An Efficient Burst Detection and Isolation Monitoring System for Water Distribution Networks Using Multivariate Statistical Techniques
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Framework
2.2. Burst Monitoring System
2.2.1. Standardized Exponential Weighted Moving Average (Standardized EWMA)
2.2.2. Principal Component Analysis
2.3. Optimal Pressure Sensor Placement
2.3.1. K-Means Clustering Approach
2.3.2. Sensitivity Analysis
2.4. Burst Identification
3. Results and discussion
3.1. Simulation of a Water Distribution Network
3.2. Monitoring of Burst Occurrence in the WDN
3.3. Robustness of the Three Monitoring Systems
3.4. Optimal Sensor Placement
3.5. Burst Isolation
4. Conclusions
Author Contributions
Acknowledgments
Conflicts of Interest
References
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Node Number | Elevation (m) | Demand (m3/day) | Link | Length (m) | Diameter (mm) |
---|---|---|---|---|---|
N1 | 125 | 86 | L1 | 1200 | 400 |
N2 | 120 | 130 | L2 | 1400 | 150 |
N3 | 121 | 86 | L3 | 500 | 150 |
N4 | 120 | 86 | L4 | 700 | 350 |
N5 | 110 | 173 | L5 | 400 | 150 |
N6 | 116 | 86 | L6 | 400 | 125 |
N7 | 117 | 86 | L7 | 600 | 350 |
N8 | 115 | 173 | L8 | 300 | 250 |
N9 | 110 | 173 | L9 | 400 | 200 |
N10 | 111 | 130 | L10 | 500 | 200 |
N11 | 114 | 173 | L11 | 400 | 200 |
N12 | 110 | 173 | L12 | 400 | 250 |
N13 | 105 | 173 | L13 | 350 | 200 |
N14 | 110 | 86 | L14 | 500 | 150 |
Scenario | Burst Flowrate (m3/day) | Burst Location | Occurrence Time (min) | Duration (min) |
---|---|---|---|---|
S1 | 10 | N14 | 902 | 2 |
S2 | 20 | N6 | 685 | 5 |
S3 | 30 | N7 | 551 | 7 |
S4 | 40 | N10 | 62 | 8 |
S5 | 50 | N2 | 589 | 7 |
S6 | 60 | N4 | 384 | 4 |
S7 | 70 | N7 | 348 | 3 |
S8 | 70 | N14 | 540 | 7 |
S9 | 80 | N1 | 744 | 4 |
S10 | 100 | N7 | 980 | 5 |
System | Burst Monitoring Method | Sensor Placement Approach | Reference |
---|---|---|---|
System 1 | CUSUM chart | Sensitivity analysis | [9] |
System 2 | Standardized EWMA chart | k-means clustering and sensitivity analysis | This study |
System 3 | Standardized EWMA and PCA | PCA, k-means clustering, and sensitivity analysis | This study |
Scenario | CUSUM (system 1) | Standardized EWMA (system 2) | Standardized EWAM-PCA (system 3) |
---|---|---|---|
S1 | - | - | 0.88 s |
S2 | - | 47.72 s | 1.63 s |
S3 | 4.11 s | 30.27 s | 6.04 s |
S4 | 3.32 s | 35.52 s | 12.98 s |
S5 | 1.83 s | 20.87 s | 4.62 s |
S6 | 1.15 s | 27.51 s | 2.59 s |
S7 | 1.01 s | 25.99 s | 1.56 s |
S8 | 0.95 s | 25.36 s | 5.33 s |
S9 | 1.09 s | 17.41 s | 2.84 s |
S10 | 0.83 s | 25.84 s | 4.10 s |
Average | 1.79 s | 28.50 s | 4.26 s |
Node | N1 | N2 | N3 | N4 | N5 | N6 | N7 |
Cumulative sensitivity () | |||||||
Node | N8 | N9 | N10 | N11 | N12 | N13 | N14 |
Cumulative sensitivity () |
Node | Parameters of Regression Model | Flow Rate | Pressure | ||||
---|---|---|---|---|---|---|---|
p1 | p2 | p3 | Mean | Standard Deviation | Mean | Standard Deviation | |
N1 | −1.94∙10−5 | −3.46∙10−4 | 225 | 88.6 | 35.34 | 224.79 | 0.11 |
N2 | −1.70∙10−5 | −5.73∙10−4 | 230 | 134.0 | 53.43 | 229.58 | 0.23 |
N3 | −2.26∙10−5 | −4.51∙10−4 | 229 | 88.6 | 35.34 | 228.75 | 0.13 |
N4 | −3.36∙10−5 | −8.12∙10−4 | 230 | 88.6 | 35.34 | 229.63 | 0.20 |
N5 | −1.09∙10−5 | −4.52∙10−4 | 225 | 178.3 | 71.10 | 239.52 | 0.26 |
N6 | −4.05∙10−5 | −8.83∙10−4 | 234 | 88.6 | 35.34 | 233.56 | 0.24 |
N7 | −4.21∙10−5 | −9.16∙10−4 | 233 | 88.6 | 35.34 | 232.54 | 0.25 |
N8 | −1.16∙10−5 | −4.45∙10−4 | 235 | 178.2 | 71.10 | 234.50 | 0.27 |
N9 | −1.20∙10−5 | −5.10∙10−4 | 240 | 178.3 | 71.10 | 239.47 | 0.28 |
N10 | −2.09∙10−5 | −6.85∙10−4 | 239 | 134.0 | 53.43 | 238.48 | 0.28 |
N11 | −1.10∙10−5 | −4.80∙10−4 | 236 | 178.3 | 71.10 | 235.51 | 0.26 |
N12 | −1.16∙10−5 | −4.67∙10−4 | 240 | 178.3 | 71.10 | 239.49 | 0.27 |
N13 | −1.20∙10−5 | −5.26∙10−4 | 245 | 178.3 | 71.10 | 244.47 | 0.28 |
N14 | −5.06∙10−5 | −9.67∙10−4 | 240 | 88.6 | 35.34 | 239.46 | 0.29 |
Number of Sensors | Number of Probable Configurations | Selected Measurement Points |
---|---|---|
1 | 14 | 2 |
2 | 91 | 2, 10 |
3 | 364 | 2, 10, 14 |
4 | 1001 | 2, 6, 10, 14 |
5 | 2002 | 2, 3, 6, 10, 14 |
6 | 3003 | 2, 3, 6, 9, 11, 14 |
7 | 3432 | 2, 3, 4, 9, 10, 11, 14 |
8 | 3003 | 2, 3, 4, 6, 9, 10, 11, 14 |
9 | 2002 | 2, 3, 4, 9, 10, 11, 12, 13, 14 |
10 | 1001 | 2, 3, 4, 5, 6, 9, 10, 11, 13, 14 |
11 | 364 | 2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14 |
12 | 91 | 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14 |
13 | 14 | 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14 |
14 | 1 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 |
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Nam, K.; Ifaei, P.; Heo, S.; Rhee, G.; Lee, S.; Yoo, C. An Efficient Burst Detection and Isolation Monitoring System for Water Distribution Networks Using Multivariate Statistical Techniques. Sustainability 2019, 11, 2970. https://doi.org/10.3390/su11102970
Nam K, Ifaei P, Heo S, Rhee G, Lee S, Yoo C. An Efficient Burst Detection and Isolation Monitoring System for Water Distribution Networks Using Multivariate Statistical Techniques. Sustainability. 2019; 11(10):2970. https://doi.org/10.3390/su11102970
Chicago/Turabian StyleNam, KiJeon, Pouya Ifaei, Sungku Heo, Gahee Rhee, Seungchul Lee, and ChangKyoo Yoo. 2019. "An Efficient Burst Detection and Isolation Monitoring System for Water Distribution Networks Using Multivariate Statistical Techniques" Sustainability 11, no. 10: 2970. https://doi.org/10.3390/su11102970