A real-time partial discharge monitoring system based on pulse current

Abstract

The present invention relates to the technical field of partial discharge monitoring, and discloses a real-time partial discharge monitoring system based on pulse current, which includes a signal acquisition module, a preprocessing module, a preliminary diagnosis module, a fault isolation module, and a deep diagnosis module. The signals collected in real time are preprocessed such as data marking, baseline calibration, and pulse extraction to accurately remove interference and ensure reliable data; through targeted detection of sensor open circuit and sampling card abnormality, the fault can be quickly located; the fault isolation module can automatically switch to a standby sensor when a fault occurs to reduce the impact of the fault; the deep diagnosis module constructs a fault feature vector using multi-dimensional features and compares it with the sample set to achieve fine classification of faults; the fault warning module can send the sensor and partial discharge fault warning signals to the operation and maintenance personnel in real time; the present invention improves the monitoring accuracy and reliability, provides a strong guarantee for the safe and stable operation of equipment, and reduces the risk of accidents.

Description

Technical Field

[0001] The present invention relates to the technical field of partial discharge monitoring, and particularly relates to a real-time partial discharge monitoring system based on pulse current. Background Art

[0002] In modern power systems, the safe and stable operation of power equipment is directly related to the reliability of the power grid and the order of social production and life. As an early typical sign of insulation deterioration of power equipment, if partial discharge is not detected and processed in time, it may lead to equipment failures or even large-scale power outages. Therefore, real-time monitoring and accurate diagnosis of partial discharge have become key technical requirements in the field of condition-based maintenance of power equipment.

[0003] Currently, the partial discharge monitoring technology based on the pulse current method has been widely applied. It captures the pulse current signals generated by partial discharge of equipment through high-frequency current sensors to realize the perception of discharge phenomena. However, there are still many limitations in the existing technology: firstly, in the signal processing link, factors such as environmental noise and electromagnetic interference easily cause a large number of invalid components to be mixed in the original pulse current signals. Traditional threshold determination or simple filtering methods are difficult to effectively distinguish real discharge pulses from interference signals, reducing the monitoring accuracy; secondly, at the fault diagnosis level, most existing systems only focus on the identification of partial discharge itself and lack the fault diagnosis ability for the hardware of the monitoring system. Once abnormal situations such as open circuits or crashes occur in the hardware, it is easy to cause distortion or interruption of monitoring data, thereby leading to misjudgment or missed judgment; thirdly, in terms of fault type identification, most systems only rely on a single feature for diagnosis, which is difficult to comprehensively reflect the complex characteristics of partial discharge and cannot accurately distinguish different types of discharge faults, restricting the depth and reliability of equipment condition assessment.

[0004] Therefore, there is an urgent need for a real-time partial discharge monitoring system that can achieve efficient preprocessing of signals, collaborative diagnosis of hardware faults and discharge faults, and multi-dimensional accurate identification of fault types, so as to overcome the deficiencies of the existing technology and improve the intelligent and reliable level of operation and maintenance of power equipment. Summary of the Invention

[0005] The purpose of the present invention is to provide a real-time partial discharge monitoring system based on pulse current, which solves the technical problems proposed in the background art.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A real-time partial discharge monitoring system based on pulse current, comprising:

[0008] A signal acquisition module, configured to continuously sense the pulse current signals generated by partial discharge through a high-frequency current sensor;

[0009] A preprocessing module for preprocessing the pulsed current signal to determine the effective pulses at relevant acquisition time nodes;

[0010] A preliminary diagnosis module for performing fault diagnosis and analysis based on the pulsed current signal to determine whether there is an open circuit fault in the high-frequency current sensor and an abnormality in the sampling card;

[0011] A fault isolation module for receiving the fault identifier sent by the preliminary diagnosis module, then triggering the corresponding preset emergency handling mechanism according to the fault identifier, and determining the sensor fault warning signal;

[0012] A deep diagnosis module, when it is determined by the preliminary diagnosis module that there is no open circuit fault in the high-frequency current sensor and no abnormality in the sampling card, identifies the fault type of partial discharge to determine the partial discharge fault warning signal;

[0013] A fault warning module for sending the sensor fault warning signal and the partial discharge fault warning signal to relevant operation and maintenance personnel;

[0014] As a further solution of the present invention: the preprocessing method is as follows:

[0015] Step S1, data marking:

[0016] Mark the pulsed current signal generated by real-time sensing of partial discharge through the high-frequency current sensor as L t , where t is the serial number of the acquisition time node of the pulsed current signal;

[0017] Within the pre-specified time window C1, obtain the pulsed current signals at multiple acquisition time nodes therein and record them as L k , where k = 1, 2,... v, and v is the number of multiple acquisition time nodes within the pre-specified time window C1;

[0018] Step S2, baseline calibration:

[0019] By: , calculate the pulsed current signal L0 after baseline calibration t ;

[0020] First, obtain the average value of multiple pulsed current signals within the pre-specified time window, and then subtract the average value from the pulsed current signal L t at the corresponding acquisition time node to obtain the pulsed current signal after baseline calibration;

[0021] Step S3, pulse extraction:

[0022] Extract the pulsed current signals L0 k and L0 k+1 of two adjacent acquisition time nodes, and the time interval T between them;

[0023] Then, through: , calculate the signal amplitude change rate R between them t ;

[0024] Extract the preset amplitude change threshold R y and the lowest signal threshold L min ;

[0025] When |R t | > R y , and L0 t > L min , then determine that the pulse current signal at the t-th acquisition time node is a valid pulse;

[0026] Otherwise, determine that the pulse current signal at the t-th acquisition time node does not belong to a valid pulse.

[0027] As a further solution of the present invention: The fault diagnosis and analysis method is as follows:

[0028] Step G1, Sensor open circuit detection:

[0029] When it is determined that the pulse current signal at the t-th acquisition time node does not belong to a valid pulse, obtain the pulse current signals at n consecutive acquisition time nodes after the t-th acquisition time node, and at the same time extract the baseline voltage:

[0030] If all the pulse current signals at the n acquisition time nodes do not belong to valid pulses, and the baseline voltage value is 0; then determine that the high-frequency current sensor has an open circuit fault, and add a fault identifier "A1" to it;

[0031] Otherwise, do not determine that the high-frequency current sensor has an open circuit fault, and add a fault identifier "A0" to it;

[0032] Among them, the baseline voltage is used to describe the reference level or stable state voltage value of the pulse current signal when there is no pulse;

[0033] Step G2, Sampling card abnormality detection:

[0034] Within the pre-specified time window C2, obtain the pulse current signals at multiple acquisition time nodes therein, and denote them as L j , j = 1, 2,... m, where m is the number of multiple acquisition time nodes within the pre-specified time window C2;

[0035] Through the formula , calculate the variance BL corresponding to L j ;

[0036] Among them, L jis the preprocessed pulsed current signal. The variance BL is used to measure the fluctuation degree of the pulsed current signal, and PL is the average value of all L j ;

[0037] Extract the preset crash threshold BL y ;

[0038] When BL < BL y , it is determined that the sampling card is abnormal, and a fault flag "B1" is added to it;

[0039] Otherwise, the high-frequency current sensor is not determined to be abnormal for the sampling card, and a fault flag "B0" is added to it;

[0040] As a further solution of the present invention: the emergency handling mechanism is as follows:

[0041] When the fault flag is "A1" or "B1", the standby high-frequency current sensor is automatically switched, and a sensor fault warning signal is generated;

[0042] As a further solution of the present invention: the fault type identification method is as follows:

[0043] Step H1, Pulse repetition rate feature:

[0044] Within the preset time window C3, count the number g of valid pulses;

[0045] By: , calculate the pulse repetition rate CF within the time window C3;

[0046] In the formula, T3 is the duration of the time window C3;

[0047] Step H2, Pulse amplitude mean feature:

[0048] Extract the pulsed current signals L corresponding to all valid pulses within the time window C3 i , where i is the acquisition time node number corresponding to the valid pulse within the time window C3;

[0049] By: , calculate the pulse amplitude mean AL within the time window C3;

[0050] Step H3, Pulse rise time mean feature:

[0051] For each valid pulse, the time elapsed from 10% of the pulse starting amplitude to 90% of the amplitude is defined as the pulse rise time;

[0052] Within the time window C3, calculate the average value of the rise times of all valid pulses and denote it as the pulse rise time mean ST;

[0053] Step H4, Fault type determination:

[0054] Take the pulse repetition rate CF, the average pulse amplitude AL, and the average pulse rise time ST as the fault feature vector W to be judged = {CF, AL, ST};

[0055] At the same time, extract the fault feature vector sample set WY corresponding to different typical fault types obtained through experiments in advance u = {CF u , AL u , ST u}, u = 1, 2,..., q, where q represents the number of fault feature vector samples corresponding to different typical fault types;

[0056] Among them, CF u is the pulse repetition rate of the u-th sample, AL u is the average pulse amplitude of the u-th sample, and ST u is the average pulse rise time of the u-th sample;

[0057] Then, through:

[0058] Calculate the distance D between the fault feature vector W to be judged and the fault feature vector samples WY corresponding to different typical fault types u ; u

[0059] Compare the magnitudes of all distances D u and obtain the distance D with the minimum value u，min ;

[0060] When D u，min ≤ Dy, it is determined that the current partial discharge fault type is the same as the fault type corresponding to the fault feature vector sample WY u , and at the same time, a partial discharge fault warning signal is generated;

[0061] Among them, Dy is a pre-set distance threshold.

[0062] Advantages of the present invention:

[0063] Precise signal acquisition and preprocessing: By using a high-frequency current sensor to capture partial discharge pulse current signals in real time, and adopting preprocessing methods such as data marking, baseline calibration, and pulse extraction, signal interference and baseline drift are effectively removed. Effective pulses are accurately determined based on the signal amplitude change rate and the lowest signal threshold, providing a reliable data basis for subsequent fault diagnosis and significantly improving the accuracy and effectiveness of signal acquisition.

[0064] ​Efficient Fault Diagnosis and Isolation: The fault diagnosis and analysis cover the detection of sensor open circuits and the detection of abnormal sampling cards. Scientific judgment rules are set based on the continuity of effective pulses and signal variance respectively, which can quickly identify the open circuit faults of high-frequency current sensors and the abnormalities of sampling cards. After receiving the fault identification, the fault isolation module quickly triggers the emergency handling mechanism, automatically switches to the standby sensor, reduces the monitoring interruption caused by faults, ensures the stable operation of the system, and reduces the scope of the impact of faults.

[0065] Deep Fault Type Identification: The deep diagnosis module constructs a fault feature vector using multi-dimensional features such as pulse repetition rate, average pulse amplitude, and average pulse rise time, and compares it with the typical fault type sample set obtained through pre-experiments. The fault type is determined by calculating the distance, realizing the refined classification of partial discharge faults, providing more detailed fault information for maintenance personnel, helping to formulate targeted maintenance strategies, and improving the efficiency of fault handling.

[0066] Real-time and Comprehensive Fault Warning: The fault warning module timely sends the sensor fault warning signal and the partial discharge fault warning signal to the maintenance personnel, enabling the maintenance personnel to grasp the operating status of the system in real time, quickly respond when a fault occurs or is about to occur, avoid the expansion of the fault leading to serious accidents, ensure the safe and stable operation of the equipment, and improve the reliability and safety of application scenarios such as power systems. Brief Description of the Drawings

[0067] The present invention will be further described below with reference to the accompanying drawings.

[0068] Figure 1 It is the system block diagram of a real-time partial discharge monitoring system based on pulse current of the present invention.

[0069] Figure 2 It is the flow schematic diagram of the preprocessing module in a real-time partial discharge monitoring system based on pulse current of the present invention.

[0070] Figure 3 It is the flow schematic diagram of the deep diagnosis module in a real-time partial discharge monitoring system based on pulse current of the present invention. Specific Embodiments

[0071] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0072] As Embodiment 1 of the present invention:

[0073] Please refer to Figure 1, Figure 2 and Figure 3 As shown in and

[0074] , the present invention is a real-time partial discharge monitoring system based on pulse current, comprising:

[0074] A signal acquisition module, configured to sense the pulse current signal generated by partial discharge through a high-frequency current sensor, and convert the analog signal corresponding to the pulse current signal into a digital signal through an analog-to-digital converter;

[0075] Meanwhile, the pulse current signal converted into a digital signal is marked as L t , where t is the acquisition time node serial number of the pulse current signal;

[0076] A preprocessing module, configured to preprocess the signal converted into a digital signal;

[0077] In this embodiment, the purpose of setting the preprocessing module is: since there are often various interference factors in the actual acquisition environment, such as electromagnetic interference, environmental noise, etc., these interferences will cause a large amount of useless information to be doped in the acquired original pulse current signal, thus affecting the accurate judgment of the subsequent partial discharge signal. Therefore, it is necessary to preprocess the signal;

[0078] The preprocessing method is as follows:

[0079] Step S1, data marking:

[0080] Within a pre-specified time window C1, obtain the pulse current signals of multiple acquisition time nodes therein, and mark them as L k , where k = 1, 2,... v, and v is the number of multiple acquisition time nodes within the pre-specified time window C1;

[0081] Step S2, baseline calibration:

[0082] By: , calculate the pulse current signal L0 after baseline calibration t ;

[0083] In this embodiment, the purpose of baseline calibration is to eliminate environmental noise interference. The noise interference in the environment is usually random and persistent, which will cause the overall acquired signal to deviate from the true partial discharge signal. Through baseline calibration, the DC offset and low-frequency noise in the signal can be effectively removed, making the subsequent analyzed signal more pure and more capable of reflecting the true situation of partial discharge;

[0084] Step S3, pulse extraction:

[0085] Extract the pulse current signals L0 k and L0 k+1 of two adjacent acquisition time nodes, and the time interval T therebetween;

[0086] Then, pass through: , calculate the signal amplitude change rate R between them t ;

[0087] Extract the preset amplitude change threshold R y and the lowest signal threshold L min ;

[0088] When |R t | > R y , and L0 t > L min , then determine that the pulsed current signal at the t-th acquisition time node is a valid pulse;

[0089] Otherwise, determine that the pulsed current signal at the t-th acquisition time node does not belong to a valid pulse;

[0090] Preliminary diagnosis module, used for fault diagnosis and analysis based on the pulsed current signal:

[0091] The specific method is as follows:

[0092] Step G1, sensor open circuit detection:

[0093] When it is determined that the pulsed current signal at the t-th acquisition time node does not belong to a valid pulse, obtain the pulsed current signals at n consecutive acquisition time nodes after the t-th acquisition time node, and at the same time extract the baseline voltage:

[0094] If all the pulsed current signals at the n acquisition time nodes do not belong to valid pulses, and the baseline voltage value is 0; then determine that the high-frequency current sensor has an open circuit fault, and add a fault identifier "A1" to it accordingly;

[0095] Otherwise, do not determine that the high-frequency current sensor has an open circuit fault, and add a fault identifier "A0" to it accordingly;

[0096] Among them, the baseline voltage is used to describe the reference level or stable state voltage value of the pulsed current signal when there is no pulse;

[0097] Step G2, sampling card anomaly detection:

[0098] Within the pre-specified time window C2, obtain the pulsed current signals at multiple acquisition time nodes therein, and denote them as L j , j = 1, 2,..., m, where m is the number of multiple acquisition time nodes within the pre-specified time window C2;

[0099] Through the formula , calculate the variance BL corresponding to L j ;

[0100] Among them, Lj is the preprocessed pulsed current signal. The variance BL is used to measure the fluctuation degree of the pulsed current signal, and PL is the average value of all L j ;

[0101] Extract the preset crash threshold BL y ;

[0102] When BL < BL y , it is determined that the sampling card is abnormal, and then a fault identifier "B1" is added to it;

[0103] Otherwise, the high-frequency current sensor is not determined to be abnormal for the sampling card, and then a fault identifier "B0" is added to it;

[0104] In this embodiment, under normal circumstances, the pulsed current signal will have a certain fluctuation, and the variance will not be too small. If the variance is too small, it means that there may be a problem with the sampling card, resulting in abnormal fluctuation of the pulsed current signal;

[0105] For example, the crash of the sampling card may cause the collected pulsed current signal to be fixed near a certain value with little change, resulting in a very small variance;

[0106] The fault isolation module is used to receive the fault identifier sent by the preliminary diagnosis module, and then trigger the corresponding preset emergency handling mechanism according to the fault identifier;

[0107] The emergency handling mechanism is as follows:

[0108] When the fault identifier is "A1" or "B1", it automatically switches to the standby high-frequency current sensor and sends a sensor fault warning signal through the fault warning module;

[0109] During the monitoring process of power equipment, sensor faults may occur at any time. If not processed in time, it will lead to the loss of monitoring data and affect the accurate judgment of the equipment operation status. Therefore, in this embodiment, when a fault such as sensor open circuit or sampling card crash is detected, the system immediately enables the standby sensor;

[0110] Among them, the sensor fault warning signal is used to notify the relevant operation and maintenance personnel so that the operation and maintenance personnel can repair or replace the faulty sensor in time according to the fault identifier corresponding to the sensor fault warning signal;

[0111] The real-time partial discharge monitoring system based on pulse current constructed in the first embodiment accurately senses and converts pulse current signals through the signal acquisition module, laying a data foundation for subsequent processing. The preprocessing module specifically adopts methods of data marking, baseline calibration, and pulse extraction to effectively eliminate useless information generated by factors such as electromagnetic interference and environmental noise, significantly improving the purity and reliability of the signals. The preliminary diagnosis module can detect faults such as the open circuit of the high-frequency current sensor and the abnormality of the sampling card in a timely manner, and the fault isolation module can quickly start the standby sensor and issue a warning according to the fault identification, greatly reducing the problem of missing monitoring data caused by sensor faults, ensuring the continuity and stability of the monitoring process of power equipment, providing strong guarantee for the safe operation of power equipment, and effectively reducing the potential risks caused by monitoring failure.

[0112] As the second embodiment of the present invention:

[0113] Please refer to Figure 1 、 Figure 2 and Figure 3 As shown, when the present application is specifically implemented, compared with the first embodiment, the difference between the technical solution of this embodiment and that of the first embodiment is only that in this embodiment, it further includes:

[0114] A deep diagnosis module, which, after determining through the preliminary diagnosis module that there is no open circuit fault in the high-frequency current sensor and no abnormality in the sampling card, identifies the fault type of the partial discharge;

[0115] The specific method is as follows:

[0116] Step H1, Pulse repetition rate feature:

[0117] Within a pre-specified time window C3, count the number g of valid pulses;

[0118] Through: , calculate the pulse repetition rate CF within the time window C3;

[0119] In the formula, T3 is the duration of the time window C3;

[0120] Step H2, Pulse amplitude mean feature:

[0121] Extract the pulse current signals L corresponding to all valid pulses within the time window C3 i , where i is the acquisition time node serial number corresponding to the valid pulse within the time window C3;

[0122] Through: , calculate the pulse amplitude mean AL within the time window C3;

[0123] Step H3, Pulse rise time mean feature:

[0124] For each valid pulse, the time taken to rise from 10% of the starting amplitude of the pulse to 90% of the amplitude is defined as the pulse rise time;

[0125] Within the time window C3, calculate the average value of all valid pulse rise times and denote it as the mean pulse rise time ST;

[0126] In this embodiment, the pulse rise time is related to the physical mechanism during the discharge process. For example, the pulse rise times of internal discharge and surface discharge may be different;

[0127] Step H4, Fault type determination:

[0128] Take the pulse repetition rate CF, the mean pulse amplitude AL, and the mean pulse rise time ST as the fault feature vector W to be judged = {CF, AL, ST};

[0129] At the same time, extract the fault feature vector sample set WY corresponding to different typical fault types obtained through experiments in advance u = {CF u 、AL u 、ST u}, u = 1, 2,..., q, where q represents the number of fault feature vector samples corresponding to different typical fault types;

[0130] In this embodiment, typical fault types include insulation air gap discharge, insulation surface discharge, insulation internal discharge, etc.;

[0131] Then, through:

[0132] Calculate the distance D between the fault feature vector W to be judged and the fault feature vector samples WY corresponding to different typical fault types u ; u ;

[0133] Compare the magnitudes of all distances D u and obtain the distance D with the minimum value u，min ;

[0134] When D u，min ≤ Dy, it is determined that the current partial discharge fault type is the same as the fault type corresponding to the fault feature vector sample WY u , and at the same time, a partial discharge fault warning signal is generated;

[0135] Among them, Dy is a pre-set distance threshold. In this embodiment, the value of Dy approaches 0;

[0136] The fault warning module is also used to identify the faulty equipment on the display interface of the monitoring system with a preset color according to the partial discharge fault warning signal, display the current partial discharge fault type, and record detailed information such as the fault occurrence time and equipment number in the system log for the operation and maintenance personnel to consult.

[0137] On the basis of Embodiment 1, Embodiment 2 introduces a deep diagnosis module, which uses features such as pulse repetition rate, average pulse amplitude, and average pulse rise time to accurately identify the types of partial discharge faults. By comparing and analyzing the fault feature vector to be judged with the pre-obtained sample set of fault feature vectors corresponding to different typical fault types, this method can accurately determine various fault types such as insulation air gap discharge, insulation surface discharge, and insulation internal discharge, and generate a detailed partial discharge fault warning signal. It not only identifies the faulty equipment on the monitoring system display interface with a specific color but also records detailed information such as the fault occurrence time and equipment number. This enables the operation and maintenance personnel to quickly and accurately grasp the fault situation, greatly improving the accuracy and efficiency of fault diagnosis, providing a precise basis for carrying out equipment maintenance work targeted, helping to prevent the deterioration of equipment faults in advance, reducing the risk of equipment damage, and enhancing the safety and reliability of the power system operation.

[0138] As Embodiment 3 of the present invention:

[0139] Please refer to Figure 1 、 Figure 2 and Figure 3 As shown, when the present application is specifically implemented, compared with Embodiment 1 and Embodiment 2, the technical solution of this embodiment lies in combining the solutions of the above-mentioned Embodiment 1 and Embodiment 2 for implementation.

[0140] Embodiment 3 organically combines the solutions of Embodiment 1 and Embodiment 2, having both the advantages of quickly detecting and isolating sensor faults and ensuring the continuity of monitoring in Embodiment 1 and the characteristics of accurately identifying the types of partial discharge faults and providing detailed fault information in Embodiment 2. This comprehensive and in-depth monitoring and diagnosis method can comprehensively ensure the operation safety of power equipment. On the one hand, it timely processes sensor faults to avoid interruption of monitoring data; on the other hand, it accurately identifies the types of partial discharge faults, provides detailed fault warnings for the operation and maintenance personnel, enables them to efficiently formulate maintenance strategies, comprehensively improves the operation and maintenance management level of power equipment, effectively reduces the probability of power equipment faults, and ensures the stable and efficient operation of the power system, providing a double guarantee for the reliability and stability of power supply.

[0141] It should be stated that all the data collected in this application are collected with the consent and authorization of the user, and the uses of the data are legal and compliant, and the use and processing of the data comply with the relevant laws, regulations and standards of the relevant regions.

[0142] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0143] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claimed rights.

Claims

1. A real-time partial discharge monitoring system based on pulsed current, characterized in that Including: A signal acquisition module, which is used to sense the pulse current signal generated by partial discharge in real time through a high-frequency current sensor; A preprocessing module, which is used to preprocess the pulse current signal to determine the effective pulses at relevant acquisition time nodes; The preprocessing method is as follows: Step S1: Mark the pulsed current signal generated by real-time sensing of partial discharge through a high-frequency current sensor as L t , where t is the serial number of the acquisition time node of the pulsed current signal; Within a pre-specified time window C1, obtain the pulsed current signals at multiple acquisition time nodes therein and denote them as L k , where k = 1, 2, …… v, and v is the number of multiple acquisition time nodes within the pre-specified time window C1; Step S2: First, obtain the average value of multiple pulsed current signals within a pre-specified time window, and then subtract the average value from the pulsed current signal L t at the corresponding acquisition time node, thereby obtaining the pulsed current signal after baseline calibration; Step S3: Extract the pulsed current signals L0 at two adjacent acquisition time nodes k and L0 k+1 , and the time interval T therebetween; Then pass through: , calculate the signal amplitude change rate R between them t ; Extract the preset amplitude change threshold R y and the lowest signal threshold L min ; When |R t | > R y , and L0 t > L min , then it is determined that the pulsed current signal at the t-th acquisition time node is a valid pulse; Otherwise, it is determined that the pulse current signal at the t-th acquisition time node does not belong to the effective pulse; A preliminary diagnosis module, which is used to perform fault diagnosis and analysis based on the pulse current signal to determine whether there is an open circuit fault in the high-frequency current sensor and an abnormality in the sampling card; A fault isolation module, which is used to receive the fault identifier sent by the preliminary diagnosis module, then trigger the corresponding preset emergency processing mechanism according to the fault identifier, and determine the sensor fault warning signal; A deep diagnosis module, when it is determined by the preliminary diagnosis module that there is no open circuit fault in the high-frequency current sensor and no abnormality in the sampling card, it identifies the fault type of the partial discharge to determine the partial discharge fault warning signal.

2. The on-line partial discharge monitoring system based on pulse current according to claim 1, wherein, The sensor open circuit detection method in the fault diagnosis and analysis is as follows: When it is determined that the pulse current signal at the t-th acquisition time node does not belong to the effective pulse, the pulse current signals at n consecutive acquisition time nodes after the t-th acquisition time node are obtained, and the baseline voltage is extracted at the same time: If all the pulse current signals at the n acquisition time nodes do not belong to the effective pulse and the baseline voltage value is 0; then the high-frequency current sensor is determined to have an open circuit fault, and the fault identifier "A1” is added to it; Otherwise, the high-frequency current sensor is not determined to have an open circuit fault, and the fault identifier "A0” is added to it; Among them, the baseline voltage is used to describe the reference level of the pulse current signal when there is no pulse.

3. The on-line partial discharge monitoring system based on pulse current according to claim 2, wherein, The sampling card abnormality detection method in the fault diagnosis and analysis is as follows: Within a pre-specified time window C2, obtain the pulsed current signals at multiple acquisition time nodes therein, and denote them as L j , where j = 1, 2, …… m, and m is the number of multiple acquisition time nodes within the pre-specified time window C2; Through the formula , the variance BL corresponding to L j is calculated; Among them, L j is the preprocessed pulsed current signal, the variance BL is used to measure the fluctuation degree of the pulsed current signal, and PL is the average value of all L j ; Extract a preset crash threshold BL y ; When BL < BL y , it is determined that the sampling card is abnormal, and then the fault identifier "B1" is added to it; Otherwise, the high-frequency current sensor is not determined to have an abnormality in the sampling card, and the fault identifier "B0” is added to it.

4. A real-time partial discharge monitoring system based on pulse current according to claim 3, characterized in that, The emergency processing mechanism is as follows: When the fault identifier is "A1” or "B1”, the high-frequency current sensor is automatically switched to the standby one, and the sensor fault warning signal is generated.

5. The real-time partial discharge monitoring system based on pulse current according to claim 1, characterized in that The fault type identification method is as follows: Extract the fault feature vector W = {CF, AL, ST} to be judged for partial discharge; Among them, CF represents the pulse repetition rate, AL represents the average pulse amplitude, and ST represents the average pulse rise time; Simultaneously extract the fault feature vector sample set WY corresponding to different typical fault types obtained through experiments in advance u = {CF u , AL u , ST u}, where u = 1, 2,..., q, and q represents the number of fault feature vector samples corresponding to different typical fault types; Among them, CF u is the pulse repetition rate of the u-th sample, AL u is the average pulse amplitude of the u-th sample, and ST u is the average pulse rise time of the u-th sample; Followed by: ; Calculate the distance D between the fault feature vector W to be judged and the fault feature vector samples WY corresponding to different typical fault types u ; u ; Compare all distances D u in terms of their magnitudes and obtain the distance D with the minimum value u，min ; When D u，min ≤ Dy, it is determined that the current partial discharge fault type is the same as the fault type corresponding to the fault feature vector sample WY u Meanwhile, a partial discharge fault warning signal is generated; where Dy is a preset distance threshold.

6. The on-line partial discharge monitoring system based on pulse current according to claim 5, wherein, The calculation method of the pulse repetition rate is: Within the pre-specified time window C3, count the number g of effective pulses; Adopted by: , the pulse repetition rate CF within the time window C3 is calculated; In the formula, T3 is the duration of the time window C3.

7. A real-time partial discharge monitoring system based on pulse current according to claim 5, characterized in that, The calculation method of the average pulse amplitude is: Extract the pulse current signal L corresponding to all valid pulses within the time window C3 i , where i is the sequence number of the acquisition time node corresponding to the valid pulses within the time window C3; Passed by: , the average pulse amplitude AL within the time window C3 is calculated.

8. A real-time partial discharge monitoring system based on pulse current according to claim 5, characterized in that, The calculation method of the average pulse rise time is: For each effective pulse, the time experienced from 10% of the pulse starting amplitude to 90% of the amplitude is defined as the pulse rise time; Within the time window C3, calculate the average value of the rise times of all effective pulses, which is the average pulse rise time ST.

9. A real-time partial discharge monitoring system based on pulsed current according to claim 1, characterized in that, It also includes: A fault warning module, which is used to send the sensor fault warning signal and the partial discharge fault warning signal to relevant operation and maintenance personnel.

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