DETECTION OF A LEAKAGE IN A SUPPLY GRID

Abstract

A method for detecting a leakage of fluid in a supply grid is disclosed. The method includes measuring a flow of the fluid at first locations by first sensors; predicting a flow at a second location by a self-learning system based on the measured flows, wherein the self-learning system has been trained to predict the flow at a specified location in a supply grid; measuring an actual flow of the fluid at the second location by a second sensor located at the second location; ascertaining a difference between the actual flow measured at the second location and the flow at the second location predicted by the trained system; and outputting a notification of an assumed leakage when the ascertained difference is greater than a specified threshold. Devices and assemblies for detecting a leakage of fluid in a supply grid are also disclosed.

Claims

1. A method for detecting a leak of fluid in a supply network, wherein the supply network has pipes through which the fluid is configured to flow, first sensors for measuring flow rates of the fluid at first locations in the supply network, and at least one second sensor for measuring a flow rate of the fluid at a second location in the supply network, the method comprising: a) measuring the flow rates of the fluid at the first locations by the first sensors; b) predicting the flow rate at the second location by a self-learning system based on values of the measured flow rates at the first locations, wherein the self-learning system has been trained to predict a flow rate at a predefined location in the supply network; c) measuring an actual flow rate of the fluid at the second location by the second sensor at the second location; d) determining a difference between the actual flow rate measured at the second location and the flow rate at the second location predicted by the trained system; and e) outputting a message of a suspected leak when the determined difference is greater than a predefined limit value.

2. The method of claim 1, wherein the self-learning system has been trained by: i) measuring flow rates of the fluid at the first locations in the supply network by the first sensors; ii) determining a flow rate at the second location by the self-learning system based on values of the flow rates at the first locations measured in act i); iii) determining a difference between the flow rate determined in act ii) and a target value; iv) adapting the self-learning system taking into account the difference determined in act iii); and v) repeating acts i) to iv) until a predefined abort criterion is achieved.

3. The method of claim 2, wherein the training of the self-learning system according to acts i) to v) and the detection of the leak according to acts a) to e) are carried out for a plurality of different second locations.

4. The method of claim 3, wherein steps acts i) to v) are repeated with the proviso that, instead of the first sensors at the first locations, the second sensors at the second locations are used and vice versa, and wherein acts a) to e) are repeated with the proviso that, instead of the first sensors at the first locations, the second sensors at the second locations are used and vice versa.

5. The method of claim 2, wherein the abort criterion comprises an average difference between the flow rate at the second location determined in act ii) and the target value falling below a predefined threshold value.

6. The method of claim 2, wherein the target value is the flow rate of the fluid through the pipe at the second location as measured by the second sensor.

7. The method of claim 2, wherein the target value is determined by a simulation.

8. The method of claim 7, wherein the simulation uses, as input data, a topology of the supply network, locations and types of consumers, and an equivalent consumption profile for each consumer.

9. The method of claim 8, wherein the input data are reduced by a series expansion before determining the target value.

10. The method of claim 1, wherein the first sensors are positioned at the first locations in the supply network in particular, in such a manner that measured values of the first sensors do not correlate with one another.

11. The method of claim 1, wherein the fluid is water, and wherein the supply network is a drinking water supply network or a wastewater network.

12. The method of claim 1, wherein the fluid is a gas, and wherein the supply network is a gas or district heating supply network.

13. An apparatus for detecting a leak of fluid in a supply network having pipes configured for having a fluid flow through the pipes, first sensors for measuring flow rates of the fluid at first locations in the supply network, and at least one second sensor for measuring a flow rate of the fluid at a second location in the supply network, the apparatus comprising: a self-learning system trained to predict a flow rate at a predefined location in the supply network; a first capture unit for capturing the flow rates of the fluid at the first locations in the supply network, as measured by the first sensors; a prediction unit for predicting a flow rate at the second location by the trained system based on values of the flow rates at the first locations, as captured by the first capture unit; a second capture unit for capturing an actual flow rate of the fluid at the second location by the second sensor at the second location; a determination unit for determining a difference between the actual flow rate measured at the second location and the flow rate at the second location predicted by the trained system; and an output unit for outputting a message of a suspected leak when the determined difference is greater than a predefined limit value.

14. An arrangement comprising: a supply network having: pipes configured for having a fluid flow through the pipes; first sensors for measuring flow rates of a fluid at first locations in the supply network; and at least one second sensor for measuring a flow rate of the fluid at a second location in the supply network; and an apparatus for detecting a leak of the fluid, the apparatus comprising: a self-learning system trained to predict a flow rate at a predefined location in the supply network; a first capture unit for capturing the flow rates of the fluid at the first locations in the supply network, as measured by the first sensors; a prediction unit for predicting a flow rate at the second location by the trained system based on values of the flow rates at the first locations, as captured by the first capture unit; a second capture unit for capturing the flow rate of the fluid at the second location by the second sensor at the second location; a determination unit for determining a difference between the flow rate measured at the second location and the flow rate at the second location predicted by the trained system; and an output unit for outputting a message of a suspected leak when the determined difference is greater than a predefined limit value.

15. The method as claimed in of claim 9, wherein the series expansion is a principal component analysis.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The disclosure is described below based on the exemplary embodiments with reference to the attached drawings, in which:

[0056] FIG. 1 illustrates an example of a supply network connected to a plurality of different consumers.

[0057] FIG. 2 illustrates a first exemplary embodiment of an apparatus for detecting a leak of fluid in a supply network.

[0058] FIG. 3 illustrates a second exemplary embodiment of an apparatus for detecting a leak of fluid in a supply network.

DETAILED DESCRIPTION

[0059] FIG. 1 illustrates, by way of example and schematically, a supply network 10 for supplying a number of consumers with drinking water. It is therefore a drinking water supply network. The disclosure is not restricted to drinking water supply networks, but rather may likewise be applied to other types of supply networks.

[0060] FIG. 1 shows a District Metering Area (DMA) which is part of a superordinate drinking water supply network. The supply network 10 shown in FIG. 1 has only a single inflow 13 and no outflows. The supply network 10 includes a number of pipes 11, wherein three or four pipes 11 respectively meet at a plurality of nodes 12 of the supply network 10. For better clarity, not all pipes 11 and nodes 12 present are referenced using reference signs in FIG. 1.

[0061] FIG. 1 also shows, by way of example, some consumers connected to the drinking water supply network 10. The consumers are divided into different categories; a plurality of houses 21, an apartment building 22, and a factory 23 are shown, by way of example, in FIG. 1. In reality, at least several dozen consumers, several hundred consumers, or several thousand consumers may be connected to a supply network in a DMA. For better clarity, only very few consumers connected to the supply network 10 are shown, by way of example, in FIG. 1.

[0062] For improved illustration of the disclosure, the topology of the supply network 10, (e.g., the number and branches of the pipes 11 and the number and type of consumers connected to the supply network 10), are therefore illustrated in a highly simplified manner in FIG. 1.

[0063] The supply network 10 shown does not have any (explicit) outflows. Nevertheless, an outflow of drinking water from the supply network 10 takes place by the consumers. However, the exact respective consumptions of the consumers are not known for practical and data protection reasons.

[0064] The supply network 10 also has three first sensors 14. These first sensors 14 are in the form of flowmeters and may measure the flow rate of the drinking water through the pipes 11 at the respective locations in the supply network 10 at which the first sensors 14 are situated. The locations at which the first sensors 14 are situated and for which the respective flow rate is measured are referred to as first locations.

[0065] The supply network 10 also has a further sensor referred to as a second sensor 15. The second sensor 15 is situated at a so-called second location in the supply network 10 and is able to measure the flow rate at this second location.

[0066] The object on which the disclosure is based is now to identify any leak in the supply network 10 during operation of the supply network 10. In other words, the intention is therefore to detect any leak in the supply network 10.

[0067] For this purpose, the disclosure uses a corresponding apparatus 30. A first exemplary embodiment of such an apparatus 30 for detecting a leak of fluid in a supply network 10 is shown in FIG. 2. In contrast, FIG. 3 shows a slightly modified exemplary embodiment of such an apparatus 30. The two exemplary embodiments differ substantially in the use of a different target value when training the self-learning system SS.

[0068] FIG. 2 first of all shows the same supply network 10 as that in FIG. 1. In order to avoid repetitions, reference is made to FIG. 1 for the description of the supply network 10 and of the consumers connected to the latter.

[0069] In addition to the supply network 10 and the consumers connected to the latter, FIG. 2 also shows an apparatus 30 for detecting a leak of fluid in the supply network 10. For this purpose, the three first sensors 14 are connected to a first capture unit E1. The first capture unit E1 is configured to capture and forward the flow rates measured by the first sensors 14. FIG. 2 also shows a second capture unit E2. In a similar manner to the first capture unit E1, this second capture unit is configured to capture the flow rate measured by the second sensor 15 at the second location in the supply network 10 and, as soon as required in the process, to forward it to a corresponding location.

[0070] During the training phase, the self-learning system SS is connected to the first capture unit E1 and to the second capture unit E2 in the first exemplary embodiment shown in FIG. 2. These connections are indicated as dashed lines in FIG. 1. The first capture unit E1 provides the input data, specifically the measured flow rates at the first locations in the supply network 10. On the basis of this, the task of the self-learning system SS is to predict or determine an (expected) flow rate at another location in the supply network. This other location is the second location which has already been mentioned, specifically the location at which the second sensor 15 is situated. At the beginning of the training phase, the flow rate at the second location determined by the self-learning system SS may not yet correspond to the actual flow rate at this location. In order to train, that is to say improve, the self-learning system SS, the concept of supervised learning is used. For this purpose, the flow rate value determined by the self-learning system SS is compared with a target value. In the present exemplary embodiment, this target value is the flow rate actually measured at the second location. The flow rate at this location is advantageously measured using the second sensor 15 which is needed anyway to carry out the method during operation of the supply network 10.

[0071] The flow rate measured by the second sensor 15 is captured by the second capture unit E2 and is forwarded to the self-learning system SS. The flow rate value measured by the second sensor 15 is then compared in the self-learning system with the previously determined/predicted value. If the correspondence is too low—which might be the norm, as indicated above, in particular at the beginning of training—new flow rate values are measured by the first sensors 14. For these new flow rate values, the self-learning system SS attempts to predict the actual flow rate value at the second location as correctly as possible.

[0072] It is advantageous if the flow rates measured by the first sensors 14 in the second run differ from the measured flow rates in the first run. This is because, if the measured flow rates at the first locations are very similar or even identical, the self-learning system SS will correctly predict the flow rate at the second location without any major problems in the second run on the basis of what has been learned from the first run. However, during operation, the self-learning system SS is able to correctly predict the flow rate at the second location for a wide variety of flow rates at the first locations.

[0073] The described acts of a run (or: iteration) are therefore: measuring the flow rates at the first locations; predicting or determining the flow rate at the second location; and comparing the predicted flow rate with the actually measured flow rate.

[0074] A sufficient number of runs are carried out to allow the predefined abort criterion to be satisfied. The abort criterion may involve the difference between the flow rate at the second location determined by the self-learning system SS and the actually present flow rate (e.g., measured by the second sensor 15 at the second location) in each case being less than 5%, less than 2%, or less than 1%, for ten successive runs.

[0075] It is advantageous to also link this abort criterion mentioned by way of example to a further condition, specifically the fact that the flow rates measured at the first locations cover a wide range of values for the ten successive runs, for example. This means that there is a difference of at least 100% between the smallest measured flow rate of each first sensor 14 and the largest measured flow rate of the same first sensor 14, for example. More complex requirements are naturally also conceivable in order to provide wide coverage of the first flow rates in different runs.

[0076] The training phase is followed by operation, also referred to as a use phase, of the supply network 10. In this case, the flow rates at the first locations in the supply network are measured again. This is carried out by the first sensors 14. The measured flow rate values are captured by the first capture unit E1 and are forwarded to the self-learning system SS which is also referred to as a “trained system” SS within the scope of this patent application after the completion of the training phase. The forwarding of the flow rates captured by the first capture unit E1 to the trained system is indicated in FIG. 1 with a solid line—in order to distinguish it from the dashed connection during the training phase.

[0077] On the basis of the measured flow rates at the first locations, the trained system SS now predicts the expected flow rate at the second location in the supply network 10. This prediction is made by a prediction unit V. The predicted flow rate value is then compared with the actually present flow rate value at the second location—the latter was measured using the second sensor 15 and was captured by the second capture unit E2. The comparison between the predicted flow rate value and the actually present flow rate value is carried out by the determination unit B. This is simply a subtraction of the smaller of the two values from the larger of the two values. The difference determined in this manner is then forwarded to an output unit. The latter outputs a message if the difference is greater than a predefined limit value G.

[0078] The output of a corresponding message is an indication to the operator of the supply network 10 that there is a leak in the supply network 10, in particular in the region of the second location at which the predicted flow rate differed from the measured flow rate. However, conditions for the validity of this conclusion are that the topology of the supply network and the number and type of consumers connected to the latter have not changed after the completion of the training phase and that the self-learning system SS was trained reliably and robustly.

[0079] If the supply network 10 has a plurality of second locations with second sensors 15 and if the predicted flow rates are compared with the measured flow rates at a plurality of second locations, the potential leak cannot only be identified but also located—within certain limits—on the basis of a difference between the predicted flow rate and the measured flow rate at precisely one second sensor (or at least a subset of second sensors). However, detailed localization using other methods is then necessary in most cases.

[0080] FIG. 3 shows a second exemplary embodiment of an apparatus 30 for detecting a leak of fluid in a supply network 10. It differs from the apparatus 30 of the first exemplary embodiment in terms of the training of the self-learning system SS.

[0081] Specifically, in the second exemplary embodiment, only the measured flow rates at the first locations are initially forwarded from the first capture unit E1 to the self-learning system SS during the training phase. The self-learning system again predicts or determines an expected flow rate at the second location in the supply network 10. However, this expected flow rate is not directly compared with the flow rate measured at the second location in act iii) of the method, but rather with a simulated flow rate at the second location. It is very important for successful training of the self-learning system SS that the simulated flow rate at the second location is trustworthy, that is to say correct, because it constitutes the target value used to train the self-learning system SS. If the target value does not correspond to reality, the trained system SS logically also cannot correctly represent or predict reality.

[0082] In the present case of a drinking water supply network, the simulation SIM is a hydraulic simulation. For this purpose, the flow rates and further parameters (e.g., pressures, flow velocities, etc.) in the supply network are simulated analytically or in a model-based manner on the basis of fluid mechanics. The challenge of a hydraulic simulation SIM may be the fact that it quickly becomes very complex even for supply networks with a relatively simple topology. In addition, a series of input data IN may be required for the hydraulic simulation SIM. These data include the topology, (e.g., the arrangement and course of the pipes 11 and nodes 12), the flow rate at the inflow 13 into the supply network 10, the arrangement and type of consumers, equivalent consumption profiles of the individual consumer types (e.g., typical or representative consumption profiles for the individual consumer types); properties of the pipes, (e.g., coefficients of friction or internal diameters).

[0083] Based on the measured flow rates at the first locations and the available input data IN, the hydraulic simulation SIM simulates the expected flow rate at the second location and passes it to the self-learning system SS. The simulated flow rate at the second location acts as a target value for the self-learning system SS and as a measure of how well the self-learning system SS has already been trained.

[0084] The use phase, or operation, of the supply network takes place in the second exemplary embodiment in an identical manner to the first exemplary embodiment, which is why reference is made to the description thereof above.

[0085] In summary, it may be stated that the disclosure provides a method, an apparatus, and an arrangement which may be used to identify and, under certain circumstances, also locate a leak of fluid in a supply network in a simple manner with the aid of a self-learning system.

[0086] It is to be understood that the elements and features recited in the appended claims may be combined in different ways to produce new claims that likewise fall within the scope of the present disclosure. Thus, whereas the dependent claims appended below depend from only a single independent or dependent claim, it is to be understood that these dependent claims may, alternatively, be made to depend in the alternative from any preceding or following claim, whether independent or dependent, and that such new combinations are to be understood as forming a part of the present specification.

[0087] Although the disclosure has been illustrated and described in detail by the exemplary embodiments, the disclosure is not restricted by the examples disclosed and other variations may be derived therefrom by a person skilled in the art without departing from the protective scope of the disclosure.