Electronic device and method for assisting in the monitoring of an electrical cabinet, electrical current distribution system comprising such a device, and associated computer program

Abstract

Electronic device and method for monitoring an electrical cabinet, electrical current distribution system comprising such a device, and associated computer program. The present invention relates to an electronic device for monitoring an electrical cabinet (10), comprising an acquisition module, configured to acquire values ​​of quantities representative of an intensity and respectively of a voltage of an electrical current, measured by an intensity sensor (41, 42, 43, 44) and respectively a voltage sensor (51, 52, 53, 54), an estimation module, configured to calculate an estimated state of the electrical cabinet (10) via a neural network and an emission module, configured to emit a message representative of the estimated state in one form among a visual form and an audible form.According to the invention, the acquisition module is further configured to acquire a value of at least one additional quantity, and the estimation module is configured to calculate the estimated state of the electrical cabinet (10) via the neural network, further based on at least one additional quantity. Figure 1 for the abstract.

Description

Title of the invention: Electronic device and method for assisting in the monitoring of an electrical cabinet, electrical current distribution system comprising such a device, and associated computer program

[0001] The present invention relates to an electronic device and method for assisting in the monitoring of an electrical cabinet, an electrical current distribution system comprising such a device, and an associated computer program.

[0002] To monitor an electrical cabinet, and in particular to detect a risk of malfunction, such as a fire in the electrical cabinet, it is known to perform spot checks on the cabinets. However, it is not always possible to identify electrical or structural faults in the cabinet during these checks. It is also known to use sensors, in particular current, voltage, and temperature sensors, located inside the electrical cabinet, and to issue an alarm signal if a value measured by one of these sensors exceeds a predefined threshold.

[0003] CN 115 880 848 A proposes to use an artificial intelligence algorithm for detect an electrical fire in an electrical cabinet, using data relating to the temperature of components inside the electrical cabinet, leakage current, intensity and voltage of an electrical current flowing in the electrical cabinet.

[0004] However, it is necessary to make detection more efficient.

[0005] The aim of the invention is therefore to propose an electronic monitoring aid device that resolves these drawbacks and improves detection.

[0006] To this end, the invention relates to an electronic device for assisting in the monitoring of an electrical cabinet, the electrical cabinet comprising an input terminal and several output terminals, and being capable of being connected to a source via the input terminal and to a plurality of loads via the output terminals and configured to distribute an electric current from the source to the plurality of loads, the electrical cabinet comprising a disconnect switch connected to the input terminal and a main bus connected between the disconnect switch and the output terminals, the device comprising: - an acquisition module, configured to acquire values ​​of quantities representative of an intensity and respectively of a voltage of an electric current flowing between the input terminal and the output terminals, measured by an intensity sensor and respectively a voltage sensor; - an estimation module, configured to calculate an estimated state of the electrical cabinet via a neural network, each quantity acquired by the acquisition module being an input variable of the neural network, the estimated state of the electrical cabinet being an output variable of the neural network; and - a transmission module, configured to emit a message representative of the estimated state in one of two forms: a visual form and an audible form.

[0007] According to the invention, the acquisition module is further configured to acquire a value of at least one additional quantity measured by at least one additional sensor, including a gas sensor and a humidity sensor, each quantity representing respectively the quantity of gas particles and the humidity inside the electrical cabinet. Also according to the invention, the estimation module is then configured to calculate the estimated state of the electrical cabinet via the neural network as a further function of at least one additional quantity, this at least one additional quantity being an additional input variable of the neural network.

[0008] Thanks to the invention, the estimation module calculates an estimated state of the electrical cabinet using at least one additional quantity, measured by at least one additional sensor. This additional quantity provides more relevant input variables for the neural network, thereby improving the reliability of the calculation of the estimated state of the electrical cabinet.

[0009] According to other advantageous aspects of the invention, the device comprises one or more of the following features, taken individually or in all technically possible combinations:

[0010] -The acquisition module is further configured to acquire the values ​​of the additional quantities measured by the humidity sensor and by the gas sensor.

[0011] - The acquisition module is further configured to acquire a value of quantity measured by a temperature sensor, the measured quantity being representative of a temperature inside the electrical cabinet.

[0012] - The acquisition module is further configured to acquire values ​​of quantities measured by a plurality of temperature sensors, the quantities being representative of the temperature inside the electrical cabinet, each temperature sensor being configured to measure one of the following temperatures: a temperature in the vicinity of the input terminal, a temperature in the vicinity of a respective output terminal, a temperature of the main bus and a temperature of an electrical protection device, connected between the main bus and a respective output terminal.

[0013] - The acquisition module is further configured to acquire a value of representative quantity of vibrations of the electrical cabinet, measured by a vibration sensor.

[0014] - The acquisition module is further configured to acquire a value of representative noise quantity in the electrical cabinet, measured by a noise sensor.

[0015] - The estimated state of the electrical cabinet is one state among the group consisting of: a normal state, abnormal state and critical state.

[0016] The invention also relates to a system for distributing an electric current between a source and a plurality of loads, the system comprising: - an electrical cabinet, comprising: • an input terminal, suitable for connection to the source, • several output terminals, capable of being connected to a plurality of charges, • a disconnect switch, connected to the input terminal, and • a main bus, connected between the disconnect switch and the plurality of output terminals,

[0017] the electrical cabinet being configured to distribute the electric current from the source to the plurality of loads; - an intensity sensor and respectively a voltage sensor, configured to measure values ​​of quantities representative of an intensity and respectively of a voltage of an electric current flowing between the input terminal and the output terminals of the electrical cabinet; - an electronic device for monitoring the electrical cabinet.

[0018] According to the invention, the system further comprises at least one additional sensor, either a gas sensor or a humidity sensor, configured to acquire a value of at least one additional quantity, representing respectively the quantity of gas particles and the humidity inside the electrical cabinet. Also according to the invention, the electronic monitoring device is as described in any one of the preceding claims.

[0019] The invention also relates to a method for assisting in the monitoring of an electrical cabinet, the electrical cabinet comprising an input terminal and several output terminals, and being capable of being connected to a source via the input terminal and to a plurality of loads via the output terminals and configured to distribute an electric current from the source to the plurality of loads, the electrical cabinet comprising a disconnect switch connected to the input terminal and a main bus connected between the disconnect switch and the output terminals, the method being implemented by an electronic monitoring device, the method comprising the following steps: - Acquisition of values ​​of quantities measured by an intensity sensor and respectively a voltage sensor, the quantities being representative of an intensity and respectively of a voltage of the electric current flowing between the input terminal and the output terminals of the electrical cabinet; - Calculation, via a neural network, of an estimated state of the electrical cabinet, each measured quantity being an input variable of the neural network, the estimated state being an output variable of the neural network; and - emission, in one form among a visual form and an audio form, of a message representative of the estimated state.

[0020] According to the invention, the method further comprises a step of acquiring a value of at least one additional quantity measured by at least one additional sensor from among a gas sensor and a humidity sensor, the quantity or quantities being representative respectively of a humidity level and a quantity of gas particles inside the electrical cabinet. Also according to the invention, during the calculation step, the at least one additional quantity is an additional input variable of the neural network.

[0021] The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement a method for assisting in the monitoring of an electrical cabinet as defined above.

[0022] The invention will become clearer upon reading the following description, given solely by way of non-limiting example, and made with reference to the drawings in which: - [Fig.1] [Fig.1] is a schematic representation of an electric current distribution system according to the invention; - [Fig.2] [Fig.2] is a schematic representation of an aid device monitoring an electrical cabinet according to the invention; - [Fig. 3] [Fig. 3] is a flowchart of a monitoring aid process of an electrical cabinet according to the invention.

[0023] Fig. 1 is a schematic representation of a system 1 for distributing an electric current between a source 3 and a plurality of charges 5, the distribution system 1 being intended to be connected to the source 3 and to the plurality of charges 5.

[0024] The source 3 is, for example, a transformer, such as a medium-voltage transformer or a medium-voltage to low-voltage transformer. Alternatively, the power source is another system for distributing an electric current. The power source 3 is capable of supplying an electric current to the distribution system 1, which is configured to distribute it among the loads 5.

[0025] The loads 5 consume the electrical current supplied by the source 3. The loads 5 are, for example, household appliances, servers, charging stations, etc.

[0026] The distribution system 1 includes an electrical cabinet 10. The electrical cabinet 10 is located, for example, inside a building and rests, for example, on the floor. The electrical cabinet 10 includes a medium-voltage switchboard and is advantageously configured to distribute a three-phase electrical current with a voltage between 1000 V and 60 kV. In this case, the electrical cabinet 10 typically consists of this medium-voltage switchboard.

[0027] Alternatively, the electrical cabinet 10 includes a low-voltage switchboard and is configured to distribute a three-phase electrical current with a voltage of 400V or less. In this variant, the electrical cabinet 10 typically consists of the low-voltage switchboard.

[0028] Alternatively, the electrical cabinet 10 comprises a low-voltage switchboard and a medium-voltage switchboard. In this variant, the electrical cabinet 10 typically consists of these medium-voltage and low-voltage switchboards.

[0029] The electrical cabinet 10 comprises an enclosure 11, inside which most of its components are arranged. The enclosure 11 is, for example, in the form of a box (not shown), capable of being completely closed, and typically comprising four side walls, a rear wall mechanically attached to the four side walls, and a front wall. The front wall generally forms a removable door or a movable door relative to the side walls between a closed configuration in which the box is closed and an open configuration allowing access to the components arranged inside the box.

[0030] The electrical cabinet 10 is configured to distribute the electric current from the source 3 to the plurality of loads 5. For this purpose, the electrical cabinet 10 includes an input terminal 12 and several output terminals 13. When the distribution system 1 is in operation, the input terminal 12 is connected to the source 3 and the output terminals 13 are each connected to a respective load 5.

[0031] The electrical cabinet 10 also includes a disconnector 16 and a main bus 17. The disconnector 16 is connected between the input terminal 12 and the main bus 17. The disconnector 16 is configured to interrupt the flow of electric current from the source 3 into the electrical cabinet 10, in particular to protect the loads 5 in the event of an electrical fault in the electrical cabinet 10 or upstream of the electrical cabinet 10, for example in the event of an electrical fault at the source 3.

[0032] Alternatively, the disconnector 16 is a circuit breaker, for example an air circuit breaker.

[0033] The main bus 17 is connected between the disconnector 16 and the output terminals 13. The main bus 17 is usually connected to the output terminals 13 indirectly.

[0034] Indeed, as shown in [Fig.1], the electrical cabinet 10 includes a first branch bus 18, a second branch bus 19, a first electrical protection device 22 and several second electrical protection devices 23.

[0035] The main bus 17 is connected to the first 18 and the second 19 branch bus.

[0036] The first branch bus 18 is connected to the first protection device 22. The first protection device 22 is for example an air circuit breaker, or a miniature circuit breaker, or a differential circuit breaker.

[0037] The second branch bus 19 is connected to the second protective devices 23. In the example of [Fig. 1], four second protective devices 23 are shown. The second protective devices 23 are advantageously miniature circuit breakers, or residual current circuit breakers.

[0038] Alternatively, not shown, the electrical cabinet 10 includes additional branch buses, not shown, each additional branch bus being connected to at least one additional electrical protection device.

[0039] The protection devices 22, 23 are configured to interrupt the flow of electric current from the electrical cabinet 10 to the loads 5, in particular in the event of an electrical fault downstream of the electrical cabinet 10, in particular between the output terminals 13 and the loads 5, or in the event of an electrical fault in the loads 5, in order to protect the distribution system 1, as well as the loads 5.

[0040] The electrical cabinet 10 advantageously includes a drawer 25. The second protective devices 23 are then advantageously housed in the drawer 25. The drawer 25 is typically accessible by a user, for example in order to handle the second protective devices 23, to replace them, to add them, or when the second protective devices 23 are circuit breakers, to reset them following an electrical fault.

[0041] According to an unrepresented variant, the electrical cabinet 10 includes several drawers, in which additional electrical protection devices are received.

[0042] The electrical cabinet 10 includes a plinth 27. The plinth 27 advantageously receives connection cables, for example between the source 3 and the disconnector, or between the first protection device 22 and the load 5. According to a particularly advantageous arrangement, not shown, the plinth receives all the cables connected to the protection devices and the loads.

[0043] The distribution system 1 also includes sensors. More specifically, in the example of [Fig. 1], the distribution system 1 includes current sensors 41, 42, 43, 44; voltage sensors 51, 52, 53, 54; gas sensors 56, 57; humidity sensors 58, 59; temperature sensors 61, 62, 63, 64, 65; a vibration sensor 67 and a noise sensor 69.

[0044] In the example of [Fig. 1], there are four intensity sensors 41, 42, 43 and 44, namely a first intensity sensor 41, a second intensity sensor 42, a third intensity sensor 43 and a fourth intensity sensor 44, and are configured to measure values ​​of a quantity representative of an intensity of the electric current flowing in the electrical cabinet 10. More specifically, the intensity sensors 41 to 44 are configured here to measure an electric current flowing respectively in the input terminal 12 by the first intensity sensor 41, in the first branch bus 18 by the second intensity sensor 42, in the main bus 17 by the third intensity sensor 43 and in the second branch bus 19 by the fourth intensity sensor 44.

[0045] Advantageously, the intensity sensors 41 to 44 are three-phase intensity sensors, i.e. configured to measure the intensity of each phase of the electric current.

[0046] In an alternative not shown, the distribution system 1 includes additional intensity sensors configured to measure the intensity of the electric current flowing further in the protection devices 22 and 23 and the output terminals 13.

[0047] Particularly advantageously, the distribution system 1 includes intensity sensors measuring the intensity of the electric current in each of the elements mentioned above, and furthermore, includes intensity sensors measuring the intensity of the electric current at different positions of the main bus 17 and of the first 18 and second 19 secondary buses, at each output terminal 13 and at each protection device 23.

[0048] In the example of [Fig. 1], there are four voltage sensors 51, 52, 53, and 54, namely a first voltage sensor 51, a second voltage sensor 52, a third voltage sensor 53, and a fourth voltage sensor 54, and they are configured to measure values ​​representative of a voltage of the electric current flowing in the cabinet 10. More precisely, the voltage sensors 51 to 54 measure a potential difference between the electric current flowing in the electrical cabinet 10 and a potential reference. In the example of [Fig. 1], the voltage sensors 51, 52, 53, and 54 are configured to measure values ​​representative of a voltage of the electric current flowing in the cabinet 10.l], the voltage sensors 51 to 54 are configured to measure a voltage of the electric current flowing respectively in the input terminal 12 by the first voltage sensor 51, in the first branch bus 18 by the second voltage sensor 52, in the main bus 17 by the third voltage sensor 53 and in the second branch bus 19 by the fourth voltage sensor 54. .

[0049] Advantageously, the voltage sensors 51 to 54 are three-phase voltage sensors, i.e. configured to measure a voltage of each phase of the electric current.

[0050] In an alternative not shown, the distribution system 1 includes additional voltage sensors configured to measure a voltage of the electric current flowing in the protection devices 22 and 23 and the output terminals 13.

[0051] Particularly advantageously, the distribution system 1 includes voltage sensors measuring the voltage of the electric current flowing in each of the elements mentioned above, and furthermore, includes voltage sensors measuring the voltage of the electric current at different positions of the main bus 17 and of the first 18 and second 19 secondary buses, at each output terminal 13 and in each of the protection devices 23.

[0052] In the example of [Fig. 1], there are two gas sensors 56 and 57, namely a first gas sensor 56 and a second gas sensor 57, configured to measure a quantity representative of the amount of gas particles inside the electrical cabinet 10, that is, inside the enclosure 11 of the electrical cabinet 10. More precisely, the gas sensors 56 and 57 measure the amount of particles of at least one given gas. Preferably, the gas sensors 56 and 57 measure the type of gas inside the electrical cabinet and the amount of particles for each type of gas.

[0053] As shown in [Fig. 1], the first gas sensor 56 is located on the casing 11, and advantageously, at the top of the casing 2, i.e., at a position furthest from the ground. The second gas sensor 57 is located inside the drawer 19, on an internal wall of the drawer 19.

[0054] In an alternative not shown, the distribution system 1 includes additional gas sensors, configured to measure the quantity of gas particles in, or near, the output terminals 13. In the case where the output terminals 13 are in close proximity to each other, i.e. any output terminal 13 is less than 10 cm away from any other output terminal 13, then a single gas sensor 56 or 57 is advantageously sufficient to measure the quantity of gas particles near all the output terminals 13.

[0055] In the example of [Fig. 1], there are two humidity sensors 58 and 59, namely a first humidity sensor 58 and a second humidity sensor 59, and they are configured to measure a quantity representative of humidity. In particular, the quantity is representative of the humidity of the air inside the envelope 11.

[0056] In practice, the humidity sensors 58 and 59 are generally also sensors of a quantity representative of ambient temperature, i.e. of a representative quantity of an air temperature in the vicinity of the humidity sensor 58, 59. By "in the vicinity of", we mean located at most a distance of a few centimeters, for example ten centimeters.

[0057] As shown in [Fig. 1], the first humidity sensor 58 is located in the vicinity of the inlet terminal 12, on the casing 11, and is configured to measure a quantity representative of the humidity of the air in the vicinity of the inlet terminal 12. The second humidity sensor 59 is located inside the drawer 19, on an internal wall of the drawer 19, and is configured to measure a quantity representative of the humidity of the air inside the drawer 19.

[0058] In an alternative not shown, the distribution system 1 includes additional humidity sensors, configured to measure humidity in, or near, the output terminals 13. In the case where the output terminals 13 are in the vicinity of each other, then a single humidity sensor 58 or 59 is advantageously sufficient to measure the humidity near all the output terminals 13.

[0059] In the example of [Fig. 1], there are five temperature sensors 61, 62, 63, 64, and 65, namely a first temperature sensor 61, a second temperature sensor 62, a third temperature sensor 63, a fourth temperature sensor 64, and a fifth temperature sensor 65. The temperature sensors 61 to 65 are configured to measure values ​​representative of the temperature of the cabinet 10. More specifically, the temperature sensors 61 to 65 are surface temperature sensors and are configured to measure values ​​representative of the temperature of a surface of the elements of the cabinet 10.

[0060] The temperature sensors 61 to 65 are here configured to measure respectively a temperature of the input terminal 12 by the first temperature sensor 61, of the first branch bus 18 by the second temperature sensor 62, of the main bus 17 by the third temperature sensor 63, of the first protection device 22 by the fourth temperature sensor 64 and of one of the second protection devices 23 by the fifth temperature sensor 65.

[0061] In an alternative not shown, the distribution system 1 includes additional temperature sensors, configured to measure a temperature of five of the following elements: the main bus 17, the second branch bus 19, the protection device 22 and the output terminals 13.

[0062] Particularly advantageously, the distribution system 1 includes temperature sensors measuring the temperature of each of the elements mentioned above, and furthermore, includes temperature sensors measuring the temperature at different positions of the main bus 17 and of the first 18 and second 19 secondary buses, and at each protection device 23.

[0063] The vibration sensor 67 is configured to acquire a value representing the vibrations of the electrical cabinet 10. The vibrations of the electrical cabinet 10 are caused, for example, by the passage of small animals such as rodents, or by poor fastening of the components of the electrical cabinet to each other. The vibration sensor 67 is, for example, an accelerometer or a gyroscope. In the example of [Fig. 1], the vibration sensor 67 is located in the plinth 27. Alternatively, the vibration sensor 67 is located on one of the following elements: the enclosure 11, the input terminal 12, the first branch bus 18, the main bus 17, the second branch bus 19, the electrical protection devices 22 and 23, and the output terminals 13; and is configured to measure the representative vibration quantity in the vicinity of the corresponding element.

[0064] According to an unrepresented variant, the distribution system 1 comprises several vibration sensors located on all or part of the elements previously mentioned.

[0065] In a particularly advantageous manner, the distribution system 1 includes vibration sensors measuring the vibrations of each of the elements mentioned above, and furthermore, includes vibration sensors measuring the vibrations at different positions of the main bus 17 and of the first 18 and second 19 secondary buses.

[0066] The noise sensor 69 is configured to acquire a value representing the noise inside the electrical cabinet 10. The noise inside the electrical cabinet 10 is caused, for example, by small animals such as rodents, by improper fastening of the cabinet components, or by a foreign object accidentally introduced into the cabinet, such as a tool left behind during maintenance. The noise sensor 69 is, for example, a microphone. In the example shown in [Fig. 1], the noise sensor 69 is placed in the drawer 25 and is configured to measure the noise representing the noise in the drawer 25.Alternatively, the noise sensor 69 is located on or near one of the following: the enclosure 11, the input terminal 12, the electrical protection devices 22 and 23, the output terminals 13 and the plinth 27; and is configured to measure the representative quantity of noise in the vicinity of the corresponding element.

[0067] According to an unrepresented variant, the distribution system 1 comprises several noise sensors located on or near all or part of the elements mentioned above, and are configured to measure noise near all or part of the elements mentioned above.

[0068] The distribution system 1 further includes an electronic device 90 for monitoring the electrical cabinet 10, also called a monitoring aid device 90. In the example of [Fig.1], the monitoring aid device 90 is a self-contained electronic device located inside the enclosure 11. In an alternative not shown, the monitoring aid device 90 is included in other electronic equipment, for example in a server, preferably located outside the envelope 11.

[0069] The monitoring aid device 90 includes an acquisition module 92, an estimation module 94 and an emission module 96, visible in [Fig.2].

[0070] In the example of [Fig.2], the monitoring aid device 90 includes an information processing unit 100 formed for example of a memory 102 and a processor 104 associated with the memory 102.

[0071] In the example of [Fig. 2], the acquisition module 92, the estimation module 94, and the transmission module 96 are each implemented as a software program, or a software component, executable by the processor 104. The memory 102 of the monitoring aid device 90 is then capable of storing acquisition software, estimation software, and transmission software. The processor 104 is then capable of executing each of the following software programs: acquisition software, estimation software, and transmission software.

[0072] In an alternative not shown, the acquisition module 92, the estimation module 94 and the transmission module 96 are each made in the form of a programmable logic component, such as an FPGA (Field Programmable Gate Array), or an integrated circuit, such as an ASIC (Application Specified Integrated Circuit).

[0073] When the monitoring aid device 90 is implemented in the form of one or more software programs, i.e., in the form of a computer program, also called a computer program product, it is further capable of being stored on a computer-readable medium, not shown. The computer-readable medium is, for example, a medium capable of storing electronic instructions and being connected to a bus of a computer system. By way of example, the readable medium is an optical disc, a magneto-optical disc, ROM, RAM, any type of non-volatile memory (e.g., FLASH or NVRAM), or a magnetic card. A computer program comprising software instructions is then stored on the readable medium.

[0074] The acquisition module 92 is configured to acquire the values ​​of the quantities measured by each of the intensity sensors 41 to 44, each of the voltage sensors 51 to 54, each of the gas sensors 56 and 57, each of the humidity sensors 58 and 59, each of the temperature sensors 61 to 64, the vibration sensor 67 and the noise sensor 69.

[0075] The estimation module 94 is configured to calculate an estimated state of the electrical cabinet 10 via an artificial neural network, or ANN (from the English Artificial Neural Network), hereafter referred to as a neural network. For this purpose, the module Estimation module 94 uses the values ​​acquired by acquisition module 92, such that each quantity acquired by acquisition module 92 is an input variable of the neural network. The estimated state is an output variable of the neural network.

[0076] The neural network comprises an ordered succession of layers of neurons, each of which takes its inputs from the outputs of the preceding layer. More precisely, each layer includes neurons that take their inputs from the outputs of the neurons in the preceding layer, or from the input variables for the first layer.

[0077] Alternatively, more complex neural network structures can be envisaged with a layer that can be linked to a layer further away than the immediately preceding layer.

[0078] Each neuron is also associated with an operation, that is to say a type of processing, to be carried out by said neuron within the corresponding processing layer.

[0079] Each layer is connected to the other layers by a plurality of synapses. A synaptic weight is associated with each synapse, and each synapse forms a link between two neurons. It is often a real number, which takes on both positive and negative values. In some cases, the synaptic weight is a complex number.

[0080] Each neuron is designed to perform a weighted sum of the value(s) received from the neurons of the preceding layer, each value being multiplied by the respective synaptic weight of each synapse, or connection, between said neuron and the neurons of the preceding layer, and then to apply an activation function, typically a non-linear function, to said weighted sum, and to deliver at the output of said neuron, in particular to the neurons of the next layer connected to it, the value resulting from the application of the activation function. The activation function introduces non-linearity into the processing performed by each neuron. The sigmoid function, the hyperbolic tangent function, and the Heaviside function are examples of activation functions.

[0081] As an optional complement, each neuron is also capable of applying, in addition, a multiplicative factor, also called bias, to the output of the activation function, and the value delivered at the output of said neuron is then the product of the bias value and the value from the activation function.

[0082] Alternatively, the neural network is a convolutional neural network. The convolutional neural network is also sometimes called a convolutional neural network or by the acronym CNN, which refers to the English term "Convolutional Neural Networks".

[0083] In a convolutional neural network, each neuron in the same layer has exactly the same connection pattern as its neighboring neurons, but at different input positions. The connection pattern is called the convolutional kernel or, more often, the "kernel" in reference to the corresponding English term.

[0084] A fully connected layer of neurons is a layer in which the neurons of said layer are each connected to all the neurons of the preceding layer.

[0085] Such a type of layer is more often referred to by the English term "fully connected", and sometimes designated by the name "dense layer".

[0086] The estimated state of the electrical cabinet 10 is advantageously one of the following states: a normal state, an abnormal state, and a critical state. The normal state is associated with the normal operation of the electrical cabinet 10; that is, the neural network estimates that no electrical, mechanical, or thermal fault is present in the electrical cabinet 10. The abnormal state is associated with the abnormal operation of the electrical cabinet 10; that is, the neural network estimates that an electrical, mechanical, or thermal fault is present, but that it does not pose a problem requiring immediate action by the user. An abnormal state is, for example, the detection of noise in the electrical cabinet 10, without any change in the other quantities measured by the sensors.The critical state is associated with a critical fault in the electrical cabinet 10, that is to say that the neural network estimates that an electrical, mechanical or thermal fault is present and that it poses a major risk to the operation of the electrical cabinet, requiring for example an emergency maintenance operation, and / or a cut-off of the electrical current flowing in the electrical cabinet 10, that is to say a shutdown of the electrical cabinet 10.

[0087] In other words, the normal state, the abnormal state, and the critical state correspond to three successive levels of criticality, the normal state being associated with a first level, the abnormal state being associated with a second level higher than the first, and finally the critical state being associated with a third level higher than the second. These states allow a user to estimate whether it is necessary to intervene on the electrical cabinet 10 for a maintenance operation, and the degree of urgency of the maintenance operation.

[0088] The neural network is trained during a preliminary training phase so that it can then correctly predict the estimated state during the inference phase, which follows this preliminary training phase. The training is performed using training data corresponding to both the input and output variables of the neural network. For example, the training data might consist of sensor measurements and the estimated state of the electrical cabinet associated with those measurements. An error calculation is performed between the predicted output variable (by the neural network from the input training data) and the corresponding output training data. Then, an adjustment of the synaptic weights of the neural network is made according to the calculated error, for example via a backpropagation algorithm and with reiteration of the above in order to minimize the error, i.e., to gradually reduce the error until a predefined minimum threshold is reached.

[0089] The learning phase is then complete when the error has been reduced to reach this threshold. The synaptic weights and any biases used for the inference of the neural network are then those obtained at the end of this learning phase, that is to say, corresponding to this minimum error between the predicted output variable and the corresponding output training data.

[0090] The transmission module 96 is configured to emit a message representing the estimated state in one of two forms: a visual message and an audible message. The transmission module 96 is, for example, a human-machine interface, or is connected to a human-machine interface, to display a message representing the estimated state of the electrical cabinet 10. The message is, for example, a color code, with each color associated with an estimated state, or an audible message, in particular an alarm if the estimation module 94 calculates that the estimated state is critical. Those skilled in the art will understand that each estimated state is associated with a respective visual or audible representation, distinct from one estimated state to another, so that the user can more easily and quickly perceive which state of the electrical cabinet 10 has been estimated by the monitoring aid device 90. In other words, each estimated state is associated with a respective color and / or a respective audible message..

[0091] Optionally, the transmission module 96 is configured to transmit additional information relating to the sensors, for example the values ​​of the quantities measured by the sensors.

[0092] Particularly advantageously, the estimation module 94 is configured to estimate the state of the electrical cabinet 10 for several distinct risks associated with the electrical cabinet 10. A first risk is, for example, a fault in the electrical cabinet 10, whether electrical, mechanical, or thermal; and a second risk is, for example, a risk of an electric arc in the electrical cabinet 10. Advantageously, other risks are estimated by the estimation module 94, such as the risk of the presence of small animals or rodents inside the electrical cabinet 10, the risk of the presence of water inside the electrical cabinet 10, etc.

[0093] In the case where the estimation module 94 is configured to estimate the state of the electrical cabinet 10 for several risks, the estimation module 94 calculates an estimated state for each of the risks. In this case, the network output variables of neurons are an estimated state of the electrical cabinet 10 for each risk, and the emission module 96 is then configured to emit a message representative of the estimated state for each risk.

[0094] Advantageously, the estimation module 94 is configured to generate a resulting estimated state equal to the estimated state for the greatest risk at the time the estimation is performed. The greatest risk is, for example, the one with the highest probability of occurrence. Alternatively, or in addition, the greatest risk is the one with the highest criticality level according to a predefined order of criticality among risks, while also having a probability of occurrence greater than a predefined minimum threshold. The transmission module 96 is then configured to transmit a message representative of the resulting estimated state, and may also transmit additional information related to the greatest risk at the time of the estimation.

[0095] Alternatively, the estimation module 94 calculates an estimated state of the electrical cabinet 10 only for the highest risk. In this case, the output variable of the neural network is the estimated state of the electrical cabinet 10 for the highest risk, and the transmission module 96 is then configured to transmit a message representative of the estimated state for said risk.

[0096] In order to best estimate the various risks, certain sensors are advantageously added, or possibly removed. In particular, in the case of an assessment of the risk of electric arc, the vibration 67 and noise 69 sensors are particularly advantageous in order to obtain the most precise and accurate prediction possible, whereas in the case of an assessment of the risk of electrical, mechanical or thermal fault of the electrical cabinet, the vibration 67 and noise 69 sensors can be omitted without this having a significant impact on the accuracy of the calculation performed by the estimation module 94.

[0097] The risk(s) taken into account for calculating the estimated state by the estimation module 94 are advantageously chosen by the user, with sensors being added according to the risk(s) the user wishes the estimation module 94 to evaluate. Alternatively, the sensors necessary for calculating the estimated state for the different risks are all present in the electrical cabinet 10, and the sensors are then activated by the monitoring aid device 90 according to the risk(s) chosen by the user.

[0098] A method for assisting in monitoring the electrical cabinet 10, implemented by the monitoring assistance device 90, is now described.

[0099] The electrical cabinet 10 is in operation, so electric current flows through the electrical cabinet 10 from the source 3 to the loads 5. The current sensors 41 to 44, the voltage sensors 51 to 54, the gas sensors 56 and 57, humidity sensors 58 and 59, temperature sensors 61 to 64, vibration sensor 67 and noise sensor 69 are in operation, is able to perform measurements according to the risk for which the estimated state is to be calculated.

[0100] During a step 201, the monitoring aid device 90, and more specifically the acquisition module 92, acquires the values ​​of the quantities measured by the intensity sensors 41 to 44, the voltage sensors 51 to 54, the gas sensors 56 and 57, the humidity sensors 58 and 59, the temperature sensors 61 to 64, the vibration sensor 67 and the noise sensor 69.

[0101] These values ​​are transmitted to the estimation module 94, which in a subsequent step 202 calculates, via the neural network, the estimated state of the electrical cabinet 10. As mentioned previously, each quantity measured by the intensity sensors 41 to 44, the voltage sensors 51 to 54, the gas sensors 56 and 57, the humidity sensors 58 and 59, the temperature sensors 61 to 64, the vibration sensor 67 and the noise sensor 69 is an input variable of the neural network, and the output variable of the neural network is the estimated state.

[0102] During a step 203 subsequent to the calculation state 202, the transmission module 96 emits, either in visual form, or in audible form, or both in visual and audible form, a message representative of the state estimated during the calculation step 202. Optionally, the transmission module 96 also emits additional data, such as the values ​​measured by the sensors.

[0103] Alternatively, in step 202, the estimation module 94 calculates an estimated state for each risk. In this case, in step 203, the transmission module 96 emits a message representing the estimated state for each risk.

[0104] According to another variant, if the estimation module 94 is configured to estimate several risks associated with the electrical cabinet, then at step 202, the estimation module 94 generates a resulting estimated state equal to the estimated state for the greatest risk at the time the estimation is performed, in addition to the calculation of the estimated state at step 202. In this case, at step 203, the transmission module 96 transmits a message representative of the resulting estimated state, and possibly also additional information related to the type of risk that is greatest at the time of the estimation.

[0105] According to an unrepresented variant, the estimation module 94 is advantageously further configured to control the disconnector 16 in the open position, corresponding to an absence of current flow in the electrical cabinet 10, when the estimated state is equal to the critical state.

[0106] Thus, the monitoring aid device 90 assists in monitoring the electrical cabinet 10. The ability to assess certain specific risks, such as the risk of electric arc, by adding specific sensors allows for customization of the Monitoring of the electrical cabinet 10 according to user preferences. The transmission of a message representing the estimated status facilitates user decision-making. Based on the message transmitted by the transmission module 96, the user decides what action to take, for example, a maintenance operation and a date for its execution, and / or a power outage affecting the electrical cabinet 10.

[0107] Any feature described for an embodiment or variant in the foregoing may be implemented for the other embodiments and variants described above, provided that it is technically feasible.

Claims

1. Demands Electronic monitoring aid device (90) for an electrical cabinet (10), the electrical cabinet (10) comprising an input terminal (12) and several output terminals (13), and being capable of being connected to a source (3) via the input terminal (12) and to a plurality of loads (5) via the output terminals (13) and configured to distribute an electric current from the source (3) to the plurality of loads (5), the electrical cabinet (10) comprising a disconnect switch (16) connected to the input terminal (12) and a main bus (17) connected between the disconnect switch (16) and the output terminals (13), the device (90) comprising: - an acquisition module (92), configured to acquire values ​​of quantities representative of an intensity and respectively of a voltage of an electric current flowing between the input terminal (12) and the output terminals (13), measured by an intensity sensor (41, 42, 43, 44) and respectively a voltage sensor (51, 52, 53, 54); - an estimation module (94), configured to calculate an estimated state of the electrical cabinet (10) via a neural network, each quantity acquired by the acquisition module (92) being an input variable of the neural network, the estimated state of the electrical cabinet (10) being an output variable of the neural network; and - a transmission module (96), configured to transmit a message representative of the estimated state in one form among a visual form and an audible form; the acquisition module being further configured to acquire a value of at least one additional quantity measured by at least one additional sensor from among a gas sensor (56, 57) and a humidity sensor (58, 59), the quantity or quantities being representative respectively of a quantity of gas particles and a humidity level inside the electrical cabinet (10), characterized in that the acquisition module (92) is further configured to acquire a value of a quantity representative of noise in the electrical cabinet (10), measured by a noise sensor (69), and in that the estimation module (94) is then configured to calculate the estimated state of the electrical cabinet (10) via the neural network as a function of at least one additional quantity, at least one additional quantity including the noise representative quantity, at least one additional quantity being an additional input variable of the neural network.

2. Device (90) according to claim 1 in which the acquisition module (92) is further configured to acquire the values ​​of the additional quantities measured by the humidity sensor (56, 57) and by the gas sensor (58, 59).

3. Device (90) according to any one of the preceding claims, wherein the acquisition module (92) is further configured to acquire a value of a quantity measured by a temperature sensor (61, 62, 63, 64, 65), the measured quantity being representative of a temperature inside the electrical cabinet (10).

4. Device (90) according to any one of the preceding claims, wherein the acquisition module (92) is further configured to acquire values ​​of quantities measured by a plurality of temperature sensors (61, 62, 63, 64, 65), the quantities being representative of the temperature inside the electrical cabinet (10), each temperature sensor (61, 62, 63, 64, 65) being configured to measure a temperature among: a temperature in the vicinity of the input terminal (12), a temperature in the vicinity of a respective output terminal (13), a temperature of the main bus (17) and a temperature of an electrical protection device (22, 23), connected between the main bus (17) and a respective output terminal (13).

5. Device (90) according to any one of the preceding claims, wherein the acquisition module (92) is further configured to acquire a value of a quantity representative of vibrations of the electrical cabinet (10), measured by a vibration sensor (67).

6. Device (90) according to any one of the preceding claims, wherein the estimated state of the electrical cabinet (10) is a state from the group consisting of: a normal state, an abnormal state and a critical state.

7. System (1) for distributing an electric current between a source (3) and a plurality of loads (5), the system (1) comprising: - an electrical cabinet (10), comprising: • an input terminal (12), suitable for being connected to the source (3), • several output terminals (13), suitable for being connected to the plurality of loads (5), • a disconnector (16), connected to the input terminal (12), and • a main bus (17), connected between the disconnector (16) and the plurality of output terminals (13), the electrical cabinet (10) being configured to distribute the electric current from the source (3) to the plurality of loads (5);- an intensity sensor (41, 42, 43, 44) and respectively a voltage sensor (51, 52, 53, 54), configured to measure values ​​of quantities representative of an intensity and respectively a voltage of an electric current flowing between the input terminal (12) and the output terminals (13) of the electrical cabinet (10); - an electronic monitoring aid device (90) for the electrical cabinet (10), the system (1) further comprising at least one additional sensor from among a gas sensor (56, 57) and a humidity sensor (58, 59), configured to acquire a value of at least one additional quantity, representative respectively of a quantity of gas particles and of humidity inside the electrical cabinet (10), characterized in that the system further comprises a noise sensor (69) and in that the electronic monitoring aid device (90) is according to any one of the preceding claims.;

8. A method for assisting in the monitoring of an electrical cabinet (10), the electrical cabinet (10) comprising an input terminal (12) and several output terminals (13), and being capable of being connected to a source (3) via the input terminal (12) and to a plurality of loads (5) via the output terminals (13) and configured to distribute a current

9. electrical from the source (3) to the plurality of loads (5), the electrical cabinet (10) comprising a disconnector (16) connected to the input terminal (12) and a main bus (17) connected between the disconnector (16) and the output terminals (13), the method being implemented by an electronic monitoring aid device (90), the method comprising the following steps: - acquisition (201) of values ​​of quantities measured by an intensity sensor (41, 42, 43, 44) and respectively a voltage sensor (51, 52, 53, 54), the quantities being representative of an intensity and respectively a voltage of the electric current flowing between the input terminal (12) and the output terminals (13) of the electrical cabinet; - calculation (202), via a neural network, of an estimated state of the electrical cabinet (10), each measured quantity being an input variable of the neural network, the estimated state being an output variable of the neural network; and - emission (203), in one form among a visual form and an audio form, of a message representative of the estimated state, the method further comprising an acquisition step (201) of a value of at least one additional quantity measured by at least one additional sensor from among a gas sensor (56, 57) and a humidity sensor (58, 59), the quantity or quantities being representative respectively of a humidity and a quantity of gas particles inside the electrical cabinet (10), characterized in that the method further comprises an acquisition step (201) of a value of a quantity representative of noise in the electrical cabinet, measured by a noise sensor (69), and in that during the calculation step (202), the at least one additional quantity includes the representative noise quantity, and the at least one additional quantity is an additional input variable of the network of neurons. Computer program comprising software instructions which, when executed by a computer, implement a method according to claim 10.

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