Method and apparatus for operating a distribution network

Abstract

A method for operating a distribution network having a specific network topology using N sensors sensing wavefront characteristics is proposed. Each of the N sensors is located at a specific measurement location in the distribution network. The method comprises the following steps a), b) and c): a) using at least a subset of the N sensors to obtain transient event parameter values ​​for specific transient events in the distribution network, b) determining a risk factor indicating a hazard in the distribution network depending on the obtained transient event parameter value and an actual value of a network parameter indicating the distribution network, and c) performing one of a plurality of actions at the distribution network depending on the determined risk factor. Furthermore, a computer program product, an apparatus and a distribution network are proposed.

Description

Technical Field

[0001] The invention relates to a method for operating a distribution network having a specific network topology using N sensors for sensing wavefront characteristics, each of the N sensors being located at a specific measurement location in the distribution network. The invention also relates to a computer program product and an apparatus for operating a distribution network having a specific network topology. Furthermore, the invention relates to a distribution network comprising a plurality of pipe sections for distributing a specific load and such an apparatus for operating the distribution network. Background Art

[0002] Distribution networks, such as water or heating networks, may fail for a variety of reasons. For example, failure of a particular segment of a distribution network may result in interruption of supply to consumers or damage to infrastructure, such as roads and other property. Therefore, there is a need to minimize the risk of failures in distribution networks, along with the task of minimizing the time required to properly detect, locate and isolate a damaged segment in a distribution network.

[0003] Conventional models and methods for assessing the risk of failure of pipe sections in distribution networks are known from references [1] to [4]. Summary of the invention

[0004] The present invention aims to enhance the operation of a distribution network.

[0005] According to a first aspect, a method for operating a distribution network having a specific network topology using N sensors for sensing wavefront characteristics is proposed. Each of the N sensors is located at a specific measurement position in the distribution network. The method comprises the following steps a), b) and c):

[0006] a) obtaining a transient event parameter value for a specific transient event in the distribution network using at least a subset of the N sensors,

[0007] b) determining a risk factor indicative of a hazard in the distribution network depending on the obtained value of the transient event parameter and an actual value of a network parameter indicative of the distribution network, and

[0008] c) performing one of a plurality of actions at the distribution network depending on the determined risk factor.

[0009] For example, the distribution network is a water distribution network, an oil distribution network, a natural gas distribution network or a heat distribution network.A distribution network may also be referred to as a network.

[0010] The hazard may be a real or actual damage to the distribution network, such as pipe congestion or pipe burst, or a high probability of damage to a pipe or pipe section in the distribution network. A pipe section may also be called a segment.

[0011] Examples of transient events include leaks, valve openings, valve closings, pump starts, or pump stops. Additionally, examples of transient event parameters include the magnitude of a particular transient event, the location of a particular transient event in the distribution network, and / or the event type of a particular transient event in the distribution network. Event types may also be referred to as types.

[0012] Depending on the magnitude of the transient event, the wavefront caused by the transient event may be detected by a specific subset of sensors or by all sensors in the distribution network. Thus, the subset of sensors is able to obtain the transient event parameter value for the specific transient event. In other words, different transient events may be detected by different subsets of sensors located at different measurement locations in the distribution network.

[0013] In particular, the topology of the distribution network is known a priori, in particular before performing the method comprising steps a) to c). In particular, information indicative of the network topology as part of the network parameters is stored in a memory, wherein said information can be used in said step b) of the method.

[0014] Furthermore, the network parameters may include information about the age of the pipes, the material of the pipes and the average and maximum static loads in the distribution network. Furthermore, the network parameters may include recorded or historical data of the distribution network.

[0015] Performing one of the actions according to step c) may include providing a replacement recommendation for replacing the pipe or pipe component or providing an alarm to an operator or service personnel of the distribution network. Furthermore, performing a specific action may include recording a determined risk factor or a plurality of determined risk factors. Furthermore, the recorded determined risk factors may be used to generate or trigger a replacement recommendation or an alarm.

[0016] Additionally, specific actions may include providing an operator or service personnel of the distribution network with a visual representation of the distribution network, such as a heat map representing the distribution network and indicating potential hazards and identified risk factors.

[0017] By using not only actual values ​​of network parameters but also obtained transient event parameter values, risk factors for hazards in the distribution network can be determined with greater accuracy. Determining risk factors with greater accuracy leads to reduced maintenance costs for the distribution network and enhanced planning of network repair strategies.

[0018] In the following, several embodiments of a method for operating a distribution network are described.

[0019] According to an embodiment, the risk factor is determined depending on the obtained transient event parameter value, the actual value of the network parameter, load related factors of the load distributed by the distribution network and external factors describing the environment of the distribution network.

[0020] According to a further embodiment, the transient event parameters comprise the amplitude of the specific transient event, in particular at a specific measurement location or certain measurement locations, the location of the specific transient event in the distribution network and / or the event type of the specific transient event in the distribution network.

[0021] According to a further embodiment, step a) comprises:

[0022] Transient event parameter values ​​are obtained for a plurality of specific transient events in the distribution network using at least the subset of the N sensors.

[0023] According to a further embodiment, step a) comprises:

[0024] using at least said subset of the N sensors to sense wavefront characteristics caused by a particular transient event in the distribution network,

[0025] collecting signals from the subset of N sensors, the signals being indicative of sensed wavefront properties, and

[0026] The collected signals are processed to obtain the transient event parameters.

[0027] According to a further embodiment, step b) comprises:

[0028] providing a model for modeling hazards in a distribution network, the model being time dependent and using a set of parameters representing at least network parameters and transient event parameters,

[0029] modeling the actual hazard by applying to the provided model at least the determined actual value of the obtained transient event parameter value and the actual values ​​of the network parameters of the parameter set, and

[0030] The risk factor is determined depending on the actual hazard modeled.

[0031] According to a further embodiment, step b) comprises:

[0032] Divide the distribution network into pipe segments,

[0033] assigning each of the pipe segments to one of a plurality of M groups according to at least one classification parameter, where M ≥ 2, and

[0034] For each of the M groups, perform the following sub-steps i) to iii):

[0035] i) providing a model for modeling hazards in a pipe section of the group, said model being time dependent and using a set of parameters representing at least network parameters and transient event parameters,

[0036] ii) modeling the actual hazard by applying the determined actual values ​​(including at least the obtained transient event parameter values) and the actual values ​​of the network parameters of the parameter set to the provided model, and

[0037] iii) Determine the risk factor for the group of pipe segments depending on the actual hazard modelled.

[0038] According to a further embodiment, the pipe segments are separated from each other and each of the pipe segments is homogeneous at least with respect to pipe material and pipe diameter.

[0039] According to a further embodiment, the model used to model the actual risk uses Cox regression.

[0040] According to a further embodiment, in Cox regression, the following formula for modeling the time-dependent and parameter set-dependent actual hazard h(t,x) is used:

[0041] h(t,x)=h0(t)exp{β T x}... (1)

[0042] In this formula, t specifies the time, x specifies the parameter set as a vector, h0(t) specifies the baseline hazard due to time t, and β specifies the coefficient vector of the parameter set.

[0043] According to a further embodiment, each of the sensors is a high-speed pressure sensor. For example, each of the sensors has a sampling rate of 100 Hz or higher, in particular 200 Hz or higher.

[0044] According to a further embodiment, step b) of determining the risk factor is performed by a central processor unit of the distribution network.

[0045] According to a further embodiment, the method comprises the step of determining the location of a specific transient event in the distribution network depending on differences in detected arrival times of wavefronts detected by sensors located at said subset of measurement locations and the positioning of the measurement locations.

[0046] In particular, the location of the measurement position is part of an a priori known network topology. Therefore, before performing the above method steps, the location of the measurement position is known.

[0047] According to a further embodiment, for each possible sensor pairing of the N sensors, a time shift between the arrival times at the two sensors of the sensor pairing is determined by cross-correlating the wavefronts received at the two sensors.

[0048] According to a further embodiment, using the determined locations of transient events in the distribution network, using the detected arrival times of the wavefronts detected by sensors located at said subset of measurement locations and using the specific network topology of the distribution network, the event type of the transient events in the distribution network is classified as leak, valve opening, valve closing, pump start or pump stop.

[0049] According to a further embodiment, the risk factor is determined depending on actual values ​​of network parameters, actual values ​​of load related factors of load distributed by the distribution network including the obtained transient event parameter value and external factors describing the environment of the distribution network.

[0050] The network parameters may include meta-information including, among other things, segment age, segment material, degree of corrosion, and the like.

[0051] Load-related factors may include pressure transients and cumulative flow rates through the corresponding segments. Additionally, external factors may include soil conditions and installation properties. For example, installation properties may describe an installation under street conditions or in heavy traffic area conditions.

[0052] The above factors can also be called covariates. Covariates can be continuous, discrete or categorical. It can be noted that covariates can be converted into discrete variables or categorical variables by using ranges.

[0053] According to a further embodiment, step b) comprises:

[0054] providing a model for modeling hazards in a distribution network, the model being time dependent and using a factor vector representing network parameters, load-related factors including transient event parameters, and external factors,

[0055] modeling the actual hazard by applying the determined actual values ​​(including at least the obtained values ​​of the load-related factors, the load-related factors including the transient event parameters), the actual values ​​of the network parameters and the actual values ​​of the external factors of the factor vector to the provided model, and

[0056] The risk factor is determined depending on the actual hazard modeled.

[0057] According to a further embodiment, step b) comprises:

[0058] Divide the distribution network into Interconnected pipe segments in by The i-th pipe segment is specified by , wherein the pipe segments are separated from each other and each of the pipe segments is homogeneous at least in terms of pipe material and pipe diameter.

[0059] Such factors may also be referred to as covariates.

[0060] According to a further embodiment, to model the life of a pipe segment, the formula

[0061]

[0062] For matching pipe sections The actual danger associated with the time Modeling is performed, where is the factor vector and t is the segment to be evaluated is the actual dangerous time, β is the coefficient vector of the model, is the function used to calculate the actual hazard which depends on the model used (eg Cox regression model or logistic regression model).

[0063] According to a further embodiment, the model for modeling the actual risk uses a Cox regression model, wherein in Cox regression, the formula

[0064]

[0065] where h o (t) specifies the baseline hazard due to time t.

[0066] The Cox regression model is a semi-parametric model. In particular, it quantifies the baseline hazard, which is the hazard caused only by the aging of the component (here, the pipe segment) in a non-parametric way. It then uses a parametric approach to speed up or slow down the hazard curve based on these factors.

[0067] According to a further embodiment, the model for modeling the actual risk uses a logistic regression model, wherein in the logistic regression model, the formula

[0068]

[0069] in Specify pipe segment probability of failure.

[0070] Logistic regression is a sigmoid function whose range is restricted to the range [0,1]. Therefore, it is suitable for modeling the failure probability of a component (here, a pipe section). It is mentioned that in this model, time is also considered as a factor vector (additional covariates) and have corresponding coefficients in the vector β. As can be seen from the equation above, this method is a parametric method whose output can be learned for historical data via the expectation maximization (EM) method or the iterative reweighted least squares (IRLS) method.

[0071] According to a further embodiment, the coefficient vector β is learned based on historical data. In particular, learning the hazard model means using historical data to infer the coefficient vector β. For factors such as pipe diameter and length, they are easy to incorporate into the model, that is, they only affect their corresponding segments and have no effect on other segments. However, transient events do affect other segments - relative to the segment on which the transient event occurs. This is because these events generate pressure transients that propagate through the network and are reflected by some components of the network (such as valves and / or fittings). Therefore, when a transient event occurs, it may affect other parts of the network.

[0072] According to a further embodiment, the model is adapted to model reflections of transient events in the distribution network and / or the factor vector comprises factors associated with different types of pressure transients. In the following, the example of pressure transients will be discussed in detail as an example of transient events:

[0073] Pressure transients cause fatigue, a phenomenon whereby failures of pipe segments are accelerated by pressure transients due to a variety of reasons, such as bursts, valve operations (opening and / or closing), pump operations (starting and / or shutting down), etc. Regardless of why the transients occur, they propagate through the pipe segments connected to the segment where the event occurred. This affects not only the segment on which the event occurred, but also other connected segments with different levels of impact due to the attenuation of the transient wave amplitude as it propagates through the network.

[0074] In addition to the propagation of the pressure transient waves, they are also reflected by some parts of the network, such as valves and fittings. These reflections therefore have an impact on the danger that is developing on the network. In addition, these reflections also pass through the connected segments and are reflected again. For reasons of ease of handling, only first-order reflections are considered below, that is, only the propagation of the original pressure transient and the propagation of the first-order reflections are considered. However, it is possible to relax the method to include more reflection stages.

[0075] First, impact specific segments of interest Segments and reflective surfaces is defined as:

[0076]

[0077] in:

[0078] -Is the impact attention segment The set of all segments and reflecting surfaces of

[0079] - is the set of all segments where events occurring will affect the segment of interest and

[0080] -Is the impact attention segment The set of all reflective surfaces

[0081] Depends on the segment of interest The distance between the segments and the rest of the network. In addition, depends on the topology of the network, since the topology gives the locations of these reflective surfaces relative to the segment of interest. For example, find One way is to consider the Segments and reflective surfaces whose corresponding distances are less than a predefined threshold.

[0082] In order to combine the pressure transient to the segment of interest First, pressure transients are divided into l types. For example, these types can include:

[0083] i. Segment burst

[0084] ii. Valve operation (open / close)

[0085] iii. Pump operation (start / stop)

[0086] Then, in addition to the above factors, the factor vector including factors related to pressure transients,

[0087] Right now

[0088] in:

[0089] - is to indicate the pressure transient as affecting the segment of interest A set of factor vectors of covariates,

[0090] -Concern segment The ξ-th factor of the ξ-th type of pressure transient on , ξ={1,2,...l}.

[0091] Therefore, in order to combine different types of pressure transients for the segment of interest The influence of the hazard curve can be considered that the influence of transients decays with the travel distance. To model this, a radial basis function (RBF) can be used.

[0092] Therefore, it is recommended to use the following formula to evaluate Elements in

[0093]

[0094] in:

[0095] - is the amplitude of the ith pressure transient wave of the ξth type,

[0096] - is a description of the segment of interest The upper segment The propagated pressure transient occurs at The effect of the attenuation of the RBF,

[0097] - is a description of the segment from which you are interested The surface on The reflected pressure transient ) of the attenuated RBF.

[0098] It may be mentioned that once a wave reaches a reflecting surface, reflection occurs on the wave. That is, when the ξ-th type event with amplitude Occurs in segment At this point, it first propagates to the reflecting surface At the same time, it decays to Then it reflects off the surface Reflect back to the segment of interest According to the reflection RBF attenuation.

[0099] A variety of RBFs can be used, one example being the Gaussian RBF, and the corresponding RBF used in equation (7) is defined in equations (8), (9) and (10).

[0100]

[0101]

[0102]

[0103] in:

[0104] - is through the network from the event location To the attention section The distance traveled by the center coordinates of

[0105] - is through the network from the event location To reflective surface The travel distance,

[0106] - is through the network from the reflective surface To the attention section The distance traveled by the center coordinates of

[0107] - is the width parameter that controls the decay rate of the propagating ξ-th type of pressure transient,

[0108] - is the width parameter which controls the decay rate of the reflected ξ-th type pressure transient.

[0109] It should be mentioned that for long segments, virtual segmentation can be used to obtain more accurate and high-resolution results. More specifically, long segments can be segmented into multiple shorter segments, making the travel distance metric more suitable for equations (8), (9) and (10).

[0110] Equation (7) contains the width parameter These can be estimated based on field experiments. It is worth mentioning that equations (8), (9) and (10) describe one type of RBF, namely Gaussian RBF, however, other types such as thin plate spline (TPS-RBF) can also be used to give examples.

[0111] Width parameter RBF ) results, the output value O is as follows Figure 7 As shown in Figure 7 In , the distance on the x-axis is measured in both directions relative to the event position. In addition, Figure 7 , the Curve VI specifies Curve V2 designation Curve V3 Assignment And curve V4 specifies

[0112] Furthermore, as an illustrative example, let us assume a hazard model with the following covariates:

[0113] 1. Segment material type (covariate x1).

[0114] 2. Segment diameter (covariate x2).

[0115] 3. Segment length (covariate x3).

[0116] 4. Valve operation transient (covariate x4).

[0117] 5. Pump operation transient (covariate x5).

[0118] 6. Burst transient (covariate x6).

[0119] In this example, three types of pressure transients are considered, i.e. l ​​= 3. The total number of covariates is 6, so the segment The covariate vector is Make does not include time, and Including time, defined as follows:

[0120]

[0121]

[0122] In equations (11) and (12), the covariates associated with pressure transients can be related to equation (7) as follows:

[0123]

[0124] Another measure of failure rate may be of interest, such as survival rate and failure probability.

[0125] These can be obtained from the hazard rate as follows:

[0126]

[0127] Furthermore, the probability of failure can be calculated as follows:

[0128]

[0129] in:

[0130] -It's a paragraph The probability of a failure occurring before time t.

[0131] The present approach described thus far quantifies the risk associated with a pipe segment, and therefore with a network.

[0132] Recommendations are provided below regarding segment replacement in the network. This raises the question of whether to place a segment and what the probability of failure is.

[0133] Since risk can be quantified in a probabilistic manner, replacement recommendations can be considered while being mindful of the uncertainty of failure.

[0134] A decision-making tool under uncertainty is proposed. In Table (1), the decision cost matrix is ​​shown. That is, two actions can be taken (replace, do not replace), and two results can occur (failure, no failure). This leads to 4 possible scenarios.

[0135] Fault No trouble Expected costs replace <![CDATA[A 11 (t)]]> <![CDATA[A 12 (t)]]> <![CDATA[p1(t)A 11 (t)+p2(t)A 12 (t)]]> No replacement <![CDATA[A 21 (t)]]> <![CDATA[A 22 (t)]]> <![CDATA[p1(t)A 21 (t)+p2(t)A 22 (t)]]> Probability <![CDATA[p1(t)]]> <![CDATA[p2(t)]]>

[0136] Table 1: Decision cost matrix

[0137] in:

[0138] A 11(t) - is the cost of taking the action of replacing the pipe section at time t, and it actually fails at t

[0139] A 12 (t) - is the cost of taking the action of replacing the pipe section at time t, and it actually does not fail at t

[0140] A 21 (t) - is the cost of taking the action of not replacing the pipe segment at time t, and it actually fails at t

[0141] A 22 (t) - is the cost of taking the action of not replacing the pipe section at time t, and no failure actually occurred at t

[0142] p1(t) - probability of failure of a pipe segment at time t

[0143] p2(t) - the probability that the pipe segment will not fail at time t

[0144] The expected cost of each action (replacement or no-replacement) is calculated as follows:

[0145]

[0146] in:

[0147] - is to take action a at time t i Expected cost

[0148] - is a set of potential actions from time t Take action a i The recommended operation is the one that minimizes the expected cost, as shown in equation (17):

[0149]

[0150] in:

[0151] a * (t) - is the recommended action at time t.

[0152] Furthermore, to recommend the replacement time, the time at which the expected cost of replacement becomes less than the expected cost of non-replacement as found in equation (18) is solved and given as the recommended replacement time.

[0153]

[0154] in:

[0155] - is the recommended replacement time

[0156] According to a second aspect, a computer program product is proposed, wherein the computer program product comprises a program code for executing the method of the first aspect or one of the embodiments of the first aspect when the program code is run on at least one computer.

[0157] A computer program product such as a computer program means may be embodied as a memory card, USB stick, CD-ROM, DVD or as a file downloadable from a server in a network. For example, a file comprising a computer program product may be provided by transmitting such a file from a wireless communication network.

[0158] According to a third aspect, a device for operating a distribution network having a specific network topology is proposed. The device comprises:

[0159] a plurality of N sensors for sensing wavefront characteristics, each of the N sensors being located at a specific measurement location in the distribution network, wherein at least a subset of the N sensors is configured to obtain a transient event parameter value for a specific transient event in the distribution network,

[0160] a determination unit for determining a risk factor indicative of a hazard in the distribution network depending on the obtained transient event parameter value and an actual value of a network parameter indicative of the distribution network, and

[0161] An execution unit is configured to execute one of a plurality of actions at the distribution network depending on the determined risk factor.

[0162] In particular, the apparatus may be or may comprise a computer-aided or computer-related system or a computer system.

[0163] The corresponding unit (e.g., determination unit or execution unit) can be implemented in hardware and / or software. If the unit is implemented in hardware, it can be implemented as a device, such as a computer or a processor or a part of a system (e.g., a computer system). If the unit is implemented in software, it can be implemented as a computer program product, a function, a routine, a program code, or an executable object.

[0164] Embodiments and features according to the first aspect are also embodiments of the third aspect.

[0165] Further possible implementations or alternative solutions of the invention also include combinations of features described above or below with respect to the embodiments - not explicitly mentioned herein. A person skilled in the art may also add individual or isolated aspects and features to the most basic form of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0166] Further embodiments, features and advantages of the present invention will become apparent from the ensuing description and dependent claims, taken in conjunction with the accompanying drawings, in which:

[0167] Figure 1 A sequence of method steps showing a first embodiment of a method for operating a distribution network;

[0168] Figure 2 A schematic diagram showing an example for a distribution network;

[0169] Figure 3 A sequence of method steps showing a second embodiment of a method for operating a distribution network;

[0170] Figure 4 shows a graph illustrating distance-dependent attenuation of transient effects on a distribution network;

[0171] Figure 5 A schematic block diagram showing an embodiment of an apparatus for operating a distribution network;

[0172] Figure 6 A sequence of method steps showing a third embodiment of a method for operating a distribution network; and

[0173] Figure 7 A schematic diagram illustrating the effect of the width parameter on the output value in RBF is shown.

[0174] In the drawings, like reference numbers indicate identical or functionally equivalent elements unless otherwise indicated. DETAILED DESCRIPTION

[0175] exist Figure 1 , a sequence of method steps for a first embodiment of a method for operating a distribution network 10 having a specific network topology using sensors 31-33 for sensing high-speed wavefront characteristics is shown. Each of the N sensors 31-33 is located at a specific measurement location L1-L3 in the distribution network 10. The distribution network 10 may be, for example, a water distribution network, an oil distribution network, a gas distribution network or a heat distribution network.

[0176] in this regard, Figure 2 A schematic diagram of an example for a distribution network 10 is shown. In particular, Figure 2 Only a small portion of such a distribution network 10 is shown. Figure 2 The distribution network 10 has a plurality of pipes 20 suitable for conducting a liquid, such as water or oil.

[0177] Without loss of generality, Figure 2 The number N of sensors 31-33 is three (N=3). Generally, N may be a positive integer, for example, in a water distribution network, N may be greater than 1000. Each of the sensors 31-33 for detecting the transient event parameter value of a specific transient event E has a sampling rate of 100 MHz or greater. Therefore, the sensors 31-33 are high-speed pressure sensors.

[0178] exist Figure 2 In the example of FIG. 1 , a burst of one of the tubes 20 is depicted as an example of a transient event E. The different paths of propagation of the pressure wave caused by the burst E are shown in FIG. Figure 2 Indicated by P1, P2 and P3. Figure 2 In the example of , the first sensor 31 is located at the measurement position L1 , the second sensor 32 is located at the measurement position L2 , and the third sensor 33 is located at the measurement position 33 .

[0179] according to Figure 1 An embodiment of the method has the following method steps S10-S30:

[0180] In step S10, a transient event parameter value of a specific transient event E in the distribution network 10 is obtained using at least a subset of the N sensors 31-33. For example, each of the subset of sensors 31-33 senses a wavefront characteristic caused by the specific transient event E. Depending on the sensed wavefront characteristic, a transient event parameter value is generated or created. Examples of transient event parameters include the amplitude of the specific transient event E (particularly at the corresponding specific measurement locations L1-L3 of the specific sensors 31-33), the location of the specific transient event E in the distribution network 10, and / or the event type of the specific transient event E in the distribution network 10. Examples of event types include leaks, congestion, valve opening, valve closing, pump start, and pump stop.

[0181] In step S20 , a risk factor indicative of a hazard in the distribution network 10 is determined depending on the obtained transient event parameter value and the actual value of the network parameter indicative of the distribution network 10 .

[0182] The network parameters may include meta-information about the distribution network 10, such as pipe age, pipe material, pipe diameter, corrosion-related characteristics, etc. In addition, the input parameters for determining the risk factors may include load-related factors of the distribution network 10 and external factors describing the environment of the distribution network 10. The external factors may include soil conditions, temperature, precipitation, etc. The load-related factors may exemplarily include parameters of the load itself, such as a density value of the load.

[0183] In step S30, one of a plurality of actions at the distribution network 10 is performed depending on the determined risk factor. Performing one of the actions may include providing a replacement recommendation for replacing a pipe or pipe component or providing an alert to an operator or service personnel of the distribution network 10.

[0184] In addition, performing a specific action may include recording a determined risk factor or multiple determined risk factors over time. In addition, a replacement suggestion or alarm may be generated or triggered based on the recorded determined risk factors.

[0185] Furthermore, certain actions may include providing an operator or service personnel of distribution network 10 with a visual representation of distribution network 10, such as in the form of a heat map indicating potential hazards and risk factors for distribution.

[0186] Figure 3 A sequence of method steps of a second exemplary embodiment of a method for operating a distribution network 10 is shown. Figure 3 The second embodiment is based on Figure 1 A first embodiment wherein Figure 1 Step S10 of the method is performed according to Figure 3 Steps S11 to S13 of the method are implemented, and Figure 1 The method step S20 is performed according to Figure 3 The method steps S21-S23 are implemented.

[0187] In this regard, method step S10 comprises steps S11-S13:

[0188] In step S11 , at least the subset of the N sensors 31 - 33 is used to sense wavefront characteristics caused by a specific transient event E in the distribution network 10 .

[0189] In step S12, signals from the subset of N sensors 31-33 are collected. The signals are indicative of the sensed wavefront properties.

[0190] In step S13 , the collected signals are processed to obtain the transient event parameters. For example, the processing may be performed by a central processing unit of the distribution network 10 .

[0191] As mentioned above, Figure 3 The method step S20 includes steps S21-S23:

[0192] In step S21, a model is provided for modeling hazards in the distribution network 10. The model is time dependent and uses a set of parameters representing at least network parameters and transient event parameters.

[0193] In step S22, the actual hazard is modeled by applying the determined actual values ​​(including at least the obtained transient event parameter values) and the actual values ​​of the network parameters of the parameter set to the provided model.

[0194] In step S23, risk factors are determined depending on the actual hazard modeled.

[0195] In particular, the step S20 may comprise dividing the distribution network 10 into pipe segments to assign each of the pipe segments to one of a plurality of M groups according to at least one classification parameter (M≥2). Then, the following sub-steps i) to iii) may be performed:

[0196] i) providing a model for modeling hazards in a pipe section of the group, said model being time dependent and using a set of parameters representing at least network parameters and transient event parameters,

[0197] ii) modeling the actual hazard by applying the determined actual values ​​(including at least the obtained transient event parameter values) and the actual values ​​of the network parameters of the parameter set to the provided model, and

[0198] iii) Determine the risk factors for the group of pipe segments depending on the actual hazard modelled.

[0199] In particular, the pipe segments are separated from each other and each of the pipe segments is homogeneous at least in terms of pipe material and pipe diameter. In particular, the model uses Cox regression. In Cox regression, the formula is used

[0200] h(t,x)=h0(t)exp{β T x}...(19)

[0201] It is used to model the time-dependent and parameter-set-dependent actual hazard h(t,x), where t specifies the time, x specifies the parameter set as a vector, h0(t) specifies the baseline risk due to time t, and β specifies the coefficient vector for the parameter set.

[0202] The parameter set x can be expressed as x = [x1,…,x p ] T , where x p Specify at least one transient event parameter, such as a pressure transient.

[0203] The pressure transient x p The transient event E in the distribution network 10 represented affects the part of the distribution network 10 where it occurs most and its effect gradually disappears with increasing travel distance. In the formula mentioned below, this is controlled via the exponent k, i.e. the larger k is, the faster the transient effect decays with travel distance.

[0204] x p =Mψ...(20)

[0205]

[0206]

[0207] In the above formula, M specifies the magnitude of the pressure transient, and d i Specify the i-th segment S from the location of the transient event E to the distribution network 10 i The distance from the center.

[0208] in this regard, Figure 4A diagram illustrating the distance-dependent attenuation of hazardous transient effects in the distribution network 10 is shown. Figure 4 In , the x-axis shows the distance d from the location of the transient event E in the distribution network 10, and the y-axis shows the exponent k of the above formula.

[0209] In the above Figure 4 , curve C0 shows the curvature when k=0, curve C1 shows the curvature when k=1, curve C2 shows the curvature when k=2, and curve C3 shows the curvature when k=3.

[0210] also, Figure 5 Shown for operation such as Figure 2 A schematic block diagram of an embodiment of a device 50 of a distribution network 10 is shown.

[0211] Figure 5 The device 50 shows a plurality of sensors 51, 31, 32, 33 for sensing wavefront characteristics. Each of the sensors 31-33 is located in the distribution network 10 (see Figure 2 ) in a specific measurement location L1-L3. At least a subset of the sensors 31-33 is configured to obtain the distribution network 10 (see Figure 2 ) is the transient event parameter value of a specific transient event E.

[0212] Furthermore, the device 50 comprises a determination unit 52 and an execution unit 53 .

[0213] The determination unit 52 is configured to determine a risk factor indicative of a hazard in the distribution network 10 depending on the obtained transient event parameter value and an actual value indicative of a network parameter of the distribution network 10 .

[0214] The execution unit 53 is configured to execute one of a plurality of actions at the distribution network 10 depending on the determined risk factor.

[0215] Figure 6 A sequence of method steps of a third exemplary embodiment of a method for operating a distribution network 10 is shown. Figure 6 The third embodiment is based on Figure 1 A first embodiment wherein Figure 1 Step S10 of the method is performed according to Figure 6 Steps S11 to S13 of the method are implemented, and Figure 1 The method step S20 is performed according to Figure 6 The method steps S20a-S20c are implemented.

[0216] In this regard, method step S10 comprises steps S11-S13:

[0217] In step S11 , at least the subset of the N sensors 31 - 33 is used to sense wavefront characteristics caused by a specific transient event E in the distribution network 10 .

[0218] In step S12, signals from the subset of N sensors 31-33 are collected. The signals are indicative of the sensed wavefront properties.

[0219] In step S13 , the collected signal is processed to obtain the transient event parameter. For example, the processing may be performed by a central processing unit of the distribution network 10 .

[0220] As mentioned above, Figure 6 The method step S20 comprises steps S20a-S20c:

[0221] In step S20a, a model is provided for modeling hazards in the distribution network 10. The model is time dependent and uses a factor vector representing network parameters, load related factors including transient event parameters, and external factors.

[0222] In step S20b, the actual hazard is modeled by applying the determined actual values ​​(including at least the obtained values ​​of the load related factors, which include the transient event parameters), the actual values ​​of the network parameters and the actual values ​​of the external factors of the factor vector to the provided model.

[0223] In step S20c, risk factors are determined depending on the actual risk modeled.

[0224] Although the invention has been described in terms of preferred embodiments, it will be apparent to those skilled in the art that modifications may be made in all embodiments.

[0225] Reference Number:

[0226] 10 Distribution Network

[0227] 20 tubes

[0228] 31, 32, 33 Sensors

[0229] 50 devices

[0230] 51+ sensors

[0231] 52 Determine unit

[0232] 53 Execution Unit

[0233] C0 Curve when k=0

[0234] C1 Curve when k=1

[0235] C2 Curve when k=2

[0236] C3 Curve when k=3

[0237] d Distance

[0238] k index

[0239] L1 Measurement position, positioning of the first sensor

[0240] L2 Measurement position, positioning of the second sensor

[0241] L3 Measurement position, positioning of the third sensor

[0242] O Output

[0243] P1 The first path of pressure wave propagation

[0244] P2 Second path of pressure wave propagation

[0245] P3 The third path of pressure wave propagation

[0246] S10-S13 Method Steps

[0247] S20, S20a, S20b, S20c Method steps

[0248] S21-S23, S30 Method Steps

[0249] V1, V2, V3, V4 curves

[0250] References:

[0251] [1] A.Large et al.: “Decision support tools: Review of risk models in drinking water network asset management”, Water Affairs Journal, vol. 10; 45-53, 2015

[0252] [2] Sinha and Clair: “State-of-the-Technology Review on Water Pipe Condition, Deterioration and Failure Rate Prediction Models”, available at RDDS

[0253] [3] “Application of Artificial Neural Networks (ANN) to model the failure of urban water mains”, Mathematical and Computer Modeling, Vol. 51, No. 9-10, May 2010, pp. 1170-1180

[0254] [4] Procedia Engineering, Vol. 186, 2017, pp. 117-126.

Claims

1. A method for operating a distribution network (10) having a specific network topology using N sensors (31-33) for sensing wavefront characteristics, each of the N sensors (31-33) being located at a specific measurement location (L1-L3) in the distribution network (10), the method comprising: a) using at least a subset of the N sensors (31-33) to obtain (S10) a transient event parameter value of a specific transient event (E) in the distribution network (10), b) determining (S20) a risk factor indicative of a hazard in said distribution network (10) depending on the obtained value of said transient event parameter and an actual value indicative of a network parameter of said distribution network (10), and c) performing ( S30 ) one of a plurality of actions at said distribution network ( 10 ) depending on the determined risk factor, The method further comprises determining the location of the specific transient event in the distribution network depending on differences in detected arrival times of wavefronts detected by sensors located at a subset of the measurement locations and at the localization of the measurement locations, and wherein, for each possible sensor pairing of the N sensors, the time shift between arrival times at the two sensors of the sensor pairing is determined by cross-correlating the wavefronts received at the two sensors.

2. The method according to claim 1, characterized in that The risk factor is determined depending on actual values ​​of the network parameters, actual values ​​of load-related factors of loads distributed by the distribution network (10) and external factors describing the environment of the distribution network (10), wherein the actual values ​​of the load-related factors include the obtained values ​​of the transient event parameters.

3. The method according to claim 1 or 2, characterized in that: The transient event parameter value comprises the magnitude of the specific transient event (E), the location of the specific transient event (E) in the distribution network (10) and / or the event type of the specific transient event (E) in the distribution network (10).

4. The method according to any one of claims 1 or 2, characterized in that: Step a) comprises: using said at least a subset of said N sensors (31-33) to obtain said transient event parameter values ​​for a plurality of specific transient events (E) in said distribution network (10).

5. The method according to claim 1 or 2, characterized in that: Step a) (S10) comprises: sensing (S11) wavefront characteristics caused by the specific transient event (E) in the distribution network (10) using the at least one subset of the N sensors (31-33), collecting (S12) signals from a subset of the N sensors (31-33), the signals being indicative of the sensed wavefront properties, and The collected signals are processed ( S13 ) to obtain the transient event parameter value.

6. The method according to claim 2, characterized in that Step b) (S20) comprises: providing (S20a) a model for modeling hazards in the distribution network (10), the model being time-dependent and using a factor vector representing the network parameters, the load-related factors including the transient event parameter values ​​and the external factors, modeling the actual hazard by applying to a provided model at least the determined actual value of the obtained load-related factor, the actual value of the network parameter, and the actual value of the external factor of the factor vector (S20b), wherein the obtained load-related factor includes the transient event parameter, and The risk factor is determined (S20c) depending on the actual hazard modelled.

7. The method according to claim 6, characterized in that Step b) comprises: The distribution network (10) is divided into N interconnected pipe sections ,in by To specify the i-th pipe segment, Therein, the pipe sections are separated from one another and each of the pipe sections is homogeneous at least with regard to the pipe material and the pipe diameter.

8. The method according to claim 7, characterized in that formula is used to align the pipe segment The actual danger associated with the time Modeling, in, is the factor vector, The segment to be evaluated the time of the actual danger, is the coefficient vector of the model, and is the function used to calculate the actual risk which depends on the model used.

9. The method according to claim 8, characterized in that The model used to model the actual risk is a Cox regression model, where the formula ， in, Specify the baseline hazard due to time t.

10. The method according to claim 8, characterized in that The model used to model the actual risk uses a logistic regression model, wherein in the logistic regression model, the formula is used ， in, Specify pipe segment probability of failure.

11. The method according to any one of claims 8 to 10, characterized in that Learning the coefficient vector based on historical data .

12. The method according to any one of claims 6 to 10, characterized in that The model is adapted to model reflections of said transient event (E) in said distribution network (10).

13. The method according to any one of claims 6 to 10, characterized in that The factor vector includes factors related to different types of pressure transients.

14. A device (50) for operating a distribution network (10) having a specific network topology, the device (50) comprising: A plurality (51) of N sensors (31-33) for sensing wavefront characteristics, each of the N sensors (31-33) being located at a specific measurement location (L1-L3) in the distribution network (10), wherein at least a subset of the N sensors (31-33) is configured to obtain a transient event parameter value of a specific transient event (E) in the distribution network (10), a determination unit (52) for determining a risk factor indicative of a hazard in the distribution network (10) depending on the obtained value of the transient event parameter and an actual value indicative of a network parameter of the distribution network (10), and an execution unit (53) for executing one of a plurality of actions at the distribution network (10) depending on the determined risk factor, wherein the location of the specific transient event in the distribution network is determined depending on differences in detected arrival times of wavefronts detected by sensors located at a subset of the measurement locations and at the localization of the measurement locations, and wherein, for each possible sensor pairing of the N sensors, the time shift between the arrival times at the two sensors of the sensor pairing is determined by cross-correlating the wavefronts received at the two sensors.

15. A distribution network (10) comprising a plurality of pipe sections for distributing specific loads and a device (50) according to claim 14 for operating the distribution network (10).

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