Advertisement

Intelligent Rainfall Monitoring System for Efficient Electric Power Transmission

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 253)

Abstract

Global climate change has given rise to disastrous heavy rainfall during typhoon seasons, wreaking havoc on our living environments. The electric power transmission lines in Taiwan are spread throughout the island, while some towers are located in high-altitude mountains, calling for good early warning and monitoring mechanisms in the face of natural disasters. This study integrates the QPESUMS radar echo system adopted by the Central Weather Bureau to develop an automatic real-time rainfall estimation and monitoring system, which takes advantage of intelligent agents to handle the massive volume of rainfall information for analysis. Rainfall estimation using adaptive algorithms monitoring the rainfall fluctuations at remote towers can provide maintenance crews with real-time information for timely repairs.

Keywords

Intelligent agent Monitoring Rainfall estimation Electric power transmission 

References

  1. 1.
    Beehler ME (1997) Reliability centered maintenance for transmission systems. IEEE Trans Power Delivery:1023–1028 Google Scholar
  2. 2.
    Brands E (1974) Radar rainfall pattern optimizing technique. NOAA Teach, Memo, ERL NSSL-67, Oklahoma, pp 16Google Scholar
  3. 3.
    Wilson JW (1970) Integration of radar and raingague data for improved rainfall measurement. J Appl Meteor 9:189–497CrossRefGoogle Scholar
  4. 4.
    Yu PS (1987) Real-time grid based distributed rainfall-runoff model for flood forecasting with weather radar. Ph.D. Thesis, University of BirminghamGoogle Scholar
  5. 5.
    Collier CG (1996) Weather radar precipitation data and their use in hydrological modeling. In: Abbott MB, Refsgaard JC (eds) Distributed hydrological modeling. Kluwer Academic Publishers, Dordrecht, Chap. 8, pp 143–163Google Scholar
  6. 6.
    Bell VA, Moore RJ (1998) A grid-based distributed flood forecasting model for use weather radar data. Part 2: case studies, hydrology and earth system sciences, vol 2. (2–3), pp 283–298 Google Scholar
  7. 7.
    Corral C, Sempere-Torres D, Revilla M, Berenguer M (2000) A semi-distributed hydrological model using rainfall estimates by radar, application to Mediterranean basins. Part B: Physics and Chemistry of the EarthGoogle Scholar
  8. 8.
    Bedient Philip B, Holder Anthony, Benavides Jude A, Vieux Baxter E (2003) Radar-based flood warning system applied to tropical storm allison. J Hydrol Eng 8(6):308–318CrossRefGoogle Scholar
  9. 9.
    Burrough PA, McDonnell RA (1998) Principles of geographical information systems. Oxford University Press, New York Google Scholar
  10. 10.
    Carter MM, Elsner JB, Bennett SP (2000) A quantitative precipitation forecast experiment for Puerto Rico. J Hydrol 239:162–178CrossRefGoogle Scholar
  11. 11.
    Chiou TK, Chen CR, Chang PL (2005) Status and outlook of a quantitative rainfall estimation technique in central weather bureau. Taiwan, Geophysical Research Abstracts, vol 7, 10637Google Scholar
  12. 12.
    Goovaerts P (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J Hydrol 228:113–129CrossRefGoogle Scholar
  13. 13.
    George YL, Wong DW (2008) An adaptive inverse-distance weighting spatial interpolation technique. J Comput Geosci 34:1044–1055CrossRefGoogle Scholar
  14. 14.
    Buytaert W, Celleri R, Willems P, Bievre BD, Wyseure G (2006) Spatial and temporal rainfall variability in mountainous areas: a case study from the south Ecuadorian Andes. J Hydrol 329:413–421CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Digital Media DesignAsia UniversityTaichungTaiwan ROC
  2. 2.Department of Electrical EngineeringNational Taipei University of TechnologyTaipeiTaiwan ROC

Personalised recommendations