Intelligent Fault Detection Algorithm of PV System Based on Cloud Computing
| International Journal of Electronics and Communication Engineering |
| © 2024 by SSRG - IJECE Journal |
| Volume 11 Issue 5 |
| Year of Publication : 2024 |
| Authors : Laith A. Abdul-Rahaim, Ahmed Swadi Rahi |
10.14445/23488549/IJECE-V11I5P110
How to Cite?
Laith A. Abdul-Rahaim, Ahmed Swadi Rahi, "Intelligent Fault Detection Algorithm of PV System Based on Cloud Computing," SSRG International Journal of Electronics and Communication Engineering, vol. 11, no. 5, pp. 99-107, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I5P110
Abstract:
Using Photovoltaic (PV) panel systems is increasingly gaining popularity as a viable alternative energy source. In order to optimize the utilization of dependable energy resources, the photovoltaic panel system must be maintained in an optimal state. Continuous maintenance and monitoring are necessary in this context. However, in the event of fluctuations in weather patterns affecting the reliability of energy production, it becomes necessary to determine if these changes are within the expected range owing to environmental factors or if they deviate from the norm due to issues such as malfunctioning equipment, shading, or accumulation of dust on solar panels. To address this need, the implementation of an intelligent monitoring system is essential. This research includes an integrated system for remote monitoring via the use of cloud computing techniques for remote monitoring through temperature, radiation, voltage, and current sensors, as well as analyzing the type of fault using Artificial Neural Network (ANN) and fuzzy theory in the MATLAB program. Ensuring energy continuity and reducing time, effort, and cost for maintenance through early fault detection and diagnosis are crucial for maintaining the performance and longevity of PV systems and for rapid decision-making.
Keywords:
Photovoltaic (PV), Fault detection and diagnosis, Cloud Computing, Fuzzy theory, DC-DC Converter.
References:
[1] Mojtaba Forouzesh et al., “Step-Up DC–DC Converters: A Comprehensive Review of Voltage-Boosting Techniques, Topologies, and Applications,” IEEE Transactions on Power Electronics, vol. 32, no. 12, pp. 9143-9178, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Fang Lin Luo, and Hong Ye, Advanced DC-DC Converters, 1st ed., CRC Press: Boca Raton, pp. 1-792, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Ebrahim Babaei et al., “Extendable Nonisolated High Gain DC–DC Converter Based on Active–Passive Inductor Cells,” IEEE Transactions on Industrial Electronics, vol. 65, no. 12, pp. 9478-9487, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Farzad Mohammadzadeh Shahir, Ebrahim Babaei, and Murtaza Farsadi, “Extended Topology for a Boost DC–DC Converter,” IEEE Transactions on Power Electronics, vol. 34, no. 3, pp. 2375-2384, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[5] B. Axelrod et al., “Steep Conversion Ration Ćuk, Zeta, and Sepic Converters Based on a Switched Coupled-Inductor Cell,” 2008 IEEE Power Electronics Specialists Conference, Rhodes, Greece, pp. 3009-3014, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Nishtha Bajoria et al., “Overview of Different Control Schemes used for Controlling of DC-DC Converters,” 2016 International Conference on Electrical Power and Energy Systems (ICEPES), Bhopal, India, pp. 75-82, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Raja Basak et al., “High Gain Boost Converter with Reduced Switching Stress,” 2015 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), Melmaruvathur, India, pp. 393-399, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Thomas Salvatierra, and Marian K. Kazimierczuk, “DC Analysis, and Design of a PWM Buck Converter Operated as a Dynamic Power Supply,” International Journal of Circuit Theory and Applications, vol. 45, no. 5, pp. 681-706, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[9] F. Mohammadzadeh Shahir, E. Babaei, and M. Farsadi, “Voltage-Lift Technique Based Nonisolated Boost DC–DC Converter: Analysis and Design,” IEEE Transactions on Power Electronics, vol. 33, no. 7, pp. 5917-5926, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Adrian Ioinovici, Power Electronics and Energy Conversion Systems: Fundamentals and Hard‐switching Converters, John Wiley & Sons, pp. 1-872, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Fang Lin Luo, and Hong Ye, “Positive Output Super-Lift Converters,” IEEE Transactions on Power Electronics, vol. 18, no. 1, pp. 105-113, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Yefim Berkovich et al., “Improved Luo Converter Modifications with Increasing Voltage Ratio,” IET Power Electronics, vol. 8, no. 2, pp. 202-212, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Kerui Li, Yafei Hu, and Adrian Ioinovici, “Generation of the Large DC Gain Step-Up Nonisolated Converters in Conjunction with Renewable Energy Sources Starting from a Proposed Geometric Structure,” IEEE Transactions on Power Electronics, vol. 32, no. 7, pp. 5323-5340, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Miao Zhu, and Fang Lin Luo, “Analysis of Positive Output Super-Lift Converter in Discontinuous Conduction Mode,” 2004 International Conference on Power System Technology, Singapore, pp. 828-833, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Shaheer Ansari et al., “A Review of Monitoring Technologies for Solar PV Systems Using Data Processing Modules and Transmission Protocols: Progress, Challenges, and Prospects,” Sustainability, vol. 13, no. 15, pp. 1-34, 2021,
[CrossRef] [Google Scholar] [Publisher Link]
[16] M. Fuentes et al., “Design of an Accurate, Low-Cost Autonomous Data Logger for PV System Monitoring using Arduino™ that Complies with IEC Standards,” Solar Energy Materials and Solar Cells, vol. 130, pp. 529-543, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Bhabendu Kumar Mohanta et al., “Survey on IoT Security: Challenges and Solution Using Machine Learning, Artificial Intelligence, and Blockchain Technology,” Internet of Things, vol. 11, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Ferran Adelantado et al., “Understanding the Limits of LoRaWAN,” IEEE Communications Magazine, vol. 55, no. 9, pp. 34-40, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Salsabeel Shapsough et al., “Smart Grid Cyber Security: Challenges and Solutions,” 2015 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE), Offenburg, Germany, pp. 170-175, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Rashmi Mishra, Shimpy Ralhan, and Khaja Mohiddin, “A Review on Frequency Stability Enhancement and Effective Energy Storage Through Various Optimization Techniques,” Mobile Information Systems, vol. 2022, pp. 1-8, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Dhanup S. Pillai, and N. Rajasekar, “A Comprehensive Review on Protection Challenges and Fault Diagnosis in PV Systems,” Renewable and Sustainable Energy Reviews, vol. 91, pp. 18-40, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Shibo Lu, B.T. Phung, and Daming Zhang, “Study on DC Series Arc Fault in Photovoltaic Systems for Condition Monitoring Purpose,” 2017 Australasian Universities Power Engineering Conference (AUPEC), Melbourne, VIC, Australia, pp. 1-6, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[23] A. Mellit, G.M. Tina, and S.A. Kalogirou, “Fault Detection and Diagnosis Methods for Photovoltaic Systems: A Review,” Renewable and Sustainable Energy Reviews, vol. 91, pp. 1-17, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Masoud Emamian et al., “Cloud Computing and IoT Based Intelligent Monitoring System for Photovoltaic Plants Using Machine Learning Techniques,” Energies, vol. 15, no. 9, pp. 1-25, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[25] D. Balakrishnan et al., “An IoT-Based System for Fault Detection and Diagnosis in Solar PV Panels,” International Conference on Smart Engineering for Renewable Energy Technologies (ICSERET-2023), vol. 387, no. 5009, pp. 1-10, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Sufyan Samara, and Emad Natsheh, “Intelligent Real-Time Photovoltaic Panel Monitoring System Using Artificial Neural Networks,” IEEE Access, vol. 7, pp. 50287-50299, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Barun Basnet, Hyunjun Chun, and Junho Bang, “An Intelligent Fault Detection Model for Fault Detection in Photovoltaic Systems,” Journal of Sensors, vol. 2020, pp. 1-11, 2020.
[CrossRef] [Google Scholar] [Publisher Link]