Intelligent distribution box remote monitoring system based on Internet of Things

Abstract

The invention discloses an intelligent distribution box remote monitoring system based on the Internet of Things, and relates to the technical field of distribution box state monitoring, and the system comprises the steps: collecting an inner wall temperature, an air temperature and a relative humidity, calculating a dew point temperature, and determining a dew point crossing event window according to a difference value between the inner wall temperature and a dew point; performing sliding frequency domain analysis on the earth leakage current in the dew point event window to obtain a harmonic amplitude ratio and a time derivative thereof, extracting transition intensity, generating a reference value by using a historical event and measurement resolution, and calculating a health margin; the transfer power and the target branch are determined by combining the total active load in the cabinet, a segmentation and contact control instruction is automatically issued, the monitoring amount is uploaded, and remote monitoring and remote operation are achieved. The non-linear leakage risk can be identified, and the overheating and discharging risk can be reduced in advance.

Description

Technical Field

[0001] This invention relates to the field of power distribution box status monitoring technology, and in particular to an intelligent power distribution box remote monitoring system based on the Internet of Things. Background Technology

[0002] Distribution boxes operate for extended periods in coastal outdoor environments, chemical plants, data centers, and other locations with significant day-night temperature differences or large humidity fluctuations. The inner wall of the cabinet often cools down before the air inside due to heat dissipation and environmental exchange. Once the inner wall temperature approaches or falls below the dew point temperature, an extremely thin water film will form on the wall surface in a short time, causing a rapid increase in leakage current to ground and nonlinear conductivity. This further induces surface discharge, contact corrosion, and abnormal temperature rise. In severe cases, it can develop into internal arcing and false tripping, resulting in power outages and reduced equipment lifespan.

[0003] Current technologies for maintaining distribution boxes to address condensation and insulation degradation primarily rely on periodic manual inspections and fixed threshold alarms. These methods fail to adapt to the specific cabinet and its current thermal and humidity conditions, leading to frequent false alarms or missed alarms during actual operation. Furthermore, they cannot guarantee the timely extraction of quantities reflecting rapid changes in nonlinear conductivity within the most sensitive time window. Consequently, it is difficult to distinguish between slow background fluctuations and rapid increases in risk, thus missing opportunities to reduce thermal load and electrical stress through remote control. Summary of the Invention

[0004] The purpose of this invention is to solve the problem in the prior art that it is difficult to accurately identify nonlinear leakage risks in a timely manner within a critical short time window across dew points, and to propose an intelligent distribution box remote monitoring system based on the Internet of Things.

[0005] To address the problems existing in the prior art, the present invention adopts the following technical solution: A remote monitoring system for intelligent distribution boxes based on the Internet of Things (IoT) includes: The dew point window determination module is used to calculate the dew point temperature based on the air temperature and relative humidity inside the distribution box, and to determine the cross-dew point event window based on the dew point temperature and the inner wall temperature of the distribution box. Harmonic ratio derivative module, used to determine the harmonic amplitude ratio based on the leakage current to ground of the distribution box within a cross-dew point event window; The reference value setting module is used to set a reference value based on the harmonic amplitude ratio. The health margin calculation module is used to calculate the health margin based on the transition strength and the reference value; The power transfer decision module is used to determine the transfer power and target branch based on health margin and total active load in the distribution box; The remote control module is used to issue control commands to the target branch based on the transfer power, and send the transition strength, health margin, transfer power and control commands as remote monitoring quantities to the monitoring terminal.

[0006] Preferably, the dew point temperature is calculated based on the air temperature and relative humidity inside the distribution box, including: Collect the temperature of the inner wall of the distribution box, the air temperature inside the distribution box, and the relative humidity inside the distribution box; Noise reduction measures are implemented for the inner wall temperature, air temperature, and relative humidity inside the distribution box. The dew point temperature of the air inside the distribution box is calculated by substituting the air temperature and relative humidity inside the distribution box into the Magnus empirical formula.

[0007] Preferably, determining the cross-dew point event window based on the dew point temperature and the inner wall temperature of the distribution box includes: Calculate the difference between the inner wall temperature and the dew point temperature of the distribution box, and take the moment when the temperature difference first becomes zero as the reference moment for crossing the dew point. The rate of change of the temperature difference across the dew point reference time is approximated by the central difference method. The duration of the time span is calculated based on the rate of change of the temperature difference across dew point reference times and the quantization resolution of the temperature sensor. Construct a cross-dew point event window centered on the cross-dew point reference time and spanning a duration equal to the length of the time window.

[0008] Preferably, determining the harmonic amplitude ratio based on the leakage current to ground of the distribution box within the cross-dew point event window includes: Synchronously collect the leakage current to ground of the distribution box within the cross-dew point event window; The sliding discrete Fourier transform of the collected leakage current to ground of the distribution box was performed to obtain the amplitude of the first harmonic and the amplitude of the third harmonic at the collection point. Calculate the ratio of the third harmonic amplitude to the first harmonic amplitude at the sampling point to obtain the harmonic amplitude ratio at the sampling point; Calculate the time derivative of the harmonic amplitude ratio at the acquisition point.

[0009] Preferably, setting a reference value based on the harmonic amplitude ratio includes: Compare the time derivatives of the harmonic amplitude ratios of all acquisition points within the dew point event window, and take the maximum value as the transition intensity; The minimum effective derivative of the harmonic amplitude ratio is obtained based on the minimum resolvable increment of the measurement system for the harmonic amplitude ratio and the sampling period. Obtain the transition intensity of the previous cross-dew point event window and compare it with the minimum effective derivative of the harmonic amplitude ratio, setting the larger value as the reference value.

[0010] Preferably, the health margin is calculated based on the transition strength and a reference value, including: By performing an exponential mapping between the transition intensity and the reference value, the health margin is obtained.

[0011] Preferably, the transfer power and target branch are determined based on the health margin and the total active load in the distribution box, including: Subtract the health margin from one to obtain the transfer power coefficient; The original transferred power is obtained by multiplying the transferred power coefficient by the total active load in the distribution box; Obtain the available load margin of adjacent feeders; The original transferred power is limited based on the available carrying capacity margin of adjacent feeders to obtain the transferred power; Calculate the difference between the active load and the transferred power of each branch, and mark the branch with the smallest absolute value of the difference as the target branch.

[0012] Preferably, the control command for the target branch is issued based on the transfer power, and the transition strength, health margin, transfer power, and control command are sent to the monitoring terminal as remote monitoring quantities, including: If the transfer power is greater than zero, a control command is generated; otherwise, no control command is generated. The control command includes: disconnecting the sectionalizing switch of the target branch and closing the tie switch of the target branch. The generated control commands are sent to the corresponding switching equipment at the distribution box site, and the operating status of the switching equipment is collected after the switching action is completed. The transition strength, health margin, transfer power, control commands, and operating status of the switching equipment are uploaded to the remote monitoring terminal.

[0013] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention calculates the dew point temperature by measuring the air temperature and relative humidity inside the distribution box, and compares it with the inner wall temperature of the distribution box to determine the dew point event window. Then, within this event window, frequency domain analysis is performed on the leakage current to ground of the distribution box to obtain the harmonic amplitude ratio and its time derivative. This allows for the measurement of rapid changes caused by condensation within the most critical short time period. At the same time, the measurement system provides the minimum resolvable increment of the harmonic amplitude ratio and the minimum effective derivative of the sampling period, and sets a reference value by combining the transition intensity of the previous dew point event window. This ensures stable risk calibration under different cabinets and different operating conditions, significantly reducing false alarms and missed alarms.

[0014] 2. This invention calculates the health margin by using transition strength and reference values, then determines the transfer power based on the health margin and the total active load in the distribution box, and limits the transfer power according to the available carrying capacity of adjacent feeders. At the same time, it selects the target branch based on the principle of minimizing the absolute value of the difference between the active load and the transfer power of each branch, realizing automatic conversion and scale adaptation from risk quantity to handling quantity. This ensures that the load is transferred from the higher-risk branch to the adjacent feeder with carrying capacity in a planned manner without breaking the network security boundary, thereby reducing thermal and electrical stress in advance and improving the operational safety margin and equipment availability.

[0015] 3. This invention generates transfer power and issues control commands to disconnect the target branch section switch and close the target branch connecting switch. After the action is completed, the operating status of the switchgear is collected, and the transition strength, health margin, transfer power and control commands are uploaded to the remote monitoring terminal, forming a closed loop from online identification, quantitative decision-making to remote execution and status verification; improving the traceability and consistency of the handling, and reducing invalid switching and mechanical wear. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a functional block diagram of an IoT-based intelligent distribution box remote monitoring system provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0018] Example: This example provides a remote monitoring system for intelligent distribution boxes based on the Internet of Things (IoT). See [link / reference]. Figure 1 Specifically, including: The dew point window determination module is used to calculate the dew point temperature based on the air temperature and relative humidity inside the distribution box, and to determine the cross-dew point event window based on the dew point temperature and the inner wall temperature of the distribution box. In an embodiment of the present invention, the dew point temperature is calculated based on the air temperature and relative humidity inside the distribution box, and a cross-dew point event window is determined based on the dew point temperature and the inner wall temperature of the distribution box, including: Collect the temperature of the inner wall of the distribution box, the air temperature inside the distribution box, and the relative humidity inside the distribution box; Noise reduction measures are implemented for the inner wall temperature, air temperature, and relative humidity inside the distribution box. Substitute the air temperature and relative humidity inside the distribution box into Magnus's empirical formula to calculate the dew point temperature of the air inside the distribution box. Specifically, the inner wall temperature of the distribution box refers to the actual temperature of the inner surface of the metal or non-metal casing of the distribution box, reflecting the wall state after heat exchange with the air inside the cabinet. It can be obtained using a patch temperature sensor to determine whether the wall surface may be below the dew point and condensation may occur. The air temperature inside the distribution box refers to the temperature of the air inside the cabinet, measured at a representative location at a certain distance from the heat source and the air inlet and outlet, and is used to calculate the condensation risk by pairing it with the dew point temperature. The relative humidity inside the distribution box refers to the ratio of the partial pressure of water vapor in the air inside the cabinet to the partial pressure of saturated water vapor at that air temperature, measured by a humidity sensor. The dew point temperature is the temperature at which air begins to condense into liquid water while keeping the water vapor content constant. When the inner wall temperature is lower than the dew point temperature, condensation is easily formed on the wall surface, which is the benchmark for assessing condensation risk and determining the trigger event window.

[0019] Specifically, when collecting data on the inner wall temperature, air temperature, and relative humidity of the distribution box, the following sensor configuration and data acquisition method are used: For the inner wall temperature, a surface-mount temperature sensor is selected and fixed to the inner wall surface of different areas of the distribution box cabinet, avoiding direct exposure to heat-generating components such as contactors and circuit breakers, and near air inlets and outlets. The sensor measurement accuracy must reach ±0.1℃ to ensure accurate reflection of the actual wall temperature. For the air temperature inside the distribution box, a sheathed thermocouple sensor is used and installed inside the cabinet at least 3 meters away from heat-generating components. A representative location of 0cm and at least 50cm from the air inlet and outlet is used to avoid the influence of local temperature fluctuations on the measurement results. The sensor's measurement range covers -10℃ to 60℃, meeting the daily operating temperature requirements of the distribution box. For the relative humidity inside the distribution box, an integrated temperature and humidity sensor is used and installed in the same area as the air temperature sensor to ensure that the measurement environment of the two is consistent. The sensor's relative humidity measurement range is 0% to 100% with an accuracy of ±2%. All sensors are connected to the data acquisition module, and the acquisition frequency is set to once per second to achieve real-time synchronous acquisition of the three types of parameters.

[0020] Specifically, when performing noise reduction on the collected data of the inner wall temperature, air temperature, and relative humidity inside the distribution box, the sources of data noise are first analyzed. These mainly include electromagnetic interference generated by electromagnetic equipment inside the cabinet and random drift of the sensors themselves. For this type of noise, a moving average filtering algorithm is used. Specifically, for each type of parameter data collected continuously, 5 to 10 consecutive sampling points are selected as a filtering window according to the time series. The arithmetic mean of all sampling points in each window is calculated, and this average value is used as the noise-reduced data of the middle sampling point of the corresponding window. The window is slid sequentially to complete the noise reduction of all data. This process can effectively smooth the data fluctuations caused by high-frequency interference and random drift, and avoid the loss of transient change characteristics of parameters due to over-filtering, thus ensuring the accuracy of the data used in subsequent calculations.

[0021] Specifically, we first need to clarify the specific form and parameter values ​​of Magnus's empirical formula. This formula calculates intermediate quantities using the relative humidity and air temperature inside the distribution box. The formula for calculating the intermediate variables is as follows:

[0022] In the formula, As an intermediate quantity, The relative humidity inside the distribution box. The air temperature inside the distribution box. and As empirical constants, the optimal combination of constants for the ambient temperature range was obtained by regression analysis of measured saturated vapor pressure data within different temperature ranges and minimizing the fitting error. 17.62 The parameter set of 243.12℃ is most commonly used for engineering humid air calculations from 0 degrees Celsius to 30 degrees Celsius. The dew point temperature is then calculated using intermediate values. The formula for calculating the dew point temperature is:

[0023] In the formula, Dew point temperature, and These are empirical constants, taken at values ​​of 17.62 and 243.12℃ respectively. This is an intermediate quantity; Specifically, the dew point temperature is calculated by substituting the air temperature and relative humidity inside the distribution cabinet into the Magnus empirical formula because there is a stable exponential relationship between the saturated vapor pressure of water vapor in the air and temperature. The Magnus empirical formula is an approximate expression built on this relationship, which can accurately describe the saturated state of air under normal pressure conditions. Calculating the dew point temperature by using air temperature and relative humidity is equivalent to finding the critical temperature at which the air reaches saturation and begins to condense liquid water, assuming the current moisture content remains unchanged. Condensation in the distribution cabinet is caused by the wall temperature falling below this critical temperature. Therefore, the dew point temperature calculated using the Magnus empirical formula can provide a physically based and quantifiable key parameter for subsequent assessment of condensation risk, ensuring that the determination of cross-dew point event windows is based on the actual thermal and moisture balance law.

[0024] Calculate the difference between the inner wall temperature and the dew point temperature of the distribution box, and take the moment when the temperature difference first becomes zero as the reference moment for crossing the dew point. The rate of change of the temperature difference across the dew point reference time is approximated by the central difference method. The duration of the time span is calculated based on the rate of change of the temperature difference across dew point reference times and the quantization resolution of the temperature sensor. Construct a cross-dew point event window centered on the cross-dew point reference time and spanning a duration equal to the length of the time window; Specifically, the dew point reference time is the moment when the temperature difference sequence first crosses zero from positive to negative or from negative to positive, marking the instant when the wall temperature equals the dew point temperature; the temperature sensor quantization resolution is the smallest temperature step that the temperature measurement system can resolve, determining the smallest temperature change that can be reliably identified; the crossing duration refers to the shortest time scale during a dew point event when the difference between the inner wall temperature of the distribution cabinet and the dew point temperature decreases from a resolvable non-zero range to zero and completes the crossing. The crossing duration reflects the speed at which the wall temperature approaches the dew point and triggers condensation, and is the basis for constructing the width of the dew point event window. This ensures that the event window covers the critical moments before and after condensation occurs, while avoiding the introduction of redundant time periods beyond the sensor's resolution capability; the dew point event window is a time interval constructed with the dew point reference time as the center and the time window length as the width, covering changes before and after the dew point crossing while avoiding the introduction of too many irrelevant time periods.

[0025] Specifically, condensation is triggered by the instant when the inner wall temperature equals the dew point temperature. Therefore, using the moment when the temperature difference first reaches zero as the cross-dew point reference moment can accurately capture the physical critical point. First, pre-processed data of the distribution box's inner wall temperature and calculated dew point temperature are acquired, and the difference between the two is calculated moment by moment according to the time series. The changing trend of this temperature difference is continuously monitored. When the temperature difference first changes from a non-zero state to zero, the corresponding moment is recorded and determined as the cross-dew point reference moment. When approximating the rate of change of the temperature difference at the cross-dew point reference moment using the central difference method, the preceding and following moments adjacent to the cross-dew point reference moment are selected. The difference between the inner wall temperature and the dew point temperature at these two moments is calculated respectively. Then, the temperature difference at the following moment is subtracted from the temperature difference at the preceding moment, and the result is divided by the time interval between these two adjacent moments to obtain the rate of change of the temperature difference at the cross-dew point reference moment. Using the central difference approximation to obtain the rate of change of the temperature difference can provide an unbiased estimate of the zero-crossing speed within a short time window and suppress the influence of measurement noise on the judgment.

[0026] Specifically, the span duration is calculated based on the rate of change of temperature difference across the dew point reference time and the quantization resolution of the temperature sensor. The quantization resolution of the temperature sensor is the smallest temperature change it can distinguish. The span duration is obtained by dividing the quantization resolution of the temperature sensor by the absolute value of the rate of change of temperature difference across the dew point reference time. Centered on the dew point reference time, the calculated span duration is used as the total length of the time window. That is, the start time of the event window is the dew point reference time minus half of the span duration, and the end time is the dew point reference time plus half of the span duration. This forms a complete and accurate cross-dew point event window that can cover the process of the inner wall temperature crossing the dew point temperature, providing a highly reliable triggering basis for subsequent health margin calculations.

[0027] Harmonic ratio derivative module, used to determine the harmonic amplitude ratio based on the leakage current to ground of the distribution box within a cross-dew point event window; In an embodiment of the present invention, determining the harmonic amplitude ratio based on the leakage current to ground of the distribution box within a cross-dew point event window includes: Synchronously collect the leakage current to ground of the distribution box within the cross-dew point event window; The sliding discrete Fourier transform of the leakage current to ground of the distribution box at the acquisition point is performed to obtain the amplitude of the first harmonic and the amplitude of the third harmonic at the acquisition point. Calculate the ratio of the third harmonic amplitude to the first harmonic amplitude at the sampling point to obtain the harmonic amplitude ratio at the sampling point; Specifically, condensation triggering is caused by the surface conductivity nonlinearity resulting from the formation of an ultrathin water film after the inner wall temperature equals the dew point temperature. Rapid changes in leakage current must be observed within a dew point event window to correspond to the actual physical process. Performing a sliding discrete Fourier transform on the ground leakage current within this time window yields first and third components, with the third component being more sensitive to nonlinear conductivity and micro-discharge. The ratio of the third to the first component eliminates the influence of dimensions and range, suppressing errors caused by load fluctuations and sensor gain drift. Furthermore, the time derivative of this ratio is calculated to highlight the speed and intensity of short-term transitions, weakening slow background changes, allowing subsequent judgments to be driven by the nonlinear effects induced across the dew point. By characterizing the nonlinear leakage transition with a high signal-to-noise ratio within the critical short time window, false alarms and false negatives are significantly reduced.

[0028] Specifically, the leakage current from the distribution box to ground is a tiny current flowing from the live components inside the cabinet through the insulating surface or structural parts to the ground. It is an important physical quantity for determining changes in surface conductivity and insulation degradation. The first harmonic amplitude is the amplitude of the leakage current in the power frequency component, reflecting the strength of the fundamental wave conduction path. The third harmonic amplitude is the amplitude of the leakage current in the three times the power frequency component, reflecting the intensity of nonlinear conduction and micro-discharge effects. The harmonic amplitude ratio is the ratio of the third harmonic amplitude to the first harmonic amplitude, used to eliminate the influence of dimensions and highlight nonlinear changes.

[0029] Specifically, a high-precision leakage current sensor is used to continuously collect the leakage current to ground of the distribution cabinet within the dew point event window. This sensor is synchronized with sensors used to collect the inner wall temperature, air temperature, and relative humidity. A sliding discrete Fourier transform is performed on the leakage current to ground of the distribution cabinet at each collection point. The length of the sliding window is determined based on the duration of the dew point event window and the sampling frequency. Typically, a window containing 2048 continuous sampling points is selected, with each sliding step of the window being one sampling point. Within each sliding window, the complex spectral values ​​of the power frequency component and the third power frequency component are obtained from the leakage current signal using the discrete Fourier transform algorithm. The magnitude of the spectral values ​​is calculated for each component. The magnitude of the power frequency component is taken as the first harmonic amplitude of the collection point, and the magnitude of the third power frequency component is taken as the third harmonic amplitude of the collection point. The ratio of the third harmonic amplitude to the first harmonic amplitude at each collection point is calculated, and this ratio is taken as the harmonic amplitude ratio of the corresponding collection point to reflect the relative distribution characteristics of the harmonic components in the leakage current.

[0030] The reference value setting module is used to set a reference value based on the harmonic amplitude ratio. In embodiments of the present invention, setting a reference value based on the harmonic amplitude ratio includes: Calculate the time derivative of the harmonic amplitude ratio at the acquisition point; Compare the time derivatives of the harmonic amplitude ratios of all acquisition points within the dew point event window, and take the maximum value as the transition intensity; The minimum effective derivative of the harmonic amplitude ratio is obtained based on the minimum resolvable increment of the measurement system for the harmonic amplitude ratio and the sampling period. Obtain the transition intensity of the previous cross-dew point event window and compare it with the minimum effective derivative of the harmonic amplitude ratio, setting the larger value as the reference value.

[0031] Specifically, for each acquisition point, the harmonic amplitude ratios corresponding to its adjacent preceding and following acquisition points are selected. The harmonic amplitude ratio of the preceding acquisition point is subtracted from the harmonic amplitude ratio of the following acquisition point, and the difference is divided by the time interval between adjacent acquisition points to obtain the time derivative of the harmonic amplitude ratio at that acquisition point, which characterizes the rate of change of the harmonic amplitude ratio with time.

[0032] Specifically, the most dramatic change in nonlinear leakage within the dew point event window is characterized by the peak value of the time derivative of the harmonic amplitude ratio. Defining the maximum value of the time derivative as the transition intensity can directly characterize the rapid transitions during the formation and dissipation of condensation without being affected by the slowly changing background. The minimum effective derivative is calculated by the measurement system based on the minimum resolvable increment of the harmonic amplitude ratio and the sampling period, which can set a physical lower limit for the criterion given by the instrument resolution, avoiding mistaking quantization jitter for a true transition. The larger value of the transition intensity and the minimum effective derivative of the previous dew point event window is then used as the reference value, allowing the benchmark to be adaptively updated with the history of the equipment and the environment.

[0033] Specifically, transition intensity refers to the maximum rate of change of the time derivative of the harmonic amplitude ratio across all sampling points within the dew point event window. It is a quantitative indicator measuring the degree of rapid nonlinear change in the leakage current to ground of the distribution cabinet during the dew point stage. This quantity is derived from the maximum value obtained by comparing the harmonic amplitude ratio time series point by point within the event window according to the sampling time after time-based differentiation, reflecting the strength of the rapid change in the surface conductive channels during condensation formation or dissipation. The reference value is a benchmark quantity used for normalized comparison of transition intensity, and is composed of the larger of the transition intensity of historical dew point event windows and the minimum resolvable increment of the harmonic amplitude ratio determined by the measurement system and the minimum effective derivative determined by the sampling period. The reference value is used to ensure the comparability of transition intensity under different equipment, different operating conditions, and different measurement resolutions.

[0034] Specifically, when comparing the time derivatives of the harmonic amplitude ratios at all sampling points within the dew point event window, the time derivatives of the harmonic amplitude ratios calculated by the central difference at each sampling point within the event window are iterated through. The values ​​of each derivative are recorded one by one, and the maximum value is selected by numerical comparison. This maximum value is determined as the transition intensity to characterize the most drastic change in the harmonic characteristics of the leakage current within the dew point event window. When obtaining the minimum effective derivative of the harmonic amplitude ratio based on the minimum resolvable increment of the harmonic amplitude ratio by the measurement system and the sampling period, the minimum resolvable increment of the harmonic amplitude ratio by the measurement system is divided by the sampling period to obtain the minimum effective derivative of the harmonic amplitude ratio, which is used to measure the minimum rate of change of the harmonic amplitude ratio over time that the system can identify. The transition intensity of the previous dew point event window is obtained, and then the previous transition intensity is compared with the minimum effective derivative of the harmonic amplitude ratio calculated in this case. The larger value is selected as the reference value to provide a valid basis for the calculation of subsequent parameters such as health margin.

[0035] The health margin calculation module is used to calculate the health margin based on the transition strength and the reference value; In an embodiment of the present invention, calculating the health margin based on the transition strength and a reference value includes: An exponential mapping is performed between the transition strength and the reference value to obtain the health margin, where the formula for calculating the health margin is:

[0036] In the formula, For health margin, The current transition strength within the cross-dew point event window. Used as a reference value; Specifically, the transition intensity determined within the current cross-dew point event window is obtained, along with a reference value obtained through comparison. Then, the ratio of the transition intensity to the reference value is calculated, and this ratio is negative, serving as the exponent of an exponential function. A natural exponential function is selected, and the negative value is substituted into it for calculation. The result is the health margin, a dimensionless health status index derived from the exponential mapping between the transition intensity and the reference value. This index characterizes the operational safety margin of the distribution cabinet during the cross-dew point phase. A health margin value closer to one indicates a healthier state, while a value close to zero indicates the need for priority intervention. Using the ratio of the transition intensity to the reference value as a dimensionless index eliminates the influence of range and equipment differences. Taking its negative value as the exponent of the exponential function yields a monotonically decreasing and bounded mapping, ensuring the health margin naturally falls between zero and one and is insensitive to small disturbances while exhibiting accelerated penalties for large anomalies. This approach effectively suppresses noise while highlighting rapid degradation during the cross-dew point phase.

[0037] The power transfer decision module is used to determine the transfer power and target branch based on health margin and total active load in the distribution box; In embodiments of the present invention, determining the transferred power and target branch based on health margin and total active load within the distribution box includes: Subtract the health margin from one to obtain the transfer power coefficient; The original transferred power is obtained by multiplying the transferred power coefficient by the total active load in the distribution box; Obtain the available load margin of adjacent feeders; The original transferred power is limited based on the available carrying capacity margin of adjacent feeders to obtain the transferred power; Calculate the difference between the active load and the transferred power of each branch, and mark the branch with the smallest absolute value of the difference as the target branch; Specifically, the transfer power coefficient is obtained by subtracting the health margin from one, which can directly map the degree of unhealthiness derived from the transition intensity and reference value to the proportion of load to be transferred, so that the scale of action adapts to the real-time risk in the cabinet; the transfer power coefficient is multiplied by the total active load in the distribution box to obtain the original transfer power, so that the calculation is consistent with the current load level and avoids over- or under-handling; then the original transfer power is limited according to the available load margin of adjacent feeders, so that the transfer can be implemented without exceeding safety constraints and equipment thermal limits; finally, the target branch is selected according to the principle of minimizing the absolute value of the difference between the active load and the transfer power of each branch, following the idea of ​​minimum readjustment, reducing the number of switching and power fluctuations.

[0038] Specifically, the transfer power factor is a coefficient obtained by subtracting the health margin from one, used to represent the proportion of load to be transferred, and is between zero and one; the total active load in the distribution box refers to the sum of the active power of all branches in the cabinet at the current moment, which serves as the basis for calculating the scale of the power to be transferred; the original transfer power is the product of the transfer power factor and the total active load in the distribution box, representing the amount of power to be transferred without considering external constraints; the available carrying capacity margin of adjacent feeders is the additional remaining active power capacity that adjacent feeders can carry under the current operating mode and safety limits, used to ensure that the transfer will not cause overload; the active load of each branch is the real-time active power of each branch in the cabinet, used to match the most suitable transfer target.

[0039] Specifically, the transfer power coefficient is obtained by subtracting the previously calculated health margin from 1. This coefficient represents the proportion of the power to be transferred to the total active load in the distribution box. The original transfer power is obtained by multiplying the obtained transfer power coefficient with the total active load monitored in real time in the distribution box. This initially determines the amount of power to be transferred from the current distribution box. The available carrying capacity margin of the adjacent feeders is obtained. The original transfer power is compared with the available carrying capacity margin. If the original transfer power is less than or equal to the available carrying capacity margin, the original transfer power is used. If the original transfer power is greater than the available carrying capacity margin, the available carrying capacity margin is used to ensure that the transfer power does not exceed the carrying capacity of the adjacent feeders. The active load data of each branch in the distribution box is collected. The difference between the active load and the transfer power of each branch is calculated and the absolute value is taken. The absolute values ​​of the differences of all branches are compared. The branch with the smallest absolute value of the difference is marked as the target branch and used as the specific branch for the load transfer operation. By calculating the original transfer power and then limiting it based on the available carrying capacity margin of adjacent feeders, the load transfer amount can adaptively change with the health status of the equipment and always remain within the allowable range of the power grid, avoiding new thermal stress or electrical risks caused by excessive transfer. Simultaneously, by calculating the difference between the active load and the transfer power of each branch and selecting the branch with the smallest absolute difference as the target branch, disturbances can be minimized without changing the overall power grid topology, reducing the number of switching operations and system fluctuations, and effectively improving the operational safety, power supply continuity, and equipment availability of the distribution cabinet.

[0040] The remote control module is used to issue control commands to the target branch based on the transfer power, and send the transition strength, health margin, transfer power and control commands as remote monitoring quantities to the monitoring terminal. In an embodiment of the present invention, a control command for the target branch is issued based on the transfer power, and the transition strength, health margin, transfer power, and control command are sent to the monitoring terminal as remote monitoring quantities, including: If the transfer power is greater than zero, a control command is generated; otherwise, no control command is generated. The control command includes: disconnecting the sectionalizing switch of the target branch and closing the tie switch of the target branch. The generated control commands are sent to the corresponding switching equipment at the distribution box site, and the operating status of the switching equipment is collected after the switching action is completed. The transition strength, health margin, transfer power, control commands, and operating status of the switching equipment are uploaded to the remote monitoring terminal. Specifically, the transfer power is derived from the health margin calculated from the transition strength and reference value. It is a quantitative requirement for the risk of nonlinear leakage caused by condensation. Control commands are only generated when the transfer power is greater than zero, which can avoid triggering the interruption operation when there is no actual load reduction requirement, and reduce unnecessary mechanical wear and arc risk. By disconnecting the sectionalizing switch of the target branch and closing the tie switch of the target branch, the load is transferred from the high-risk branch to the adjacent feeder with the load-bearing capacity according to the predetermined transfer power, directly eliminating the risk source and thermal stress. After the command is issued to the switchgear, the operating status of the switchgear is collected to form a closed-loop verification of the action.

[0041] Specifically, the magnitude of the transferred power is determined. If the transferred power is greater than zero, a control command is generated, which includes disconnecting the sectionalizing switch of the target branch and closing the tie switch of the target branch. If the transferred power is not greater than zero, no control command is generated. After the control command is generated, it is sent to the corresponding switching equipment at the distribution box site via the distribution box's communication module. This switching equipment includes the sectionalizing switch and tie switch of the target branch. After the switching equipment completes its actions according to the control command, the operating status of the switching equipment is collected by the status acquisition unit, specifically the open / closed status of the sectionalizing switch and tie switch. The previously calculated transition strength, health margin, determined transferred power, generated control command, and collected operating status of the switching equipment are uploaded to a remote monitoring terminal via the distribution network's communication network. The remote monitoring terminal is the monitoring platform of the distribution network dispatch center, enabling maintenance personnel to monitor the operation and control status and health status of the distribution box in real time.

[0042] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A remote monitoring system for intelligent distribution boxes based on the Internet of Things, characterized in that, include: The dew point window determination module is used to calculate the dew point temperature based on the air temperature and relative humidity inside the distribution box, and to determine the cross-dew point event window based on the dew point temperature and the inner wall temperature of the distribution box. Harmonic ratio derivative module, used to determine the harmonic amplitude ratio based on the leakage current to ground of the distribution box within a cross-dew point event window; The reference value setting module is used to set a reference value based on the harmonic amplitude ratio. The health margin calculation module is used to calculate the health margin based on the transition strength and the reference value; The power transfer decision module is used to determine the transfer power and target branch based on health margin and total active load in the distribution box; The remote control module is used to issue control commands to the target branch based on the transfer power, and send the transition strength, health margin, transfer power and control commands as remote monitoring quantities to the monitoring terminal.

2. The IoT-based intelligent distribution box remote monitoring system according to claim 1, characterized in that, Calculate the dew point temperature based on the air temperature and relative humidity inside the distribution box, including: Collect the temperature of the inner wall of the distribution box, the air temperature inside the distribution box, and the relative humidity inside the distribution box; Noise reduction measures are implemented for the inner wall temperature, air temperature, and relative humidity inside the distribution box. The dew point temperature of the air inside the distribution box is calculated by substituting the air temperature and relative humidity inside the distribution box into the Magnus empirical formula.

3. The IoT-based intelligent distribution box remote monitoring system according to claim 2, characterized in that, The dew point event window is determined based on the dew point temperature and the inner wall temperature of the distribution box, including: Calculate the difference between the inner wall temperature and the dew point temperature of the distribution box, and take the moment when the temperature difference first becomes zero as the reference moment for crossing the dew point. The rate of change of the temperature difference across the dew point reference time is approximated by the central difference method. The duration of the time span is calculated based on the rate of change of the temperature difference across dew point reference times and the quantization resolution of the temperature sensor. Construct a cross-dew point event window centered on the cross-dew point reference time and spanning a duration equal to the length of the time window.

4. The IoT-based intelligent distribution box remote monitoring system according to claim 1, characterized in that, Determining the harmonic amplitude ratio based on the leakage current to ground of the distribution box within the cross-dew point event window includes: Synchronously collect the leakage current to ground of the distribution box within the cross-dew point event window; The sliding discrete Fourier transform of the collected leakage current to ground of the distribution box was performed to obtain the amplitude of the first harmonic and the amplitude of the third harmonic at the collection point. Calculate the ratio of the third harmonic amplitude to the first harmonic amplitude at the sampling point to obtain the harmonic amplitude ratio at the sampling point.

5. The IoT-based intelligent distribution box remote monitoring system according to claim 4, characterized in that, Reference values ​​are set based on the harmonic amplitude ratio, including: Calculate the time derivative of the harmonic amplitude ratio at the acquisition point; Compare the time derivatives of the harmonic amplitude ratios of all acquisition points within the dew point event window, and take the maximum value as the transition intensity; The minimum effective derivative of the harmonic amplitude ratio is obtained based on the minimum resolvable increment of the measurement system for the harmonic amplitude ratio and the sampling period. Obtain the transition intensity of the previous cross-dew point event window and compare it with the minimum effective derivative of the harmonic amplitude ratio, setting the larger value as the reference value.

6. The IoT-based intelligent distribution box remote monitoring system according to claim 1, characterized in that, The health margin is calculated based on the transition strength and a reference value, including: By performing an exponential mapping between the transition intensity and the reference value, the health margin is obtained.

7. The IoT-based intelligent distribution box remote monitoring system according to claim 1, characterized in that, The transferred power and target branch are determined based on the health margin and the total active load in the distribution box, including: Subtract the health margin from one to obtain the transfer power coefficient; The original transferred power is obtained by multiplying the transferred power coefficient by the total active load in the distribution box; Obtain the available load margin of adjacent feeders; The original transferred power is limited based on the available carrying capacity margin of adjacent feeders to obtain the transferred power; Calculate the difference between the active load and the transferred power of each branch, and mark the branch with the smallest absolute value of the difference as the target branch.

8. The IoT-based intelligent distribution box remote monitoring system according to claim 1, characterized in that, Based on the transfer power, control commands are issued to the target branch, and the transition strength, health margin, transfer power, and control commands are sent to the monitoring terminal as remote monitoring parameters, including: If the transfer power is greater than zero, a control command is generated; otherwise, no control command is generated. The control command includes: disconnecting the sectionalizing switch of the target branch and closing the tie switch of the target branch. The generated control commands are sent to the corresponding switching equipment at the distribution box site, and the operating status of the switching equipment is collected after the switching action is completed. The transition strength, health margin, transfer power, control commands, and operating status of the switching equipment are uploaded to the remote monitoring terminal.

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