Fault diagnosis method and device for motor GIS disconnector

Abstract

The application provides a fault diagnosis method and device for a GIS disconnector of a motor. The method comprises the following steps: firstly, obtaining a power curve in a motor operation process, and preprocessing the power curve to obtain a first target curve, wherein the preprocessing at least comprises outlier filtering and missing value completion; then, calculating a correlation coefficient based on the first target curve through a hill climbing algorithm to obtain a target coefficient; finally, comparing the target coefficient with a first threshold to determine whether a disconnector of the motor has a fault. The application draws a power curve based on a current and a voltage of the motor, and determines the correlation based on the power curve optimized through the hill climbing algorithm, thereby avoiding the interference of different periods of the power curve, and solving the problem of low accuracy in judging the mechanism blockage and mechanism misplacement faults in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of motor diagnosis, and in particular to a fault diagnosis method for a motor GIS isolating switch, a fault diagnosis device for a motor GIS isolating switch, a computer-readable storage medium, and a motor. Background Art

[0002] Disconnectors are important equipment for changing the operation mode of power grids. However, due to their fully enclosed nature, the status of primary equipment cannot be visually viewed during daily maintenance. Currently, the main method for confirming the position of disconnectors relies solely on opening and closing indicators, connecting rod markings, travel switches, etc. There is no quantitative judgment on the position, and it is impossible to determine problems such as mechanism obstruction.

[0003] A search of prior art documents revealed a Chinese patent application with the number CN202011491841.9, titled "A GIS Disconnector Fault Diagnosis Method Based on Multiple Feature Quantities." This invention uses the GIS disconnector motor current, spindle angle, and image information as feature quantities for fault diagnosis. However, this invention simply sets thresholds for the average and extreme values ​​of each feature quantity, making it difficult to identify minor stuck faults. Further analysis and identification of GIS disconnector fault characteristics is required. Consequently, the existing technology has low accuracy in diagnosing mechanism stuck and misaligned faults. Summary of the Invention

[0004] The main purpose of this application is to provide a fault diagnosis method for a motor GIS isolating switch, a fault diagnosis device for a motor GIS isolating switch, a computer-readable storage medium and a motor, so as to at least solve the problem of low accuracy in judging the faults of mechanism jamming and mechanism not being in place in the prior art.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a fault diagnosis method for a motor GIS disconnector is provided, comprising: obtaining a power curve during the operation of the motor, and preprocessing the power curve to obtain a first target curve, wherein the preprocessing at least includes outlier filtering and missing value completion; calculating a correlation coefficient based on the first target curve by a hill climbing algorithm to obtain a target coefficient; and comparing the target coefficient with a first threshold to determine whether the disconnector of the motor has a fault.

[0006] Obtaining a power curve during motor operation includes: monitoring the voltage of the motor in real time through a voltage sensor to obtain a second target curve; monitoring the voltage of the motor in real time through a current sensor to obtain a third target curve; and performing calculations based on the second target curve and the third target curve to obtain a power curve during motor operation.

[0007] Optionally, preprocessing the power curve includes: traversing the power curve, and determining the sampling point as a first target sample point when the value of any sampling point is less than a second threshold; determining the sampling point as the first target sample point when the value of any sampling point is greater than or equal to the average of other sampling points in the power curve; and deleting the first target sample point in the power curve to complete outlier filtering of the power curve.

[0008] Optionally, preprocessing the power curve to obtain the first target curve further includes: traversing the power curve and determining missing sampling points in the power curve as second target sampling points; and completing the second target sampling points based on linear interpolation to obtain the first target curve.

[0009] Optionally, the correlation coefficient is calculated based on the first target curve by a hill climbing algorithm to obtain a target coefficient, including: randomly intercepting the first target curve based on a first preset step size to obtain a second target curve P1=[p 11 ,p 12 ,…,p n1 ] and P2=[p 21 ,p 22 ,…,p 2n ]; Substitute the second target curve into the first preset formula And based on the hill climbing algorithm, the second target power curve P2 = [p 21 ,p 22 ,…,p 2n ] is optimized to maximize R(P1, P2) and obtain the target coefficient, where E is the mathematical expectation value.

[0010] Optionally, the second target power curve P2=[p 21 ,p 22 ,…,p 2n ] is optimized to maximize R(P1, P2) and obtain the target coefficient, including: according to the second target power curve P1=[p 11 ,p 12 ,…,p 1n ] and P2=[p 21 ,p 22 ,…,p 2n ], set the objective function N is a natural number; the independent variable x takes an arbitrary value as the current solution, and a neighborhood set is constructed based on the initial solution and the second preset step size; based on the samples in the neighborhood set, the objective function is substituted, and the independent variable x that makes the objective function reach the maximum value is determined as the optimal solution of the current neighborhood set; the neighborhood set is updated with the optimal solution as the current solution, and the optimal solution is updated based on the updated neighborhood set until the optimal solutions are the same in two consecutive updating processes or the number of updates reaches the maximum number, thereby obtaining the target solution; the target solution is substituted into the first preset formula to obtain the target coefficient.

[0011] Optionally, a comparison is performed based on the target coefficient and a first threshold value to determine whether the isolating switch of the motor is faulty, including: when the minimum value of the target coefficient is greater than or equal to the first threshold value, determining that the isolating switch of the motor is not faulty; when the minimum value of the target coefficient is less than the first threshold value, determining that the isolating switch of the motor is faulty.

[0012] According to another aspect of the present application, a fault diagnosis device for a motor GIS disconnector is provided, the device comprising: an acquisition unit for acquiring a power curve during motor operation, and preprocessing the power curve to obtain a first target curve, wherein the preprocessing at least includes outlier filtering and missing value completion; a calculation unit for calculating a correlation coefficient based on the first target curve by a hill climbing algorithm to obtain a target coefficient; and a determination unit for comparing the target coefficient with a first threshold to determine whether the disconnector of the motor has a fault.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described.

[0014] According to another aspect of the present application, a motor is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of the methods described.

[0015] The technical solution of the application is applied to first acquire a power curve in the operation process of a motor, and pre-process the power curve to obtain a first target curve, the pre-processing at least including outlier filtering and missing value completion; then, a correlation coefficient is calculated based on the first target curve through a hill climbing algorithm to obtain a target coefficient; finally, the target coefficient and a first threshold value are compared to determine whether a disconnecting switch of the motor has failed. The application draws a power curve based on the current and voltage of the motor, and performs correlation determination according to the power curve optimized through the hill climbing algorithm, compared with the threshold value determination in the prior art through the average values of the motor current, main shaft rotation angle and image information and other characteristic quantities, the application determines through the correlation coefficient, avoids the interference of different periods of the power curve, and solves the problem of low accuracy of the existing technology in determining the mechanism jamming and mechanism misplacement failure. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A hardware structure block diagram of a mobile terminal for diagnosing a fault of a motor GIS disconnecting switch is shown according to an embodiment of the application;

[0017] Figure 2 A flowchart of a motor GIS disconnecting switch fault diagnosis method is shown according to an embodiment of the application;

[0018] Figure 3 A power curve diagram of a normal disconnecting switch is shown according to an embodiment of the application;

[0019] Figure 4 A power curve diagram of a slightly jammed disconnecting switch is shown according to an embodiment of the application;

[0020] Figure 5 A structure block diagram of a motor GIS disconnecting switch fault diagnosis device is shown according to an embodiment of the application.

[0021] Among the above drawings, the following reference signs are included:

[0022] 102, processor; 104, memory; 106, transmission device; 108, input / output device. DETAILED DESCRIPTION

[0023] It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other without conflict. The application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0024] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] As introduced in the background technology, in the prior art, threshold judgment is performed based on the average values ​​of characteristic quantities such as motor current, spindle angle and image information. In order to solve the problem of low accuracy in judging mechanism jamming and mechanism out-of-position faults, the embodiments of the present application provide a method for diagnosing a motor GIS isolating switch fault, a device for diagnosing a motor GIS isolating switch fault, a computer-readable storage medium and a motor.

[0027] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0028] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a fault diagnosis method of a motor GIS isolating switch according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown) a processor 102 (the processor 102 may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1more or less components than those shown, or configured differently from those shown, as Figure 1 described.

[0029] The memory 104 can be used to store computer programs, such as software programs of application software and modules, such as a computer program corresponding to the device information display method of the embodiments of the present application. The processor 102 can execute various functional applications and data processing, i.e., implement the above method, by running the computer program stored in the memory 104. The memory 104 can include a high-speed random access memory, and can further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include a memory remotely arranged with respect to the processor 102, and the remote memory can be connected to the mobile terminal through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The specific examples of the above network can include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet in a wireless manner.

[0030] In the present embodiment, a fault diagnosis method of a motor GIS disconnector running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown.

[0031] Figure 2 is a flowchart of the fault diagnosis method of the motor GIS disconnector according to the embodiments of the present application. As Figure 2 shown, the method includes the following steps:

[0032] In step S201, a power curve in a motor running process is obtained, and the power curve is preprocessed to obtain a first target curve. The preprocessing at least includes outlier filtering and missing value completion.

[0033] Specifically, during the operation of the motor, power data of the motor is collected in real time through corresponding sensors or monitoring systems, and these data usually exist in the form of time series, i.e., a power curve changing with time. The power curve data is preprocessed, i.e., the power curve is processed, and if there is a sampling point value that is negative or greater than 10 times the average value of all elements in the set, it is identified as abnormal data. Linear interpolation is used to complete the missing data and the identified abnormal data values.

[0034] In step S202, a correlation coefficient is calculated based on the first target curve by using a hill climbing algorithm, and a target coefficient is obtained.

[0035] Specifically, the hill climbing algorithm is a local search algorithm used to find the optimal solution in a given data set. In this step, the hill climbing algorithm is used to analyze the preprocessed first target curve and calculate one or more correlation coefficients. These coefficients may represent the similarity between different parts of the curve, the continuity of the change trend, or other characteristics related to the status of the disconnecting switch. After processing by the hill climbing algorithm, one or more target coefficients are obtained, which will serve as the basis for subsequent fault judgment.

[0036] In step S203, the target coefficient is compared with a first threshold value to determine whether the disconnecting switch of the motor has failed.

[0037] Specifically, one or more first threshold values are set based on historical data, empirical knowledge, or industry standards. These threshold values are used to define the boundary between the normal operation and failure state of the disconnecting switch.

[0038] As can be seen, the embodiments of the present application provide a fault diagnosis method for a GIS disconnecting switch of a motor. The method obtains and analyzes the power curve during the operation of the motor, calculates the correlation coefficient using the hill climbing algorithm, and compares it with the preset threshold value, thereby achieving effective diagnosis of the fault of the GIS disconnecting switch of the motor. This method has the advantages of real-time, high degree of automation, etc., and helps to improve the efficiency and accuracy of equipment maintenance.

[0039] As a possible implementation manner, the power curve during the operation of the motor is obtained, including: monitoring the voltage of the motor in real time through a voltage sensor to obtain a second target curve; monitoring the current of the motor in real time through a current sensor to obtain a third target curve; and calculating based on the second target curve and the third target curve to obtain the power curve during the operation of the motor.

[0040] Specifically, the voltage and current of the motor are collected by the voltage sensor and the current sensor, and the real-time voltage value and the current value are multiplied to calculate the power curve during the operation of the motor.

[0041] As a possible implementation, the power curve is preprocessed, including the following steps:

[0042] Step 301, traverse the power curve, and determine the sampling point as the first target sample point if the value of any sampling point is less than the second threshold value;

[0043] Specifically, the system checks each sampling point in the power curve in turn, and each sampling point represents a power value at a certain time. The second threshold value is usually set according to experience or historical data to distinguish between normal operation state and abnormal state.

[0044] Step 302, determine the sampling point as the first target sample point if the value of any sampling point is greater than or equal to the mean value of other sampling points in the power curve;

[0045] Specifically, while determining the first target sample point, the mean value of all sampling points in the power curve except the first target sample point is calculated. For each sampling point, if its value is greater than or equal to the above calculated mean value, the sampling point is determined as the first target sample point. This step is mainly to filter out those sampling points that deviate significantly from the normal operation state.

[0046] Step 303, delete the first target sample point in the power curve to complete the outlier filtering of the power curve.

[0047] Specifically, all data points in the power curve that are marked as the first target sample point are deleted. This helps to eliminate abnormal fluctuations in the power curve and ensures the quality of the basic data for subsequent analysis.

[0048] In summary, in the preprocessing stage of the motor GIS disconnector fault diagnosis method, the system can effectively preprocess the power curve, remove outliers, and retain data reflecting the normal operation of the motor. This not only improves the accuracy of fault diagnosis, but also ensures the reliability and stability of the analysis results. So that the subsequent analysis can accurately reflect the running state of the motor.

[0049] As a possible implementation, the power curve is preprocessed to obtain the first target curve, and further comprising: traversing the power curve, determining the missing sampling point in the power curve as the second target sampling point; based on the linear interpolation method, the second target sampling point is completed to obtain the first target curve.

[0050] Specifically, the linear interpolation method is a simple and effective interpolation method, which assumes that the data change between two known data points is linear. Through the above steps, the system can effectively preprocess the power curve, complete the missing sampling points, and ensure the integrity of the power curve.

[0051] As a possible implementation manner, the correlation coefficient is calculated based on the first target curve through a hill climbing algorithm to obtain a target coefficient, including:

[0052] The first target curve is randomly intercepted based on a first preset step length to obtain a second target curve P1=[p 11 ,p 12 ,…,p 1n ] and P2=[p 21 ,p 22 ,…,p 2n ];

[0053] Specifically, a random starting point is selected, and a data segment with a length of the first preset step length is intercepted to form the second target curve. This step ensures that the intercepted data segment can reflect the running state of the motor and has a certain randomness.

[0054] The second target curve is substituted into a first preset formula and the second target power curve P2=[p 21 ,p 22 ,…,p 2n ] is optimized based on the hill climbing algorithm to maximize R(P1, P2) to obtain the target coefficient, where E is a mathematical expectation value.

[0055] Through the above steps, the system can calculate the correlation coefficient based on the first target curve through the hill climbing algorithm to obtain the target coefficient. This process not only improves the accuracy of fault diagnosis, but also ensures the reliability and stability of the analysis result.

[0056] As a possible implementation manner, the second target power curve P2=[p 21 ,p 22 ,…,p 2n ] is optimized based on the hill climbing algorithm to maximize R(P1, P2) to obtain the target coefficient, including the following steps:

[0057] Step 401, according to the second target power curve P1=[p 11 ,p 12 ,…,p 1n ] and P2=[p 21 ,p 22 ,…,p 2n ], set the objective function N is a natural number;

[0058] Step 402, take an arbitrary value of the independent variable x as the current solution, and construct a neighborhood set based on the initial solution and a second preset step length;

[0059] Specifically, the initial solution x = 0 can be selected, the neighborhood set N(x) = [x-m, x-m+1, …, x+m] can be selected, where m is a neighborhood range parameter set, and m = 10 is usually taken in the implementation of the application.

[0060] In step 403, based on the samples in the neighborhood set, the independent variable x that makes the objective function reach the maximum value is determined as the optimal solution of the current neighborhood set.

[0061] Specifically, the optimal solution is selected from the neighborhood set, and the solution that maximizes the objective function is selected:

[0062] In step 404, the optimal solution is used to update the neighborhood set and update the optimal solution based on the updated neighborhood set until the optimal solution is the same in the two consecutive update processes or the number of updates reaches the maximum number, and the target solution is obtained.

[0063] In step 405, the target solution is substituted into the first preset formula to obtain the target coefficient.

[0064] Specifically, the neighborhood set N(x') and the optimal solution are repeatedly updated until the optimal solution is found as the optimal value of E(P1P2), at which time the correlation coefficient The fault judgment basis of the isolating switch is: for any power curve P, the qualified standard is minR(P, P i )≥0.95, where Pi is any historical comparison power curve set or interphase comparison power curve set.

[0065] Through the above steps, the system can optimize the second target power curve based on the hill climbing algorithm to maximize the correlation with the reference data, thereby obtaining the target coefficient. This process not only improves the accuracy of fault diagnosis, but also ensures the reliability and stability of the analysis results.

[0066] As a possible implementation, the target coefficient is compared with the first threshold to determine whether the isolating switch of the motor has failed, including: in the case where the minimum value of the target coefficient is greater than or equal to the first threshold, it is determined that the isolating switch of the motor has not failed; in the case where the minimum value of the target coefficient is less than the first threshold, it is determined that the isolating switch of the motor has failed.

[0067] Through the above steps, the system can accurately determine whether the isolating switch of the motor has failed based on the comparison of the target coefficient and the first threshold.

[0068] In order for those skilled in the art to more clearly understand the technical solutions of the present application, the implementation process of the fault diagnosis method of the motor GIS isolating switch of the present application will be described in detail below with specific embodiments.

[0069] The embodiment relates to a fault diagnosis method of a specific motor GIS disconnector, and in the specific implementation of the application, the motor voltage and current of two disconnectors during nearly three times of disconnection of a normal disconnector and a slightly blocked disconnector are collected, the data sampling interval is 1 ms, and a power curve is calculated, Figure 3 is a power curve diagram of the normal disconnector, Figure 4 is a power curve diagram of the slightly blocked disconnector, and ABC are A phase, B phase and C phase of a circuit.

[0070] Table 1

[0071]

[0072] According to the result, the method can accurately determine that the correlation coefficient of the AC phase and the AB phase power curve is low, so that it is determined that the A phase has a defect. Meanwhile, by comparing the calculation result without optimization by the hill climbing algorithm with the calculation result of the method, it can be seen that the correlation coefficients of the power curves of the typical normal disconnector and the fault disconnector after optimization have obvious differences, and the correlation coefficients before optimization are prone to be interfered to cause the fault condition to be unable to be determined.

[0073] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a group of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0074] The embodiment of the application further provides a fault diagnosis device for a motor GIS disconnector, and it should be noted that the fault diagnosis device for the motor GIS disconnector of the embodiment of the application can be used to execute the fault diagnosis method for the motor GIS disconnector provided by the embodiment of the application. The device is used to realize the above-mentioned embodiment and preferred embodiment, and details are not repeated. As used below, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the device described in the following embodiment is preferably realized in software, realization of hardware, or a combination of software and hardware is also possible and is conceived.

[0075] The fault diagnosis device for the motor GIS disconnector provided by the embodiment of the application is introduced below.

[0076] Figure 5 is a structure block diagram of the fault diagnosis device for the motor GIS disconnector according to the embodiment of the application. As shown in Figure 5 , the device comprises an acquisition unit 10, a calculation unit 20 and a determination unit 30.

[0077] The acquisition unit 10 is configured to acquire a power curve during the operation of the motor, and pre-process the power curve to obtain a first target curve. The pre-processing at least includes outlier filtering and missing value completion.

[0078] Specifically, during the operation of the motor, the power data of the motor is collected in real time through corresponding sensors or monitoring systems. These data usually exist in the form of time series, i.e., a curve of power changing with time. The power curve data is pre-processed, i.e., the power curve. If a certain sampling point value is negative or greater than 10 times the average value of all elements in the set, it is identified as abnormal data. Linear interpolation is used to complete the missing data and the identified abnormal data value.

[0079] The calculation unit 20 is configured to calculate a correlation coefficient based on the first target curve by using a hill climbing algorithm to obtain a target coefficient.

[0080] Specifically, the hill climbing algorithm is a local search algorithm used to find the optimal solution in a given data set. In this step, the hill climbing algorithm is used to analyze the pre-processed first target curve and calculate one or more correlation coefficients. These coefficients may represent the similarity between different parts of the curve, the continuity of the change trend, or other characteristics related to the status of the disconnecting switch. After the hill climbing algorithm is processed, one or more target coefficients are obtained, which will be used as the basis for subsequent fault judgment.

[0081] The determination unit 30 is configured to compare the target coefficient with a first threshold to determine whether the disconnecting switch of the motor has failed.

[0082] Specifically, one or more first thresholds are set according to historical data, experience knowledge or industry standards. These thresholds are used to define the boundary between the normal working state and the fault state of the disconnecting switch.

[0083] As can be seen, the embodiment of the application provides a fault diagnosis device for a GIS disconnecting switch of a motor. The device includes an acquisition unit, a calculation unit and a determination unit. By acquiring and analyzing the power curve during the operation of the motor, using the hill climbing algorithm to calculate the correlation coefficient, and comparing it with the preset threshold, the effective diagnosis of the fault of the GIS disconnecting switch of the motor is realized. This method has the advantages of real-time, high degree of automation, etc., which helps to improve the efficiency and accuracy of equipment maintenance.

[0084] As a possible implementation manner, the acquisition unit includes a voltage acquisition module, a current acquisition module and a power curve module.

[0085] The voltage acquisition module is configured to monitor the voltage of the motor in real time through the voltage sensor to obtain a second target curve; the current acquisition module is configured to monitor the voltage of the motor in real time through the current sensor to obtain a third target curve; and the power curve module is configured to calculate based on the second target curve and the third target curve to obtain a power curve in the motor running process.

[0086] Specifically, the voltage and current of the motor are collected by the voltage sensor and the current sensor, and the real-time voltage value and the current value are multiplied to calculate the power curve in the motor running process.

[0087] As a possible implementation manner, the acquisition unit further includes a first traversal module, a first target module and a filtering module.

[0088] The first traversal module is configured to traverse the power curve, and in a case where a value of any one sampling point is less than a second threshold value, determine the sampling point as a first target sample point.

[0089] Specifically, the system sequentially checks each sampling point in the power curve, and each sampling point represents a power value at a certain time. The second threshold value is usually set according to experience or historical data, and is used to distinguish between normal running state and abnormal state.

[0090] The first target module is configured to, in a case where a value of any one sampling point is greater than or equal to an average value of other sampling points in the power curve, determine the sampling point as a first target sample point.

[0091] Specifically, while determining the first target sample point, the average value of all sampling points in the power curve except the first target sample point is calculated. For each sampling point, if its value is greater than or equal to the average value calculated above, the sampling point is determined as the first target sample point. This step is mainly to screen out those sampling points that obviously deviate from the normal running state.

[0092] The filtering module is configured to delete the first target sample point in the power curve to complete the filtering of abnormal values of the power curve.

[0093] Specifically, all data points in the power curve that are marked as the first target sample point are deleted. This helps to eliminate abnormal fluctuations in the power curve and ensure the quality of the basic data for subsequent analysis.

[0094] In summary, in the preprocessing stage of the motor GIS disconnector fault diagnosis method, the system can effectively preprocess the power curve, remove abnormal values and retain data reflecting the normal running of the motor. This not only improves the accuracy of fault diagnosis, but also ensures the reliability and stability of the analysis results. So that the subsequent analysis can accurately reflect the running state of the motor.

[0095] As a possible implementation manner, the acquisition unit further includes: a second traversal module and a completion module.

[0096] The second traversal module is used to traverse the power curve and determine the missing sampling points in the power curve as second target sampling points; the completion module is used to complete the second target sampling points based on linear interpolation to obtain the first target curve.

[0097] Specifically, linear interpolation is a simple and effective interpolation method that assumes that the data change between two known data points is linear. Through the above steps, the system can effectively preprocess the power curve, fill in the missing sampling points, and ensure the integrity of the power curve.

[0098] As a possible implementation manner, the calculation unit includes: a second target module and a substitution module.

[0099] The second target module is used to randomly intercept the first target curve based on the first preset step size to obtain the second target curve P1=[p 11 ,p 12 ,…,p 1n ] and P2=[p 21 ,p 22 ,…,p 2n ];

[0100] Specifically, a random starting point is selected and a data segment having a length of a first preset step length is intercepted to form a second target curve. This step ensures that the intercepted data segment can reflect the operating state of the motor and has a certain degree of randomness.

[0101] Substitution module, used to substitute the second target curve into the first preset formula And based on the hill climbing algorithm, the second target power curve P2 = [p 21 ,p 22 ,…,p 2n ] is optimized to maximize R(P1, P2) and obtain the target coefficient, where E is the mathematical expectation value.

[0102] Through the above steps, the system can calculate the correlation coefficient based on the first target curve using a hill climbing algorithm to obtain the target coefficient. This process not only improves the accuracy of fault diagnosis, but also ensures the reliability and stability of the analysis results.

[0103] As a possible implementation manner, the substitution module includes: a setting submodule, a construction submodule, a substitution submodule, an update submodule and a target coefficient submodule.

[0104] Set up a submodule for 11 ,p12 ,…,p 1n ] and P2 = [p 21 ,p 22 ,…,p 2n ], set the objective function N is a natural number;

[0105] The construction submodule is configured to take an arbitrary value of the independent variable x as the current solution and construct a neighborhood set based on the initial solution and a second preset step size.

[0106] Specifically, the initial solution x = 0 can be selected, and the neighborhood set N(x) = [x-m, x-m+1, …, x+m] can be selected, where m is a neighborhood range parameter set, and m = 10 is usually taken in the specific implementation of the application.

[0107] The substitution submodule is configured to substitute the samples in the neighborhood set into the objective function to determine the independent variable x that maximizes the objective function as the optimal solution of the current neighborhood set.

[0108] Specifically, the optimal solution is selected from the neighborhood set to maximize the objective function.

[0109] The update submodule is configured to update the neighborhood set with the optimal solution as the current solution and update the optimal solution based on the updated neighborhood set until the optimal solution is the same in two consecutive update processes or the number of updates reaches a maximum number, thereby obtaining a target solution.

[0110] The target coefficient submodule is configured to substitute the target solution into a first preset formula to obtain a target coefficient.

[0111] Specifically, the neighborhood set N(x') and the optimal solution are repeatedly updated until the optimal solution is found as the optimal value of E(P1P2), at which point the correlation coefficient The disconnector fault judgment basis is: for any power curve P, the qualified standard is minR(P, P i ) ≥ 0.95, where Pi is any historical comparison power curve set or interphase comparison power curve set.

[0112] Through the above steps, the system can optimize the second target power curve based on the hill climbing algorithm to maximize the correlation with the reference data, thereby obtaining the target coefficient. This process not only improves the accuracy of fault diagnosis, but also ensures the reliability and stability of the analysis results.

[0113] As a possible implementation manner, the determining unit includes: a first determining module and a second determining module.

[0114] The first determination module is configured to determine that the disconnecting switch of the motor is not faulty when the minimum value of the target coefficient is greater than or equal to the first threshold value.

[0115] Through the above steps, the system can accurately determine whether the disconnecting switch of the motor is faulty based on the comparison between the target coefficient and the first threshold value.

[0116] The fault diagnosis device of the motor GIS disconnecting switch includes a processor and a memory, and the above-mentioned acquisition unit, calculation unit and determination unit are all stored in the memory as program units, and the corresponding functions are realized by the processor executing the above-mentioned program units stored in the memory. The above-mentioned modules are located in the same processor; or, the above-mentioned modules are located in different processors in any combination.

[0117] The processor contains a core, and the core retrieves the corresponding program unit from the memory. The core can be set to one or more, and the efficiency of communication can be improved by adjusting the core parameters.

[0118] The memory can include a non-permanent memory in a computer readable medium, a random access memory (RAM) and / or a non-volatile memory such as a read-only memory (ROM) or a flash memory (flash RAM), and the memory includes at least one memory chip.

[0119] The embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium includes a stored program, wherein the program controls the device where the computer readable storage medium is located to execute the fault diagnosis method of the motor GIS disconnecting switch when the program runs.

[0120] The embodiment of the present application provides a motor, which includes one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include a program for executing the fault diagnosis method of the motor GIS disconnecting switch.

[0121] It should be apparent to those skilled in the art that the modules or steps of the application described above can be implemented with a general purpose computer, and can be centralized in a single computer or distributed among a network of computers, and can be implemented with program code executable by a computer, and thus can be stored in a storage device and executed by a computer, and in some cases, the steps shown or described can be executed in a different order than shown or described, or can be implemented as separate integrated circuit modules or as a single integrated circuit module, and thus the application is not limited to any particular combination of hardware and software.

[0122] Those skilled in the art will appreciate that embodiments of the application can be devised for a method, a system, or a computer program product. Accordingly, the application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can be in the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk memory, CD-ROMs, optical storage media, etc.) embodying computer readable program code.

[0123] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0124] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0125] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart illustrations and / or block diagrams.Figure 1 one or more processes and / or functions specified in one or more blocks Figure 1 one or more processes and / or functions specified in one or more blocks

[0126] In one typical arrangement, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0127] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) and / or cache memory, non-volatile memory, such as read-only memory (ROM), EPROM, and / or flash memory, etc. The memory is an example of computer readable media.

[0128] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassette, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0129] It should also be noted that the terms "comprising", "containing", or any other variant thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not only include those elements, but can also include other elements not expressly listed or inherent to such process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0130] From the above description, it can be seen that the above-mentioned embodiments of the present application achieve the following technical effects:

[0131] 1)、the motor GIS disconnecting switch fault diagnosis method of the application, first obtain the power curve in the motor running process, and pretreat the power curve to obtain the first target curve, the pretreatment at least includes outlier filtering and missing value completion;Then, based on the first target curve, the correlation coefficient is calculated by hill climbing algorithm to obtain the target coefficient;Finally, the target coefficient and the first threshold value are compared to determine whether the motor disconnecting switch fails. The application draws the power curve based on the current and voltage of the motor, and determines the correlation according to the power curve optimized by hill climbing algorithm. Compared with the average value of the characteristic quantity such as motor current, main shaft rotation angle and image information in the prior art, the application determines by correlation coefficient, avoids the interference of different periods of power curve, and solves the problem of low accuracy of existing technology in judging the mechanism block and mechanism out of position fault.

[0132] 2)、the motor GIS disconnecting switch fault diagnosis device of the application, the device comprises: an acquisition unit, a calculation unit and a determination unit, by acquiring and analyzing the power curve in the motor running process, the correlation coefficient is calculated by hill climbing algorithm, and compared with the preset threshold value, so as to realize the effective diagnosis of motor GIS disconnecting switch fault. This method has the advantages of real-time, high degree of automation, etc., which helps to improve the efficiency and accuracy of equipment maintenance.

[0133] The above is only the preferred embodiment of the application and does not limit the application. For those skilled in the art, the application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A method for diagnosing a fault of a GIS disconnector of an electric machine, characterized in that, The method comprises the following steps: obtaining a power curve during motor operation, and preprocessing the power curve to obtain a first target curve, wherein the preprocessing at least includes outlier filtering and missing value completion; calculating a correlation coefficient based on the first target curve by a hill climbing algorithm to obtain a target coefficient, wherein the target coefficient is used to represent the similarity or the continuity of the change trend between different parts of the first target curve; The correlation coefficient is calculated based on the first target curve by a hill climbing algorithm to obtain a target coefficient, including: the first target curve is randomly intercepted based on a first preset step to obtain a second target power curve and ; substituting the second target power curve into a first preset formula , and optimizing the second target power curve based on a hill climbing algorithm to make reach a maximum value, to obtain the target coefficient, wherein E is a mathematical expectation value; comparing the target coefficient with a first threshold to determine whether the disconnector of the motor fails.

2. The method of claim 1, wherein, The method for obtaining the power curve during motor operation comprises the following steps: obtaining a second target curve by monitoring the voltage of the motor in real time through a voltage sensor; obtaining a third target curve by monitoring the voltage of the motor in real time through a current sensor; calculating based on the second target curve and the third target curve to obtain the power curve during motor operation.

3. The method of claim 1, wherein, The preprocessing of the power curve comprises the following steps: traversing the power curve, and determining a sampling point as a first target sampling point if the value of the sampling point is less than a second threshold; determining the sampling point as the first target sampling point if the value of the sampling point is greater than or equal to the average value of other sampling points in the power curve; deleting the first target sampling point in the power curve to complete the outlier filtering of the power curve.

4. The method of claim 3, wherein, The preprocessing of the power curve to obtain the first target curve further comprises the following steps: traversing the power curve, and determining a missing sampling point in the power curve as a second target sampling point; completing the second target sampling point based on a linear interpolation method to obtain the first target curve.

5. The method of claim 1, wherein, based on a hill climbing algorithm on a second target power curve is optimized to maximize a maximum value, to obtain the target coefficient, comprising: According to the second target power curve and , a target function , N is a natural number; taking an arbitrary value of the independent variable x as a current solution, and constructing a neighborhood set based on an initial solution and a second preset step size; determining the independent variable x that makes the objective function reach a maximum value as an optimal solution of the current neighborhood set based on the sample in the neighborhood set; updating the neighborhood set by taking the optimal solution as the current solution, and updating the optimal solution based on the updated neighborhood set, until the optimal solutions in two consecutive updating processes are the same or the updating times reach a maximum number, to obtain a target solution; substituting the target solution into the first preset formula to obtain the target coefficient.

6. The method of claim 1, wherein, The comparison of the target coefficient with the first threshold to determine whether the disconnector of the motor fails comprises the following steps: if the minimum value of the target coefficient is greater than or equal to the first threshold, determining that the disconnector of the motor does not fail; if the minimum value of the target coefficient is less than the first threshold, determining that the disconnector of the motor fails.

7. A fault diagnosis device for a GIS disconnector of an electric machine, characterized in that The device comprises: an obtaining unit, configured to obtain a power curve during motor operation, and preprocess the power curve to obtain a first target curve, wherein the preprocessing at least includes outlier filtering and missing value completion; a calculating unit, configured to calculate a correlation coefficient based on the first target curve by a hill climbing algorithm to obtain a target coefficient, wherein the target coefficient is used to represent the similarity or the continuity of the change trend between different parts of the first target curve; The computing unit comprises a second target module configured to randomly intercept the first target curve based on a first preset step to obtain a second target power curve and ; A substitution module is configured to substitute the second target power curve into a first preset formula and based on a hill climbing algorithm, the second target power curve is optimized to have a maximum, resulting in the target coefficient, wherein E is the mathematical expectation value; A determination unit is configured to compare the target coefficient with a first threshold to determine whether a disconnector of the motor is faulty.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, controls a device in which the computer-readable storage medium is located to perform the method of any one of claims 1-6.

9. An electric machine characterized by comprise: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing the method of any one of claims 1-6.

Images