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RETRACTED ARTICLE: IOT based statistical performance improvement technique on the power output of photovoltaic system

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This article was retracted on 30 May 2022

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Abstract

Green electricity gains emphasis not only due to technological progress but also because of deployment of non-renewable energy resources and abundant of renewable energy resources. Global demand for the power increasing yearly is met with the help of green energy. The sources which paved the way for green power generation are solar, wind, tidal etc. The parameters of green power generation to be monitored becomes essential to enhance the power output. Without human interaction there is a simple and easy way for monitoring the parameters, with the help of Internet of Things (IOT). Among available resources PV (Photovoltaic) plant generation is advantageous among all other resources because of easy installation and power production. PV plants of MW capacity are sometimes installed in very remote locations. Interfacing the plant using internet helps to monitor and evaluate the performance of PV arrays continuously. Performance impairment could be due to several factors like drop in efficiency, hotspots, manufacturing defects, insufficient insolation, shadow, orientation of panels, soiling etc. Quality control and good engineering practice helps in overcoming most of these factors. Soiling or accumulation of dust is one important factor which requires frequent attention as dust accumulation will hinder the light reaching the solar panels. PV panel orientation varies throughout the dawn and based on geographical location tilt angle varies throughout the year. For curtailment in losses due to orientation, tilt angle, accumulation of dust, an integrated smart system with wireless technology is proposed in this paper.

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References

  • Akram MN, Lotfifard S (2015) Modeling and health monitoring of DC side of photovoltaic array. IEEE Trans Sustain Energy 6(4):1245–1253

    Article  Google Scholar 

  • Ambuj G, Rishabh J, Rakshita J, Ravi S (2017) Real time remote solar monitoring system. In: 2017 3rd International Conference on Advances in Computing, Communication and Automation

  • Arabatzis I, Todorova N, Fasaki I, Tsesmeli C, Peppas A, Li WX, Zhao Z (2018) Photocatalytic, self-cleaning, antireflective coating for photovoltaic panels: characterization and monitoring in real conditions. Sol Energy 159:251–259

    Article  Google Scholar 

  • Bunyan H, Ali W, Alnaser M (2016) Enhancing the performance of photovoltaic panel by proper washing periods in Kuwait. Smart Grid Renew Energy 7:190–196

    Article  Google Scholar 

  • Carletti V, Greco A, Saggese A et al (2019) An intelligent flying system for automatic detection of faults in photovoltaic plants. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01212-6

    Article  Google Scholar 

  • Cheng Y, Zou X, Zhuang G, Teng G, Qu W (2015) Remote monitoring system for distributed photovoltaic based on ZigBee. In: 2015 4th International Conference on Computer Science and Network Technology (ICCSNT). doi: 10.1109/iccsnt.2015.7490878

  • Curtis T, Tatapudi S, TamizhMani G (2018) Design and operation of a waterless PV soiling monitoring station. In: 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC and 34th EU PVSEC). doi: 10.1109/pvsc.2018.8547820

  • Dey S, Kar T, Iqbal A, Roychoudhury D, Laha S, Chowdhury A, Dey R (2017) An IoT framework for smart power management system. In: 2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). https://doi.org/10.1109/iemcon.2017.8117195

  • Figgis B, Brophy B (2018) PV Coatings and Particle Adhesion Forces. online: www.net/publication/283676562_PV_Coatings_and_Particle_Adhesion_Forces

  • Gheitasi A, Almaliky A, Albaqawi N (2015) Development of an automatic cleaning system for photovoltaic plants. In: Proceedings of the IEEE PES Asia-Pacific Power Energy Engineering Conference, Brisbane, Australia, 15–18 November 2015; pp 1–4. [CrossRef]

  • Guo B, Javed W, Figgis BW, Mirza T (2015) Effect of dust and weather conditions on photovoltaic performance in Doha, Qatar. In: 2015 1st Work Smart Grid Renew Energy Sgre 2015

  • Hartman WT, Hansen A, Vasquez E, El-Tawab S, Altaii K (2018) Energy monitoring and control using Internet of Things (IoT) system. In: 2018 Systems and Information Engineering Design Symposium (SIEDS). doi: 10.1109/sieds.2018.8374723

  • Illavarason P, Kamachi Sundaram B (2019a) A study of intrusion detection system using machine learning classification algorithm based on different feature selection approach. In: IEEE 3rd International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC 2019), ISBN: 978-1-7281-4365-1

  • Illavarason P, Arokia-Renjith J, Mohan-Kumar P (2019b) Medical diagnosis of cerebral palsy rehabilitation using eye images in machine learning techniques. J Med Syst Springer 43(8):278–302. https://doi.org/10.1007/s10916-019-1410-6

    Article  Google Scholar 

  • Javed W, Guo B, Wubulikasimu Y, Figgis B (2016) Photovoltaic performance degradation due to soiling and characterization of the accumulated dust. In: Proceedings of the IEEE International Conference on Power

  • Jiang Y, Lu L, Lu H (2016) A novel model to estimate the cleaning frequency for dirty solar photovoltaic (PV) modules in desert environment. Sol Energy 140:236–240

    Article  Google Scholar 

  • Kang M-S, Kang H, Choi E-C (2013) Monitoring technology available for measuring multiple-photovoltaic panel arrays. In: IEEE 2013 Tencon—Spring. doi: 10.1109/tenconspring.2013.6584487

  • Kekre A, Gawre SK (2017) Solar photovoltaic remote monitoring system using IOT. In: 2017 International conference on recent innovations in signal processing and embedded systems (RISE). doi: 10.1109/rise.2017.8378227

  • Khan ZA, Zafar A, Javaid S et al (2019) Hybrid meta-heuristic optimization-based home energy management system in smart grid. J Ambient Intell Human Comput 10:4837–4853. https://doi.org/10.1007/s12652-018-01169-y

    Article  Google Scholar 

  • Kim YD, Shin J-H, Cho J-Y, Choi H-J, Lee H (2014) Nanosized patterned protective glass exhibiting high transmittance and self-cleaning effects for photovoltaic systems. Phys Status Solidi 211:1822–1827

    Article  Google Scholar 

  • Lu H, Zhao W (2018) Effects of particle sizes and tilt angles on dust deposition characteristics of a ground-mounted solar photovoltaic system. Appl Energy 220:514–526

    Article  Google Scholar 

  • Lu H, Lu L, Wang Y (2016) Numerical investigation of dust pollution on a solar photovoltaic (PV) system mounted on an isolated building. Appl Energy 180:27–36

    Article  Google Scholar 

  • Mishra S, Koduvere H, Palu I, Kuhi-Thalfeldt R (2016) Modelling of solar-wind hybrid renewable energy system architectures. In: 2016 IEEE International Energy Conference

  • Mohammed AH, Yusuf B, Muammer K (2019) Assessment of various dry photovoltaic cleaning techniques and frequencies on the power output of CdTe-Type modules in dusty environments—2019 Sustainability, MDPI Journal

  • Nalamwar HS, Ivanov MA, Baidali SA (2017) Automated intelligent monitoring and the controlling software system for solar panels. J Phys: Conf Ser 803:012107. https://doi.org/10.1088/1742-6596/803/1/012107

    Article  Google Scholar 

  • Oh W, Kang B, Choi S, Bae S, Jeong S, Kim SM, Seok LH, Donghwan K, Heon H, Sung C (2016) Evaluation of anti-soiling and anti-reflection coating for photovoltaic modules. J Nanosci Nanotechnol 16:10689–10692

    Article  Google Scholar 

  • Padmavti CM, Mane PB (2017) Power management system in smart home based on WSN. Int J Electron Electr Comput Syst 6(12):176–185

  • Pan J, Jain R, Paul S, Vu T, Saifullah A, Sha M (2015) An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype, and Experiments. IEEE Internet of Things Journal 2(6):527–537. https://doi.org/10.1109/jiot.2015.2413397

    Article  Google Scholar 

  • Patil S, Mallaradhya MH (2016) Design and implementation of microcontroller based automatic dust cleaning system for solar panel. Int J Eng Res Adv Technol 2:187–190

    Google Scholar 

  • Patil SM, Vijayalashmi M, Tapaskar R (2017) IoT based solar energy monitoring system. In: 2017 International conference on energy, communication, data analytics and soft computing (ICECDS). doi: 10.1109/icecds.2017.8389711

  • Piliougine M, Cañete C, Moreno R, Carretero J, Hirose J, Ogawa S, De Cardona MS (2013) Comparative analysis of energy produced by photovoltaic modules with anti-soiling coated surface in arid climates. Appl Energy 112:626–634

    Article  Google Scholar 

  • Rosario M, De Claudio C, Gaetano F, Subhas CM (2017) A Smart power meter to monitor energy flow in smart grids: the role of advanced sensing and IoT in the electric grid of the future. IEEE Sens J 17(23):7828–7837. https://doi.org/10.1109/JSEN.2017.2760014

    Article  Google Scholar 

  • Sivagami P, Jothi-Swaroopan NM (2020) Smart methodology for performance improvement of energy sources for home application. J Microprocess Microsyst. https://doi.org/10.1016/j.micpro.2020.103042

    Article  Google Scholar 

  • Tanesab J, Parlevliet D, Whale J, Urmee T (2017) Seasonal effect of dust on the degradation of PV modules performance deployed in different climate areas. Renew Energy 111:105–115

    Article  Google Scholar 

  • Vijayakumar K, Arun C (2017) Automated risk identification using NLP in cloud-based development environments. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-017-0503-7

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, India

    P. Sivagami

  2. Department of Electrical and Electronics Engineering, RMK Engineering College, Chennai, India

    N. M. Jothiswaroopan

Authors
  1. P. Sivagami
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  2. N. M. Jothiswaroopan
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Correspondence to P. Sivagami.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-03973-z

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Sivagami, P., Jothiswaroopan, N.M. RETRACTED ARTICLE: IOT based statistical performance improvement technique on the power output of photovoltaic system. J Ambient Intell Human Comput 12, 5029–5043 (2021). https://doi.org/10.1007/s12652-020-01954-8

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  • DOI: https://doi.org/10.1007/s12652-020-01954-8

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