scholarly journals Leakage localization using pressure sensors and spatial clustering in water distribution systems

2021 ◽  
Author(s):  
Xin Li ◽  
Shipeng Chu ◽  
Tuqiao Zhang ◽  
Tingchao Yu ◽  
Yu Shao
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Spatial Clustering ◽  
Water Distribution Systems ◽  
Pressure Sensors ◽  
Multiple Time ◽  
Efficient Manner ◽  
Localization Accuracy ◽  
Water Demands ◽  
Novel Model

Abstract Leakages in water distribution systems (WDSs) are a worldwide problem, which can result in an intolerable burden in satisfying the water demands of the consumers. There is an urgent demand to develop technologies that can detect and localize the leakage in a timely and efficient manner. The monitoring data of the WDS is a typical time series, and there is a certain spatiotemporal correlation between the data provided by the devices distributed at different locations of the WDS. This paper proposes a novel model-based method for WDS leakage localization. The method is characterized by (1) developing the dominant sensor sequence for each candidate leakage node to improve the localization accuracy based on the spatial correlation analysis; (2) utilizing multiple time steps of the measurements which are temporal varying correlated; (3) ranking leakage regions and nodes by their possibility to contain the true leakage. A realistic WDS is used to evaluate the performance of the method. Results show that the method can accurately and efficiently localize the leakage.

scholarly journals Optimal selection of number and location of pressure sensors in water distribution systems using geostatistical tools coupled with genetic algorithm

2019 ◽  
Vol 21 (6) ◽  
pp. 1030-1047 ◽  
Author(s):  
Fattah Soroush ◽  
Mohammad J. Abedini
Keyword(s):  
Genetic Algorithm ◽  
Distribution Systems ◽  
Water Distribution ◽  
Random Search ◽  
Water Distribution Systems ◽  
Pressure Sensors ◽  
Monitoring Network ◽  
Search Optimization ◽  
First Time

Abstract This paper presents a novel methodology for designing an optimal pressure sensor to make average pressure field in water distribution systems (WDS) more accurate via geostatistical tools coupled with genetic algorithm (GA) under normal operating condition. In light of this, the objective function is introduced based on geostatistical technique as variance of residual of block ordinary kriging (BOK). In order to solve the problem of sensor placement, three different approaches, so-called, simplified, exhaustive, and random search optimization are considered. To the best of the authors' knowledge, this is the first time whereby geostatistical tools are used to design a pressure monitoring network in the WDS. The proposed methodology is first tested and verified on a literature case study of Anytown WDS and then is applied to a real-world case study referred to as C-Town consisting of five district metered areas (DMAs). The proposed methodology has several advantages over existing more conventional approaches which will be demonstrated in this paper. The results indicate that this method outperforms the conventional paradigms in current use in terms of mathematical labor and the results are quite promising.

scholarly journals Time-Series-Based Leakage Detection Using Multiple Pressure Sensors in Water Distribution Systems

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3070 ◽  
Author(s):  
Yu Shao ◽  
Xin Li ◽  
Tuqiao Zhang ◽  
Shipeng Chu ◽  
Xiaowei Liu
Keyword(s):  
Time Series ◽  
Distribution Systems ◽  
Water Distribution ◽  
Time Window ◽  
Water Distribution Systems ◽  
Pressure Sensors ◽  
Leak Detection ◽  
Window Length ◽  
Threshold Selection ◽  
Scanning Time

Leak detection is nowadays an important task for water utilities as leakages in water distribution systems (WDS) increase economic costs significantly and create water resource shortages. Monitoring data such as pressure and flow rate of WDS fluctuate with time. Diagnosis based on time series monitoring data is thought to be more convincing than one-time point data. In this paper, a threshold selection method for the correlation coefficient based on time series data is proposed based on leak scenario falsification, to explore the advantages of data interpretation based on time series for leak detection. The approach utilizes temporal varying correlation between data from multiple pressure sensors, updates the threshold values over time, and scans multiple times for a scanning time window. The effect of scanning time window length on threshold selection is also tested. The performance of the proposed method is tested on a real, full-scale water distribution network using synthetic data, considering the uncertainty of demand and leak flow rates, sensor noise, and so forth. The case study shows that the scanning time window length of 3–6 achieves better performance; the potential of the method for leak detection performance improvement is confirmed, though affected by many factors such as modeling and measurement uncertainties.

scholarly journals Real-time burst detection based on multiple features of pressure data

2021 ◽  
Author(s):  
Xiangqiu Zhang ◽  
Zhihong Long ◽  
Tian Yao ◽  
Hua Zhou ◽  
Tingchao Yu ◽  
...  
Keyword(s):  
Real Time ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Classification Model ◽  
Absolute Pressure ◽  
Detection Accuracy ◽  
Multiple Time ◽  
Burst Detection ◽  
Time Step

Abstract Pipe bursts are an essential issue for water loss in water distribution systems. This study proposes a real-time burst detection method that combines multiple data features of multiple time steps. The method sets burst thresholds in three dimensions according to different moments at a specific monitoring point, and achieves burst identification based on a classification model. First, three data features, namely, absolute pressure value, predicted deviation value obtained by prediction model, and pressure variation value, of historical pressure at each time step are scored based on the Western Electric Company rules. The scores represent different abnormalities. Then, the scores corresponding to the three features are used as input of the decision tree classification model. The trained model is used for detecting burst events. Results show that this method achieves 99.56% detection accuracy, indicating that it is effective for burst detection. The proposed method outperformed the single feature-based method and provides good results in water distribution systems.

An Ontological Approach to Water Distribution Systems Management

WRPMD'99 ◽  
1999 ◽  
Author(s):  
P. Costa ◽  
A. Esposito ◽  
C. Gualtieri ◽  
D. Pianese ◽  
G. Pulci Doria ◽  
...  
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Systems Management ◽  
Ontological Approach

Robust Estimation of Integrated Hydraulics and Parameters in Water Distribution Systems

2001 ◽  
Author(s):  
Mietek A. Brdys ◽  
Kazimierz Duzinkiewicz ◽  
Michal Grochowski ◽  
Tomasz Rutkowski
Keyword(s):  
Robust Estimation ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems
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