Development of Solar Powered Feeding Scheme for Wireless Sensor Networks in low Solar Density Conditions / Bezvadu Sensoru Tīklu Elektroapgādes Sistēmas Izstrāde, Kas Izmanto Saules Paneļus Un Darbojas Pazeminātas Saules Radiācijas Apstākļos

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Abstract

In the recent years, there has been significant research focus on the safety and reliability of data harvesting and optimal energy consuming by wireless sensor network nodes. If external electrical power fails, the node needs to be able to send notifications to the utility demanding the use of backup energy strategies. The authors of the research offer an approach that can help to use PV panels as an alternative power source for WSN nodes in particular irradiation conditions. Survey and testing of the main types of PV panels offered on the market in conditions closed to real ones, in which WSN nodes are maintained, have been implemented. Based on the test results, maximum power control module parameters can be calculated in order to achieve the best effectiveness of the power control system for a selected type of PV panel or panel group. The novelty of the research is an approach that includes an original test bed design for PV testing, PV testing method and selection of design and MPP control module parameters, which ensure maximum effectiveness of WSN node power feeding.

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  • 1. Jayadevan V.T. Rodriguez J. Lonij V.P.A. and Cronin A.D. (2012). Forecasting solar power intermittency using ground-based cloud imaging. In Proceedings of American Solar Energy Society Meeting.

  • 2. Lonij V.P. Jayadevan V.T. Brooks A.E. Koch K. Leuthold M. and Cronin A.D. (2012). Improving forecasts of PV power output using real-time measurements of PV output of 100 residential PV installs. In Proceedings of the 38th IEEE Photovoltaics Specialists Conference.

  • 3. Cronin A. Pulver S. Cormode D. Jordan D. Kurtz S. and Smith R. (2010). Measuring degradation rates without irradiance data. In Proceedings of the 36th IEEE Photovoltaics Specialists Conference March 2010.

  • 4. Alippi C. and Galperti C. (2008). An adaptive system for optimal solar energy harvesting in wireless sensor network nodes. IEEE Transactions on Circuits and Systems-I. 55 (6).

  • 5. Brunelli D. Benini L. Moser C. and Thiele L. (2008). An efficient solar energy harvester for wireless sensor nodes. In DATE ‘08 Proceedings of the Conference on Design Automation and Test in Europe (pp. 104-109).

  • 6. Zabašta A. Dambrauskas V. Deksnis J. Deksnis V. Gudele I. Kondratjevs K. Kriaučeliūnas A. Kuņicina N. Navalinskaite K. Nolendorfs A. and Šeļmanovs- Plešs V. (2013). Smart Metering. In Project (LLIV-312) “Smart Metering” 2013 (pp. 1-110). Ventspils: Engineering Research Institute Ventspils International Radio Astronomy Centre of Ventspils University College.

  • 7. Global Horizontal Irradiation (GHI) GeoModel Solar (2015). Retrieved 20 May 2015 from http://solargis.info/doc/free-solar-radiation-maps-GHI.

  • 8. Nedumgatt J. J. Jayakrishnan K. B. Umashankar S. Vijayakumar D. and Kothari D. P. (2011). Perturb and observe MPPT algorithm for solar PV systems-modelling and simulation. In Annual IEEE India Conference (INDICON) Dec. 2011 (pp. 1-6).

  • 9. Honsberg C. and Bowden S. (n.d.). Measurement of solar cell efficiency. Retrieved 20 May 2015 from http://www.pveducation.org/pvcdrom/characterisation/measurementof-solar-cell-efficiency.

  • 10. Part II - Photovoltaic Cell I-V Characterization Theory and LabVIEW Analysis Code. (2012). Retrieved 20 May 2015 from http://www.ni.com/white-paper/7230/en/.

  • 11. Onat N. (2010). Recent developments in maximum power point tracking technologies for photovoltaic systems. International Journal of Photoenergy 2010 Article ID 245316 (11 p.).

  • 12. Chao K. H. and Li C. J. (2010). An intelligent maximum power point tracking method based on extension theory for PV systems. Expert Systems with Applications 37 (2) 1050-1055.

  • 13. International Journal of Electrical and Electronic Engineering & Telecommunications 1.4 (1) January 2015 (23 p.). ISSN 2319 - 2518.

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CiteScore 2018: 0.32

SCImago Journal Rank (SJR) 2018: 0.147
Source Normalized Impact per Paper (SNIP) 2018: 0.325

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