Advertisement

Wireless Networks

, Volume 24, Issue 5, pp 1667–1681 | Cite as

Energy management of WSN-based charge measurement system of ultra high-voltage direct-current transmission line

  • Dawei Deng
  • Haiwen Yuan
  • Yong Cui
  • Yong Ju
Article
  • 161 Downloads

Abstract

With the construction of ultra-high-voltage direct current (UHVDC) transmission lines, the complex electromagnetic environment around the lines has been a widespread concern. The ZigBee-based field measurement system is widely used in ground space charge density measurements of HVDC transmission projects. In actual use, the power consumption of the space charge density measurement system is a key limitation of the device performance.Research on low-power and energy-management strategies of this measurement system can improve the device lifetimes. This capability is very important for improving monitoring efficiency of the surrounding electromagnetic environment of HVDC transmission projects.

Keywords

Low power consumption Energy management Wireless sensor network Space charge density measurement Electromagnetic environment 

Notes

Acknowledgements

This research is supported by State Grid Corporation of China (GYB17201400178).

References

  1. 1.
    Antolin, D., Medrano, N., & Calvo. B. (2016). Reliable lifespan evaluation of a remote environment monitoring system based on wireless sensor networks and global system for mobile communications. Journal of Sensors.Google Scholar
  2. 2.
    Abdul-Salaam, G., Abdullah, A. H., Anisi, M. H., Gani, A., & Alelaiwi, A. (2016). A comparative analysis of energy conservation approaches in hybrid wireless sensor networks data collection protocols. Telecommunication Systems, 61(1), 159–179.CrossRefGoogle Scholar
  3. 3.
    Egarter, D., Monacchi, A., Khatib, T., & Elmenreich, W. (2016). Integration of legacy appliances into home energy management systems. Journal of Ambient Intelligence and Humanized Computing, 7(2), 171–185.CrossRefGoogle Scholar
  4. 4.
    Anisi, M. H., Abdul-Salaam, G., & Abdullah, A. H. (2015). A survey of wireless sensor network approaches and their energy consumption for monitoring farm fields in precision agriculture. Precision Agriculture, 16(2), 216–238.CrossRefGoogle Scholar
  5. 5.
    Shahzad, G., Yang, H., Ahmad, A. W., & Lee, C. (2016). Energy-efficient intelligent street lighting system using traffic-adaptive control. IEEE Sensors Journal, 16(13), 5397–5405.CrossRefGoogle Scholar
  6. 6.
    Anisi, M. H., Abdul-Salaam, G., Idris, M. Y. I., Wahab, A. W. A., & Ahmedy, I. (2015). Energy harvesting and battery power based routing in wireless sensor networks. Wireless Networks 1–18.Google Scholar
  7. 7.
    Srbinovski, B., Magno, M., Edwards-Murphy, F., Pakrashi, V., & Popovici, E. (2016). An energy aware adaptive sampling algorithm for energy harvesting WSN with energy hungry sensors. Sensors, 16(4), 448.CrossRefGoogle Scholar
  8. 8.
    Chandrakasan, A., Amirtharajah, R., & Cho, S. H. (1999). Design considerations for distributed microsensor systems. Custom Integrated Circuits 279–286.Google Scholar
  9. 9.
    Srie, V. J. E., Ganeshkumar, P., & Vasantha, S. G. (2013). A survey on algorithms for cluster head selection in WSN. International Journal of Advanced Research in Computer Engineering & Technology, 2(5), 2278.Google Scholar
  10. 10.
    Abhishek, C., & Sumedha, S. (2014). Minimization of average energy consumption to prolong lifetime of wireless sensor network. In IEEE Global conference on wireless computing and networking.Google Scholar
  11. 11.
    Amrit, A. R., Shikha, N., & Sanjay, P. (2014). Achieving energy efficiency and increasing network life in wireless sensor networks. In IEEE International advance computing conference 171–175.Google Scholar
  12. 12.
    Kumar, V., Jain, S., & Tiwari, S. (2011). Energy efficient clustering algorithms in wireless sensor networks: A survey. International Journal of Computer Science Issues, 8(5), 259–268.Google Scholar
  13. 13.
    Ljiljana, S., Stevan, M. B., & Kevin, W. S. (2008). Partner choice and power allocation for energy efficient cooperation in wireless sensor networks. ICC, 2008, 4255–4260.Google Scholar
  14. 14.
    Bruno, B., Francky, C., & Denis, C. (2008). Energy efficiency of the IEEE 802.15.4 Standard in dense wireless microsensor networks: Modeling and improvement perspectives. Europe: Springer.Google Scholar
  15. 15.
    Wang, Q., & Yang, W. (2007). Energy consumption model for power management in wireless sensor networks (pp. 142–151). San Diego: IEEE Press.Google Scholar
  16. 16.
    Wang, C., Shih, J., & Pan, B. (2014). A network lifetime enhancement method for sink relocation and its analysis in wireless sensor networks. IEEE Sensors Journal, 14(6), 1932–1942.CrossRefGoogle Scholar
  17. 17.
    Wang, Q., Hempstead, M., & Yang, W. (2006). A realistic power consumption model for wireless sensor network devices. IEEE Secon, 2006(1), 286–295.Google Scholar
  18. 18.
    Cigdem, E., Merve, S. V., & Cagri, G. (2014). Lifetime analysis of wireless sensor nodes in different smart grid environments. Wireless Networks, 20, 2053–2062.CrossRefGoogle Scholar
  19. 19.
    Sallabi, F. M., Gaouda, A. M., & EI-Hag, A. H. (2014). Evaluation of Zigbee wireless sensor networks under high power disturbances. IEEE Transactions on Power Delivery, 29(1), 13–20.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina

Personalised recommendations