Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
  • G01M3/00—Investigating fluid-tightness of structures
  • G01M3/02—Investigating fluid-tightness of structures by using fluid or vacuum
  • G01M3/26—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors
  • G01M3/28—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for pipes, cables or tubes; for pipe joints or seals; for valves ; for welds
  • G01M3/2807—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for pipes, cables or tubes; for pipe joints or seals; for valves ; for welds for pipes
  • G01M3/2815—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for pipes, cables or tubes; for pipe joints or seals; for valves ; for welds for pipes using pressure measurements
• • E—FIXED CONSTRUCTIONS
  • E03—WATER SUPPLY; SEWERAGE
  • E03B—INSTALLATIONS OR METHODS FOR OBTAINING, COLLECTING, OR DISTRIBUTING WATER
  • E03B7/00—Water main or service pipe systems
  • E03B7/07—Arrangement of devices, e.g. filters, flow controls, measuring devices, siphons or valves, in the pipe systems
  • E03B7/071—Arrangement of safety devices in domestic pipe systems, e.g. devices for automatic shut-off
• • E—FIXED CONSTRUCTIONS
  • E03—WATER SUPPLY; SEWERAGE
  • E03B—INSTALLATIONS OR METHODS FOR OBTAINING, COLLECTING, OR DISTRIBUTING WATER
  • E03B7/00—Water main or service pipe systems
  • E03B7/07—Arrangement of devices, e.g. filters, flow controls, measuring devices, siphons or valves, in the pipe systems
  • E03B7/075—Arrangement of devices for control of pressure or flow rate
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
  • F17D—PIPE-LINE SYSTEMS; PIPE-LINES
  • F17D5/00—Protection or supervision of installations
  • F17D5/02—Preventing, monitoring, or locating loss
  • F17D5/06—Preventing, monitoring, or locating loss using electric or acoustic means
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
  • G01M3/00—Investigating fluid-tightness of structures
  • G01M3/02—Investigating fluid-tightness of structures by using fluid or vacuum
  • G01M3/04—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point
  • G01M3/24—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using infrasonic, sonic, or ultrasonic vibrations
  • G01M3/243—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using infrasonic, sonic, or ultrasonic vibrations for pipes
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q10/00—Administration; Management
  • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
  • G06Q50/06—Energy or water supply
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
  • Y02A20/00—Water conservation; Efficient water supply; Efficient water use
  • Y02A20/15—Leakage reduction or detection in water storage or distribution

Definitions

• the present invention relates to a method for operating a distribution network having a certain network topology using N sensors for sensing wavefront characteristics, each of said N sensors being located at a certain measurement location in the distribution network.
• the present invention further relates to a computer program product and to a device for operating a distribution network having a certain network topology.
• the present invention relates to a distribution network comprising a plurality of pipe segments for distributing a certain load and such a device for operating the distribution network.
• Distribution networks may be subject to failure due to various reasons. For example, failures of particular segments of the distribution network may lead to interrupts in the supply for consumers or damages of the infrastructure, like roads and other properties. Therefore, together with the task of minimizing the time required to correctly detect, localize and isolate a damaged segment in the distribution network, there is a need to minimize the risk of a failure in the distribution network.
• a method for operating a distribution network having a certain network topology using N sensors for sensing wavefront characteristics is suggested.
• Each of said N sensors is located at a certain measurement location in the distribution network.
• the method comprises the following steps a) , b) and c) : a) obtaining values of transient event parameters of a certain transient event in the distribution network using at least a subset of the N sensors,
• the distribution network is a water distribution network, an oil distribution network, a gas distribution network or a heat distribution network.
• the distribution network may also be referenced as network.
• a hazard may be a real or actual damage of the distribution network, like a pipe congestion or a pipe burst, or a high probability of a damage of a pipe or pipe segment in the distribution network.
• the pipe segment may also be referenced as segment .
• transient events examples include leaks, valve openings, valve closures, pump starts or pump stops.
• transient event parameters include a magnitude of the certain transient event, a location of the certain transient event in the distribution network and/or an event type of the certain transient event in the distribution network.
• the event type may also be referenced as type.
• the wavefront caused by said transient event is detectable by a certain subset of the sensors or by all sensors in the distribution network.
• this subset of the sensors is able to obtain values of the transient event parameters of a certain transient event.
• different transient events may be detectable by different subsets of the sensors located at different measurement locations in the distribution network.
• the topology of the distribution network is known a priori, in particular before the method including steps a) to c) is executed.
• information indicating the network topology as part of the network parameters is stored in a memory, wherein said information may be used in said step b) of the method.
• 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 logged or historical data of the distribution network.
• Executing one of the actions according to step c) may include to provide a replacement recommendation for replacing a pipe or a pipe part or to provide an alarm to an operator or a service personnel of the distribution network. Moreover, said executing a certain action may include logging the determined risk factor or determined risk factors. Further, a replacement recommendation or an alarm may be generated or triggered using the logged determined risk factors.
• a certain action may include to provide a visual representation of the distribution network, like a heat map of a graph representing the distribution network and indicating potential hazards and determined risk factors, for the operator or service personnel of the distribution network.
• the risk factor for a hazard in the distribution network can be determined with a higher accuracy. Determining risk factors with a higher accuracy results in lowering the costs for the maintenance of the distribution network and in enhancing the planning of network rehabilitation strategies.
• the risk factor is determined in dependence on the obtained values of the transient event parameters, the actual values of the network parameters, load- related factors of a load distributed by the distribution network and external factors describing an environment of the distribution network.
• the transient event parameters include a magnitude of the certain transient event, in particular at a certain measurement location or at certain measurement locations, a location of the certain transient event in the distribution network and/or an event type of the certain transient event in the distribution network.
• the step a) includes:
• the step a) includes:
• the step b) includes:
• model for modeling a hazard in the distribution network, said model being time-dependent and using a parameter set at least representing the network parameters and the transient event parameters,
• modeling an actual hazard by applying determined actual values at least including the obtained values of the transient event parameters and the actual values of the network parameters for the parameter set to the provided model, and
• the step b) includes:
• the pipe segments are disjunct to other and each of the pipe segments is homogenous at least regarding pipe material and pipe diameter.
• the model for modeling the actual hazard uses a Cox regression.
• t designates time
• x designates the parameter set as a vector
• ho(t) designates a base line hazard due to time t
• b designates a vector of coefficients for the parameter set.
• each of the sensors is a high-rate pressure sensor.
• each of the sensors has a sampling . rate of 100 Hz or greater, in particular of 200 Hz or greater.
• the step b) of determining the risk factor is executed by a central processor unit of the distribution network.
• the method includes a step of determining the location of the certain transient event in the distribution network in dependence on the differences of detected arrival times of the wavefront detected by the sensors located at said subset of the measurement locations and positions of the measurement locations.
• the positions of the measurement locations are part of the a priori known network topology.
• the positions of the measurement locations are known before the above-mentioned method steps are executed.
• the time shift between the arrival times at the two sensors of the sensor pair is determined by cross-correlating the wavefronts received at said two sensors.
• the event type of the transient event in the distribution network is classified as a leak, as a valve opening, as a valve closure, as a pump start or as a pump stop using the determined location of the transient event in the distribution network, using the detected arrival times of the wavefront detected by the sensors located at said subset of the measurement locations, and using the certain network topology of the distribution network.
• the risk factor is determined in dependence on the actual values of the network parameters, on the actual values of load-related factors of a load distributed by the distribution network including the obtained values of the transient event parameters, and external factors describing an environment of the distribution network.
• the network parameters may comprise meta information, said meta information particularly including segment age, segment material, corrosion level and the like.
• the load-related factors may include pressure transients and the accumulated flow rate through the respective segment.
• the external factors may include soil conditions and the installation nature.
• the installation nature may describe an installation under street or under a heavy traffic area, for example.
• the above factors may be also referenced as covariates.
• the covariates may be continuous, discrete or categorical. It may be noted, that the covariate may be converted into a discrete one or a categorical one by using means of ranges.
• the step b) includes:
• model for modeling a hazard in the distribution network, said model being time-dependent and using a factors vector representing the network parameters, the load-related factors including the transient event parameters, and the external factors,
• modeling an actual hazard by applying determined actual values at least including obtained values of the load-related factors including the transient event parameters, the actual values of the network parameters and the actual values of the external factors for the factor vector to the provided model, and
• the step b) includes:
• Said factors may be also referred to as covariates.
• x Sa is the factor vector
• t is the time at which the actual hazard for the segment S a is to be evaluated
• b is the coefficients vector of the model
• x Sa is the factor vector
• t is the time at which the actual hazard for the segment S a is to be evaluated
• b is the coefficients vector of the model
• x Sa is the factor vector
• t is the time at which the actual hazard for the segment S a is to be evaluated
• b is the coefficients vector of the model
• ) is the function used to compute the actual hazard being dependent on the used model, e.g. a Cox regression model or a logistic regression model.
• the model for modeling the actual hazard uses a Cox regression model, wherein, in the Cox regression, the formula
• h 0 (t) designates a base line hazard due to time t .
• the Cox regression model is a semi parametric model. In particular, it quantifies the baseline hazard, which is a hazard resulting from solely aging of the component (here pipe segment) in a nonparametric manner. Then, it uses a parametric method for either accelerating or deaccelerating the hazard profile based on the factors.
• the model for modeling the actual hazard uses a logistic regression model, wherein, in the logistic regression model, the formula
• the logistic regression is a sigmoid function whose range is limited in the range [0, 1] . Consequently, it is appropriate for being used in modeling failure probability of a component (here a pipe segment) . It is mentioned that in this model, time is also considered as a part in the factors vector 3 ⁇ 43 ⁇ 4 (an additional covariate) , and has a corresponding coefficient in the vector b.
• this method is a parametric method, whose output can be learnt for historical data via methods of expectation maximization (EM) , or iteratively reweighted least square (IRLS) method.
• EM expectation maximization
• IRLS iteratively reweighted least square
• the coefficients vector b is learnt based on historical data.
• learning the hazard model represents using historical data for inferring the coefficients vector b.
• the transients events do affect the other segments - with respect to the segments on which they took place. This is due to the fact that those events generate pressure transients that propagate through the network and are subject to being reflected by some components of the network such as valves and/or fittings. Therefore, when a transient event takes place, it may affect other parts of the network.
• the model is adapted for modeling reflections of the transient event in the distribution network and/or the factors vector includes factors relating to pressure transients of different types. This is discussed in detail for the example of pressure transients as an example for transient events, in the following:
• Pressure transients yield into fatigue, a phenomenon that accelerates the failure of a pipe segments Pressure transients happen due to several reasons, for example, bursts, valve operation (opening and/or closing) , pumps operation (start up and/or shut down) and the like. Regardless of why transients happen, they propagate through the pipe segments connected to the segment where the event took place. This affects not only the segment on which they happened, but also on the other connected segments with different levels of effect due to the decay of the transients wave amplitude as the transients travel through the network.
• ⁇ A a — is the set of all segments and reflection surfaces that affect the segment of interest S a ,
• ⁇ S a is the set of all segments, where events happening will affect the segment of interest S a .
• SR a — is the set of all reflection surfaces that affect the segment of interest S a
• ⁇ S a depends on the distances between the segment of interest S a and the rest of the segments in the network. Further, fR a depends on the topology of the network, as the topology gives the locations of these reflection surfaces relative to the segment of interest S a . For example, one way to find ⁇ 3 a is to consider the segments and reflection surfaces whose respective distance relative to the segment of interest S a is less than a pre-defined threshold.
• the pressure transients are divided into € types.
• these types can include: i. Segment burst
• the factors vector x Sa - in addition to the aforementioned ⁇ factors - includes factors related to pressure transients ,
• a radial basis function (RBF) may be used.
• ⁇ is the RBF describing the decay of the effect of the propagated pressure transient happening at segment Si on the segment of interest S a , the RBF describing the decay of the effect of the reflected pressure transient ⁇ M ⁇ fr 3 ⁇ , 1 ⁇ ) from surface Jlj on the segment of interest S a .
• the reflection happens on the wave once it reaches the reflection surface. That is, when the x th type event happens at segment t with amplitude it first propagates to the reflection surface Rj whilst decaying to be , then it is reflected back from the reflection surface Jlj to the segment of interest S a decaying as per the reflection
• RBF may be used, one example is the Gaussian RBF, the corresponding RBF used in equation (7) are defined as in equations (8), (9), and (10).
• c Es J— is the traveled distance through the network from the event location (c Es ) to the reflection surface R j , is the traveled distance through the network from the reflection surface l j to the center coordiantes of the segment of interest S a ,
• i p — is the width parameter which controls the decay rate of the propagated x th type pressure transient
• t — is the width parameter which controls the decay rate of the reflected x th type pressure transient.
• Equation (7) encompasses the width parameters These can be estimated based on field experiments. It may be mentioned though, that equations (8), (9) and (10) describe one type of RBF, namely, Gaussian RBF, however, other types can be used such as thin plate spline (TPS-RBF) to give an example .
• TPS-RBF thin plate spline
• curve V2 designates t , curve V2 designates t t curve V3 designates t ⁇ 2 , and curve V4 designates t ⁇ 2 .
• Segment material type (covariate x x) .
• Bursts transients (covariate x 6 ) .
• equations (11) and (12) the covariates related to pressure transients can be linked to equation (7) as follows:
• Another measure of the failure rate can be of interest, such as survival rate and probability of failure.
• the probability of failure can be calculated as follows :
• Pf ⁇ s a (t)— is the probability that segment S a will have failed before time t.
• ⁇ iiC ⁇ is the cost taking the action of replacing the pipe segment at time t and it actually fails at t
• a i2 (.t) — is the cost taking the action of replacing the pipe segment at time t and it actually does not fails at t
• the recommended action is the one that minimizes the expected cost as in equation (17):
• a * (t)— is the recommended action at time t.
• the time at which the replacement expected cost becomes less than the not-replacement expected cost is found as in equation (18) is solved and given as the recommended time of replacement.
• T— is the recommended time of segment replacement
• a computer program product comprises a program code for executing the method of the first aspect or of one of the embodiments of the first aspect when the program code is run on at least one computer.
• 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 which may be downloaded from a server in a network.
• a file may be provided by transferring the file comprising the computer program product from a wireless communication network.
• a device for operating a distribution network having a certain network topology comprises:
• a plurality N of sensors for sensing wavefront characteristics, each of said N sensors being located at a certain measurement location in the distribution network, wherein at least a subset of the N sensors is configured to obtain values of transient event parameters of a certain transient event in the distribution network, a determining unit for determining a risk factor indicating a hazard in the distribution network in dependence on the obtained values for the transient event parameters and actual values of network parameters being indicative for the distribution network, and
• an executing unit for executing one of a number of actions at the distribution network in dependence on the determined risk factor.
• the device may be or may include a computer- aided or computer-related system or a computer system.
• the respective unit e.g. the determining unit or the executing unit, may be implemented in hardware and/or in software. If said unit is implemented in hardware, it may be embodied as a device, e.g. as a computer or as a processor or as a part of a system, e.g. a computer system. If said unit is implemented in software it may be embodied as a computer program product, as a function, as a routine, as a program code or as an executable object.
• Fig. 1 shows a sequence of method steps of a first embodiment of a method for operating a distribution network
• Fig. 2 shows a schematic diagram of an example for a distribution network
• Fig. 3 shows a sequence of method steps of a second embodiment of a method for operating a distribution network
• Fig. 4 shows a diagram illustrating the distance-dependent decay of the transient effects to a distribution network
• Fig. 5 shows a schematic block diagram of an embodiment of a device for operating a distribution network
• Fig. 6 shows a sequence of method steps of a third embodiment of a method for operating a distribution network
• Fig. 7 shows a schematic diagram illustrating the effect of the width parameter in RBF on the output value.
• Fig. 1 a sequence of method steps of a first embodiment of a method for operating a distribution network 10 having a certain network topology using sensors 31 - 33 for sensing high-speed wavefront characteristics is shown.
• Each of said N sensors 31 - 33 is located at a certain measurement location LI - L3 in the distribution network 10.
• the distribution network 10 may be a water distribution network, an oil distribution network, a gas distribution network or a heat distribution network, for example.
• Fig. 2 shows a schematic diagram of an example for the distribution network 10.
• Fig. 2 shows only a small part of such a distribution network 10.
• the distribution network 10 of Fig. 2 has a plurality of pipes 20 which are adapted to conduct a liquid, like water or oil.
• N may be a positive integer, for example in a water distribution network N may be greater than 1,000.
• Each of the sensors 31 - 33 for detecting values of transient event parameters of a certain transient event E has a sampling rate of 100 MHz or greater.
• the sensors 31 - 33 are high-rate pressure sensors.
• a burst of one of the pipes 20 as an example of a transient event E is depicted.
• the different paths of the pressure wave propagation caused by said burst E are indicated in Fig. 2 by PI, P2, and P3.
• a first sensor 31 is located at measurement location LI
• a second sensor 32 is located at measurement location L2
• a third sensor 33 is located at measurement location 33.
• the embodiment of the method according to Fig. 1 has the following methods steps S10 - S30:
• step S10 values of transient event parameters of a certain transient event E in the distribution network 10 are obtained using at least a subset of the N sensors 31 - 33.
• each of said subset of sensors 31 - 33 senses wavefront characteristics caused by said certain transient event E.
• the values of the transient event parameters are generated or created.
• the transient event parameters include a magnitude of the certain transient event E, in particular at the respective certain measurement location LI - L3 of the certain sensor 31 - 33, a location of the certain transient event E in the distribution network 10 and/or an event type of the certain transient event E in the distribution network 10.
• the event type include a leak, a congestion, a valve opening, a valve closure, a pump start and a pump stop.
• a risk factor indicating a hazard in the distribution network 10 is determined in dependence on the obtained values for the transient event parameters and actual values of network parameters being indicative for the distribution network 10.
• the network parameters may include meta-information about the distribution network 10, like pipe age, pipe material, pipe diameter, corrosion-related feature and others.
• input parameters for determining the risk factor may include load- related factors of the load by the distribution network 10 and external factors describing an environment of the distribution network 10.
• the external factors may include soil conditions, temperature and precipitation and the like.
• the load-related factors may exe plarily include parameters of the load as such, like a density value of the load.
• step S30 one of a number of actions at the distribution network 10 is executed in dependence on the determined risk factor. Executing one of the actions may include to provide a replacement recommendation for replacing a pipe or a pipe part or to provide an alarm to an operator or to service personnel of the distribution network 10.
• said executing a certain action may include to log the determined risk factor or determined risk factors over time. Further, a replacement recommendation or an alarm may be generated or triggered from the logged determined risk factors .
• a certain action may include to provide a visual representation of the distribution network 10, e.g. in form of a heat map indicating potential hazards and allocated risk factors, to the operator or to service personnel of the distribution network 10.
• Fig. 3 shows a sequence of method steps of a second embodiment of a method for operating a distribution network 10.
• the second embodiment of Fig. 3 is based on the first embodiment of Fig. 1, wherein method step S10 of Fig. 1 is embodied by method steps S-ll to S13 according to Fig. 3 and method step S20 of Fig. 1 is embodied by method steps S21 - S23 according to Fig. 3.
• method step S10 includes steps Sll - S13:
• step Sll wavefront characteristics caused by the certain transient event E in the distribution network 10 are sensed using at least said subset of the N sensors 31 - 33.
• step S12 signals from said subset of the N sensors 31 - 33 are collected. Said signals are indicative for the sensed wavefront characteristics.
• step S13 the collected signals are processed for obtaining said transient event parameters.
• said processing may be executed by a central processing unit of the distribution network 10.
• method step S20 of Fig. 3 includes steps S21 - S23 :
• step S21 a model for modeling a hazard in the distribution network 10 is provided.
• Said model is time-dependent and uses a parameter set at least representing the network parameters and the transient event parameters.
• step S22 an actual hazard is modeled by applying determined actual values at least including the obtained values of the transient event parameters and the actual values of the network parameters for the parameter set to the provided model.
• step S23 the risk factor is determined in dependence on the modeled actual hazard.
• said step S20 may include to divide the distribution network 10 into pipe segments to allocate each of the pipe segments to one of a plurality of M groups according to at last one categorical parameter (M 3 2) . Then, the following sub-steps i) to iii) may be executed:
• the pipe segments are disjunct to each other and each of the pipe segments is homogeneous at least regarding pipe material and pipe diameter.
• the model uses a Cox regression. In the Cox regression, the formula
• h(t, x) h 0 (t) ex r[b t c] ... (19) is used for modeling the time-dependent and parameter-set- dependent actual hazard h(t,x), where t designates the time, x designates the parameter set as a vector, ho(t) designates a base line hazard due to time t and b designates a vector of coefficients for the parameter set.
• the transient event E in the distribution network 10 represented by said pressure transient x P effects the segments of the distribution network 10 where it happened the most, and its effect fades away as the travel distance increases. In the below-mentioned formula this is controlled via the exponent k, i.e. the larger k is, the faster the decay of the transience effect with the travel distance is.
• M designates the magnitude of the pressure transient
• di designates the distance from the location of the transient event E to the center of the i-th segment Si of the distribution network 10.
• Fig. 4 shows a diagram illustrating the distance-dependent decay of the transient effects to hazards in the distribution network 10.
• the x-axis shows the distance d from the location of the transient event E in the distribution network 10
• the y-axis shows the exponent k of above-noted formulas.
• Fig. 5 shows a schematic block diagram of an embodiment of a device 50 for operating a distribution network 10, as depicted in Fig. 2.
• the device 50 of Fig. 5 shows a plurality 51 of sensors 31, 32, 33 for sensing wavefront characteristics.
• Each of the sensors 31 - 33 is located at a certain measurement location LI - L3 in the distribution network 10 (see Fig. 2).
• At least a subset of the sensors 31 - 33 is configured to obtain values of transient event parameters of a certain transient event E in the distribution network 10 (see Fig. 2).
• the device 50 includes a determining unit 52 and an executing unit 53.
• the determining unit 52 is configured to determine a risk factor indicating a hazard in the distribution network 10 in dependence on the obtained values for the transient event parameters and actual values of network parameters being indicative for the distribution network 10.
• the executing unit 53 is configured to execute one of a number of actions at the distribution network 10 in dependence on the determined risk factor.
• Fig. 6 shows a ⁇ sequence of method steps of a third embodiment of a method for operating a distribution network 10.
• the third embodiment of Fig. 6 is based on the first embodiment of Fig. 1, wherein method step S10 of Fig. 1 is embodied by method steps Sll to S13 according to Fig. 6 and method step S20 of Fig. 1 is embodied by method steps S20a - S20c according to Fig. 6.
• method step S10 includes steps Sll - S13:
• step Sll wavefront characteristics caused by the certain transient event E in the distribution network 10 are sensed using at least said subset of the N sensors 31 - 33.
• step S12 signals from said subset of the N sensors 31 - 33 are collected. Said signals are indicative for the sensed wavefront characteristics.
• step S13 the collected signals are processed for obtaining said transient event parameters.
• said processing may be executed by a central processing unit of the distribution network 10.
• method step S20 of Fig. 6 includes steps S20a - S20c :
• step S20a a model for modeling a hazard in the distribution network 10 is provided.
• Said model is time- dependent and uses a factor vector representing the network parameters, the load-related factors including the transient event parameters, and the external factors.
• step S20b an actual hazard is modeled by applying determined actual values at least including obtained values of load-related factors including the transient event parameters, the actual values of the network parameters and the actual values of the external factors for the factor vector to the provided model.
• step S20c the risk factor is determined in dependence on the modeled actual hazard.

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• 2019
  • 2019-04-30 EP EP19736832.7A patent/EP3942271A1/de active Pending
  • 2019-04-30 CN CN201980095885.9A patent/CN113906280A/zh active Pending
  • 2019-04-30 WO PCT/RU2019/000304 patent/WO2020185113A1/en unknown

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