Journal of Solar Energy Research

Smart Maintenance with Regression Analysis for Efficiency Improvement in Photovoltaic Energy Systems

Document Type : Original Article

Authors

1 Department of Alternative Energy Resources Technology Program, Hacettepe Ankara Chamber of Industry 1st Organized Industrial Zone Vocational School, Hacettepe University, Ankara, Türkiye

2 Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Kırıkkale University, Kırıkkale, Türkiye.

3 National Technical University of Athens, Athens, Greece

4 North West Regional College, Londonderry, Northern Ireland

5 Sincan District Directorate of National Education, Ankara, Türkiye

6 Impektra IT Software, Ankara, Türkiye

7 Yenikent Ahmet Çiçek Vocational and Technical Anatolian High School, Ankara, Türkiye

8 Private Ankara Chamber of Industry Technical College Vocational and Technical Anatolian High School, Ankara, Türkiye

9 Ankara Chamber of Industry 1st Organized Industrial Zone Management, Ankara, Türkiye

10 Oryx-Data Incubator EURL, Paris, France

10.22059/jser.2023.363200.1335

Abstract

This research had the overarching goal of optimizing maintenance intervals and reducing the maintenance workload by enhancing accessibility for individuals lacking technical expertise in the upkeep of photovoltaic systems, with a particular focus on rooftop applications. The study achieved this objective by employing a linear regression algorithm to analyse climatic parameters such as wind speed, humidity, ambient temperature, and light intensity, collected from the installation site of a photovoltaic solar energy system. Simultaneously, the current and voltage values obtained from the system were also examined. This analysis not only facilitated the determination of power generation within the system but also enabled real-time detection of potential issues such as pollution, shadowing, bypass, and panel faults on the solar panels. Additionally, an artificial intelligence-supported interface was developed within the study, attributing any decline in power generation to specific causes and facilitating prompt intervention to rectify malfunctions, thereby ensuring more efficient system operation.

Keywords

[1] Sobri, S., Koohi-Kamali, S. and Rahim, N. A. (2018). Solar photovoltaic generation forecasting methods: A review, Energy Convers Manag, 156, 459–497. DOI: 10.1016/J.ENCONMAN.2017.11.019.
[2] Lahouar, A., Mejri, A. and Ben Hadj Slama, J. (2017). Importance based selection method for day-ahead photovoltaic power forecast using random forests, International Conference on Green Energy and Conversion Systems, GECS 2017. DOI: 10.1109/GECS.2017.8066171.
[3] Shi, J., Lee, W. J., Liu, Y., Yang, Y. and Wang, P. (2012). Forecasting power output of photovoltaic systems based on weather classification and support vector machines, IEEE Trans Ind Appl, 48(3), 1064-1069. DOI: 10.1109/TIA.2012.2190816.
[4] Garud, K. S., Jayaraj, S. and Lee, M. Y. (2021). A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models, Int J Energy Res, 45(1), 6–35. DOI: 10.1002/ER.5608.
[5] Ahmad, M. W., Mourshed, M. and Rezgui, Y. (2018). Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression, Energy, 164, 465–474. DOI: 10.1016/J.ENERGY.2018.08.207.
[6] Sundaram, K. M., Padmanaban, S., Holm-Nielsen, J. B.  and Pandiyan, P. (2022). Photovoltaic Systems: Artificial Intelligence-based Fault Diagnosis and Predictive Maintenance, 1st ed. CRC Press. DOI: 10.1201/9781003202288.
[7] Sfetsos A. and Coonick, A. H. (2000). Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques, Solar Energy, 68(2), 169–178. DOI: 10.1016/S0038-092X(99)00064-X.
[8] Dorvlo, A. S. S., Jervase, J. A. and Al-Lawati, A. (2002). Solar radiation estimation using artificial neural networks, Appl Energy, 71(4), 307–319. DOI: 10.1016/S0306-2619(02)00016-8.
[9] Mellit, A., Kalogirou, S. A., Hontoria, L. and Shaari, S. (2009). Artificial intelligence techniques for sizing photovoltaic systems: A review, Renewable and Sustainable Energy Reviews, 13(2), 406–419. DOI: 10.1016/J.RSER.2008.01.006.
[10] Gligor, A., Dumitru, C. D. and Grif, H. S. (2018). Artificial intelligence solution for managing a photovoltaic energy production unit, Procedia Manuf, 22, 626–633. DOI: 10.1016/J.PROMFG.2018.03.091.
[11] VanDeventer W. et al., (2019). Short-term PV power forecasting using hybrid GASVM technique, Renew Energy, 140, 367–379. DOI: 10.1016/J.RENENE.2019.02.087.
[12] Tüfekci, P. (2014). Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods, International Journal of Electrical Power & Energy Systems, 60, 126–140. DOI: 10.1016/J.IJEPES.2014.02.027.
[13] Savaş S. et al., (2022). Innovative and Smart Maintenance in Solar Energy Systems, Journal of Information Systems and Management Research, 4(2), 35–49.
[14] Abubakar, A., Almeida, C. F. M. and Gemignani, M. (2021). Review of Artificial Intelligence-Based Failure Detection and Diagnosis Methods for Solar Photovoltaic Systems, Machines 9(12), 328. DOI: 10.3390/MACHINES9120328.
[15] Mellit A. and Kalogirou, S. (2022). Handbook of Artificial Intelligence Techniques in Photovoltaic Systems: Modeling, Control, Optimization, Forecasting and Fault Diagnosis, Handbook of Artificial Intelligence Techniques in Photovoltaic Systems: Modeling, Control, Optimization, Forecasting and Fault Diagnosis, 1–358. DOI: 10.1016/C2019-0-00960-0.
[16] Caron J. R. and Littmann, B. (2013). Direct monitoring of energy lost due to soiling on first solar modules in California, IEEE J Photovolt, 3(1), 336–340. DOI: 10.1109/JPHOTOV.2012.2216859.
[17] Kalogirou, S. A., Agathokleous, R. and Panayiotou, G. (2013). On-site PV characterization and the effect of soiling on their performance, Energy, 51, 439–446. DOI: 10.1016/J.ENERGY.2012.12.018.
[18] Guo, B., Javed, W., Figgis, B. W. and Mirza, T. (2015). Effect of dust and weather conditions on photovoltaic performance in Doha, Qatar, 2015 1st Workshop on Smart Grid and Renewable Energy, SGRE 2015. DOI: 10.1109/SGRE.2015.7208718.
[19] Tovilović D. M. and Đurišić, Ž. R. (2022). Tree-based machine learning models for photovoltaic output power forecasting that consider photovoltaic panel soiling, International Journal of Sustainable Energy, 41(9), 1279–1302. DOI: 10.1080/14786451.2022.2045989.
[20] Mohammad, A. and Mahjabeen, F.  (2023). Revolutionizing solar energy: The impact of artificial intelligence on photovoltaic systems. International Journal of Multidisciplinary Sciences and Arts, 2(1), 117-127. DOI: 10.47709/ijmdsa.v2i1.2599
[21] Yoon, Y. (2019). Smart Monitoring System to Improve Solar Power System Efficiency. The Journal of The Institute of Internet, Broadcasting and Communication, 19(1), 219–224. DOI: 10.7236/JIIBC.2019.19.1.219
[22] Kingsley-Amaehule, M., Uhunmwangho, R., Nwazor, N. and Okedu, K. E. (2022). Smart Intelligent Monitoring and Maintenance Management of Photo-voltaic Systems. International Journal of Smart Grid, 6(4), 110-122. DOI: 10.20508/ijsmartgrid.v6i4.260.g246
[23] Rani, D. P., Suresh, D., Kapula, P. R., Akram, C. M., Hemalatha, N. and Soni, P. K. (2023). IoT based smart solar energy monitoring systems. Materials Today: Proceedings, 80, 3540-3545. DOI: 10.1016/j.matpr.2021.07.293
[24] Sharma, M., Singla, M. K., Nijhawan, P., Ganguli, S. and Rajest, S. S. (2020). An application of IOT to develop concept of smart remote monitoring system. Business Intelligence for Enterprise Internet of Things, 233-239. DOI: 10.1007/978-3-030-44407-5_15
[25] Chen, Z., Sivaparthipan, C. B. and Muthu, B. (2022). IoT based smart and intelligent smart city energy optimization. Sustainable Energy Technologies and Assessments, 49, 101724. DOI: 10.1016/j.seta.2021.101724
[26] Hema, N., Krishnamoorthy, N., Chavan, S. M., Kumar, N. M. G., Sabarimuthu, M. and Boopathi, S. (2023). A Study on an Internet of Things (IoT)-Enabled Smart Solar Grid System. In Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT, 290-308. IGI Global. DOI: 10.4018/978-1-6684-8098-4.ch017
[27] Shakya, S. (2021). A self monitoring and analyzing system for solar power station using IoT and data mining algorithms. Journal of Soft Computing Paradigm, 3(2), 96-109. DOI: 10.36548/jscp.2021.2.004
[28] Panda, D. K. and Das, S. (2021). Smart grid architecture model for control, optimization and data analytics of future power networks with more renewable energy. Journal of Cleaner Production, 301, 126877. DOI: 10.1016/j.jclepro.2021.126877
[29] Hasankhani, A. and Hakimi, S. M. (2021). Stochastic energy management of smart microgrid with intermittent renewable energy resources in electricity market. Energy, 219, 119668. DOI:  10.1016/j.energy.2020.119668
[30] Steindl, G., Stagl, M., Kasper, L., Kastner, W. and Hofmann, R. (2020). Generic digital twin architecture for industrial energy systems. Applied Sciences, 10(24), 8903. DOI: 10.3390/app10248903
[31] Shihavuddin, A. S. M., Rashid, M. R. A., Maruf, M. H., Hasan, M. A., ul Haq, M. A., Ashique, R. H., & Al Mansur, A. (2021). Image based surface damage detection of renewable energy installations using a unified deep learning approach. Energy Reports, 7, 4566-4576. DOI: 10.1016/j.egyr.2021.07.045
[32] Raza, M. W., Amin, R., Malik, A. S., Kasi, M., Kasi, B. and Muhammad, F. (2017), Analysis of The Impact of Environmental Factors on Efficiency of Different Types of Solar Cells, Journal of Applied and Emerging Sciences, 7(1), 76-90. DOI: 10.36785/JAES.71219.
[33] Ay, İ., Kademli, M., Karabulut, Ş. and Savaş, S. (2022). Affecting Factors of Efficiency in Photovoltaic Energy Systems and Productivity-Enhancing Suggestions, 2022 Innovations in Intelligent Systems and Applications Conference (ASYU), 1–6. DOI: 10.1109/ASYU56188.2022.9925271.
[34] Yıldıran, Y. (2022). Doğrusal Regresyon Modeli, in Teori ve Uygulamada Makine Öğrenmesi, Savaş S. and Buyrukoğlu, S. Eds., 1.Ankara: Nobel Akademik Yayıncılık Eğitim Danışmanlık TİC. LTD. ŞTİ., 21–36.
[35] Kutner, M. H., Nachtsheim, C. J., Neter, J. and Li, W. (2005). Applied linear statistical models, 5, McGraw-Hill Irwin Boston.
[36] Colmenares-Quintero, R. F., Rojas-Martinez, E. R., Macho-Hernantes, F., Stansfield, K. E. and Colmenares-Quintero, J. C. (2021). Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks, Cogent Eng, 8(1). DOI: 10.1080/23311916.2021.1981520.
Journal of Solar Energy Research
Volume 8, Issue 4
October 2023
Pages 1663-1679