상하수도학회지 (Journal of Korean Society of Water and Wastewater)

  • 제29권3호
  • /
  • Pages.415-425
  • /
  • 2015
  • /
  • 1225-7672(pISSN)
  • /
  • 2287-822X(eISSN)
대한상하수도학회 (The Korean Society of Water and Wastewater)
권호 표지
DOI QR코드

DOI QR Code

상수관망 블록의 대표적인 용수사용 유형에 대한 최소 용수사용 시간의 결정

Determining the Time of Least Water Use for the Major Water Usage Types in District Metered Areas

  • 박수완 (부산대학교건설융합학부) ;
  • 정소연 (부산대학교건설융합학부) ;
  • Park, Suwan (Department of Civil and Environmental Engineering, Pusan National University) ;
  • Jung, So-Yeon (Department of Civil and Environmental Engineering, Pusan National University) ;
  • Sahleh, Vahideh (Department of Civil and Environmental Engineering, Pusan National University)
  • 투고 : 2015.02.02
  • 심사 : 2015.03.05
  • 발행 : 2015.06.15
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Aging water pipe networks hinder efficient management of important water service indices such as revenue water and leakage ratio due to pipe breakage and malfunctioning of pipe appurtenance. In order to control leakage in water pipe networks, various methods such as the minimum night flow analysis and sound waves method have been used. However, the accuracy and efficiency of detecting water leak by these methods need to be improved due to the increase of water consumption at night. In this study the Principal Component Analysis (PCA) technique was applied to the night water flow data of 426 days collected from a water distribution system in the interval of one hour. Based on the PCA technique, computational algorithms were developed to narrow the time windows for efficient execution of leak detection job. The algorithms were programmed on computer using the MATLAB. The presented techniques are expected to contribute to the efficient management of water pipe networks by providing more effective time windows for the detection of the anomaly of pipe network such as leak or abnormal demand.

키워드

참고문헌

  1. Bougadis, J., Adamowski, K., Diduch, R. (2005) Short-term municipal water demand forecasting, Hydrological Processing, 19(1), pp. 137-148. https://doi.org/10.1002/hyp.5763
  2. Covas, D., Ramos, H., Lopes, N., Almeida, A.B. (2006) Water pipe system diagnosis by transient pressure signals. Water Distribution Systems Analysis Symposium 2006, ASCE, USA, pp. 1-19.
  3. Gertler, J., Romera, J., Puig, V., Quevedoanos, J. (2010) Leak detection and isolation in water distribution networks using principal component analysis and structured residuals. Control and Fault-Tolerant Systems (SysTol) 2010, IEEE, Nice, France, October 6-8.
  4. Hanil network engineering Inc. (2004) Development of management techniques to prevent leaks and to monitor individual water use for block systems of water-supply networks, Ministry of Environment, pp. 19-21.
  5. Jang, G.H. (2005) Study on the characteristics of night flow components for leakage management in district metered areas, M.S. Thesis, University of Seoul, Seoul, Korea. [Korean Literature]
  6. Kang, S.R. (2012) Design and Implementation of an Abnormal Water Flow Detection System based on Outlier and Missing Value Correction., M.S. Thesis, Incheon National University, Incheon, Korea. [Korean Literature]
  7. Lambert, A., Hirner, W. (2000) Losses from Water Supply Systems: Standard Terminology and Performance Measures, IWA, IWA the blue pages October 2000, pp. 1-13.
  8. Lee, M.R. (2010) A Study on Deterioration Evaluation Model of Water Main using Integrated PCA and ANN., M.S. dissertation, University of Seoul, Seoul, Korea. [Korean Literature]
  9. Mounce, S.R., Boxall, J., Machell, J. (2009) Development and verification of an online artificial intelligence system for detection of bursts and other abnormal flows, Journal of Water Resources Planning and Management-ASCE, 136(3), pp. 309-318. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000030
  10. Palau, C.V., Arregui. F., Ferrer, A. (2004) Using Multivariate Principal Component Analysis of Injected Water Flows to Detect Anomalous Behaviors in a Water Supply System. A Caes Study, Water Science & Technology: Water Supply, 4(3), pp. 169-181.
  11. Palau, C. V., Arregui, F. J., Carlos, M. (2012) Burst detection in water networks using principal component analysis, Journal of Water Resources Planning and Management, 138(1), pp. 47-54. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000147
  12. Park, G.B. (2004) Multivariate analysis, Hakjisa, pp. 65-67., pp. 77-79.
  13. Park, S., Jeon, D.H., Jung, S.Y., Kim, J.H., Lee, D.J. (2013) Identifying an Appropriate Analysis Duration for the Principal Component Analysis of Water Pipe Flow Data, Journal of Korean Society of Water and Wastewater, KSWW, 27(3), pp. 351-361. https://doi.org/10.11001/jksww.2013.27.3.351

피인용 문헌

  1. 상수관망의 누수감지를 위한 주성분 분석의 적용 가능성에 대한 연구 vol.33, pp.2, 2019, https://doi.org/10.11001/jksww.2019.33.2.159