Circuit breaker fault diagnosis system

Abstract

The invention relates to a circuit breaker fault diagnosis system. Relates to the technical field of high-voltage circuit breaker fault detection. The system comprises a control module, a switching value acquisition module, a current acquisition module and a displacement acquisition module. The input end of the control module is respectively connected with the output ends of the switching value acquisition module, the current acquisition module and the displacement acquisition module; the switching value acquisition module acquires opening and closing positions of the circuit breaker; based on the opening and closing position of the circuit breaker, a control module collects vibration information through a vibration sensor, and obtains vibration information, current information and stroke information of a circuit breaker touch stroke in the opening and closing process of the circuit breaker; and the control module extracts feature vectors corresponding to the vibration information, the current information and the stroke information, obtains a composite feature vector, and compares the composite feature vector with a preset value to realize fault detection of the circuit breaker. By adopting the system, the detection accuracy of circuit breaker fault detection can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of high-voltage circuit breaker fault detection, and in particular to a circuit breaker fault diagnosis system. Background Art

[0002] High-voltage circuit breakers play a vital role in the safe and stable operation of power grids. Circuit breakers can perform switching operations, disconnect normal operating currents, and change the operation mode of power systems. Circuit breakers can also disconnect short-circuit fault currents, reduce the scope of power outages, protect the safety of power equipment, and maintain system stability. Therefore, circuit breaker fault detection is crucial.

[0003] At present, the fault detection and intelligent diagnosis of circuit breakers are mainly carried out through regular inspections, vibration, sound print and other single parameters or combinations. However, vibration and sound print signals are easily affected by the environment, which affects the accuracy of fault diagnosis. Therefore, there is currently a problem of low detection accuracy of circuit breaker fault detection. Summary of the invention

[0004] Based on this, it is necessary to provide a circuit breaker fault diagnosis system to address the above-mentioned technical problem of low detection accuracy of circuit breaker fault detection.

[0005] The present application provides a circuit breaker fault diagnosis system, the system comprising: a control module, a switch quantity acquisition module, a current acquisition module and a displacement acquisition module;

[0006] The input end of the control module is respectively connected to the output end of the switch quantity acquisition module, the output end of the current acquisition module and the output end of the displacement acquisition module;

[0007] The switch quantity acquisition module acquires the opening and closing positions of the circuit breaker; based on the opening and closing positions of the circuit breaker, the control module acquires vibration information through a vibration sensor to obtain vibration information of the circuit breaker during the opening and closing process, the current acquisition module acquires current information of the circuit breaker during the opening and closing process, and the displacement acquisition module acquires travel information of the circuit breaker's actuation travel;

[0008] The control module extracts the characteristic vectors corresponding to the vibration information, the current information and the stroke information respectively, obtains a composite characteristic vector based on the characteristic vectors, and compares the composite characteristic vector with a preset value to realize fault detection of the circuit breaker.

[0009] In one of the embodiments, the system further includes a fault diagnosis model, which is obtained by training composite feature samples of the circuit breaker through a SVM algorithm based on a convolutional neural network.

[0010] In one embodiment, the system further includes a power bus; the power bus is used to connect the control module, the switch quantity acquisition module, the current acquisition module and the displacement acquisition module.

[0011] In one embodiment, the system further includes a power module; an output end of the power module is connected to the power bus to provide electrical energy to the system.

[0012] In one of the embodiments, the current information during the opening and closing process of the circuit breaker includes the opening and closing current and the energy storage motor current.

[0013] In one of the embodiments, after the current acquisition module acquires the current, the method further comprises: performing current waveform processing on the acquired current to obtain characteristic currents corresponding to a plurality of moments, and transmitting the characteristic currents to the control module.

[0014] In one of the embodiments, the control module uses a chip type of A40i.

[0015] In one of the embodiments, the vibration sensor of the control module collects the vibration information through a 485 communication interface.

[0016] In one embodiment, the vibration information of the circuit breaker during the opening and closing process collected by the vibration sensor of the control module includes: opening and closing contactor action, closing coil core impact action, moving and static contact contact action, and connecting rod mechanism movement.

[0017] In one embodiment, the displacement acquisition module includes: a displacement sensor and an acceleration sensor; the displacement sensor and the acceleration sensor are used to obtain characteristic parameters of the circuit breaker triggering stroke; wherein the characteristic parameters include: contact stroke during the opening and closing process, opening and closing action speed, maximum displacement during the opening and closing process, just-closing speed, and just-opening speed.

[0018] The above-mentioned circuit breaker fault diagnosis system includes: a control module, a switch quantity acquisition module, a current acquisition module and a displacement acquisition module; wherein the input end of the control module is respectively connected to the output end of the switch quantity acquisition module, the output end of the current acquisition module and the output end of the displacement acquisition module; first, the switch quantity acquisition module acquires the opening and closing positions of the circuit breaker; then, based on the opening and closing positions of the circuit breaker, the control module acquires vibration information through a vibration sensor to obtain vibration information of the circuit breaker during the opening and closing process, the current acquisition module acquires current information of the circuit breaker during the opening and closing process, and the displacement acquisition module acquires travel information of the circuit breaker's actuation travel; finally, the control module extracts feature vectors corresponding to the vibration information, current information and travel information respectively, obtains a composite feature vector based on the feature vector, compares the composite feature vector with a preset value, and realizes fault detection of the circuit breaker. In the process of circuit breaker fault diagnosis, by combining the vibration information, current information and travel information of the circuit breaker's actuation stroke collected during the opening and closing process, the characteristic vectors corresponding to the three types of information are obtained, and the characteristic vectors are compounded and compared with the preset values. According to the comparison results, the circuit breaker fault can be judged. By integrating multi-dimensional information such as vibration, current and displacement, the performance of the circuit breaker can be more comprehensively evaluated. By comparing the characteristic vectors with the preset values, potential faults can be discovered in time, and the circuit breaker maintenance can be carried out in time, which improves the accuracy of the circuit breaker fault diagnosis system, thereby improving the detection accuracy of circuit breaker fault detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 A schematic diagram of the structure of circuit breaker fault diagnosis in one embodiment;

[0021] Figure 2 A schematic diagram of the structure of circuit breaker fault diagnosis in another embodiment;

[0022] Figure 3 A schematic diagram of a typical waveform of a circuit breaker coil current in an embodiment;

[0023] Figure 4 A schematic diagram of a vibration signal waveform in one embodiment;

[0024] Figure 5 A schematic diagram of a typical closing displacement curve in an embodiment;

[0025] Figure 6FIG. 1 is a schematic diagram of a flow chart of circuit breaker fault diagnosis in an embodiment. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0028] It can be understood that the terms "first", "second", etc. used in the present application can be used in this article to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from another element.

[0029] It should be noted that when an element is considered to be "connected" to another element, it can be directly connected to the other element, or connected to the other element through an intermediate element. In addition, the "connection" in the following embodiments should be understood as "electrical connection", "communication connection", etc. if there is transmission of electrical signals or data between the connected objects.

[0030] When used herein, the singular forms "a", "an", and "said / the" may also include plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "include / comprise" or "have" etc. specify the presence of stated features, wholes, steps, operations, components, parts or combinations thereof, but do not exclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts or combinations thereof.

[0031] In an exemplary embodiment, Figure 1 As shown, a circuit breaker fault diagnosis system is provided, comprising: a control module, a switch quantity acquisition module, a current acquisition module and a displacement acquisition module;

[0032] The input end of the control module is respectively connected to the output end of the switch quantity acquisition module, the output end of the current acquisition module and the output end of the displacement acquisition module;

[0033] The switch quantity acquisition module acquires the opening and closing positions of the circuit breaker; based on the opening and closing positions of the circuit breaker, the control module acquires vibration information through a vibration sensor to obtain vibration information of the circuit breaker during the opening and closing process, the current acquisition module acquires current information of the circuit breaker during the opening and closing process, and the displacement acquisition module acquires travel information of the circuit breaker's actuation travel;

[0034] The control module extracts the characteristic vectors corresponding to the vibration information, the current information and the stroke information respectively, obtains the composite characteristic vector based on the characteristic vector, compares the composite characteristic vector with the preset value, and realizes the fault detection of the circuit breaker.

[0035] Among them, the control module is the core module of the circuit breaker fault diagnosis system, which is responsible for receiving the input data of each acquisition module, processing and analyzing the received data, and coordinating the work between the various modules to ensure the effective acquisition of data and the realization of fault detection; the switch quantity acquisition module is used to collect the opening and closing position information of the circuit breaker, that is, the working status of the circuit breaker, and provides information whether the circuit breaker is in the open or closed state, which is the basis for the control module to make decisions; the current acquisition module is used to monitor the current passing through the equipment during the opening and closing process of the circuit breaker; the displacement acquisition module is used to collect the stroke information of the circuit breaker's actuation stroke and monitor the physical movement of the circuit breaker components, which can be used to determine whether the movement of the circuit breaker is normal, such as whether the contacts are completely closed or open; the feature vector is a key parameter extracted from the collected data; the composite feature vector is a comprehensive feature integrated from various feature vectors, which can reflect the overall operating status of the circuit breaker; the preset value is a benchmark value set based on the performance indicators of the circuit breaker under normal operating conditions.

[0036] The above-mentioned circuit breaker fault diagnosis system includes: a control module, a switch quantity acquisition module, a current acquisition module and a displacement acquisition module; wherein the input end of the control module is respectively connected to the output end of the switch quantity acquisition module, the output end of the current acquisition module and the output end of the displacement acquisition module; first, the switch quantity acquisition module acquires the opening and closing positions of the circuit breaker; then, based on the opening and closing positions of the circuit breaker, the control module acquires vibration information through a vibration sensor to obtain vibration information of the circuit breaker during the opening and closing process, the current acquisition module acquires current information of the circuit breaker during the opening and closing process, and the displacement acquisition module acquires travel information of the circuit breaker's actuation travel; finally, the control module extracts feature vectors corresponding to the vibration information, current information and travel information respectively, obtains a composite feature vector based on the feature vector, compares the composite feature vector with a preset value, and realizes fault detection of the circuit breaker. In the process of circuit breaker fault diagnosis, by combining the vibration information, current information and travel information of the circuit breaker triggering stroke collected during the opening and closing process, the characteristic vectors corresponding to the three kinds of information are obtained, and the characteristic vectors are compounded to obtain the composite characteristic vector and compare it with the preset value. According to the comparison result, the circuit breaker fault can be judged. By integrating multi-dimensional information such as vibration, current and displacement, the performance of the circuit breaker can be more comprehensively evaluated. By comparing the characteristic vector with the preset value, potential faults can be discovered in time and the circuit breaker maintenance can be carried out in time, which improves the accuracy of the circuit breaker fault diagnosis system and thus improves the detection accuracy of circuit breaker fault detection.

[0037] In an exemplary embodiment, the system further includes a fault diagnosis model, which is obtained by training composite feature samples of the circuit breaker through a SVM algorithm based on a convolutional neural network.

[0038] Among them, the fault diagnosis model can be a mathematical or computer program used to identify and judge circuit breaker faults, which determines the state of the circuit breaker system by analyzing data, and then identifies the circuit breaker fault; the characteristic samples are the basic data used to train the fault diagnosis model, which is the same as the method of obtaining the composite feature vector in the above embodiment; the convolutional neural network is a deep learning model suitable for processing data with a grid structure, such as images; the SVM algorithm is a support vector machine, which is a supervised learning algorithm that divides data points into different categories by finding the decision boundary with the largest interval.

[0039] As an embodiment, in a fault diagnosis system, a convolutional neural network is used to extract spatial features from composite feature samples to enhance the model's ability to identify faults; a support vector machine is used to process the features extracted by the convolutional neural network and perform fault classification.

[0040] In this embodiment, by combining the feature extraction capability of the convolutional neural network with the classification capability of the SVM algorithm, different types of faults can be identified more accurately, the accuracy of diagnosis can be improved, and accurate fault identification can timely discover potential problems of the circuit breaker, effectively reducing the losses caused by sudden failures of the circuit breaker.

[0041] In one embodiment, the system further includes a power bus; the power bus is used to connect the control module, the switch quantity acquisition module, the current acquisition module and the displacement acquisition module.

[0042] Among them, the power bus is used to transmit power in the circuit breaker fault diagnosis system, connecting the control module, switch quantity acquisition module, current acquisition module and displacement acquisition module, which can ensure that each module in the system can obtain a stable power supply and ensure the normal operation of each module.

[0043] Furthermore, if Figure 2 As shown, in one embodiment, the system further includes a power module; the output end of the power module is connected to the power bus to provide electrical energy to the system.

[0044] Among them, the power module is a module that converts power input into the voltage and current required by the system, and may include circuit structures such as transformers, rectifiers, and voltage stabilizers; the output end of the power module can transmit the converted electric energy to the power bus, providing stable electric energy for each module in the system. The stability and reliability of the power supply are crucial to the performance of the entire system, and can effectively prevent failures caused by insufficient power.

[0045] In an exemplary embodiment, the current information during the opening and closing process of the circuit breaker includes the opening and closing current and the energy storage motor current.

[0046] Among them, the opening and closing process refers to the process of the circuit breaker allowing current to pass and cutting off current; the opening and closing current refers to the current flowing through the circuit breaker during the opening and closing operation of the circuit breaker; the energy storage motor current refers to the current consumed by the motor used to drive the mechanical action of the circuit breaker.

[0047] As an embodiment, the opening process is to cut off the circuit and cut off the current input; the closing process is to reconnect the circuit and allow current to pass; the size of the opening and closing current needs to be strictly monitored, and excessive opening and closing current may cause circuit breaker failure or directly cause equipment damage; the energy storage motor is used to store energy when the circuit breaker is closed, and releases energy when the circuit breaker is opened.

[0048] In this embodiment, by real-time monitoring of the opening and closing current and the energy storage motor current, abnormal conditions such as overload, short circuit, etc. can be quickly discovered, and an alarm can be issued in time, so as to promptly determine that the circuit breaker has failed; by analyzing the current information, the performance of the circuit breaker can be adjusted to improve the overall efficiency and stability of the system.

[0049] Furthermore, in one embodiment, after the current acquisition module acquires the current, it includes: performing current waveform processing on the acquired current to obtain characteristic currents corresponding to multiple moments, and transmitting the characteristic currents to the control module.

[0050] Among them, current waveform processing is the process of analyzing and converting the collected current information, which usually includes steps such as filtering, shaping, amplitude extraction and feature extraction to eliminate noise and improve signal quality; characteristic current can be the current data point extracted after processing, which can be used to describe the specific state or characteristics of the current waveform. The characteristics may include peak current, effective value, average value, waveform distortion, etc., which can reflect the dynamic changes of the current; multiple moments refer to the collection and processing of the current waveform at different time points to form a series of characteristic current data;

[0051] In this embodiment, the characteristic current at multiple moments is obtained by processing the collected current. Multi-moment collection helps to observe the trend of current changes; by transmitting the characteristic current to the control module, the monitoring and control capabilities of the system can be effectively improved.

[0052] In one embodiment, the chip type used by the control module is A40i.

[0053] Specifically, the control module uses A40i, which contains 4 ARM Cortex-A7 cores, each with a main frequency of up to 1.2GHz, and has powerful computing power; A40i has a SATA interface and supports large-capacity data storage. It can store a large amount of model parameters and historical data. It can continuously train and improve the diagnostic model through historical data to improve the accuracy of fault diagnosis.

[0054] Furthermore, in one embodiment, the vibration sensor of the control module collects vibration information through the 485 communication interface.

[0055] As an embodiment, the control module has a 485 communication function, and can collect the vibration signal of the vibration sensor through 485 communication, reduce the noise and decompose the vibration signal, complete the extraction of characteristic signals and the travel information, the opening and closing current, and the energy storage motor current to jointly construct a composite feature vector for the input of the convolutional neural network algorithm. After being processed by the convolutional neural network algorithm, it is used as the input of the SVM algorithm, and after training, a fault diagnosis module model is formed.

[0056] In one embodiment, the vibration information of the circuit breaker during the opening and closing process collected by the vibration sensor of the control module includes: the action of the opening and closing contactor, the impact action of the closing coil core, the contact action of the moving and static contacts, and the movement of the connecting rod mechanism.

[0057] As an embodiment, the opening and closing contactor action refers to the physical movement of the circuit breaker contactor when opening and closing, which directly affects the connection status of the circuit. The vibration sensor can monitor the vibration characteristics of the action to evaluate the working condition of the contactor; the closing coil core impact action refers to the rapid movement of the iron core due to the electromagnetic force when the electromagnet is closed and the coil is energized, and collides with other structures to generate a specific vibration signal; the moving and static contact contact action refers to the contact process between the moving contact and the static contact during the opening and closing process of the circuit breaker; the connecting rod mechanism movement refers to the dynamic movement of the connecting rod structure inside the circuit breaker during the closing and opening process.

[0058] In this embodiment, the vibration sensor can monitor the vibration signal in real time during the opening and closing process of the circuit breaker, and then obtain the status of the circuit breaker. By analyzing different status information, the fault information of the circuit breaker is obtained, which can effectively improve the fault monitoring and maintenance management efficiency of the circuit breaker.

[0059] Furthermore, in one embodiment, the displacement acquisition module includes: a displacement sensor and an acceleration sensor; the displacement sensor and the acceleration sensor are used to obtain characteristic parameters of the circuit breaker triggering stroke; wherein the characteristic parameters include: the contact stroke during the opening and closing process, the opening and closing action speed, the maximum displacement during the opening and closing process, the just-closing speed, and the just-opening speed.

[0060] As an embodiment, a displacement sensor is a device used to measure the change in the position of an object. It can accurately monitor the travel of the circuit breaker contacts during the opening and closing process, obtain displacement data and transmit it to the control module; an acceleration sensor is used to measure the acceleration change of an object. In the application of circuit breakers, the acceleration sensor can provide dynamic behavior data of contact movement; the contact travel refers to the movement distance of the contacts during the opening and closing process of the circuit breaker; the opening and closing action speed refers to the speed at which the contacts move during the opening and closing process; the maximum displacement refers to the maximum movement distance of the contacts during the opening and closing process; the just-closing speed refers to the speed of the contacts at the moment of closing; accordingly, the just-opening speed refers to the speed of the contacts at the moment of opening.

[0061] Through the above embodiments, by collecting data such as the opening and closing action speed, the closing speed, and the opening speed, the data changes can be monitored in time to prevent the data from being too large or too small to cause adverse effects on the system, thereby improving the stability and reliability of the system.

[0062] Further, in a detailed embodiment, Figure 3As shown, a schematic diagram of a typical waveform of the circuit breaker coil current is provided. The current acquisition module is used to collect the opening and closing current and the energy storage motor current. The opening and closing characteristic quantity of the circuit breaker is obtained through the current curve. The sampled current waveform is analyzed to obtain the moments t0, t1, t2, t3, and t4, and the characteristic currents I0, I1, I2, I3, and I4 can be obtained at the same time.

[0063] Moreover, the vibration collection process is as follows, the vibration signal of the circuit breaker is divided into the following parts: the action of the opening and closing contactor, the impact of the closing coil core on the tripping device, the process from the moving and static contacts to the full contact, the movement of the connecting rod mechanism, and the acquisition of vibration signals through communication, such as Figure 4 The displacement acquisition module is used to measure the travel time characteristics of the circuit breaker, and is used to obtain the circuit breaker's opening and closing time, contact travel, opening and closing action speed, maximum displacement during the opening and closing process, just-closing speed, just-opening speed and other characteristic information, such as Figure 5 As shown in the figure, a schematic diagram of a typical closing displacement curve is provided.

[0064] Specifically, Figure 6 As shown, a schematic diagram of a circuit breaker fault diagnosis process is provided, wherein a circuit breaker fault diagnosis method may include the following steps:

[0065] Step S601, extracting features from various signals of the circuit breaker.

[0066] Among them, the characteristic vector of the coil opening and closing current is obtained, that is, the circuit breaker opening and closing signal is obtained, the coil current information during the circuit breaker opening and closing process is obtained, the current information is derived, and the derivative at the extreme point is 0, the inflection point time and inflection point current of the coil opening and closing process are obtained, and the second order is retained according to the accuracy requirements. The calculation principle is: - h= , where f(x i ) is the current sampling point, f(x i+1 ),f(x i+2 ) is the first sampling point and the second sampling point after the current sampling moment, the characteristic combination vector of time t and current I is obtained, the characteristic vector matrix of (t, I) is constructed, and the coil current characteristic vector in the opening and closing process is obtained.

[0067] More importantly, the characteristic vector of the vibration signal (vibration information) during the opening and closing process is obtained: The vibration signal is obtained. The vibration composite signal during the opening and closing process can be described as: + (t) ; Where i is the number of vibrations, An is the maximum amplitude of the nth vibration, is the amplitude attenuation coefficient, u (t)is a step function, is the start time of each sub-event, is the frequency of each vibration event, (t) is noise. The vibration signal is transformed by Hilbert transform, and the Hilbert transform of the single component signal x(t) is defined as: , define the parsing signal: Then the original vibration signal can be expressed as: ; Calculate the power spectrum of the vibration signal, and obtain the mean value, standard deviation, skewness, kurtosis, root mean square, amplitude factor, waveform factor, impact factor, margin factor and other characteristic vectors of each signal.

[0068] In addition, the characteristic vector of the travel information in the opening and closing process is obtained, including the acquisition of the displacement signal. The opening and closing speed is calculated by v=s / t, and the average speed within 5ms after closing is calculated as the closing speed, and the average speed within 5ms after opening is calculated as the opening speed. The maximum displacement in the opening and closing process is calculated, and the opening and closing time, contact travel, opening and closing action speed, opening and closing speed, and travel characteristic vector of the opening and closing process of the circuit breaker are obtained.

[0069] Step S602: construct a composite feature vector set.

[0070] Based on the stroke data, opening and closing current, and vibration signal characteristic vectors, a composite reference vector of the circuit breaker's normal state and sub-healthy states such as low coil voltage, high coil voltage, coil turn-to-turn short circuit, long core movement stroke, core jamming, and loose base is constructed.

[0071] Step S603: perform data processing through a convolutional neural network.

[0072] The composite feature vector is trained through the SVM algorithm based on the convolutional neural network to form a diagnostic model. The convolutional neural network is used to extract the features of the composite feature vector, and then these features are used as the input of the SVM algorithm. The image recognition ability of the convolutional neural network is fully utilized, and the dimension of the load feature vector can be reduced at the same time, thereby reducing the complexity of the SVM algorithm and greatly improving the accuracy of classification.

[0073] Step S604, comparing the composite feature samples in the decision model library with the feature vector to be identified to obtain a fault diagnosis result.

[0074] Among them, the decision model library is a database composed of composite feature samples that constitute the fault diagnosis model; in actual operation, the recognition data set is obtained according to the real-time operation data, and the vector set to be identified is constructed. Then, classification and identification are carried out by calculating the distance between the feature vector to be identified and the multi-dimensional space clustering of the multi-feature vectors in the sample library. If the spatial distance with a certain type of fault composite feature vector set is the smallest, it belongs to this classification, thereby completing fault diagnosis and identification.

[0075] In the above embodiment, a composite feature vector matrix is ​​constructed by a fault identification method combining vibration information, waveform information and travel information, and a fault diagnosis model is generated by using a convolutional neural network-based SVM learning algorithm, thereby realizing circuit breaker fault diagnosis and identification.

[0076] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0077] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0078] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0079] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0080] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A circuit breaker fault diagnosis system, characterized in that: The system comprises: a control module, a switch quantity acquisition module, a current acquisition module and a displacement acquisition module; The input end of the control module is respectively connected to the output end of the switch quantity acquisition module, the output end of the current acquisition module and the output end of the displacement acquisition module; The switch quantity acquisition module acquires the opening and closing positions of the circuit breaker; based on the opening and closing positions of the circuit breaker, the control module acquires vibration information through a vibration sensor to obtain vibration information of the circuit breaker during the opening and closing process, the current acquisition module acquires current information of the circuit breaker during the opening and closing process, and the displacement acquisition module acquires travel information of the circuit breaker's actuation travel; The control module extracts the characteristic vectors corresponding to the vibration information, the current information and the stroke information respectively, obtains a composite characteristic vector based on the characteristic vectors, and compares the composite characteristic vector with a preset value to realize fault detection of the circuit breaker.

2. The system according to claim 1, characterized in that The system also includes a fault diagnosis model, which is obtained by training the composite feature samples of the circuit breaker through a SVM algorithm based on a convolutional neural network.

3. The system according to claim 1, characterized in that The system also includes a power bus; The power bus is used to connect the control module, the switch quantity acquisition module, the current acquisition module and the displacement acquisition module.

4. The system according to claim 3, characterized in that The system also includes a power module; The output end of the power module is connected to the power bus to provide electrical energy to the system.

5. The system according to claim 1, characterized in that The current information during the opening and closing process of the circuit breaker includes the opening and closing current and the energy storage motor current.

6. The system according to claim 5, characterized in that After the current acquisition module acquires the current, the method includes: The collected current is processed into a current waveform to obtain characteristic currents corresponding to a plurality of moments, and the characteristic currents are transmitted to the control module.

7. The system according to claim 1, characterized in that The chip type used by the control module is A40i.

8. The system according to claim 7, characterized in that The vibration sensor of the control module collects the vibration information through a 485 communication interface.

9. The system according to claim 1, characterized in that The vibration information of the circuit breaker during the opening and closing process collected by the vibration sensor of the control module includes: The action of the opening and closing contactors, the impact action of the closing coil core, the contact action of the moving and static contacts, and the movement of the connecting rod mechanism.

10. The system according to claim 1, characterized in that The displacement acquisition module includes: a displacement sensor and an acceleration sensor; The displacement sensor and the acceleration sensor are used to obtain characteristic parameters of the circuit breaker actuation stroke; wherein the characteristic parameters include: contact stroke during the opening and closing process, opening and closing action speed, maximum displacement during the opening and closing process, closing speed and opening speed.

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