System and method for pressure-dependent demand optimization for leakage detection

1. A method for leakage detection in a hydraulic network comprising

loading a hydraulic simulation model corresponding to a real-world hydraulic network in a hydraulic modeling and simulation application executing on a computer system, the hydraulic simulation model configured to represent leakages as pressure dependent emitter flow at selected nodes identified as leakage nodes;

receiving optimization criteria including a specified maximum of possible leakage nodes;

generating, by a genetic algorithm (GA), a plurality of trial solutions for an optimization, each trial solution representing locations for leakage nodes and corresponding emitter coefficients;

performing a hydraulic analysis for the trial solutions to generate model-simulated results;

comparing the model-simulated results to field-observed data for the real-world hydraulic network and generating goodness-of-fit values, each goodness-of-fit value based on a combination of a calculated head difference between model-simulated hydraulic grade and field-observed hydraulic grade and a calculated flow difference between model-simulated flow and field-observed flow;

repeating the generating, performing and comparing to attempt to minimize the head difference and the flow difference, until a particular goodness-of-fit value is achieved or a maximum number of iterations is reached; and

displaying, in a user interface of the computer system, predicted locations for leakage nodes from a particular selected trial solution.

2. The method of claim 1 , wherein the hydraulic network is a water distribution system.

3. The method of claim 1 , wherein the specified maximum of possible leakage nodes is specified by a user interacting with the user interface of the computer system.

4. The method of claim 1 , wherein optimization criteria further comprise maximum emitter coefficients allowed for nodes.

5. The method of claim 1 wherein the displaying also displays emitter coefficients corresponding to the leakage nodes, a larger emitter coefficient indicating more leakage at a node.

6. The method of claim 1 , wherein the steps of generating, performing, comparing, and repeating collectively operate to search for

{right arrow over (X)}=(LN i n K i n ); LN i n εJ n ; n=1, . . . , NGroup; i=1, . . . , NLeak″

that minimizes

F ( {right arrow over (X)} )

subject to

0

≤

K

i

n

≤

K

n

_

P

i

n

>

0

∑

n

=

1

NGroup

⁢

NL

dup

n

=

0

where LN i n is a leakage node index for leakage node i within a demand group n, K i n is an emitter coefficient for leakage node i in group n, J n is a set of nodes within node group n, NGroup is a number of node groups, NLeak n is a number of specified leakage nodes to be identified for node group n, K n is a maximum emitter coefficient for group n, P i n is a pressure head at detected leakage node i within group n, NL dup n is a number of duplicated nodes that are identified as leakage emitters in one solution for group n, and F({right arrow over (X)}) is an objective function that evaluates goodness-of-fit.

7. The method of claim 6 , wherein F({right arrow over (X)}) operates to minimize a sum of difference squares, minimize a sum of absolute differences or minimize a maximum absolute difference.

8. An apparatus for leakage detection in a hydraulic network, comprising:

a processor; and

a memory configured to store computer-executable instructions for execution on the processor, the computer-executable instructions implementing,

a hydraulic simulation model corresponding to a real-world hydraulic network, the hydraulic simulation model configured to represent leakages as pressure dependent emitter flow at selected nodes identified as leakage nodes,

a user interface configured to receive optimization criteria including a specified maximum of possible leakage nodes,

a genetic algorithm (GA)-based optimizer configured to generate a plurality of trial solutions for an optimization, each trial solution representing locations for leakage nodes and corresponding emitter coefficients, and

an evaluation module configured to perform a hydraulic analysis for the trial solutions to generate model-simulated results, and compare the model-simulated results to field-observed data for the real-world hydraulic network and generate goodness-of-fit values, each goodness-of-fit value based on a combination of a calculated head difference between model-simulated hydraulic grade and field-observed hydraulic grade and a calculated flow difference between model-simulated flow and field-observed flow, and in response to one or more of the goodness-of-fit values, to select a particular trial solution as a solution.

9. The apparatus of claim 8 , wherein the user interface is further configured to display predicted locations for leakage nodes from the particular selected trial solution.

10. The apparatus of claim 9 , wherein the user interface is further configured to display emitter coefficients corresponding to the leakage nodes of the particular selected trial solution, a larger emitter coefficient indicating more leakage at a node.

11. The apparatus of claim 8 , wherein the hydraulic network is a water distribution system.

12. The apparatus of claim 8 , wherein the user interface is configured to receive from a user the specified maximum of possible leakage nodes.

13. The apparatus of claim 8 , wherein the optimization criteria further comprise maximum emitter coefficients allowed for nodes.

14. A non-transitory computer-readable storage medium including instructions executable by a processor, the instructions when executed by the processor to:

load a hydraulic simulation model corresponding to a real-world hydraulic network in a hydraulic modeling and simulation application executing on a computer system, the hydraulic simulation model configured to represent leakages as pressure dependent emitter flow at selected nodes identified as leakage nodes;

receive optimization criteria;

generate, by a genetic algorithm (GA), a plurality of trial solutions for an optimization, each trial solution representing locations for leakage nodes and corresponding emitter coefficients;

perform a hydraulic analysis for the trial solutions to generate model-simulated results;

compare the model-simulated results to other data and generate goodness-of-fit values based on a combination of a calculated head difference and a calculated flow difference between model-simulated results and the field-observed data;

repeat the instructions to generate, perform, and compare to attempt to minimize the head difference and the flow difference, until a particular goodness-of-fit value is achieved or a maximum number of iterations is reached; and

display predicted locations for leakage nodes from a particular selected trial solution.

15. The non-transitory computer-readable storage medium of claim 14 , wherein the hydraulic network is a water distribution system.

16. The non-transitory computer-readable storage medium of claim 14 , wherein the optimization criteria comprise a specified maximum of possible leakage nodes.

17. The non-transitory computer-readable storage medium of claim 16 , wherein the optimization criteria further comprise maximum emitter coefficients allowed for nodes.