Method and system for assisting in the prioritization of maintenance interventions in an electrical distribution network

Abstract

A method for assisting in the prioritization of maintenance interventions in an electrical distribution network comprising a plurality of sensors (Cij) capable of communicating within the network comprises: a determination (21), for at least one sensor, of a probability value of success of a remote maintenance intervention, from a prediction model supplied by a plurality of variables (Vij) representative of a communication link of the sensor within the network; for at least one sensor having a communication malfunction, a collection of contextual data (Dctxt(ij)); an analysis (22) of the contextual data to assign a malfunction typology to the sensor; a transmission, to an HMI (23), of the probability value of success of remote intervention and the malfunction typology, for restitution to a human to decide on at least one priority maintenance intervention. Abstract figure: Figure 3

Description

Title of the invention: Method and system for assisting in the prioritization of maintenance interventions in an electrical distribution network Technical field

[0001] The present disclosure relates to the field of electrical distribution networks equipped with communicating meters. More specifically, the present disclosure relates to the management and maintenance of a fleet of such communicating meters, in particular for the purpose of optimizing its performance level. Prior art

[0002] A communicating meter is a meter using so-called AMR (Automated Meter Reading) technologies which measures electricity consumption in detail and precisely, in real time, and is configured to transmit this measurement data to the electricity distribution network manager responsible for metering.

[0003] Some of these communicating meters are also remotely programmable and equipped with a remote cut-off device; this is referred to as “AMM” technology (for “Advanced Meter Management”). The introduction of such communicating meters into distribution networks allows their evolution towards “smart grids”.

[0004] Communicating meters also allow certain operations to be carried out remotely, without a technician having to travel or the customer being present. They also improve and speed up diagnostics in the event of a malfunction in an electrical installation. By eliminating the manual tasks of reading, changing power, cutting off or re-commissioning, these meters allow a reduction in energy distribution costs and intervention times and contribute, for the network manager, to guaranteeing a balance between supply and demand.

[0005] Indeed, to respond to the growing problems of optimizing the energy balance and reducing the carbon footprint, electricity distribution network managers must face increasingly strict objectives, particularly during peak electricity consumption in winter. To meet these objectives, it is necessary for them to be able to carry out targeted load shedding operations, by carrying out local supply cuts with fine granularity, and one-off power shaving operations, which consist of example, to limit the power of a customer installation to 20 or 30% of the subscribed power, for a short period of a few hours, to avoid the occurrence of a general blackout (in French "power failure") on all or part of the distribution network.

[0006] These operations are made possible thanks to the widespread use of communicating meters in the distribution network, which can be controlled individually, by remote disconnection or sending a power clipping instruction from the network manager's central information system. Communication with the meters can take place via various channels.

[0007] In the French electricity distribution network operated by Enedis®, the communicating meters are organized into clusters of meters connected to the same concentrator forming an intermediate level between the central information system and the meters of the cluster that it is responsible for controlling. Communication between the meters of a cluster and the concentrator to which they are attached is carried out by a Power Line Communication (PLC) technology. The PLC technology then forms a first level of communication, on the low voltage network. Depending on the geographical configuration and the density of the distribution network mesh, a concentrator can thus control a cluster of approximately 1 to 1400 meters. It is generally installed in an HTA / LV (High Voltage A / Low Voltage) transformer and distribution station.

[0008] Among its various functions, the concentrator plays in particular the role of communication relay between the meters of the cluster and the central information system, with which it communicates by radio waves, for example via mobile telephone networks of type 2G, 3G, 4G or more, which constitute a second level of communication. The concentrator thus constitutes a gateway between the PLC communication network and the networks of the telecommunications operators.

[0009] The French electricity distribution network currently relies on a fleet of around 35 million communicating meters, the level of performance of which is highly dependent on the performance of the communication link between the meters and the central information system.

[0010] Indeed, when the communication link is broken, the advantages of a remotely operated (or teleoperated) system disappear, in particular the services provided quickly and without constraints for customers. The prolonged absence of meter communication and the difficulties in teleoperating them are complex to diagnose and lead to: - daily requests to expert technicians in the distribution network; - an additional delay in the provision of intervention services; - a deterioration in the quality of value-added services.

[0011] There is therefore a need for a technique for maintaining the performance level of a fleet of communicating meters, by automatically detecting possible communication malfunctions and by characterizing the typology of malfunction, so as to enable maintenance interventions on the fleet of meters to be prioritized effectively. Summary

[0012] The present disclosure meets this need.

[0013] A method for assisting in the prioritization of maintenance interventions in an electrical distribution network is proposed, implemented by computer means, the electrical distribution network comprising a plurality of sensors capable of communicating within the network, comprising: a. a determination, for at least one sensor of the plurality of sensors, of a probability value of success of a remote maintenance intervention on the sensor(s), from a prediction model supplied by a plurality of variables representative of a communication link of the sensor(s) within the network; b. for at least one sensor of the plurality of sensors exhibiting a communication malfunction, collecting contextual data relating to the sensor; c. an analysis of the contextual data collected to assign, to the sensor presenting a communication malfunction, a malfunction typology, based on the contextual data analyzed; d. a transmission, to a human-machine interface, of the probability value of success of remote intervention and the typology of malfunction assigned to the sensor(s), for restitution to a human to decide on at least one priority maintenance intervention on at least one of the sensors of the plurality of sensors.

[0014] According to another aspect, a computer system is proposed for assisting in the prioritization of maintenance interventions in an electrical distribution network, the electrical distribution network comprising a plurality of sensors capable of communicating within the network, the computer system comprising at least one processor configured to execute steps of: a. determination, for at least one sensor of the plurality of sensors, of a probability value of success of a remote maintenance intervention on the sensor(s), from a prediction model supplied by a plurality of variables representative of a communication link of the sensor(s) within the network; b. collection of contextual data relating to at least one sensor of the plurality of sensors exhibiting a communication malfunction; c. analysis of the contextual data collected to assign, to the sensor presenting a communication malfunction, a malfunction typology, based on the contextual data analyzed; a. transmission, to a human-machine interface, of the probability value of success of remote intervention and the typology of malfunction assigned to the sensor(s), for restitution to a human to decide on at least one priority maintenance intervention on at least one of the sensors of the plurality of sensors.

[0015] According to another aspect, there is provided a computer program comprising instructions for implementing all or part of a method as defined herein when this program is executed by a processor.

[0016] According to another aspect, there is provided a non-transitory, computer-readable recording medium on which such a program is recorded.

[0017] Such a recording medium may be any entity or device capable of storing the program. For example, the medium may comprise a storage means, such as a ROM, for example a CD ROM or a microelectronic circuit ROM, or a magnetic recording means, for example a USB key or a hard disk.

[0018] On the other hand, such a recording medium may be a transmissible medium such as an electrical or optical signal, which may be conveyed via an electrical or optical cable, by radio or by other means, so that the computer program it contains is remotely executable. The program according to the invention may in particular be downloaded over a network, for example the Internet.

[0019] Alternatively, the recording medium may be an integrated circuit in which the program is incorporated, the circuit being adapted to execute or to be used in the execution of the aforementioned method for assisting in the prioritization of maintenance interventions.

[0020] The features set out in the following paragraphs may, optionally, be implemented, independently of one another or in combination with one another:

[0021] The variables representative of a communication link of the sensor(s) belong to the group comprising: - state variables of the sensor(s) within the network; - state variables of a concentrator to which the sensor(s) is(are) attached within the network; - state variables of a communicating link between the sensor(s) and the concentrator to which it is attached; - collection variables from the sensor(s); - collection variables from the concentrator to which the sensor(s) is(are) attached; - discovery variables of the sensor(s) within a local communication network with the concentrator to which it is attached; - reboot variables of the concentrator to which the sensor(s) is / are attached; - variables representative of radio coverage of the extended communication network to which the concentrator to which the sensor(s) is(are) attached belongs.

[0022] The probability value of success of a remote maintenance intervention is a teleoperability indicator capable of taking a value from among: - poor teleoperability value; - an average teleoperability value; - a good teleoperability value; - excellent teleoperability value.

[0023] The analysis of the collected contextual data implements an elimination algorithm from a corpus of sensors presenting a communication malfunction, and one of the malfunction typologies delivered at the output of the algorithm is a probability of hardware failure of the sensor(s).

[0024] The corpus of sensors includes all sensors not collecting for a given number of days.

[0025] The collected contextual data belong to the group comprising: - data relating to a communication performance of sensors of a cluster of sensors to which the sensor(s) exhibiting a communication malfunction belong(s); - data relating to at least one communication event from the sensor(s) occurring over the determined number of days; - data relating to a contractual situation of the sensor(s) presenting a communication malfunction; - data relating to a hardware intervention on the sensor(s) presenting a communication malfunction.

[0026] The sensors are communicating electric meters. Brief description of the drawings

[0027] Other characteristics, details and advantages will appear on reading the detailed description below, and on analyzing the attached drawings, in which: Fig.l

[0028] [Fig.l] presents in the form of a synoptic diagram an electrical distribution network, from the HTA arrival to the final connection at the customer's premises. Fig. 2

[0029] [Fig.2] schematically represents the communicating chain of an electrical distribution network according to one embodiment. Fig. 3

[0030] [Fig.3] presents a flowchart of the processing carried out by the CPU of the processing unit of the central information system of [Fig.l] according to one embodiment. Fig. 4

[0031] [Fig.4] shows the failure detection sensitivity and accuracy curves used to choose a teleoperation success prediction model according to one embodiment. Fig. 5

[0032] [Fig.5] shows the success detection sensitivity and accuracy curves used to choose a teleoperation success prediction model according to one embodiment. Fig. 6

[0033] [Fig.6] shows a curve representative of a performance of the prediction models, as a function of a proportion of failed teleoperations, according to one embodiment. Fig. 7

[0034] [Fig.7] shows a synthetic flowchart of an algorithm for determining a typology of meter malfunction according to one embodiment. Description of the embodiments

[0035] The technique of the present disclosure is based on a completely new and inventive approach to optimizing the performance of a sensor park in an electrical distribution network. Indeed, it is based on the joint determination of two important parameters for optimizing the management of maintenance interventions on such a park, namely: - the probability of success of a remote maintenance intervention on a sensor, which provides an interesting indication as to whether or not it is appropriate to have a technician travel to intervene on a sensor presenting a communication malfunction; - a typology of sensor malfunction, which makes it possible in particular to identify whether a sensor communication malfunction results from a hardware failure of the sensor or from an environmental context, for example. This typology of malfunction can advantageously be taken into account when deciding on the opportunity or necessity of moving a technician to intervene on the sensor.

[0036] Such a technique is advantageously implemented in the information system of the sensor park of the electrical distribution network, this information system comprising means of communication with all of the sensors of the park, for the collection of measurement data and contextual data, and one or more processors configured to calculate a probability of success of a remote maintenance intervention on the basis of a predictive algorithm based for example on a gradient amplification method of the XGBoost type, and to determine a typology of malfunction of a sensor exhibiting a communication malfunction.

[0037] These two parameters, which are transmitted by the information system to be presented on a human-machine interface, allow the personnel of the electricity distribution network manager, and in particular non-expert personnel, to carry out a rapid and reliable diagnosis of the faults observed on the sensor park.

[0038] The first technical effect of determining and transmitting these two parameters is to enable the identification of strictly necessary maintenance interventions, since they correspond to a hardware failure of the sensor which requires the travel of a technician, and on the contrary, to avoid any unnecessary travel of the technician, for example when the probability of success of a remote maintenance intervention is high and the malfunction does not result from a hardware failure of the sensor. It is thus possible to optimize the technicians' intervention schedule, therefore reducing the waiting time before a strictly necessary intervention, by eliminating all cases of unnecessary interventions.This also has the effect of reducing the carbon footprint of the electricity distribution network manager, by avoiding unnecessary travel by intervention technicians and by optimizing the management of the vehicle fleet made available to them and the resulting fuel consumption.

[0039] The determination and transmission of these two parameters also has the technical effect of improving the efficiency of maintenance interventions on the sensor park, and therefore of maintaining an optimal level of performance of the park, which is capable of satisfying the load shedding and power clipping objectives in all circumstances, thanks to remote operations targeted on the sensors.

[0040] Reference is now made to [Fig. 1], which shows in the form of a block diagram the structure of a low-voltage electrical distribution network, from the high-voltage inlet A to the final customer connection.

[0041] An HTA / BT transformer station referenced 10 comprises one or more HTA / BT transformers 11, which are supplied at the input by the HTA arrival referenced 12 (typically of the order of 20000V approximately) and allow the voltage of the distributed electricity to be lowered, by providing at the output a LV (low voltage) source dipole referenced 13. Such an HTA / LV transformer station 10 generally houses a concentrator Ki, to which is associated a low voltage panel referenced 14. Each line at the output of the panel corresponds to a low voltage feeder, or LV feeder, referenced 15 (i.e. a star branch behind the low voltage panel). One or more LV sections (i.e. portion(s) of the low voltage network) referenced 16 allow a LV feeder 15 to be connected to one or more connections referenced 17. A connection referenced 18 (defined as a point in the low voltage network to which the customer's installation is connected) connects a connection 17 to a communicating meter Cij.

[0042] [Fig.2] schematically represents the communicating chain 1 of the electrical distribution network of [Fig.l], which comprises: - a central information system SI referenced 2; - a plurality of concentrators referenced Kl, K2, ..., KN, which communicate by radio with the IS referenced 2; - a plurality of sensors Cij, where i refers to the concentrator Ki to which a sensor Cij is attached, and where j refers to an index of the sensor Cij in the cluster of sensors controlled by the concentrator Ki.

[0043] In the following, we will focus on describing a particular example of embodiment, in which the sensors considered are communicating meters; for example, in the case of the French electricity distribution network, Linky® meters. In the following, we may designate by the term sensor, or meter, or even communicating meter, such a sensor of an electricity distribution network, configured to carry out measurements of electricity consumption of a customer installation, to transmit them directly or indirectly to a central information system of the electricity distribution network, and remotely programmable by the latter.

[0044] It is recalled that a concentrator Ki is an industrial computer, installed in an HTA / LV transformer station 10, which is responsible for controlling a cluster of 1 to 1400 communicating meters Cij. As a simple illustrative, and non-limiting, example, [Fig.l] shows a cluster of three meters referenced Cil to C13 for the concentrator Kl, a cluster of four sensors referenced C21 to C24 for the concentrator K2, and a cluster of six sensors referenced CN1 to CN6 for the concentrator KN.

[0045] In the example of [Fig.l], the communication within a cluster between a concentrator Ki and the communicating meters Cij is based on a power line carrier technology, of generation G1 or G3 for example. The communication between a concentrator Ki and the central information system SI referenced 2 is based on transmission by radio waves, for example by a radio communications network 2G, 3G, 4G, or in the future 5G or beyond, mobile phones from a telecommunications operator.

[0046] The information system SI referenced 2 conventionally comprises memories M associated with one or more CPU processor(s). The memories can be of the ROM (Read Only Memory) or RAM (Random Access Memory) or Flash type. They allow in particular the storage of data received from the concentrators Ki by the Tx / Rx transmission / reception module of the information system. This Tx / Rx transmission / reception module also controls the transmission, to a human machine interface IHM referenced 3, of the various indicators and parameters determined and calculated by the CPU processor, as will be seen in more detail later in relation to [Fig.3].

[0047] It will be noted that the term module can correspond to a software component as well as to a hardware component or a set of hardware and software components, a software component itself corresponding to one or more computer programs or subroutines or more generally to any element of a program capable of implementing a function or a set of functions as described for the modules concerned. In the same way, a hardware component corresponds to any element of a hardware assembly capable of implementing a function or a set of functions for the module concerned (integrated circuit, smart card, memory card, etc.).

[0048] More generally, such an information system SI referenced 2 comprises a random access memory (for example a RAM memory), a processing unit equipped for example with a CPU processor, and controlled by a computer program, representative of the method for assisting in the prioritization of maintenance interventions, stored in a read-only memory (for example a ROM memory or a hard disk). Upon initialization, the code instructions of the computer program are for example loaded into the random access memory before being executed by the CPU processor of the processing unit. The random access memory contains in particular different variables representative of the communication links of the sensors Cij within the communicating chain referenced 1, as well as contextual data collected in the communicating chain 1.The processor of the processing unit controls the execution of a prediction model for determining probability values of success of a remote maintenance intervention on the communicating meters Cij, as well as the analysis of the contextual data stored in the RAM to assign, to each of the meters Cij presenting a communication malfunction, a malfunction typology. It also controls the transmission, by the transmission / reception module Tx / Rx, of the parameters that it has calculated, to 1THM referenced 3, which can take for example the form of a web application.

[0049] [Fig.2] illustrates only one particular way, among several possible ones, of implementing the information system SI referenced 2, so that it performs the steps of the method detailed below, in relation to [Fig.3] (in any one of the different embodiments, or in a combination of these embodiments). Indeed, these steps can be carried out indifferently on a reprogrammable computing machine (a PC computer, a DSP processor or a microcontroller) executing a program comprising a sequence of instructions, or on a dedicated computing machine (for example a set of logic gates such as an FPGA or an ASIC, or any other hardware module).

[0050] We now present, in relation to [Fig.3], the processing carried out by the CPU of the processing unit of the IS referenced 2, in one embodiment.

[0051] We consider a corpus {Cij} of communicating meters, as illustrated for example in [Fig.2]. From this corpus of sensors, two main indicators are determined: - an indicator of the probability of success of a teleoperation PSUCc(ij); - in the case of a sensor Cij exhibiting a communication malfunction, a malfunction typology TYP_DYS(ij), which may take the form, in a simplified embodiment, of an indicator of high probability of hardware failure of the communicating meter Cij.

[0052] We will first of all describe below in more detail the step referenced 21 of determining a value of probability of success of a tele-operation Psucc(ij) on a counter Cij, denoted PRED({Vij}). This step PRED({Vij}) referenced 21 is based on a data science algorithm, for example of the gradient accelerator type (i.e. XGBoost), supplied as input by a set of variables {Vij} representative of the operation of the communicating chain 1, which can be grouped into the following domains: - the state in the communicating chain (installed, uncovered, etc.) of the Cij meters and the Ki concentrators; - the communicating link between the Cij meter and the Ki concentrator; - the collection of Cij meters; - the collection of Ki concentrators; - the discovery of Cij counters; - the “reboot” (in French, restarting) of the Ki concentrators; - the WAN (Wide Area Network) coverage of the Ki (session radius) concentrator.

[0053] These variables make it possible to exhaustively represent all aspects of the operation of the communicating chain 1 and constitute a set of relevant data to feed the prediction model PRED({Vij}).

[0054] It is recalled that the term “collection” designates the daily cyclical reading of the electricity consumption data of a customer installation measured by a meter Cij; the term “discovery” designates the process of integrating a meter Cij into the local PAN network (for “Private Area Network”) connecting the concentrator Ki to all the meters Cij in its cluster.

[0055] Without being exhaustive here as to the set of variables {Vij} which can be taken into account to feed the prediction model, it can be indicated, by way of example only, that the variables representative of the communicating link between the meter Cij and the concentrator Ki can include for example indicators of proportion of time where the current Cij-Ki link is labeled non-existent, loyal or secure; similarly, the variables representative of the collection of the concentrators Ki can include for example a number of distinct concentrators having collected an index or a maximum power of the meters Cij, a duration in hours before the reading of the index by the concentrator, or even an average of the daily duration in hours before reading of the maximum power of a meter Cij by the concentrator Ki.These different variables depend in particular on the organization of the information system of the electricity distribution network manager, and we understand that the reliability and precision of the model for predicting the probability of success of the tele-operation depend in particular on the volume and diversity of the variables {Vij} which feed it.

[0056] To build an effective prediction model, it is possible to preprocess the data {Vij}, in particular to eliminate non-significant variables from the set of variables.

[0057] In order to build the most efficient model possible, different models are trained on training data and then tested on test data. Thus, for example, the proportion of failed services in the training data is varied per iteration, between 1% and 50% in steps of 1%. At each iteration, an XGBoost model is built with all the indicators defined above. The performances are tested for each of the models with a success / failure threshold varying per iteration from 0 to 100% in steps of 1%. The following metrics are calculated for each model built and for each success / failure threshold: - the accuracy of detection of teleoperation failures; - the sensitivity of detection of teleoperation failures; - the accuracy of detection of teleoperation successes; - the sensitivity of detection of teleoperation successes; - failure detection performance defined as follows: failure detection accuracy + 1.5 x failure detection sensitivity (this formula favors sensitivity over accuracy).

[0058] These latter metrics make it possible to obtain for an XGBoost model with a given proportion of failed tele-operations (for example, a common tele-operation allowing the programming of communicating meters, and called SMC01 for “Service Métier Comptage” in the Enedis® network), precision and sensitivity curves for the detection of successes and failures, illustrated in figures 4 and 5. Thus, [Fig.4] presents the sensitivity curves (curve referenced 30) and precision curves (curve referenced 31) for the detection of failures, in the case of a proportion of SMC01 failures of 10% in the training sample; [Fig.5] presents the sensitivity curves (curve referenced 40) and precision curves (curve referenced 41) for the detection of successes, in the case of a proportion of SMC01 failures of 10% in the training sample.

[0059] By calculating the acceleration of these curves, it is possible to determine the following three thresholds: - a failure threshold, referenced 32: first success / failure threshold in ascending order for which the acceleration of the sensitivity of failure detection changes sign and is strictly less than 20 in absolute value for the sixth time; - a success threshold, referenced 42: first success / failure threshold in decreasing order for which the acceleration of the sensitivity of success detection changes sign and is strictly less than 20 in absolute value for the sixth time; - an excellent success probability threshold, referenced 43: first success / failure threshold in decreasing order for which the acceleration of the precision of success detection changes sign and is strictly less than 20 in absolute value for the sixth time.

[0060] Selecting the sixth change of sign of the acceleration, in ascending or descending order, makes it possible to position oneself after the strong increase in sensitivity for the detection of failures and before the strong drop in sensitivity for the detection of successes. In the case of the failure threshold referenced 32, the method for determining this value aims to detect failures efficiently. For the success threshold referenced 42, the method focuses on detecting successes with excellent precision and high sensitivity. Finally, the method for determining the threshold of excellent success probability, referenced 43, makes it possible to define the level from which the detection of successes offers very high precision.

[0061] We then obtain three thresholds which define the teleoperability ranges (poor, average, good and excellent) for each model with a given proportion of teleoperations (for example, SMC01) in failure.

[0062] The selected model is the one for which the failure detection performance, defined above, is the highest, as illustrated in [Fig.6].

[0063] At the end of a training phase of the selected prediction model, it is used during the step referenced 21, to determine, for each counter Cij of the starting corpus referenced 20, a probability of success of a remote maintenance intervention operation, also called probability of success of tele-operation, and noted Psucc(ij).

[0064] In one embodiment, this probability Psucc(ij) takes four distinct discrete values: - excellent, for a probability Psucc(ij) greater than a threshold “threshold_excel” 43 of excellent success probability defining the limit between the good teleoperability zone and the excellent teleoperability zone; - poor, for a probability Psucc(ij) lower than a failure threshold “threshold_ech” 32 defining the limit between the poor teleoperability zone and the average teleoperability zone; - average, for a probability Psucc(ij) greater than the failure threshold “threshold_ech” 32 and less than a success threshold “threshold_succ” 42 defining the limit between the average teleoperability zone and the good teleoperability zone; - good, for a probability PSUCc(ij), higher than the “threshold_succ” threshold 42 of success and lower than the “threshold_excel” threshold 43 of excellent success probability.

[0065] The restitution 23 to a non-expert user, on a human-machine interface HMI, of one of these four discrete values, corresponding to the four ranges of teleoperability (poor, average, good and excellent), provides simple and directly accessible information regarding the chances of success of a remote maintenance intervention.

[0066] We now present in more detail the processing carried out by the CPU processor of the central information system during the step referenced 22 of analysis of the malfunction typology of a counter AN_TYPij.

[0067] Meter malfunctions identified by central information system supervision are mainly caused by alarms reported by non-silent meters. However, a significant proportion of non-communicating meters have malfunctions that are worth characterizing in order to then deal with them correctly: PLC noise, concentrator malfunction, individual main circuit breaker (ICCB) cut-off, etc.

[0068] Within a corpus of meters exhibiting a communication malfunction, identifying the meter(s) exhibiting a high probability of failure aims to identify malfunctions attributable to the meter, and which require maintenance.

[0069] In a simple variant, the step referenced 22 implements the elimination processing algorithm illustrated in [Fig.7], which allows the detection of meters presenting a high probability of hardware failure. Such an algorithm is fed at the input by the set {Cij_dys} referenced 60 of the meters presenting a communication malfunction and allows, over the course of the steps referenced E1 to E7, to eliminate all the meters from this corpus 60 for which there is an external cause of malfunction, or a disturbance which could explain the absence or malfunction of communication. The set {Cij_dys} referenced 60 includes for example all the meters of the distribution network which have not been collecting for more than sixty days (i.e. which have not transmitted a consumption index of the customer installation to which they belong to the central information system for more than sixty days). In another embodiment, this set 60 can include all the meters not collecting for more than fifteen days, which represents a larger volume of data to process, but makes it possible to optimize the operation of the meter pool.

[0070] At the end of step E7, we obtain the set {Cij_def} referenced 70 of the isolated counters on their cluster, with neighboring hardware functioning properly, without obvious reason for non-communication. They are then suspected of hardware failure.

[0071] The meter failure algorithm of [Fig.7] is applied for example every day to the entire meter pool in order to have a daily list of meters suspected of hardware failure. For example, we consider that the algorithm of [Fig.7] is executed by the CPU processor of the central information system on the day noted DAY_REF.

[0072] During step El, the central information system collects the list of meters not collecting for more than sixty days at JOUR_REF with some of their characteristics, such as: - an identifier of the reference concentrator; - an identifier of the concentrator communicating with the meter (which may be different from the reference concentrator); - a connection identifier 17; - an identifier for section 16; - Reconciliation date (i.e. date of convergence of the data sent by the meter via the communication link and the data entered into the central information system for this meter by a technician) of the meter; - Indicator of a collection in the past; - Date of last collection; - Date of first collection; - Date of installation of the meter; - Date of installation of the concentrator; - PLC generation of the meter; - PLC generation from the concentrator.

[0073] These data are sorted and analyzed to identify, within the set {Cij_dys} referenced 60, the silent counters, i.e. reconciled, with a first collection date that is not empty and prior to JOUR_REF-60 days: they feed a step referenced E2. The counters excluded during this identification are kept in a list of non-silent counters {Cij_Typl} referenced 61, optionally with the reason for this exclusion.

[0074] During step E2, the CPU of the central information system analyzes the eligibility for failure of the list of meters received from step El. For a meter in this list to be eligible for failure, it must meet the following conditions: - reference hub identifier not empty and different from '0'; - line identifier not empty and different from '0'; - departure identifier 14 not empty and different from '0'; - section identifier 16 not empty and different from '0'; - CPL generation of the meter and CPL generation of the concentrator non-zero and identical; - Date of installation of the concentrator strictly less than DAY_REF - 5 days; - Date of removal of the empty meter or after JOUR_REF.

[0075] The counters eligible for failure at the end of the processing step E2 feed the processing step E3. The counters not eligible for failure are kept in a list of counters not eligible for failure {Cij_Typ2} referenced 62, optionally with the reason for their non-eligibility.

[0076] Furthermore, the meters with a high probability of failure for DAY_REF - 1 day are imported. If at DAY_REF, certain meters are no longer suspected of failure whereas they were the day before, the reason for the failure exit is studied. In certain specific cases, the meters are then caught up to keep them in the list of faulty ones processed throughout steps E2 to E7, because the cause of their exit is not attributable to the meter. For example, if a meter was faulty the day before, there is no reason why it should no longer be so at DAY_REF because its concentrator had too low a collection performance.

[0077] For each analysis level E2 to E7 allowing the exclusion of counters from the list of potential faults, a referenced list 63 of the counters which exit the failure (EXIT_DEF) and a list 62 of the counters which are simply excluded from the failure are supplied, optionally with the reason for the exit or exclusion.

[0078] For meters that failed at DAY_REF - 1 day and that did not fail at DAY_REF, we check whether or not we have collected them for more than sixty days using data collected by the central information system. If, for a meter, we If a number of consecutive collection days is greater than or equal to 1, or a number of consecutive non-collection days is less than sixty, it is added to list 63 of failure exits.

[0079] Similarly, if a meter faulty at DAY_REF - 1 day is not faulty at DAY_REF due to a removal date prior to DAY_REF, it is added to list 63 of fault outputs.

[0080] During a step referenced E3, the clusters to which the counters suspected of failure at the output of step E2 are attached are then studied, in order to ensure that they are in a high-performance environment.

[0081] To keep a meter in the list of potential faults, we then check that at least one of the following conditions is met: - On the line: at least five meters and only one meter not having collected at DAY_REF - 1 day; - On departure: at least five meters and only one meter not having collected on DAY_REF - 1 day; - On the section: at least five meters and only one meter not having collected on DAY_REF - 1 day; - On the connection: at least five meters and more than 80% of the connection meters having collected at JOUR_REF or at JOUR_REF - 1 day.

[0082] Depending on their previous status, counters that do not meet any of the previous conditions are added to list 63 of failure outputs or to list 62 of failure exclusions.

[0083] During a step referenced E4, the number of meters that stopped collecting on the same date is checked for each concentrator. Thus, if several meters stopped collecting on the same day, they are eliminated from the list of meters suspected of failure which feeds the following step E5. Depending on their state the day before, the meters that have simultaneous stops are added to the list 63 of failure outputs or to the list 62 of failure exclusions.

[0084] During a step referenced E5, for each meter suspected of failure constituting an input data of this step, potential communications from this meter are sought, which could lead to its exclusion from the list of potential failures feeding the following step E6. These communications are sought over the period [DAY_REF - 60; DAY_REF], from the data collected by the central information system.

[0085] Depending on their standby status, the meters for which a communication is observed (for example, a maximum power collection, or a successful teleoperation) are added to the list 63 of failure outputs or to the list 62 of failure exclusions.

[0086] During a step referenced E6, the list of meters with a high probability of failure provided at the output of step E5 is analyzed to identify meters having a cut-off at the circuit breaker (CCPI) or a cut-off SMC01 (Service Métier Comptage 1, remote programming service, operated remotely and allowing the meter breaker to be opened): the meters thus identified are excluded from the list of potential failures provided at the input of the following step E7.

[0087] Depending on their previous day status, the meters affected by suspected outage are added to list 63 of failure outputs or to list 62 of failure exclusions.

[0088] During a step referenced E7, for each Measurement Reference Point (PRM - a series of numbers that identifies a customer installation on the electricity distribution network) for which a meter failure is suspected at the output of step E6, it is verified that there has been no meter change: if the existence of another meter at a date later than the installation date of the initial meter and with the status 'NEW' is found on the same PRM, then the PRM and the meters are excluded from the list of faulty meters delivered at the output of step E7. The existence of the different meters on the PRMs, the installation date and the status of the meters are part of the data collected by the central information system.

[0089] Depending on their previous day status, the counters excluded due to change on the PRM are added to the list 63 of failure outputs or to the list 62 of failure exclusions.

[0090] Finally, as symbolized by the arrow referenced 64, a catch-up of faulty counters is carried out: the counters appearing in the list 63 of exit of the failure due to context (step E3) or SMC01 of cut-off (step E6), are reintegrated into the list 70 {Cij_def} of counters with a high probability of failure for JOUR_REF.

[0091] Alternatively, each counter in the list 62 of failure exclusions is recorded in association with a reason for failure exclusion, as identified during one of steps E2 to E7, making it possible to identify a typology of malfunction of the counter.

[0092] During the step referenced 23 of [Fig.3], this typology of malfunction can be returned to a user on an HMI; as a variant, this restitution can consist of simply providing information of high probability of hardware failure for the relevant meters of the list 70 {Cij_def}.

[0093] For example, this HMI takes the form of a web application, which a user can query from a meter, concentrator, or transformer station identifier of the distribution network. The restitution display can take the form of a map of the distribution network, on which these different devices are reported, in association with some of their characteristics. The user can, for example, access a detailed sheet for each meter, listing data such as its status, installation date, line ID, type of equipment, concentrator ID, any planned intervention date, etc. In addition, this sheet indicates for each meter: - a teleoperability indicator (excellent, good, average or poor) as determined during step referenced 21 of [Fig.3]; - a probability of hardware failure (or a typology of malfunction), as determined during step referenced 22 of [Fig.3].

[0094] The user can then use these two indicators to define the maintenance activity of the meter fleet, by targeting maintenance interventions as precisely as possible and limiting unnecessary actions, and therefore the associated losses of time, money and fuel. These indicators can also be used to better guide the processing of customer intervention requests, remotely or locally: they thus make it possible to limit the travel of maintenance technicians and improve customer satisfaction. Industrial application

[0095] These technical solutions can be applied in any type of electrical distribution network comprising communicating sensors. List of reference signs

[0096] 1: communicating chain; 2: central information system; 3: HMI; 10: HTA / LV transformer station; 11: HTA / LV transformer; 12: HTA arrival; 13: BT source dipole; 14: BT table; 15: BT departure; 16: BT section; 17: connection; 18: connection; Ki: concentrator i; Cij: counter j of cluster i; Vij: variables representing a communication link of the counter Cij; Dctxt(ij): contextual data relating to the counter Cij; 20: corpus of counters {Cij}; 21: algorithm for determining a probability of success of teleoperation; 22: algorithm for determining a typology of dysfunction; 23: HMI; 30: Failure detection sensitivity curve; 31: Failure detection accuracy curve; 32: failure threshold; 40: success detection accuracy curve; 41: success detection sensitivity curve; 42: success threshold; 43: excellent probability threshold; 60: corpus {Cij_dys} of meters presenting a communication malfunction; 61: list of non-silent counters; 62: list of counters excluded from hardware failure; 63: list of counters exiting from failure; 64: correction of faulty meters; 70: list of counters with high probability of hardware failure; El: collection of contextual data and exclusion of non-silent meters E2: failure eligibility analysis; E3: contextual environment analysis; E4: search for simultaneous stops on a cluster; E5: analysis of communications; E6: search for SMC01 cut-off; E7: search for meter change.

Claims

Claims

1. Method for assisting in the prioritization of maintenance interventions in an electrical distribution network, implemented by computer means (2), said electrical distribution network comprising a plurality of sensors (Cij) capable of communicating within said network, comprising: a. a determination (21), for at least one sensor of said plurality of sensors, of a probability value of success of a remote maintenance intervention on said at least one sensor, from a prediction model supplied by a plurality of variables (Vij) representative of a communication link of said at least one sensor within said network; b. for at least one sensor of said plurality of sensors exhibiting a communication malfunction, a collection of contextual data (Dctxt(ij)) relating to said sensor; c. an analysis (22) of said collected contextual data to assign, to said sensor presenting a communication malfunction, a malfunction typology, based on said analyzed contextual data; d. a transmission, to a human-machine interface (23), of said remote intervention success probability value and said malfunction typology assigned to said at least one sensor, for restitution to a human to decide on at least one priority maintenance intervention on at least one of the sensors of said plurality of sensors.

2. Method according to claim 1, characterized in that said variables (Vij) representative of a communication link of said at least one sensor belong to the group comprising: - state variables of said at least one sensor within said network; - state variables of a concentrator (Ki) to which said at least one sensor is attached within said network; - state variables of a communicating link between said at least one sensor and said concentrator to which it is attached; - collection variables of said at least one sensor; - collection variables of said concentrator to which said at least one sensor is attached; - discovery variables of said at least one sensor within a local communication network with said concentrator to which it is attached; - restart variables of said concentrator to which said at least one sensor is attached; - variables representative of radio coverage of the extended communication network to which said concentrator to which said at least one sensor is attached belongs.

3. Method according to any one of claims 1 and 2, characterized in that said value of probability of success of a remote maintenance intervention is a teleoperability indicator capable of taking a value from among: - a poor teleoperability value; - an average teleoperability value; - a good teleoperability value; - an excellent teleoperability value.

4. Method according to any one of claims 1 to 3, characterized in that said analysis (22) of said collected contextual data implements an algorithm by elimination from a corpus (60) of sensors presenting a communication malfunction, and in that one of said malfunction typologies delivered at the output of said algorithm is a probability of hardware failure of said at least one sensor.

5. Method according to claim 4, characterized in that said corpus (60) of sensors comprises all the sensors not collecting for a determined number of days.

6. Method according to claim 5, characterized in that said collected contextual data belong to the group comprising: - data relating to a communication performance of sensors of a cluster of sensors to which said at least one sensor exhibiting a communication malfunction belongs; - data relating to at least one communication event of said at least one sensor occurring during said determined number of days; - data relating to a contractual situation of said at least one sensor exhibiting a communication malfunction; - data relating to a hardware intervention on said at least one sensor exhibiting a communication malfunction.

7. Method according to any one of claims 1 to 6, characterized in that said sensors are communicating electricity meters.

8. Computer system (2) for assisting in the prioritization of maintenance interventions in an electrical distribution network, said electrical distribution network comprising a plurality of sensors (Cij) capable of communicating within said network, said computer system comprising at least one processor (CPU) configured to execute steps of: a. determining (21), for at least one sensor of said plurality of sensors, a value of probability of success of a remote maintenance intervention on said at least one sensor, from a prediction model supplied by a plurality of variables representative of a communication link of said at least one sensor within said network; b. collecting contextual data relating to at least one sensor of said plurality of sensors exhibiting a communication malfunction; c.analysis (22) of said collected contextual data to assign, to said sensor exhibiting a communication malfunction, a malfunction typology, as a function of said analyzed contextual data; d. transmission, to a human-machine interface (23), of said remote intervention success probability value and of said malfunction typology assigned to said at least one sensor, for restitution to a human to decide on at least one priority maintenance intervention on at least one of the sensors of said plurality of sensors.

9. Computer program comprising instructions for implementing the method according to one of claims 1 to 7 when this program is executed by a processor.

10. Non-transitory recording medium readable by a computer on which is recorded a program for implementing the method according to one of claims 1 to 7 when this program is executed by a processor.

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