitt and Canny Operator”, Frontiers in Physics, Vol. 9, pp. 701462-701474, 2021.
- Q. Ling and N.A.M. Isa, “Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey”, IEEE Access, Vol. 11, pp. 15921-15944, 2023.
- V.S. Bharath Kurukuru, A. Haque and M.A. Khan, “Fault Classification for Photovoltaic modules using Thermography and Image Processing”, Proceedings of International Conference on Industry Applications, pp. 1-6, 2019.
- D.P. Winston, “Solar PV’s Micro Crack and Hotspots Detection Technique using NN and SVM”, IEEE Access, Vol. 9, pp. 127259-127269, 2021.
- B. Su, Z. Zhou and H. Chen, “PVEL-AD: A Large-Scale Open-World Dataset for Photovoltaic Cell Anomaly Detection”, IEEE Transactions on Industrial Informatics, Vol. 19, No. 1, pp. 404-413, 2023.
- R. Rajeshkanna and P. Ganesh Kumar, “Fault-Related Feature Discrimination Network for Cell Partitioning and Defect Classification in Real-Time Solar Panel Manufacturing”, Journal of Process Mechanical Engineering, Vol. 78, No. 2, pp. 1-13, 2023.
- N.T.N. Trinh, D.T. Hung, N.H.T. Dat and P.Q. Dung, “Application of Artificial Intelligence in Detecting and Classifying Faults of Solar Panels”, Proceedings of International Conference on Communications and Electronics, pp. 513-518, 2022.
- Hossam A. Abd El-Ghany, Ahmed E. ElGebaly and Ibrahim B.M. Taha, “A New Monitoring Technique for Fault Detection and Classification in PV Systems based on Rate of Change of Voltage-Current Trajectory”, International Journal of Electrical Power and Energy Systems, Vol. 133, pp. 1-12, 2021.
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