تشخیص و شناسایی خطا در سیستم‌های فتوولتائیک با استفاده از شبکه عصبی عمیق VGG16

چکیده مقاله :

تشخیص خطا در آرایه های فتوولتائیک (PV) جهت افزایش توان خروجی و همچنین طول عمر مفید یک سیستم PV ضروری است. وجود شرایطی مانند سایه جزئی، خطاهای امپدانس بالا و وجود سامانه ردیاب نقطه حداکثر توان (MPPT)، تشخیص خطا را در شرایط محیطی به چالش می کشد. بیشتر تحقیقات انجام‌شده در این زمینه فقط در چند سناریو از عیوب به شناسایی و طبقه بندی پرداخته اند. این پژوهش با استفاده از شبکه ی عصبی کانولوشنی عمیق از پیش آموزش داده شده VGG16)) و با بهره گیری از ویژگی ها استخراج شده بوسیله اسکالوگرام های دوبعدی تولیدشده از داده های سیستم PV، به شناسایی و طبقه‌بندی خطا در سیستم PV با استفاده از یک شبکه عصبی کاملا متصل می پردازد. برخلاف روش های قبلی پیشنهادشده در ادبیات موضوع تشخیص و طبقه‌بندی عیوب، موارد مختلف معیوب همراه با ترکیب MPPT در مطالعه ما در نظر گرفته‌شده است. در این تحقیق نشان داده‌شده است که روش پیشنهادی شاملCNN از پیش آموزش‌دیده تنظیم‌شده، از روش های موجود بهتر عمل می کند و به‌دقت تشخیص خطای 375/83 درصد دست پیدا می کند.

چکیده انگلیسی :

Fault detection in photovoltaic (PV) arrays is necessary to increase the output power and also the useful life of a PV system. The presence of conditions such as partial shade, high impedance faults, and the maximum power point detector (MPPT) system make the fault detection of PV in environmental conditions more challenging. The literature identified and classified defects just in few scenarios. In this study two-dimensional scalograms are generated from PV system data. The VGG16 as a pretrained convolutional neural network is used for feature extraction. Finally, to identify and classify faults in the PV system a fully connected neural network is trained. Unlike the previous methods proposed in the literature on the subject of defect detection and classification, various defective cases with MPPT combination are considered in this research. It has been shown that the proposed method including pre-trained CNN performs better than the existing methods and achieves an error detection accuracy of 83.375%.

منابع و مأخذ:

A. Dhoke, R. Sharma, and T. K. Saha, "A technique for fault detection, identification and location in solar photovoltaic systems," Solar Energy, vol. 206, pp. 864-874, 2020.
A. Jäger-Waldau, "Snapshot of photovoltaics—February 2020," Energies, vol. 13, no. 4, p. 930, 2020.
C. Buerhop, D. Schlegel, M. Niess, C. Vodermayer, R. Weißmann, and C. Brabec, "Reliability of IR-imaging of PV-plants under operating conditions," Solar Energy Materials and Solar Cells, vol. 107, pp. 154-164, 2012.
P. B. Quater, F. Grimaccia, S. Leva, M. Mussetta, and M. Aghaei, "Light Unmanned Aerial Vehicles (UAVs) for cooperative inspection of PV plants," IEEE Journal of Photovoltaics, vol. 4, no. 4, pp. 1107-1113, 2014.
J. Tsanakas and P. Botsaris, "An infrared thermographic approach as a hot-spot detection tool for photovoltaic modules using image histogram and line profile analysis," International Journal of Condition Monitoring, vol. 2, no. 1, pp. 22-30, 2012.
D. S. Pillai and N. Rajasekar, "An MPPT-based sensorless line–line and line–ground fault detection technique for PV systems," IEEE Transactions on Power Electronics, vol. 34, no. 9, pp. 8646-8659, 2018.
D. 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.
Y. Zhao, J.-F. De Palma, J. Mosesian, R. Lyons, and B. Lehman, "Line–line fault analysis and protection challenges in solar photovoltaic arrays," IEEE transactions on Industrial Electronics, vol. 60, no. 9, pp. 3784-3795, 2012.
B. P. Kumar, G. S. Ilango, M. J. B. Reddy, and N. Chilakapati, "Online fault detection and diagnosis in photovoltaic systems using wavelet packets," IEEE Journal of Photovoltaics, vol. 8, no. 1, pp. 257-265, 2017.
F. Aziz, A. U. Haq, S. Ahmad, Y. Mahmoud, M. Jalal, and U. Ali, "A novel convolutional neural network-based approach for fault classification in photovoltaic arrays," IEEE Access, vol. 8, pp. 41889-41904, 2020.
K. AbdulMawjood, S. S. Refaat, and W. G. Morsi, "Detection and prediction of faults in photovoltaic arrays: A review," in 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018), 2018, pp. 1-8: IEEE.
R. Hariharan, M. Chakkarapani, G. S. Ilango, and C. Nagamani, "A method to detect photovoltaic array faults and partial shading in PV systems," IEEE Journal of Photovoltaics, vol. 6, no. 5, pp. 1278-1285, 2016.
M. Catelani, L. Ciani, D. Galar, and G. Patrizi, "Optimizing maintenance policies for a yaw system using reliability-centered maintenance and data-driven condition monitoring," IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 9, pp. 6241-6249, 2020.
S. Voutsinas, D. Karolidis, I. Voyiatzis, and M. Samarakou, "Development of a multi-output feed-forward neural network for fault detection in Photovoltaic Systems," Energy Reports, vol. 8, pp. 33-42, 2022.
J. Van Gompel, D. Spina, and C. Develder, "Satellite based fault diagnosis of photovoltaic systems using recurrent neural networks," Applied Energy, vol. 305, p. 117874, 2022.
M. Dhimish, V. Holmes, B. Mehrdadi, and M. Dales, "Multi‐layer photovoltaic fault detection algorithm," High voltage, vol. 2, no. 4, pp. 244-252, 2017.
E. Garoudja, F. Harrou, Y. Sun, K. Kara, A. Chouder, and S. Silvestre, "Statistical fault detection in photovoltaic systems," Solar Energy, vol. 150, pp. 485-499, 2017.
A. Dhoke, R. Sharma, and T. K. Saha, "PV module degradation analysis and impact on settings of overcurrent protection devices," Solar Energy, vol. 160, pp. 360-367, 2018.
A. V. de Oliveira, M. Aghaei, and R. Rüther, "Automatic fault detection of photovoltaic array by convolutional neural networks during aerial infrared thermography," in Proceedings of the 36th European Photovoltaic Solar Energy Conference and Exhibition, Marseille, France, 2019, pp. 9-13.
S. A. Zaki, H. Zhu, M. A. Fakih, A. R. Sayed, and J. Yao, "Deep‐learning–based method for faults classification of PV system," IET Renewable Power Generation, vol. 15, no. 1, pp. 193-205, 2021.

S.-D. Lu, M.-H. Wang, S.-E. Wei, H.-D. Liu, and C.-C. Wu, "Photovoltaic module fault detection based on a convolutional neural network," Processes, vol. 9, no. 9, p. 1635, 2021. I. S. Ramírez, J. R. P. Chaparro, and F. P. G. Márquez, "Machine Learning techniques implemented in IoT platform for fault detection in photovoltaic panels," in 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 2021, pp. 429-434: IEEE.
Z. Yi and A. H. Etemadi, "Line-to-line fault detection for photovoltaic arrays based on multiresolution signal decomposition and two-stage support vector machine," IEEE Transactions on Industrial Electronics, vol. 64, no. 11, pp. 8546-8556, 2017.
A. Y. Appiah, X. Zhang, B. B. K. Ayawli, and F. Kyeremeh, "Long short-term memory networks based automatic feature extraction for photovoltaic array fault diagnosis," IEEE Access, vol. 7, pp. 30089-30101, 2019.
M. N. Akram and S. Lotfifard, "Modeling and health monitoring of DC side of photovoltaic array," IEEE Transactions on Sustainable Energy, vol. 6, no. 4, pp. 1245-1253, 2015.
S. R. Madeti and S. Singh, "A comprehensive study on different types of faults and detection techniques for solar photovoltaic system," Solar Energy, vol. 158, pp. 161-185, 2017.
V. Kongphet, A. Migan-Dubois, C. Delpha, D. Diallo, and J.-Y. Lechenadec, "Photovoltaic Fault Detection and Diagnosis: Which Level of Granularity for PV Modeling?," in 2020 Prognostics and Health Management Conference (PHM-Besançon), 2020, pp. 180-186: IEEE.
M. Köntges et al., "Review of failures of photovoltaic modules," 2014
S. R. Madeti and S. Singh, "Online modular level fault detection algorithm for grid-tied and off-grid PV systems," Solar Energy, vol. 157, pp. 349-364, 2017.
A. Triki-Lahiani, A. B.-B. Abdelghani, and I. Slama-Belkhodja, "Fault detection and monitoring systems for photovoltaic installations: A review," Renewable and Sustainable Energy Reviews, vol. 82, pp. 2680-2692, 2018.
Y.-Y. Hong and R. A. Pula, "Methods of photovoltaic fault detection and classification: A review," Energy Reports, vol. 8, pp. 5898-5929, 2022.
[1] A. Dhoke, R. Sharma, and T. K. Saha, "A technique for fault detection, identification and location in solar photovoltaic systems," Solar Energy, vol. 206, pp. 864-874, 2020. [2] A. Jäger-Waldau, "Snapshot of photovoltaics—February 2020," Energies, vol. 13, no. 4, p. 930, 2020. [3] C. Buerhop, D. Schlegel, M. Niess, C. Vodermayer, R. Weißmann, and C. Brabec, "Reliability of IR-imaging of PV-plants under operating conditions," Solar Energy Materials and Solar Cells, vol. 107, pp. 154-164, 2012. [4] P. B. Quater, F. Grimaccia, S. Leva, M. Mussetta, and M. Aghaei, "Light Unmanned Aerial Vehicles (UAVs) for cooperative inspection of PV plants," IEEE Journal of Photovoltaics, vol. 4, no. 4, pp. 1107-1113, 2014. [5] J. Tsanakas and P. Botsaris, "An infrared thermographic approach as a hot-spot detection tool for photovoltaic modules using image histogram and line profile analysis," International Journal of Condition Monitoring, vol. 2, no. 1, pp. 22-30, 2012. [6] D. S. Pillai and N. Rajasekar, "An MPPT-based sensorless line–line and line–ground fault detection technique for PV systems," IEEE Transactions on Power Electronics, vol. 34, no. 9, pp. 8646-8659, 2018. [7] D. 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. [8] Y. Zhao, J.-F. De Palma, J. Mosesian, R. Lyons, and B. Lehman, "Line–line fault analysis and protection challenges in solar photovoltaic arrays," IEEE transactions on Industrial Electronics, vol. 60, no. 9, pp. 3784-3795, 2012. [9] B. P. Kumar, G. S. Ilango, M. J. B. Reddy, and N. Chilakapati, "Online fault detection and diagnosis in photovoltaic systems using wavelet packets," IEEE Journal of Photovoltaics, vol. 8, no. 1, pp. 257-265, 2017. [10] F. Aziz, A. U. Haq, S. Ahmad, Y. Mahmoud, M. Jalal, and U. Ali, "A novel convolutional neural network-based approach for fault classification in photovoltaic arrays," IEEE Access, vol. 8, pp. 41889-41904, 2020. [11] K. AbdulMawjood, S. S. Refaat, and W. G. Morsi, "Detection and prediction of faults in photovoltaic arrays: A review," in 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018), 2018, pp. 1-8: IEEE. [12] R. Hariharan, M. Chakkarapani, G. S. Ilango, and C. Nagamani, "A method to detect photovoltaic array faults and partial shading in PV systems," IEEE Journal of Photovoltaics, vol. 6, no. 5, pp. 1278-1285, 2016. [13] M. Catelani, L. Ciani, D. Galar, and G. Patrizi, "Optimizing maintenance policies for a yaw system using reliability-centered maintenance and data-driven condition monitoring," IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 9, pp. 6241-6249, 2020. [14] S. Voutsinas, D. Karolidis, I. Voyiatzis, and M. Samarakou, "Development of a multi-output feed-forward neural network for fault detection in Photovoltaic Systems," Energy Reports, vol. 8, pp. 33-42, 2022. [15] J. Van Gompel, D. Spina, and C. Develder, "Satellite based fault diagnosis of photovoltaic systems using recurrent neural networks," Applied Energy, vol. 305, p. 117874, 2022. [16] M. Dhimish, V. Holmes, B. Mehrdadi, and M. Dales, "Multi‐layer photovoltaic fault detection algorithm," High voltage, vol. 2, no. 4, pp. 244-252, 2017. [17] E. Garoudja, F. Harrou, Y. Sun, K. Kara, A. Chouder, and S. Silvestre, "Statistical fault detection in photovoltaic systems," Solar Energy, vol. 150, pp. 485-499, 2017. [18] A. Dhoke, R. Sharma, and T. K. Saha, "PV module degradation analysis and impact on settings of overcurrent protection devices," Solar Energy, vol. 160, pp. 360-367, 2018. [19] A. V. de Oliveira, M. Aghaei, and R. Rüther, "Automatic fault detection of photovoltaic array by convolutional neural networks during aerial infrared thermography," in Proceedings of the 36th European Photovoltaic Solar Energy Conference and Exhibition, Marseille, France, 2019, pp. 9-13. [20] S. A. Zaki, H. Zhu, M. A. Fakih, A. R. Sayed, and J. Yao, "Deep‐learning–based method for faults classification of PV system," IET Renewable Power Generation, vol. 15, no. 1, pp. 193-205, 2021. [21] S.-D. Lu, M.-H. Wang, S.-E. Wei, H.-D. Liu, and C.-C. Wu, "Photovoltaic module fault detection based on a convolutional neural network," Processes, vol. 9, no. 9, p. 1635, 2021. [22] I. S. Ramírez, J. R. P. Chaparro, and F. P. G. Márquez, "Machine Learning techniques implemented in IoT platform for fault detection in photovoltaic panels," in 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 2021, pp. 429-434: IEEE. [23] Z. Yi and A. H. Etemadi, "Line-to-line fault detection for photovoltaic arrays based on multiresolution signal decomposition and two-stage support vector machine," IEEE Transactions on Industrial Electronics, vol. 64, no. 11, pp. 8546-8556, 2017. [24] A. Y. Appiah, X. Zhang, B. B. K. Ayawli, and F. Kyeremeh, "Long short-term memory networks based automatic feature extraction for photovoltaic array fault diagnosis," IEEE Access, vol. 7, pp. 30089-30101, 2019. [25] M. N. Akram and S. Lotfifard, "Modeling and health monitoring of DC side of photovoltaic array," IEEE Transactions on Sustainable Energy, vol. 6, no. 4, pp. 1245-1253, 2015. [26] S. R. Madeti and S. Singh, "A comprehensive study on different types of faults and detection techniques for solar photovoltaic system," Solar Energy, vol. 158, pp. 161-185, 2017. [27] V. Kongphet, A. Migan-Dubois, C. Delpha, D. Diallo, and J.-Y. Lechenadec, "Photovoltaic Fault Detection and Diagnosis: Which Level of Granularity for PV Modeling?," in 2020 Prognostics and Health Management Conference (PHM-Besançon), 2020, pp. 180-186: IEEE. [28] M. Köntges et al., "Review of failures of photovoltaic modules," 2014. [29] S. R. Madeti and S. Singh, "Online modular level fault detection algorithm for grid-tied and off-grid PV systems," Solar Energy, vol. 157, pp. 349-364, 2017. [30] A. Triki-Lahiani, A. B.-B. Abdelghani, and I. Slama-Belkhodja, "Fault detection and monitoring systems for photovoltaic installations: A review," Renewable and Sustainable Energy Reviews, vol. 82, pp. 2680-2692, 2018. [31] Y.-Y. Hong and R. A. Pula, "Methods of photovoltaic fault detection and classification: A review," Energy Reports, vol. 8, pp. 5898-5929, 2022. [32] D. Revati and E. Natarajan, "IV and PV characteristics analysis of a photovoltaic module by different methods using Matlab software," Materials Today: Proceedings, vol. 33, pp. 261-269, 2020.
A. Narin, "Detection of focal and non-focal epileptic seizure using continuous wavelet transform-based scalogram images and pre-trained deep neural networks," Irbm, vol. 43, no. 1, pp. 22-31, 2022.
J. Garcia, S. Muller, E. Caicedo, T. Bastos Filho, and A. Souza, "Non-fatigating brain computer interface based on SSVEP and ERD to command an autonomous car," Advances in Data Science and Adaptive Analysis, vol. 1, pp. 1-11, 2018.
S. Tammina, "Transfer learning using vgg-16 with deep convolutional neural network for classifying images," International Journal of Scientific and Research Publications (IJSRP), vol. 9, no. 10, pp. 143-150, 2019. [36] F. Chollet, Deep learning with Python. Simon and Schuster, 2021
C. Szegedy et al., "Going deeper with convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1-9.
X. Ning, P. Duan, W. Li, and S. Zhang, "Real-time 3D face alignment using an encoder-decoder network with an efficient deconvolution layer," IEEE Signal Processing Letters, vol. 27, pp. 1944-1948, 2020.