An intelligent monitoring method and system for the running state of a power distribution cabinet

Abstract

The application relates to the technical field of power distribution cabinet monitoring, and discloses a power distribution cabinet operation state intelligent monitoring method and an intelligent monitoring system. The power distribution cabinet operation state intelligent monitoring method comprises the following steps: step S101, three-point median filtering processing; step S102, dew point temperature calculation; step S103, dew potential calculation; step S104, update of an asymmetric hysteresis moisture storage state; step S105, nonlinear risk power calculation; step S106, online risk index calculation; step S107, self-normalization intensity calculation; and step S108, output of the power distribution cabinet operation state. The application focuses on the influence of cold spot condensation, embodies the hysteresis characteristics of the wet state of an insulating surface, avoids the problem that a fixed threshold value is difficult to adapt to different cabinet working conditions, realizes continuous monitoring of an implicit insulation deterioration risk, and the output operation state can provide corresponding basis for power distribution cabinet operation and maintenance.

Description

Technical Field

[0001] This invention relates to the field of power distribution cabinet monitoring technology, and more specifically, to a method and system for intelligent monitoring of the operating status of power distribution cabinets. Background Technology

[0002] Distribution cabinets are critical equipment in power systems, and their insulation performance directly determines the safe and stable operation of the power system. During operation, the internal environment of a distribution cabinet is affected by factors such as fluctuations in external temperature and humidity, differences in ventilation conditions, and the characteristics of the cabinet structure, which can easily lead to the formation of cold spots in localized low-temperature areas. These cold spots are the first areas where moisture condenses, and condensation increases the equivalent conductivity of the insulation surface, thus causing latent insulation degradation. This degradation may not immediately trigger the protection device, but it will erode the insulation performance over a long period of time, eventually potentially leading to short-circuit faults.

[0003] Current monitoring methods typically collect average air temperature and humidity inside the cabinet, issuing warnings based on whether the relative humidity exceeds the standard or the air temperature is below the dew point. This approach has significant drawbacks. Firstly, it neglects the cold-point surface temperature; the relationship between cold-point temperature and dew point is the core basis for assessing condensation risk, and relying solely on average temperature and humidity leads to serious biases in risk assessment. Secondly, current methods often use fixed thresholds for binary judgment, completely ignoring the hysteresis characteristic of insulation surfaces—rapid moisture absorption but slow desiccation. Even when the environment dries, residual moisture on the surface continues to affect insulation performance, and latent risks cannot be effectively captured. Furthermore, different distribution cabinets have varying installation locations and ventilation structures, making it difficult for fixed thresholds to adapt to all operating conditions, easily resulting in some cabinets frequently triggering false alarms while others fail to trigger alarms. These problems prevent current monitoring methods from timely identifying latent insulation degradation risks, hindering accurate data collection for operation and maintenance, and seriously threatening the safe and stable operation of the power system. Summary of the Invention

[0004] This invention provides an intelligent monitoring method and system for the operating status of power distribution cabinets, solving the technical problems mentioned in the background.

[0005] This invention provides an intelligent monitoring method for the operating status of a power distribution cabinet, comprising the following steps:

[0006] Step S101: Collect the air temperature inside the distribution cabinet, the relative humidity inside the cabinet, and the surface temperature of the cold spot. Perform three-point median filtering on the three to obtain a smoothed time series.

[0007] Step S102: Calculate the dew point temperature based on the smoothed air temperature and relative humidity inside the cabinet.

[0008] Step S103: Calculate the dew point excess relative to the cold point surface temperature. Use the exponential moving mean and exponential moving variance of the dew point excess to perform adaptive normalization mapping on the dew point excess to obtain the condensation potential.

[0009] Step S104: Use the condensation potential as input to update the asymmetric hysteresis moisture storage state. During the update process, set the moisture absorption time constant to be smaller than the desiccation time constant so that the rate of increase of the asymmetric hysteresis moisture storage state when the condensation potential increases is higher than the rate of decrease when the condensation potential decreases.

[0010] Step S105: Perform a power operation with an exponent greater than one on the asymmetric hysteresis moisture storage state to obtain the nonlinear risk power;

[0011] Step S106: Perform an exponential sliding integral on the nonlinear risk power to obtain the online risk index;

[0012] Step S107: Calculate the relative deviation of the online risk index based on the long-term mean and long-term variance of the online risk index to obtain the self-normalized strength.

[0013] Step S108: The self-normalized intensity is compared with the preset grading threshold, and the operating status including normal, moisture accumulation increase, and high risk of implicit insulation degradation is output.

[0014] This invention provides an intelligent monitoring system for the operating status of a power distribution cabinet, comprising:

[0015] The median filtering module collects the air temperature, relative humidity, and cold spot surface temperature inside the distribution cabinet, and performs three-point median filtering on each of the three to obtain a smoothed time series.

[0016] The dew point temperature calculation module calculates the dew point temperature based on the smoothed air temperature and relative humidity inside the cabinet.

[0017] The condensation potential calculation module calculates the dew point excess relative to the cold point surface temperature. It then uses the exponential moving mean and exponential moving variance of the dew point excess to perform an adaptive normalization mapping on the dew point excess, thereby obtaining the condensation potential.

[0018] The asymmetric hysteresis moisture storage state update module takes the condensation potential as input to update the asymmetric hysteresis moisture storage state. During the update process, the moisture absorption time constant is set to be smaller than the desiccation time constant, so that the rate of increase of the asymmetric hysteresis moisture storage state when the condensation potential increases is higher than the rate of decay when the condensation potential decreases.

[0019] The nonlinear risk power calculation module performs a power operation with an exponent greater than one on the asymmetric hysteresis moisture storage state to obtain the nonlinear risk power.

[0020] The online risk index calculation module performs an exponential moving integral on the nonlinear risk power to obtain the online risk index.

[0021] The self-normalized intensity calculation module calculates the relative deviation of the online risk index based on the long-term mean and long-term variance of the online risk index, and obtains the self-normalized intensity.

[0022] The operating status output module compares the self-normalized intensity with the preset grading threshold and outputs operating statuses including normal, moisture accumulation increase, and high risk of implicit insulation degradation.

[0023] The beneficial effects of this invention are as follows: Addressing the insulation monitoring needs of distribution cabinets, this invention collects air temperature, relative humidity, and cold spot surface temperature inside the cabinet and performs three-point median filtering. By calculating dew point temperature and condensation potential, and considering the characteristic that the moisture absorption time constant is less than the dehumidification time constant, the asymmetric hysteresis moisture storage state is updated. Then, a nonlinear risk power is obtained through exponential calculation, and an online risk index is formed through exponential moving integral. Finally, the self-normalized intensity is calculated based on the long-term mean and variance of the risk index, and the operating status is classified. This invention focuses on the core impact of cold spot condensation, reflects the hysteresis characteristics of the wet state of the insulation surface, avoids the problem of fixed thresholds being difficult to adapt to different cabinet operating conditions, achieves continuous monitoring of implicit insulation degradation risks, and the output operating status can provide corresponding basis for distribution cabinet operation and maintenance, improving the consistency of monitoring results under different cabinets and operating conditions. Attached Figure Description

[0024] Figure 1 This is a flowchart of an intelligent monitoring method for the operating status of a power distribution cabinet according to the present invention;

[0025] Figure 2 This is a schematic diagram of an intelligent monitoring system for the operating status of a power distribution cabinet according to the present invention.

[0026] In the figure: Median filtering module 201, dew point temperature calculation module 202, condensation potential calculation module 203, asymmetric hysteresis moisture storage state update module 204, nonlinear risk power calculation module 205, online risk index calculation module 206, self-normalized intensity calculation module 207, and operation status output module 208. Detailed Implementation

[0027] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0028] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of the present invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in one or more embodiments of the present invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" indicate that the element or object preceding the term encompasses the elements or objects listed following the term and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0029] like Figure 1 As shown, a method for intelligent monitoring of the operating status of a power distribution cabinet includes the following steps:

[0030] Step S101: Collect the air temperature inside the distribution cabinet, the relative humidity inside the cabinet, and the surface temperature of the cold spot. Perform three-point median filtering on the three to obtain a smoothed time series.

[0031] Step S102: Calculate the dew point temperature based on the smoothed air temperature and relative humidity inside the cabinet.

[0032] Step S103: Calculate the dew point excess relative to the cold point surface temperature. Use the exponential moving mean and exponential moving variance of the dew point excess to perform adaptive normalization mapping on the dew point excess to obtain the condensation potential.

[0033] Step S104: Use the condensation potential as input to update the asymmetric hysteresis moisture storage state. During the update process, set the moisture absorption time constant to be smaller than the desiccation time constant so that the rate of increase of the asymmetric hysteresis moisture storage state when the condensation potential increases is higher than the rate of decrease when the condensation potential decreases.

[0034] Step S105: Perform a power operation with an exponent greater than one on the asymmetric hysteresis moisture storage state to obtain the nonlinear risk power;

[0035] Step S106: Perform an exponential sliding integral on the nonlinear risk power to obtain the online risk index;

[0036] Step S107: Calculate the relative deviation of the online risk index based on the long-term mean and long-term variance of the online risk index to obtain the self-normalized strength.

[0037] Step S108: The self-normalized intensity is compared with the preset grading threshold, and the operating status including normal, moisture accumulation increase, and high risk of implicit insulation degradation is output.

[0038] In one embodiment of the present invention, the air temperature inside the distribution cabinet, the relative humidity inside the cabinet, and the surface temperature of the cold spot are collected, and three-point median filtering is performed on each of the three to obtain a smoothed time series, including:

[0039] At a fixed sampling period Below, the air temperature inside the cabinet was collected. Relative humidity inside the cabinet and cold spot surface temperature ,in Indicates the discrete sampling time. This represents the time interval between adjacent discrete sampling moments;

[0040] Calculate the smoothed air temperature inside the cabinet using the following formula. :

[0041]

[0042] in , , These are the original values ​​of the air temperature inside the cabinet at the current discrete sampling time, one sampling period before the current discrete sampling time, and two sampling periods before the current discrete sampling time, respectively. This is an operator that takes the median value after sorting the three values ​​within the parentheses by size.

[0043] Calculate the smoothed relative humidity inside the cabinet using the following formula. :

[0044]

[0045] in This represents the smoothed relative humidity inside the cabinet at the current discrete sampling time. , , These are the original values ​​of the relative humidity inside the cabinet at the current discrete sampling time, one sampling period before the current discrete sampling time, and two sampling periods before the current discrete sampling time, respectively.

[0046] Calculate the smoothed cold spot surface temperature using the following formula. :

[0047]

[0048] in This represents the smoothed surface temperature of the cold spot at the current discrete sampling time. , , These are the original values ​​of the cold spot surface temperature at the current discrete sampling time, one sampling period before the current discrete sampling time, and two sampling periods before the current discrete sampling time, respectively.

[0049] It should be noted that the fixed sampling period is the standard time interval for performing three-point median filtering, preferably between 5 and 30 seconds. Too short a period will increase computational load, while too long a period will lose details of condensation trends. The cabinet interior air temperature (raw value) is the temperature of the air inside the distribution cabinet, reflecting its thermal state. It can be collected using a contact temperature sensor (such as a thermistor) or a non-contact temperature sensor (such as an infrared thermometer). The cabinet interior relative humidity (raw value) is the percentage of water vapor in the air inside the distribution cabinet, reflecting its humid state. It can be collected using a capacitive humidity sensor or a resistive humidity sensor. The cold spot surface temperature (raw value) is the surface temperature of the low-temperature, condensation-prone area inside the distribution cabinet, reflecting the thermal state of this area. It can be collected using a surface-mounted temperature sensor, which must be attached to the cold spot, such as the side panel near the cable trench. The current discrete sampling time marks the time node of the current data acquisition. The smoothed cabinet interior air temperature (current discrete sampling time value) is the cabinet interior air temperature value after three-point median filtering, reflecting the stable thermal state of the air inside the cabinet after filtering. The smoothed relative humidity inside the cabinet (value at the current discrete sampling time) is the relative humidity value inside the cabinet after three-point median filtering, reflecting the stable humidity state of the air inside the cabinet after filtering. The smoothed cold spot surface temperature (value at the current discrete sampling time) is the cold spot surface temperature value after three-point median filtering, reflecting the stable thermal state of the easily condensing area after filtering.

[0050] It should be noted that the cabinet air temperature and relative humidity are used to calculate the dew point, while the cold spot surface temperature is used to determine condensation conditions. Filtering them separately avoids numerical interference from different physical quantities. The core operation of three-point median filtering is to select the original values ​​of the current and the two previous sampling periods, sort them by value, and take the median value as the smoothed value. This step is crucial for resisting single-point glitches, which are abnormal values ​​caused by instantaneous sensor jumps or communication jitter. The median value after sorting can eliminate the influence of abnormal values. For example, if the original value of the cabinet air temperature jumps by 50 degrees Celsius at a certain moment, and the values ​​at the two previous moments are 25 degrees Celsius and 26 degrees Celsius, then 26 degrees Celsius is taken as the smoothed value after sorting to avoid the jump interfering with subsequent calculations. In addition, the original values ​​of three consecutive sampling periods constitute the filtering window. The window slides with the sampling time to achieve point-by-point smoothing of the time-series data. Compared with single-point or two-point processing, the smoothing effect of three-point median filtering is more stable and does not introduce excessive offset due to data lag.

[0051] It should be noted that the specific range of the fixed sampling period can be adjusted according to the usage scenario of the distribution cabinet. For outdoor distribution cabinets with fluctuating environments, the sampling period can be shortened to 5 to 15 seconds; for indoor distribution cabinets with stable environments, the sampling period can be extended to 15 to 30 seconds. The time interval between discrete sampling moments is equal to the fixed sampling period, and the time difference between two adjacent sampling moments remains constant. For example, if the fixed sampling period is 10 seconds, the sampling interval is 10 seconds, ensuring the equal time interval characteristic of the data. If the original sampled value at a certain moment is missing or abnormal, the smoothed value from the previous moment is used to replace the original value at that moment in the filtering calculation. Furthermore, if the three original values ​​are equal when sorting, no additional priority determination is needed; any one of the equal values ​​is directly taken as the median value. This determination rule is simple and efficient and does not affect the filtering effect.

[0052] It should be noted that this invention collects time-series data of three key physical quantities within the distribution cabinet separately, and employs a three-point median filtering method to smooth the data, eliminating interference from single-point outliers in subsequent calculations. Separate filtering ensures the independence of each data type, avoiding numerical interference between different physical quantities and improving the accuracy of subsequent dew point calculations and condensation potential assessments. Three-point median filtering effectively suppresses glitches caused by instantaneous sensor fluctuations or communication jitter, ensuring data stability. Equal time interval sampling maintains the temporal regularity of the data, facilitating the construction of subsequent dynamic models. Thus, through a standardized data preprocessing workflow, high-quality foundational data is provided for the entire intelligent data inspection method, contributing to the accurate monitoring of hidden insulation degradation risks.

[0053] In one embodiment of the present invention, calculating the dew point temperature based on the smoothed air temperature and relative humidity inside the cabinet includes:

[0054] Calculate the intermediate variables according to the following formula :

[0055]

[0056] in This represents the smoothed relative humidity inside the cabinet at the current discrete moment, expressed as a percentage. This represents the smoothed air temperature inside the cabinet at the current discrete moment, in degrees Celsius. The first empirical coefficient is set to 17.62. This is the second empirical coefficient, with a value of 243.12, in degrees Celsius. The operator for natural logarithms;

[0057] Calculate the dew point temperature using the following formula. :

[0058]

[0059] in Dew point temperature, in degrees Celsius; It is an intermediate variable.

[0060] It should be noted that the smoothed relative humidity inside the cabinet is the time series data of relative humidity inside the cabinet after three-point median filtering, reflecting the smoothed state of the air moisture content inside the distribution cabinet. The smoothed air temperature inside the cabinet is the time series data of air temperature inside the cabinet after three-point median filtering, reflecting the smoothed state of the air temperature inside the distribution cabinet. The first empirical coefficient is an empirical constant used to calculate the dew point temperature, reflecting the fitting characteristics of the relationship between air humidity and temperature. The preferred value is 17.62, which is a classic fitting parameter for calculating dew point temperature in the meteorological field and is suitable for temperature and humidity correlation models under normal atmospheric conditions. The second empirical coefficient is an empirical constant used to calculate the dew point temperature, reflecting the fitting characteristics of the influence of air temperature on dew point temperature. The preferred value is 243.12, which is a classic fitting parameter for calculating dew point temperature in the meteorological field. Combined with the first empirical coefficient, it can achieve accurate calculation of dew point temperature under normal conditions. The first value is the result of dividing the smoothed relative humidity inside the cabinet by one hundred and taking the natural logarithm, reflecting the logarithmic characteristics of the relative humidity inside the cabinet after normalization. The second value is the product of the smoothed air temperature inside the cabinet and the first empirical coefficient, reflecting the linear correlation between air temperature and the empirical coefficient. The third value is the sum of the smoothed air temperature inside the cabinet and the second empirical coefficient, reflecting the superposition characteristic of air temperature and the empirical coefficient. The intermediate variable is the sum of the quotients of the first and second values ​​divided by the third value, reflecting the intermediate correlation characteristic after the temperature and humidity data have been fitted. The fourth value is the product of the intermediate variable and the second empirical coefficient, reflecting the linear amplification characteristic of the intermediate variable and the second empirical coefficient. The fifth value is the difference between the first empirical coefficient and the intermediate variable, reflecting the difference characteristic between the first empirical coefficient and the intermediate variable. The dew point temperature is the quotient of the fourth value divided by the fifth value, reflecting the critical temperature at which water vapor in the air begins to condense under the current temperature and humidity conditions.

[0061] It should be noted that when the distribution cabinet is in a high-altitude environment, the air density is reduced, and the first empirical coefficient can be adjusted to 17.8 and the second empirical coefficient to 245.5. This adjustment is based on the changing characteristics of air moisture saturation in high-altitude environments. When the distribution cabinet is in a high-dust environment, dust particles will adsorb moisture, and the first empirical coefficient can be adjusted to 17.5 and the second empirical coefficient to 242.8. This adjustment is based on the attenuation characteristics of the actual water vapor content in the air under dusty conditions. Furthermore, under normal atmospheric conditions, the intermediate variable ranges from -5 to 5. If the intermediate variable exceeds this range, it is necessary to check whether the smoothed temperature and humidity data are abnormal, such as whether the relative humidity exceeds 100% or falls below 0%, or whether the air temperature exceeds the normal operating temperature range of the distribution cabinet. This invention calculates the dew point temperature based on the smoothed cabinet temperature and humidity data using a mathematical model fitted with empirical coefficients. This temperature is the core indicator for judging air moisture condensation. Moreover, the calculation steps are adapted to the enclosed environment inside the distribution cabinet, avoiding the equipment cost of directly measuring the dew point temperature. Meanwhile, the fixed value of the empirical coefficient ensures the consistency and repeatability of the calculation. The steps are clearly broken down, which facilitates the deployment and execution of edge computing devices without increasing the computational load. It is suitable for the real-time requirements of intelligent data inspection and can also be compatible with temperature and humidity monitoring scenarios of different types of power distribution cabinets.

[0062] In one embodiment of the present invention, the dew point excess relative to the cold point surface temperature is calculated, and the dew point excess is adaptively normalized using the exponential moving mean and exponential moving variance of the dew point excess to obtain the condensation potential, including:

[0063] Calculate the dew point excess using the following formula. :

[0064]

[0065] in Dew point temperature, The surface temperature of the cold spot after smoothing. The unit is Celsius;

[0066] The update coefficient is calculated according to the following formula. : ;

[0067] in The sampling period is It is a time constant. This is the natural exponentiation operator;

[0068] Calculate the exponential moving average using the following formula. With exponential moving variance :

[0069]

[0070]

[0071] in The unit is Celsius. The unit is degrees Celsius squared;

[0072] Calculate the exponential moving standard deviation using the following formula. :

[0073]

[0074] in It is a very small positive real number;

[0075] Calculate the condensation potential using the following formula :

[0076]

[0077] in It is a dimensionless quantity.

[0078] It should be noted that dew point temperature is a physical quantity calculated based on the smoothed air temperature and relative humidity inside the cabinet, reflecting the critical temperature at which air moisture begins to condense. Smoothed cold point surface temperature is a physical quantity obtained by applying a three-point median filter to the original cold point surface temperature value, reflecting the true temperature state at the cold point. Dew point excess is the difference between the dew point temperature and the smoothed cold point surface temperature, reflecting the degree of deviation of the cold point temperature from the dew point temperature. Sampling period is the time interval between two consecutive data acquisitions, reflecting the frequency of data acquisition, with a preferred value of 300 seconds. Time constant is the update rate control parameter for the exponential moving average statistic, reflecting the statistic's response speed to new data, with a preferred value of 3600 seconds. Update coefficient is a weighted coefficient calculated based on the sampling period and time constant, reflecting the weight of the current data in the exponential moving average statistic. Exponential moving mean is a statistic obtained by weighted averaging of dew point excess, reflecting the long-term average level of dew point excess. Exponential moving variance is a statistic obtained by weighted averaging of the squared differences between dew point excess and the exponential moving mean, reflecting the dispersion of dew point excess. The exponential moving standard deviation is the sum of the square root of the exponential moving variance and the minimum positive real number, reflecting the fluctuation range of dew point exceedance. The minimum positive real number is a parameter introduced to avoid the exponential moving standard deviation being zero, and is preferably taken as 10 to the power of -6. The condensation potential is a continuous quantity from 0 to 1 obtained after adaptively normalizing the dew point exceedance, reflecting the potential intensity of condensation at the cold point.

[0079] It should be noted that the initial values ​​of the exponential moving mean and exponential moving variance can be set to the dew point exceedance at the first sampling time and zero. For example, if the dew point exceedance at the first sampling time is 0.5 degrees Celsius, the initial value of the exponential moving mean is set to 0.5 degrees Celsius, and the initial value of the exponential moving variance is set to 0. Subsequent times are updated according to the recursive formula. The time constant can be set from 1800 seconds to 7200 seconds. The value needs to be adjusted according to the operating environment of the distribution cabinet. A larger value, such as 7200 seconds, can be used in a stable indoor environment, while a smaller value, such as 1800 seconds, can be used in a fluctuating outdoor environment. This invention calculates the dew point excess to reflect the temperature difference between the cold point and the dew point, and then uses the exponential moving average and variance to characterize the statistical features of the dew point excess, achieving adaptive normalization mapping to obtain the condensation potential. This avoids the limitations of using fixed thresholds to adapt to different distribution cabinet operating conditions, allowing the condensation potential to be dynamically adjusted based on the cabinet's own historical data, accurately reflecting the potential intensity of condensation at the cold point. At the same time, it transforms the condensation condition into a continuous quantity from 0 to 1, providing a stable and physically meaningful input for subsequent updates to the asymmetric hysteresis moisture storage state, improving the algorithm's versatility and consistency in different scenarios, and ensuring that the judgment of the potential intensity of condensation is more in line with actual operating conditions.

[0080] In one embodiment of the present invention, the condensation potential is used as input to update the asymmetric hysteresis moisture storage state. During the update process, the moisture absorption time constant is set to be smaller than the desiccation time constant, so that the rate of increase of the asymmetric hysteresis moisture storage state when the condensation potential increases is higher than the rate of decrease when the condensation potential decreases. This includes:

[0081] Set the moisture absorption time constant With dehumidification time constant Satisfy the following formula:

[0082]

[0083] The moisture absorption discrete renewal coefficient is calculated according to the following formula. With dehumidification discrete update coefficient :

[0084]

[0085]

[0086] in The sampling period is This is the natural exponentiation operator;

[0087] Update the asymmetric hysteresis moisture storage state according to the following formula. :

[0088]

[0089] in It is a dimensionless quantity. This represents the asymmetric hysteresis-induced moisture storage state at the previous discrete time step. The condensation potential is the current discrete moment.

[0090] It should be noted that the moisture absorption time constant is a time parameter characterizing the rate of surface wetting, reflecting the typical time it takes for a surface to go from dry to wet. A preferred value is 30 to 300 seconds, determined based on the airflow rate within the distribution cabinet and the moisture absorption characteristics of the surface material; smaller values ​​are used for metal surfaces due to faster moisture absorption, and larger values ​​are used for insulating materials due to slower moisture absorption. The desiccation time constant is a time parameter characterizing the rate of surface drying, reflecting the typical time it takes for a surface to go from wet to dry. A preferred value is 600 to 1800 seconds, determined based on the ventilation conditions within the distribution cabinet and the evaporation rate of residual moisture on the surface; larger values ​​are used for poor ventilation, and smaller values ​​are used for good ventilation. The moisture absorption discrete update coefficient is a coefficient calculated based on the sampling period and the moisture absorption time constant, reflecting the update rate of the moisture storage state when the condensation potential increases. The desiccation discrete update coefficient is a coefficient calculated based on the sampling period and the desiccation time constant, reflecting the update rate of the moisture storage state when the condensation potential decreases. The condensation potential at the current discrete moment is an index characterizing the potential intensity of cold point condensation, calculated in the previous step, reflecting the magnitude of the driving force for condensation at the current moment. The asymmetric hysteresis moisture storage state at the previous discrete moment is an index characterizing the equivalent surface moisture content calculated at the previous sampling moment, reflecting the influence of historical moisture on the current state. The asymmetric hysteresis moisture storage state at the current discrete moment is an index characterizing the equivalent surface moisture content updated at the current sampling moment, reflecting the actual surface moisture level at the current moment.

[0091] It should be noted that the core logic of setting the moisture absorption time constant to be less than the dehumidification time constant is based on the actual physical characteristics of the cold spot surface inside the distribution cabinet: In actual scenarios, when the cold spot comes into contact with high humidity air, moisture will be quickly absorbed onto the surface, and the moisture absorption process takes a short time; however, when the air humidity decreases, the residual moisture on the surface needs to be removed through evaporation, and due to the relatively enclosed space inside the distribution cabinet, the dehumidification process takes a longer time; for example, the surface of the cold spot made of metal may become wet within 30 seconds when the humidity rises from 60% to 90%, while it may take more than 10 minutes to dry when the humidity drops from 90% to 60%. The update coefficient is selected based on the relationship between the condensation potential and the moisture storage state at the previous moment in order to distinguish between the moisture absorption and desiccation stages of the surface: when the condensation potential is greater than or equal to the moisture storage state at the previous moment, it indicates that the ambient humidity is rising and the surface is in the process of moisture absorption. At this time, the moisture absorption discrete update coefficient is used to allow the moisture storage state to quickly keep up with the environmental changes; when the condensation potential is less than the moisture storage state at the previous moment, it indicates that the ambient humidity is decreasing and the surface is in the process of desiccation. At this time, the desiccation discrete update coefficient is used to allow the moisture storage state to decrease slowly, thereby simulating the hysteresis characteristics of the wet state.

[0092] It should be noted that the initial value of the moisture storage state is set to 0. This value represents that the cold point surface is initially in a completely dry state, which meets the normal environmental conditions before equipment startup. The moisture storage state boundary correction measure is as follows: when the calculated moisture storage state value is less than 0, it is directly corrected to 0; when the value is greater than 1, it is directly corrected to 1. This correction measure is to ensure the physical meaning of the moisture storage state, with 0 corresponding to a completely dry surface and 1 corresponding to a completely wet surface, avoiding invalid values ​​that exceed the physical range. The environmental adaptation adjustment of the time constant is based on the following: when the ambient temperature of the distribution cabinet is higher than 25 degrees Celsius, the moisture evaporation rate is faster, and the dehumidification time constant can be adjusted to 600 to 1200 seconds; when the relative humidity of the environment is consistently higher than 80%, the driving force of the moisture absorption process is stronger, and the moisture absorption time constant can be adjusted to 30 to 150 seconds.

[0093] It should be noted that this invention is based on the physical reality that the surface of cold spots in distribution cabinets absorbs moisture quickly but dehumidifies slowly. By setting different time constants for moisture absorption and dehumidification, an asymmetric hysteresis moisture storage state update mechanism is constructed. The corresponding update coefficient is selected based on the relationship between condensation potential and historical moisture storage state, achieving accurate simulation of the surface moisture change process. This accurately reflects the hysteresis characteristics of surface moisture, avoiding misjudgments caused by instantaneous environmental changes, while retaining the influence of historical moisture on the current state. This provides more realistic moisture indicators for subsequent risk assessment, improving the accuracy of the entire inspection method in identifying hidden insulation degradation risks.

[0094] In one embodiment of the present invention, performing a power operation with an exponent greater than one on the asymmetric hysteresis moisture storage state to obtain the nonlinear risk power includes:

[0095] Set the power index Satisfy the following formula: ;

[0096] Calculate the nonlinear risk power according to the following formula : ;

[0097] in For nonlinear risk power, which is a dimensionless quantity; It is an asymmetric hysteresis moisture storage state and is a dimensionless quantity; It is a power exponent.

[0098] It should be noted that the power exponent is used to perform a power operation on the asymmetric hysteresis moisture storage state, reflecting the degree of nonlinear risk amplification. A value of 2 or 3 is preferred, as this range matches the nonlinear risk evolution characteristics of insulation degradation. Risk growth is gradual with slight moisture storage, but accelerates as the water film becomes continuous. The asymmetric hysteresis moisture storage state is the equivalent surface water content state obtained based on the updated condensation potential, reflecting the wet memory and hysteresis characteristics of the switchgear insulation surface. The nonlinear risk power is a quantitative risk indicator obtained after performing a power operation on the asymmetric hysteresis moisture storage state, reflecting the degree of nonlinear growth of insulation degradation risk. The effect of surface moisture on insulation performance is not linear. When slightly damp, the decrease in insulation performance is small, and the corresponding risk increases slowly. However, when the moisture level rises to near the point of forming a continuous water film, the conductive paths on the insulation surface increase dramatically, and the risk tends to rise at an accelerated pace. This nonlinear relationship can be quantified by using power operations with an exponent greater than 1. For example, when the moisture level is 0.3 and the exponent is 2, the nonlinear risk power is 0.09, and the risk growth is gradual. When the moisture level is 0.8 and the exponent is 2, the nonlinear risk power is 0.64, and the risk growth is significantly accelerated. This approach aligns with the actual risk evolution of insulation degradation, ensuring that the risk assessment is more consistent with engineering practice.

[0099] It should be noted that this invention is based on the nonlinear influence of wet conditions on the insulation performance of distribution cabinets. Through power-law operations with an exponent greater than 1, the asymmetric hysteresis-induced moisture accumulation state, reflecting surface wetness, is mapped to a nonlinear risk power. This ensures a gradual increase in risk power during the mild moisture accumulation phase, preventing the system from becoming overly sensitive and triggering false alarms. When the moisture accumulation state approaches a critical value, the risk power increases rapidly, highlighting the early warning value of high-risk phases and providing realistic input for subsequent risk accumulation calculations.

[0100] In one embodiment of the present invention, performing an exponential moving integral on the nonlinear risk power to obtain an online risk index includes:

[0101] Calculate the discrete update coefficients according to the following formula. : ;

[0102] in The sampling period is The time constant for risk accumulation. This is the natural exponentiation operator;

[0103] Calculate the online risk index using the following formula. :

[0104]

[0105] in Let be the online risk index at the current discrete moment, and be a dimensionless quantity. The online risk index for the previous discrete time step; The nonlinear risk power at the current discrete moment; These are the discrete update coefficients.

[0106] It should be noted that the risk accumulation time constant is a characteristic parameter used to calculate the discrete update coefficients. It reflects the duration of the impact of historical nonlinear risk power on the current online risk index, and is preferably taken as 3600 to 86400 seconds. This time range can cover the risk contribution of short-term fluctuations and long-term accumulation, matching the gradual characteristics of the implicit insulation degradation of the distribution cabinet. The discrete update coefficients are calculated based on the sampling period and the risk accumulation time constant, reflecting the weight ratio of the current nonlinear risk power in the online risk index update. The nonlinear risk power at the current discrete moment is the risk quantification value calculated based on the asymmetric hysteresis moisture storage state at the current sampling moment, reflecting the risk intensity caused by the wet state of the insulation surface at the current moment. The online risk index at the current discrete moment is the risk index value updated at the current sampling moment, reflecting the level of insulation degradation risk accumulation up to the current moment.

[0107] It should be noted that this invention employs an exponential moving integral method to accumulate nonlinear risk power on a rolling basis, balancing the weights of historical and current risks through discretely updated coefficients. This approach preserves the impact of accumulated historical risks, avoiding misjudgments due to single risk fluctuations, while also promptly incorporating the contribution of current risks to reflect dynamic changes in risk. Furthermore, it eliminates the need to store large amounts of historical data, requiring only the online risk index from the previous moment, thus reducing the storage pressure on edge computing devices. This makes it suitable for edge computing deployments in power distribution cabinets and ensures the engineering feasibility of the method.

[0108] In one embodiment of the present invention, the relative deviation of the online risk index is calculated based on the long-term mean and long-term variance of the online risk index to obtain the self-normalized strength, including:

[0109] The update coefficient is calculated according to the following formula. : ;

[0110] in The sampling period is The time constant of long-term statistics This is the natural exponentiation operator;

[0111] The long-term mean is calculated recursively using the following formula. With long-term variance :

[0112]

[0113]

[0114] in It is the long-term mean of the current discrete moment, and is a dimensionless quantity; Let V be the long-term variance at the current discrete moment, which is a dimensionless quantity; This represents the online risk index at the current discrete moment. It is the long-term mean of the previous discrete time step; Let V be the long-term variance of the previous discrete time step;

[0115] Calculate the long-term standard deviation using the following formula :

[0116]

[0117] in It is a very small positive real number;

[0118] Calculate the self-normalized strength according to the following formula. :

[0119]

[0120] in It is a dimensionless quantity.

[0121] It should be noted that the time constant of long-term statistics is a time parameter used to update the long-term mean and long-term variance, reflecting the memory length of long-term statistics. A preferred value is 86,400 to 604,800 seconds, as this range reflects seasonal or long-term operating condition trends and covers the typical operating cycle of the distribution cabinet. The long-term mean at the current discrete moment is the average long-term statistical level of the online risk index at the current sampling moment, reflecting the average risk baseline including the current data. The long-term variance at the current discrete moment is the degree of long-term statistical dispersion of the online risk index at the current sampling moment, reflecting the fluctuation range of the risk baseline. The long-term standard deviation is the square root of the long-term variance at the current discrete moment, reflecting the fluctuation amplitude of the risk baseline. The self-normalization strength is the degree of deviation of the current risk from its own long-term baseline, reflecting the relative anomaly level of the current risk. In addition, the update coefficient ranges from 0.0001 to 0.1. If the update coefficient is too small, the long-term statistics will be updated too slowly and will not be able to track changes in risk trends. If the update coefficient is too large, the long-term statistics will be too affected by the current data, resulting in poor baseline stability. This range can balance the stability and tracking of the baseline and ensure the reliability of the self-normalization strength.

[0122] It should be noted that this invention constructs a risk baseline for the distribution cabinet itself by calculating the long-term mean and long-term variance of the online risk index, and then compares the current risk index with this baseline to obtain the relative deviation, i.e., the self-normalized intensity. This aims to solve the problem of incomparable absolute risk values ​​due to the differences between different distribution cabinets, while reducing data storage requirements through recursive calculations and adapting to the resource limitations of edge computing architectures. Therefore, the self-normalized intensity can objectively reflect the degree of anomaly of the current risk relative to the equipment's historical operating state, improving the consistency of risk assessment across cabinets and seasons, and avoiding misjudgments or omissions caused by fixed thresholds.

[0123] In one embodiment of the present invention, the self-normalized intensity is compared with a preset grading threshold, and an operating status including normal, moisture accumulation increase, and high risk of implicit insulation degradation is output, including:

[0124] Set the grading threshold constant and Satisfy the following formula: ;

[0125] Determine the running state variables according to the following formula :

[0126]

[0127] in These are runtime state variables, and they are discrete variables. The self-normalized intensity at the current discrete moment is a dimensionless quantity. This is the first-level threshold constant; This is the second-level threshold constant.

[0128] It should be noted that the first-level threshold constant is the critical value distinguishing between the normal state and the moisture accumulation rising state, reflecting the initial critical level of risk classification. The second-level threshold constant is the critical value distinguishing between the moisture accumulation rising state and the high-risk state of implicit insulation degradation, reflecting the advanced critical level of risk classification. The operating status variable is a discrete identifier characterizing the insulation risk level of the distribution cabinet, reflecting the current risk status of the distribution cabinet. The first-level threshold constant is preferably set to 2, with a value range of 1.5 to 2.5. This range corresponds to a deviation of 1.5 to 2.5 standard deviations in statistics, avoiding both frequent misjudgments due to excessively small values ​​and missed early risks due to excessively large values. The second-level threshold constant is preferably set to 3, with a value range of 2.5 to 3.5. This range corresponds to a deviation of 2.5 to 3.5 standard deviations in statistics, effectively distinguishing high-risk states requiring intervention, balancing the reliability and safety of risk identification. The difference between the first and second level threshold constants is preferably set to 1, with a range of 0.5 to 2. A difference that is too small will result in an overly narrow range for the moisture accumulation rise state, leading to frequent state switching; a difference that is too large will result in an overly wide range for the moisture accumulation rise state, making it difficult to distinguish high-risk states in a timely manner. A range of 0.5 to 2 balances the range width and the accuracy of the judgment. The dynamic adjustment of the level threshold constants is based on the historical operating data of the distribution cabinet and the incidence rate of risk events. If the judgment result of moisture accumulation rise occurs frequently under normal conditions, the first level threshold can be increased by 0.2 to 0.3; if high-risk events are missed, the second level threshold can be decreased by 0.2 to 0.3. The adjustment cycle is once a month, and after adjustment, the state judgment results need to be observed for one week. After confirming the rationality, the new threshold is fixed. In addition, the output method of the operating status variables is that the edge computing device transmits the operating status variables to the remote monitoring platform through industrial Ethernet or wireless communication module. The output format is status code plus timestamp. The status code rule is 0 for normal, 1 for moisture accumulation increase, and 2 for high risk of implicit insulation degradation. The timestamp is accurate to the second, which makes it easy for maintenance personnel to trace the time of change of risk status.

[0129] It should be noted that the self-normalized intensity represents the degree of deviation of the current risk from its own long-term baseline. Since the long-term baselines differ among different distribution cabinets, directly using absolute values ​​to determine risk will lead to biased results. By presetting two tiered thresholds, the continuous degree of deviation is converted into discrete three-state states, achieving a unified determination of risk levels. Furthermore, the three states correspond to different operation and maintenance strategies: the normal state indicates that the risk is within the baseline range and no additional intervention is required; the rising moisture accumulation state indicates that the risk significantly deviates from the baseline, requiring increased monitoring frequency; and the high-risk state of implicit insulation degradation indicates that the risk significantly deviates from the baseline, requiring timely investigation and handling, which will not be elaborated upon here.

[0130] In one embodiment of the present invention, such as Figure 2As shown, an intelligent monitoring system for the operating status of a power distribution cabinet includes:

[0131] The median filtering module 201 collects the air temperature inside the distribution cabinet, the relative humidity inside the cabinet, and the surface temperature of the cold spot, and performs three-point median filtering on the three to obtain a smoothed time series.

[0132] Dew point temperature calculation module 202 calculates the dew point temperature based on the smoothed air temperature and relative humidity inside the cabinet.

[0133] The condensation potential calculation module 203 calculates the dew point excess relative to the cold point surface temperature. It then uses the exponential moving mean and exponential moving variance of the dew point excess to perform an adaptive normalization mapping on the dew point excess to obtain the condensation potential.

[0134] The asymmetric hysteresis moisture storage state update module 204 uses the condensation potential as input to update the asymmetric hysteresis moisture storage state. During the update process, the moisture absorption time constant is set to be less than the desiccation time constant, so that the rate of increase of the asymmetric hysteresis moisture storage state when the condensation potential increases is higher than the rate of decrease when the condensation potential decreases.

[0135] The nonlinear risk power calculation module 205 performs a power operation with a power exponent greater than one on the asymmetric hysteresis moisture storage state to obtain the nonlinear risk power.

[0136] The online risk index calculation module 206 performs an exponential sliding integral on the nonlinear risk power to obtain the online risk index.

[0137] The self-normalized intensity calculation module 207 calculates the relative deviation of the online risk index based on the long-term mean and long-term variance of the online risk index to obtain the self-normalized intensity.

[0138] The operation status output module 208 compares the self-normalized intensity with the preset grading threshold and outputs the operation status, including normal, moisture accumulation increase, and high risk of implicit insulation degradation.

[0139] It should be noted that the interval and threshold sizes are set for ease of comparison. The size of the threshold depends on the amount of sample data and the base number set by those skilled in the art for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value. Furthermore, the above formulas are all dimensionless calculations, and the formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0140] The embodiments of this example have been described above. However, this example is not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of this example, and all of them are within the protection scope of this example.

Claims

1. A method for intelligent monitoring of the operating status of a power distribution cabinet, characterized in that, Includes the following steps: Step S101: Collect the air temperature inside the distribution cabinet, the relative humidity inside the cabinet, and the surface temperature of the cold spot. Perform three-point median filtering on the three to obtain a smoothed time series. Step S102: Calculate the dew point temperature based on the smoothed air temperature and relative humidity inside the cabinet. Step S103: Calculate the dew point excess relative to the cold point surface temperature. Use the exponential moving mean and exponential moving variance of the dew point excess to perform adaptive normalization mapping on the dew point excess to obtain the condensation potential. Step S104: Use the condensation potential as input to update the asymmetric hysteresis moisture storage state. During the update process, set the moisture absorption time constant to be smaller than the desiccation time constant so that the rate of increase of the asymmetric hysteresis moisture storage state when the condensation potential increases is higher than the rate of decrease when the condensation potential decreases. Step S105: Perform a power operation with an exponent greater than one on the asymmetric hysteresis moisture storage state to obtain the nonlinear risk power; Step S106: Perform an exponential sliding integral on the nonlinear risk power to obtain the online risk index; Step S107: Calculate the relative deviation of the online risk index based on the long-term mean and long-term variance of the online risk index to obtain the self-normalized strength. Step S108: Compare the self-normalization intensity with the preset grading threshold and output the operating status including normal, moisture accumulation increase and high risk of implicit insulation degradation. Set the moisture absorption time constant to be smaller than the desiccation time constant; Based on the sampling period and the moisture absorption time constant, calculate the natural exponential function value by subtracting the value of the natural exponential function with the negative of the ratio of the sampling period to the moisture absorption time constant as the exponent, and obtain the moisture absorption discrete update coefficient. Based on the sampling period and the dehumidification time constant, calculate the natural exponential function value by subtracting the inverse of the ratio of the sampling period to the dehumidification time constant, and obtain the dehumidification discrete update coefficient. The condensation potential at the current discrete moment is compared with the asymmetric hysteresis moisture storage state at the previous discrete moment. If the condensation potential at the current discrete moment is greater than or equal to the asymmetric hysteresis moisture storage state at the previous discrete moment, the moisture absorption discrete update coefficient is selected. The condensation potential at the current discrete moment is subtracted from the asymmetric hysteresis moisture storage state at the previous discrete moment to obtain the difference. The difference is multiplied by the moisture absorption discrete update coefficient, and the resulting product is added to the asymmetric hysteresis moisture storage state at the previous discrete moment to obtain the asymmetric hysteresis moisture storage state at the current discrete moment. If the condensation potential at the current discrete moment is less than the asymmetric hysteresis moisture storage state at the previous discrete moment, select the dehumidification discrete update coefficient; subtract the asymmetric hysteresis moisture storage state at the previous discrete moment from the condensation potential at the current discrete moment to obtain the difference; multiply the difference with the dehumidification discrete update coefficient; and add the resulting product with the asymmetric hysteresis moisture storage state at the previous discrete moment to obtain the asymmetric hysteresis moisture storage state at the current discrete moment. Based on the sampling period and the time constant of the long-term statistic, calculate the natural exponential function value by subtracting the inverse of the ratio of the sampling period to the time constant of the long-term statistic, and obtain the update coefficient. Multiply the difference between 1 and the update coefficient by the long-term mean of the previous discrete time step to obtain the first intermediate value; Multiply the updated coefficient by the online risk index at the current discrete moment to obtain the second intermediate value; add the first intermediate value and the second intermediate value to obtain the long-term mean at the current discrete moment. Subtract the long-term mean of the current discrete time from the online risk index at the current discrete time to obtain the difference, and calculate the square of the difference; multiply the difference by the update coefficient and the long-term variance of the previous discrete time to obtain the third intermediate value. Multiply the update coefficient by the square of the difference to obtain the fourth intermediate value; add the third intermediate value to the fourth intermediate value to obtain the long-term variance at the current discrete time. Perform the square root operation on the long-term variance at the current discrete time, and add the result to the smallest positive real number to obtain the long-term standard deviation; The self-normalized strength is obtained by subtracting the long-term mean of the current discrete time from the online risk index at the current discrete time and dividing by the long-term standard deviation.

2. The intelligent monitoring method for the operating status of a power distribution cabinet according to claim 1, characterized in that, Under a fixed sampling period, the air temperature inside the cabinet, the relative humidity inside the cabinet, and the surface temperature of the cold spot were collected respectively. For the air temperature inside the cabinet, the original value at the current discrete sampling time, the original value of the previous sampling period, and the original value of the previous two sampling periods are selected. The three original values ​​are sorted according to their numerical values ​​and the median value is taken to obtain the smoothed air temperature inside the cabinet at the current discrete sampling time. For the relative humidity inside the cabinet, the original value at the current discrete sampling time, the original value of the previous sampling period, and the original value of the previous two sampling periods are selected. The three original values ​​are sorted according to their numerical values ​​and the median value is taken to obtain the smoothed relative humidity value inside the cabinet at the current discrete sampling time. For the cold spot surface temperature, select the original value at the current discrete sampling time, the original value of the previous sampling period, and the original value of the previous two sampling periods. Sort the three original values ​​according to their numerical values ​​and take the median value to obtain the smoothed cold spot surface temperature at the current discrete sampling time.

3. The intelligent monitoring method for the operating status of a power distribution cabinet according to claim 1, characterized in that, Divide the smoothed relative humidity inside the cabinet by 100, and perform a natural logarithmic operation on the quotient to obtain the first value; Multiply the smoothed air temperature inside the cabinet by the first empirical coefficient to obtain the second value; add the smoothed air temperature inside the cabinet to the second empirical coefficient to obtain the third value; calculate the quotient of the second value divided by the third value, and add the quotient to the first value to obtain the intermediate variable. Multiply the intermediate variable by the second empirical coefficient to obtain the fourth value; subtract the intermediate variable from the first empirical coefficient to obtain the fifth value; calculate the quotient of the fourth value and the fifth value to obtain the dew point temperature.

4. The intelligent monitoring method for the operating status of a power distribution cabinet according to claim 1, characterized in that, Subtract the smoothed cold point surface temperature from the dew point temperature to obtain the dew point excess. The update coefficients are calculated based on the sampling period and time constant. Using the update coefficients, the exponential moving average of the dew point excess at the current discrete moment is weighted and summed with the exponential moving average at the previous discrete moment to obtain the exponential moving average at the current discrete moment. Subtract the exponential moving average of the current discrete time from the dew point exceedance at the current discrete time to obtain the difference, and then calculate the square of the difference. Using the updated coefficients, a weighted sum is performed on the square of the difference and the exponential moving variance of the previous discrete time step to obtain the exponential moving variance of the current discrete time step. Perform the square root operation on the exponential moving variance at the current discrete time, and add the result to the smallest positive real number to obtain the exponential moving standard deviation; Subtract the exponential moving mean of the current discrete time from the dew point excess at the current discrete time, and divide by the exponential moving standard deviation to obtain the quotient; perform natural exponential operation on the negative of the quotient, add one to the result and take the reciprocal to obtain the dew point potential.

5. The intelligent monitoring method for the operating status of a power distribution cabinet according to claim 1, characterized in that, Set a power exponent with a value greater than one; using the asymmetric hysteresis moisture storage state as the base and the power exponent as the exponent, perform a power operation to obtain the nonlinear risk power.

6. The intelligent monitoring method for the operating status of a power distribution cabinet according to claim 1, characterized in that, Based on the sampling period and the time constant of risk accumulation, calculate the natural exponential function value by subtracting the inverse of the ratio of the sampling period to the time constant of risk accumulation, and obtain the discrete update coefficients. Multiply the difference between 1 and the discrete update coefficient by the online risk index of the previous discrete time step to obtain the first product; The discrete update coefficients are multiplied by the nonlinear risk power at the current discrete time to obtain the second product; Add the first product to the second product to obtain the online risk index at the current discrete moment.

7. The intelligent monitoring method for the operating status of a power distribution cabinet according to claim 1, characterized in that, Set a first-level threshold constant and a second-level threshold constant, wherein the first-level threshold constant is less than the second-level threshold constant; The self-normalization intensity at the current discrete moment is numerically compared with the first and second graded threshold constants. If the self-normalization intensity at the current discrete moment is less than the first grade threshold constant, the running state variable is determined to be normal; If the self-normalization intensity at the current discrete moment is greater than or equal to the first grade threshold constant and less than the second grade threshold constant, the operating state variable is determined to be moisture storage increase. If the self-normalization intensity at the current discrete moment is greater than or equal to the second-level threshold constant, the operating state variable is determined to be at high risk of implicit insulation degradation.

8. An intelligent monitoring system for the operating status of a power distribution cabinet, characterized in that, The method for intelligent monitoring of the operating status of a power distribution cabinet as described in any one of claims 1 to 7 includes: The median filtering module collects the air temperature, relative humidity, and cold spot surface temperature inside the distribution cabinet, and performs three-point median filtering on each of the three to obtain a smoothed time series. The dew point temperature calculation module calculates the dew point temperature based on the smoothed air temperature and relative humidity inside the cabinet. The condensation potential calculation module calculates the dew point excess relative to the cold point surface temperature. It then uses the exponential moving mean and exponential moving variance of the dew point excess to perform an adaptive normalization mapping on the dew point excess, thereby obtaining the condensation potential. The asymmetric hysteresis moisture storage state update module takes the condensation potential as input to update the asymmetric hysteresis moisture storage state. During the update process, the moisture absorption time constant is set to be smaller than the desiccation time constant, so that the rate of increase of the asymmetric hysteresis moisture storage state when the condensation potential increases is higher than the rate of decay when the condensation potential decreases. The nonlinear risk power calculation module performs a power operation with an exponent greater than one on the asymmetric hysteresis moisture storage state to obtain the nonlinear risk power. The online risk index calculation module performs an exponential moving integral on the nonlinear risk power to obtain the online risk index. The self-normalized intensity calculation module calculates the relative deviation of the online risk index based on the long-term mean and long-term variance of the online risk index, and obtains the self-normalized intensity. The operating status output module compares the self-normalized intensity with the preset grading threshold and outputs operating statuses including normal, moisture accumulation increase, and high risk of implicit insulation degradation.

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