Nothing Special   »   [go: up one dir, main page]

skip to main content
research-article

PVoT: Reconfigurable Photovoltaic Array for Indoor Light Energy-Powered Batteryless Devices

Published: 01 November 2022 Publication History

Abstract

Multiple photovoltaic (PV) modules are often used to provide enhanced harvesting capability for light energy-based Internet of Things (IoT) devices. PV modules facing multiple directions can lead to a situational energy loss when parts of the modules are shaded. To address this issue, existing solutions exploit reconfigurable PV arrays to acquire the optimal configuration in a given situation. However, conventional techniques are not energy efficient in estimating the harvesting capability of PV modules, and require high computation to find the optimal PV array at runtime. In this article, we propose PVoT, an energy-efficient reconfigurable PV array, which maximizes the harvesting energy for indoor IoT devices. To this end, we propose the use of photoresistors to estimate the harvesting capability with minimal energy overhead. We also provide hardware and software schemes, which perform event-driven light change detection in an energy-efficient way. Furthermore, we develop a power imbalance threshold metric to quickly find the optimal PV array at runtime. We implemented a prototype PVoT with off-the-shelf components and accompanying software. Experiments with the prototype hardware showed that PVoT achieves a gain of up to 23.9% in harvested energy compared to the existing directly connected PV array scheme.

References

[1]
J. Hester and J. Sorber, “The future of sensing is batteryless, intermittent, and awesome,” in Proc. 15th ACM Conf. Embedded Netw. Sens. Syst. (SenSys), Delft, Netherlands, Nov. 2017, pp. 1–6.
[3]
B. Lucia, V. Balaji, A. Colin, K. Maeng, and E. Ruppel, “Intermittent computing: Challenges and opportunities,” in Proc. 2nd Summit Adv. Program. Lang. (SNAPL), May 2017, pp. 1–14.
[4]
K. Kato and H. Koizumi, “A study on effect of blocking and bypass diodes on partial shaded PV string with compensating circuit using voltage equalizer,” in Proc. IEEE Int. Symp. Circuits Syst. (ISCAS), Lisbon, Portugal, May 2015, pp. 241–244.
[5]
A. M. Ajmal, T. S. Babu, V. K. Ramachandaramurthy, D. Yousri, and J. B. Ekanayake, “Static and dynamic reconfiguration approaches for mitigation of partial shading influence in photovoltaic arrays,” Sustain. Energy Technol. Assess., vol. 40, Aug. 2020, Art. no. 10.1016/j.seta.2020.100738.
[6]
Y. Wang, X. Lin, Y. Kim, N. Chang, and M. Pedram, “Architecture and control algorithms for combating partial shading in photovoltaic systems,” IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., vol. 33, no. 6, pp. 917–930, Jun. 2014. 10.1109/TCAD.2014.2302383.
[7]
J. Randall, O. Amft, J. Bohn, and M. Burri, “LuxTrace: Indoor positioning using building illumination,” Pers. Ubiquit. Comput., vol. 11, no. 6, pp. 417–428, Mar. 2007. 10.1007/s00779-006-0097-0.
[8]
D. Kim, J. Ahn, J. Shin, and H. Cha, “Ray tracing-based light energy prediction for indoor batteryless sensors,” Proc. ACM Interact. Mobile Wearable Ubiquitous Technol., vol. 5, no. 1, pp. 1–27, Mar. 2021. 10.1145/3448086.
[9]
Panasonic Amorphous Silicon Solar Cells.” Accessed: Sep. 26, 2021. [Online]. Available: https://panasonic.co.jp/ls/psam/en/products/pdf/Catalog_Amorton_ENG.pdf
[10]
R. Faranda, S. Leva, and V. Maugeri, “MPPT techniques for PV systems: Energetic and cost comparison,” in Proc. IEEE Power Energy Soc. Gen. Meeting Convers. Del. Electr. Energy 21st Century, Pittsburgh, PA, USA, Jul. 2008, pp. 1–6.
[11]
N. Rajasekar, N. K. Kumar, and R. Venugopalan, “Bacterial foraging algorithm based solar PV parameter estimation,” Solar Energy, vol. 97, pp. 255–265, Nov. 2013. 10.1016/j.solener.2013.08.019.
[12]
MSP430FR599x, MSP430FR596x mixed-signal microcontrollers.” Data Sheet MSP430FR5962, Texas Instrum., Dallas, TX, USA, 2021. [Online]. Available: https://www.ti.com/lit/ds/symlink/msp430fr5994.pdf?ts=1644744253429&ref_url=https%253A%252F%252Fwww.google.com%252F
[13]
Y. Xue, M. Manjrekar, C. Lin, M. Tamayo, and J. N. Jiang, “Voltage stability and sensitivity analysis of grid-connected photovoltaic systems,” in Proc. IEEE Power Energy Soc. General Meeting, Detroit, MI, USA, Jul. 2011, pp. 1–7.
[14]
G. Velasco-Quesada, F. Guinjoan-Gispert, R. Piqué-López, M. Román-Lumbreras, and A. Conesa-Roca, “Electrical PV array reconfiguration strategy for energy extraction improvement in grid-connected PV systems,” IEEE Trans. Ind. Electron., vol. 56, no. 11, pp. 4319–4331, Nov. 2009. 10.1109/TIE.2009.2024664.
[15]
A. Dolara, G. C. Lazaroiu, S. Leva, and G. Manzolini, “Experimental investigation of partial shading scenarios on PV (photovoltaic) modules,” Energy, vol. 55, pp. 466–475, Jun. 2013. 10.1016/j.energy.2013.04.009.
[16]
E. Garoudja, A. Chouder, K. Kara, and S. Silvestre, “An enhanced machine learning based approach for failures detection and diagnosis of PV systems,” Energy Convers. Manag., vol. 151, pp. 496–513, Nov. 2017. 10.1016/j.enconman.2017.09.019.
[17]
H. Patel and V. Agarwal, “MATLAB-based modeling to study the effects of partial shading on PV array characteristics,” IEEE Trans. Energy Convers., vol. 23, no. 1, pp. 302–310, Mar. 2008.
[18]
S. Park, S. Narayanaswamy, and S. Chakraborty, “Design- time optimization of reconfigurable PV architectures for irregular surfaces,” in Proc. IEEE 38th Int. Conf. Comput. Des. (ICCD), Hartford, CT, USA, Oct. 2020, pp. 518–524.
[19]
X. Lin, Y. Wang, S. Yue, D. Shin, N. Chang, and M. Pedram, “Near-optimal, dynamic module reconfiguration in a photovoltaic system to combat partial shading effects,” in Proc. Des. Autom. Conf. (DAC), San Francisco, CA, USA, Jun. 2012, pp. 516–521.
[20]
S. K. Das, D. Verma, S. Nema, and R. K. Nema, “Shading mitigation techniques: State-of-the-art in photovoltaic applications,” Renew. Sustain. Energy Rev., vol. 78, pp. 369–390, Oct. 2017. 10.1016/j.rser.2017.04.093.
[21]
W. Lee, Y. Kim, Y. Wang, N. Chang, M. Pedram, and S. Han, “Versatile high-fidelity photovoltaic module emulation system,” in Proc. IEEE/ACM Int. Symp. Low Power Electron. Des. (ISLPED), Fukuoka, Japan, Aug. 2011, pp. 91–96.
[22]
E. Duran, M. Piliougine, M. Sidrach-de-Cardona, J. Galan, and J. M. Andujar, “Different methods to obtain the I–V curve of PV modules: A review,” in Proc. 33rd IEEE Photovolt. Spec. Conf., San Diego, CA, USA, May 2008, pp. 1–6.
[23]
H. Nagayoshi, “I–V curve simulation by multi-module simulator using I–V magnifier circuit,” Sol. Energy Mater. Sol. Cells, vol. 82, nos. 1–2, pp. 159–167, May 2004. 10.1016/j.solmat.2004.01.014.
[24]
Y. Zhao, L. Yang, B. Lehman, J.-F. de Palma, J. Mosesian, and R. Lyons, “Decision tree-based fault detection and classification in solar photovoltaic arrays,” in Proc. 27th Ann. IEEE Appl. Power Electron. Conf. Exposit. (APEC), Orlando, FL, USA, Feb. 2012, pp. 93–99.
[25]
R. Benkercha and S. Moulahoum, “Fault detection and diagnosis based on C4.5 decision tree algorithm for grid connected PV system,” Solar Energy, vol. 173, pp. 610–634, Oct. 2018. 10.1016/j.solener.2018.07.089.
[26]
H. Zhu, L. Lu, J. Yao, S. Dai, and Y. Hu, “Fault diagnosis approach for photovoltaic arrays based on unsupervised sample clustering and probabilistic neural network model,” Solar Energy, vol. 176, pp. 395–405, Dec. 2018. 10.1016/j.solener.2018.10.054.
[27]
Y. Feng, W. Hao, H. Li, N. Cui, D. Gong, and L. Gao, “Machine learning models to quantify and map daily global solar radiation and photovoltaic power,” Renew. Sustain. Energy Rev., vol. 118, Feb. 2020, Art. no. 10.1016/j.rser.2019.109393.
[28]
M. Abdel-Basset, H. Hawash, R. K. Chakrabortty, and M. Ryan, “PV-Net: An innovative deep learning approach for efficient forecasting of short-term photovoltaic energy production,” J. Clean. Prod., vol. 303, Jun. 2021, Art. no. 10.1016/j.jclepro.2021.127037.
[29]
Y. Mahmoud and E. F. El-Saadany, “Enhanced reconfiguration method for reducing mismatch losses in PV systems,” IEEE J. Photovolt., vol. 7, no. 6, pp. 1746–1754, Nov. 2017. 10.1109/JPHOTOV.2017.2752708.
[30]
M. U. Olanipekun, J. L. Munda, and Y. Hamam, “A multi-start greedy algorithm for optimal reconfiguration of solar photovoltaic arrays for maximum power output in real-time application,” Int. Rev. Electr. Eng., vol. 12, pp. 432–439, Sep. 2017. 10.15866/iree.v12i5.12763.
[31]
Y. Mahmoud and E. El-Saadany, “Fast reconfiguration algorithm for improving the efficiency of PV systems,” in Proc. 8th Int. Renew. Energy Congr. (IREC), Amman, Jordan, Mar. 2017, pp. 1–5.
[32]
S. N. Deshkar, S. B. Dhale, J. S. Mukherjee, T. S. Babu, and N. Rajasekar, “Solar PV array reconfiguration under partial shading conditions for maximum power extraction using genetic algorithm,” Renew. Sustain. Energy Rev., vol. 43, pp. 102–110, Mar. 2015. 10.1016/j.rser.2014.10.098.
[33]
N. A. Rajan, K. D. Shrikant, B. Dhanalakshmi, and N. Rajasekar, “Solar PV array reconfiguration using the concept of standard deviation and genetic algorithm,” Energy Procedia, vol. 117, pp. 1062–1069, Jun. 2017. 10.1016/j.egypro.2017.05.229.
[34]
M. Dhimish, V. Holmes, B. Mehrdadi, M. Dales, and P. Mather, “Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system,” Energy, vol. 140, pp. 276–290, Dec. 2017. 10.1016/j.energy.2017.08.102.
[35]
H. I. Solis-Cisneroset al., “A dynamic reconfiguration method based on neuro-fuzzy control algorithm for partially shaded PV arrays,” Sustain. Energy Technol. Assess., vol. 52, Aug. 2022, Art. no. 10.1016/j.seta.2022.102147.
[36]
J. Park, H. Joshi, H. G. Lee, S. Kiaei, and U. Y. Ogras, “Flexible PV-cell modeling for energy harvesting in wearable IoT applications,” in Proc. 12th IEEE/ACM/IFIP Int. Conf. Hardw. Softw. Codes. Syst. Synth. (CODES+ISSS), Seoul, South Korea, Oct. 2017, pp. 1–20.
[37]
D. Nguyen and B. Lehman, “A reconfigurable solar photovoltaic array under shadow conditions,” in Proc. 23rd Annu. IEEE Appl. Power Electron. Conf. Exposit., Austin, TX, USA, Feb. 2008, pp. 980–986.

Index Terms

  1. PVoT: Reconfigurable Photovoltaic Array for Indoor Light Energy-Powered Batteryless Devices
          Index terms have been assigned to the content through auto-classification.

          Recommendations

          Comments

          Please enable JavaScript to view thecomments powered by Disqus.

          Information & Contributors

          Information

          Published In

          Publisher

          IEEE Press

          Publication History

          Published: 01 November 2022

          Qualifiers

          • Research-article

          Contributors

          Other Metrics

          Bibliometrics & Citations

          Bibliometrics

          Article Metrics

          • 0
            Total Citations
          • 0
            Total Downloads
          • Downloads (Last 12 months)0
          • Downloads (Last 6 weeks)0
          Reflects downloads up to 22 Sep 2024

          Other Metrics

          Citations

          View Options

          View options

          Get Access

          Login options

          Media

          Figures

          Other

          Tables

          Share

          Share

          Share this Publication link

          Share on social media