Partial discharge fault monitoring method and system applicable to power cabinet

Abstract

The present invention relates to the technical field of electromagnetic signal detection, and in particular to a local discharge fault monitoring method and system suitable for a power cabinet. Suspected abnormal sensors are obtained by screening according to signal abnormality indicators of local discharge signals of various sensors, and the suspected abnormal sensors are combined in pairs. Suspected abnormal sensors are continuously screened according to the abnormal change degree of two adjacent sensors in various sensors associated with them to obtain interference center sensors. Then, according to the signal interference caused by each interference center sensor to other sensors, the interference signal strength of each sensor is obtained, so as to correct the local discharge signal of each sensor, and obtain the local discharge signal of each sensor with the electromagnetic signal interference of each electrical component in the power cabinet removed, which can effectively solve the technical problem of inaccurate local discharge signal detection caused by the interference of the electromagnetic signal of the electrical components in the power cabinet.

Description

Technical Field

[0001] The present invention relates to the technical field of electromagnetic signal detection, and in particular to a partial discharge fault monitoring method and system suitable for a power cabinet. Background Art

[0002] As a key part of the power transmission network, power cabinets are important equipment for distributing and controlling electric energy. They are mainly responsible for transmitting electric energy from the distribution system to various electrical equipment. The health of the power cabinet is directly related to the stable and safe operation of the power grid. Partial discharge refers to the microscopic discharge phenomenon in the insulation system of the power cabinet. Its essence is the rapid release of charge caused by the electric field strength exceeding the breakdown strength of the insulating material. It is precisely because of this breakdown that timely detection of partial discharge in the power cabinet can avoid more serious power accidents.

[0003] When monitoring partial discharge faults in power cabinets, commonly used methods include radio frequency detection and ultrasonic detection. The partial discharge signals are obtained through sensors. Although these methods can locate the location of partial discharge by analyzing the partial discharge signals, in the power cabinet, there are many types of electrical components, such as circuit breakers, current transformers, voltage transformers, capacitors, etc. These components are arranged in the power cabinet according to the circuit requirements, and the arrangement of the electrical components is not simple and regular, so a multi-level three-dimensional structure is formed. These electrical components will generate different electromagnetic signals when they are running, and when the local discharge signal is generated, it will be affected by the electromagnetic signals generated by these electrical components, thereby affecting the accuracy of the detection of the partial discharge signal. Summary of the invention

[0004] In order to solve the technical problem of low accuracy of partial discharge signal detection of existing power cabinets, the purpose of the present invention is to provide a partial discharge fault monitoring method and system suitable for power cabinets. The technical solution adopted is as follows:

[0005] In a first aspect of the present invention, a partial discharge fault monitoring method applicable to a power cabinet is provided, wherein a plurality of sensors are arranged in the power cabinet for detecting partial discharge signals, and the partial discharge fault monitoring method comprises:

[0006] Obtain the signal anomaly index of the partial discharge signal of each sensor at the current moment, and screen out suspected abnormal sensors according to the signal anomaly index;

[0007] The suspected abnormal sensors are combined in pairs to obtain multiple groups of suspected abnormal sensor pairs, and the suspected abnormal sensors are continuously screened according to the abnormal change degree of two adjacent sensors in the sensor set corresponding to the suspected abnormal sensor pair to obtain the interference center sensor; the sensor set includes the suspected abnormal sensor pair and each sensor associated therewith;

[0008] According to the signal interference caused by each interference center sensor to other sensors, the interference signal strength of each sensor is obtained;

[0009] According to the interference signal strength of each sensor, the partial discharge signal of each sensor is corrected.

[0010] In an exemplary embodiment, obtaining the signal abnormality index of the partial discharge signal of each sensor at the current moment includes:

[0011] Obtain the difference between the intensity value of the partial discharge signal of each sensor at the current moment and the preset signal intensity reference value, and obtain the signal abnormality index according to the difference;

[0012] The process of obtaining the signal strength reference value includes:

[0013] Obtaining the partial discharge signal strength value of the sensor at each first sampling moment in a monitoring time period that meets a preset condition; the preset condition indicates that each electrical component in the power cabinet is in a normal operating state; the each first sampling moment is each sampling moment in the monitoring time period;

[0014] Obtaining a weight coefficient of each first sampling moment according to the time interval between each first sampling moment and the current moment, wherein the weight coefficient is inversely proportional to the time interval;

[0015] Performing weighted summation of the local discharge signal strength values ​​at each first sampling moment and the weight coefficients at each first sampling moment to obtain a weighted value of the local discharge signal strength of the sensor in the monitoring time period;

[0016] The signal strength reference value of the sensor is obtained by fusing the partial discharge signal strength weighted value, the mode and the median of the partial discharge signal strength values ​​at each first sampling moment.

[0017] In an exemplary embodiment, the process of acquiring each sensor associated with the suspected abnormal sensor pair includes:

[0018] According to the power cabinet, a coordinate system is constructed;

[0019] According to the setting position of each sensor in the power cabinet, each coordinate point of each sensor in the coordinate system is obtained;

[0020] According to the coordinate points of the suspected abnormal sensor pair, obtaining a connecting line of the suspected abnormal sensor pair in the coordinate system;

[0021] Sensors whose vertical distances to the connecting line are less than a preset distance threshold are obtained to constitute the sensors associated with the suspected abnormal sensor pair; and the two suspected abnormal sensors in the suspected abnormal sensor pair and the associated sensors are sorted according to the direction from one suspected abnormal sensor in the suspected abnormal sensor pair to the other suspected abnormal sensor.

[0022] In an exemplary embodiment, the suspected abnormal sensors are combined in pairs to obtain multiple groups of suspected abnormal sensor pairs, and the suspected abnormal sensors are continuously screened according to the abnormal change degree of two adjacent sensors in the sensor set corresponding to the suspected abnormal sensor pair to obtain the interference center sensor, including:

[0023] Combining the suspected abnormal sensors in pairs to obtain multiple groups of suspected abnormal sensor pairs, and using a suspected abnormal sensor deletion process to obtain the retained suspected abnormal sensors;

[0024] The process of deleting the suspected abnormal sensor includes: obtaining the overall level of abnormal change of the suspected abnormal sensor pair according to the abnormal change degrees of two adjacent sensors in the sensor set corresponding to the suspected abnormal sensor pair; if the difference between the overall level of abnormal change of the suspected abnormal sensor pair and the abnormal change degree of the corresponding suspected abnormal sensor pair is less than a preset difference threshold, removing the suspected abnormal sensor with the smallest signal abnormality index in the suspected abnormal sensor pair;

[0025] The retained suspected abnormal sensors are combined in pairs to obtain multiple groups of suspected abnormal sensor pairs, and the suspected abnormal sensor deletion process is used to obtain the retained suspected abnormal sensors; and so on, until the difference between the overall level of abnormal changes of all suspected abnormal sensor pairs and the abnormal change degree of the corresponding suspected abnormal sensor pairs is greater than or equal to the preset difference threshold, the finally retained suspected abnormal sensors are obtained as the interference center sensors.

[0026] In an exemplary embodiment, the process of obtaining the abnormal variation degree includes:

[0027] Acquire a signal anomaly index of each second sampling moment of the sensor in a historical time period, wherein each second sampling moment is each sampling moment of the historical time period;

[0028] According to the signal abnormality index of each second sampling moment of the sensor, the abnormal moment is obtained by screening out from each second sampling moment;

[0029] According to the abnormal time of the first sensor and the abnormal time of the second sensor in the historical time period, a comprehensive abnormal time corresponding to the first sensor and the second sensor is obtained; the first sensor and the second sensor are two different sensors;

[0030] Obtaining the intensity difference between the local discharge signal intensity characteristic values ​​of the first sensor and the second sensor at each comprehensive abnormal moment, and obtaining the maximum value of the local discharge signal intensity characteristic values ​​of the first sensor and the second sensor at each comprehensive abnormal moment;

[0031] The ratio of the intensity difference to the maximum value at each comprehensive abnormal moment is obtained, and then the average value of the ratio is calculated to obtain the abnormal change degree of the first sensor and the second sensor.

[0032] In an exemplary embodiment, the process of acquiring the characteristic value of the local discharge signal strength of the first sensor or the second sensor at any comprehensive abnormal moment includes:

[0033] Acquire a time window centered on the comprehensive abnormal moment, and acquire the local discharge signal strength value at each second sampling moment in the time window;

[0034] Obtaining a weight coefficient of each second sampling moment in the time window according to the time interval between each second sampling moment in the time window and the comprehensive abnormality moment, wherein the weight coefficient is inversely proportional to the time interval;

[0035] The partial discharge signal strength values ​​at each second sampling moment in the time window and the weight coefficients at each second sampling moment in the time window are weighted and summed to obtain the partial discharge signal strength characteristic value at the comprehensive abnormal moment.

[0036] In an exemplary embodiment, obtaining the overall level of abnormal change of the suspected abnormal sensor pair according to the abnormal change degrees of two adjacent sensors in the sensor set corresponding to the suspected abnormal sensor pair includes:

[0037] The sum of the abnormal change degrees of two adjacent sensors in the sensor set is calculated as the overall abnormal change level of the corresponding suspected abnormal sensor pair.

[0038] In an exemplary embodiment, according to the signal interference caused by each interference center sensor to other sensors, the interference signal strength of each sensor is obtained, including:

[0039] The following calculation formula is used to obtain the signal interference caused by each interference center sensor to other sensors:

[0040] ;

[0041] in, Indicates The sensor is The signal interference caused by the interference center sensor, The sensors are Any sensor other than the interference center sensor, Indicates The intensity value of the partial discharge signal of the interference center sensor, Indicates The sensor and The overall level of abnormal changes corresponding to the interference center sensors;

[0042] The interference signal strength of each sensor is obtained using the following calculation formula:

[0043] ;

[0044] in, Indicates The interference signal strength of each sensor, Indicates the number of interference center sensors.

[0045] In an exemplary embodiment, correcting the partial discharge signal of each sensor according to the interference signal strength of each sensor includes:

[0046] ;

[0047] in, Represents the corrected The partial discharge signal of each sensor, Indicates The partial discharge signal of each sensor, Indicates The signal strength baseline value of each sensor, represents the absolute value function, Represents an exponential function with the natural constant e as the base.

[0048] In a second aspect of the present invention, a partial discharge fault monitoring system suitable for a power cabinet is provided, comprising: a memory and a processor; the memory is connected to the processor; the memory is used to store program instructions; the processor is used to implement the above-mentioned partial discharge fault monitoring method suitable for a power cabinet when the program instructions are executed.

[0049] The present invention has the following beneficial effects: since each sensor may be interfered by the electromagnetic signal of the electrical components in the power cabinet, the signal anomaly index of the local discharge signal of each sensor at the current moment is first obtained, and then the suspected abnormal sensors are screened according to the signal anomaly index, and then multiple groups of suspected abnormal sensor pairs are obtained by combining the suspected abnormal sensors in pairs, so as to continuously screen the suspected abnormal sensors according to the abnormal change degree related to the suspected abnormal sensor pairs to obtain the interference center sensor, and then for each interference center sensor, the signal interference caused by each interference center sensor to other sensors is obtained to obtain the interference signal strength of each sensor in the power cabinet, and finally, according to the interference signal strength of each sensor, the local discharge signal of each sensor is corrected to obtain the local discharge signal of each sensor without the interference of the electromagnetic signal of each electrical component in the power cabinet, which can effectively solve the technical problem of inaccurate local discharge signal detection caused by the interference of the electromagnetic signal of the electrical components in the power cabinet. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of a partial discharge fault monitoring method applicable to a power cabinet provided by an embodiment of the present invention;

[0051] Figure 2 is a flow chart for obtaining a signal strength reference value provided by an embodiment of the present invention;

[0052] Figure 3 is a flowchart of obtaining each sensor associated with a suspected abnormal sensor pair provided by an embodiment of the present invention;

[0053] Figure 4 is a flow chart of a process for obtaining an abnormal change degree provided by an embodiment of the present invention;

[0054] Figure 5 It is a flow chart of obtaining the characteristic value of the strength of a partial discharge signal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0056] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0057] The present embodiment provides a partial discharge fault monitoring method applicable to a power cabinet, in which a plurality of sensors are arranged in the power cabinet used for the method, each sensor is used to detect a partial discharge signal at a corresponding position, the sensor is specifically a partial discharge sensor, and the partial discharge signal is specifically an electromagnetic signal during partial discharge. On the basis of being able to effectively detect partial discharge signals, the specific type of the sensor is set according to actual needs, such as a separate ultrasonic sensor, a transient ground voltage / ground wave sensor, an ultra-high frequency sensor, etc., or a multifunctional partial discharge sensor integrating online ultra-high frequency, ground wave / ultrasound. In an exemplary embodiment, the sensor takes an ultra-high frequency sensor as an example. The number of sensors used to detect partial discharge signals is configured according to actual conditions and is not limited.

[0058] It should be understood that the power cabinet is also equipped with multiple electrical components according to power needs, such as circuit breakers, current transformers, voltage transformers, capacitors, etc. First of all, the positions of the electrical components arranged in the power cabinet are configured according to power needs, and this embodiment is not limited. The setting positions of each sensor in the power cabinet are set according to actual needs. The installation positions of the sensors can be near equipment such as transformers, current transformers, voltage transformers and switchgear in the power cabinet that are prone to partial discharge, and the distribution is relatively dense. Moreover, the sensor distribution method can adopt a grid distribution method. It should be understood that the sampling frequency of each sensor is set according to actual needs, such as 5 times per second.

[0059] At different locations in the power cabinet, the sensors are affected differently by various electrical components, and their interference to the partial discharge signal will also be different. At the same time, the partial discharge signal passes through different paths, and the weakening and influence of the electrical components on the partial discharge signal on different paths are also different.

[0060] Considering the working nature of the power cabinet, many electrical components in it will work within a relatively stable working parameter range, making the entire power cabinet work stably, which makes the interference electromagnetic signals generated by many electrical components in the power cabinet relatively stable. This further leads to the fact that many partial discharge signals detected by sensors at various locations will show a relatively stable periodic change within their respective time ranges.

[0061] Considering that each sensor in the power cabinet detects the result of the joint action of multiple electrical components nearby, and because the distance between sensors is usually close, when the working state of an electrical component changes, high-frequency electromagnetic waves are generated, and other electrical components linked to it will also change due to the change in the current working state of the electrical components, and correspondingly, they will also produce certain electromagnetic wave abnormalities. In this way, since these electrical components are not distributed in the same place, changes will occur on multiple sensors, and multiple peaks will be generated due to the distance between these sensors and those electrical components.

[0062] When a partial discharge signal is generated, it is interfered by different electromagnetic signals on different paths and propagates to sensors at various locations. The partial discharge signals detected by sensors at various locations are not the same. That is, the detected signal is a superposition signal of various electromagnetic waves generated by the original many electrical components and the electromagnetic waves generated by partial discharge. Therefore, by removing the many electromagnetic signals generated by the electrical components near the sensor after the detected signal, the partial discharge signal without the interference of many electrical components can be obtained.

[0063] like Figure 1 As shown, the partial discharge fault monitoring method applicable to the power cabinet includes the following steps:

[0064] Step 1: Obtain the signal anomaly index of the partial discharge signal of each sensor at the current moment, and screen the suspected abnormal sensors according to the signal anomaly index.

[0065] Each sensor collects a local discharge signal at the current moment, and then obtains the signal abnormality index of the local discharge signal of each sensor at the current moment, and then screens out suspected abnormal sensors from each sensor based on the signal abnormality index of the local discharge signal of each sensor at the current moment.

[0066] In an exemplary embodiment, the difference between the intensity value of the partial discharge signal of each sensor at the current moment and the preset signal intensity reference value of each sensor is obtained, and the signal abnormality index of each sensor is obtained according to the difference.

[0067] The power cabinet is an important facility that transmits electric energy from the power distribution system to various electrical equipment. Therefore, when it is running, it operates stably under certain operating conditions in most cases. Therefore, the operating conditions (working conditions) of various electrical components are relatively stable most of the time, which also causes the electromagnetic signals generated by many electrical components to be relatively stable when they are working. Based on this, the partial discharge signals detected by each sensor in a preset time period before the current moment are analyzed to obtain the normal partial discharge signal of the area detected by the sensor.

[0068] In an exemplary embodiment, Figure 2 As shown, the process of obtaining the signal strength reference value includes:

[0069] Step 1-1: Obtain the partial discharge signal strength value of the sensor at each first sampling moment in a monitoring time period that meets a preset condition.

[0070] The signal strength reference value of the sensor represents the strength value of the local discharge signal of the sensor when each electrical component in the power cabinet is operating normally. Therefore, a monitoring time period is preset, and the monitoring time period is not set arbitrarily, but needs to meet preset conditions. The preset conditions represent that each electrical component in the power cabinet is in a normal operating state, then, the monitoring time period is a time period when each electrical component in the power cabinet is in a normal operating state. It should be understood that the monitoring time period is a time period before the current moment, and the length of the monitoring time period is set by the actual situation. The monitoring time period includes multiple sampling moments, and for the sake of convenience of explanation, each sampling moment of the monitoring time period is defined as each first sampling moment.

[0071] For any sensor, the partial discharge signal strength value of the sensor at each first sampling moment in the monitoring time period is obtained.

[0072] Step 1-2: Obtain a weight coefficient of each first sampling moment according to the time interval between each first sampling moment and the current moment.

[0073] When the power cabinet is in operation, various electrical components, lines, etc. will gradually age or be damaged, resulting in different electromagnetic waves generated by various electrical components at different times. The closer to the current moment, the longer the power cabinet has been running, the more serious the aging of each electrical component may be, and at the same moment, the electromagnetic signals generated by various electrical components working under the same working conditions are more similar. Therefore, the weight coefficient of each first sampling moment is obtained according to the time interval between each first sampling moment and the current moment, and the weight coefficient is inversely proportional to the time interval, that is, the closer to the current moment, the greater the weight coefficient of the corresponding first sampling moment. In an exemplary embodiment, the time interval is negatively correlated and normalized, and the time interval after negative correlation normalization is used as the weight coefficient corresponding to the first sampling moment. The method of negative correlation normalization in this embodiment can be: , It represents the object that needs to be normalized, and exp represents the exponential function with the natural constant e as the base.

[0074] Step 1-3: Perform weighted summation on the local discharge signal strength values ​​at each first sampling moment and the weight coefficients at each first sampling moment to obtain a weighted value of the local discharge signal strength of the sensor in the monitoring time period.

[0075] For any sensor, the local discharge signal strength value of the sensor at each first sampling moment in the monitoring time period is multiplied by the weight coefficient of the corresponding first sampling moment to obtain the product corresponding to each first sampling moment, and then the sum of the products corresponding to all the first sampling moments is calculated to implement the weighted summation operation, and the obtained sum is the weighted value of the local discharge signal strength of the sensor in the monitoring time period.

[0076] Step 1-4: The partial discharge signal strength weighted value is integrated with the mode and median of the partial discharge signal strength values ​​at each first sampling moment to obtain a signal strength reference value of the sensor.

[0077] Since not all electrical components at the first sampling moment work with the same parameters during the monitoring period, some low-frequency or high-frequency signals different from normal signals will appear. Therefore, only using the above-mentioned weighted method to describe the electromagnetic signal when the power cabinet is working normally will be biased. Therefore, in an exemplary embodiment, the mode and median of the local discharge signal strength values ​​at each first sampling moment during the monitoring period are obtained, and the mode and median are introduced to balance the deviation caused by the weighted value. The signal strength reference value of the sensor is obtained by integrating the weighted value of the local discharge signal strength and the mode and median of the local discharge signal strength values ​​at each first sampling moment.

[0078] For any sensor, the calculation formula of the signal strength baseline value of the sensor is as follows:

[0079] ;

[0080] in, Indicates The signal strength baseline value of each sensor represents the normal working state of each electrical component in the power cabinet. The partial discharge signal measured by each sensor; Indicates The weighted value of the partial discharge signal strength of each sensor; Indicates The mode of the partial discharge signal strength values ​​of the sensors at each first sampling moment, Indicates The median of the partial discharge signal strength values ​​of the sensors at each first sampling moment.

[0081] By adopting the above process, the signal strength reference value of each sensor is obtained.

[0082] When the sensor is detecting, the greater the difference between the actual partial discharge signal detected and the partial discharge signal (i.e., the signal strength reference value) when the power cabinet is working normally, the closer the corresponding sensor is to the electrical component whose working state has changed, and the higher the possibility that the partial discharge signal detected by it is an abnormal interference signal. Then, the difference between the strength value of the partial discharge signal of each sensor at the current moment and the corresponding signal strength reference value of each sensor is obtained. The difference is specifically the absolute value of the difference between the strength value of the partial discharge signal and the signal strength reference value. Then, the signal abnormality index of each sensor is obtained based on the obtained differences of each sensor, as follows:

[0083] ;

[0084] in, Indicates The signal abnormality indicator of each sensor, Indicates The intensity value of the partial discharge signal detected by each sensor at the current moment; Indicates The signal strength baseline value of each sensor; It represents the absolute value function; is a normalization function. The normalization method in this embodiment can be: , It represents the object that needs to be normalized, and exp represents the exponential function with the natural constant e as the base.

[0085] The numerous electrical components in the power cabinet affect each other, that is, the change of the working state of each electrical component will cause the change of the working state of other electrical components. Since the numerous electrical components are relatively not gathered together, and each electrical component has a corresponding sensor that is closest to it. Based on this, when the working state of a series of electrical components changes and generates electromagnetic waves, there will be multiple points with large intensity values ​​in the partial discharge signals detected by the numerous sensors, which can be defined as maximum points. Then, as the distance from the numerous electrical components increases, the detected partial discharge signal gradually decreases.

[0086] In an exemplary embodiment, based on the signal anomaly index of each sensor, suspected abnormal sensors are screened from each sensor. Specifically: the signal anomaly index of each sensor is sorted from large to small, and then the first preset number of signal anomaly indexes or the first preset percentage of signal anomaly indexes are obtained, and the sensors corresponding to the obtained signal anomaly indexes are used as suspected abnormal sensors. As other implementations, an abnormal index threshold value can also be preset, and the value of the abnormal index threshold value is set according to actual needs. The signal anomaly index of each sensor is compared with the abnormal index threshold value, and the sensors corresponding to the signal anomaly index greater than or equal to the abnormal index threshold value are used as suspected abnormal sensors.

[0087] Through this step, multiple suspected abnormal sensors can be obtained as the basis for analysis in subsequent steps.

[0088] Step 2: Combine the suspected abnormal sensors in pairs to obtain multiple groups of suspected abnormal sensor pairs. According to the abnormal change degree of two adjacent sensors in the sensor set corresponding to the suspected abnormal sensor pair, the suspected abnormal sensors are continuously screened to obtain the interference center sensor.

[0089] This step is a continuous iterative process. Through multiple cyclic updates, in each cycle, the suspected abnormal sensors that do not meet the requirements are removed to continuously update the retained suspected abnormal sensors, and the finally retained suspected abnormal sensors are used as interference center sensors.

[0090] In each cycle, the suspected abnormal sensors need to be combined in pairs to obtain multiple groups of suspected abnormal sensor pairs, each suspected abnormal sensor pair includes two suspected abnormal sensors. It should be understood that the combination rules based on the combination of suspected abnormal sensors in pairs are set according to actual needs, such as random combination in pairs, or taking any suspected abnormal sensor as the starting point, defining it as the starting suspected abnormal sensor, obtaining the distance between the position of each other suspected abnormal sensor and the starting suspected abnormal sensor, finding the shortest distance, and forming a suspected abnormal sensor pair with the starting suspected abnormal sensor; then, selecting any suspected abnormal sensor from the remaining suspected abnormal sensors as the starting point, defining it as the starting suspected abnormal sensor, obtaining the distance between the position of each suspected abnormal sensor in the remaining suspected abnormal sensors and the starting suspected abnormal sensor, finding the shortest distance, and forming a suspected abnormal sensor pair with the starting suspected abnormal sensor; and so on, finally combining all suspected abnormal sensors in pairs to obtain multiple groups of suspected abnormal sensor pairs. It should be understood that if in each cycle, the number of suspected abnormal sensors is an odd number when combined in pairs, the last suspected abnormal sensor will be retained to the next cycle process and participate in the pairwise combination in the next cycle process.

[0091] In this step, during a continuous cycle, it is necessary to obtain a sensor set of each suspected abnormal sensor pair, where the sensor set includes the suspected abnormal sensor pair and each sensor associated therewith, that is, each sensor associated with each suspected abnormal sensor pair is obtained.

[0092] In an exemplary embodiment, Figure 3 As shown, the acquisition process of each sensor associated with the suspected abnormal sensor pair includes:

[0093] Step 2-1: Construct a coordinate system based on the power cabinet.

[0094] A coordinate system is constructed according to the arrangement of sensors in the power cabinet. If all sensors are arranged in the same plane formed by width and height, the width and height directions of the power cabinet can be used as the horizontal and vertical axes of the two-dimensional coordinate system to construct a two-dimensional coordinate system; if all sensors are arranged in a three-dimensional space in the power cabinet, the width, height and depth directions of the power cabinet can be used as the X-axis, Y-axis and Z-axis of the three-dimensional coordinate system to construct a three-dimensional coordinate system. This embodiment takes a two-dimensional coordinate system as an example.

[0095] Step 2-2: According to the setting position of each sensor in the power cabinet, obtain each coordinate point of each sensor in the coordinate system.

[0096] Based on the position of each sensor in the power cabinet, each sensor has a corresponding coordinate point, and the coordinate points of each sensor in the coordinate system can be obtained according to the setting position of each sensor in the power cabinet. Then, the distance between any two sensors is the distance between the coordinate points of the two sensors, and the distance between any two suspected abnormal sensors is obtained.

[0097] Step 2-3: According to the coordinate points of the suspected abnormal sensor pair, obtain the connection line of the suspected abnormal sensor pair in the coordinate system.

[0098] For any suspected abnormal sensor pair, the coordinate points of two suspected abnormal sensors in the suspected abnormal sensor pair are connected to obtain a connecting line of the suspected abnormal sensor pair in the coordinate system.

[0099] Step 2-4: Acquire sensors whose vertical distances to the connecting line are less than a preset distance threshold, and constitute individual sensors associated with the suspected abnormal sensor pair.

[0100] For any suspected abnormal sensor pair, obtain each other sensor except the suspected abnormal sensor pair, and obtain the vertical distance between each other sensor and the line connecting the suspected abnormal sensor pair. It should be understood that each other sensor can be used as a starting point to draw a perpendicular line to the line connecting the suspected abnormal sensor pair, and obtain the length of each perpendicular line, and the length of each perpendicular line is the vertical distance. Among them, the connecting line in this embodiment refers to the line segment between two suspected abnormal sensors. Then, if the intersection point of the perpendicular line of other sensors and the line connecting the suspected abnormal sensor pair is outside the line segment, these sensors are not considered and are not included in the consideration range of the associated sensors.

[0101] A distance threshold is preset, and the specific value of the preset distance threshold is set according to actual needs. Since the preset distance threshold is used to obtain the associated sensor, the value of the preset distance threshold should not be set too large.

[0102] The sensors whose vertical distances to the connecting line are less than a preset distance threshold are obtained, and the obtained sensors are used as sensors associated with the suspected abnormal sensor pair, so as to obtain sensors associated with each suspected abnormal sensor pair in each cycle.

[0103] For any suspected abnormal sensor pair, the sensors in the sensor set of the suspected abnormal sensor pair are sorted according to the direction from one of the suspected abnormal sensors in the suspected abnormal sensor pair to the other suspected abnormal sensor, that is, the two suspected abnormal sensors in the suspected abnormal sensor pair and the associated sensors are sorted. One of the suspected abnormal sensors and the other suspected abnormal sensor in the suspected abnormal sensor pair are determined according to actual needs. Then, the order obtained is: one of the suspected abnormal sensors in the suspected abnormal sensor pair, the first associated sensor, the second associated sensor, the third associated sensor, and so on, the last associated sensor, and the other suspected abnormal sensor.

[0104] It should be understood that this embodiment is not limited to only obtaining the sensors associated with each pair of suspected abnormal sensors. By adopting the above-mentioned process of obtaining associated sensors, the associated sensors corresponding to any two sensors can also be obtained.

[0105] In an exemplary embodiment, the process of obtaining the interference center sensor includes:

[0106] First cycle: Combine the suspected abnormal sensors obtained in step 1 in pairs to obtain multiple groups of suspected abnormal sensor pairs, and use the following suspected abnormal sensor deletion process to obtain the suspected abnormal sensors retained in the first cycle.

[0107] The process of deleting suspected abnormal sensors includes:

[0108] For any suspected abnormal sensor pair, the overall abnormal change level of the suspected abnormal sensor pair is obtained according to the abnormal change degree of two adjacent sensors in the sensor set corresponding to the suspected abnormal sensor pair. In an exemplary embodiment, the abnormal change degree acquisition process is as follows: Figure 4 As shown, including:

[0109] Step 2-5: Obtain the signal anomaly index of each second sampling moment of the sensor in the historical time period, where each second sampling moment is each sampling moment in the historical time period.

[0110] When the working state of some electrical components changes, the electromagnetic signal changes, which are refracted and scattered by many other electrical components and detected by many sensors. When the electromagnetic signal propagates, the shorter the electromagnetic signal propagation distance, the clearer the abnormal signal detected by the sensor. This causes the interference signal generated by the working change of each electrical component to be circular and decrease outward with the change of distance. Based on this, the abnormal change degree between any two sensors is analyzed.

[0111] A historical time period is preset, and the historical time period is a time period before the current moment. As a specific implementation, the end moment of the historical time period can be the moment before the current moment, and the length of the historical time period is set according to actual needs. It should be understood that the historical time period includes multiple sampling moments. For the sake of clarity, each sampling moment of the historical time period is defined as each second sampling moment.

[0112] The signal anomaly index at each second sampling moment of the sensor in the historical time period is obtained, wherein the calculation method of the signal anomaly index in step 1 is used for calculation, and the intensity value of the partial discharge signal detected at the current moment needs to be replaced with the intensity value of the partial discharge signal detected at the corresponding second sampling moment. Thus, the signal anomaly index at each second sampling moment of each sensor is obtained.

[0113] Step 2-6: According to the signal abnormality index at each second sampling moment of the sensor, the abnormal moment is screened out from each second sampling moment.

[0114] A signal anomaly threshold is preset, and the signal anomaly threshold is set according to actual judgment needs, such as 0.3.

[0115] For any sensor, compare the signal anomaly index of each second sampling moment of the sensor with the signal anomaly threshold, obtain the signal anomaly index greater than or equal to the signal anomaly threshold, and take the second sampling moment corresponding to the signal anomaly index greater than or equal to the signal anomaly threshold as the abnormal moment corresponding to the sensor.

[0116] By adopting the above process, the abnormal time corresponding to each sensor is obtained.

[0117] Step 2-7: Obtain the comprehensive abnormal time corresponding to the first sensor and the second sensor according to the abnormal time of the first sensor and the abnormal time of the second sensor in the historical time period.

[0118] For the convenience of explanation, the first sensor and the second sensor are assumed to be two different sensors. It should be understood that the first sensor and the second sensor may be two adjacent sensors or may not be adjacent sensors.

[0119] According to the abnormal time of the first sensor and the abnormal time of the second sensor in the historical time period, the comprehensive abnormal time corresponding to the first sensor and the second sensor is obtained.

[0120] In an exemplary embodiment, the abnormal moment corresponding to the first sensor in the historical time period is obtained, and the union of the abnormal moment corresponding to the second sensor in the historical time period is obtained to obtain multiple abnormal moments, and these abnormal moments are used as the comprehensive abnormal moments corresponding to the first sensor and the second sensor. For example: the abnormal moments corresponding to the first sensor in the historical time period are moment 1, moment 3, and moment 4, and the abnormal moments corresponding to the second sensor in the historical time period are moment 2, moment 3, moment 4, and moment 5, then the comprehensive abnormal moments corresponding to the first sensor and the second sensor include: moment 1, moment 2, moment 3, moment 4, and moment 5.

[0121] Step 2-8: Obtain the intensity difference between the local discharge signal intensity characteristic values ​​of the first sensor and the second sensor at each comprehensive abnormal moment, and obtain the maximum value of the local discharge signal intensity characteristic values ​​of the first sensor and the second sensor at each comprehensive abnormal moment.

[0122] The local discharge signal strength characteristic values ​​of the first sensor and the second sensor can be directly the strength values ​​of the local discharge signals of the first sensor and the second sensor. In an exemplary embodiment, in order to comprehensively consider the influence of several moments before and after a certain moment on the strength value of the local discharge signal at the moment, a specific acquisition process of the local discharge signal strength characteristic value is given as follows: Figure 5 As shown, including:

[0123] Step 2-8-1: Obtain a time window centered on the comprehensive abnormal moment, and obtain the local discharge signal strength value at each second sampling moment in the time window.

[0124] For any comprehensive abnormality moment, a time window centered on the comprehensive abnormality moment is obtained, and the length of the time window is set according to actual needs. For example, the 5 second sampling moments before the comprehensive abnormality moment, the comprehensive abnormality moment, and the 5 second sampling moments after the comprehensive abnormality moment are used as the time window centered on the comprehensive abnormality moment.

[0125] And obtain the local discharge signal strength value of each second sampling moment of the first sensor in the time window of the comprehensive abnormal moment, and the local discharge signal strength value of each second sampling moment of the second sensor.

[0126] Step 2-8-2: Obtain the weight coefficient of each second sampling moment in the time window according to the time interval between each second sampling moment in the time window and the comprehensive abnormality moment.

[0127] The time interval between each second sampling moment in the time window of the comprehensive abnormal moment and the comprehensive abnormal moment is obtained. Since the closer to the comprehensive abnormal moment, the greater the influence on the local discharge signal intensity value at the comprehensive abnormal moment, the weight coefficient of each second sampling moment in the time window of the comprehensive abnormal moment is obtained according to the time interval between each second sampling moment in the time window of the comprehensive abnormal moment and the comprehensive abnormal moment, and the weight coefficient is inversely proportional to the time interval. In an exemplary embodiment, the time interval is negatively correlated and normalized, and the time interval after negative correlation normalization is used as the weight coefficient corresponding to the second sampling moment.

[0128] Step 2-8-3: perform weighted summation of the local discharge signal strength values ​​at each second sampling moment in the time window and the weight coefficients at each second sampling moment in the time window to obtain the local discharge signal strength characteristic value at the comprehensive abnormal moment.

[0129] For the first sensor, the local discharge signal strength value at each second sampling moment in the time window of the comprehensive abnormal moment of the first sensor is multiplied by the weight coefficient of the corresponding second sampling moment to obtain the product corresponding to each second sampling moment of the first sensor, and then the products of all the second sampling moments in the time window of the comprehensive abnormal moment of the first sensor are added, and the sum value obtained is the local discharge signal strength characteristic value of the first sensor at the comprehensive abnormal moment.

[0130] Similarly, for the second sensor, the local discharge signal strength value at each second sampling moment in the time window of the comprehensive abnormal moment of the second sensor is multiplied by the weight coefficient of the corresponding second sampling moment to obtain the product corresponding to each second sampling moment of the second sensor, and then the products of all the second sampling moments in the time window of the comprehensive abnormal moment of the second sensor are added, and the sum obtained is the local discharge signal strength characteristic value of the second sensor at the comprehensive abnormal moment.

[0131] Thus, by adopting the above process, the characteristic values ​​of the partial discharge signal strengths of the first sensor and the second sensor at each comprehensive abnormal moment are obtained.

[0132] Since the first sensor has a local discharge signal strength characteristic value and the second sensor also has a local discharge signal strength characteristic value at each comprehensive abnormal moment, then the intensity difference between the local discharge signal strength characteristic values ​​of the first sensor and the second sensor at each comprehensive abnormal moment is obtained, and the intensity difference is specifically the absolute value of the difference between the local discharge signal strength characteristic values ​​of the first sensor and the second sensor at each comprehensive abnormal moment. Moreover, the maximum value of the local discharge signal strength characteristic values ​​of the first sensor and the second sensor at each comprehensive abnormal moment is obtained as the maximum value of the local discharge signal strength characteristic at each comprehensive abnormal moment.

[0133] Step 2-9: Obtain the ratio of the intensity difference to the maximum value at each comprehensive abnormal moment, and then calculate the average value of the ratio to obtain the abnormal change degree of the first sensor and the second sensor.

[0134] For any comprehensive abnormal moment, the ratio of the intensity difference at the comprehensive abnormal moment to the maximum value of the local discharge signal intensity characteristic is calculated to obtain the ratio at each comprehensive abnormal moment. Then, the average value of the ratios at all comprehensive abnormal moments is calculated, and the average value is the abnormal variation degree of the first sensor and the second sensor.

[0135] By adopting the above process, the abnormal variation degree of each two sensors is obtained. Then, the abnormal variation degree of each two adjacent sensors in the sensor set corresponding to each suspected abnormal sensor pair is obtained. Since the suspected abnormal sensor pair includes two suspected abnormal sensors, the abnormal variation degree of the two suspected abnormal sensors in each suspected abnormal sensor pair is also obtained.

[0136] For any suspected abnormal sensor pair, the overall abnormal change level of the suspected abnormal sensor pair is obtained according to the abnormal change degree of two adjacent sensors in the sensor set corresponding to the suspected abnormal sensor pair. Specifically, the average value of the abnormal change degree of two adjacent sensors in the sensor set corresponding to the suspected abnormal sensor pair is calculated as the overall abnormal change level of the suspected abnormal sensor pair. Thus, the overall abnormal change level of each suspected abnormal sensor pair is obtained.

[0137] It should be understood that since this embodiment is not limited to obtaining only the sensors associated with each pair of suspected abnormal sensors, the associated sensors corresponding to any two sensors can also be obtained. Then, by using the above process, the overall level of abnormal changes of any two sensors can be obtained.

[0138] The difference between the overall level of abnormal changes of the suspected abnormal sensor pair and the abnormal change degree of the suspected abnormal sensor pair is obtained. It should be understood that the process of obtaining the difference is: calculating the absolute value of the difference between the overall level of abnormal changes of the suspected abnormal sensor pair and the abnormal change degree of the suspected abnormal sensor pair, and then normalizing them. The normalized result is the difference between the overall level of abnormal changes of the suspected abnormal sensor pair and the abnormal change degree of the suspected abnormal sensor pair.

[0139] A difference threshold is preset, and the specific value of the preset difference threshold is set according to actual judgment needs, such as 0.8.

[0140] Compare the difference between the overall level of abnormal changes of the suspected abnormal sensor pair and the abnormal change degree of the suspected abnormal sensor pair with the preset difference threshold. If the difference between the overall level of abnormal changes of the suspected abnormal sensor pair and the abnormal change degree of the suspected abnormal sensor pair is less than the preset difference threshold, it means that the overall level of abnormal changes of the suspected abnormal sensor pair is similar to the abnormal change degree of the suspected abnormal sensor pair, indicating that the two suspected abnormal sensors of the suspected abnormal sensor pair may have detected the same interference signal. Then, the suspected abnormal sensor with the smallest signal abnormality index in the suspected abnormal sensor pair is removed (removal here means that it will no longer be used for subsequent interference with the central sensor), and the suspected abnormal sensor with the largest signal abnormality index in the suspected abnormal sensor pair is retained.

[0141] By using the above process, each suspected abnormal sensor pair is judged, so as to remove the suspected abnormal sensor with the smallest signal abnormality index among the suspected abnormal sensor pairs that meet the requirements, and retain the suspected abnormal sensor with the largest signal abnormality index among the suspected abnormal sensor pairs. In this way, the suspected abnormal sensors are screened and updated in the first cycle.

[0142] Second cycle: The suspected abnormal sensors retained in the first cycle are combined in pairs to obtain multiple groups of suspected abnormal sensor pairs, and the suspected abnormal sensors deleted in the first cycle are used to obtain the retained suspected abnormal sensors as the suspected abnormal sensors retained in the second cycle.

[0143] The third cycle: The suspected abnormal sensors retained in the second cycle are combined in pairs to obtain multiple groups of suspected abnormal sensor pairs, and the suspected abnormal sensors deleted in the first cycle are used to obtain the retained suspected abnormal sensors as the suspected abnormal sensors retained in the third cycle.

[0144] By analogy, until the difference between the overall level of abnormal changes of all suspected abnormal sensor pairs and the abnormal change degree of the corresponding suspected abnormal sensor pairs is greater than or equal to the preset difference threshold, the cycle is no longer performed, and the final retained suspected abnormal sensor is obtained and used as the interference center sensor. Each interference center sensor serves as the interference center point of each interference signal.

[0145] Step 3: According to the signal interference caused by each interference center sensor to other sensors, the interference signal strength of each sensor is obtained.

[0146] After the interference center points of each interference signal are screened, the interference signal strength of each sensor is obtained according to the signal interference caused by each interference center sensor to other sensors. In an exemplary embodiment, the signal interference caused by each interference center sensor to other sensors is obtained using the following calculation formula:

[0147] ;

[0148] in, Indicates The sensor is The signal interference caused by the interference center sensor, Indicates the current moment The intensity value of the partial discharge signal of the interference center sensor, Indicates The sensor and The overall level of abnormal changes corresponding to the interference center sensors is calculated through step 2.

[0149] It should be understood that The sensors are Any sensor other than the interference center sensor, that is, The sensors are all the sensors arranged in the power cabinet, except any sensor other than the central sensor, then The sensor can also be One of the interference center sensors other than the interference center sensors.

[0150] The signal interference caused by each interference signal to each sensor is obtained above. During detection, many interference signals act on the sensor together, that is, the interference signal detected at each sensor is generated by the superposition of many interference signals. Therefore, the interference signal strength of each sensor is obtained by the following calculation formula:

[0151] ;

[0152] in, Indicates The interference signal strength of each sensor, Indicates the number of interference center sensors.

[0153] By adopting the above calculation method, all sensors are traversed to obtain the interference signal strength of each sensor.

[0154] Step 4: Correct the partial discharge signal of each sensor according to the interference signal strength of each sensor.

[0155] After obtaining the interference signal strength of each sensor, the interference signal needs to be subtracted from the local discharge signal detected by each sensor to correct the local discharge signal of each sensor. In an exemplary embodiment, the correction is performed using the following calculation formula:

[0156] ;

[0157] in, Represents the corrected The partial discharge signal of each sensor, Indicates The partial discharge signal of each sensor (i.e. the partial discharge signal before correction), Indicates The signal strength baseline value of each sensor, represents the absolute value function, Represents an exponential function with the natural constant e as the base.

[0158] Thus, a clean partial discharge signal is obtained from each sensor after the interference is removed.

[0159] Subsequently, the local discharge signals of each sensor may be detected and acquired by radio frequency detection method, and then the time of the local discharge signals detected by each sensor may be compared, and the location of the local discharge may be located according to the corresponding time difference. This part is not part of the technical solution provided in this embodiment and is not specifically limited.

[0160] This embodiment also provides a partial discharge fault monitoring system applicable to a power cabinet, comprising: a memory and a processor; the memory is connected to the processor, and the memory is used to store program instructions; the processor is used to implement the steps in the above-mentioned partial discharge fault monitoring method embodiment applicable to a power cabinet when the program instructions are executed.

[0161] In an exemplary embodiment, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the above-mentioned partial discharge fault monitoring method embodiment applicable to a power cabinet.

[0162] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0163] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A partial discharge fault monitoring method applicable to a power cabinet, wherein a plurality of sensors are arranged in the power cabinet for detecting partial discharge signals, wherein the method comprises: The partial discharge fault monitoring method comprises: Obtain the signal anomaly index of the partial discharge signal of each sensor at the current moment, and screen out suspected abnormal sensors according to the signal anomaly index; The suspected abnormal sensors are combined in pairs to obtain multiple groups of suspected abnormal sensor pairs, and the suspected abnormal sensors are continuously screened according to the abnormal change degree of two adjacent sensors in the sensor set corresponding to the suspected abnormal sensor pair to obtain the interference center sensor; the sensor set includes the suspected abnormal sensor pair and each sensor associated therewith; According to the signal interference caused by each interference center sensor to other sensors, the interference signal strength of each sensor is obtained; Correcting the partial discharge signal of each sensor according to the interference signal strength of each sensor; The suspected abnormal sensors are combined in pairs to obtain multiple groups of suspected abnormal sensor pairs, and the suspected abnormal sensors are continuously screened according to the abnormal change degree of two adjacent sensors in the sensor set corresponding to the suspected abnormal sensor pair to obtain the interference center sensor, including: Combining the suspected abnormal sensors in pairs to obtain multiple groups of suspected abnormal sensor pairs, and using a suspected abnormal sensor deletion process to obtain the retained suspected abnormal sensors; The process of deleting the suspected abnormal sensor includes: obtaining the overall level of abnormal change of the suspected abnormal sensor pair according to the abnormal change degrees of two adjacent sensors in the sensor set corresponding to the suspected abnormal sensor pair; if the difference between the overall level of abnormal change of the suspected abnormal sensor pair and the abnormal change degree of the corresponding suspected abnormal sensor pair is less than a preset difference threshold, removing the suspected abnormal sensor with the smallest signal abnormality index in the suspected abnormal sensor pair; The retained suspected abnormal sensors are combined in pairs to obtain multiple groups of suspected abnormal sensor pairs, and the suspected abnormal sensor deletion process is used to obtain the retained suspected abnormal sensors; and so on, until the difference between the overall level of abnormal changes of all suspected abnormal sensor pairs and the abnormal change degree of the corresponding suspected abnormal sensor pairs is greater than or equal to the preset difference threshold, the finally retained suspected abnormal sensors are obtained as the interference center sensors.

2. A partial discharge fault monitoring method applicable to a power cabinet as claimed in claim 1, characterized in that: Obtain the signal abnormality index of the partial discharge signal of each sensor at the current moment, including: Obtain the difference between the intensity value of the partial discharge signal of each sensor at the current moment and the preset signal intensity reference value, and obtain the signal abnormality index according to the difference; The process of obtaining the signal strength reference value includes: Obtaining the partial discharge signal strength value of the sensor at each first sampling moment in a monitoring time period that meets a preset condition; the preset condition indicates that each electrical component in the power cabinet is in a normal operating state; the each first sampling moment is each sampling moment in the monitoring time period; Obtaining a weight coefficient of each first sampling moment according to the time interval between each first sampling moment and the current moment, wherein the weight coefficient is inversely proportional to the time interval; Performing weighted summation of the local discharge signal strength values ​​at each first sampling moment and the weight coefficients at each first sampling moment to obtain a weighted value of the local discharge signal strength of the sensor in the monitoring time period; The signal strength reference value of the sensor is obtained by fusing the partial discharge signal strength weighted value, the mode and the median of the partial discharge signal strength values ​​at each first sampling moment.

3. A partial discharge fault monitoring method applicable to a power cabinet as claimed in claim 1, characterized in that: The process of acquiring each sensor associated with the suspected abnormal sensor pair includes: According to the power cabinet, a coordinate system is constructed; According to the setting position of each sensor in the power cabinet, each coordinate point of each sensor in the coordinate system is obtained; According to the coordinate points of the suspected abnormal sensor pair, obtaining a connecting line of the suspected abnormal sensor pair in the coordinate system; Sensors whose vertical distances to the connecting line are less than a preset distance threshold are obtained to constitute the sensors associated with the suspected abnormal sensor pair; and the two suspected abnormal sensors in the suspected abnormal sensor pair and the associated sensors are sorted according to the direction from one suspected abnormal sensor in the suspected abnormal sensor pair to the other suspected abnormal sensor.

4. A partial discharge fault monitoring method applicable to a power cabinet as claimed in claim 1, characterized in that: The process of obtaining the abnormal change degree includes: Acquire a signal anomaly index of each second sampling moment of the sensor in a historical time period, wherein each second sampling moment is each sampling moment of the historical time period; According to the signal abnormality index of each second sampling moment of the sensor, the abnormal moment is obtained by screening out from each second sampling moment; According to the abnormal time of the first sensor and the abnormal time of the second sensor in the historical time period, a comprehensive abnormal time corresponding to the first sensor and the second sensor is obtained; the first sensor and the second sensor are two different sensors; Obtaining the intensity difference between the local discharge signal intensity characteristic values ​​of the first sensor and the second sensor at each comprehensive abnormal moment, and obtaining the maximum value of the local discharge signal intensity characteristic values ​​of the first sensor and the second sensor at each comprehensive abnormal moment; The ratio of the intensity difference to the maximum value at each comprehensive abnormal moment is obtained, and then the average value of the ratio is calculated to obtain the abnormal change degree of the first sensor and the second sensor.

5. A partial discharge fault monitoring method applicable to a power cabinet as claimed in claim 4, characterized in that: The process of obtaining the characteristic value of the local discharge signal strength of the first sensor or the second sensor at any comprehensive abnormal moment includes: Acquire a time window centered on the comprehensive abnormal moment, and acquire the local discharge signal strength value at each second sampling moment in the time window; Obtaining a weight coefficient of each second sampling moment in the time window according to the time interval between each second sampling moment in the time window and the comprehensive abnormality moment, wherein the weight coefficient is inversely proportional to the time interval; The partial discharge signal strength values ​​at each second sampling moment in the time window and the weight coefficients at each second sampling moment in the time window are weighted and summed to obtain the partial discharge signal strength characteristic value at the comprehensive abnormal moment.

6. A partial discharge fault monitoring method applicable to a power cabinet as claimed in claim 1, characterized in that: Obtaining the overall level of abnormal change of the suspected abnormal sensor pair according to the abnormal change degrees of two adjacent sensors in the sensor set corresponding to the suspected abnormal sensor pair includes: The sum of the abnormal change degrees of two adjacent sensors in the sensor set is calculated as the overall abnormal change level of the corresponding suspected abnormal sensor pair.

7. A partial discharge fault monitoring method applicable to a power cabinet as claimed in claim 1, characterized in that: According to the signal interference caused by each interference center sensor to other sensors, the interference signal strength of each sensor is obtained, including: The following calculation formula is used to obtain the signal interference caused by each interference center sensor to other sensors: ； in, Indicates The sensor is The signal interference caused by the interference center sensor, The sensors are Any sensor other than the interference center sensor, Indicates The intensity value of the partial discharge signal of the interference center sensor, Indicates The sensor and The overall level of abnormal changes corresponding to the interference center sensors; The interference signal strength of each sensor is obtained using the following calculation formula: ； in, Indicates The interference signal strength of each sensor, Indicates the number of interference center sensors.

8. A partial discharge fault monitoring method applicable to a power cabinet as claimed in claim 7, characterized in that: According to the interference signal strength of each sensor, correct the partial discharge signal of each sensor, including: ； in, Represents the corrected The partial discharge signal of each sensor, Indicates The partial discharge signal of each sensor, Indicates The signal strength baseline value of each sensor, represents the absolute value function, Represents an exponential function with the natural constant e as the base.

9. A partial discharge fault monitoring system suitable for a power cabinet, characterized in that it comprises: Memory and processor; The memory is connected to the processor; The memory is used to store program instructions; The processor is used to implement the partial discharge fault monitoring method applicable to a power cabinet as described in any one of claims 1 to 8 when the program instructions are executed.

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