Intelligent integrated distribution box online monitoring method and system

Abstract

This application discloses an online monitoring method and system for intelligent integrated distribution boxes. The method includes: establishing a topological association between a temperature measuring point and the terminals of each outgoing circuit within its heat conduction range, and simultaneously collecting the effective value of the current of each outgoing circuit and the temperature value of the measuring point; establishing a multivariate linear model using the square of the effective value of the associated circuit current, the ambient reference temperature, and the temperature value of the measuring point, and obtaining an estimated value of the thermal contribution coefficient of each terminal through least squares regression within a sliding time window; establishing a baseline value based on the estimated value during the initial operation phase; calculating the rate of change of the current estimated value of the thermal contribution coefficient relative to the baseline value during the continuous monitoring phase; and generating directional early warning information pointing to the circuit to which the corresponding terminal belongs when the rate of change meets a preset deterioration judgment condition. This application can realize online identification and positioning of the contact status of terminals using regional temperature information.

Description

Technical Field

[0001] This application relates to the field of power distribution equipment monitoring technology, specifically to an online monitoring method and system for intelligent integrated power distribution boxes. Background Technology

[0002] As low-voltage power distribution systems evolve towards digital and intelligent operation and maintenance, intelligent integrated distribution boxes typically possess the capability for online acquisition of operating parameters such as current and temperature. However, existing distribution boxes often have multiple outgoing circuits sharing a busbar, with tightly packed terminals. Poor terminal contact, oxidation, or loosening can lead to increased contact resistance and localized abnormal heating. Current temperature monitoring relies heavily on temperature sensors placed near the busbar or terminals, using absolute temperature values, temperature rise, or fixed thresholds for alarms. However, the same temperature measurement point is often simultaneously affected by the heating of multiple adjacent terminals, load changes, and the ambient temperature within the box. The measured temperature reflects the cumulative effect of regional heat, making it difficult to directly identify specific abnormal circuits. Especially in the early stages of degradation, although contact resistance has gradually increased, the absolute temperature may not yet have reached the alarm threshold, easily causing a delayed warning. Increasing the granularity of location by adding more temperature measurement points would increase the costs of sensors, wiring, installation, and maintenance. Therefore, improving the identification and location capabilities of early contact degradation of terminals under existing acquisition conditions remains a problem that needs to be solved for online monitoring of intelligent integrated distribution boxes. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this manual provides an online monitoring method and system for intelligent integrated distribution boxes.

[0004] According to a first aspect of the embodiments of this application, an online monitoring method for an intelligent integrated distribution box is provided, comprising: Configure topology relationships to establish the correspondence between each temperature measuring point and the wiring terminals of each outgoing circuit within its heat conduction range; synchronously collect the effective current value of each outgoing circuit and the temperature value of each temperature measuring point at a fixed sampling period; For each temperature measuring point, a multivariate linear model is established with the square of the effective value of the current of each associated circuit as the independent variable, the ambient reference temperature as the additional independent variable, and the temperature value of the temperature measuring point as the dependent variable. The coefficients corresponding to each circuit in the multivariate linear model are the thermal contribution coefficients of the circuit terminals to the temperature measuring point. Within the sliding time window, least squares regression is performed on the multivariate linear model to obtain the estimated thermal contribution coefficient of each terminal. During the initial operation phase, the estimated thermal contribution coefficients of each terminal are averaged to obtain the baseline value of each terminal. During the continuous monitoring phase, the rate of change of the current estimated thermal contribution coefficient of each terminal relative to the baseline value is calculated. When the rate of change of a certain terminal meets the preset deterioration judgment condition, a directional early warning message pointing to the circuit to which the terminal belongs is generated.

[0005] As an optional approach, before performing least squares regression on the multivariate linear model, a design matrix is ​​constructed based on the squares of the effective values ​​of the currents of each associated loop at each sampling time within the sliding time window and the ambient reference temperature, and the condition number of the design matrix is ​​calculated. When the condition number does not exceed the condition number threshold, the least squares regression is performed. After the solution, the standard error of the estimated thermal contribution coefficient of each terminal is calculated based on the residual variance and the design matrix. When the relative standard error of the estimated thermal contribution coefficient of a certain terminal exceeds the preset standard error threshold, the estimated thermal contribution coefficient of that terminal within the current sliding time window is marked as low confidence and is not included in the subsequent calculation of the rate of change.

[0006] As an optional approach, the degradation determination criteria include an amplitude condition and a trend condition that are satisfied simultaneously; the amplitude condition is that the rate of change exceeds a preset degradation warning threshold; the trend condition is that the rate of change in a consecutive preset number of valid windows all exceed the degradation warning threshold and show an increasing trend, and the increasing trend is determined by performing a univariate linear fitting on the rate of change corresponding to the consecutive preset number of valid windows and determining that the fitting slope is positive.

[0007] As an optional approach, during the continuous monitoring phase, for terminals that have not triggered the degradation judgment condition, each time a new thermal contribution coefficient estimate is generated, the baseline value is updated with a preset update weight. The new baseline value is obtained by multiplying the update weight by the current thermal contribution coefficient estimate and the remaining weight by the original baseline value.

[0008] As an optional approach, the model-estimated temperature value at each sampling moment within the sliding time window is calculated based on the multivariate linear model and the estimated thermal contribution coefficient. The difference between the model-estimated temperature value and the corresponding actual temperature value at each sampling moment is taken as the residual, and the root mean square value of the residual is calculated. When the root mean square value of the residual exceeds a preset residual threshold, the sliding time window is marked as an abnormal fit and is not included in the subsequent calculation of the rate of change.

[0009] As an optional solution, the thermal contribution coefficient is the product of the contact resistance of the corresponding terminal and the thermal resistance of the thermal conduction path between the terminal and the temperature measuring point; the thermal conduction range is the area on both sides of the temperature sensor installation position along the busbar direction that does not exceed a preset distance, and the preset distance is determined according to the busbar material and cross-sectional area.

[0010] As an optional approach, the length of the sliding time window is not shorter than the time required for each outgoing circuit load to undergo a complete periodic change; the fixed sampling period is taken as 1 / 10 to 1 / 5 of the bus exhaust heat time constant.

[0011] As an optional approach, when the heat conduction ranges of two adjacent temperature measuring points overlap, and the terminals of the same outgoing circuit are simultaneously associated with the two adjacent temperature measuring points, the two adjacent temperature measuring points independently perform the least squares regression solution to obtain the estimated value of the heat contribution coefficient of the terminal of the outgoing circuit and its estimation standard error. The estimated value of the heat contribution coefficient of the temperature measuring point with the smaller relative standard error of the estimated value of the terminal is selected as the estimated value of the heat contribution coefficient of the terminal.

[0012] As an optional approach, before establishing the multivariate linear model, the collected data within the sliding time window is preprocessed: for outgoing circuits that have no current within the window, the independent variable term corresponding to the circuit is removed from the multivariate linear model; for sampling times where the effective current value exceeds a preset multiple of the rated current of the circuit, the data at that sampling time is removed from the data participating in the least squares regression solution.

[0013] According to a second aspect of the embodiments of this application, an online monitoring system for intelligent integrated distribution boxes is also provided, comprising: The topology configuration and data acquisition module is used to configure the topology association, establish the correspondence between each temperature measurement point and the wiring terminals of each outgoing circuit within its heat conduction range, and synchronously acquire the effective current value of each outgoing circuit and the temperature value of each temperature measurement point at a fixed sampling period. The model building module is used to establish a multivariate linear model for each temperature measuring point, with the square of the effective value of the current of each associated circuit as the independent variable, the ambient reference temperature as the additional independent variable, and the temperature value of the temperature measuring point as the dependent variable. The coefficients corresponding to each circuit in the multivariate linear model are the thermal contribution coefficients of the circuit terminals to the temperature measuring point. The regression solution module is used to perform least squares regression on the multivariate linear model within a sliding time window to obtain the estimated thermal contribution coefficient of each terminal. The baseline monitoring and early warning module is used to average the estimated thermal contribution coefficients of each terminal during the initial operation phase to obtain the baseline value of each terminal; during the continuous monitoring phase, it calculates the rate of change of the current estimated thermal contribution coefficient of each terminal relative to the baseline value; when the rate of change of a certain terminal meets the preset deterioration judgment condition, it generates directional early warning information pointing to the circuit to which the terminal belongs.

[0014] This application leverages the characteristic that the load of each outgoing circuit varies over time to decompose the temperature response formed by multiple terminals at the temperature measurement point into the independent thermal contribution of each terminal. The continuous change of the thermal contribution coefficient relative to the baseline value characterizes the change in contact state. Simultaneously, condition number and residual tests are used to filter out unreliable regression results, ensuring that the degradation judgment is based on data with separability and fitting reliability. Therefore, without increasing the number of temperature sensors, early contact degradation of specific terminals can be identified and located from regional temperature monitoring results. This reduces the impact of load fluctuations, ambient temperature changes, and local thermal coupling on the judgment results, and generates early warning information with clear circuit indication before the absolute temperature reaches the traditional alarm threshold, thereby improving the timeliness and location effectiveness of online monitoring of intelligent integrated distribution boxes. Attached Figure Description

[0015] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this specification and, together with the description, serve to explain the principles of this specification.

[0016] Figure 1 This is a flowchart of an online monitoring method for an intelligent integrated distribution box according to one embodiment of this application.

[0017] Figure 2 This is a schematic diagram illustrating the thermal contribution decomposition principle of the temperature measurement point associated loop provided in the embodiments of this application.

[0018] Figure 3 This is a schematic diagram illustrating the determination of the validity of sliding time window regression provided in an embodiment of this application.

[0019] Figure 4 This is a schematic diagram illustrating the rate of change of thermal contribution coefficient and the determination of degradation, provided in an embodiment of this application.

[0020] Figure 5 This is a schematic block diagram of an online monitoring system for an intelligent integrated distribution box according to this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] This embodiment provides an online monitoring method for intelligent integrated distribution boxes, applicable to intelligent integrated distribution boxes with multiple outgoing circuits. The integrated distribution box has a shared busbar, and the terminals of multiple outgoing circuits are arranged along the busbar with small spacing between adjacent terminals, all within the same heat conduction area. The distribution box is equipped with current transformers and temperature sensors. Each outgoing circuit is configured with a current transformer to collect the effective current value. Several temperature sensors are installed in the busbar area, each corresponding to a temperature measurement point, covering multiple terminals within a certain range around it. The monitoring terminal of the distribution box has data acquisition, storage, and processing capabilities, for example, using an ARM Cortex-M4 or equivalent embedded processor with at least 64KB of memory. The load types supplied by each outgoing circuit differ, such as lighting, power equipment, and HVAC, and the current in each circuit exhibits different variation patterns over time.

[0023] The implementation process of the method described in this application will be explained in detail below with reference to specific embodiments.

[0024] Please see Figure 1 , Figure 1 This is a flowchart of an online monitoring method for an intelligent integrated distribution box according to one embodiment of this application, such as... Figure 1 As shown, the method includes steps 101-104: Step 101: Configure the topology association relationship and synchronously collect the effective current value of each outgoing circuit and the temperature value of each temperature measuring point.

[0025] In some embodiments, the monitoring terminal pre-stores a topology association table, recording the correspondence between each temperature measuring point and the terminals of each outgoing circuit within its heat conduction range. The heat conduction range refers to the area within a certain distance on either side of the busbar where the temperature sensor is installed, where heat generated by the terminals can be conducted through the busbar to the temperature measuring point and cause observable temperature changes. The specific distance is determined based on the busbar material and cross-sectional area; for example, when the cross-sectional area of ​​the copper busbar is 60mm × 6mm, the effective heat conduction distance on one side is approximately 80mm to 120mm. During the installation and commissioning phase of the distribution box, rated current is applied to each circuit one by one, and the temperature response of each temperature measuring point is observed to determine the association relationship. The topology association table remains unchanged during operation.

[0026] For example, a certain integrated distribution box is equipped with 3 temperature measurement points and 8 outgoing circuits. The topology table is as follows:

[0027] In some embodiments, the monitoring terminal synchronously collects the effective current value of each outgoing circuit and the temperature value of each temperature measuring point at a fixed sampling period. The sampling period is determined based on the busbar thermal time constant, typically ranging from 1 / 10 to 1 / 5 of the busbar thermal time constant, ensuring that the sampling resolution of the temperature change process meets the needs of subsequent regression analysis. The busbar thermal time constant refers to the time required for the busbar temperature to change from its initial state to 63.2% of its steady-state value, typically ranging from several minutes to tens of minutes, and can be determined through a step load test. Accordingly, the sampling period can be configured, for example, from 1 minute to 5 minutes. Simultaneously, the temperature of a reference temperature measuring point located away from the heat-generating area within the enclosure is collected as an environmental reference temperature. This reference temperature measuring point is installed inside the enclosure away from the busbar and circuit breaker, such as on the inside of the enclosure door. The current transformer accuracy class is not lower than 1.0, and the temperature sensor accuracy is not lower than 0.5 degrees Celsius.

[0028] Step 102: Establish a multivariate linear model for each temperature measurement point.

[0029] In some embodiments, for each temperature measurement point P in the topology association table, k outgoing loops are associated within its heat conduction range. The temperature of temperature measurement point P is affected by the heat generated by each associated terminal and the ambient temperature. Heat conduction within the busbar follows Fourier's law, and its governing equation is a linear partial differential equation. Therefore, the contribution of the heat generation power of each terminal to the temperature of the temperature measurement point strictly satisfies a linear superposition relationship. The heat path effect of each terminal conducted through the busbar to the temperature measurement point is already included in its respective heat contribution coefficient. The heat interaction between terminals through the busbar does not constitute a nonlinear coupling term outside the model. The temperature-dependent changes in material thermal conductivity (approximately 4% for copper within the normal operating temperature range) and the nonlinear effect of convective heat dissipation are both second-order small quantities. Windows with excessively large approximate deviations are screened out through subsequent residual checks. Accordingly, using the square of the effective value of the current of each associated loop as the independent variable, the ambient reference temperature as an additional independent variable, and the temperature value of the temperature measurement point as the dependent variable, the following multivariate linear model is established: ; in, Let temperature measurement point P be at time t. The temperature measurement value is expressed in degrees Celsius. For the first The associated loop at time The effective value of the current, in amperes; For the first The thermal contribution coefficient of the circuit terminal to the temperature measuring point P is the temperature rise per unit current square caused by the heat generated at the circuit terminal due to the current passing through it, which is conducted to the temperature measuring point through the busbar. The unit is the square of degrees Celsius per ampere. This is the ambient reference temperature, in degrees Celsius. The ambient temperature influence coefficient reflects the proportion of the transfer of ambient temperature to the temperature measurement point reading. This is a constant offset term, in degrees Celsius. This is the residual term, expressed in degrees Celsius.

[0030] Among them, the thermal contribution coefficient The physical meaning is the product of the contact resistance of the corresponding terminal and the thermal resistance of the heat conduction path between the terminal and the temperature measuring point. Specifically, the heat generation power at the terminal is... ,in The contact resistance is expressed in ohms; the temperature rise caused by heat conduction from the busbar material to the measuring point is also related to the thermal resistance of the conduction path. Relevant. When a terminal experiences contact deterioration, such as thickened oxide film or loose bolts, the contact resistance... Increase, corresponding thermal contribution coefficient This increases accordingly. Through continuous tracking of various... The change in the value can reflect the change in the contact state of the corresponding terminal.

[0031] Since the busbar geometry and material remain unchanged during operation, the thermal resistance of the heat conduction path is low. The thermal conductivity of copper is mainly determined by the cross-sectional area of ​​the busbar, the terminal spacing, and the thermal conductivity of the material. The variation in thermal conductivity of copper within the normal operating temperature range of the distribution box (20℃ to 100℃) is approximately 4%, far less than the degradation warning threshold (30% to 50%). It can be considered approximately constant, thermal contribution coefficient The significant changes mainly reflect the contact resistance The changes. Meanwhile, changes in ambient temperature and convection conditions are common-mode factors affecting all terminals, already included in the ambient reference temperature term in the model. Explicit modeling and absorption avoid confusion with contact degradation of a single terminal; this forms the physical basis for this method to locate degradation.

[0032] Figure 2 This is a schematic diagram illustrating the thermal contribution decomposition principle of the temperature measurement point associated loop provided in the embodiments of this application, as shown below. Figure 2 As shown, multiple outgoing circuit terminals are installed on the common busbar, connecting circuits 1, 2, and 3 to circuit... Collect the effective value of the current respectively , , to Its square , , to This characterizes the heat input of each associated terminal block. The heat from each terminal block is conducted to the temperature measuring point via the busbar. Meanwhile, ambient reference temperature This affects the temperature measurement point, and the measured temperature value is... The sum of the squares of the effective values ​​of the currents in each circuit and the ambient reference temperature is used. As the independent variable, with A multivariate linear model is established for the dependent variable, where , , to The thermal contribution coefficient of each terminal block. The environmental temperature influence coefficient. For constant offset terms, The residual term is obtained through regression analysis. , , to Estimated thermal contribution coefficients for each terminal block.

[0033] Step 103: Perform least squares regression on the multivariate linear model within the sliding time window.

[0034] In some embodiments, the monitoring terminal maintains a sliding time window, the window length being... The time required for each outgoing circuit load to undergo a complete cycle change is not less than the time needed for each circuit to complete a full cycle. For most integrated distribution boxes, the load exhibits a clear diurnal variation pattern. A 24-hour period is sufficient to cover a complete daily load cycle; for scenarios with longer load variation cycles, The timeframe is 48 to 72 hours. The sliding window advances at fixed intervals, which are smaller than the window length. For example, if the window length is 24 hours, the interval is 12 hours. Adjacent regressions share half of the data. The cumulative number of sampling points within each window... ,by For 24 hours, sampling cycle Taking 5 minutes as an example, It is 288.

[0035] Specifically, before performing regression analysis, it is first verified whether the data meets the solvability conditions. A design matrix is ​​constructed based on the squares of the effective values ​​of the currents in each associated loop at each sampling time within the sliding time window and the ambient reference temperature. The matrix is OK Each column corresponds to an observation vector at a sampling time. Calculate the condition number of the design matrix. The condition number is defined as the ratio of the maximum singular value to the minimum singular value of a matrix, reflecting the degree of linear correlation between the square signals of the currents in each associated loop. The larger the condition number, the more synchronous the load changes in each loop and the worse the signal separability.

[0036] Before constructing the design matrix X, the squares of the effective values ​​of the currents in each associated loop are normalized to eliminate numerical degradation caused by differences in the rated current magnitudes of each loop. Specifically, the normalization uses maximum absolute value scaling. ,in For the first The rated current value of the circuit, in amperes; ambient reference temperature. We directly substitute the Celsius values ​​without normalization to maintain the interpretability of the intercept term.

[0037] The specific arrangement format of the design matrix X is as follows: the row index corresponds to the sampling time within the window. (Sorted in ascending order by time), column indexes are as follows: The last column is a vector of all 1s, corresponding to the constant offset term. The design matrix is ​​directly input into the least squares solver without requiring additional data format conversion. Correspondingly, the parameter vector... The solution needs to be adjusted after output. Individual thermal contribution coefficients are calculated according to Perform inverse normalization reduction, where The normalization coefficients obtained from the regression are denoted as .

[0038] Optionally, condition number threshold The method for determining this is as follows: Given a sensor sampling accuracy of 16-bit AD conversion, the relative quantization accuracy is approximately... The relative error amplification factor of the regression coefficients is approximately equal to the product of the condition number and the quantization precision; to ensure that the relative error of the estimated thermal contribution coefficient does not exceed 5%, we take... Considering practical factors such as sensor noise and model approximation errors, in engineering, Choose a value between 500 and 1000, for example, 800.

[0039] Among them, when the condition number Exceeding the condition number threshold If the sliding time window is marked as having insufficient confidence, it will not participate in the subsequent rate of change calculation. The monitoring terminal will continue to collect data and slide the window until the load variability increases, at which point the solution will be recalculated. When the number of conditions does not exceed the condition number threshold, the next step of regression solution will be performed.

[0040] In some embodiments, after the condition number verification is passed, least squares regression is performed on the multivariate linear model within a sliding time window to obtain the estimated thermal contribution coefficients of each terminal block. The coefficients to be determined are then combined into a parameter vector. Temperature observations form a vector The least squares solution is obtained through the normal equation: ; in Given the aforementioned design matrix, Ordinary Least Squares (OLS) finds the optimal parameter estimate by minimizing the sum of squared residuals. Its principle is that it has the minimum variance among all linear unbiased estimates. In practice, it first calculates... get Given a symmetric positive definite matrix, we can find its inverse through Cholesky decomposition. Cholesky decomposition decomposes the symmetric positive definite matrix into the product of a lower triangular matrix and its transpose, with a computational complexity of O(n). Typical The values ​​range from 3 to 8, corresponding to matrix dimensions of 5 to 10, and a single solution takes no more than 100 milliseconds on an embedded processor.

[0041] After obtaining parameter estimates Then, calculate the residual variance: ; Based on this, the covariance matrix of the parameter estimation is obtained: ; Estimated thermal contribution coefficients of each terminal block standard error The relative standard error of the covariance matrix is ​​the square root of its diagonal elements: ; When the relative standard error of a certain terminal exceeds the preset standard error threshold, it indicates that the thermal sensitivity of that terminal is insufficient or the data separability is inadequate within the current window. The estimated thermal contribution coefficient of that terminal within the current window is marked as low confidence and will not be included in subsequent rate of change calculations. The standard error threshold is set to... Extracting the diagonal elements of a matrix without additional matrix operations.

[0042] For example, temperature measurement point P1 is associated with 3 loops, and the 288 sets of data within the window form a design matrix of 288 rows and 5 columns. The solution is obtained as follows: , , (All units are degrees Celsius per ampere squared). , Celsius.

[0043] In some embodiments, after obtaining the thermal contribution coefficient estimate, the model-estimated temperature value at each sampling time within the sliding time window is calculated based on the multivariate linear model and the thermal contribution coefficient estimate. The difference between the model-estimated temperature value and the actual temperature value at the corresponding sampling time is used as the residual. Calculate the root mean square value of the residuals. When the root mean square value of the residual exceeds a preset residual threshold, the sliding time window is marked as an abnormal fit and will not be included in the subsequent rate of change calculation.

[0044] The residual threshold is determined by statistically analyzing the root mean square (RMS) residual values ​​of each effective window during the initial operation phase, and taking a preset multiple of the average as the residual threshold. This preset multiple is typically 2 to 3 times the average. For example, if the average RMS residual value of each effective window during the initial operation phase is 0.4 degrees Celsius, then taking 3 times the average would result in a residual threshold of 1.2 degrees Celsius. This method adapts the residual threshold to the actual model fitting accuracy level of the current distribution box, avoiding reliance solely on the nominal accuracy of the sensors while ignoring model structural errors. An RMS residual value exceeding this threshold indicates that the fitting deviation of the current window significantly deviates from the normal level during the initial operation phase, potentially indicating abnormal operating conditions.

[0045] Following the previous example, the calculated root mean square value of the residual is 0.3 degrees Celsius, which is below the threshold of 1.5 degrees Celsius. The regression result of this window is valid, and its estimated thermal contribution coefficient will be used in the subsequent baseline comparison and rate of change calculation.

[0046] Figure 3 This is a schematic diagram illustrating the effectiveness determination of sliding time window regression provided in an embodiment of this application, such as... Figure 3 As shown, within a sliding time window, the squares of the effective values ​​of the currents in each associated loop, the ambient reference temperature, and the temperature values ​​at the measurement points are obtained, and a design matrix X is constructed accordingly. The condition number of the design matrix X is calculated. If the condition number exceeds a preset condition number threshold, the window is marked as having insufficient confidence and is not included in subsequent rate of change calculations. If the condition number does not exceed the condition number threshold, least squares regression is performed to obtain the estimated thermal contribution coefficient of each terminal. Based on the estimated thermal contribution coefficient, the model-estimated temperature value at each sampling time is calculated, and the difference between the model-estimated temperature value and the corresponding actual temperature value is used as the residual. The root mean square value of the residual is further calculated. If the root mean square value of the residual exceeds a preset residual threshold, the window is marked as an abnormal fit and is not included in subsequent rate of change calculations; otherwise, the window is considered a valid window, and its estimated thermal contribution coefficient is included in subsequent rate of change calculations.

[0047] Step 104: Baseline establishment, rate of change calculation, and degradation assessment.

[0048] In some embodiments, during the initial operation phase, the estimated thermal contribution coefficients of each terminal are averaged to obtain the baseline value. Specifically, after the system is put into operation, it first undergoes a contact stabilization waiting period, which lasts from 3 to 7 days after commissioning. During this period, each terminal undergoes an initial contact surface fine-tuning process, and data is collected but not used for baseline calculation. After the contact stabilization waiting period ends, the initial operation phase begins, lasting from 7 to 14 days, at which point the contact state of each terminal has tended to stabilize. During the initial operation phase, regression results from multiple effective windows are collected. The effective window refers to the sliding time window that has passed condition number verification and residual test. The estimated thermal contribution coefficients of each terminal within the effective window are averaged to obtain the baseline value of each terminal. : ; in The number of valid windows during the initial running phase. For the first The first valid window Estimated value of the thermal contribution coefficient of the circuit terminal block. At least five effective windows are used to reduce random fluctuations in the initial estimate. After the initial operation phase, the system enters the continuous monitoring phase.

[0049] Figure 4 This is a schematic diagram illustrating the rate of change of thermal contribution coefficient and degradation determination provided in the embodiments of this application, as shown below. Figure 4 As shown, the rate of change of the thermal contribution coefficient of each terminal is continuously monitored, using the effective window as a sequence. The rate of change is calculated according to... The calculation is performed, with the vertical axis representing the rate of change (%) and the horizontal axis representing the window number (effective window). During the initial run, the previous... Establish baseline values ​​within a valid window. During the continuous monitoring phase, the rate of change is continuously obtained according to the window number and compared with the degradation warning threshold (%). When the rate of change exceeds the preset threshold, and within a preset number of consecutive periods... When all valid windows exceed the threshold and show an increasing trend (i.e., the fitting slope is greater than 0), the degradation confirmation stage begins, and the degradation judgment condition is met. The figure also shows the baseline value. , , … , , … , , … The evolution process is as follows: When the degradation judgment condition is not triggered, the baseline value is updated according to the preset update weight; when the degradation judgment condition is triggered, the update of the baseline value stops. If the degradation judgment condition is not met, monitoring continues; if it is met, a directional early warning message is generated, pointing to the circuit to which the terminal belongs.

[0050] In one embodiment, during the continuous monitoring phase, the rate of change of the estimated current thermal contribution coefficient of each terminal relative to the baseline value is calculated. Specifically, after entering the continuous monitoring phase, the rate of change of the estimated current thermal contribution coefficient of each terminal relative to the baseline value is calculated each time a new effective window regression result is generated. ; A positive value indicates that the thermal contribution coefficient is increased relative to the baseline, reflecting that the contact resistance of the corresponding terminal is increased relative to the initial state.

[0051] In some embodiments, a targeted early warning message is generated when the rate of change meets the degradation judgment criteria. Specifically, the degradation judgment criteria include an amplitude condition and a trend condition that are met simultaneously, wherein the amplitude condition is the rate of change. Exceeding the preset degradation warning threshold The trend condition is a continuous preset quantity. The rate of change within the effective window all exceeded the degradation warning threshold and showed an increasing trend; the increasing trend was observed through continuous... The change rate values ​​corresponding to each effective window are subjected to univariate linear fitting, and the slope of the fitting is determined to be positive. Univariate linear fitting is to fit a linear function with the window number as the independent variable and the change rate as the dependent variable, and take the sign of the slope as the criterion.

[0052] Among them, the degradation warning threshold The determination is based on the fact that when the contact resistance of the terminal increases to 1.5 times its initial value, the heat generation power increases by 50%, and the risk of temperature rise increases significantly. The warning threshold is set at the level corresponding to a 30% to 50% increase in contact resistance, since the heat contribution coefficient is proportional to the contact resistance. This directly corresponds to the increase in contact resistance, with a value ranging from 30% to 50%, for example, 40%.

[0053] Among them, the number of consecutive windows Based on the window step interval and false alarm suppression requirements, when the window step interval is 12 hours, A value of 3 to 5 corresponds to a continuous exceedance of the threshold for 1.5 to 2.5 consecutive days. If the value is too small, seasonal temperature fluctuations may trigger false alarms. If the value is too large, the warning delay will increase. For example, taking... The value is 3.

[0054] In one embodiment, when both the magnitude condition and the trend condition are satisfied, the determination is made for the first... When contact deterioration occurs at a circuit terminal, a directional warning message is generated pointing to the circuit to which the terminal belongs, clearly identifying the circuit number.

[0055] For example, a temperature measurement point is associated with 3 loops, and the baseline value is... After six months of operation, the estimated heat contribution coefficients of loop 2 in three consecutive effective windows were 0.0040, 0.0042, and 0.0045, respectively, with corresponding change rates of 43%, 50%, and 61%, all exceeding [the required percentage]. Furthermore, it shows an increasing trend, the slope of the univariate linear fitting is positive, and both the amplitude condition and the trend condition are met. The system generates directional early warning information, such as "abnormal contact status of the terminal block of circuit 2".

[0056] In some embodiments, the heat dissipation conditions of the distribution box change slowly with ambient temperature and seasonal variations, and the thermal resistance of the heat conduction path fluctuates seasonally. If the baseline value is not updated for a long period, it will lead to a systematic deviation in the rate of change. Optionally, during the continuous monitoring phase, for wiring terminals that have not triggered the degradation judgment conditions, each time a new estimated value of the thermal contribution coefficient is generated, the baseline value is updated with a preset update weight: ; in To update the weights, the new baseline value is the sum of the updated weight multiplied by the current estimated thermal contribution coefficient and the remaining weight multiplied by the original baseline value.

[0057] Among them, updating weights The method for determining this is as follows: the time scale of seasonal variation is approximately 90 days, and the equivalent update time constant of the baseline should match this, i.e., it needs to be approximately... Each window step cycle covers 90 days; when the window step interval is 12 hours, 90 days corresponds to 180 step cycles. Pick The actual value range is from 0.005 to 0.02, for example, 0.01. The smaller the value, the slower the baseline updates, the weaker the ability to follow seasonal changes, but the stronger the ability to resist disturbances.

[0058] Specifically, for wiring terminals that have triggered the degradation judgment conditions, the update of the baseline value is stopped to prevent the degradation process from being absorbed by the baseline and masking the anomaly. After maintenance personnel complete the repair, the system will manually confirm the reset and re-enter the initial operation phase to collect a new baseline.

[0059] In some embodiments, the collected data within the sliding time window is preprocessed before establishing the multivariate linear model. For outgoing circuits that consistently show no current within the window, it indicates that the circuit is out of service and cannot provide effective load change information. The independent variable term corresponding to this circuit is removed from the multivariate linear model, and only circuits with load changes are retained for regression. For sampling times where the effective current value exceeds a preset multiple of the circuit's rated current, the data for this period may be outside the applicability range of the linear model. Data for this sampling time is removed from the data participating in the least squares regression solution; the preset multiple is, for example, 1.5 times.

[0060] In some embodiments, when the temperature value at a temperature measurement point experiences a sudden change, remains constant, or exceeds a preset physically reasonable range, the regression solution of the multivariate linear model corresponding to that temperature measurement point is paused until the temperature value returns to the physically reasonable range. The physically reasonable range is determined based on the installation environment and operating conditions of the distribution box, for example, from -10 degrees Celsius to 200 degrees Celsius. The criterion for a sudden change is that the difference between the temperature values ​​at two adjacent sampling times exceeds a preset jump threshold, for example, 10 degrees Celsius; the criterion for remaining constant is that the temperature values ​​are exactly the same for a consecutive preset number of sampling times, for example, 10 consecutive sampling points with unchanged readings. These abnormal situations are usually caused by sensor malfunctions; the sensor malfunction information is reported while the regression calculation is paused.

[0061] In some embodiments, the heat conduction ranges of two adjacent temperature measuring points overlap, and the terminals of the same outgoing circuit are simultaneously associated with two temperature measuring points. In this case, the two temperature measuring points independently perform the aforementioned multivariate linear model establishment and least squares regression solution process, each obtaining an estimated value of the heat contribution coefficient of the terminal in that outgoing circuit. Based on the covariance matrix of the aforementioned parameter estimation, the relative standard error (i.e., the ratio of the standard error to the estimated value) of the estimated value of the heat contribution coefficient of the terminal at each of the two temperature measuring points is calculated. The estimated value of the heat contribution coefficient corresponding to the temperature measuring point with the smaller relative standard error is selected as the estimated value of the heat contribution coefficient of that terminal and used in the subsequent calculation of the rate of change. A smaller relative standard error indicates that the temperature measuring point has higher thermal sensitivity and parameter identification accuracy for that terminal, and its estimation result is more reliable.

[0062] Alternatively, the models of two adjacent temperature measurement points can be jointly solved into a single system of equations. The consistency constraint of the overlapping loop in the two equations can be used to improve the estimation accuracy. Specifically, the observation vectors of the two temperature measurement points are vertically concatenated into a joint design matrix, and the thermal contribution coefficient of the overlapping loop in the two temperature measurement points is constrained to be equal. The solution is obtained by the least squares method with equality constraints.

[0063] In some embodiments, the computation scheduling of the monitoring terminal is arranged to be performed during periods of stable load, such as the early morning hours. A single regression calculation involves a matrix dimension of 5 to 10, with a storage overhead of approximately 10KB (calculated using a 24-hour window, a 5-minute sampling period, and 8 associated loops). The computation time on the embedded processor does not exceed 100 milliseconds. Early warning information can be uploaded to the remote monitoring platform via a communication module, using methods such as 4G or LoRa. In some embodiments, the regression calculation can also be centrally performed on the remote monitoring platform, with the monitoring terminal only responsible for data collection and uploading.

[0064] This method leverages the load timing differences in each outgoing circuit of a comprehensive distribution box due to varying power supply targets. It decomposes the mixed regional temperature signal into independent thermal contribution parameters for each terminal. By tracking the relative trends of these parameters over a long period, it can identify early contact degradation at specific terminals and provide clear circuit location information before the absolute temperature value reaches the traditional alarm threshold. This method requires no increase in the number of temperature sensors, is implemented solely at the monitoring terminal software level, and is suitable for upgrading existing intelligent integrated distribution boxes.

[0065] Please see Figure 5 , Figure 5 This is a schematic block diagram of an online monitoring system for an intelligent integrated distribution box according to this application. Figure 5 As shown, the system includes: The topology configuration and data acquisition module 501 is used to configure the topology association relationship, establish the correspondence between each temperature measuring point and the wiring terminals of each outgoing circuit within its heat conduction range, and synchronously acquire the effective value of the current of each outgoing circuit and the temperature value of each temperature measuring point at a fixed sampling period. The model building module 502 is used to establish a multivariate linear model for each temperature measuring point, with the square of the effective value of the current of each associated circuit as the independent variable, the ambient reference temperature as the additional independent variable, and the temperature value of the temperature measuring point as the dependent variable. The coefficients corresponding to each circuit in the multivariate linear model are the thermal contribution coefficients of the circuit terminals to the temperature measuring point. The regression solution module 503 is used to perform least squares regression on the multivariate linear model within a sliding time window to obtain the estimated value of the thermal contribution coefficient of each terminal. The baseline monitoring and early warning module 504 is used to average the estimated thermal contribution coefficients of each terminal during the initial operation phase to obtain the baseline value of each terminal; during the continuous monitoring phase, it calculates the rate of change of the current estimated thermal contribution coefficient of each terminal relative to the baseline value; when the rate of change of a certain terminal meets the preset deterioration judgment condition, it generates directional early warning information pointing to the circuit to which the terminal belongs.

[0066] The implementation process of the functions and roles of each module in the above system is detailed in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0067] Based on the same concept as the above-described method, this application also provides a machine-readable storage medium storing a plurality of computer instructions. When executed by a processor, these computer instructions can implement the online monitoring method for intelligent integrated distribution boxes disclosed in the above examples of this application. The machine-readable storage medium can be any electronic, magnetic, optical, or other physical storage device, and can contain or store information such as executable instructions, data, etc.

[0068] Based on the same application concept as the above method, this application embodiment also provides a computer program product, which includes a computer program that, when executed by a processor, implements the online monitoring method for intelligent integrated distribution boxes disclosed in the above examples of this application.

[0069] In the above embodiments, the descriptions of each embodiment have different focuses. Parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. The above descriptions are merely preferred embodiments of this application and explanations of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by specific combinations of the above technical features, but should also cover other technical solutions formed by arbitrary combinations of the above technical features or their equivalent features without departing from the inventive concept.

Claims

1. A method for online monitoring of an intelligent integrated distribution box, characterized in that, include: Configure topology relationships and establish the correspondence between each temperature measuring point and the wiring terminals of each outgoing circuit within its heat conduction range; The effective current value of each outgoing circuit and the temperature value of each temperature measuring point are collected synchronously at a fixed sampling period. For each temperature measuring point, a multivariate linear model is established with the square of the effective value of the current of each associated circuit as the independent variable, the ambient reference temperature as the additional independent variable, and the temperature value of the temperature measuring point as the dependent variable. The coefficients corresponding to each circuit in the multivariate linear model are the thermal contribution coefficients of the circuit terminals to the temperature measuring point. Within the sliding time window, least squares regression is performed on the multivariate linear model to obtain the estimated thermal contribution coefficient of each terminal. During the initial operation phase, the estimated thermal contribution coefficients of each terminal are averaged to obtain the baseline value of each terminal. During the continuous monitoring phase, the rate of change of the current estimated thermal contribution coefficient of each terminal relative to the baseline value is calculated. When the rate of change of a certain terminal meets the preset deterioration judgment condition, a directional early warning message pointing to the circuit to which the terminal belongs is generated.

2. The method according to claim 1, characterized in that, Before performing least squares regression on the multivariate linear model, a design matrix is ​​constructed based on the squares of the effective values ​​of the currents of each associated loop at each sampling time within the sliding time window and the ambient reference temperature, and the condition number of the design matrix is ​​calculated; when the condition number does not exceed the condition number threshold, the least squares regression is performed. After solving, the standard error of the estimated thermal contribution coefficient of each terminal is calculated based on the residual variance and the design matrix. When the relative standard error of the estimated thermal contribution coefficient of a certain terminal exceeds the preset standard error threshold, the estimated thermal contribution coefficient of that terminal in the current sliding time window is marked as low confidence and is not included in the subsequent calculation of the rate of change.

3. The method according to claim 1, characterized in that, The degradation judgment conditions include an amplitude condition and a trend condition that are satisfied simultaneously; the amplitude condition is that the rate of change exceeds a preset degradation warning threshold; the trend condition is that the rate of change in a consecutive preset number of valid windows all exceed the degradation warning threshold and show an increasing trend, and the increasing trend is determined by performing a univariate linear fitting on the rate of change corresponding to the consecutive preset number of valid windows and determining that the fitting slope is positive.

4. The method according to claim 1, characterized in that, During the continuous monitoring phase, for terminals that have not triggered the degradation judgment condition, each time a new thermal contribution coefficient estimate is generated, the baseline value is updated with a preset update weight. The new baseline value is obtained by multiplying the update weight by the current thermal contribution coefficient estimate and the remaining weight by the original baseline value.

5. The method according to claim 1, characterized in that, The model-estimated temperature value at each sampling time within the sliding time window is calculated based on the multivariate linear model and the estimated thermal contribution coefficient. The difference between the model-estimated temperature value and the corresponding actual temperature value at each sampling time is taken as the residual, and the root mean square value of the residual is calculated. When the root mean square value of the residual exceeds a preset residual threshold, the sliding time window is marked as an anomaly and will not participate in the subsequent calculation of the rate of change.

6. The method according to claim 1, characterized in that, The thermal contribution coefficient is the product of the contact resistance of the corresponding terminal and the thermal resistance of the thermal conduction path between the terminal and the temperature measuring point; the thermal conduction range is the area on both sides of the temperature sensor installation position along the busbar direction that does not exceed a preset distance, and the preset distance is determined according to the busbar material and cross-sectional area.

7. The method according to claim 1, characterized in that, The length of the sliding time window is not shorter than the time required for each outgoing circuit load to undergo a complete periodic change; the fixed sampling period is 1 / 10 to 1 / 5 of the bus exhaust heat constant.

8. The method according to claim 1, characterized in that, When the heat conduction ranges of two adjacent temperature measuring points overlap, and the terminals of the same outgoing circuit are simultaneously associated with the two adjacent temperature measuring points, the two adjacent temperature measuring points independently perform the least squares regression solution to obtain the estimated value of the heat contribution coefficient of the terminal of the outgoing circuit and its estimation standard error. The estimated value of the heat contribution coefficient of the temperature measuring point with the smaller relative standard error of the estimated value of the terminal is selected as the estimated value of the heat contribution coefficient of the terminal.

9. The method according to claim 1, characterized in that, Before establishing the multivariate linear model, the collected data within the sliding time window is preprocessed: for outgoing circuits that have no current within the window, the independent variable term corresponding to the circuit is removed from the multivariate linear model; for sampling times where the effective current value exceeds a preset multiple of the rated current of the circuit, the data at that sampling time is removed from the data participating in the least squares regression solution.

10. An intelligent integrated distribution box online monitoring system, characterized in that, include: The topology configuration and data acquisition module is used to configure the topology association, establish the correspondence between each temperature measurement point and the wiring terminals of each outgoing circuit within its heat conduction range, and synchronously acquire the effective current value of each outgoing circuit and the temperature value of each temperature measurement point at a fixed sampling period. The model building module is used to establish a multivariate linear model for each temperature measuring point, with the square of the effective value of the current of each associated circuit as the independent variable, the ambient reference temperature as the additional independent variable, and the temperature value of the temperature measuring point as the dependent variable. The coefficients corresponding to each circuit in the multivariate linear model are the thermal contribution coefficients of the circuit terminals to the temperature measuring point. The regression solution module is used to perform least squares regression on the multivariate linear model within a sliding time window to obtain the estimated thermal contribution coefficient of each terminal. The baseline monitoring and early warning module is used to average the estimated thermal contribution coefficients of each terminal during the initial operation phase to obtain the baseline value of each terminal. During the continuous monitoring phase, the rate of change of the estimated current thermal contribution coefficient of each terminal relative to the baseline value is calculated; when the rate of change of a certain terminal meets the preset deterioration judgment condition, directional early warning information pointing to the circuit to which the terminal belongs is generated.

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