Fault prediction and alarm method and system for intelligent power distribution cabinet

Abstract

The invention provides an intelligent power distribution cabinet fault prediction alarm method and system, and relates to the technical field of power distribution cabinet fault prediction, contact resistance detectors are respectively arranged on the surfaces of moving and static contacts of a circuit breaker in a power distribution cabinet, and a distributed insulation resistance sensor group is deployed at weak points of a line insulation layer in the power distribution cabinet; the method comprises the steps of collecting contact resistance and insulation resistance in real time; calculating a contact resistance fluctuation coefficient according to the contact resistance, and calculating an insulation resistance attenuation rate according to the insulation resistance; obtaining a contact abrasion probability according to the contact resistance fluctuation coefficient, and obtaining an insulation aging probability according to the insulation resistance attenuation rate; and triggering a multi-stage fault early warning mechanism according to the contact wear probability and / or the insulation aging probability, and outputting a fault position according to the sensor orientation when the fault early warning grade is greater than or equal to a preset fault grade. According to the method provided by the invention, the power distribution cabinet fault caused by contact wear and / or insulation aging can be predicted, and the fault position can be fed back in time.

Description

Technical Field

[0001] This disclosure generally relates to the field of power distribution cabinet fault prediction technology, and specifically to an intelligent power distribution cabinet fault prediction alarm method and system. Background Technology

[0002] As the core equipment for power distribution and control in a power system, the switch cabinet is widely used in industrial production, building construction and other scenarios. Its operational reliability directly affects power supply safety and system stability.

[0003] In existing technologies, the detection or prediction of power distribution cabinet faults either relies solely on the electrical parameters within the cabinet or combines electrical parameters with environmental parameters. Regardless of the method, only fault detection or prediction is possible, not fault location. For power distribution cabinets with frequently accessed switching contacts, faults often occur at these contacts, preventing traditional methods from identifying faults in advance. This often leads to sudden power outages after warnings are issued, resulting in economic losses and safety hazards. Summary of the Invention

[0004] In view of the above-mentioned defects or deficiencies in the existing technology, it is desirable to provide a method and system for predicting and alarming faults in intelligent power distribution cabinets to solve the above problems.

[0005] The first aspect of this invention provides a fault prediction and alarm method for intelligent distribution cabinets, wherein contact resistance detectors are respectively installed on the surfaces of the moving and stationary contacts of the circuit breakers inside the distribution cabinet, and a distributed insulation resistance sensor group is deployed at weak points in the insulation layer of the lines inside the distribution cabinet; the method includes:

[0006] Real-time acquisition of contact resistance from a contact resistance detector and insulation resistance from a distributed insulation resistance sensor group;

[0007] When it is determined that the circuit breaker is triggered, the contact resistance fluctuation coefficient is calculated based on the contact resistance, and the insulation resistance attenuation rate is calculated based on the insulation resistance according to the set period.

[0008] The contact resistance fluctuation coefficient is input into the first mapping database to obtain the contact wear probability; the insulation resistance attenuation rate is input into the second mapping database to obtain the insulation aging probability.

[0009] A multi-level fault warning mechanism is triggered based on the contact wear probability and / or the insulation aging probability, and the fault location is output based on the position of the contact resistance detector and / or insulation resistance sensor group when the fault warning level is greater than or equal to the preset fault level.

[0010] According to the technical solution provided by the present invention, when determining that the circuit breaker is triggered, calculating the contact resistance fluctuation coefficient based on the contact resistance includes:

[0011] When it is determined that the circuit breaker contacts are triggered, all the contact resistances collected by the contact resistance detector within a set time window before the current moment and during the circuit breaker triggering process are obtained;

[0012] Calculate the mean and standard deviation of all contact resistances acquired within the set time window;

[0013] The contact resistance fluctuation coefficient is calculated based on the mean and standard deviation of the contact resistance, using the following formula:

[0014]

[0015] Where, ΔR w μ(R) represents the contact resistance fluctuation coefficient. w ) represents the average contact resistance within a set time window, σ(R) w ) represents the standard deviation of the contact resistance within the set time window.

[0016] According to the technical solution provided by the present invention, the step of calculating the insulation resistance attenuation rate based on the insulation resistance according to a set period includes:

[0017] When the end time of the set period is determined, the insulation resistance at the start time and the insulation resistance at the end time of the set period are obtained;

[0018] The insulation resistance attenuation rate is calculated based on the insulation resistance at the start and end of the set period, using the following formula:

[0019]

[0020] Where, ΔR i R represents the insulation resistance attenuation rate. i (t0) represents the insulation resistance at the previous moment, R i (t1) represents the insulation resistance at the current moment.

[0021] According to the technical solution provided by the present invention, the moving contacts and stationary contacts of the circuit breaker in the distribution cabinet are respectively provided with contact temperature sensors, and the method further includes:

[0022] When it is determined that the circuit breaker contacts are triggered, the contact temperatures are obtained from the contact temperature sensor within a set time window before the current moment, and during each trigger of the circuit breaker.

[0023] Calculate the temperature trend index based on all the contact temperatures obtained within the set time window;

[0024] Calculate the time series entropy reflecting the complexity of the signal based on all the contact resistances obtained within the set time window;

[0025] The contact wear probability is corrected based on the temperature trend index and the time series entropy.

[0026] According to the technical solution provided by the present invention, the distribution cabinet is further equipped with a humidity sensor, a particle concentration sensor, and an ambient temperature sensor; the insulation layer of the lines inside the distribution cabinet is further equipped with a Rogowski coil partial discharge sensor; and a power frequency phase sensor is installed on the incoming line side of the distribution cabinet; the method further includes:

[0027] When the end time of the set period is determined, the ambient humidity at the current time collected by the humidity sensor, the smoke particle concentration at the current time collected by the particle concentration sensor, and the ambient temperature at the current time collected by the ambient temperature sensor are obtained.

[0028] The environmental humidity, dust particle concentration, and environmental temperature are normalized and weighted to obtain the environmental coupling coefficient.

[0029] The system acquires all pulse amplitudes, pulse frequencies, signal main frequencies, and high-frequency energy collected in real time by the Rogowski coil partial discharge sensor within the set period, as well as all phase distributions collected in real time by the power frequency phase sensor within the set period.

[0030] A five-dimensional partial discharge feature vector is obtained based on all the pulse amplitudes, pulse frequencies, signal main frequencies, high-frequency energy, and phase distributions acquired within the set period.

[0031] The insulation aging probability is corrected based on the environmental coupling coefficient and the five-dimensional partial discharge feature vector.

[0032] According to the technical solution provided by the present invention, after determining that the circuit breaker contacts are triggered, after acquiring all the contact resistances collected by the contact resistance detector within a set time window before the current moment and during the circuit breaker triggering process, the method further includes:

[0033] Within the set time window, a sliding data window covering the triggering process is formed with a preset duration as the sliding window length;

[0034] Obtain all contact resistances within the sliding data window;

[0035] Sort all contact resistances within the sliding data window by their numerical values;

[0036] The median value of the sorted contact resistance is selected as the filtering result of the center point of the sliding data window;

[0037] The sliding data window is slid in a preset step size until the filtered results of all contact resistances within the set time window are obtained.

[0038] According to the technical solution provided by the present invention, the method of outputting the fault location based on the position of the contact resistance detector and / or insulation resistance sensor group when the fault warning is greater than or equal to the set level includes:

[0039] The insulation resistance sensor group deployed at each weak point of the insulation layer of the line in the power distribution cabinet is divided into a region group. The resistance drop of each region group is calculated, and the target region group whose resistance drop exceeds the preset drop threshold is locked.

[0040] Obtain the three-dimensional coordinates of all insulation resistance sensors within the target area group, and calculate the specific location of insulation aging;

[0041] Extract the contact resistance fluctuation coefficient, contact temperature difference, and number of contact state switching times at the current moment;

[0042] When it is determined that the contact resistance fluctuation coefficient exceeds a preset abnormal threshold, the position of the contact resistance detector is located, the contact temperature difference is compared with a preset temperature threshold, and the number of state switching times is compared with a preset number threshold.

[0043] When the temperature difference of the contact point exceeds the preset temperature threshold and the number of state switching exceeds the preset number threshold, the specific location of the contact point wear is determined.

[0044] According to the technical solution provided by the present invention, after determining that the end time of the set period has been reached, and after acquiring the ambient humidity collected by the humidity sensor, the smoke particle concentration collected by the particle concentration sensor, and the ambient temperature collected by the ambient temperature sensor at the current time, the method further includes:

[0045] When the ambient temperature is determined to be greater than the alarm temperature threshold, the concentration of all smoke and dust particles collected within the set time period prior to the current moment is obtained;

[0046] Concentration variation curves are constructed based on the concentrations of all the soot particles, and multiple concentration variation segments are divided on the concentration variation curves;

[0047] Calculate the mean curvature value for each concentration change segment, and generate a fault alarm signal when any mean curvature value is greater than the preset curvature threshold.

[0048] According to the technical solution provided by the present invention, after acquiring the contact resistance acquired by the real-time contact resistance detector and the insulation resistance acquired by the distributed insulation resistance sensor, the method further includes:

[0049] Determine whether any abnormal values ​​are found in the contact resistance and / or insulation resistance collected at least three consecutive times;

[0050] When an abnormal value is found in the insulation resistance, the abnormal point data is supplemented according to the distance-weighted difference method based on the measured values ​​of two adjacent insulation resistance sensors.

[0051] When the contact resistance shows an abnormal value, switch to another contact resistance detector in the same group of contacts to collect the contact resistance and mark the location of the abnormal contact resistance detector.

[0052] A second aspect of the present invention provides an intelligent distribution cabinet fault prediction and alarm system for executing the intelligent distribution cabinet fault prediction and alarm method described above, the system comprising:

[0053] The acquisition module is configured to acquire in real time the contact resistance acquired by the contact resistance detector and the insulation resistance acquired by the distributed insulation resistance sensor group.

[0054] The calculation module is configured to determine when the circuit breaker is triggered, calculate the contact resistance fluctuation coefficient based on the contact resistance, and calculate the insulation resistance attenuation rate based on the insulation resistance according to a set period.

[0055] A mapping module is configured to input the contact resistance fluctuation coefficient into a first mapping database to obtain the contact wear probability; and input the insulation resistance attenuation rate into a second mapping database to obtain the insulation aging probability.

[0056] The early warning module is configured to trigger a multi-level fault early warning mechanism based on the contact wear probability and / or the insulation aging probability, and output the fault location based on the position of the contact resistance detector and / or the insulation resistance sensor group when the fault early warning level is greater than or equal to the preset fault level.

[0057] Compared with existing technologies, the advantages of this invention are as follows: By installing contact resistance detectors on the moving and stationary contact surfaces of the circuit breakers in the distribution cabinet, and deploying distributed insulation resistance sensor groups at weak points in the insulation layer of the lines within the distribution cabinet, it is possible to collect contact resistance and insulation resistance in real time. Based on this, the contact resistance fluctuation coefficient (characterizing the wear degree of the circuit breaker contacts) and the insulation resistance attenuation rate (characterizing the aging degree of the line insulation layer) can be calculated. Then, the contact wear probability and insulation aging probability are obtained through a preset mapping database. A multi-level fault early warning mechanism is triggered based on the magnitude of these probabilities, facilitating the implementation of different handling measures for different fault early warning levels. Furthermore, it can quickly locate the fault location when the fault early warning level is too high, facilitating maintenance personnel to carry out repairs. This solution can predict distribution cabinet faults caused by contact wear and insulation aging, solving the problems of existing technologies being unable to locate fault points and unable to identify contact faults in advance, leading to sudden power outages. It can promptly report the fault location, improve the reliability of distribution cabinet operation, and reduce economic losses and safety hazards. Attached Figure Description

[0058] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0059] Figure 1 This is a flowchart of the steps of the intelligent power distribution cabinet fault prediction and alarm method provided in Embodiment 1 of the present invention;

[0060] Figure 2 This is a schematic diagram of the intelligent power distribution cabinet fault prediction and alarm system provided in Embodiment 6 of the present invention.

[0061] The reference numerals are as follows: 10, data acquisition module; 20, calculation module; 30, mapping module; 40, early warning module. Detailed Implementation

[0062] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0063] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0064] Example 1

[0065] Please refer to Figure 1This embodiment 1 provides a method for predicting and alarming faults in an intelligent distribution cabinet. Contact resistance detectors are installed on the surfaces of the moving and stationary contacts of the circuit breaker within the distribution cabinet, and a distributed insulation resistance sensor group is deployed at weak points in the insulation layer of the wiring within the distribution cabinet. The method includes:

[0066] S100: Real-time acquisition of contact resistance from the contact resistance detector and insulation resistance from the distributed insulation resistance sensor group.

[0067] Specifically, a distribution cabinet typically contains multiple circuit breakers, each with at least one set of stationary and moving contacts. A contact resistance detector is installed on each of these contacts, requiring multiple detectors within the cabinet. These detectors are connected to a predictive alarm system, continuously monitoring the contact resistance at the stationary and moving contacts and sending the data to the system. The distribution cabinet also contains multiple insulation resistance sensor groups, positioned at weak points in the insulation layer, such as cable joints and branches. Each group consists of multiple insulation resistance sensors evenly distributed across these weak points. These sensors are also connected to the predictive alarm system, continuously monitoring the insulation resistance and sending the data to the system.

[0068] In step S100, the predictive alarm system stores the contact resistance value sent by the contact resistance detector and the insulation resistance value detected by the insulation resistance sensor in real time, and establishes a contact resistance sequence and an insulation resistance sequence corresponding to the time and value according to the acquisition time, so as to facilitate the tracking of the contact resistance and insulation resistance values ​​in the later stage.

[0069] S200: When the circuit breaker is triggered, calculate the contact resistance fluctuation coefficient based on the contact resistance, and calculate the insulation resistance attenuation rate based on the insulation resistance according to the set period.

[0070] Specifically, the predictive alarm system is also connected to the circuit breaker, and monitors the status of the circuit breaker contacts in real time. In step S200, when the predictive alarm system detects that the circuit breaker has been triggered, it calculates the contact resistance fluctuation coefficient by retrieving the contact resistance sequence from step S100. The contact resistance fluctuation coefficient is used to characterize the wear degree of the circuit breaker contacts. Furthermore, the predictive alarm system calculates the insulation resistance attenuation rate once according to a set period. The insulation resistance attenuation rate is used to characterize the aging degree of the line insulation layer, and the insulation resistance attenuation rate is calculated by retrieving the insulation resistance sequence from step S100.

[0071] Furthermore, in step S200, when determining that the circuit breaker is triggered, calculating the contact resistance fluctuation coefficient based on the contact resistance includes:

[0072] S211: When it is determined that the circuit breaker contacts are triggered, acquire all the contact resistances collected by the contact resistance detector within a set time window before the current moment and during the circuit breaker triggering process.

[0073] Specifically, circuit breaker contact triggering includes switching the contacts from open to closed, or from closed to open. In step S211, when the predictive alarm system detects a state switch (open / close) of any circuit breaker contact, it retrieves the contact resistance values ​​within a set time window from the contact resistance sequence. The set time window is set according to requirements. In this embodiment, the set time window is 10 seconds, meaning that the contact resistance values ​​retrieved from the contact resistance sequence within 10 seconds prior to the current moment, during the circuit breaker's open / close process, are only acquired during the circuit breaker triggering process. For example, if the contact resistance detector has a sampling frequency of 100Hz and the triggering process lasts 100ms, then 10 contact resistance values ​​will be acquired in a single trigger. If only one trigger occurs within the 10-second set time window, then the valid data acquired are the 10 contact resistance values ​​during that triggering process. It should be noted that under normal circumstances, the contact will be triggered at most once within the 10-second set time window. If the number of triggers exceeds one, a malfunction may occur.

[0074] S212: Calculate the mean and standard deviation of all contact resistances acquired within the set time window.

[0075] Specifically, in step S212, the mean and standard deviation of the contact resistance are calculated based on the values ​​of the 10 contact resistances retrieved in step S211. The mean and standard deviation can reflect the fluctuation of the contact resistance.

[0076] S213: Calculate the contact resistance fluctuation coefficient based on the mean and standard deviation of the contact resistance, using the following formula:

[0077]

[0078] Where, ΔR w μ(R) represents the contact resistance fluctuation coefficient. w ) represents the average contact resistance within a set time window, σ(R) w ) represents the standard deviation of the contact resistance within the set time window.

[0079] Specifically, in step S213, the calculated contact resistance fluctuation coefficient can characterize the wear degree of the circuit breaker contacts. The larger the value of the contact resistance fluctuation coefficient, the higher the wear degree of the contacts; the smaller the value of the contact resistance fluctuation coefficient, the lower the wear degree of the contacts.

[0080] Furthermore, to improve the accuracy of the contact resistance fluctuation coefficient calculation, after step S211 and before step S212, the retrieved contact resistance value is subjected to sliding window mid-value filtering. The filtering process includes:

[0081] S211-1: Within the set time window, a sliding data window covering the triggering process is formed with a preset duration as the sliding window length.

[0082] Specifically, the sliding window length is less than or equal to the length of the set time window, but in most cases it is less than the set time window length. Taking the set time window length of 100ms as an example, in this embodiment, the sliding window length is 50ms. Starting from the trigger time, the first sliding data window captures the contact resistance value from 0 to 50ms, and then slides in 10ms increments until all 10 data points are covered.

[0083] To facilitate understanding, we will continue to use the values ​​of the 10 contact resistors in step S211 above as an example. However, to demonstrate the filtering effect, the sixth value of the 10 contact resistors will be replaced with 500 for easier explanation. Thus, the contact resistors within the time window are set to include: 118, 122, 125, 121, 119, 500, 120, 124, 122, and 121.

[0084] S211-2: Obtain all contact resistances within the sliding data window.

[0085] Specifically, in step S211-2, the first sliding data window is used as an example to obtain the contact resistance values ​​from 0 to 50ms, namely 118, 122, 125, 121, and 119.

[0086] S211-3: Sort all contact resistances within the sliding data window by their numerical values.

[0087] Specifically, in step S211-3, the values ​​of the five contact resistances obtained in step S211-2 are sorted to obtain the first sorting sequence: 118, 119, 121, 122, 125.

[0088] S211-4: Select the median value of the contact resistance after sorting as the filtering result of the center point of the sliding data window.

[0089] Specifically, in step S211-4, based on the first sorting sequence obtained in step S211-3, the middle value 121 is selected as the filtering result, thus completing the filtering of the first sliding data window.

[0090] S211-5: Slide the sliding data window by a preset step size until the filtering results of all contact resistances within the set time window are obtained.

[0091] Specifically, in step S211-5, with a preset step size of 10ms, the second sliding data window acquires the contact resistance values ​​from 10ms to 60ms, i.e., 122, 125, 121, 119, and 500. These five contact resistance values ​​are sorted to obtain a second sorted sequence: 119, 121, 122, 125, and 500. The median value, 122, is selected as the filtering result, and the outlier 500 is removed, completing the filtering for the second sliding data window. This process is repeated for a total of six sliding data windows, yielding six filtered results: 121, 122, 121, 121, 122, and 122. Step S211 is then executed based on these six filtered results. Steps S211-1 to S211-5 remove outliers from the contact resistance values, ensuring more accurate calculations of the contact resistance fluctuation coefficient, thereby improving the accuracy of fault warnings and preventing false alarms from the predictive alarm system caused by outliers.

[0092] Furthermore, in step S200, calculating the insulation resistance attenuation rate based on the insulation resistance according to a set period includes:

[0093] S221: When the end time of the set period is determined, the insulation resistance at the start time and the insulation resistance at the end time of the set period are obtained.

[0094] Specifically, the set period can be selected according to the actual situation. In this embodiment, the set period is selected as 10 days. In step S21, when the prediction alarm system determines that the end time of the set period has been reached, it retrieves the insulation resistance values ​​at the start time t0 and the end time t1 from the insulation resistance sequence. It should be noted that the insulation resistance corresponding to each weak point in the insulation layer of the line needs to be obtained at the same time, and the insulation resistance attenuation rate of each weak point in the insulation layer needs to be calculated at the same time.

[0095] S222: Calculate the insulation resistance attenuation rate based on the insulation resistance at the start and end of the set period, using the following formula:

[0096]

[0097] Where, ΔR iR represents the insulation resistance attenuation rate. i (t0) represents the insulation resistance at the initial moment, R i (t1) represents the insulation resistance at the end time.

[0098] Specifically, in step S222, since multiple insulation resistance sensors are deployed at the weak points of the line insulation layer, multiple insulation resistance values ​​at the start time and multiple insulation resistance values ​​at the end time will be obtained for the same weak point. The weighted average of the insulation resistance values ​​at all start times is then calculated to obtain R. i (t0), the weighted average of the insulation resistance values ​​at all ending times is used to obtain R. i (t1), the weight in the calculation is inversely proportional to the distance between the insulation resistance sensor and the weak point of the line insulation layer. The calculated insulation resistance attenuation rate can characterize the aging degree of the line insulation layer. The smaller the insulation resistance attenuation rate, the lighter the aging degree of the line insulation layer; the larger the insulation resistance attenuation rate, the heavier the aging degree of the line insulation layer.

[0099] S300: Input the contact resistance fluctuation coefficient into the first mapping database to obtain the contact wear probability; input the insulation resistance attenuation rate into the second mapping database to obtain the insulation aging probability.

[0100] Specifically, in step S300, to obtain the contact wear probability, a first mapping database needs to be established in advance. The first mapping database includes multiple contact resistance fluctuation coefficient ranges and the mapping relationship between them and the contact wear probability corresponding to each contact resistance fluctuation coefficient range. The first mapping database is illustrated in Table 1 below:

[0101] Table 1

[0102] Contact resistance fluctuation coefficient range Contact wear probability mapping relationship Wear and tear description <![CDATA[0≤ΔR w ≤5%]]> <![CDATA[P w =2×ΔR w ]]> The contact surface is smooth and free from wear. <![CDATA[5%<ΔR w ≤10%]]> <![CDATA[P w =4×ΔR w -10%]]> Slight wear, oxide film thickening <![CDATA[10%<ΔR w ≤15%]]> <![CDATA[P w =6×ΔR w -30%]]> Moderate wear, microscopic signs of wear. <![CDATA[15%<ΔR w ≤20%]]> <![CDATA[P w =5×ΔR w -15%]]> Severe wear, increased pit depth <![CDATA[ΔR w >20%]]> <![CDATA[P w =95%]]> Risk of contact failure; immediate repair required.

[0103] Among them, P w This represents the probability of contact wear. After calculating the contact resistance fluctuation coefficient according to step S213, the probability of contact wear can be obtained by comparing it with the first mapping database. In practical applications, the mapping relationship of the contact wear probability can be adjusted as needed.

[0104] In step S300, to obtain the insulation aging probability, a second mapping database needs to be established in advance. The second mapping database includes multiple insulation resistance attenuation rate ranges and the corresponding insulation aging probability mapping relationship for each insulation resistance attenuation rate range. The second mapping database is illustrated in Table 2 below:

[0105] Table 2

[0106] Insulation resistance attenuation rate range Insulation aging probability mapping relationship Description of aging <![CDATA[0≤ΔR i ≤0.2]]> <![CDATA[P i =(50×ΔR i )×100%]]> The insulation layer is intact and there is no obvious damage. <![CDATA[0.2<ΔR i ≤0.5]]> <![CDATA[P i =(60×ΔR i -2)×100%]]> Microcracks form on the surface of the insulation layer <![CDATA[0.5<ΔR i ≤1.0]]> <![CDATA[P i =60×ΔR i ×100%]]> Structural damage to the insulation layer <![CDATA[1<ΔR i ≤2.0]]> <![CDATA[P i =(30×ΔR i +30)×100%]]> The insulation layer is severely damaged and nearing breakdown. <![CDATA[ΔR i >2.0]]> <![CDATA[P i =95%]]> Insulation layer breakdown failure

[0107] Among them, P i This represents the insulation aging probability. After calculating the insulation resistance attenuation rate according to step S222, the insulation aging probability can be obtained by comparing it with the second mapping database. In practical applications, the mapping relationship of the insulation aging probability can be adjusted as needed.

[0108] S400: Trigger a multi-level fault warning mechanism based on the contact wear probability and / or the insulation aging probability, and output the fault location based on the position of the contact resistance detector and / or insulation resistance sensor group when the fault warning level is greater than or equal to the preset fault level.

[0109] Specifically, in step S400, after obtaining the contact wear probability and / or insulation aging probability according to step S300, the fault level is predicted according to a preset multi-level fault warning mechanism. The triggering rules of the multi-level fault warning mechanism are shown in Table 3 below:

[0110] Table 3

[0111] Triggering rules Fault warning level <![CDATA[20%≤P w <40% or 30% ≤ P i <50%]]> Level 1 <![CDATA[40%≤P w <60% or 50% ≤ P i <70%]]> Level 2 <![CDATA[60%≤P w <80% or 70% ≤ P i <90%]]> Level 3 <![CDATA[P w ≥80% or P i ≥90% Level 4

[0112] By matching the contact wear probability with triggering rules, the corresponding fault warning level can be obtained. The predictive alarm system generates different execution action control commands for different fault warning levels. When the fault warning level is Level 1, the predictive alarm system illuminates the warning indicator light on the distribution cabinet; when the fault warning level is Level 2, the predictive alarm system controls the display of a visual reminder on the computer used to monitor the status of the distribution cabinet and sends a prompt to the maintenance personnel's mobile phone to increase the number of inspections; when the warning level is Level 3, the predictive alarm system controls the display of an alarm indicator on the computer used to monitor the status of the distribution cabinet and sends an alarm command to the maintenance personnel's mobile phone to prompt the maintenance personnel to perform timely maintenance on the distribution cabinet; when the warning level is Level 4, the predictive alarm system controls the power supply of the distribution cabinet to be cut off and the backup power supply to be connected.

[0113] In this embodiment, fault prediction can be performed for a single fault scenario, i.e., obtaining the fault warning level solely based on the contact wear probability or the insulation aging probability; or fault prediction can be performed for a composite scenario, i.e., obtaining the fault warning level simultaneously based on both the contact wear probability and the insulation aging probability. It should be noted that when performing fault prediction for a composite fault scenario, a "highest-priority principle" is adopted, i.e., the corresponding fault prediction level is obtained separately based on the contact wear probability and the insulation aging probability, and then the two obtained fault prediction levels are compared, with the higher level selected as the fault prediction level output by the predictive alarm system. Therefore, the method provided in this embodiment can predict faults inside the distribution cabinet from multiple perspectives, improving both the comprehensiveness and accuracy of fault prediction.

[0114] Specifically, in this embodiment, the preset fault level is selected as level three. When the fault prediction level output by the predictive alarm system is greater than or equal to level three, in addition to sending corresponding execution action instructions for different levels, the fault location corresponding to the fault with a fault prediction level greater than the predicted fault level is also sent simultaneously, so that maintenance personnel can quickly locate the fault location and thus ensure timely maintenance of the fault location.

[0115] Furthermore, in step S400, the method for outputting the fault location based on the position of the contact resistance detector and / or insulation resistance sensor group when the fault warning level is greater than or equal to the set level includes:

[0116] For the location method of insulation aging fault, please refer to the following steps S400-1 to S400-2:

[0117] S400-1: Divide the insulation resistance sensor group deployed at each weak point of the insulation layer of the line in the distribution cabinet into a region group, calculate the resistance drop of each region group, and lock the target region group whose resistance drop exceeds the preset drop threshold.

[0118] Specifically, in step S400-1, when it is determined that the fault is caused by insulation aging, it indicates that insulation aging has occurred in a certain section of the line insulation layer. Then, the resistance reduction rate is calculated for each area group corresponding to the insulation layer of each line within the distribution cabinet. For example, in this embodiment, assuming the lines within the distribution cabinet are divided into 8 area groups (AH) according to the insulation resistance sensor group, the resistance reduction rate of each area group is calculated using the following formula:

[0119]

[0120] When the resistance drop in area group B exceeds the preset drop threshold, area group B is locked as the target area group. At this time, it can be determined that the insulation layer of the line corresponding to area group B is faulty, which serves as a rough location.

[0121] S400-2: Obtain the three-dimensional coordinates of all insulation resistance sensors within the target area group, and calculate the specific location of insulation aging.

[0122] Specifically, in step S400-2, after determining that region group B is the target region group, the three-dimensional coordinates of all insulation resistance sensors in region group B are obtained. Assuming that region group B includes three insulation resistance sensors with coordinates S1(15,25,30), S2(10,15,30), and S3(20,20,30), the insulation aging location is calculated using the triangulation method:

[0123]

[0124] Where (x,y,z) are the three-dimensional coordinates of the insulation aging location.

[0125] For the location method of contact wear fault, please refer to the following steps S400-3 to S400-5:

[0126] S400-3: Extract the contact resistance fluctuation coefficient and the number of times the contact is triggered at the current moment.

[0127] Specifically, in step S400-3, the contact resistance fluctuation coefficient can be obtained from step S213, and the number of electric shocks can be monitored by the predictive alarm system.

[0128] S400-4: When it is determined that the contact resistance fluctuation coefficient exceeds the preset abnormal threshold, locate the position of the contact resistance detector and compare the number of triggers with the preset number threshold.

[0129] Specifically, in step S400-4, it is first determined whether the contact resistance fluctuation coefficient is abnormal. When the contact resistance fluctuation coefficient is abnormal, the fault location is coarsely located according to the three-dimensional coordinates of the corresponding contact resistance detector. Then, it is determined whether the number of triggers has reached the preset threshold number to verify whether there is really wear at the location.

[0130] S400-5: When the number of state switching exceeds the preset threshold, locate the specific location of the wear on the contact point.

[0131] Specifically, in step S400-5, when the number of triggers of the contact is too high, it is determined that the contact has indeed failed, and then the three-dimensional coordinates of the contact resistance sensor corresponding to the contact are used as the contact wear position.

[0132] Example 2

[0133] Based on Embodiment 1 above, this embodiment provides another intelligent power distribution cabinet fault prediction and alarm method. The same content as Embodiment 1 will not be repeated here; the difference lies in:

[0134] The moving and stationary contacts of the circuit breaker in the distribution cabinet are also respectively equipped with contact temperature sensors; after step S300 and before step S400, the following is also included:

[0135] S311: When the circuit breaker contacts are triggered, acquire all contact temperatures collected by the contact temperature sensor within a set time window before the current moment and during the circuit breaker triggering process.

[0136] Specifically, contact temperature sensors are installed on the surfaces of the stationary and moving contacts. These sensors are connected to a predictive alarm system, which collects contact temperatures in real time and sends the collected data to the predictive alarm system. The predictive alarm system then uses the contact temperature values ​​combined with the collection time to establish a contact temperature sequence for later tracking. In step S311, after the predictive alarm system detects contact triggering of the circuit breaker, it retrieves the contact temperature values ​​within a 10-second set time window and within 100ms of the circuit breaker triggering process.

[0137] S312: Calculate the temperature trend index based on all the contact temperatures obtained within the set time window.

[0138] Specifically, in step S312, the predictive alarm system first calculates the average of all contact temperature values ​​obtained in step S311 to obtain the average contact temperature of the current triggering process, and stores the average contact temperature of the current triggering process; then, the predictive alarm system retrieves the average contact temperature of the previous triggering process of the contact, and calculates the temperature trend index based on the average contact temperature of the two triggering processes. The formula for calculating the temperature trend index is as follows:

[0139]

[0140] TTI stands for Temperature Trend Index. This represents the average contact temperature during the current triggering process. This represents the average contact temperature during the previous triggering process.

[0141] S313: Calculate the time series entropy reflecting the complexity of the signal based on all the contact resistances obtained within the set time window.

[0142] Specifically, in step S313, the values ​​of all contact resistances after filtering in steps S211-1 to S211-5 are first obtained, and then μ(R) is calculated based on the obtained values ​​of all contact resistances. w Then, the value of each contact resistance is compared with μ(R) one by one. w ), greater than μ(R) w ) is recorded as "1" (high level), which is less than μ(R) w ) is recorded as "0" (low level), and all contact resistance values ​​are compared with μ(R) w After comparison, the sequence composed of the original contact resistance values ​​is converted into a symbol sequence [0,1,0,0,1,1]; then, the time series entropy is calculated based on the symbol sequence. The formula for calculating the time series entropy is as follows:

[0143] H = -(p0log2p0 + p1log2p1)

[0144] Where H represents the time series entropy, p0 represents the probability of "0" appearing in the symbol sequence, and p1 represents the probability of "1" appearing in the symbol sequence.

[0145] S314: Correct the contact wear probability based on the temperature trend index and the time series entropy.

[0146] Specifically, in step S314, the temperature trend correction coefficient is first calculated based on the temperature trend index (TTI). The calculation formula is as follows:

[0147]

[0148] Where, k TTI TTI represents the temperature trend correction coefficient, α represents the correction weight of the temperature trend index, and TTI is the temperature trend correction factor. thr In this embodiment, TTI represents the temperature trend threshold. thr Select 10%;

[0149] Next, the entropy correction factor is calculated based on the time series entropy H. The calculation formula is as follows:

[0150]

[0151] Where, k H H represents the entropy correction coefficient, β represents the correction weight for the time series entropy, and H... min H is the theoretical minimum value of time series entropy. max This represents the theoretical maximum value of the time series entropy.

[0152] Finally, the contact wear probability is corrected based on the temperature trend correction coefficient and the entropy correction coefficient. The corrected contact wear probability is shown below:

[0153] P w ′=P w ×k TTI ×k H ;

[0154] Among them, P w ′ represents the corrected contact wear probability.

[0155] Example 3

[0156] Based on Embodiment 1 above, this embodiment provides another intelligent power distribution cabinet fault prediction and alarm method. The same content as Embodiment 1 will not be repeated here; the difference lies in:

[0157] The distribution cabinet is also equipped with a humidity sensor, a particle concentration sensor, and an ambient temperature sensor. A Rogowski coil partial discharge sensor is also installed in the insulation layer of the wiring inside the distribution cabinet. A power frequency phase sensor is installed on the incoming line side of the distribution cabinet. After step S300 and before step S400, the following is also included:

[0158] S321: When the end time of the set period is reached, acquire the ambient humidity at the current time collected by the humidity sensor, the dust particle concentration at the current time collected by the particle concentration sensor, and the ambient temperature at the current time collected by the ambient temperature sensor.

[0159] Specifically, the humidity sensor, particle concentration sensor, and ambient temperature sensor are installed inside the distribution cabinet near weak points in the line insulation. These sensors are connected to a predictive alarm system to collect real-time data on ambient humidity, particulate matter concentration, and ambient temperature. The system then sends these data to the predictive alarm system, which stores them in conjunction with the time of collection to obtain separate sequences for ambient humidity, particulate matter concentration, and ambient temperature, facilitating subsequent tracking. In step S321, when the predictive alarm system determines that the set period has ended, it retrieves the current ambient humidity, particulate matter concentration, and ambient temperature from the ambient humidity, particulate matter concentration, and ambient temperature sequences, respectively.

[0160] S322: Normalize and weight the ambient humidity, dust particle concentration and ambient temperature to obtain the environmental coupling coefficient.

[0161] Specifically, in step S322, the environmental coupling coefficient is calculated based on the acquired ambient humidity, dust particle concentration, and ambient temperature at the current moment. The calculation formula is as follows:

[0162]

[0163] Where, k h H represents the environmental coupling coefficient, ω1 represents the weight of environmental humidity, and H represents the environmental coupling coefficient. h H represents the ambient humidity at the current moment. hmax and H hmin These represent the upper and lower limits of the normal range of ambient humidity, respectively; ω2 represents the weight of the smoke particle concentration; C h C represents the current concentration of smoke and dust particles. hmax and C hmin These represent the upper and lower limits of the normal range for smoke and dust particle concentration, respectively; ω3 represents the weighting of ambient temperature; and T... h T represents the ambient temperature at the current moment. hmax and T hminThese represent the upper and lower limits of the normal range of ambient temperature, respectively.

[0164] S323: Acquire all pulse amplitudes, pulse frequencies, signal main frequencies, and high-frequency energy collected in real time by the Rogowski coil partial discharge sensor within the set period, and all phase distributions collected in real time by the power frequency phase sensor within the set period.

[0165] Specifically, the Rogowski coil partial discharge sensor is connected to the predictive alarm system to detect pulse amplitude, pulse frequency, signal main frequency, and high-frequency energy in real time. It then sends these parameters to the predictive alarm system, which stores them in conjunction with the acquisition time. A power frequency phase sensor is also connected to the predictive alarm system to detect phase distribution in real time and sends the phase distribution data to the predictive alarm system, which stores it in conjunction with the acquisition time. In step S323, the predictive alarm system retrieves all pulse amplitudes, pulse frequencies, signal main frequencies, high-frequency energy, and phase distributions within a set time period.

[0166] S324: Obtain a five-dimensional partial discharge feature vector based on all the pulse amplitudes, pulse frequencies, signal main frequencies, high-frequency energy, and phase distributions acquired within the set period.

[0167] Specifically, in step S324, the average pulse amplitude of the set period is calculated. pulse frequency average and high-frequency energy average Based on the phase distribution, it is concentrated in Determine the phase eigenvalue φ c Next, based on the average pulse amplitude... pulse frequency average Signal main frequency f0, average high-frequency energy and phase eigenvalue φ c Five-dimensional features were obtained Five-dimensional features Normalized to the [0,1] interval, construct a five-dimensional partial discharge feature vector.

[0168] S325: Correct the insulation aging probability based on the environmental coupling coefficient and the five-dimensional partial discharge feature vector.

[0169] Specifically, in step S325, firstly, based on the environmental coupling coefficient k... h The environmental coupling correction coefficient is calculated using the following formula:

[0170] k h ′=1+k h ×0.2

[0171] Where k h ′ represents the environmental coupling correction coefficient;

[0172] Next, the five-dimensional partial discharge feature vector PD is mapped to the partial discharge correction coefficient k using a machine learning model. PD The machine learning model can adopt the random forest model;

[0173] Finally, the insulation aging probability is corrected based on the environmental coupling correction factor and the partial discharge correction factor. The corrected insulation aging probability is shown below:

[0174] P i ′=P i ×k e ′×k PD

[0175] Among them, P i ′ represents the corrected insulation aging probability.

[0176] Example 4

[0177] Based on Embodiment 3 above, this embodiment provides another intelligent power distribution cabinet fault prediction and alarm system. The contents that are the same as in Embodiment 1 will not be repeated here; the differences are as follows:

[0178] Following step S321, the method further includes:

[0179] S321-1: When the ambient temperature is determined to be greater than the alarm temperature threshold, obtain the concentration of all smoke particles collected within the set time period before the current moment.

[0180] Specifically, in step S321-1, the prediction alarm system determines that when the ambient temperature collected by the ambient temperature sensor is greater than the alarm temperature threshold, it indicates that the temperature inside the power distribution cabinet is too high. This may be caused by a short circuit or open flame inside the power distribution cabinet, thus preliminarily determining that an abnormality has occurred inside the power distribution cabinet. Then, the prediction alarm system acquires the concentration of all smoke particles within the set time period in the particle concentration sequence.

[0181] S321-2: Construct a concentration change curve based on the concentration of all the dust particles, and divide the concentration change curve into multiple concentration change segments.

[0182] Specifically, in step S321-2, after the predictive alarm system obtains the concentration of all smoke particles, it establishes a particle concentration change curve. This curve characterizes the change in smoke concentration over a set time period prior to the current moment. By analyzing the trend and rate of change of the smoke concentration, abnormal phenomena occurring within the distribution cabinet can be verified. To make the judgment more accurate, the predictive alarm system divides the established concentration change curve into multiple concentration change segments, each with an equal time length.

[0183] S321-3: Calculate the mean curvature of each concentration change segment, and generate a fault alarm signal when any mean curvature is greater than the preset curvature threshold.

[0184] Specifically, in step S321-3, firstly, a concentration change segment is selected, and the curvature corresponding to each smoke particle concentration value is calculated. The mean curvature of the concentration change segment is obtained by calculating the mean curvature of all smoke particle concentration values ​​in the segment. All concentration change segments are traversed, and the mean curvature of each segment is calculated. All mean curvature values ​​are compared with a preset curvature threshold. When any mean curvature value exceeds the preset curvature threshold, it indicates that the smoke particle concentration has increased rapidly at the corresponding time point, which may be due to smoke from the line causing a rapid change in smoke particles. At this time, it is determined that a fault has occurred in the distribution cabinet. Then, the predictive alarm unit generates an alarm signal and sends the alarm signal to the computer used to monitor the status of the distribution cabinet and the mobile phone of the maintenance personnel to indicate that the distribution cabinet has failed. The alarm signal will also trigger the broadcast system to issue an alarm notification.

[0185] Example 5

[0186] Based on Embodiment 1 above, this embodiment provides another intelligent power distribution cabinet fault prediction and alarm method. The same content as Embodiment 1 will not be repeated here; the difference lies in:

[0187] Following step S100, the method further includes:

[0188] S100-1: Determine whether the contact resistance and / or insulation resistance collected at least three times consecutively exceed the normal value range.

[0189] Specifically, in step S100-1, the predictive alarm system detects whether abnormal values ​​appear in the contact resistance and insulation resistance in real time, in order to determine whether the contact resistance detector and insulation resistance sensor are faulty, so as to avoid inaccurate data acquisition caused by the fault of the contact resistance detector and insulation resistance sensor itself, thereby affecting the accuracy of fault prediction.

[0190] S100-2: When the insulation resistance exceeds the normal range, the abnormal point data is supplemented according to the distance weighted difference method based on the measured values ​​of two adjacent insulation resistance sensors.

[0191] Specifically, in step S100-2, when the predictive alarm system detects that the contact resistance values ​​collected by the same insulation resistance sensor three consecutive times all exceed the normal range, the predictive alarm system generates a maintenance command based on the coordinates of the faulty sensor to prompt maintenance personnel to perform timely repairs. Furthermore, it supplements the insulation resistance at the abnormal location of the insulation resistance sensor using a weighted interpolation method based on the insulation resistance values ​​collected by the two nearest adjacent insulation resistance sensors, ensuring continued fault prediction. The insulation resistance is supplemented using a weighted interpolation method, assigning weights to the distances of adjacent insulation resistance sensors from the fault point; the closer the distance, the larger the weight.

[0192] S100-3: When the contact resistance exceeds the normal range, switch to another contact resistance detector in the same group of contacts to collect the contact resistance and mark the location of the abnormal contact resistance detector.

[0193] Specifically, in step S100-3, when the predictive alarm system detects that the contact resistance values ​​collected by the same contact resistance detector three times in a row all exceed the normal range, the predictive alarm system generates a maintenance instruction based on the coordinates of the fault detector so that maintenance personnel can perform timely maintenance. Furthermore, if the faulty detector is installed on the moving contact, the system switches to the detector on the corresponding stationary contact for temporary testing to ensure that fault prediction can proceed normally.

[0194] Example 6

[0195] Please refer to Figure 2 This embodiment provides an intelligent distribution cabinet fault prediction and alarm system for executing the intelligent distribution cabinet fault prediction and alarm method as described in Embodiments 1-5. The system includes:

[0196] The acquisition module 10 is configured to acquire in real time the contact resistance acquired by the contact resistance detector and the insulation resistance acquired by the distributed insulation resistance sensor group.

[0197] Calculation module 20 is configured to determine when the circuit breaker is triggered, calculate the contact resistance fluctuation coefficient based on the contact resistance, and calculate the insulation resistance attenuation rate based on the insulation resistance according to a set period.

[0198] The mapping module 30 is configured to input the contact resistance fluctuation coefficient into a first mapping database to obtain the contact wear probability; and input the insulation resistance attenuation rate into a second mapping database to obtain the insulation aging probability.

[0199] The early warning module 40 is configured to trigger a multi-level fault early warning mechanism based on the contact wear probability and / or the insulation aging probability, and output the fault location based on the position of the contact resistance detector and / or the insulation resistance sensor group when the fault early warning level is greater than or equal to the preset fault level.

[0200] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for predicting and alarming faults in an intelligent power distribution cabinet, characterized in that, The method includes: contact resistance detectors are installed on the surfaces of the moving and stationary contacts of the circuit breaker inside the distribution cabinet; distributed insulation resistance sensor groups are deployed at weak points in the insulation layer of the lines inside the distribution cabinet; Real-time acquisition of contact resistance from a contact resistance detector and insulation resistance from a distributed insulation resistance sensor group; When it is determined that the circuit breaker is triggered, the contact resistance fluctuation coefficient is calculated based on the contact resistance, and the insulation resistance attenuation rate is calculated based on the insulation resistance according to the set period. The contact resistance fluctuation coefficient is input into the first mapping database to obtain the contact wear probability; the insulation resistance attenuation rate is input into the second mapping database to obtain the insulation aging probability. A multi-level fault warning mechanism is triggered based on the contact wear probability and / or the insulation aging probability, and the fault location is output based on the position of the contact resistance detector and / or insulation resistance sensor group when the fault warning level is greater than or equal to the preset fault level.

2. The intelligent power distribution cabinet fault prediction and alarm method according to claim 1, characterized in that, When the circuit breaker is triggered, the contact resistance fluctuation coefficient is calculated based on the contact resistance, including: When it is determined that the circuit breaker contacts are triggered, all the contact resistances collected by the contact resistance detector within a set time window before the current moment and during the circuit breaker triggering process are obtained; Calculate the mean and standard deviation of all contact resistances acquired within the set time window; The contact resistance fluctuation coefficient is calculated based on the mean and standard deviation of the contact resistance, using the following formula: Where, ΔR w μ(R) represents the contact resistance fluctuation coefficient. w ) represents the average contact resistance within a set time window, σ(R) w ) represents the standard deviation of the contact resistance within the set time window.

3. The intelligent power distribution cabinet fault prediction and alarm method according to claim 2, characterized in that, The step of calculating the insulation resistance attenuation rate based on the insulation resistance according to a set period includes: When the end time of the set period is determined, the insulation resistance at the start time and the insulation resistance at the end time of the set period are obtained; The insulation resistance attenuation rate is calculated based on the insulation resistance at the start and end of the set period, using the following formula: Where, ΔR i R represents the insulation resistance attenuation rate. i (t0) represents the insulation resistance at the previous moment, R i (t1) represents the insulation resistance at the current moment.

4. The intelligent power distribution cabinet fault prediction and alarm method according to claim 3, characterized in that, The moving and stationary contacts of the circuit breaker in the distribution cabinet are respectively equipped with contact temperature sensors, and the method further includes: When it is determined that the circuit breaker contacts are triggered, the contact temperatures are obtained from the contact temperature sensor within a set time window before the current moment, and during each trigger of the circuit breaker. Calculate the temperature trend index based on all the contact temperatures obtained within the set time window; Calculate the time series entropy reflecting the complexity of the signal based on all the contact resistances obtained within the set time window; The contact wear probability is corrected based on the temperature trend index and the time series entropy.

5. The intelligent power distribution cabinet fault prediction and alarm method according to claim 4, characterized in that, The distribution cabinet is also equipped with a humidity sensor, a particle concentration sensor, and an ambient temperature sensor. A Rogowski coil partial discharge sensor is also installed in the insulation layer of the wiring inside the distribution cabinet. A power frequency phase sensor is installed on the incoming line side of the distribution cabinet. The method further includes: When the end time of the set period is determined, the ambient humidity at the current time collected by the humidity sensor, the smoke particle concentration at the current time collected by the particle concentration sensor, and the ambient temperature at the current time collected by the ambient temperature sensor are obtained. The environmental humidity, dust particle concentration, and environmental temperature are normalized and weighted to obtain the environmental coupling coefficient. The system acquires all pulse amplitudes, pulse frequencies, signal main frequencies, and high-frequency energy collected in real time by the Rogowski coil partial discharge sensor within the set period, as well as all phase distributions collected in real time by the power frequency phase sensor within the set period. A five-dimensional partial discharge feature vector is obtained based on all the pulse amplitudes, pulse frequencies, signal main frequencies, high-frequency energy, and phase distributions acquired within the set period. The insulation aging probability is corrected based on the environmental coupling coefficient and the five-dimensional partial discharge feature vector.

6. The intelligent power distribution cabinet fault prediction and alarm method according to claim 5, characterized in that, When determining that the circuit breaker contacts are triggered, acquiring all contact resistances collected by the contact resistance detector within a set time window prior to the current moment and during the circuit breaker triggering process, further includes: Within the set time window, a sliding data window covering the triggering process is formed with a preset duration as the sliding window length; Obtain all contact resistances within the sliding data window; Sort all contact resistances within the sliding data window by their numerical values; The median value of the sorted contact resistance is selected as the filtering result of the center point of the sliding data window; The sliding data window is slid in a preset step size until the filtered results of all contact resistances within the set time window are obtained.

7. The intelligent power distribution cabinet fault prediction and alarm method according to claim 6, characterized in that, The method for outputting the fault location based on the position of the contact resistance detector and / or insulation resistance sensor group when the fault warning level is greater than or equal to the set level includes: The insulation resistance sensor group deployed at each weak point of the insulation layer of the line in the power distribution cabinet is divided into a region group. The resistance drop of each region group is calculated, and the target region group whose resistance drop exceeds the preset drop threshold is locked. Obtain the three-dimensional coordinates of all insulation resistance sensors within the target area group, and calculate the specific location of insulation aging; Extract the contact resistance fluctuation coefficient, contact temperature difference, and number of contact state switching times at the current moment; When it is determined that the contact resistance fluctuation coefficient exceeds a preset abnormal threshold, the position of the contact resistance detector is located, the contact temperature difference is compared with a preset temperature threshold, and the number of state switching times is compared with a preset number threshold. When the temperature difference of the contact point exceeds the preset temperature threshold and the number of state switching exceeds the preset number threshold, the specific location of the contact point wear is determined.

8. The intelligent power distribution cabinet fault prediction and alarm method according to claim 7, characterized in that, When determining that the end time of the set period has been reached, after acquiring the ambient humidity collected by the humidity sensor, the dust particle concentration collected by the particle concentration sensor, and the ambient temperature collected by the ambient temperature sensor, the method further includes: When the ambient temperature is determined to be greater than the alarm temperature threshold, the concentration of all smoke and dust particles collected within the set time period prior to the current moment is obtained; Concentration variation curves are constructed based on the concentrations of all the soot particles, and multiple concentration variation segments are divided on the concentration variation curves; Calculate the mean curvature value for each concentration change segment, and generate a fault alarm signal when any mean curvature value is greater than the preset curvature threshold.

9. The intelligent power distribution cabinet fault prediction and alarm method according to claim 8, characterized in that, After acquiring the contact resistance from the real-time contact resistance detector and the insulation resistance from the distributed insulation resistance sensor, the system further includes: Determine whether any abnormal values ​​are found in the contact resistance and / or insulation resistance collected at least three consecutive times; When an abnormal value is found in the insulation resistance, the abnormal point data is supplemented according to the distance-weighted difference method based on the measured values ​​of two adjacent insulation resistance sensors. When the contact resistance shows an abnormal value, switch to another contact resistance detector in the same group of contacts to collect the contact resistance and mark the location of the abnormal contact resistance detector.

10. A smart power distribution cabinet fault prediction and alarm system, characterized in that, The system is used to execute the intelligent distribution cabinet fault prediction and alarm method as described in any one of claims 1-9, the system comprising: Acquisition module (10), the acquisition module (10) is configured to acquire in real time the contact resistance acquired by the contact resistance detector and the insulation resistance acquired by the distributed insulation resistance sensor group; The calculation module (20) is configured to determine when the circuit breaker is triggered, calculate the contact resistance fluctuation coefficient based on the contact resistance, and calculate the insulation resistance attenuation rate based on the insulation resistance according to a set period. The mapping module (30) is configured to input the contact resistance fluctuation coefficient into a first mapping database to obtain the contact wear probability; and input the insulation resistance attenuation rate into a second mapping database to obtain the insulation aging probability. The early warning module (40) is configured to trigger a multi-level fault early warning mechanism based on the contact wear probability and / or the insulation aging probability, and output the fault location based on the position of the contact resistance detector and / or the insulation resistance sensor group when the fault early warning level is greater than or equal to the preset fault level.

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