A method and apparatus for online insulation monitoring of electric motors.

Abstract

This invention discloses a method for online insulation monitoring of electric motors, comprising: Step 1, connecting an adjustable resistor in series with the insulation resistance of the motor under test, and calculating the real-time resistance value of the insulation resistance of the motor under test by measuring the voltage divider; Step 2, based on the real-time resistance value of the insulation resistance of the motor under test, confirming whether the alarm conditions are met, and simultaneously confirming the insulation life level of the motor, issuing an alarm and displaying the insulation life level. This invention also discloses a device for online insulation monitoring of electric motors. This invention has online testing capabilities, high testing efficiency, accurate results, and does not affect the normal operation of the motor, significantly improving the reliability of the motor's normal operation.

Description

Technical Field

[0001] This invention relates to the field of power technology. More specifically, this invention relates to a method and apparatus for online insulation monitoring of electric motors. Background Technology

[0002] Large electric motors are crucial equipment in the power industry. With the development of the power industry and technological advancements, electric motors are evolving towards larger capacity and higher voltage. Increased single-unit capacity makes it possible to improve motor efficiency, reduce costs, and mitigate the environmental impact of power generation. However, this also places higher demands on the safe and reliable operation of these motors. Failures in large-capacity electric motors can cause power outages and even jeopardize the stable operation of the power system. Such incidents involve a wide range of issues, long repair cycles, high costs, and significant economic losses. Therefore, the safe and reliable operation of large electric motors has become a major concern for the power system.

[0003] The primary threat to the operational safety of large electric motors stems from their insulation system, determined by the motor's insulation structure, operating environment, and operating conditions. During operation, the stator insulation of an electric motor is subjected to electric field effects, thermal stress, mechanical stress, chemical reactions, and the influence of the external environment, inevitably suffering damage and gradually deteriorating over long-term operation. Therefore, to improve the operational reliability of electric motors, the insulation condition of the stator windings must be closely monitored. Traditional methods often involve periodic checks using high-voltage insulation testers or megohmmeters during non-operating periods. Motor equipment is also frequently equipped with motor safety control cabinets, which provide protection against phase loss and overcurrent. However, these detection and protection devices cannot display real-time insulation data of the motor's operating status or reflect changes in the actual insulation condition. Consequently, they cannot track internal contamination and insulation values ​​in many critical motor components in real time, hindering the effective scheduling of preventative maintenance and replacement work. This results in incomplete assurance of safe motor operation, preventing the equipment from reaching its maximum lifespan and allowing the motor to be fully utilized. Summary of the Invention

[0004] This invention provides a method and apparatus for online insulation monitoring of electric motors, which has online testing function, high testing efficiency, accurate results, and does not affect the normal operation of the electric motor, thus significantly improving the reliability of the electric motor's normal operation.

[0005] To achieve these objectives and other advantages according to the present invention, a method for implementing online insulation monitoring of electric motors is provided, comprising:

[0006] Step 1: Connect the adjustable resistor in series with the insulation resistance of the motor under test, and calculate the real-time resistance value of the insulation resistance of the motor under test by measuring the voltage divider.

[0007] Step two: Based on the real-time resistance value of the insulation resistance of the motor under test, confirm whether the alarm conditions are met, and at the same time confirm the insulation life level of the motor, and set up an alarm and display the insulation life level.

[0008] Preferably, the insulation life rating in step two is achieved through a motor life assessment model, the establishment of which includes:

[0009] S1: Collect the real-time insulation resistance values ​​of different motors as their lifespan continues to decrease, establish the correspondence between motor lifespan and insulation resistance information, and build a database.

[0010] S2: The correspondence between the motor insulation resistance information and the motor insulation life value of the one-dimensional time-series signal in the database is converted into two-dimensional image features by using a recursive graph algorithm. A pre-trained neural network model is established using the ImageNet dataset. The two-dimensional image features are then processed by the pre-trained neural network model to determine the motor life assessment model.

[0011] Preferably, in S1:

[0012] S11: For a single motor, collect the real-time resistance value of the motor's insulation resistance as the motor's lifespan continues to decrease, and establish the correspondence between the motor's lifespan and insulation resistance information.

[0013] S12: For different motor objects, collect the real-time resistance value of the insulation resistance of the motor as its lifespan continues to decrease, and establish the correspondence between the lifespan of different motors and the insulation resistance information.

[0014] S13: Based on the data results obtained from S11 and S12, build a database.

[0015] An online insulation monitoring device for electric motors includes:

[0016] The online detection module includes a power supply module, an adjustable resistor module, and a voltage measurement module. The adjustable resistor module is connected in series with the insulation resistance of the motor under test. The power supply module provides voltage to the series circuit. The voltage measurement module measures and outputs the real-time voltage of the adjustable resistor module.

[0017] The data processing module includes an insulation resistance calculation module, a motor alarm configuration module, and an alarm analysis module. The insulation resistance calculation module receives the real-time voltage of the adjustable resistor module, calculates and outputs the real-time resistance value of the insulation resistance of the motor under test. The motor alarm configuration module configures the motor alarm conditions. The alarm analysis module receives and compares the real-time resistance value of the insulation resistance of the motor under test with the alarm conditions.

[0018] The motor life assessment module receives the real-time resistance value of the insulation resistance of the motor under test, calls the motor life assessment model, and confirms the insulation life level of the motor based on the real-time resistance value of the insulation resistance of the motor under test.

[0019] The monitoring and alarm display module includes a monitoring display module and an alarm display module. The monitoring display module receives and displays the real-time resistance value of the motor insulation resistance and the motor insulation life rating. The alarm display module receives and displays alarm prompt information.

[0020] Preferably, the establishment of the motor life assessment model includes:

[0021] S1: The insulation resistance calculation module collects the real-time insulation resistance values ​​of different motors as their lifespan continues to decrease, establishes the correspondence between motor lifespan and insulation resistance information, and builds a database.

[0022] S2: The motor life assessment module uses a recursive graph algorithm to convert the correspondence between the motor insulation resistance information and the motor insulation life value of the one-dimensional time-series signal in the database into two-dimensional image features. A pre-trained neural network model is established using the ImageNet dataset. The two-dimensional image features are then processed by the pre-trained neural network model to determine the motor life assessment model.

[0023] Preferably, in S1:

[0024] S11: For a single motor, collect the real-time resistance value of the motor's insulation resistance as the motor's lifespan continues to decrease, and establish the correspondence between the motor's lifespan and insulation resistance information.

[0025] S12: For different motor objects, collect the real-time resistance value of the insulation resistance of the motor as its lifespan continues to decrease, and establish the correspondence between the lifespan of different motors and the insulation resistance information.

[0026] S13: Based on the data results obtained from S11 and S12, build a database.

[0027] An electronic device includes: 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 to cause the at least one processor to perform the method.

[0028] A storage medium on which a computer program is stored, which, when executed by a processor, implements the method described thereon.

[0029] A computer program product comprising a computer program that, when executed by a processor, implements the method described.

[0030] The present invention has at least the following beneficial effects:

[0031] First, the method of the present invention realizes real-time monitoring and abnormal alarm of the insulation status of the motor. It can detect the insulation resistance status online without stopping the motor, and will not affect the normal operation of the motor.

[0032] Secondly, when this invention is applied to motors with different insulation resistance ranges, the measurement range and accuracy of online monitoring can be improved by adjusting the resistance value of the adjustable resistor, thereby improving the measurement reliability and applicability of the system.

[0033] Third, this invention establishes a motor life assessment model by pre-establishing the correspondence between motor life and insulation resistance information. Based on the measured insulation resistance, the insulation life level of the motor insulation resistance can be obtained, thus realizing online real-time assessment of the motor insulation life level.

[0034] Fourth, this invention can determine the insulation life level of the motor under test by collecting the insulation resistance information of the motor, ensuring the timeliness and accuracy of insulation life assessment and significantly improving the reliability of the motor.

[0035] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating a method for implementing a technical solution of the present invention;

[0037] Figure 2 A flowchart illustrating the insulation life rating determination process for an electric motor, representing one technical solution of the present invention.

[0038] Figure 3 This is a structural diagram of an apparatus for implementing a technical solution of the present invention. Detailed Implementation

[0039] The present invention will now be described in further detail with reference to the accompanying drawings, so that those skilled in the art can implement it based on the description.

[0040] It should be understood that terms such as “having,” “comprising,” and “including” as used herein do not exclude the presence or addition of one or more other elements or combinations thereof.

[0041] like Figure 1As shown, the present invention provides a method for implementing online insulation monitoring of electric motors, comprising:

[0042] Step 1: Connect the adjustable resistor in series with the insulation resistance of the motor under test. The resistance value of the adjustable resistor can be adjusted according to the range of insulation resistance of the user and the motor under test to ensure that the monitoring system has sufficient measurement accuracy and provides a stable voltage U1 to the series circuit. The real-time resistance value of the motor under test is calculated by measuring the voltage divider. That is, first measure the real-time voltage U2 of the adjustable resistor after the series circuit is energized, and then calculate the real-time voltage and real-time resistance value of the motor under test.

[0043] Step two: Based on the real-time resistance value of the insulation resistance of the motor under test, confirm whether the alarm conditions are met. The alarm conditions are set with a threshold range. Once the real-time resistance value falls within the threshold range, the alarm conditions are met. At the same time, confirm the insulation life level of the motor. The insulation life level corresponds to the degree of emergency maintenance of the motor's life. Issue an alarm and display the insulation life level.

[0044] The above technical solution achieves real-time monitoring and abnormal alarm of the motor insulation status. It allows for online detection of insulation resistance without stopping the motor, without affecting its normal operation. Furthermore, for motors with different insulation resistance ranges, the measurement range and accuracy of online monitoring can be improved by adjusting the value of the adjustable resistor, thus enhancing the system's measurement reliability and applicability.

[0045] In another technical solution, such as Figure 2 As shown, the insulation life level in step two is achieved through a motor life assessment model. This model is determined based on the correspondence between motor insulation resistance information and motor insulation life. The establishment of the motor life assessment model includes:

[0046] S1: Collect the real-time insulation resistance values ​​of different motors as their lifespan continues to decrease, establish the correspondence between motor lifespan and insulation resistance information, and build a database.

[0047] As a preferred option, in S1:

[0048] S11: For a single motor, collect the real-time resistance value of the motor's insulation resistance as the motor's lifespan continues to decrease, and establish the correspondence between the motor's lifespan and insulation resistance information.

[0049] S12: Replace different motor objects. For different motor objects, collect the real-time resistance value of the insulation resistance of the motor as its lifespan continues to decrease, and establish the correspondence between the lifespan of different motors and the insulation resistance information.

[0050] S13: Based on the data results obtained from S11 and S12, build the database;

[0051] S2: The correspondence between the motor insulation resistance information and the motor insulation life value of the one-dimensional time-series signal in the database is converted into two-dimensional image features using a recursive graph algorithm. A training strategy is adopted to process the classic dataset in the database to obtain a pre-trained neural network model. The classic dataset is a transfer learning strategy in the field of deep learning, that is, learning basic features in other datasets first, and then achieving the purpose of transfer learning in this dataset through the similarity between images, which can greatly improve the learning ability of the model. The ImageNet dataset is a public, large-scale visualization database used for research on visual object recognition software. The model is trained and tested based on the database obtained in S13. A pre-trained neural network model is established through the ImageNet dataset. The two-dimensional image features are processed through the pre-trained neural network model to determine the motor life assessment model.

[0052] In the above technical solution, by establishing a motor life assessment model based on the pre-established correspondence between motor life and insulation resistance information, the insulation life level of the motor insulation resistance can be obtained based on the measured insulation resistance, thereby realizing online real-time assessment of the motor insulation life level.

[0053] This invention can determine the insulation life level of a motor under test by collecting its insulation resistance information, ensuring the timeliness and accuracy of insulation life assessment and significantly improving the reliability of the motor.

[0054] like Figure 3 As shown, an online insulation monitoring device for electric motors includes:

[0055] The online detection module includes a power supply module, an adjustable resistor module, and a voltage measurement module. The adjustable resistor module is connected in series with the insulation resistance of the motor under test. The resistance value of the adjustable resistor module can be adjusted according to the user and the insulation resistance range of the motor under test to ensure that the monitoring system has sufficient measurement accuracy. The power supply module provides a stable voltage U1 for the series circuit. The voltage measurement module measures the real-time voltage U2 of the adjustable resistor module and outputs the data to the data processing module.

[0056] The data processing module includes an insulation resistance calculation module, a motor alarm configuration module, and an alarm analysis module. The insulation resistance calculation module receives the real-time voltage from the adjustable resistor module, calculates and outputs the real-time insulation resistance value of the motor under test. The real-time resistance value is sent to the motor life assessment module, alarm analysis module, and monitoring display module. The motor alarm configuration module is configured with motor alarm conditions. The alarm analysis module receives and compares the real-time resistance value of the insulation resistance of the motor under test with the alarm conditions. The alarm analysis module compares the real-time resistance value with the alarm conditions stored in the motor alarm configuration module one by one. If the alarm conditions are met, the alarm signal is sent to the alarm display module to trigger an alarm; otherwise, no alarm is triggered.

[0057] The motor life assessment module receives the real-time insulation resistance value of the motor under test, calls the motor life assessment model, which is determined based on the correspondence between the motor insulation resistance information and the motor insulation life. Based on the real-time insulation resistance value of the motor under test, it confirms the insulation life level of the motor and sends the life level and display information to the monitoring display module and the alarm display module. The insulation life level is divided into 1-6 levels, with the life time decreasing sequentially. This invention gives "No action required" for level 1, "Continuous monitoring" for levels 2 and 3, "Maintenance required" for levels 4 and 5, and "Urgent maintenance needed" for level 6, which can prevent the motor from failing due to insulation life issues.

[0058] The monitoring and alarm display module displays the received real-time resistance information on the monitoring screen. If an alarm signal is detected, an alarm window will pop up. The monitoring screen includes the collected data and time of the motor insulation resistance. The alarm window includes the insulation resistance, insulation life, alarm time, and alarm level. It includes a monitoring display module and an alarm display module. The monitoring display module receives and displays the real-time resistance value of the motor insulation resistance and the real-time status of the motor life assessment based on the motor insulation life level. The alarm display module receives and displays alarm prompts and other abnormal data.

[0059] The establishment of the electric motor life assessment model includes:

[0060] S1: The insulation resistance calculation module collects the real-time insulation resistance values ​​of different motors as their lifespan continues to decrease, establishes the correspondence between motor lifespan and insulation resistance information, and builds a database.

[0061] As a preferred option, in S1:

[0062] S11: For a single motor, collect the real-time resistance value of the motor's insulation resistance as the motor's lifespan continues to decrease, and establish the correspondence between the motor's lifespan and insulation resistance information.

[0063] S12: Replace different motor objects. For different motor objects, collect the real-time resistance value of the insulation resistance of the motor as its lifespan continues to decrease, and establish the correspondence between the lifespan of different motors and the insulation resistance information.

[0064] S13: Based on the data results obtained from S11 and S12, build the database;

[0065] S2: The motor life assessment module uses a recursive graph algorithm to convert the correspondence between the motor insulation resistance information and the motor insulation life value of the one-dimensional time-series signal in the database into two-dimensional image features. A training strategy is adopted to process the classic dataset in the database to obtain a pre-trained neural network model. The classic dataset is a transfer learning strategy in the field of deep learning, that is, learning basic features in other datasets first, and then achieving the purpose of transfer learning in this dataset through the similarity between images, which can greatly improve the learning ability of the model. The ImageNet dataset is a public, large-scale visualization database used for research on visual object recognition software. The model is trained and tested based on the database obtained in S13. A pre-trained neural network model is established through the ImageNet dataset. The two-dimensional image features are processed by the pre-trained neural network model to determine the motor life assessment model.

[0066] The present invention also provides an electronic device, 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 cause the at least one processor to perform the SQL dynamic assembly method based on the JDBC protocol. This electronic device can be any terminal device including mobile phones, laptops, desktop computers, tablets, PDAs (Personal Digital Assistants), POS (Point of Sales) terminals, in-vehicle computers, etc.

[0067] The present invention also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0068] The present invention also provides a computer program product comprising a computer program that, when executed by a processor, implements the method described.

[0069] Through the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware, and of course, it can also be implemented by special hardware including application-specific integrated circuits, special CPUs, special memory, special components, etc. Generally, any function performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or special-purpose circuits. However, for the present invention, software program implementation is more often the preferred implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, portable hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0070] The number of devices and processing scale described herein are for the purpose of simplifying the description of the invention. Applications, modifications, and variations of the invention will be readily apparent to those skilled in the art.

[0071] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A method for implementing online insulation monitoring of electric motors, characterized in that, include: Step 1: Connect the adjustable resistor in series with the insulation resistance of the motor under test, and calculate the real-time resistance value of the insulation resistance of the motor under test by measuring the voltage divider. Step 2: Based on the real-time resistance value of the insulation resistance of the motor under test, confirm whether the alarm conditions are met, and at the same time confirm the insulation life level of the motor, and set up an alarm and display the insulation life level. The insulation life rating in step two is achieved through a motor life assessment model, the establishment of which includes: S1: Collect the real-time insulation resistance values ​​of different motors as their lifespan continues to decrease, establish the correspondence between motor lifespan and insulation resistance information, and build a database. S2: The correspondence between the motor insulation resistance information and the motor insulation life value of the one-dimensional time-series signal in the database is converted into two-dimensional image features by using a recursive graph algorithm. A pre-trained neural network model is established using the ImageNet dataset. The two-dimensional image features are then processed by the pre-trained neural network model to determine the motor life assessment model.

2. The method for online insulation monitoring of electric motors as described in claim 1, characterized in that, In S1: S11: For a single motor, collect the real-time resistance value of the motor's insulation resistance as the motor's lifespan continues to decrease, and establish the correspondence between the motor's lifespan and insulation resistance information. S12: For different motor objects, collect the real-time resistance value of the insulation resistance of the motor as its lifespan continues to decrease, and establish the correspondence between the lifespan of different motors and the insulation resistance information. S13: Based on the data results obtained from S11 and S12, build a database.

3. A device for online insulation monitoring of electric motors, characterized in that, include: The online detection module includes a power supply module, an adjustable resistor module, and a voltage measurement module. The adjustable resistor module is connected in series with the insulation resistance of the motor under test. The power supply module provides voltage to the series circuit. The voltage measurement module measures and outputs the real-time voltage of the adjustable resistor module. The data processing module includes an insulation resistance calculation module, a motor alarm configuration module, and an alarm analysis module. The insulation resistance calculation module receives the real-time voltage of the adjustable resistor module, calculates and outputs the real-time resistance value of the insulation resistance of the motor under test. The motor alarm configuration module configures the motor alarm conditions. The alarm analysis module receives and compares the real-time resistance value of the insulation resistance of the motor under test with the alarm conditions. The motor life assessment module receives the real-time resistance value of the insulation resistance of the motor under test, calls the motor life assessment model, and confirms the insulation life level of the motor based on the real-time resistance value of the insulation resistance of the motor under test. A monitoring and alarm display module, comprising a monitoring display module and an alarm display module, wherein the monitoring display module receives and displays the real-time resistance value of the motor insulation resistance and the motor insulation life level, and the alarm display module receives and displays alarm prompt information; The establishment of the electric motor life assessment model includes: S1: The insulation resistance calculation module collects the real-time insulation resistance values ​​of different motors as their lifespan continues to decrease, establishes the correspondence between motor lifespan and insulation resistance information, and builds a database. S2: The motor life assessment module uses a recursive graph algorithm to convert the correspondence between the motor insulation resistance information and the motor insulation life value of the one-dimensional time-series signal in the database into two-dimensional image features. A pre-trained neural network model is established using the ImageNet dataset. The two-dimensional image features are then processed by the pre-trained neural network model to determine the motor life assessment model.

4. The device for online insulation monitoring of electric motors as described in claim 3, characterized in that, In S1: S11: For a single motor, collect the real-time resistance value of the motor's insulation resistance as the motor's lifespan continues to decrease, and establish the correspondence between the motor's lifespan and insulation resistance information. S12: For different motor objects, collect the real-time resistance value of the insulation resistance of the motor as its lifespan continues to decrease, and establish the correspondence between the lifespan of different motors and the insulation resistance information. S13: Based on the data results obtained from S11 and S12, build a database.

5. An electronic device, characterized in that, include: 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 cause the at least one processor to perform the method according to any one of claims 1 to 2.

6. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method described in any one of claims 1 to 2.

7. A computer program product comprising a computer program, characterized in that, When the program is executed by the processor, it implements the method described in any one of claims 1 to 2.

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