# Using SCADA to Detect and Locate Bursts in a Long-Distance Water Pipeline

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

## 2. Pipe Burst Detection

#### 2.1. State Changes of Pressure and Flow at Pumping Station

- State 1
- When the number of operating pumps increases, the pressure and flow both increase (ΔP and ΔQ are positive);
- State 2
- When some pumps are shut off, the pressure drops and the flow also decreases (ΔP and ΔQ are negative);
- State 3
- If the flow decreases, the pressure rises when the number of pumps operating remains constant (ΔQ is negative and ΔP is positive); and
- State 4
- If the flow increases, the pressure descends when the number of pumps stays constant (ΔQ is positive and ΔP is negative).

#### 2.2. Pressure and Flow Fluctuation Distributions

#### 2.3. Abnormality Risk Function

_{ΔP}is the mean of pressure fluctuations; σ

_{ΔP}is the mean square deviation of pressure fluctuations; µ

_{ΔQ}is the mean of flow fluctuations; and σ

_{ΔQ}is the mean square deviation of flow fluctuations.

#### 2.4. Combining Pressure and Flow Risk Functions

^{Θ}. If B is a nonempty subset of Θ, its probability assignment function is denoted by m(B), which represents trust in the degree of evidential support for proposition B:

_{i}(Equation (6)). Bel(Φ) is also zero. Another fundamental function of D–S theory, the plausibility function Pl, is defined as:

_{1}and m

_{2}are given and their corresponding focal elements are A

_{i}and B

_{j}, the combination rule of D–S is defined by:

_{1}and m

_{2}contradict each other.

## 3. Pipe Burst Localization

^{6}, which means the drag of the pipe is proportional to speed squared, so usually n = 2); L is the length of the pipe section; and Q is the flow.

_{1}, and the slope of the water head line will change at the burst point. The slope of the line B

_{1}increases as the upstream flow increases. The pressure difference ΔP

_{2}also increases between B

_{1}and C (C is parallel to line A) in the outlet direction of the pipe, due to the burst flow. In contrast, the slope of the line B

_{2}decreases and the corresponding ΔP

_{2}decreases too.

_{1}and P

_{2}are the upstream and downstream pressures under regular operating conditions; C = $\rho g(z2-z1)$; and Q

_{0}is the normal flow. After a burst occurs at location x, Equation (13) becomes:

_{1}and P’

_{2}are the upstream and downstream pressures after the burst; Q

_{1}and Q

_{2}are the upstream and downstream flows; and ΔQ is the burst flow. The burst location is calculated by:

_{ac}and x

_{ad}are the distances between the burst point and sensor a; x

_{bc}and x

_{bd}are the distances between the burst point and the sensor b; l

_{a}and l

_{b}are the distances from the pump station to the sensors a and b; $\overline{x}$ is the estimated average distance from the pump station; Q is the flow before the burst event; and Q + ΔQ is the flow after the burst event. The flow data are measured from the pump station, and pressure data are gathered from each monitoring point every 5 min.

## 4. Case Study

^{3}/h. The dual pipeline is constructed mostly of steel pipes, and some pipe sections have been replaced by GRP sand pipes or ductile iron pipes during maintenance. Sixteen remote-transmitting pressure sensors and two flow sensors are installed along each pipeline. The pipeline configuration is shown in Figure 9.

#### 4.1. Pipe Burst Detection

#### 4.1.1. Risk Threshold

#### 4.1.2. Two Burst Events

^{3}/h. The pressure–flow risk curve jumped sometimes as pressure and flow fluctuated, but the combined risk value remained below the red threshold because of its filtering effect. At the time of the burst (shown as the vertical dotted line), the pressure suddenly dropped to 255 kPa, and the flow increased to 27 500 m

^{3}/h. The pressure risk value was almost 1.00, and the flow risk value increased to 0.99. The combined risk value jumped to 0.99. Fifteen minutes later, workers shut down the pumps so pressure and flow abruptly dropped to 0 and the combined risk returned to the normal range.

^{3}/h. Pressure and flow risk values before the burst sometimes become large because of random fluctuations in the sensors. However, the combined risk was below 0.4. When the burst occurred, the pressure dropped to 126 kPa, and the flow increased to 27,400 m

^{3}/h. At the same time, the pressure risk increased to 0.98, the flow risk became 1.00, and the combined risk increased to 0.99. After the pumps were shut down, flow and pressure sensors returned error values (extremely large values or zero). The combined risk fluctuated sharply when the burst emergency was dealt with.

#### 4.2. Pipe Burst Location

#### 4.3. Parameter Sensitivity Analysis

## 5. Discussion and Conclusions

- The pressure sensors that are used for burst detection in a long-distance water transportation pipeline should be evenly distributed, and the distance between sensors should not exceed 5000 m. It is not necessary to increase the density of sensors because there would be little improvement in the results but the management costs would greatly increase.
- The sampling return period of the pressure sensors should not exceed 5 min. If the sampling time is too long, the backflow of water in the pipe after the burst point will affect the sensor readings, which will lead to large deviations in the calculations. More frequent sampling results will require more power, but will not greatly increase precision. A reasonable sampling frequency is necessary to ensure the feasibility and effectiveness of the monitoring system.
- The data fluctuations observed in a long-distance water pipeline are consistent with the behavior of a water distribution system. In practice, the accuracy of instrumental monitoring can be improved by taking account of the statistical characteristics of monitored data during normal operation of the system.

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 6.**Cumulative probability curves of (

**a**) pressure and (

**b**) flow fluctuations at the pumping station.

Pressure Risk | Flow Risk | Combined Risk | ||||||
---|---|---|---|---|---|---|---|---|

Risk | Counts | Proportion | Risk | Counts | Proportion | Risk | Counts | Proportion |

0.0–0.1 | 813 | 2.32% | 0.0–0.1 | 615 | 1.75% | 0.0–0.1 | 7901 | 22.55% |

0.1–0.2 | 123 | 0.35% | 0.1–0.2 | 1677 | 4.78% | 0.1–0.2 | 26,525 | 75.70% |

0.2–0.3 | 31,608 | 90.21% | 0.2–0.3 | 24,359 | 69.51% | 0.2–0.3 | 403 | 1.15% |

0.3–0.6 | 1807 | 5.15% | 0.3–0.6 | 7848 | 22.39% | 0.3–0.6 | 116 | 0.33% |

0.6–0.9 | 380 | 1.08% | 0.6–0.9 | 362 | 1.03% | 0.6–0.9 | 71 | 0.20% |

0.9–1.0 | 309 | 0.88% | 0.9–1.0 | 179 | 0.51% | 0.9–1.0 | 24 | 0.07% |

Sensor | 1# | 3# | 5# | 7# | 10# | 18# | 25# |
---|---|---|---|---|---|---|---|

P_{b} (MPa) | 0.330 | 0.332 | 0.314 | 0.303 | 0.281 | 0.203 | 0.121 |

P_{a} (MPa) | 0.255 | 0.227 | 0.197 | 0.165 | 0.177 | 0.138 | 0.116 |

P_{b} − P_{a} (MPa) | −0.075 | −0.105 | −0.118 | −0.138 | −0.105 | −0.065 | −0.005 |

Distance from pumping station (m) | 0 | 1200 | 2200 | 3050 | 4646 | 8459 | 12403 |

_{b}= pressure before the burst; P

_{a}= pressure after the burst.

Pipe Section | 1–10 | 3–10 | 5–10 | 7–10 |
---|---|---|---|---|

S × 10^{11} (kPa·h^{2}/m^{7}) | 3.49 | 3.80 | 3.15 | 3.73 |

C (kPa) | −27.70 | 4.00 | −2.67 | −3.53 |

X (m) | 2683.06 | 2365.52 | 1181.89 | −1362.80 |

Distance from pumping station (m) | 2683.06 | 3565.52 | 3381.89 | 1687.20 |

^{3}/h (cubic meter per hour) in applying Equations (13) and (15).

Pipe Section | Items | S | S − 20% | S − 10% | S + 10% | S + 20% |
---|---|---|---|---|---|---|

1–10 | S | 3.49 | 2.79 | 3.14 | 3.84 | 4.19 |

x (m) | 2682.519 | 3355.552 | 2981.526 | 2438.018 | 2234.365 | |

Location (m) | 2682.519 | 3355.552 | 2981.526 | 2438.018 | 2234.365 | |

Deviation (m) | 0 | 673.033 | 299.007 | −244.501 | −448.154 | |

3–10 | S | 3.80 | 3.04 | 3.42 | 4.18 | 4.56 |

x (m) | 2365.520 | 3042.988 | 2666.617 | 2119.168 | 1913.874 | |

Location (m) | 3565.520 | 4242.988 | 3866.617 | 3319.168 | 3113.874 | |

Deviation (m) | 0 | 677.468 | 301.097 | −246.352 | −451.646 |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Cheng, W.; Fang, H.; Xu, G.; Chen, M.
Using SCADA to Detect and Locate Bursts in a Long-Distance Water Pipeline. *Water* **2018**, *10*, 1727.
https://doi.org/10.3390/w10121727

**AMA Style**

Cheng W, Fang H, Xu G, Chen M.
Using SCADA to Detect and Locate Bursts in a Long-Distance Water Pipeline. *Water*. 2018; 10(12):1727.
https://doi.org/10.3390/w10121727

**Chicago/Turabian Style**

Cheng, Weiping, Hongji Fang, Gang Xu, and Meijun Chen.
2018. "Using SCADA to Detect and Locate Bursts in a Long-Distance Water Pipeline" *Water* 10, no. 12: 1727.
https://doi.org/10.3390/w10121727