Abstract
For several years, fault diagnosis of photovoltaic (PV) plants has been manually performed by the human operator by a visual inspection or automatically, by evaluating electrical measures collected by sensors mounted on each PV module. In recent years, a notable interest of the scientific community has been devoted towards the definition of algorithms able to automatically analyse the sequence of images acquired by a thermal camera mounted on board of an unmanned aerial vehicle (UAV) for early PV anomaly detection. In this paper, we define a model-based approach for the detection of the panels, which uses the structural regularity of the PV string and a novel technique for local hot spot detection, based on the use of a fast and effective algorithm for finding local maxima in the PV panel region. Finally, we introduce the concept of global hot spot detection, namely a multi-frame recognition of PV faults which further improves the anomaly detection accuracy of the proposed method. The algorithm has been designed and optimized so as to run in real-time directly on an embedded system on board of the UAV. The accuracy of the proposed approach has been experimented on several video sequences with a standard protocol in terms of Precision, Recall and F-Score, so that our dataset and our quantitative results can be used for future comparisons and to evaluate the reliability of computer vision techniques designed for thermographic PV inspection.
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The dataset, together with the annotations, is available for benchmarking purposes upon request at http://mivia.unisa.it.
References
Addabbo P, Angrisano A, Bernardi ML, Gagliarde G, Mennella A, Nisi M, Ullo S (2017) A UAV infrared measurement approach for defect detection in photovoltaic plants. In: IEEE international workshop on metroaerospace, pp 345–350
Aghaei M, Bellezza Quater P, Grimaccia F, Leva S, Mussetta M (2014) Unmanned aerial vehicles in photovoltaic systems monitoring applications. In: European photovoltaic solar energy 29th conference and exhibition, pp 2734–2739
Aghaei M, Gandelli A, Grimaccia F, Leva S, Zich R (2015a) IR real-time analyses for PV system monitoring by digital image processing techniques. In: IEEE international conference on event-based control, communication, and signal processing (EBCCSP), pp 1–6
Aghaei M, Grimaccia F, Gonano CA, Leva S (2015b) Innovative automated control system for PV fields inspection and remote control. IEEE Trans Ind Electron 62(11):7287–7296
Aghaei M, Dolara A, Leva S, Grimaccia F (2016a) Image resolution and defects detection in PV inspection by unmanned technologies. In: IEEE power and energy society general meeting (PESGM), pp 1–5
Aghaei M, Leva S, Grimaccia F (2016b) PV power plant inspection by image mosaicing techniques for IR real-time images. In: IEEE photovoltaic specialists conference (PVSC), pp 3100–3105
Arenella A, Greco A, Saggese A, Vento M (2017) Real time fault detection in photovoltaic cells by cameras on drones. In: Springer international conference on image analysis and recognition (ICIAR), pp 617–625
Buerhop C, Scheuerpflug H (2014) Field inspection of PV-modules using aerial, drone-mounted thermography. In: EU-PVSEC
Buerhop C, Schlegel D, Niess M, Vodermayer C, Weißmann R, Brabec C (2012a) Reliability of IR-imaging of PV-plants under operating conditions. Elsevier Sol Energy Mater Sol Cells 107:154–164
Buerhop C, Weißmann R, Scheuerpflug H, Auer R, Brabec C (2012b) Quality control of pv-modules in the field using a remote-controlled drone with an infrared camera. In: European photovoltaic solar energy conference and exhibition
Carletti V, Greco A, Saggese A, Vento M (2018) Multi-object tracking by flying cameras based on a forward–backward interaction. IEEE Access 6:43905–43919
DiLascio R, Foggia P, Percannella G, Saggese A, Vento M (2013) A real time algorithm for people tracking using contextual reasoning. Comput Vis Image Underst 117(8):892–908
Djordjevic S, Parlevliet D, Jennings P (2014) Detectable faults on recently installed solar modules in western australia. Elsevier Renew Energy 67:215–221
Dotenco S, Dalsass M, Winkler L, Würzner T, Brabec C, Maier A, Gallwitz F (2016) Automatic detection and analysis of photovoltaic modules in aerial infrared imagery. In: IEEE international conference on applications of computer vision (WACV), pp 1–9
Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (VOC) challenge. Springer Int J Comput Vis 88(2):303–338
Goutte C, Gaussier E (2005) A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: Springer European conference on information retrieval, pp 345–359
Grimaccia F, Aghaei M, Mussetta M, Leva S, Quater PB (2015) Planning for PV plant performance monitoring by means of unmanned aerial systems (UAS). Springer Int J Energy Environ Eng 6(1):47–54
Grimaccia F, Leva S, Dolara A, Aghaei M (2017a) Survey on PV modules common faults after an o&m flight extensive campaign over different plants in italy. IEEE J Photovolt 7(3):810–816
Grimaccia F, Leva S, Niccolai A (2017b) PV plant digital mapping for modules defects detection by unmanned aerial vehicles. IET Renew Power Gener 11(10):1221–1228
Guerriero P, Daliento S (2017) Automatic edge identification for accurate analysis of thermographic images of solar panels. In: IEEE international conference on clean electrical power (ICCEP), pp 768–772
Hu Y, Cao W, Ma J, Finney SJ, Li D (2014a) Identifying PV module mismatch faults by a thermography-based temperature distribution analysis. IEEE Trans Device Mater Reliab 14(4):951–960
Hu Y, Cao W, Wu J, Ji B, Holliday D (2014b) Thermography-based virtual MPPT scheme for improving PV energy efficiency under partial shading conditions. IEEE Trans Power Electron 29(11):5667–5672
Jiang L, Su J, Li X (2016) Hot spots detection of operating PV arrays through IR thermal image using method based on curve fitting of gray histogram. In: MATEC web of conferences, vol 61, p 06017
Leva S, Aghaei M, Grimaccia F (2015) Pv power plant inspection by UAS: correlation between altitude and detection of defects on PV modules. In: IEEE international conference on environment and electrical engineering (EEEIC), pp 1921–1926
Li X, Hu W, Shen C, Zhang Z, Dick A, Hengel AVD (2013) A survey of appearance models in visual object tracking. ACM Trans Intell Syst Technol 4(4):58
Quater PB, Grimaccia F, Leva S, Mussetta M, Aghaei M (2014) Light unmanned aerial vehicles (UAVS) for cooperative inspection of pv plants. IEEE J Photovolt 4(4):1107–1113
Tsanakas JA, Chrysostomou D, Botsaris P, Gasteratos A (2015) Fault diagnosis of photovoltaic modules through image processing and canny edge detection on field thermographic measurements. Int J Sustain Energy 34(6):351–372
Tsanakas JA, Ha L, Buerhop C (2016) Faults and infrared thermographic diagnosis in operating C–Si photovoltaic modules: a review of research and future challenges. Elsevier Renew Sustain Energy Rev 62:695–709
Tsanakas JA, Ha LD, Al Shakarchi F (2017) Advanced inspection of photovoltaic installations by aerial triangulation and terrestrial georeferencing of thermal/visual imagery. Elsevier Renew Energy 102:224–233
Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):13
Zhang X, Yan J, Feng S, Lei Z, Yi D, Li SZ (2012) Water filling: unsupervised people counting via vertical kinect sensor. In: IEEE international conference on advanced video and signal-based surveillance (AVSS), pp 215–220
Acknowledgements
This research has been partially supported by A.I. Tech s.r.l. (http://www.aitech.vision). We would like to thank Topview s.r.l. (http://www.topview.it) for providing the videos used in our experimentation.
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Carletti, V., Greco, A., Saggese, A. et al. An intelligent flying system for automatic detection of faults in photovoltaic plants. J Ambient Intell Human Comput 11, 2027–2040 (2020). https://doi.org/10.1007/s12652-019-01212-6
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DOI: https://doi.org/10.1007/s12652-019-01212-6