Online intelligent detection method for partial discharge of elbow-type plug head of power cable

Abstract

The invention discloses an online intelligent detection method for partial discharge of a power cable elbow-type plug, and relates to the technical field of online detection, and the method comprises the following steps: collecting monitoring data of the power cable elbow-type plug, the monitoring data comprising environmental influence data and internal detection data; establishing a data analysis model based on machine learning according to the monitoring data, and generating a partial discharge monitoring coefficient; and comparing and analyzing the partial discharge monitoring coefficient with a preset partial discharge assessment safety threshold, and maintaining the elbow-type plug of the power cable according to a comparison and analysis result. According to the method, possible faults and maintenance requirements are predicted by analyzing the partial discharge data of the elbow-type plug of the power cable, so that proper prevention measures are taken.

Description

Technical Field

[0001] The present invention relates to the technical field of online detection, and more specifically to an online intelligent detection method for partial discharge of an elbow plug of a power cable. Background Art

[0002] The power cable elbow plug is an important device used in power systems. It is mainly used to connect and branch cables and has plug-in and pull-out functions. It is usually installed in locations where flexible connections are required, such as substations, corners of transmission lines, and between connecting cables and equipment. These plug-ins can connect cables when needed, or be disassembled and reconnected when maintaining or replacing cables, so as to achieve adjustment and maintenance of the power system.

[0003] Deficiencies of existing technologies:

[0004] At present, the operation and maintenance personnel use the method of periodic offline detection of elbow-type plugs installed in the distribution network ring network cabinet by manually holding an ultrasonic partial discharge detector, or realizing online detection by sampling high-frequency induced voltage signals. Periodic offline detection of handheld partial discharge detectors: high cost, an ultrasonic partial discharge instrument costs tens of thousands of yuan; poor anti-interference ability, greatly affected by the external environment, such as the vibration of insect wings, footsteps and raindrops, etc., which can cause the instrument to move; strong limitations, can only detect ultrasonic signals, cannot detect electrical signals during partial discharge, and ultrasonic waves only appear in the late stage of the discharge process, so discharge defects cannot be discovered in time.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0006] In order to overcome the above defects of the prior art, an embodiment of the present invention provides an online intelligent detection method for partial discharge of an elbow-type plug-in connector of a power cable, and solves the problems raised in the above background technology through the online intelligent detection method for partial discharge of an elbow-type plug-in connector of a power cable.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The invention discloses an online intelligent detection method for partial discharge of an elbow plug of a power cable, comprising the following steps: collecting monitoring data of the elbow plug of the power cable, wherein the monitoring data includes environmental impact data and internal detection data; establishing a data analysis model based on machine learning according to the monitoring data to generate a partial discharge monitoring coefficient; comparing and analyzing the partial discharge monitoring coefficient with a preset partial discharge assessment safety threshold, and maintaining the elbow plug of the power cable according to the comparison and analysis results.

[0009] In a preferred embodiment, the environmental impact data includes a temperature impact coefficient, and the internal detection data includes current monitoring data and sound wave detection data.

[0010] In a preferred embodiment, the specific steps of comparing and analyzing the local discharge monitoring coefficient with a preset local discharge assessment safety threshold and maintaining the power cable elbow plug head according to the comparison and analysis results are as follows: comparing and analyzing the local discharge monitoring coefficient generated by data analysis with a preset local discharge assessment safety threshold; when the local discharge monitoring coefficient is lower than the safety threshold, it is determined that the discharge does not need to be repaired, and continuous monitoring is performed; when the local discharge monitoring coefficient is higher than the safety threshold, it is determined that the discharge needs to be repaired, and early warning and maintenance work are promptly carried out on the plug head.

[0011] In a preferred embodiment, the method for obtaining the temperature influence coefficient is as follows: measuring the initial resistance of the transmission line at a reference temperature, and measuring the actual temperature of the transmission line in different time periods, and the corresponding transmission line resistance at different temperatures, and obtaining the temperature influence coefficient through the above measured data.

[0012] In a preferred embodiment, the method for obtaining the current monitoring data is as follows: when local discharge occurs in the elbow-type plug-in head, the high-frequency current generated by the discharge on the shielded grounding wire is obtained; when the elbow-type plug-in head operates normally, the capacitive power-frequency induced current flowing through the shielded grounding wire is obtained; the high-frequency partial discharge current and the capacitive power-frequency induced current are added together to calculate the current monitoring data.

[0013] In a preferred embodiment, the method for acquiring the acoustic wave detection data is as follows: collecting acoustic wave signals, preprocessing and filtering the acoustic wave signals; drawing a waveform graph based on the processed acoustic wave signals, and converting the waveform graph from the time domain to the frequency domain through Fourier transform; analyzing the frequency domain graph to obtain the acoustic wave frequency, and combining it with a preset sound speed to calculate the acoustic wave detection data. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a structural schematic diagram of the on-line intelligent detection method for partial discharge of the elbow-type plug-in connector of a power cable according to the present invention. DETAILED DESCRIPTION

[0015] 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.

[0016] Embodiment 1, Figure 1The invention provides an online intelligent detection method for partial discharge of an elbow-type plug-in connector of a power cable.

[0017] S10, collecting monitoring data of the power cable elbow plug, wherein the data includes environmental impact data and internal detection data;

[0018] The environmental impact data includes a temperature impact coefficient, and the internal detection data includes current monitoring data and sound wave detection data;

[0019] The method for obtaining the temperature influence coefficient is as follows:

[0020] Measuring the initial resistance of the transmission line at a reference temperature, measuring the actual temperature of the transmission line in different time periods, and the corresponding resistance of the transmission line at different temperatures, and obtaining the temperature influence coefficient through the above measured data;

[0021] The calculation formula of the temperature influence coefficient is as follows:

[0022]

[0023] Where T0 is the known reference temperature, R0 is the resistance of the transmission line measured at the reference temperature, and T i is the actual temperature of the transmission line in different time periods, R i is the resistance measured at different temperatures, It is the average value of the temperature measured in different time periods;

[0024] The role of temperature influence coefficient in online intelligent detection of partial discharge of power cable elbow plugs can be explained as follows:

[0025] Impact on sensor performance stability: In intelligent detection systems, the performance of commonly used sensors such as voltage sensors and current sensors is greatly affected by temperature. The temperature influence coefficient helps evaluate the performance changes of sensors under different temperature conditions, thereby correcting sensor data and ensuring the accuracy and reliability of detection results.

[0026] Impact on the insulation properties of equipment: Temperature has a significant impact on the insulation properties of power equipment. For power equipment such as elbow plugs, the insulation properties may change with temperature changes, resulting in an increased possibility of partial discharge. By understanding the temperature influence coefficient, the insulation condition of the equipment at different operating temperatures can be predicted, so that maintenance measures can be taken in advance to avoid partial discharge and possible failures.

[0027] Temperature compensation and calibration: In intelligent detection systems, the temperature influence coefficient can be used to develop temperature compensation algorithms. These algorithms can adjust the sensor's measurement value based on real-time temperature data to make it more accurately reflect the actual situation. For example, for partial discharge detection, the threshold and analysis algorithm of the discharge signal need to be adjusted according to temperature changes to ensure that potential partial discharge phenomena can be effectively detected;

[0028] Improve the stability and reliability of the monitoring system: The understanding and application of the temperature influence coefficient can help design a more stable and reliable intelligent detection system. By correctly handling and utilizing the temperature influence coefficient, the negative impact of temperature changes on the monitoring system can be reduced, and the stability and reliability of the system under different environmental conditions can be improved;

[0029] Among them, the specific method for obtaining current monitoring data is as follows:

[0030] When partial discharge occurs in the elbow plug, the high-frequency current generated by the discharge on the shield grounding wire is obtained;

[0031] When the elbow plug operates normally, the capacitive power-frequency induced current flowing through the shielded grounding wire is obtained; the high-frequency partial discharge current and the capacitive power-frequency induced current are added together to obtain current monitoring data.

[0032] The calculation formula of the current monitoring data is as follows:

[0033] I=I power frequency + I high frequency

[0034] I is the current monitoring data, I 工频 It is the capacitive power frequency induced current flowing through the equipment when it is working normally, I 高频 It is the high-frequency current generated by partial discharge;

[0035] Current monitoring data plays an important role in the online intelligent detection of partial discharge of power cable elbow plugs. Partial discharge is a common fault form in power equipment, which can cause equipment damage or even accidents. In order to discover and locate these problems in time, power equipment is usually monitored online, among which current monitoring data is one of the key indicators. The role of current monitoring data in the online intelligent detection of partial discharge of power cable elbow plugs is as follows:

[0036] Reflecting current fluctuation: Current monitoring data describes the stability and fluctuation of current. In the partial discharge detection of plugs, partial discharge activities usually cause small changes or pulsations in current. By monitoring the current monitoring data, the changes in current can be evaluated, including weak signals that may be caused by partial discharge.

[0037] Auxiliary discharge feature analysis: Although the signals of partial discharges themselves may be weak and interfered with, they will have a specific impact on the current. The current monitoring data can be used as an auxiliary feature to help confirm whether there is partial discharge activity. Especially in complex power equipment environments, accurate identification of partial discharge signals is very critical;

[0038] Reduce false alarm rate: Through the analysis of current monitoring data, it can help reduce the false alarm rate. Sometimes there may be natural fluctuations or interference in the current during the operation of the equipment. If these fluctuations are mistakenly identified as partial discharge signals, it will cause false alarms. The stability factor can help the system determine whether there are actual abnormal signals, thereby improving the reliability and accuracy of the monitoring system;

[0039] Monitoring system optimization: Incorporating current monitoring data into the intelligent system of partial discharge detection helps optimize monitoring algorithms and strategies. The system can adjust the monitoring sensitivity according to the real-time changes in current fluctuations, thereby more accurately capturing the changes and development trends of partial discharge activities;

[0040] The specific method for obtaining the acoustic wave detection data is as follows:

[0041] First, it is necessary to use devices such as acoustic sensors or microphones to collect acoustic signals from the target. These acoustic signals may come from mechanical vibrations of the equipment, fluid flow sounds, or other sources.

[0042] The collected original sound wave signal contains noise or unnecessary frequency components, so it is usually necessary to perform preprocessing and filtering operations to remove background noise or frequencies unrelated to the target signal while retaining the main detection signal;

[0043] After the signal is preprocessed, the acoustic signal waveform is drawn, the time domain is converted into the frequency domain through Fourier transform, the frequency domain diagram is analyzed to obtain the acoustic wave frequency, and the acoustic wave detection data is calculated based on the preset sound speed;

[0044] Among them, the specific calculation formula of the acoustic wave detection data is as follows:

[0045] B=lnC*F

[0046] In the formula, B is the sound wave detection data, C is the sound speed, and F is the sound wave frequency;

[0047] Acoustic wave detection can play an important role in the online intelligent detection of partial discharge of power cable elbow plugs, which is specifically manifested in the following aspects:

[0048] Non-contact detection: Acoustic wave detection is a non-contact detection method that can be performed without affecting the operation of power equipment. For partial discharge detection objects such as elbow plugs, it is particularly suitable to use acoustic wave technology for long-term online monitoring to achieve real-time monitoring and early warning;

[0049] Highly sensitive detection: Acoustic wave detection can detect tiny sound changes, so it is very effective for high-sensitivity detection of partial discharge. Even tiny discharge phenomena can be captured and analyzed in a timely manner through acoustic wave detection;

[0050] Real-time monitoring and data analysis: The acoustic wave detection system can realize continuous real-time monitoring and collect a large amount of acoustic wave data. Through data analysis and processing, it can identify the type, frequency and other information of the discharge signal, providing data support for fault prediction and prevention;

[0051] Diversified signal processing technologies: The acoustic wave detection system combines a variety of signal processing technologies to deeply mine the useful information contained in the acoustic wave data and improve the accuracy and reliability of detection;

[0052] Reduce maintenance costs and risks: The acoustic wave online monitoring system can detect potential problems in power equipment early, help take timely maintenance measures, avoid sudden failures and downtime losses of equipment, and thus reduce maintenance costs and operating risks;

[0053] S20, establishing a data analysis model based on machine learning according to the data to generate a partial discharge monitoring coefficient;

[0054] The specific calculation formula of the partial discharge monitoring coefficient is as follows:

[0055]

[0056] J is the partial discharge monitoring coefficient, B is the acoustic wave detection data, I is the current monitoring data, and WD is the temperature influence coefficient;

[0057] In the online intelligent detection of partial discharge of power cable elbow plugs, the partial discharge monitoring system plays a key role, which is specifically reflected in the following aspects:

[0058] Real-time monitoring and early warning: The partial discharge monitoring system can monitor the discharge activity inside the cable elbow plug in real time. Through continuous monitoring, the system can detect and record the discharge signal in time, which enables the system to issue an alarm in advance to indicate potential electrical faults and take necessary maintenance measures to avoid fault expansion and equipment damage;

[0059] Data analysis and trend monitoring: The monitoring system can not only capture discharge events in real time, but also conduct in-depth analysis and processing of the collected data. Through signal processing technology, it can identify the characteristics of discharge signals, determine the type of discharge, and evaluate its potential impact on the equipment. In addition, the system can also monitor the trend changes of discharge activities and provide long-term stability and health status assessment;

[0060] Reduce downtime and maintenance costs: Through real-time monitoring and early warning functions, the partial discharge monitoring system can help power companies or operators plan and schedule maintenance work more effectively, and promptly detect potential problems with cable elbow plugs, which can reduce the occurrence of sudden failures, reduce downtime and maintenance costs;

[0061] Improve safety and reliability: Online partial discharge monitoring systems can significantly improve the safety and reliability of power equipment. Through predictive maintenance, the system can identify and repair problems before major equipment failures occur, which not only extends the service life of the equipment, but also enhances the stability and reliability of the power grid.

[0062] Remote monitoring and management: Some partial discharge monitoring systems usually support remote monitoring and management functions. Operators can access the system remotely to view the status and performance data of the equipment in real time. This remote capability makes the monitoring system more flexible and convenient, and can effectively respond to various operational needs and emergencies.

[0063] S30, comparing and analyzing the partial discharge monitoring coefficient with a preset partial discharge assessment safety threshold, and maintaining the power cable elbow plug according to the comparison and analysis result;

[0064] The specific steps of comparing and analyzing the partial discharge monitoring coefficient with the preset partial discharge assessment safety threshold and maintaining the power cable elbow plug according to the comparative analysis results are as follows:

[0065] Definition of partial discharge monitoring coefficient: The partial discharge monitoring coefficient is usually a comprehensive indicator that reflects the degree and frequency of partial discharge inside the equipment, usually achieved using data analysis technology;

[0066] Set the safety threshold for partial discharge assessment: The safety threshold is a threshold that is pre-set based on the equipment design specifications, operating experience and safety standard factors. This threshold is generally the upper limit of the partial discharge monitoring coefficient. Exceeding this threshold means that the partial discharge of the equipment has reached or exceeded the limit of safe operation and maintenance and repair are required;

[0067] Comparative analysis process: First, obtain real-time or periodic partial discharge data through partial discharge monitoring equipment or sensors; use data analysis technology to calculate and generate partial discharge monitoring coefficients; compare the calculated partial discharge monitoring coefficients with the pre-set safety thresholds; if the monitoring coefficient is lower than the safety threshold, it indicates that the degree of partial discharge is still within an acceptable range and the equipment can continue to operate. At this time, it is usually recommended to conduct continuous monitoring and regular inspections to ensure that partial discharge does not gradually increase to a dangerous level; if the monitoring coefficient is higher than the safety threshold, it indicates that the insulation system of the equipment has serious partial discharge problems and there may be potential safety risks. At this time, measures should be taken immediately, such as arranging maintenance personnel to inspect and maintain the equipment to prevent the discharge problem from further developing and causing equipment damage or safety accidents;

[0068] Implement early warning and maintenance work: When the local discharge monitoring coefficient exceeds the safety threshold, the relevant responsible personnel should be notified immediately to carry out maintenance. Early warning can be achieved through an automated monitoring system to enable timely response and arrangement of maintenance work; maintenance work may include locating the local discharge source, replacing or repairing insulation materials to restore the equipment to a safe operating state;

[0069] The online intelligent detection of partial discharge of power cable elbow plugs solves the following key problems:

[0070] Early fault diagnosis and warning:

[0071] Partial discharge is one of the early signs of aging or defects in electrical equipment. Through online intelligent detection, it is possible to monitor in real time whether partial discharge occurs around the cable elbow plug, so as to detect potential problems of power equipment early and issue early warnings to avoid the risk of fault escalation or sudden power outages.

[0072] Improve the reliability and stability of power systems:

[0073] Timely detection and treatment of partial discharge problems at cable plugs can help improve the operational reliability of the power system. Preventive maintenance and troubleshooting can avoid sudden equipment failures and ensure the continuity and stability of power supply.

[0074] Reduce maintenance costs and improve efficiency:

[0075] Traditional regular inspection and maintenance may lead to waste of resources and production interruption. The intelligent partial discharge detection system can accurately monitor the equipment status, reduce the number of unnecessary maintenance, improve maintenance efficiency, and perform targeted maintenance when necessary, reducing maintenance costs.

[0076] Optimize equipment life and resource utilization:

[0077] Timely detection and treatment of partial discharge problems can extend the service life of power equipment, reduce equipment replacement or major repair costs caused by unexpected failures, improve resource utilization, and save energy and material resources by optimizing equipment use and maintenance;

[0078] Improve safety and environmental benefits:

[0079] Partial discharge may cause local overheating, damage or even fire of equipment. The real-time early warning function of the intelligent detection system can effectively reduce these potential safety hazards. At the same time, by reducing equipment failures and downtime, it helps to reduce environmental impact and improve the environmental benefits of the power system.

[0080] In summary, the online intelligent detection of partial discharge of power cable elbow plugs not only improves the safety, reliability and stability of the power system, but also effectively reduces maintenance costs and optimizes the service life and resource utilization of equipment. It is an important technology and application in modern power equipment management.

[0081] The benefits of online intelligent detection of partial discharge of power cable elbow plugs include but are not limited to the following:

[0082] Improve equipment safety and reliability: Detection can promptly detect partial discharge inside the elbow plug. This early warning helps prevent equipment damage or accidents caused by discharge, thereby improving the safety and reliability of power equipment;

[0083] Prevent and reduce equipment failures: Through continuous online monitoring, discharge problems inside the equipment can be discovered in advance, and preventive maintenance measures can be taken in time to prevent equipment damage and failures caused by discharge, thereby avoiding downtime and production losses caused by them;

[0084] Reduce maintenance and operating costs: Online intelligent detection systems can help optimize maintenance plans and resource allocation, making maintenance more accurate and efficient. By reducing sudden failures and predictive maintenance, maintenance costs and the overall operating costs of equipment can be effectively reduced;

[0085] Extend equipment life: Timely detection and treatment of partial discharge problems can help reduce equipment loss and aging, thereby extending the service life of power equipment and improving the long-term value of assets;

[0086] Improve operating efficiency and energy utilization: Through the intelligent monitoring system, real-time monitoring and data analysis of equipment status can be achieved, optimizing equipment operation and energy utilization efficiency, ensuring that the equipment operates in the best state;

[0087] Comply with regulatory and safety standards: Modern power equipment usually needs to comply with strict regulatory and safety standards. Online intelligent detection systems can help power companies or operators track the health status of equipment in real time, meet regulations and standards, and ensure the safe operation of equipment.

[0088] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0089] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0090] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0091] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0092] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0093] 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. An online intelligent detection method for partial discharge of power cable elbow plugs, characterized in that: The following steps are involved: Collect monitoring data of power cable elbow plugs, the monitoring data including environmental impact data and internal detection data; According to the monitoring data, a data analysis model is established based on machine learning to generate partial discharge monitoring coefficients; The partial discharge monitoring coefficient is compared and analyzed with the pre-set partial discharge assessment safety threshold, and the power cable elbow plug is maintained based on the comparative analysis results.

2. The on-line intelligent detection method for partial discharge of power cable elbow plugs according to claim 1 is characterized in that: The environmental impact data includes a temperature impact coefficient, and the internal detection data includes current monitoring data and sound wave detection data.

3. The on-line intelligent detection method for partial discharge of power cable elbow plugs according to claim 1 is characterized in that: The specific steps of comparing and analyzing the partial discharge monitoring coefficient with the preset partial discharge assessment safety threshold and maintaining the power cable elbow plug according to the comparison and analysis results are as follows: Compare and analyze the partial discharge monitoring coefficient generated by data analysis with the pre-set partial discharge assessment safety threshold; When the partial discharge monitoring coefficient is lower than the safety threshold, it is determined that the discharge does not need to be repaired and continuous monitoring is performed; When the partial discharge monitoring coefficient is higher than the safety threshold, it is determined that the discharge needs to be repaired, and early warning and maintenance work are issued on the plug-in head in a timely manner.

4. The on-line intelligent detection method for partial discharge of power cable elbow plugs according to claim 2 is characterized in that: The method for obtaining the temperature influence coefficient is as follows: The initial resistance of the transmission line is measured at a reference temperature, and the actual temperature of the transmission line in different time periods and the corresponding transmission line resistance at different temperatures are measured, and the temperature influence coefficient is calculated using a preset temperature influence coefficient calculation formula.

5. The on-line intelligent detection method for partial discharge of power cable elbow plugs according to claim 4 is characterized in that: The method for obtaining the current monitoring data is as follows: When partial discharge occurs in the elbow plug, the high-frequency current generated by the discharge on the shield grounding wire is obtained; When the elbow plug is operating normally, the capacitive power-frequency induced current flowing through the shielded grounding wire is obtained; the high-frequency partial discharge current and the capacitive power-frequency induced current are added together to obtain the current monitoring data.

6. The on-line intelligent detection method for partial discharge of power cable elbow plugs according to claim 5 is characterized in that: The method for obtaining the acoustic wave detection data is as follows: Collecting sound wave signals, and performing preprocessing and filtering operations on the sound wave signals; Draw a waveform diagram according to the processed sound wave signal, and convert the waveform diagram from time domain to frequency domain through Fourier transform; The frequency domain diagram is analyzed to obtain the sound wave frequency, and combined with the preset sound speed, the sound wave detection data is calculated.

7. The on-line intelligent detection method for partial discharge of power cable elbow plugs according to claim 1 is characterized in that: The calculation formula of the partial discharge monitoring coefficient is as follows: J is the partial discharge monitoring coefficient, B is the acoustic wave detection data, I is the current monitoring data, and WD is the temperature influence coefficient.

8. The on-line intelligent detection method for partial discharge of power cable elbow plugs according to claim 2 is characterized in that: The calculation formula of the temperature influence coefficient is as follows: Where T0 is the known reference temperature, R0 is the resistance of the transmission line measured at the reference temperature, and T i is the actual temperature of the transmission line in different time periods, R i is the measured resistance at different temperatures, It is the average value of the temperature measured in different time periods.

9. The on-line intelligent detection method for partial discharge of power cable elbow plugs according to claim 7 is characterized in that: The calculation formula of the current monitoring data is as follows: I=I power frequency + I high frequency I is the current monitoring data, I 工频 It is the capacitive power frequency induced current flowing through the equipment when it is working normally, I 高频 It is the high-frequency current generated by local discharge.

10. The on-line intelligent detection method for partial discharge of power cable elbow plugs according to claim 7, characterized in that: The calculation formula of the acoustic wave detection data is as follows: B=ln C*F Wherein, B is the sound wave detection data, C is the sound speed, and F is the sound wave frequency.

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