Generator brush fault online monitoring and judging method and system

Abstract

The disclosure provides a generator brush fault online monitoring and judging method and system, the method comprising: obtaining individual temperatures of each brush of a generator; dividing each brush into multiple regions according to the distribution characteristics of the brushes of the generator, calculating regional temperatures of each region based on the individual temperatures of each brush in each region, and calculating an overall temperature of all regions based on the individual temperatures of all brushes; judging whether there is a significant difference between the multiple regional temperatures, if there is a significant difference, judging that the generator has a centrifugal oscillation; if there is no significant difference, comparing whether the multiple regional temperatures are greater than the overall temperature respectively, if greater, judging that the brush current distribution is abnormal; and if not greater than the overall temperature, determining whether each brush has a fault based on the individual temperature of each brush, the overall temperature and a temperature threshold. According to the method of the disclosure, the reliability of brush operation state monitoring can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of online monitoring of generator faults, and in particular to a method and system for online monitoring and judgment of generator brush faults. Background Technology

[0002] Currently, most synchronous generators in China use static excitation, applying the current and voltage signals generated by the excitation device to the rotor excitation windings. The brushes play a crucial role in this process, ensuring a good electrical connection between the two relatively rotating components. During generator operation, friction constantly exists between the brushes and slip rings. This causes a certain degree of temperature rise and wear on the brushes, but as long as it is within permissible limits, regular replacement of the carbon brushes can ensure the safe and stable operation of the generator.

[0003] However, the actual operating conditions of generators are quite complex, and due to various factors, brushes often experience abnormal temperatures or burnout. The main reasons include: poor or even broken contact between the brush braid and the carbon brush, leading to increased current flowing through other brushes and severe brush overheating; uneven pressure of the constant-pressure spring fixing the carbon brush; when the pressure decreases, the contact resistance between the carbon brush and the slip ring increases, causing temperature rise; and centrifugal vibration of the generator can also lead to poor contact between the carbon brush and the slip ring, resulting in abnormal temperatures. Therefore, online monitoring of the generator carbon brush operating status and timely warning of abnormal conditions can effectively prevent carbon brush burnout.

[0004] Currently, monitoring the operating status of generator carbon brushes is mainly done manually, which has low efficiency and reliability. Some power plants use measuring instruments to replace manual methods for online monitoring, but the collected data still needs to be judged and analyzed manually, which has a certain lag. In addition, there are online monitoring systems that combine measuring instruments with intelligent algorithms, but there are certain deviations in data processing and analysis and fault classification. Summary of the Invention

[0005] This disclosure provides a method and system for online monitoring and judgment of generator brush faults, the main purpose of which is to achieve online monitoring of brush faults and more accurately determine the fault type.

[0006] According to a first aspect of this disclosure, a method for online monitoring and judgment of generator brush faults is provided, including:

[0007] Obtain the individual temperature of each brush in the generator;

[0008] Based on the distribution characteristics of the generator brushes, each brush is divided into multiple regions. The region temperature of each region is calculated based on the individual temperature of each brush in each region, and the overall temperature of all regions is calculated based on the individual temperature of all brushes.

[0009] Determine whether there is a significant difference in temperature between the multiple regions. If there is a significant difference in temperature between the multiple regions, then determine that the generator is experiencing centrifugal oscillation.

[0010] If there is no significant difference between the temperatures of the multiple regions, then compare whether the temperatures of the multiple regions are greater than the overall temperature. If they are greater than the overall temperature, then it is determined that the brush current distribution is abnormal.

[0011] If the temperature is not greater than the overall temperature, then the presence or absence of a fault in each brush is determined based on the individual temperature of each brush, the overall temperature, and the temperature threshold.

[0012] In one embodiment of this disclosure, the absence of significant differences between the temperatures of the plurality of regions means that the numerical values ​​of the temperatures of the plurality of regions are equal or approximately equal.

[0013] In one embodiment of this disclosure, determining whether each brush is faulty based on its individual temperature, the overall temperature, and a temperature threshold includes: determining whether the individual temperature of each brush is greater than the overall temperature; filtering out target brushes whose individual temperature is greater than the overall temperature; determining whether the individual temperature of the target brush is greater than the temperature threshold; if it is greater, then the target brush is faulty.

[0014] In one embodiment of this disclosure, dividing each brush into multiple regions according to the distribution characteristics of the generator brushes includes: according to the distribution characteristics of the generator brushes, equating the distribution positions of all brushes to a circular region concentric with the generator shaft, and then dividing the circular region into four regions, each region containing the same number of brushes.

[0015] In one embodiment of this disclosure, the calculation of the region temperature based on the individual temperature of each brush in each region includes: selecting any region, calculating the average value of the individual temperatures of all brushes in that region, using the average value as the region temperature of that region, and then obtaining the region temperatures of other regions.

[0016] In one embodiment of this disclosure, the step of calculating the overall temperature of all regions based on the individual temperatures of all brushes includes: calculating the average value of the individual temperatures of all brushes, and using the average value as the overall temperature of all regions.

[0017] According to a second aspect embodiment of this disclosure, an online monitoring and judgment system for generator brush faults is also provided, comprising:

[0018] Multiple temperature sensors, data acquisition and storage units, data processing and analysis center, and monitoring platform;

[0019] The multiple temperature sensors are used to collect the individual temperature of each brush of the generator;

[0020] The data acquisition and storage unit and the plurality of temperature sensors are used to transmit the acquired individual temperatures of each brush to the data processing and analysis center.

[0021] The data processing and analysis center is used to divide each brush into multiple regions based on the distribution characteristics of the generator brushes, calculate the region temperature of each region based on the individual temperature of each brush in each region, and calculate the overall temperature of all regions based on the individual temperatures of all brushes; determine whether there is a significant difference between the temperatures of the multiple regions. If there is a significant difference, it is determined that the generator has a centrifugal oscillation fault; if there is no significant difference, it compares whether the temperatures of the multiple regions are greater than the overall temperature. If they are greater than the overall temperature, it is determined that there is an abnormal brush current distribution fault; if they are not greater than the overall temperature, it determines whether each brush has a fault based on the individual temperature of each brush, the overall temperature, and a temperature threshold.

[0022] The monitoring platform is used to display the faults identified by the data processing and analysis center.

[0023] In one embodiment of this disclosure, the data processing and analysis center, when determining whether there is a significant difference between the temperatures of the multiple regions, is specifically configured to: determine whether the values ​​of the temperatures of the multiple regions are equal or approximately equal; if the values ​​of the temperatures of the multiple regions are equal or approximately equal, then there is no significant difference between the temperatures of the multiple regions; otherwise, there is a significant difference between the temperatures of the multiple regions.

[0024] In one embodiment of this disclosure, the data processing and analysis center is used to determine whether each brush is faulty based on the individual temperature of each brush, the overall temperature, and a temperature threshold if the temperature is not greater than the overall temperature. Specifically, it is used to: if the temperature is not greater than the overall temperature, determine whether the individual temperature of each brush is greater than the overall temperature, filter out target brushes whose individual temperature is greater than the overall temperature, determine whether the individual temperature of the target brush is greater than the temperature threshold, and if it is greater, then the target brush is faulty.

[0025] According to a third aspect of the present disclosure, an electronic device is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the generator brush fault online monitoring and judgment method proposed in the first aspect of the present disclosure.

[0026] In one or more embodiments of this disclosure, the individual temperature of each brush of the generator is obtained; based on the distribution characteristics of the generator brushes, each brush is divided into multiple regions; the region temperature of each region is calculated based on the individual temperature of each brush in each region; and the overall temperature of all regions is calculated based on the individual temperatures of all brushes. It is determined whether there is a significant difference between the temperatures of the multiple regions. If a significant difference exists, it is determined that the generator is experiencing centrifugal oscillation; if no significant difference exists, it is determined whether the temperatures of the multiple regions are greater than the overall temperature. If they are greater, it is determined that the brush current distribution is abnormal; if they are not greater than the overall temperature, it is determined whether each brush is faulty based on the individual temperature of each brush, the overall temperature, and a temperature threshold. In this case, by dividing the region according to the brush distribution characteristics to obtain the individual temperature, region temperature, and overall temperature of the generator brushes, and then comprehensively analyzing the three types of temperatures, it is possible to achieve online monitoring of brush faults while more accurately determining the fault type, improving the efficiency and reliability of brush operating status monitoring, and reducing the workload of operation and maintenance personnel.

[0027] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0028] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0029] Figure 1 A flowchart illustrating the first method for online monitoring and judgment of generator brush faults provided in this disclosure is shown.

[0030] Figure 2 This diagram illustrates the brush region distribution provided in an embodiment of the present disclosure.

[0031] Figure 3 A flowchart illustrating the second method for online monitoring and judgment of generator brush faults provided in this embodiment of the present disclosure is shown.

[0032] Figure 4 This diagram shows a block diagram of an online monitoring and judgment device for generator brush faults provided in an embodiment of this disclosure;

[0033] Figure 5 This diagram illustrates an online monitoring and judgment system for generator brush faults provided in an embodiment of the present disclosure.

[0034] Figure 6 This is a block diagram of an electronic device used to implement the online monitoring and judgment method for generator brush faults according to embodiments of the present disclosure. Detailed Implementation

[0035] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this disclosure as detailed in the appended claims.

[0036] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0037] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise expressly and specifically defined. It should also be understood that the term "and / or" as used in this disclosure refers to and includes any or all possible combinations of one or more associated listed items.

[0038] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0039] In the first embodiment, Figure 1 This diagram illustrates a flowchart of the first method for online monitoring and judgment of generator brush faults provided in this embodiment. Figure 2 This diagram illustrates the brush region distribution provided in an embodiment of the present disclosure. Figure 3 This diagram illustrates a flowchart of a second method for online monitoring and judgment of generator brush faults provided in an embodiment of this disclosure. Figure 1 As shown, specifically, the online monitoring and judgment method for generator brush faults includes:

[0040] S101, obtain the individual temperature of each brush of the generator.

[0041] In step S101, the generator has multiple brushes, the number of which can be represented by n, and the individual temperature of each brush can be represented by T. i Let represent , where i = 1, 2, 3...n.

[0042] In step S101, each brush of the generator is equipped with a temperature sensor, which can collect the temperature of the corresponding brush. Brushes can also be called carbon brushes or excitation brushes.

[0043] In some embodiments, the temperature information (i.e., temperature data) collected by the temperature sensor is an analog signal. It is necessary to convert the analog signal into a digital signal, filter out certain interference components using a digital filter, and then extract the main components (see [link to documentation]). Figure 3 To obtain the required individual temperature T for each brush. i .

[0044] In some embodiments, the individual temperature of each brush can be obtained in time intervals. Specifically, the start time of each time interval can be represented by t0. The individual temperature of each brush at the start time t0 of different time intervals is obtained to obtain the individual temperature of each brush in the corresponding time interval.

[0045] S102, based on the distribution characteristics of the generator brushes, divide each brush into multiple regions, calculate the region temperature of each region based on the individual temperature of each brush in each region, and calculate the overall temperature of all regions based on the individual temperature of all brushes.

[0046] Specifically, in step S102, based on the distribution characteristics of the generator brushes, each brush is divided into multiple regions, including: based on the distribution characteristics of the generator brushes, the distribution positions of all brushes are equivalent to a circular region concentric with the generator shaft, and then the circular region is divided into multiple regions. The number of brushes included in each region can be the same or different.

[0047] In step S102, as Figure 2 As shown, the distribution of all brushes can be approximated as a circular region, with the generator shaft at the same center. This circular region is divided into four equal areas, each containing the same number of brushes. Using the center of the circle as the origin, these four areas are analogous to the four quadrants of a Cartesian coordinate system, and can be represented by the symbols I, II, III, and IV. Specifically, brushes located on the half-axis at boundary point A belong to area I; brushes located on the half-axis at boundary point B belong to area IV; brushes located on the half-axis at boundary point C belong to area III; and brushes located on the half-axis at boundary point D belong to area II.

[0048] In step S102, after dividing the region, the regional temperature of each region is calculated. That is, the temperature information of all the collected brushes (i.e., individual temperature) is divided into multiple regions (the division process is the data classification process), and the regional temperature of each region is obtained by using the individual temperature of all the brushes in each region.

[0049] In step S102, the region temperature of each region is calculated based on the individual temperature of each brush in each region. This includes: selecting any region, calculating the average individual temperature of all brushes in that region, using the average value as the region temperature of that region, and then obtaining the region temperatures of other regions. The region temperature of each region can be represented by T. j This indicates that j corresponds to one of the four brush distribution regions, i.e., j = I, II, III, IV. The average value is, for example, the arithmetic mean.

[0050] In step S102, the overall temperature of all regions is calculated based on the individual temperatures of all brushes, including: calculating the average value of the individual temperatures of all brushes, and using the average value as the overall temperature of all regions (i.e., the overall temperature of the entire region). Taking the initial time t0 of each time period as an example, the individual temperatures of all brushes at the initial time t0 are obtained, and the individual temperatures T of each brush are calculated. i Calculate the arithmetic mean to obtain the overall temperature T0 at the initial time t0, and thus obtain the overall temperature for the corresponding time period.

[0051] In step S102, the acquisition of the regional temperature and the overall temperature can be performed simultaneously or sequentially. For example, the overall temperature can be acquired first, followed by the regional temperature (see [link to relevant documentation]). Figure 3 ).

[0052] S103, determine whether there is a significant difference in temperature between multiple regions. If there is a significant difference in temperature between multiple regions, then determine that the generator is experiencing centrifugal oscillation.

[0053] In step S103, a machine learning-based classification learning algorithm is used for analysis and judgment. Specifically, taking four regions as an example, the average brush temperature T of the four regions is first determined. j If there is a significant difference in the temperature of the area, it can be determined that the generator is experiencing centrifugal oscillation (i.e., centrifugal oscillation of the generator rotor), which causes the contact resistance between the brush and the slip ring on the side of the shaft that is opposite to the center to increase, resulting in severe heat generation; if there is no significant difference, proceed to the next step.

[0054] In step S103, "no significant difference between the temperatures of multiple regions" means that the values ​​of the temperatures of multiple regions are equal or approximately equal. Specifically, "the values ​​of the temperatures of multiple regions are approximately equal" means that the difference between the values ​​of the temperatures of each region does not exceed 5%.

[0055] S104 If there is no significant difference between the temperatures of multiple regions, compare whether the temperatures of each region are greater than the overall temperature. If they are greater than the overall temperature, then the brush current distribution is considered abnormal.

[0056] Specifically, in step S104, the average brush temperature T of each region at the current moment is determined. j Is the overall brush temperature greater than the average value T0 (i.e., the overall temperature) at time t0? If T j A reading >T0 indicates that the overall brush temperature in the area is too high. This can be largely attributed to uneven excitation current distribution caused by abnormal connections in individual brushes (i.e., abnormal brush current distribution), resulting in the current flowing through other brushes exceeding the rated value and causing the temperature to be higher than normal. If T... j If the value is less than or equal to T0, proceed to the next step.

[0057] S105, if the temperature is not greater than the overall temperature, then determine whether each brush is faulty based on the individual temperature of each brush, the overall temperature, and the temperature threshold.

[0058] In step S105, determining whether each brush is faulty is based on the individual temperature, overall temperature, and temperature threshold of each brush, including: determining whether the individual temperature of each brush is greater than the overall temperature, filtering out target brushes with temperatures greater than the overall temperature, determining whether the target brush is greater than the temperature threshold, and if it is, the target brush is faulty.

[0059] Specifically, in step S105, the individual temperatures of each brush are screened to identify abnormal brushes (i.e., target brushes) whose temperature values ​​are greater than the overall temperature T0, and the individual temperature T of the abnormal brushes is determined. i Does it exceed the temperature threshold T during normal brush operation? ref If T i >T ref If the abnormal brush fails, it can be determined that the brush has malfunctioned; otherwise, the brush is operating normally.

[0060] In some embodiments, a machine learning-based classification learning algorithm (such as steps S103 to S105 above) can be used to analyze the fault information and generate a brush operation status report.

[0061] In some embodiments, the real-time operating status of the brush obtained through the analysis of steps S103 to S105 above is sent to the monitoring platform, and the fault information determined is sent to the generator protection unit so as to serve as the judgment logic condition for other protection functions.

[0062] The online monitoring and judgment method for generator brush faults in this embodiment of the present disclosure obtains the individual temperature of each brush of the generator; divides each brush into multiple regions according to the distribution characteristics of the generator brushes; calculates the region temperature of each region based on the individual temperature of each brush in each region; calculates the overall temperature of all regions based on the individual temperatures of all brushes; determines whether there is a significant difference between the temperatures of multiple regions; if there is a significant difference, it is determined that the generator is experiencing centrifugal oscillation; if there is no significant difference, it compares whether the temperatures of multiple regions are greater than the overall temperature; if they are greater, it is determined that the brush current distribution is abnormal; if they are not greater than the overall temperature, it determines whether the individual temperature of each brush is greater than the overall temperature, filters out target brushes whose individual temperatures are greater than the overall temperature, and determines whether the individual temperature of the target brush is greater than a temperature threshold; if it is greater, the target brush is faulty. In this scenario, the area is divided according to the characteristics of the brush distribution, resulting in three types of brush temperature values: individual temperature, area temperature, and overall temperature. A comprehensive analysis of these three types of temperatures enables online monitoring of brush faults and more accurate identification of fault categories. This improves the efficiency and reliability of brush operation status monitoring and reduces the workload of operation and maintenance personnel.

[0063] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0064] Please see Figure 4 , Figure 4 This diagram illustrates a block diagram of an online monitoring and judgment device for generator brush faults provided in an embodiment of this disclosure. This online monitoring and judgment device for generator brush faults can be implemented as all or part of a system through software, hardware, or a combination of both. The online monitoring and judgment device 10 for generator brush faults includes an acquisition module 11, a calculation module 12, a first judgment module 13, a second judgment module 14, and a third judgment module 15, wherein:

[0065] The acquisition module 11 is used to acquire the individual temperature of each brush of the generator;

[0066] The calculation module 12 is used to divide each brush into multiple regions according to the distribution characteristics of the generator brushes, calculate the region temperature of each region based on the individual temperature of each brush in each region, and calculate the overall temperature of all regions based on the individual temperature of all brushes.

[0067] The first judgment module 13 is used to determine whether there is a significant difference between the temperatures of multiple regions. If there is a significant difference between the temperatures of multiple regions, it is determined that the generator is experiencing centrifugal oscillation.

[0068] The second judgment module 14 is used to compare whether the temperature of each of the multiple regions is greater than the overall temperature if there is no significant difference between the temperatures of the multiple regions. If the temperature of the multiple regions is greater than the overall temperature, the brush current distribution is judged to be abnormal.

[0069] The third judgment module 15 is used to determine whether each brush is faulty based on the individual temperature of each brush, the overall temperature and the temperature threshold if the temperature is not greater than the overall temperature.

[0070] Optionally, the third judgment module 15 is specifically used to: determine whether the individual temperature of each brush is greater than the overall temperature, filter out target brushes whose individual temperature is greater than the overall temperature, determine whether the individual temperature of the target brush is greater than the temperature threshold, and if it is, the target brush is faulty.

[0071] Optionally, the calculation module 12 is specifically used to: select any region, calculate the average value of the individual temperatures of all brushes in that region, use the average value as the region temperature of that region, and then obtain the region temperatures of other regions; calculate the average value of the individual temperatures of all brushes, and use the average value as the overall temperature of all regions.

[0072] It should be noted that the generator brush fault online monitoring and judgment device provided in the above embodiments is only illustrated by the division of the above functional modules when executing the generator brush fault online monitoring and judgment method. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the electronic device can be divided into different functional modules to complete all or part of the functions described above. In addition, the generator brush fault online monitoring and judgment device and the generator brush fault online monitoring and judgment method embodiments provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0073] The sequence numbers of the embodiments disclosed above are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0074] In the generator brush fault online monitoring and judgment device of this disclosure embodiment, the acquisition module acquires the individual temperature of each brush of the generator; the calculation module divides each brush into multiple regions according to the distribution characteristics of the generator brushes, calculates the region temperature of each region based on the individual temperature of each brush in each region, and calculates the overall temperature of all regions based on the individual temperatures of all brushes; the first judgment module judges whether there is a significant difference between the temperatures of multiple regions. If there is a significant difference, it judges that the generator is experiencing centrifugal oscillation; the second judgment module, if there is no significant difference, compares whether the temperatures of multiple regions are greater than the overall temperature. If they are greater, it judges that the brush current distribution is abnormal; the third judgment module, if the temperature is not greater than the overall temperature, judges whether the individual temperature of each brush is greater than the overall temperature, filters out target brushes whose individual temperatures are greater than the overall temperature, and judges whether the individual temperature of the target brush is greater than a temperature threshold. If it is greater, the target brush is faulty. In this scenario, based on the characteristic that the brushes are distributed circumferentially around the generator shaft, the brushes are divided into regions to obtain three types of brush temperature values: individual temperature, regional temperature, and overall temperature. Then, based on a classification learning algorithm, these three types of temperatures are comprehensively analyzed. This enables online monitoring of brush faults and more accurate identification of fault types, improving the efficiency and reliability of brush operation status monitoring and reducing the workload of operation and maintenance personnel.

[0075] The following are system embodiments of this disclosure, which can be used to execute the method embodiments of this disclosure. For details not disclosed in the system embodiments of this disclosure, please refer to the method embodiments of this disclosure.

[0076] Please see Figure 5 , Figure 5 This diagram illustrates an online monitoring and judgment system for generator brush faults provided in an embodiment of this disclosure. This system primarily monitors and judges the operating status of the brushes by collecting their temperature data. The online monitoring and judgment system 30 includes multiple temperature sensors 31, a data acquisition and storage unit 32, a data processing and analysis center 33, and a monitoring platform 34, wherein:

[0077] Multiple temperature sensors 31 are used to collect the individual temperature of each brush of the generator;

[0078] The data acquisition and storage unit 32 and multiple temperature sensors 31 are used to transmit the acquired individual temperature of each brush to the data processing and analysis center 33.

[0079] The data processing and analysis center 33 is used to divide each brush into multiple regions based on the distribution characteristics of the generator brushes, calculate the region temperature of each region based on the individual temperature of each brush in each region, and calculate the overall temperature of all regions based on the individual temperatures of all brushes; determine whether there are significant differences between the temperatures of multiple regions. If there are significant differences, it is determined that there is a centrifugal oscillation fault in the generator; if there are no significant differences, it compares whether the temperatures of multiple regions are greater than the overall temperature. If they are greater than the overall temperature, it is determined that there is an abnormal brush current distribution fault; if they are not greater than the overall temperature, it determines whether there is a fault in each brush based on the individual temperature of each brush, the overall temperature, and the temperature threshold.

[0080] The monitoring platform 34 is used to display the faults identified by the data processing and analysis center 33.

[0081] Optionally, each brush (i.e., carbon brush) is connected to a temperature sensor.

[0082] Optionally, each temperature sensor is a high-precision resistive temperature sensor, connected to the end of the brush away from the slip ring. The connection wires of the temperature sensors have a certain margin, similar to that of the brush braid, for easy stretching. Furthermore, the connection wires of the temperature sensors have shielding functionality to ensure the accuracy of the measured temperature signal and avoid interference from surrounding electromagnetic fields.

[0083] Optionally, the other end of the temperature sensor is connected to the data acquisition and storage unit 32 via a shielded transmission line.

[0084] Optionally, the data acquisition and storage unit 32 includes a server and a wireless transmission module. The data acquisition and storage unit 32 can package the received brush temperature information (i.e., the individual temperature of each brush), convert the temperature information analog signal into a digital signal, filter out certain interference components through a digital filter, and then upload it to the background data processing and analysis center 33 through the server and wireless transmission module.

[0085] Optionally, the data processing and analysis center 33, when used to determine whether there is a significant difference between the temperatures of multiple regions, is specifically used to: determine whether the values ​​of the temperatures of multiple regions are equal or approximately equal; if the values ​​of the temperatures of multiple regions are equal or approximately equal, then there is no significant difference between the temperatures of multiple regions; otherwise, there is a significant difference between the temperatures of multiple regions.

[0086] Optionally, the data processing and analysis center 33 is used to determine whether each brush is faulty based on the individual temperature of each brush, the overall temperature, and the temperature threshold if the temperature is not greater than the overall temperature. Specifically, it is used to: if the temperature is not greater than the overall temperature, determine whether the individual temperature of each brush is greater than the overall temperature, filter out target brushes whose individual temperature is greater than the overall temperature, determine whether the individual temperature of the target brush is greater than the temperature threshold, and if it is, the target brush is faulty.

[0087] Optionally, the data processing and analysis center 33 can perform principal component extraction on the collected temperature information to obtain the individual temperature T of each brush. i Then, based on the individual temperature T of each brush... i The brush's real-time operating status is monitored by performing a machine learning-based classification learning algorithm (i.e., steps S102 to S105 in the method embodiment).

[0088] Optionally, the data processing and analysis center 33 can analyze the real-time operating status of the brush (including various faults) and send it to the monitoring platform 34, while also sending the fault information to the generator protection unit to serve as the judgment logic condition for other protection functions.

[0089] Optionally, the monitoring platform 34 can also display the real-time operating status of other brushes obtained from the analysis of the data processing and analysis center 33.

[0090] It should be noted that the foregoing explanation of the embodiment of the online monitoring and judgment method for generator brush faults also applies to the online monitoring and judgment system for generator brush faults in this embodiment, and will not be repeated here.

[0091] The generator brush fault online monitoring and judgment system of this disclosure only needs to collect brush temperature, without current and voltage signals, which simplifies the structure of the brush monitoring system and is economical. In addition, the data processing and analysis center of the system divides the brushes into regions according to the characteristic that the brushes are distributed in a circle around the generator shaft, thereby obtaining the individual, regional and overall temperature values ​​of the brushes. Then, based on the classification learning algorithm, the three types of data are comprehensively analyzed, which improves the efficiency and reliability of brush operation status monitoring.

[0092] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0093] Figure 6This is a block diagram of an electronic device used to implement the online monitoring and judgment method for generator brush faults according to embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable electronic devices, and other similar computing devices. The components, connections and relationships between components, and functions shown in this disclosure are merely illustrative and are not intended to limit the implementation of the present disclosure as described and / or claimed herein.

[0094] like Figure 6 As shown, the electronic device 20 includes a computing unit 21, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 22 or a computer program loaded from a storage unit 28 into a random access memory (RAM) 23. The RAM 23 may also store various programs and data required for the operation of the electronic device 20. The computing unit 21, ROM 22, and RAM 23 are interconnected via a bus 24. An input / output (I / O) interface 25 is also connected to the bus 24.

[0095] Multiple components in electronic device 20 are connected to I / O interface 25, including: input unit 26, such as keyboard, mouse, etc.; output unit 27, such as various types of monitors, speakers, etc.; storage unit 28, such as disk, optical disk, etc., which is communicatively connected to computing unit 21; and communication unit 29, such as network card, modem, wireless transceiver, etc. Communication unit 29 allows electronic device 20 to exchange information / data with other electronic devices through computer networks such as the Internet and / or various telecommunications networks.

[0096] The computing unit 21 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 21 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 21 performs the various methods and processes described above, such as performing a generator brush fault online monitoring and judgment method. For example, in some embodiments, the generator brush fault online monitoring and judgment method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 28. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 20 via ROM 22 and / or communication unit 29. When the computer program is loaded into RAM 23 and executed by the computing unit 21, one or more steps of the generator brush fault online monitoring and judgment method described above can be performed. Alternatively, in other embodiments, the computing unit 21 may be configured by any other suitable means (e.g., by means of firmware) to perform a method for online monitoring and judgment of generator brush faults.

[0097] Various embodiments of the systems and techniques described above in this disclosure can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic electronic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0098] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0099] In this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or electronic device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or electronic devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage electronics, magnetic storage electronics, or any suitable combination of the foregoing.

[0100] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0101] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0102] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0103] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this disclosure does not impose any restrictions here.

[0104] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for online monitoring and judgment of generator brush faults, characterized in that, include: Obtain the individual temperature of each brush in the generator; Based on the distribution characteristics of the generator brushes, each brush is divided into multiple regions. The region temperature of each region is calculated based on the individual temperature of each brush in each region, and the overall temperature of all regions is calculated based on the individual temperature of all brushes. The step of dividing each brush into multiple regions based on the distribution characteristics of the generator brushes includes: based on the distribution characteristics of the generator brushes, the distribution positions of all brushes are equivalent to a circular region concentric with the generator shaft, and then the circular region is divided into 4 regions, each region containing the same number of brushes. Determine whether there is a significant difference in temperature between the multiple regions. If there is a significant difference in temperature between the multiple regions, then determine that the generator is experiencing centrifugal oscillation. If there is no significant difference between the temperatures of the multiple regions, then compare whether the temperatures of the multiple regions are greater than the overall temperature. If they are greater than the overall temperature, then it is determined that the brush current distribution is abnormal. If the temperature is not greater than the overall temperature, then determine whether each brush is faulty based on the individual temperature of each brush, the overall temperature and the temperature threshold, including: determining whether the individual temperature of each brush is greater than the overall temperature, filtering out target brushes with individual temperatures greater than the overall temperature, determining whether the individual temperature of the target brush is greater than the temperature threshold, and if it is greater, then the target brush is faulty. The calculation of the regional temperature based on the individual temperature of each brush in each region includes: Select any region, calculate the average individual temperature of all brushes in that region, use the average value as the region temperature, and then obtain the region temperatures of other regions. The calculation of the overall temperature of all regions based on the individual temperatures of all brushes includes: Calculate the average individual temperature of all brushes and use this average as the overall temperature of all regions.

2. The method for online monitoring and judgment of generator brush faults as described in claim 1, characterized in that, The statement that there is no significant difference between the temperatures of the multiple regions means that the numerical values ​​of the temperatures of the multiple regions are equal or approximately equal.

3. A generator brush fault online monitoring and judgment system, characterized in that, include: Multiple temperature sensors, data acquisition and storage units, data processing and analysis center, and monitoring platform; The multiple temperature sensors are used to collect the individual temperature of each brush of the generator; The data acquisition and storage unit and the plurality of temperature sensors are used to transmit the acquired individual temperatures of each brush to the data processing and analysis center. The data processing and analysis center is used to divide each brush into multiple regions according to the distribution characteristics of the generator brushes, calculate the region temperature of each region based on the individual temperature of each brush in each region, and calculate the overall temperature of all regions based on the individual temperatures of all brushes; determine whether there is a significant difference between the temperatures of the multiple regions, and if there is a significant difference, determine that the generator has a centrifugal oscillation fault. If there is no significant difference, compare the temperatures of the multiple regions to see if they are greater than the overall temperature. If they are greater than the overall temperature, it is determined that there is an abnormal brush current distribution fault. If the temperature is not greater than the overall temperature, then the presence or absence of a fault in each brush is determined based on the individual temperature of each brush, the overall temperature, and the temperature threshold. The step of dividing each brush into multiple regions according to the distribution characteristics of the generator brushes includes: representing the distribution positions of all brushes as a circular region concentric with the generator shaft, and then dividing the circular region into four equal regions, each containing the same number of brushes. Determining whether each brush is faulty based on its individual temperature, the overall temperature, and the temperature threshold includes: determining whether the individual temperature of each brush is greater than the overall temperature, selecting target brushes with individual temperatures greater than the overall temperature, and determining whether the individual temperature of the target brush is greater than the temperature threshold; if it is, the target brush is faulty. The monitoring platform is used to display the faults identified by the data processing and analysis center; The calculation of the regional temperature based on the individual temperature of each brush in each region includes: Select any region, calculate the average individual temperature of all brushes in that region, use the average value as the region temperature, and then obtain the region temperatures of other regions. The calculation of the overall temperature of all regions based on the individual temperatures of all brushes includes: Calculate the average individual temperature of all brushes and use this average as the overall temperature of all regions.

4. The generator brush fault online monitoring and judgment system as described in claim 3, characterized in that, The data processing and analysis center, when determining whether there are significant differences in temperature between the multiple regions, is specifically used for: Determine whether the temperature values ​​of multiple regions are equal or approximately equal. If the temperature values ​​of multiple regions are equal or approximately equal, then there is no significant difference between the temperatures of the multiple regions; otherwise, there is a significant difference between the temperatures of the multiple regions.

5. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the generator brush fault online monitoring and judgment method according to any one of claims 1-2.

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