Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
권호 표지
DOI QR코드

DOI QR Code

Leakage Detection of Water Distribution System using Adaptive Kalman Filter

적응 칼만필터를 이용한 상수관망의 누수감시 기법

  • Kim, Seong-Won (Researcher, K-water Institute, Korea Water Resources Corporation) ;
  • Choi, Doo Yong (Researcher, K-water Institute, Korea Water Resources Corporation) ;
  • Bae, Cheol-Ho (Researcher, K-water Institute, Korea Water Resources Corporation) ;
  • Kim, Juhwan (Researcher, K-water Institute, Korea Water Resources Corporation)
  • Received : 2013.05.22
  • Accepted : 2013.08.08
  • Published : 2013.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

Leakage in water distribution system causes social and economic losses by direct water loss into the ground, and additional energy demand for water supply. This research suggests a leak detection model of using adaptive Kalman filtering on real-time data of pipe flow. The proposed model takes into account hourly and daily variations of water demand. In addition, the model's prediction accuracy is improved by automatically calibrating the covariance of noise through innovation sequence. The adaptive Kalman filtering shows more accurate result than the existing Kalman method for virtual sine flow data. Then, the model is applied to data from two real district metered area in JE city. It is expected that the proposed model can be an effective tool for operating water supply system through detecting burst leakage and abnormal water usage.

수돗물의 공급과정에서 발생되는 상수관망의 누수는 소중한 수자원의 손실, 공급에너지의 추가적인 소요 등 사회경제적인 손실을 초래한다. 본 연구에서는 관로 상에 설치되어 실시간으로 계측되는 유량자료를 이용하여 누수를 감시하는 모형을 적응 칼만필터 기법을 이용하여 제시하였다. 제안된 누수감시 알고리즘에서는 수돗물 사용량의 시간적 변화와 요일적 변동을 고려함으로써 예측의 신뢰도를 향상시키는 방안을 제시하였다. 또한 기존의 칼만필터 기법에 혁신과정을 추가하여 잡음의 공분산에 대한 자동보정을 통하여 예측의 정확도를 개선하였다. 개발된 모형은 사인형태의 가상 유량자료에 대한 모의실험을 통하여 적응 칼만필터 기법의 예측정확도를 기존의 칼만필터 기법과 비교하였으며, JE시의 2개소 블록유량자료에 대한 현장 적용성 평가를 실시하였다. 본 연구의 결과는 관로의 파열에 의한 누수 및 비정상적인 용수사용량에 대한 감시를 통하여 상수관망의 효율적인 운영관리에 적용될 수 있을 것으로 기대된다.

Keywords

References

  1. Bae, D.H., Lee, B.J., and Georgakakos, K.P. (2009). "Stochastic continuous storage function model with ensemble Kalman filtering (I): Model development." J. of Korea Water Res. Assoc., KWRA, Vol. 42, No. 11, pp. 953-961. https://doi.org/10.3741/JKWRA.2009.42.11.953
  2. Ha, S.-R., Ryu, Y.-H., and Park, S.-Y. (2006). "Probabilistic neural network for prediction of leakage in water distribution network." J. of Korean Soc. of Water and Wastewater, KSWW, Vol. 20, No. 6, pp. 799-811.
  3. Kalman, R.E. (1960). "A new approach to linear filtering and prediction problems." Trans. ASME J. Basic Engineering, Vol. 82, pp. 35-45. https://doi.org/10.1115/1.3662552
  4. Khan, A., Widdop, P.D., Day, A.J., Wood, A.S., Mounce, S.R., and Machell, J. (2005). "Artificial neural network model for a low cost failure sensor: Performance assessment in pipeline distribution." Int. J. Math. Comp. Sci., Vol. 1, No. 1, pp. 27-33.
  5. Medha, R.K. (1970). "On the identification of variances and adaptive Kalman filtering." IEEE Trans. Autom. Control, Vol. 15, pp. 175-184. https://doi.org/10.1109/TAC.1970.1099422
  6. Ministry of Environment (MoE)-Korea (2012). 2011 Statistics of Waterworks. Seoul.
  7. Mounce, S.R., Khan, A., Wood, A.S., Day, A.J., Widdop, P.D., and Machell, J. (2003). "Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system." Inf. Fusion, Vol. 4, No. 3, pp. 217-229. https://doi.org/10.1016/S1566-2535(03)00034-4
  8. Yoo, C., Hwang, J., and Kim, J. (2012). "Use of the extended Kalman filter for the real-time quality improvement of runoff data: 1. Algorithm construction and application to one station." J. of Korea Water Res. Assoc., KWRA, Vol. 45, No. 7, pp. 697-711. https://doi.org/10.3741/JKWRA.2012.45.7.697