Enhancing Photovoltaic Reliability: A Global and Local Feature Selection Approach with Improved Harris Hawks Optimization for Efficient Hotspot Detection Using Infrared Imaging
This article is part of Special Issue:
Muhammad Umair Ali,
Amad Zafar,
Waqas Ahmed,
Muhammad Aslam,
Seong Han Kim,
Muhammad Umair Ali
Department of Artificial Intelligence and Robotics , Sejong University , Seoul , 05006 , Republic of Korea , sejong.ac.kr ,
Search for more papers by this authorAmad Zafar
Department of Artificial Intelligence and Robotics , Sejong University , Seoul , 05006 , Republic of Korea , sejong.ac.kr ,
Search for more papers by this authorWaqas Ahmed
Department of Supply Chain Operations Management , School of Engineering , Jonkoping University , Jonkoping , 55318 , Sweden , ju.se
Search for more papers by this authorMuhammad Aslam
Department of Artificial Intelligence Data Science , Sejong University , Seoul , 05006 , Republic of Korea , sejong.ac.kr
Search for more papers by this authorCorresponding Author
Seong Han Kim
Department of Artificial Intelligence and Robotics , Sejong University , Seoul , 05006 , Republic of Korea , sejong.ac.kr ,
Search for more papers by this authorMuhammad Umair Ali,
Amad Zafar,
Waqas Ahmed,
Muhammad Aslam,
Seong Han Kim,
Muhammad Umair Ali
Department of Artificial Intelligence and Robotics , Sejong University , Seoul , 05006 , Republic of Korea , sejong.ac.kr ,
Search for more papers by this authorAmad Zafar
Department of Artificial Intelligence and Robotics , Sejong University , Seoul , 05006 , Republic of Korea , sejong.ac.kr ,
Search for more papers by this authorWaqas Ahmed
Department of Supply Chain Operations Management , School of Engineering , Jonkoping University , Jonkoping , 55318 , Sweden , ju.se
Search for more papers by this authorMuhammad Aslam
Department of Artificial Intelligence Data Science , Sejong University , Seoul , 05006 , Republic of Korea , sejong.ac.kr
Search for more papers by this authorCorresponding Author
Seong Han Kim
Department of Artificial Intelligence and Robotics , Sejong University , Seoul , 05006 , Republic of Korea , sejong.ac.kr ,
Search for more papers by this authorAcademic Editor: Dongdong Yuan
Abstract
The photovoltaic (PV) systems’ inherent ability to transform solar light directly into electrical energy has contributed to their increasing popularity. However, malfunctions can reduce system dependability. Therefore, rapid hotspot identification is critical for efficient, dependable, and risk-free PV operation. This work presents a method for determining the most optimal hybrid features using the infrared (IR) images of PV panels for hotspot and fault detection. The information at the global (texture, HoG, and color histograms) and local (local binary pattern, SURF, and KAZE) levels were extracted from the IR images of PV panels using a uniform window size of 8 × 8. A binary improved Harris hawks optimization (b-IHHO) optimal feature selection strategy was used to get the optimal feature subset for model training using PV IR images. The IR images of PV were acquired to test the presented framework. The findings suggested that the proposed framework can classify the IR images of solar panels with an accuracy of 98.41% with lesser feature vector size into three classes (normal, hotspot, and defective). Furthermore, the findings were also compared with the latest literature. The presented technique plays a vital role in carbon-free cities and is simple to adopt for PV system inspection.
Conflicts of Interest
The authors declare no conflict of interest.
Open Research
Data Availability
The data that support the findings of this study are available from the corresponding authors upon reasonable request.
References
- 1 IEA, Global Energy & CO2 Status Report 2019, 2019, IEA (International Energy Agency), Paris, France.
- 2 Hussain S., Ali M. U., Park G.-S., Nengroo S. H., Khan M. A., and Kim H.-J., A real-time Bi-adaptive controller-based energy management system for battery–supercapacitor hybrid electric vehicles, Energies. (2019) 12, no. 24, https://doi.org/10.3390/en12244662, 4662.
- 3 Ali M. U., Zafar A., Nengroo S. H., Hussain S., Alvi M. J., and Kim H.-J., Towards a smarter battery management system for electric vehicle applications: a critical review of lithium-ion battery state of charge estimation, Energies. (2019) 12, no. 3, https://doi.org/10.3390/en12030446, 2-s2.0-85060935068, 446.
- 4 Nengroo S. H., Ali M. U., Zafar A., Hussain S., Murtaza T., Alvi M. J., Raghavendra K. V. G., and Kim H. J., An optimized methodology for a hybrid photo-voltaic and energy storage system connected to a low-voltage grid, Electronics. (2019) 8, no. 2, https://doi.org/10.3390/electronics8020176, 2-s2.0-85063499135, 176.
- 5 Nengroo S., Kamran M., Ali M., Kim D.-H., Kim M.-S., Hussain A., and Kim H., Dual battery storage system: an optimized strategy for the utilization of renewable photovoltaic energy in the United Kingdom, Electronics. (2018) 7, no. 9, https://doi.org/10.3390/electronics7090177, 2-s2.0-85053611100, 177.
- 6 Jäger-Waldau A., Snapshot of photovoltaics—May 2023, EPJ Photovoltaics. (2023) 14, https://doi.org/10.1051/epjpv/2023016, 23.
- 7 Kumar M. and Kumar A., Performance assessment and degradation analysis of solar photovoltaic technologies: a review, Renewable and Sustainable Energy Reviews. (2017) 78, 554–587, https://doi.org/10.1016/j.rser.2017.04.083, 2-s2.0-85018736064.
- 8 Li G., Akram M. W., Jin Y., Chen X., Zhu C., Ahmad A., Arshad R. H., and Zhao X., Thermo-mechanical behavior assessment of smart wire connected and busbarPV modules during production, transportation, and subsequent field loading stages, Energy. (2019) 168, 931–945, https://doi.org/10.1016/j.energy.2018.12.002, 2-s2.0-85059299875.
- 9 Esfahani S. N., Asghari S., and Rashid-Nadimi S., A numerical model for soldering process in silicon solar cells, Solar Energy. (2017) 148, 49–56, https://doi.org/10.1016/j.solener.2017.03.065, 2-s2.0-85016451471.
- 10 Dhimish M., Thermal impact on the performance ratio of photovoltaic systems: a case study of 8000 photovoltaic installations, Case Studies in Thermal Engineering. (2020) 21, https://doi.org/10.1016/j.csite.2020.100693, 100693.
- 11 Dhimish M., Mather P., and Holmes V., Evaluating power loss and performance ratio of hot-spotted photovoltaic modules, IEEE Transactions on Electron Devices. (2018) 65, no. 12, 5419–5427, https://doi.org/10.1109/TED.2018.2877806, 2-s2.0-85056356154.
- 12 Dhimish M. and Badran G., Current limiter circuit to avoid photovoltaic mismatch conditions including hot-spots and shading, Renewable Energy. (2020) 145, 2201–2216, https://doi.org/10.1016/j.renene.2019.07.156.
- 13 Čabo F. G., Marinić-Kragić I., Garma T., and Nižetić S., Development of thermo-electrical model of photovoltaic panel under hot-spot conditions with experimental validation, Energy. (2021) 230, https://doi.org/10.1016/j.energy.2021.120785, 120785.
- 14 Madeti S. R. and Singh S. N., Monitoring system for photovoltaic plants: a review, Renewable and Sustainable Energy Reviews. (2017) 67, 1180–1207, https://doi.org/10.1016/j.rser.2016.09.088, 2-s2.0-84988957044.
- 15 Breitenstein O., Bauer J., Lotnyk A., and Wagner J.-M., Defect induced non-ideal dark—characteristics of solar cells, Superlattices and Microstructures. (2009) 45, no. 4-5, 182–189, https://doi.org/10.1016/j.spmi.2008.10.025, 2-s2.0-62949243905.
- 16 Trupke T., Mitchell B., Weber J. W., McMillan W., Bardos R. A., and Kroeze R., Photoluminescence Imaging for Photovoltaic Applications, Energy Procedia, 2012, 15, 135–146.
- 17 Deitsch S., Christlein V., Berger S., Buerhop-Lutz C., Maier A., Gallwitz F., and Riess C., Automatic classification of defective photovoltaic module cells in electroluminescence images, Solar Energy. (2019) 185, 455–468, https://doi.org/10.1016/j.solener.2019.02.067, 2-s2.0-85064925364.
- 18 Aghaei M., Grimaccia F., Gonano C. A., and Leva S., Innovative automated control system for PV fields inspection and remote control, IEEE Transactions on Industrial Electronics. (2015) 62, no. 11, 7287–7296, https://doi.org/10.1109/TIE.2015.2475235, 2-s2.0-84944128471.
- 19
Niazi K.,
Akhtar W.,
Khan H. A.,
Sohaib S., and
Nasir A. K., Binary classification of defective solar PV modules using thermography, 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC), 2018, June, Waikoloa, HI, USA, IEEE, 753–757, https://doi.org/10.1109/PVSC.2018.8548138, 2-s2.0-85059913582.
10.1109/PVSC.2018.8548138 Google Scholar
- 20 Dunderdale C., Brettenny W., Clohessy C., and Dyk E. E., Photovoltaic defect classification through thermal infrared imaging using a machine learning approach, Progress in Photovoltaics: Research and Applications. (2020) 28, no. 3, 177–188, https://doi.org/10.1002/pip.3191.
- 21 Ali M. U., Saleem S., Masood H., Kallu K. D., Masud M., Alvi M. J., and Zafar A., Early hotspot detection in photovoltaic modules using color image descriptors: an infrared thermography study, International Journal of Energy Research. (2022) 46, no. 2, 774–785, https://doi.org/10.1002/er.7201.
- 22 Ali M. U., Khan H. F., Masud M., Kallu K. D., and Zafar A., A machine learning framework to identify the hotspot in photovoltaic module using infrared thermography, Solar Energy. (2020) 208, 643–651, https://doi.org/10.1016/j.solener.2020.08.027.
- 23 Berardone I., Lopez Garcia J., and Paggi M., Analysis of electroluminescence and infrared thermal images of monocrystalline silicon photovoltaic modules after 20 years of outdoor use in a solar vehicle, Solar Energy. (2018) 173, 478–486, https://doi.org/10.1016/j.solener.2018.07.055, 2-s2.0-85050934882.
- 24 Niazi K. A. K., Akhtar W., Khan H. A., Yang Y., and Athar S., Hotspot diagnosis for solar photovoltaic modules using a Naive Bayes classifier, Solar Energy. (2019) 190, 34–43, https://doi.org/10.1016/j.solener.2019.07.063, 2-s2.0-85073701266.
- 25 Ahmed W., Hanif A., Kallu K. D., Kouzani A. Z., Ali M. U., and Zafar A., Photovoltaic panels classification using isolated and transfer learned deep neural models using infrared thermographic images, Sensors. (2021) 21, no. 16, https://doi.org/10.3390/s21165668, 5668.
- 26 Alves R. H. F., Júnior G. A.d D., Marra E. G., and Lemos R. P., Automatic fault classification in photovoltaic modules using convolutional neural networks, Renewable Energy. (2021) 179, 502–516, https://doi.org/10.1016/j.renene.2021.07.070.
- 27 Manno D., Cipriani G., Ciulla G., Di Dio V., Guarino S., and Brano V. L., Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images, Energy Conversion and Management. (2021) 241, https://doi.org/10.1016/j.enconman.2021.114315, 114315.
- 28 Ahmed W., Ali M. U., Hussain S. J., Zafar A., and Hasani S. A., Visual vocabulary based photovoltaic health monitoring system using infrared thermography, IEEE Access. (2022) 10, 14409–14417, https://doi.org/10.1109/ACCESS.2022.3148138.
- 29 Akram M. W., Li G., Jin Y., Chen X., Zhu C., and Ahmad A., Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning, Solar Energy. (2020) 198, 175–186, https://doi.org/10.1016/j.solener.2020.01.055.
- 30
Minkina W. and
Dudzik S., Infrared Thermography: Errors and Uncertainties, 2009, John Wiley & Sons.
10.1002/9780470682234 Google Scholar
- 31
Haralick R. M.,
Shanmugam K., and
Dinstein I. H., Textural features for image classification, IEEE Transactions on Systems, Man, and Cybernetics. (1973) SMC-3, no. 6, 610–621, https://doi.org/10.1109/TSMC.1973.4309314, 2-s2.0-0015680481.
10.1109/TSMC.1973.4309314 Google Scholar
- 32 Armi L. and Fekri-Ershad S., Texture image analysis and texture classification methods—a review, 2019, arXiv preprint arXiv: 1904.06554.
- 33
Dalal N. and
Triggs B., Histograms of oriented gradients for human detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005, IEEE, 886–893, https://doi.org/10.1109/CVPR.2005.177, 2-s2.0-33645146449.
10.1109/CVPR.2005.177 Google Scholar
- 34 Banerji S., Sinha A., and Liu C., Haarhog: improving the hog descriptor for image classification, 2013 IEEE International Conference on Systems,Man, and Cybernetics, 2013, IEEE, 4276–4281.
- 35 Fleyeh H. and Roch J., Benchmark evaluation of HOG descriptors as features for classification of traffic signs, in: working papers in transport, tourism, information technology and microdata analysis, Högskolan Dalarna, Borlänge. (2013) 18.
- 36 Estrada F. J. and Jepson A. D., Benchmarking image segmentation algorithms, International Journal of Computer Vision. (2009) 85, no. 2, 167–181, https://doi.org/10.1007/s11263-009-0251-z, 2-s2.0-68849129923.
- 37 Bay H., Ess A., Tuytelaars T., and Van Gool L., Speeded-up robust features (SURF), Computer Vision and Image Understanding. (2008) 110, no. 3, 346–359, https://doi.org/10.1016/j.cviu.2007.09.014, 2-s2.0-43049174575.
- 38 Alcantarilla P. F., Bartoli A., and Davison A. J., KAZE features, European Conference on Computer Vision, 2012, Springer, 214–227.
- 39 Naeem S., Ali A., Qadri S., Mashwani W. K., Tairan N., Shah H., Fayaz M., Jamal F., Chesneau C., and Anam S., Machine-learning based hybrid-feature analysis for liver cancer classification using fused (MR and CT) images, Applied Sciences. (2020) 10, no. 9, https://doi.org/10.3390/app10093134, 3134.
- 40
Hartigan J. A. and
Wong M. A., Algorithm AS 136: a k-means clustering algorithm, Journal of the Royal Statistical Society. Series C (Applied Statistics). (1979) 28, 100–108.
10.2307/2346830 Google Scholar
- 41 Kanungo T., Mount D. M., Netanyahu N. S., Piatko C. D., Silverman R., and y. Wu A., An efficient k-means clustering algorithm: analysis and implementation, IEEE transactions on pattern analysis and machine intelligence. (2002) 24, no. 7, 881–892.
- 42 Heidari A. A., Mirjalili S., Faris H., Aljarah I., Mafarja M., and Chen H., Harris hawks optimization: algorithm and applications, Future Generation Computer Systems. (2019) 97, 849–872, https://doi.org/10.1016/j.future.2019.02.028, 2-s2.0-85063421586.
- 43 Peng L., Cai Z., Heidari A. A., Zhang L., and Chen H., Hierarchical Harris hawks optimizer for feature selection, Journal of Advanced Research. (2023) 53, 261–278, https://doi.org/10.1016/j.jare.2023.01.014.
- 44 Agrawal P., Abutarboush H. F., Ganesh T., and Mohamed A. W., Metaheuristic algorithms on feature selection: a survey of one decade of research (2009–2019, IEEE Access. (2021) 9, 26766–26791.
- 45
Verma S. and
Rubin J., Fairness definitions explained, 2018 IEEE/ACM International Workshop on Software Fairness (FairWare), 2018, IEEE, 1–7.
10.1145/3194770.3194776 Google Scholar
- 46 Hassouneh Y., Turabieh H., Thaher T., Tumar I., Chantar H., and Too J., Boosted whale optimization algorithm with natural selection operators for software fault prediction, IEEE Access. (2021) 9, 14239–14258.
- 47 Mirjalili S., Saremi S., Mirjalili S. M., and Coelho L. S., Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization, Expert Systems with Applications. (2016) 47, 106–119, https://doi.org/10.1016/j.eswa.2015.10.039, 2-s2.0-84949033140.
- 48 Altabeeb A. M., Mohsen A. M., and Ghallab A., An improved hybrid firefly algorithm for capacitated vehicle routing problem, Applied Soft Computing. (2019) 84, https://doi.org/10.1016/j.asoc.2019.105728, 2-s2.0-85070962088, 105728.
- 49 Dhimish M., Holmes V., Mehrdadi B., and Dales M., Comparing Mamdani Sugeno fuzzy logic and RBF ANN network for PV fault detection, Renewable Energy. (2018) 117, 257–274, https://doi.org/10.1016/j.renene.2017.10.066, 2-s2.0-85032285798.
- 50 Ahmed W., Ali M. U., Mahmud M. A. P., Niazi K. A. K., Zafar A., and Kerekes T., A comparison and introduction of novel solar panel’s fault diagnosis technique using deep-features shallow-classifier through infrared thermography, Energies. (2023) 16, 1043.
- 51 Jaffery Z. A., Dubey A. K., Irshad, and Haque A., Scheme for predictive fault diagnosis in photo-voltaic modules using thermal imaging, Infrared Physics & Technology. (2017) 83, 182–187, https://doi.org/10.1016/j.infrared.2017.04.015, 2-s2.0-85019077038.
