Multi-dimensional partial discharge map analysis method based on artificial intelligence

Abstract

The invention discloses a multi-dimensional partial discharge map analysis method based on artificial intelligence, and particularly relates to the technical field of partial discharge intelligent detection. Comprising the steps of numbering partial discharge spectrum sources, acquiring partial discharge spectrum characteristic data, analyzing electrical signal waveform form safety, analyzing partial discharge spectrum intensity, comprehensively evaluating partial discharge spectrum characteristics, early warning partial discharge spectrum stability and monitoring an industrial equipment partial discharge environment. The electric signal spectrum data of the partial discharge spectrum of each discharge activity of the industrial equipment is acquired through the sensor, the electric signal waveform form analysis index and the partial discharge spectrum intensity evaluation index are calculated, the partial discharge stability evaluation coefficient is further calculated, and the monitoring and intelligent diagnosis of the equipment state are realized. And by calculating the industrial equipment partial discharge signal environment influence index, the efficiency of maintenance work is improved, and the automation and intelligence level of equipment management is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of partial discharge intelligent detection, and more specifically, to a multi-dimensional partial discharge spectrum analysis method based on artificial intelligence. Background Art

[0002] Partial discharge refers to the discharge phenomenon between unpenetrated electrodes caused by defects in equipment or devices during the production process or other external reasons under the action of high electric field strength. The field of industrial equipment fault diagnosis has also benefited widely from partial discharge spectrum analysis enabled by artificial intelligence.

[0003] With the development of electricity, partial discharge is becoming more and more common in high-voltage equipment experiments. Multi-dimensional partial discharge maps display information on multiple dimensions of discharge, helping engineers quickly identify possible insulation defects, cracks, contamination or other forms of faults in the equipment. These maps can not only help locate the source of the fault, but also provide an assessment of the severity of the fault.

[0004] However, there are still some shortcomings in its actual use. For example, the multi-dimensional map contains a large number of data points. There is no unified standard and specification for how to generate, analyze and process these data through partial discharge maps, which leads to inconsistency and misleading of existing data processing results. The generation and analysis process of multi-dimensional partial discharge maps still relies heavily on manual operation and experience judgment. There is a lack of signal processing technology to conduct more in-depth analysis and evaluation of partial discharge signals in order to extract useful information and build a discharge signal stability evaluation system, resulting in a low level of automation and intelligence in map analysis. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a multi-dimensional partial discharge spectrum analysis method based on artificial intelligence, which is used to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a multi-dimensional partial discharge spectrum analysis method based on artificial intelligence, comprising the following steps: Step S01: Partial discharge spectrum source numbering: used to locate the specific position of the discharge source in the industrial equipment using electromagnetic wave positioning technology, perform partial discharge monitoring on the industrial equipment, and number the generated partial discharge spectrum of the industrial equipment.

[0007] Step S02: partial discharge spectrum characteristic data acquisition: used to acquire the electrical signal spectrum data of the partial discharge spectrum of each discharge activity of the industrial equipment through a sensor, wherein the partial discharge spectrum characteristic data acquisition includes an electrical signal waveform data acquisition unit and a partial discharge intensity data acquisition unit.

[0008] Step S03: Safety analysis of electrical signal waveform morphology: Calculate the electrical signal waveform morphology analysis index of the partial discharge spectrum of each discharge activity of the industrial equipment according to the electrical signal waveform data acquisition unit, screen out the discharge activities with abnormal electrical signal waveforms, and issue an abnormal warning for the partial discharge spectrum of the industrial equipment.

[0009] Step S04: partial discharge spectrum intensity analysis: calculating the partial discharge spectrum intensity evaluation index of the partial discharge spectrum of each discharge activity of the industrial equipment according to the partial discharge intensity data acquisition unit.

[0010] Step S05: Comprehensive evaluation of partial discharge spectrum characteristics: Calculate the partial discharge stability evaluation coefficient of the industrial equipment based on the electrical signal waveform morphology analysis index and the partial discharge spectrum intensity evaluation index of the partial discharge spectrum of each discharge activity of the industrial equipment.

[0011] Step S06: Partial discharge spectrum stability warning: used to obtain the partial discharge stability evaluation coefficient of the industrial equipment, compare it with the preset partial discharge stability evaluation coefficient, and process it.

[0012] Step S07: partial discharge environment monitoring of industrial equipment: used to collect the temperature and noise amplitude of each discharge activity of the industrial equipment through sensors, calculate the industrial equipment partial discharge signal environment impact index of the industrial equipment based on the partial discharge stability evaluation coefficient of the industrial equipment, and screen out industrial equipment with abnormal electrical signal environment monitoring.

[0013] Preferably, the partial discharge spectrum source number is specifically: The UHF antenna array is installed at the signal receiving position of the industrial equipment, and the signal acquisition device is started. The partial discharge signal of each discharge activity of the industrial equipment is captured by the UHF antenna, and the partial discharge spectrum is generated from the captured partial discharge signal of each discharge activity, and the partial discharge spectrum of each discharge activity of the industrial equipment is numbered 1, 2,... ,... .

[0014] Preferably, the partial discharge spectrum characteristic data collection is specifically as follows: Electrical signal waveform data acquisition unit: The discharge pulse width, discharge pulse number, and discharge pulse duration of the partial discharge spectrum of each discharge activity of the industrial equipment are collected through sensors and marked as , , ,in =1,2,... , Expressed as The number of the partial discharge map of the secondary discharge activity; Partial discharge intensity data acquisition unit: The sensor collects the electrical signal amplitude, electrical signal peak amplitude, and total discharge amount of the partial discharge spectrum of each discharge activity of the industrial equipment, which are marked as , , .

[0015] Preferably, the electrical signal waveform morphology safety analysis is specifically as follows: Step S001: Obtain the discharge pulse width and discharge pulse duration of the partial discharge spectrum of each discharge activity of the industrial equipment, and calculate the electrical signal energy output index:

[0016] in, Expressed as The electrical signal energy output index of the partial discharge spectrum of the secondary discharge activity, Expressed as The discharge pulse width of the partial discharge spectrum of the secondary discharge activity, Represents the preset discharge pulse width, Expressed as The discharge pulse duration of the partial discharge spectrum of the secondary discharge activity, It is represented as the preset discharge pulse duration; Step S002: Obtain the number of discharge pulses and the duration of the discharge pulses in the partial discharge spectrum of each discharge activity of the industrial equipment, and calculate the frequency index of the discharge activity:

[0017] in, Expressed as The frequency index of the discharge activity of the partial discharge map of the discharge activity, Expressed as The number of discharge pulses in the partial discharge spectrum of the discharge activity, Expressed as the mean value of the number of discharge pulses, It is expressed as the mean value of the discharge pulse duration, and n is expressed as the total number of discharges in the partial discharge spectrum; Step S003: The calculation formula of the electrical signal waveform morphology analysis index is:

[0018] in, Expressed as The electrical signal waveform morphology analysis index of the partial discharge spectrum of the secondary discharge activity, Expressed as The energy output of the electrical signal of the partial discharge spectrum of the secondary discharge activity fluctuates. Expressed as The frequency of discharge activity in the partial discharge map of the secondary discharge activity fluctuates; Step S004: Obtain the electrical signal waveform morphology analysis index of the local discharge spectrum of each discharge activity of the industrial equipment, compare it with the preset electrical signal waveform morphology analysis index, screen out the numbers of the local discharge spectrum of the discharge activities whose electrical signal waveform morphology analysis index is greater than the preset electrical signal waveform morphology analysis index, mark them as discharge activities with abnormal electrical signal waveforms, locate the position of the local discharge source of the discharge activity in the industrial equipment, and issue an abnormal warning.

[0019] Preferably, the calculation formula of the partial discharge spectrum intensity evaluation index is:

[0020] in Expressed as The partial discharge spectrum intensity evaluation index of the partial discharge spectrum of the secondary discharge activity, Expressed as The electrical signal amplitude of the partial discharge spectrum of the secondary discharge activity, Expressed as The peak amplitude of the electrical signal of the partial discharge spectrum of the secondary discharge activity, Expressed as The total discharge amount of the partial discharge spectrum of the discharge activity, It is expressed as the dielectric constant of industrial equipment, and n is expressed as the total number of discharges in the partial discharge spectrum.

[0021] Preferably, the calculation formula of the partial discharge stability evaluation coefficient is:

[0022] in, Expressed as the partial discharge stability assessment coefficient, Expressed as The electrical signal waveform morphology analysis index of the partial discharge spectrum of the secondary discharge activity, Expressed as The partial discharge spectrum intensity evaluation index of the partial discharge spectrum of the secondary discharge activity.

[0023] Preferably, the partial discharge spectrum stability warning is specifically: Obtain the local discharge stability assessment coefficient of the industrial equipment and compare it with the preset local discharge stability assessment coefficient. If the local discharge stability assessment coefficient of the industrial equipment is greater than the preset local discharge stability assessment coefficient, it indicates that there is an abnormality in the stability of the discharge activity of the industrial equipment, and the management personnel should be notified to inspect the industrial equipment. Otherwise, it indicates that the stability assessment of the discharge activity of the industrial equipment is normal.

[0024] Preferably, the partial discharge environment monitoring of industrial equipment is specifically: Step S001: The temperature and noise amplitude of each discharge activity of the industrial equipment are collected by sensors and marked as , ,in =1,2,... , Expressed as The serial number of the industrial equipment; Step S002: The calculation formula of the environmental impact index of the partial discharge signal of industrial equipment is:

[0025] in, Expressed as Environmental impact index of partial discharge signal of industrial equipment for each industrial equipment, Expressed as The temperature of industrial equipment, Expressed as The noise amplitude of industrial equipment, Expressed as the preset noise amplitude, Expressed as Partial discharge stability evaluation coefficient of industrial equipment, Expressed as the mean value of the partial discharge stability evaluation coefficient, Expressed as the heat resistance level of industrial equipment; Step S003: Obtain the industrial equipment partial discharge signal environmental impact index of the industrial equipment, compare it with the preset industrial equipment partial discharge signal environmental impact index, screen out the industrial equipment partial discharge signal environmental impact index greater than the preset industrial equipment partial discharge signal environmental impact index, mark it as industrial equipment with abnormal electrical signal environment monitoring, and notify maintenance personnel to deal with the abnormal equipment in time.

[0026] Technical effects and advantages of the present invention: The present invention provides a multi-dimensional partial discharge spectrum analysis method based on artificial intelligence, which collects electrical signal spectrum data of the partial discharge spectrum of each discharge activity of industrial equipment through a sensor, calculates the electrical signal waveform morphology analysis index of the partial discharge spectrum of each discharge activity of the industrial equipment according to the electrical signal waveform data acquisition unit, calculates the partial discharge spectrum intensity evaluation index of the partial discharge spectrum of each discharge activity of the industrial equipment according to the partial discharge intensity data acquisition unit, and further calculates the partial discharge stability evaluation coefficient, which is compared with the preset partial discharge stability evaluation coefficient. If the partial discharge stability evaluation coefficient of the industrial equipment is greater than the preset partial discharge stability evaluation coefficient, it indicates that the stability of the discharge activity of the industrial equipment is abnormal, and the management personnel should be notified to detect the industrial equipment. Otherwise, it indicates that the stability evaluation of the discharge activity of the industrial equipment is normal. By introducing the spectrum analysis method based on artificial intelligence, the factory can achieve Continuous monitoring and intelligent diagnosis of equipment status can timely discover safety hazards and deal with potential faults, ensure the continuous and stable operation of equipment, and improve the automation and intelligence level of industrial equipment management. The present invention provides a multi-dimensional partial discharge spectrum analysis method based on artificial intelligence, which uses industrial equipment partial discharge environment monitoring to collect the temperature and noise amplitude of each discharge activity of industrial equipment through sensors, and calculates the industrial equipment partial discharge signal environmental impact index of industrial equipment based on the partial discharge stability evaluation coefficient of industrial equipment, and screens out industrial equipment with abnormal electrical signal environment monitoring. Partial discharge is a precursor to many industrial equipment failures. By monitoring the environmental impact index, potential failures of equipment can be predicted in advance and corresponding preventive measures can be taken, which is conducive to reducing the risk of equipment failure and maintenance costs and extending the service life of equipment. At the same time, maintenance personnel can conduct more targeted inspections and maintenance, thereby improving the efficiency and accuracy of maintenance work. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the connection of the method steps of the present invention.

[0028] Figure 2 It is a schematic diagram of the partial discharge spectrum characteristic data acquisition structure of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] See also Figure 1As shown, the present invention provides a multi-dimensional partial discharge spectrum analysis method based on artificial intelligence, including partial discharge spectrum source numbering, partial discharge spectrum feature data collection, electrical signal waveform morphology safety analysis, partial discharge spectrum intensity analysis, partial discharge spectrum feature comprehensive evaluation, partial discharge spectrum stability warning, and industrial equipment partial discharge environment monitoring.

[0031] The local discharge spectrum source number is connected with the local discharge spectrum feature data collection, the local discharge spectrum feature data collection is connected with the electric signal waveform morphology safety analysis, the electric signal waveform morphology safety analysis is connected with the local discharge spectrum intensity analysis, the local discharge spectrum intensity analysis is connected with the local discharge spectrum feature comprehensive evaluation, the local discharge spectrum feature comprehensive evaluation is connected with the local discharge spectrum stability warning, and the local discharge spectrum stability warning is connected with the industrial equipment local discharge environment monitoring.

[0032] The step S01: partial discharge spectrum source numbering is used to locate the specific position of the discharge source in the industrial equipment using the electromagnetic wave positioning technology, perform partial discharge monitoring on the industrial equipment, and number the generated partial discharge spectrum of the industrial equipment.

[0033] In a possible design, the partial discharge spectrum source number is specifically: The UHF antenna array is installed at the signal receiving position of the industrial equipment, and the signal acquisition device is started. The partial discharge signal of each discharge activity of the industrial equipment is captured by the UHF antenna, and the partial discharge spectrum is generated from the captured partial discharge signal of each discharge activity, and the partial discharge spectrum of each discharge activity of the industrial equipment is numbered 1, 2,... ,... .

[0034] See also Figure 2 As shown, the step S02: partial discharge spectrum characteristic data acquisition is used to collect electrical signal spectrum data of the partial discharge spectrum of each discharge activity of the industrial equipment through a sensor, and the partial discharge spectrum characteristic data acquisition includes an electrical signal waveform data acquisition unit and a partial discharge intensity data acquisition unit.

[0035] In a possible design, the partial discharge spectrum characteristic data collection is specifically as follows: Electrical signal waveform data acquisition unit: The discharge pulse width, discharge pulse number, and discharge pulse duration of the partial discharge spectrum of each discharge activity of the industrial equipment are collected through sensors and marked as , , ,in =1,2,... , Expressed as The number of the partial discharge map of the secondary discharge activity; Partial discharge intensity data acquisition unit: The sensor collects the electrical signal amplitude, electrical signal peak amplitude, and total discharge amount of the partial discharge spectrum of each discharge activity of the industrial equipment, which are marked as , , .

[0036] The step S03: electrical signal waveform morphology safety analysis is used to receive the electrical signal spectrum data transmitted by the partial discharge spectrum characteristic data acquisition step, calculate the electrical signal waveform morphology analysis index of the partial discharge spectrum of each discharge activity of the industrial equipment according to the electrical signal waveform data acquisition unit, screen out the discharge activities with abnormal electrical signal waveforms, and issue an abnormal warning for the partial discharge spectrum of the industrial equipment.

[0037] In a possible design, the electrical signal waveform morphology safety analysis is specifically as follows: Step S001: Obtain the discharge pulse width and discharge pulse duration of the partial discharge spectrum of each discharge activity of the industrial equipment, and calculate the electrical signal energy output index:

[0038] in, Expressed as The electrical signal energy output index of the partial discharge spectrum of the secondary discharge activity, Expressed as The discharge pulse width of the partial discharge spectrum of the secondary discharge activity, Represents the preset discharge pulse width, Expressed as The discharge pulse duration of the partial discharge spectrum of the secondary discharge activity, It is represented as the preset discharge pulse duration; Step S002: Obtain the number of discharge pulses and the duration of the discharge pulses in the partial discharge spectrum of each discharge activity of the industrial equipment, and calculate the frequency index of the discharge activity:

[0039] in, Expressed as The frequency index of the discharge activity of the partial discharge map of the discharge activity, Expressed as The number of discharge pulses in the partial discharge spectrum of the discharge activity, It is expressed as the mean value of the number of discharge pulses and is calculated as: , It is expressed as the mean value of the discharge pulse duration, and the calculation formula is: , n represents the total number of discharges in the partial discharge spectrum; Step S003: The calculation formula of the electrical signal waveform morphology analysis index is:

[0040] in, Expressed as The electrical signal waveform morphology analysis index of the partial discharge spectrum of the secondary discharge activity, Expressed as The energy output of the electrical signal of the partial discharge spectrum of the secondary discharge activity fluctuates. Expressed as The frequency of discharge activity in the partial discharge map of the secondary discharge activity fluctuates; Step S004: Obtain the electrical signal waveform morphology analysis index of the local discharge spectrum of each discharge activity of the industrial equipment, compare it with the preset electrical signal waveform morphology analysis index, screen out the numbers of the local discharge spectrum of the discharge activities whose electrical signal waveform morphology analysis index is greater than the preset electrical signal waveform morphology analysis index, mark them as discharge activities with abnormal electrical signal waveforms, locate the position of the local discharge source of the discharge activity in the industrial equipment, and issue an abnormal warning.

[0041] In this embodiment, it should be specifically noted that the electrical signal waveform morphology analysis index also includes: Step S001: Calculate the electrical signal energy output change fluctuation analysis formula:

[0042] in, Expressed as The energy output of the electrical signal of the partial discharge spectrum of the secondary discharge activity fluctuates. Expressed as The electrical signal energy output index of the partial discharge spectrum of the secondary discharge activity, It is expressed as the maximum value of the electrical signal energy output index. It is expressed as the minimum value of the electrical signal energy output index, n is expressed as the total number of discharges in the partial discharge spectrum, Expressed as The number of the partial discharge map of the secondary discharge activity; Step S002: Calculate the discharge activity frequency fluctuation analysis formula:

[0043] in, Expressed as The frequency of discharge activity in the partial discharge spectrum of the discharge activity fluctuates. Expressed as The frequency index of the discharge activity of the partial discharge map of the discharge activity, Expressed as The number of the partial discharge spectrum of the secondary discharge activity.

[0044] The step S04: partial discharge spectrum intensity analysis is used to receive the electrical signal spectrum data transmitted by the partial discharge spectrum characteristic data acquisition step, and calculate the partial discharge spectrum intensity evaluation index of the partial discharge spectrum of each discharge activity of the industrial equipment according to the partial discharge intensity data acquisition unit.

[0045] In a possible design, the calculation formula of the partial discharge spectrum intensity evaluation index is:

[0046] in Expressed as The partial discharge spectrum intensity evaluation index of the partial discharge spectrum of the secondary discharge activity, Expressed as The electrical signal amplitude of the partial discharge spectrum of the secondary discharge activity, Expressed as The peak amplitude of the electrical signal of the partial discharge spectrum of the secondary discharge activity, Expressed as The total discharge amount of the partial discharge spectrum of the discharge activity, It is expressed as the dielectric constant of industrial equipment, and n is expressed as the total number of discharges in the partial discharge spectrum.

[0047] The step S05: comprehensive evaluation of partial discharge spectrum characteristics calculates the partial discharge stability evaluation coefficient of the industrial equipment according to the electrical signal waveform morphology analysis index and the partial discharge spectrum intensity evaluation index of the partial discharge spectrum of each discharge activity of the industrial equipment.

[0048] In a possible design, the calculation formula of the partial discharge stability evaluation coefficient is:

[0049] in, Expressed as the partial discharge stability assessment coefficient, Expressed as The electrical signal waveform morphology analysis index of the partial discharge spectrum of the secondary discharge activity, Expressed as The partial discharge spectrum intensity evaluation index of the partial discharge spectrum of the secondary discharge activity.

[0050] The step S06: the partial discharge spectrum stability warning is used to obtain the partial discharge stability evaluation coefficient of the industrial equipment, compare it with the preset partial discharge stability evaluation coefficient, and process it.

[0051] In a possible design, the partial discharge spectrum stability warning is specifically: Obtain the local discharge stability assessment coefficient of the industrial equipment and compare it with the preset local discharge stability assessment coefficient. If the local discharge stability assessment coefficient of the industrial equipment is greater than the preset local discharge stability assessment coefficient, it indicates that there is an abnormality in the stability of the discharge activity of the industrial equipment, and the management personnel should be notified to inspect the industrial equipment. Otherwise, it indicates that the stability assessment of the discharge activity of the industrial equipment is normal.

[0052] The step S07: the partial discharge environment monitoring of industrial equipment is used to collect the temperature and noise amplitude of each discharge activity of the industrial equipment through sensors, calculate the industrial equipment partial discharge signal environment impact index of the industrial equipment based on the partial discharge stability evaluation coefficient of the industrial equipment, and screen out industrial equipment with abnormal electrical signal environment monitoring.

[0053] In a possible design, the partial discharge environment monitoring of industrial equipment is specifically as follows: Step S001: The temperature and noise amplitude of each discharge activity of the industrial equipment are collected by sensors and marked as , ,in =1,2,... , Expressed as The serial number of the industrial equipment; Step S002: The calculation formula of the environmental impact index of the partial discharge signal of industrial equipment is:

[0054] in, Expressed as Environmental impact index of partial discharge signal of industrial equipment for each industrial equipment, Expressed as The temperature of industrial equipment, Expressed as The noise amplitude of industrial equipment, Expressed as the preset noise amplitude, Expressed as Partial discharge stability evaluation coefficient of industrial equipment, Expressed as the mean value of the partial discharge stability evaluation coefficient, Expressed as the heat resistance level of industrial equipment; Step S003: Obtain the industrial equipment partial discharge signal environmental impact index of the industrial equipment, compare it with the preset industrial equipment partial discharge signal environmental impact index, screen out the industrial equipment partial discharge signal environmental impact index greater than the preset industrial equipment partial discharge signal environmental impact index, mark it as industrial equipment with abnormal electrical signal environment monitoring, and notify maintenance personnel to deal with the abnormal equipment in time.

[0055] In the present embodiment, it is necessary to specifically explain that the present invention collects the electric signal spectrum data of the local discharge spectrum of each discharge activity of the industrial equipment through the sensor, calculates the electric signal waveform morphology analysis index of the local discharge spectrum of each discharge activity of the industrial equipment according to the electric signal waveform data acquisition unit, calculates the local discharge spectrum intensity evaluation index of the local discharge spectrum of each discharge activity of the industrial equipment according to the local discharge intensity data acquisition unit, and further calculates the local discharge stability evaluation coefficient, and compares it with the preset local discharge stability evaluation coefficient. If the local discharge stability evaluation coefficient of the industrial equipment is greater than the preset local discharge stability evaluation coefficient, it indicates that the stability of the discharge activity of the industrial equipment is abnormal, and the management personnel should be notified to detect the industrial equipment. Otherwise, it indicates that the stability evaluation of the discharge activity of the industrial equipment is normal. By introducing the spectrum analysis method based on artificial intelligence, the factory can realize continuous monitoring and intelligent diagnosis of the equipment status, timely discover safety hazards and handle potential faults, ensure the continuous and stable operation of the equipment, and improve the automation and intelligence level of industrial equipment management. The partial discharge environmental monitoring of industrial equipment is used to collect the temperature and noise amplitude of each discharge activity of industrial equipment through sensors. Based on the partial discharge stability evaluation coefficient of industrial equipment, the environmental impact index of the partial discharge signal of industrial equipment is calculated to screen out industrial equipment with abnormal electrical signal environment monitoring. Partial discharge is a precursor to failure of many industrial equipment. By monitoring the environmental impact index, potential failures of equipment can be predicted in advance and corresponding preventive measures can be taken, which is conducive to reducing the risk of equipment failure and maintenance costs and extending the service life of equipment. At the same time, maintenance personnel can conduct inspections and maintenance more targetedly, improving the efficiency and accuracy of maintenance work.

[0056] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-dimensional partial discharge spectrum analysis method based on artificial intelligence, characterized in that: The following steps are involved: Step S01: Partial discharge spectrum source numbering: used to locate the specific location of the discharge source in the industrial equipment using electromagnetic wave positioning technology, perform partial discharge monitoring on the industrial equipment, and number the generated partial discharge spectrum of the industrial equipment; Step S02: partial discharge spectrum characteristic data acquisition: used to acquire the electric signal spectrum data of the partial discharge spectrum of each discharge activity of the industrial equipment through the sensor, wherein the partial discharge spectrum characteristic data acquisition includes an electric signal waveform data acquisition unit and a partial discharge intensity data acquisition unit; Step S03: Safety analysis of electrical signal waveform morphology: Calculate the electrical signal waveform morphology analysis index of the partial discharge spectrum of each discharge activity of the industrial equipment according to the electrical signal waveform data acquisition unit, screen out the discharge activities with abnormal electrical signal waveforms, and conduct abnormal warning of partial discharge spectrum of the industrial equipment; Step S04: partial discharge spectrum intensity analysis: calculating the partial discharge spectrum intensity evaluation index of the partial discharge spectrum of each discharge activity of the industrial equipment according to the partial discharge intensity data acquisition unit; Step S05: Comprehensive evaluation of partial discharge spectrum characteristics: Calculate the partial discharge stability evaluation coefficient of the industrial equipment based on the electrical signal waveform morphology analysis index and the partial discharge spectrum intensity evaluation index of the partial discharge spectrum of each discharge activity of the industrial equipment; Step S06: Partial discharge spectrum stability warning: used to obtain the partial discharge stability evaluation coefficient of the industrial equipment, compare it with the preset partial discharge stability evaluation coefficient, and process it; Step S07: partial discharge environment monitoring of industrial equipment: used to collect the temperature and noise amplitude of each discharge activity of the industrial equipment through sensors, calculate the industrial equipment partial discharge signal environment impact index of the industrial equipment based on the partial discharge stability evaluation coefficient of the industrial equipment, and screen out industrial equipment with abnormal electrical signal environment monitoring.

2. The multi-dimensional partial discharge spectrum analysis method based on artificial intelligence according to claim 1 is characterized in that: The specific source numbers of the partial discharge spectrum are: The UHF antenna array is installed at the signal receiving position of the industrial equipment, and the signal acquisition device is started. The partial discharge signal of each discharge activity of the industrial equipment is captured by the UHF antenna, and the partial discharge spectrum is generated from the captured partial discharge signal of each discharge activity, and the partial discharge spectrum of each discharge activity of the industrial equipment is numbered 1, 2,... ,... .

3. The multi-dimensional partial discharge spectrum analysis method based on artificial intelligence according to claim 1 is characterized in that: The partial discharge spectrum characteristic data collection is specifically as follows: Electrical signal waveform data acquisition unit: The discharge pulse width, discharge pulse number, and discharge pulse duration of the partial discharge spectrum of each discharge activity of the industrial equipment are collected through sensors and marked as , , ,in =1,2,... , Expressed as The number of the partial discharge map of the secondary discharge activity; Partial discharge intensity data acquisition unit: The sensor collects the electrical signal amplitude, electrical signal peak amplitude, and total discharge amount of the partial discharge spectrum of each discharge activity of the industrial equipment, which are marked as , , .

4. The multi-dimensional partial discharge spectrum analysis method based on artificial intelligence according to claim 1 is characterized in that: The electrical signal waveform morphology safety analysis is specifically as follows: Step S001: Obtain the discharge pulse width and discharge pulse duration of the partial discharge spectrum of each discharge activity of the industrial equipment, and calculate the electrical signal energy output index: in, Expressed as The electrical signal energy output index of the partial discharge spectrum of the secondary discharge activity, Expressed as The discharge pulse width of the partial discharge spectrum of the secondary discharge activity, Represents the preset discharge pulse width, Expressed as The discharge pulse duration of the partial discharge spectrum of the secondary discharge activity, It is represented as the preset discharge pulse duration; Step S002: Obtain the number of discharge pulses and the duration of the discharge pulses in the partial discharge spectrum of each discharge activity of the industrial equipment, and calculate the frequency index of the discharge activity: in, Expressed as The frequency index of the discharge activity of the partial discharge map of the discharge activity, Expressed as The number of discharge pulses in the partial discharge spectrum of the discharge activity, Expressed as the mean value of the number of discharge pulses, It is expressed as the mean value of the discharge pulse duration, and n is expressed as the total number of discharges in the partial discharge spectrum; Step S003: The calculation formula of the electrical signal waveform morphology analysis index is: in, Expressed as The electrical signal waveform morphology analysis index of the partial discharge spectrum of the secondary discharge activity, Expressed as The energy output of the electrical signal of the partial discharge spectrum of the secondary discharge activity fluctuates. Expressed as The frequency of discharge activity in the partial discharge map of the secondary discharge activity fluctuates; Step S004: Obtain the electrical signal waveform morphology analysis index of the local discharge spectrum of each discharge activity of the industrial equipment, compare it with the preset electrical signal waveform morphology analysis index, screen out the numbers of the local discharge spectrum of the discharge activities whose electrical signal waveform morphology analysis index is greater than the preset electrical signal waveform morphology analysis index, mark them as discharge activities with abnormal electrical signal waveforms, locate the position of the local discharge source of the discharge activity in the industrial equipment, and issue an abnormal warning.

5. The multi-dimensional partial discharge spectrum analysis method based on artificial intelligence according to claim 1 is characterized in that: The calculation formula of the partial discharge spectrum intensity evaluation index is: in Expressed as The partial discharge spectrum intensity evaluation index of the partial discharge spectrum of the secondary discharge activity, Expressed as The electrical signal amplitude of the partial discharge spectrum of the secondary discharge activity, Expressed as The peak amplitude of the electrical signal of the partial discharge spectrum of the secondary discharge activity, Expressed as The total discharge amount of the partial discharge spectrum of the discharge activity, It is expressed as the dielectric constant of industrial equipment, and n is expressed as the total number of discharges in the partial discharge spectrum.

6. The multi-dimensional partial discharge spectrum analysis method based on artificial intelligence according to claim 5 is characterized in that: The calculation formula of the partial discharge stability evaluation coefficient is: in, Expressed as the partial discharge stability assessment coefficient, Expressed as The electrical signal waveform morphology analysis index of the partial discharge spectrum of the secondary discharge activity, Expressed as The partial discharge spectrum intensity evaluation index of the partial discharge spectrum of the secondary discharge activity.

7. The multi-dimensional partial discharge spectrum analysis method based on artificial intelligence according to claim 1 is characterized in that: The partial discharge spectrum stability warning is specifically: Obtain the local discharge stability assessment coefficient of the industrial equipment and compare it with the preset local discharge stability assessment coefficient. If the local discharge stability assessment coefficient of the industrial equipment is greater than the preset local discharge stability assessment coefficient, it indicates that there is an abnormality in the stability of the discharge activity of the industrial equipment, and the management personnel should be notified to inspect the industrial equipment. Otherwise, it indicates that the stability assessment of the discharge activity of the industrial equipment is normal.

8. The multi-dimensional partial discharge spectrum analysis method based on artificial intelligence according to claim 1 is characterized in that: The industrial equipment partial discharge environmental monitoring is specifically: Step S001: The temperature and noise amplitude of each discharge activity of the industrial equipment are collected by sensors and marked as , ,in =1,2,... , Expressed as The serial number of the industrial equipment; Step S002: The calculation formula of the environmental impact index of the partial discharge signal of industrial equipment is: in, Expressed as Environmental impact index of partial discharge signal of industrial equipment for each industrial equipment, Expressed as The temperature of industrial equipment, Expressed as The noise amplitude of industrial equipment Represented as the preset noise amplitude, Expressed as Partial discharge stability evaluation coefficient of industrial equipment, Expressed as the mean value of the partial discharge stability evaluation coefficient, Expressed as the heat resistance level of industrial equipment; Step S003: Obtain the industrial equipment partial discharge signal environmental impact index of the industrial equipment, compare it with the preset industrial equipment partial discharge signal environmental impact index, screen out the industrial equipment partial discharge signal environmental impact index greater than the preset industrial equipment partial discharge signal environmental impact index, mark it as industrial equipment with abnormal electrical signal environment monitoring, and notify maintenance personnel to deal with the abnormal equipment in time.

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