The document summarizes testing of an online leak localization method using hydraulic modeling. In a case study by SES Water UK, the method detected a leak within 24 hours, located it within 42 hours, reducing water loss by 53% compared to no detection/localization. Testing in 10 DMAs found the method reduced search areas by 81% on average when additional head loss from leaks was over 0.5m. The study concludes the method can significantly reduce water losses from bursts and leaks when combined with rapid detection.

1. Testing of a cost-effective leak localisation method
12 September 2022
M. Bakker PhD

3. Leakage detection & localization benefits
Case KNT20
Case study: SES Water (UK)
Burst of 3 l/s (initially) at KNT20
1. Detection runtime = 24 hours
2. Location runtime = 42 Hours
3. Repair runtime = 77 Hours
4. Total leak runtime = 143 hours
5. Total leak volume 1.9Ml lost
Goals:
1. 24/7 surveillance
2. Awareness before client call
3. Reduce non-revenue water
Leak break out
19/9 14:30
MNF analyses
20/9 14:10
Leak Located
22/09 08:00
Leak
Repaired
25/9 13:00
Total Leak Volume = 1.9Ml

4. Contents
• Introduction
• Overview leakage reduction methods
• On-line leak localisation method
• Correction for deviations in non-leak situation
• Testing the method in 10 different DMAs
• Results of leak tests
• Conclusions

5. Introduction
• Leakage and burst large problem for
water companies
• Existing leaks
• New leaks and bursts:
• Detect
• Locate
• Repair
• UK water company SES Water deployed
Aquasuite BURST Alert in all DMAs
• Development leak localisation method

6. Overview leakage reduction methods
Each leak generates a specific profile / fingerprint, e.g.
1. Transient ((milli) seconds to minutes)
2. Deviation in noise (continuous)
3. Deviation in flow / pressure (minutes to hours)
4. Deviation in temperature (minutes to hours)
5. Deviation in sound reflections of/in the pipe
(continuous)
6. Deviation in light (fiber optic) reflections in the pipe
(continuous)
7. Deviation in visual images (by camera) in the pipe
(continuous)
8. Deviation in soil characteristics in satellite images
(days to weeks)

7. Overview leakage reduction methods
Each leak generates a specific profile / fingerprint, e.g.
1. Transient ((milli) seconds to minutes)
2. Deviation in noise (continuous)
3. Deviation in flow / pressure (minutes to hours)
4. Deviation in temperature (minutes to hours)
5. Deviation in sound reflections of/in the pipe
(continuous)
6. Deviation in light (fiber optic) reflections in the pipe
(continuous)
7. Deviation in visual images (by camera) in the pipe
(continuous)
8. Deviation in soil characteristics in satellite images
(days to weeks)
Requires:
1. Cheap sensors
2. Available hydraulic models
But also:
1. DMA infrastructure (small areas of ± 1,000
connections)
2. Well dimensioned networks

8. •API calls to data source
•Every 15 minutes update
BURST steps
1. Collect data
2. Predict
3. Filter and compute limits
4. Detect and Alert
5. Locate

9. •Self-learning
•“Normal” and “Special” days / periods
•Flow and pressure
BURST steps
1. Collect data
2. Predict
3. Filter and compute limits
4. Detect and Alert
5. Locate
6. Integrate

10. •Signals filtered: 1 minute to 4 hours
•Dynamic limits based on prediction accuracy
•Result: large event quick, smaller events slightly
delayed
BURST steps
1. Collect data
2. Predict
3. Filter and compute limits
4. Detect and Alert
5. Locate

11. •Alarm triggered when limit is exceeded
•E-mail notification
•Medium, High and Critical Alarms
•Add Label, Action, Comment
BURST steps
1. Collect data
2. Predict
3. Filter and compute limits
4. Detect and Alert
5. Locate

12. •6-10 pressure sensors per DMA
•Analyse leak impact in hydraulic model
•Estimated leak flow form Alert
•Show probable leak search area on the map
BURST steps
1. Collect data
2. Predict
3. Filter and compute limits
4. Detect and Alert
5. Locate

13. On-line leak localisation method
Characteristics:
• Applied on DMA level (500-1,500 conn.)
• Using hydraulic model
• Install 6-10 pressure sensors at relevant
locations
• Analyses every 5 of 15 minutes, when new
sensor data is received
• Analyse additional head loss caused by leak
• Localisation of leaks > 1.0 l/s (3.6 m3/h)
• Highest probability plotted on a map

14. Correction for deviations in non-leak situation
• Hydraulic models are not perfect: always deviation between model and measurement:
• Static offset  error in elevation in model or sensor
• Flow dependent deviation  resistance different (due to e.g. pipe roughness or closed valve)
• Can we predict (or “model”) this deviation?
blue: sensor data
orange: model data
green: difference (error)
grey: mean value

15. Correction for deviations in non-leak situations
• Dh = (hmeasured - hmodel) = C1 + C2 * Flow2
• Determine C1 + C2 based on previous 72 hours of data
• Apply correction:
hmodel, cor = hmodel + (C1 + C2 * Flow2)
• Results:
• Most sensors have a good fit after correction (R2 > 85%)
• Some sensors have a bad fit after correction (R2 < 85%)
• Exclude “bad sensors” from leak localisation

16. Testing the method in 10 different DMAs
Localisation method is based on:
• Analysing additional head loss due to leak
• Analyses is done with hydraulic model
Goal of SES Water: locate leaks of 1 l/s (3.6 m3/h) and larger
The expected accuracy determined by:
• Additional head loss due to a leak of 1 l/s  additional head loss must be sufficiently large
• Accuracy of pressure sensors  sensors must be sufficiently accurate
• Accuracy of the hydraulic model  model must be sufficiently accurate

17. Testing the method in 10 different DMAs
Evaluation suitability:
• High: additional head loss > 0.5 m
• Medium 0.25 m ≤ additional head loss ≤ 0.5 m
• Low: additional head loss < 0.25 m
High suitability
Good results expected
Low suitability
No good results expected
Medium suitability
Expected results unclear

18. Results of leak tests
• Executed by SES Water, Jack Nicol (https://www.linkedin.com/in/jack-nicol-540012162/)
• 12 DMA’s. 6-8 Technolog Cello pressure sensors. Every minute 1 data point
• Leak simulation: 1.5 hour at 0.8 l/s (3 m3/h); 1 hour at 1.5 l/s (5,5 m3/h)
18

19. Results of leak tests
19

20. Leak simulation: results examples
20

21. Results of leak tests
Score: false-positive rate = percentage of nodes with higher probability than real leak node
0,00
0,10
0,20
0,30
0,40
0,50
0,60
0,70
0,80
0,00 0,50 1,00 1,50 2,00 2,50 3,00 3,50
False
Positive
Rate
Additional head loss [m]
Performance of localisation method

22. Leakage detection & localization benefits
Case KNT20
Case study: SES Water (UK)
Burst of 3 l/s (initially) at KNT20
1. Detection runtime = 24 hours
2. Location runtime = 42 Hours
3. Repair runtime = 77 Hours
4. Total leak runtime = 143 hours
5. Total leak volume 1.9Ml lost
Goals:
1. 24/7 surveillance
2. Awareness before client call
3. Reduce non-revenue water
Leak break out
19/9 14:30
MNF analyses
–20/9 14:10
Leak
Located
22/09 08:00
Leak
Repaired
25/9 13:00
Total Leak Volume = 1.9Ml

23. Leak break
out
19/9 @14:30
Leak Located
20/09 @00:00
Burst detected
20/9 @16:00
Leak
Repaired
23/9 @13:00
Total Saved
Volume = 1.0
Ml
53% reduction water
loss
Leakage detection & localization benefits
Case KNT20, with detection and localization
Case study: SES Water (UK)
Burst of 3 l/s (initially) at KNT20
1. Detection runtime = 1.5 hours
2. Location runtime = 4 Hours
3. Repair runtime = 77 Hours
4. Total leak runtime = 82 hours
5. Total leak volume 1.0 Ml lost
Result
1. 53% water loss reduced
Total Leak Volume = 0.9
Ml (900 m3)

24. Conclusions
1. Leak localisation method able to reduce search area by 81%
2. Combination of detection + localisation may reduce leakage by 53%
3. Additional head loss due to leak fair indicator for expected accuracy of localisation
method

25. Acknowledgements
SES Water:
Daniel Woodworth
Jack Nicol
Sian Evans
Tanya Dady
Royal HaskoningDHV:
Maria Wünsch de Alvarenga
Martijn Deenen
Zixi Meng