System and method for monitoring resources in a water utility network

• US 7,920,983 B1
• Filed: 03/04/2010
• Issued: 04/05/2011
• Est. Priority Date: 03/04/2010
• Status: Active Grant

• Current Assignee: TaKaDu Ltd.
• Original Assignee: TaKaDu Ltd.
• Inventors: Armon, Amitai, Peleg, Amir, Gutner, Shai, Barkay, Uri, Scolnicov, Haggai
• Primary Examiner(s): Wong; Albert K
• Application Number:
  US12/717,944
• Time in Patent Office: 397 Days
• Field of Search: 340/870.02, 340/605, 73/40, 73/405.R, 137/1, 702/50, 702/51, 702/100
• US Class Current: 702/100
• CPC Class Codes: G01M 3/2807 for pipes G01M3/2892, G01M3/30 take precedence
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: Y02B 90/20 Smart grids as enabling technology in buildings sector smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00
• CPC Class Codes: Y04S 20/30 Smart metering, e.g. specially adapted for remote reading
• CPC Class Codes: Y10T 137/0318 Processes

Abstract

A computerized method for monitoring a water utility network, the water utility network comprising a network of pipes for delivering water to consumers and a plurality of meters positioned within the pipes across the water distribution network. The method includes receiving meter data representing parameters measured by the meters, such as flow, pressure, chlorine level, pH and turbidity of the water being distributed through the pipes. The method also includes receiving secondary data from sources external to the meters and representing conditions affecting consumption of water in a region serviced by the water utility network such as weather and holidays. The meter and secondary data is analyzed using statistical techniques to identify water network events including leakage events and other events regarding quantity and quality of water flowing through the pipes and operation of the water network. The events are reported to users via a user interface.

Claims

1. A computerized method for monitoring a water utility network, the water utility network comprising at least a network of pipes for delivering water to consumers and a plurality of meters positioned within the water utility network, the method comprising:

receiving meter data, the meter data representing a plurality of parameters measured by the meters, the parameters including at least flow or pressure of the water through the pipes;

receiving secondary data from one or more sources external to the meters, the secondary data representing one or more conditions affecting flow or consumption of water in a region serviced by the water utility network;

analyzing the meter data by statistically predicting meter data for a first meter based on second meter data from the water utility network and secondary data, wherein the second meter data comprises meter data other than the received first meter data, and comparing the received first meter data with the predicted meter data for the first meter to identify one or more water utility network events comprising at least one or more leakage events by detecting an anomaly if the received first meter data deviates from the predicted meter data for the first meter by a statistical deviation; and

reporting the one or more water network events to a user via a user interface.

2. The method of claim 1, comprising storing the water network events in a database.

3. The method of claim 1, wherein receiving meter data comprises receiving Supervisory Control and Data Acquisition (SCADA) data.

4. The method of claim 1, wherein identifying one or more network events comprises detecting an anomaly if the received meter data from the first meter deviates from the predicted meter data for the first meter by a predefined statistical deviation, and comprising classifying the anomaly as an a leakage event.

5. The method of claim 1, wherein detecting the anomaly comprises determining if a duration of the anomaly exceeds a predefined threshold.

6. The method of claim 1, wherein detecting the anomaly comprises determining if a frequency of the anomaly within a predefined window of time exceeds a predefined threshold.

7. The method of claim 1, wherein statistically predicting meter data for the first meter based on other second meter data from the water utility network comprises selecting one or more second meters as one or more corresponding meters and correlating meter data received from the first meter with meter data received from the one or more corresponding meters.

8. The method of claim 7, wherein selecting one or more second meters as the one or more corresponding meters comprises:

correlating historical meter data for the one or more second meters with historical meter data for the first meter; and

selecting the one or more second meters for which a correlation with the first meter exists.

9. The method of claim 7, wherein selecting the one or more second meters as the one or more corresponding meters comprises identifying the one or more second meters within the water utility network which are positioned within the water utility network so as to be unaffected by anomalies affecting the first meter.

10. The method of claim 1, wherein statistically predicting meter data for the first meter based on second other meter data from the water utility network comprises correlating meter data received from the first meter with at least stored historical meter data for the first meter.

11. The method of claim 1, wherein statistically predicting meter data for the first meter comprises calculating a statistical distribution of likely values for the first meter.

12. The method of claim 1, wherein the water network events further comprise identifying one or more informational events regarding quantity or quality of water flowing through the pipes and operation of the water utility network.

13. The method of claim 1, wherein receiving secondary data comprises receiving at least one of:

weather data representing weather conditions in the region of the water utility network;

calendar data representing one or more factors affecting water consumption on a given date;

repair data representing one or more repairs performed on the water utility network; and

structural data representing a structure of the water utility network.

14. The method of claim 12, wherein the one or more informational events comprise at least one of:

an unexpected increase in consumption pattern, a change in consumption pattern, a theft of water, a zonal boundary breach, a utility meter fault, and a network device malfunction.

15. The method of claim 1, comprising processing the meter data before analyzing it including filtering out noise from the meter data.

20. The method of claim 12, wherein receiving meter data comprises receiving over time water quality data representing turbidity, chlorine and pH of the water delivered over the network, and wherein identifying one or more network events comprises detecting a change in the water quality data over time in excess of a threshold.

16. A computerized system for monitoring a water utility network, the water utility network comprising a network of pipes for delivering water to consumers and a plurality of meters positioned within the water utility network, the system comprising:

a network information database for storing meter data representing a plurality of parameters measured by the meters, the parameters including at least flow or pressure of the water through the pipes, and secondary data from one or more sources external to the meters, the secondary data representing one or more conditions affecting flow or consumption of water in a region serviced by the water utility network;

an analysis engine configured to analyze the meter data by statistically predicting meter data for a first meter based on second meter data from the water utility network and secondary data, wherein the second meter data comprises meter data other than the received first meter data, and comparing the received first meter data with the predicted meter data for the first meter to identify anomalies;

an event classification engine configured to identify water utility network events based on the anomalies, the water network events comprising leakage events and informational events regarding quantity or quality of water flowing through the pipes and operation of the water utility network; and

an event database for storing water utility network event data representing the one or more water network events identified by the event classification engine.

17. The system of claim 16, wherein the analysis engine comprises a plurality of predictor modules for generating a statistical distribution of likely values of the meter data for a given meter and a plurality of anomaly detector modules for comparing the meter data for the given meter to the distribution of likely values to detect anomalies in the meter data.

18. The system of claim 16, comprising a plurality of interface modules for retrieving water utility network event data from the event database and reporting it to users.

19. A computerized method for managing a water utility network, the water utility network comprising a network of pipes for delivering water to consumers and a plurality of meters positioned within the water utility network, the method comprising:

sending meter data to an analysis engine, the meter data representing a plurality of parameters measured by the meters, the parameters including at least flow of the water through the pipes;

receiving from the analysis engine data representing water utility network events, the water network events comprising leakage events and informational events regarding quantity or quality of water flowing through the pipes and operation of the water utility network, the water utility network event data having been identified as a result of analysis of the meter data by statistically predicting meter data for a first meter based on second meter data from the water utility network and secondary data, the secondary data representing one or more conditions affecting consumption or flow of water in a region serviced by the water utility network, wherein the second meter data comprises meter data other than the received first meter data, and comparing the received first meter data with the predicted meter data for the first meter to identify anomalies; and

displaying the received water utility network events to a user on a computerized display device.