Abstract
Advancements in photovoltaics and information technology makes it possible for cities to produce a fraction, if not everything, of the electrical power they require. Power generation in cities can be mainly achieved by photovoltaic installations located in the roofs of houses and buildings unevenly distributed among large areas. Suboptimal conditions such as soling, partial shadowing and electrical faults, are common in PV installations, decreasing their efficiency. It is advisable to detect when these suboptimal conditions occur to correct them and improve the overall performance of the PV installation. Nevertheless, the majority of suboptimal-detection techniques reported in the literature only consider large PV systems, neglecting rooftop PV installations. In this paper a low-cost embedded system suitable for detecting suboptimal conditions in small PV installation is presented. This system was validated for the detection of partial shadow that may occur due to strange objects in a specific region of the PV array. The system obtained a sensitivity of 87%, specificity of 87.9% and accuracy of 87.6%.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Fina, B., Auer, H., Friedl, W.: Profitability of pv sharing in energy communities: Use cases for different settlement patterns. Energy 189(116), 148 (2019)
Parag, Y., Sovacool, B.K.: Electricity market design for the prosumer era. Nat. Energy 1(4), 1–6 (2016)
Li, Z., Ma, T.: Peer-to-peer electricity trading in grid-connected residential communities with household distributed photovoltaic. Appl. Energy 278(115), 670 (2020)
González-Romera, E., et al.: Advantages of minimizing energy exchange instead of energy cost in prosumer microgrids. Energies 12(4), 719 (2019)
Appiah, A.Y., Zhang, X., Ayawli, B.B.K., Kyeremeh, F.: Review and performance evaluation of photovoltaic array fault detection and diagnosis techniques. Int. J. Photoenergy 2019, 1–19 (2019)
Tyutyundzhiev, N., Lovchinov, K., Martínez-Moreno, F., Leloux, J., Narvarte, L.: Advanced PV modules inspection using multirotor UAV. In: 31st European Photovoltaic Solar Energy Conference and Exhibition, Hamburg (2015)
Chaudhary, A.S., Chaturvedi, D.: Thermal image analysis and segmentation to study temperature effects of cement and bird deposition on surface of solar panels. Int. J. Image Graph. Signal Process. 9(12), 12–22 (2017)
Köntges, M.: Reviewing the practicality and utility of electroluminescence and thermography (2014)
Leva, S., Aghaei, M., Grimaccia, F.: PV power plant inspection by UAS: correlation between altitude and detection of defects on PV modules. In: 2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC). IEEE (2015)
Cardinale-Villalobos, L., Rimolo-Donadio, R., Meza, C.: Solar panel failure detection by infrared UAS digital photogrammetry: a case study. Int. J. Renew. Energy Res. (IJRER) 10(3), 1154–1164 (2020)
Figueroa-García, J.C., Garay-Rairán, F.S., Hernández-Pérez, G.J., Díaz-Gutierrez, Y. (eds.): WEA 2020. CCIS, vol. 1274. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61834-6
Murillo-Soto, L., Meza, C.: Fault detection in solar arrays based on an efficiency threshold. In: 2020 IEEE 11th Latin American Symposium on Circuits Systems (LASCAS), pp. 1–4 (2020)
Cardinale-Villalobos, L., Meza, C., Murillo-Soto, L.D.: Experimental comparison of visual inspection and infrared thermography for the detection of soling and partial shading in photovoltaic arrays. In: Nesmachnow, S., Hernández Callejo, L. (eds.) ICSC-CITIES 2020. CCIS, vol. 1359, pp. 302–321. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69136-3_21
Mellit, A., Tina, G., Kalogirou, S.: Fault detection and diagnosis methods for photovoltaic systems: a review. Renew. Sustain. Energy Rev. 91, 1–17 (2018)
Mäki, A., Valkealahti, S.: Power losses in long string and parallel-connected short strings of series-connected silicon-based photovoltaic modules due to partial shading conditions. IEEE Trans. Energy Conv. 27(1), 173–183 (2012)
Maghami, M.R., Hizam, H., Gomes, C., Radzi, M.A., Rezadad, M.I., Hajighorbani, S.: Power loss due to soiling on solar panel: a review. Renew. Sustain. Energy Rev. 59, 1307–1316 (2016)
Javed, W., Wubulikasimu, Y., Figgis, B., Guo, B.: Characterization of dust accumulated on photovoltaic panels in doha, qatar. Solar Energy 142, 123–135 (2017)
Quater, P.B., Grimaccia, F., Leva, S., Mussetta, M., Aghaei, M.: Light unmanned aerial vehicles (uavs) for cooperative inspection of pv plants. IEEE J. Photovolt. 4(4), 1107–1113 (2014)
Mekki, H., Mellit, A., Salhi, H.: Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules. Simul. Model. Pract. Theory 67, 1–13 (2016)
Ma, J., Pan, X., Man, K.L., Li, X., Wen, H., Ting, T.O.: Detection and assessment of partial shading scenarios on photovoltaic strings. IEEE Trans. Ind. Appl. 54(6), 6279–6289 (2018)
Bastidas-Rodriguez, J.D., Franco, E., Petrone, G., Ramos-Paja, C.A., Spagnuolo, G.: Model-based degradation analysis of photovoltaic modules through series resistance estimation. IEEE Trans. Ind. Electron. 62(11), 7256–7265 (2015)
Mellit, A., Chine, W., Massi Pavan, A., Lughi, V.: Fault diagnosis in photovoltaic arrays. In: 2015 International Conference on Clean Power (ICCEP) (2015)
Murillo-Soto, L.D., Figueroa-Mata, G., Meza, C.: Identification of the internal resistance in solar modules under dark conditions using differential evolution algorithm. In: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), IEEE, pp. 1–9 (2018)
Tango, T.: Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials. Taylor & Francis Group, Tokyo Japan (2017)
Pyzdek, T.: Descriptive statistics. The Lean Healthcare Handbook. MP, pp. 145–149. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69901-7_12
Pintea, S., Moldovan, R.: The Receiver-Operating Characteristic (ROC) analysis: fundamentals and applications in clinical psychology. J. Cogn. Behav. Psychother. 9(1), 49–66 (2009)
Tu, W.: Basic principles of statistical inference. In: Ambrosius, W. (ed.) Topics in Biostatistics, vol. 404, pp. 53–72. Humana Press (2007)
Huitema, B.: The Analysis of Covariance and Alternatives: Statistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies, 2nd edn. John Wiley & Sons, Hoboken (2011)
Ruxton, G.D., Beauchamp, G.: Time for some a priori thinking about post hoc testing. Behav. Ecol 19(3), 690–693 (2008)
Acknowledgement
This paper is part of a project entitled: Fault Identification in Photovoltaic Systems, ID 5402-1360-4201, financed by the Costa Rica Institute of Technology.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Corrales, D., Cardinale-Villalobos, L., Meza, C., Murillo-Soto, L.D. (2022). Detection of Suboptimal Conditions in Photovoltaic Installations for Household-Prosumers. In: Corchado, J.M., Trabelsi, S. (eds) Sustainable Smart Cities and Territories. SSCTIC 2021. Lecture Notes in Networks and Systems, vol 253. Springer, Cham. https://doi.org/10.1007/978-3-030-78901-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-78901-5_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78900-8
Online ISBN: 978-3-030-78901-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)