PENGSen,CHENGRui,WUQing,et al.Identification of Water Supply Network Pipe Burst Based on Extreme Learning Machine Algorithm[J].China Water & Wastewater,2022,38(7):56-62.
Identification of Water Supply Network Pipe Burst Based on Extreme Learning Machine Algorithm
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第38卷
Number:
第7期
Page:
56-62
Column:
Date of publication:
2022-04-01
- Keywords:
- water supply network; identification of pipe burst area; ELM algorithm; K-means clustering algorithm
- Abstract:
- Water supply network pipe burst has the characteristics of difficult location and wide range of influence, which has troubled water supply enterprises for a long time. To solve the problem of identifying the pipe burst area of water supply networks, the burst conditions were comprehensively considered under various influencing factors, the pipe burst sample data set was constructed by using eigenvalue matrix, and the model for identifying the pipe burst area was established by extreme learning machine (ELM) algorithm. The similarity of node hydraulic change characteristics was analyzed by using K-means clustering algorithm. On this basis, the monitoring area of the pipe network was divided and the monitoring points were arranged to form a variety of monitoring schemes. The identification performance of ELM under different monitoring schemes and noise impact was analyzed by combining the identification rate of pipe burst and other parameters. The burst area was identified and analyzed in a practical pipe network. It was found that the model could effectively identify the pipe burst area. At the same time，it could effectively improve the identification rate of pipe burst by combining different zoning schemes. The addition of monitoring points could reduce the noise impact from pressure monitoring data.
Last Update:
2022-04-01