Intelligent low-voltage switch cabinet system based on building intelligent function

Abstract

The application belongs to the technical field of low-voltage switch cabinet operation control, and specifically discloses a smart low-voltage switch cabinet system based on building intelligent functions, which comprises an operation sensing module, a space topology module, a fusion diagnosis module, a fault positioning module and a dynamic protection module. The system synchronously detects current and insulation resistance at the circuit wiring terminal of the low-voltage switch cabinet, collects temperature and humidity distribution data of the surrounding environment, and then fuses the coupling characteristics between the current and temperature rise, and the time sequence correlation of the insulation resistance and humidity change. The coupling and time sequence correlation visualization display is realized in the three-dimensional heat-electricity correlation topology graph constructed in the switch cabinet, so that comprehensive identification of potential faults is realized. The method effectively improves the sensitivity and accuracy of fault identification, can detect early fault signs such as poor contact and insulation deterioration earlier, enhances the system early warning capability, and ensures the safety and stability of the switch cabinet operation.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of low-voltage switch cabinet operation control, and specifically discloses a smart low-voltage switch cabinet system based on building intelligent functions. BACKGROUND

[0002] A low-voltage switch cabinet is a key device for realizing power distribution, circuit control and electrical protection in a power system, and is widely used in power distribution networks of commercial buildings and public facilities. The low-voltage switch cabinet has a relatively closed structure and is internally integrated with terminal connectors, circuit breakers, busbars and other components. During operation, the temperature may rise due to large currents, contact resistance or partial discharge. In addition, in a high-humidity environment, problems such as moisture penetration and condensation may also occur. Abnormal temperature and humidity can significantly affect the stability of the circuit operation and even cause short-circuit faults. Therefore, real-time monitoring and intelligent control of the internal environment of the low-voltage switch cabinet are key measures to ensure its safe and stable operation.

[0003] There are related schemes for monitoring and controlling the internal environment of a low-voltage switch cabinet in the prior art. For example, a safety warning device for a high-low voltage transformer cabinet is disclosed in Patent No. CN207518151U, which realizes overall monitoring of the temperature inside the cabinet through a temperature detection module and achieves a certain degree of environmental control by combining remote warning and automatic heat dissipation functions.

[0004] However, this scheme only detects the overall environment inside the cabinet in terms of temperature monitoring, without considering the spatial differences in the distribution of internal circuits of the switch cabinet. Since the heat generated by electrical equipment is locally concentrated, it is difficult to accurately reflect the actual thermal state of key circuit nodes by relying solely on overall temperature detection, which may lead to a lag in fault identification or misjudgment.

[0005] For example, Patent No. CN111146696A proposes a low-voltage switch cabinet fault detection device, which detects the temperature of each electrical component inside the low-voltage switch cabinet through a mobile temperature measurement mechanism. Compared with the traditional fixed multi-sensor scheme, this device has certain advantages in terms of cost reduction and reduction of redundant data transmission.

[0006] This scheme realizes local temperature monitoring of electrical components at different positions and issues an alarm based on a temperature threshold, improving temperature measurement flexibility and system stability. However, despite the improvement in spatial positioning, it still relies solely on a single temperature parameter as the basis for fault judgment, without considering the coupling relationship between current changes and temperature rises. Ignoring the dynamic correlation between current operating state and temperature rise may lead to false alarms or missed alarms: on the one hand, temperature rise caused by normal load fluctuations may be misjudged as a fault; on the other hand, some early faults such as poor contact may have caused current abnormalities before the temperature has significantly risen, making it difficult to issue an early warning and affecting the sensitivity and accuracy of fault identification.

[0007] In addition, the above two schemes do not monitor the humidity state inside the low-voltage switch cabinet. In a humid environment, moisture in the air may penetrate into the cabinet, easily inducing short circuit or discharge failure. Lack of humidity sensing capability will greatly reduce the operation safety of the system in complex environment, and it is difficult to achieve comprehensive evaluation and early warning of the comprehensive environment state in the cabinet. SUMMARY

[0008] In view of this, the present application aims to propose a smart low-voltage switch cabinet system based on building intelligent function, which collects temperature and humidity at the circuit layout position in the low-voltage switch cabinet, and cooperates with the current and insulation resistance of the circuit for fusion analysis, effectively improving the identification ability and early warning accuracy of potential fault types, effectively solving the problems raised in the background art.

[0009] The purpose of the present application can be achieved by the following technical scheme: a smart low-voltage switch cabinet system based on building intelligent function, comprising: an operation perception module: real-time acquisition of current effective value, insulation resistance value of circuit wiring terminal in switch cabinet and cabinet environment data including temperature, humidity distribution around circuit wiring terminal.

[0010] A spatial topology module: establishes a spatial mapping relationship between the physical coordinates of the circuit wiring terminal and the temperature / humidity sensor, and generates a three-dimensional thermal-electric correlation topology graph.

[0011] A fusion diagnosis module: the current change rate and temperature rise rate of the circuit wiring terminal in the switch cabinet are identified through covariance analysis, and the time shift relationship between insulation resistance drop and humidity rise is monitored to identify wet-electric asynchronous events.

[0012] A fault location module: marks the occurrence area of thermal-electric decoupling events and wet-electric asynchronous events in the three-dimensional thermal-electric correlation topology graph, outputs the fault type of the corresponding area when the two areas do not overlap, and calculates the centroid coordinates of the overlapping area when the two areas overlap, takes the centroid as the center of the sphere, generates a fault volume sphere with thermal diffusion speed and humidity diffusion speed as the radius, and outputs the dynamic radius value of the fault volume sphere.

[0013] A dynamic protection module: performs gradient protection according to the radius expansion rate of the fault volume sphere.

[0014] The specific implementation process of the operation perception module: according to the internal circuit layout diagram of the switch cabinet, the circuit wiring terminal part is determined, and the current effective value and insulation resistance value under the circuit operating state are obtained based on the measurement instrument data of the wiring terminal part.

[0015] The thermocouple sensor array and humidity sensing unit are embedded and arranged around the circuit wiring terminal part to obtain temperature distribution data and humidity distribution data.

[0016] The space topology module includes the following: a three-dimensional rectangular coordinate system is established based on the physical structure of the switch cabinet, and the space coordinates of the circuit wiring terminals in the cabinet are labeled.

[0017] The physical installation coordinates of the wiring terminals are recorded as reference nodes, and an electrical topology matrix is constructed based on the electrical connection relationship between the main bus and the branch circuit.

[0018] The temperature sensors and humidity sensors deployed in the switch cabinet are matched with their actual layout positions in space coordinates, and then the current effective value, insulation resistance, temperature distribution and humidity distribution data are respectively mapped to the corresponding coordinate positions to generate a multi-dimensional sensing point set.

[0019] Based on the multi-dimensional attribute point set, a three-dimensional thermal-electric correlation topology graph is generated on the electrical topology matrix.

[0020] The thermal-electric decoupling event identification refers to the following operations: extracting the current effective value at the circuit wiring terminal position in the switch cabinet according to the set collection period, and then drawing the current change curve based on the change of the current effective value over time, and then extracting the current change rate in the monitoring time window from the current change curve.

[0021] Temperature data is collected around the circuit wiring terminal to draw a temperature rise curve, and the temperature rise rate in the monitoring time window is extracted by first-order differential operation on the curve.

[0022] The current change rate sequence and the temperature rise rate sequence are time-domain aligned, and the covariance value of the two sequences is calculated as the thermal-electric degradation index.

[0023] The thermal-electric degradation index is compared with the preset reference range. When the thermal-electric degradation index is within the reference range, it is determined that the thermal-electric coupling state is normal, and when the thermal-electric degradation index deviates from the reference range, it is determined that a thermal-electric decoupling event occurs.

[0024] The wet-electric asynchronous event identification is as follows: extracting the circuit insulation resistance data in the switch cabinet according to the set collection period, and then drawing the insulation resistance time sequence curve.

[0025] Humidity monitoring data is collected around the circuit wiring terminal to construct a humidity time sequence curve.

[0026] The insulation resistance time sequence curve and the humidity time sequence curve are analyzed by point-by-point slope to obtain a number of insulation resistance drop points and a number of humidity rise points.

[0027] The identified insulation resistance drop points and humidity rise points are numbered in time sequence, and time points with the same number are matched one by one to form a time sequence matching point group.

[0028] Calculate the time difference between the humidity rising point and the insulation resistance falling point for each time sequence matching point group, and mark it as the time offset of the group.

[0029] Compare all time offsets with the thermal response time constant of the cabinet steel plate, and define the proportion of the number of point groups that meet the time constant as the wet-electric synchronization degree index.

[0030] Compare the wet-electric synchronization degree index with the preset judgment threshold. If the synchronization degree index reaches or exceeds the judgment threshold, it is determined that the wet-electric synchronous change state occurs, otherwise it is determined that the wet-electric asynchronous event occurs.

[0031] When the two regions do not overlap, the fault type of the corresponding region is output as follows: in the three-dimensional thermal-electric correlation topology graph, the connector aging fault type is output for the thermal-electric decoupling region.

[0032] In the three-dimensional thermal-electric correlation topology graph, the environmental intrusion fault type is output for the wet-electric asynchronous region only.

[0033] When the two regions overlap, the centroid coordinates of the overlapping region are calculated as follows: in the three-dimensional thermal-electric correlation topology graph, the spatial overlapping region of the thermal-electric decoupling event region and the wet-electric asynchronous event region is discretized into several small volume units.

[0034] Based on the spatial coordinates of each volume unit and its volume weight, the weighted average position is calculated as the centroid coordinates of the overlapping region.

[0035] The fault volume sphere is generated as follows: taking the centroid of the overlapping region as the center, expanding outward along multiple radially distributed radial paths, and arranging a plurality of sampling points on each path at a set interval to form a temperature and humidity joint measurement grid covering the periphery of the overlapping region.

[0036] Real-time temperature data and humidity data at the corresponding position are collected at the sampling points on each radial path.

[0037] For the time sequence data of the sampling points under each radial path, the heat diffusion rate and humidity diffusion rate on the corresponding path are estimated based on heat conduction and moisture diffusion respectively, and the diffusion rate results of all paths are classified and aggregated according to the spatial distribution characteristics to determine the typical heat diffusion rate and typical humidity diffusion rate.

[0038] The typical heat diffusion rate and typical humidity diffusion rate are fused by weighting, and the fusion diffusion distance under the thermal-hygro coupling effect is obtained combined with the length of the monitoring time window.

[0039] Taking the centroid coordinates as the center of the sphere and the fusion diffusion distance as the radius of the sphere, a fault volume sphere in three-dimensional space is constructed.

[0040] The determination of the typical heat diffusion rate and the typical humidity diffusion rate is as follows: clustering the heat diffusion rates on all radial paths to divide into several heat diffusion clustering clusters, and clustering the humidity diffusion rates of each path to divide into several humidity diffusion clustering clusters.

[0041] The number of paths contained in each heat diffusion clustering cluster is counted, and the proportion of the number of paths in the total number of paths is calculated as the proportion of the number of paths, and the angle difference of adjacent paths is obtained based on the spatial distribution position of each path, and then the standard deviation of the angle difference of adjacent paths is calculated as the heat diffusion distribution uniformity.

[0042] The product of the proportion of the number of radial paths and the heat diffusion distribution uniformity is defined as a comprehensive evaluation index, and thus the mean value of the heat diffusion rate of the paths in the cluster with the highest comprehensive evaluation index among all heat diffusion clustering clusters is selected as the typical heat diffusion rate.

[0043] Similarly, the same comprehensive evaluation process is performed on the humidity diffusion clustering cluster to select the mean value of the humidity diffusion rate of all paths in the cluster with the highest comprehensive evaluation index as the typical humidity diffusion rate.

[0044] The implementation process of the dynamic protection module is as follows: the expansion rate is calculated based on the dynamic radius of the fault volume sphere, and compared with the warning threshold, if the warning threshold is reached, the first level response is started: the circuit breaker covering all circuits of the fault volume sphere is disconnected, and the standby power supply incoming line contactor is closed.

[0045] If the warning threshold is not reached, the second level response is started: when the heat diffusion rate is higher than the humidity diffusion rate, local temperature rise monitoring and current limiting protection are started; when the humidity diffusion rate is higher than the heat diffusion rate, insulation state strengthening monitoring and dehumidification linkage control are started.

[0046] Compared with the prior art, the beneficial effects of the present application are as follows: 1. The present application synchronously detects current and insulation resistance at the circuit wiring terminal of the low-voltage switch cabinet, and collects the temperature and humidity distribution data of the surrounding environment, integrates the coupling characteristics between current and temperature rise, and the time sequence correlation of insulation resistance and humidity change, to realize comprehensive identification of potential faults. This method effectively improves the sensitivity and accuracy of fault identification, can detect early fault signs such as poor contact and insulation deterioration earlier, enhances the system warning capability, and ensures the safety and stability of the operation of the switch cabinet.

[0047] 2、The application identifies the thermal-electric decoupling area by constructing a three-dimensional thermal-electric correlation topology graph in the switch cabinet and combining the current and temperature coupling relationship, determines the wet-electric asynchronous area according to the insulation resistance and humidity time delay relationship, utilizes the spatial overlap characteristics of the two to perform fault positioning and dynamic protection, realizes the visual expression of the fault evolution process, improves the intuitiveness and decision efficiency of fault identification, can take control measures in time according to the fault diffusion trend, and significantly enhances the safety and stability of system operation. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0049] Figure 1 It is a schematic diagram of the system in the present application.

[0050] Figure 2 It is a flow chart of the thermal-electric decoupling event identification in the present application.

[0051] Figure 3 It is a flow chart of the wet-electric asynchronous event identification in the present application. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0053] The present application proposes a smart low-voltage switch cabinet system based on intelligent building functions, which includes an operation perception module, a spatial topology module, a fusion diagnosis module, a fault positioning module and a dynamic protection module.

[0054] Referring to Figure 1 The above modules are connected in sequence to form a closed-loop monitoring and intelligent response system, ensuring the automation and intelligentization of the whole process from data acquisition to fault processing.

[0055] The operation perception module is used to collect the current effective value, insulation resistance value and cabinet environment data including temperature and humidity distribution around the circuit wiring end in the switch cabinet in real time.

[0056] In the preferred embodiment of the present application, the above-mentioned module is implemented as follows: the circuit terminal positions are determined according to the internal circuit layout of the switch cabinet, and the current effective value and insulation resistance value in the circuit operating state are obtained based on the meter data of the terminal positions.

[0057] It should be noted that the current parameter used in the present application is the current effective value, which is defined as the thermal effect generated by an alternating current in one complete cycle and the corresponding value of the equivalent direct current, and is the core physical quantity for evaluating the transmission capacity of alternating current energy. Compared with the instantaneous current value, the current effective value can more accurately reflect the actual energy transmission level in the circuit and the real operating state of the load. Therefore, selecting the current effective value as the input parameter when analyzing the coupling relationship between current and temperature helps to improve the accuracy and stability of the thermal-electric decoupling event identification and enhance the system's ability to capture potential fault features.

[0058] The temperature distribution data and humidity distribution data are obtained by embedding the thermocouple sensor array and humidity sensing unit around the space of the circuit terminal position.

[0059] It should be understood that the terminal in the low-voltage switch cabinet is a key physical interface for realizing circuit connection and energy transmission, and its contact state directly affects the current flow efficiency and local heating level. Due to structural characteristics and environmental factors, moisture easily enters along the wire channel or cabinet gap and accumulates in the terminal area, resulting in a decrease in insulation performance and an increase in short circuit or discharge risk. Therefore, the terminal position is a typical area where thermal-electric coupling effect and wet-electric interaction occur.

[0060] Taking the terminal as the key monitoring object helps to improve the sensitivity of early fault identification and significantly reduce the positioning range of potential fault areas. In addition, the internal switch cabinet is usually equipped with measuring instruments for monitoring the operating state of the circuit, such as ammeters, insulation resistance testing devices, etc., which facilitates direct acquisition of key electrical parameters and provides reliable data support for fault diagnosis.

[0061] The spatial topology module is used to establish the spatial mapping relationship between the physical coordinates of the circuit terminal and the temperature / humidity sensors, and generate a three-dimensional thermal-electric correlation topology map.

[0062] In another preferred embodiment of the present application, the above-mentioned module includes the following content: a three-dimensional rectangular coordinate system is established based on the physical structure of the switch cabinet, and the circuit terminals in the cabinet are marked with spatial coordinates according to the equipment installation position.

[0063] The specific implementation applied to the above scheme is as follows: taking the bottom left corner of the front face of the switch cabinet as a reference datum point, the datum point is set as the origin of the coordinate system; the X-axis extends from left to right along the width direction of the cabinet; the Y-axis extends from bottom to top along the height direction of the cabinet; and the Z-axis extends from front to back along the depth direction of the cabinet.

[0064] Further, after establishing the three-dimensional rectangular coordinate system, the physical installation position of the wiring terminal is determined according to the internal structure drawing and equipment layout drawing of the switch cabinet, and the three-dimensional space coordinate of the wiring terminal is marked to form a unique identifier of the wiring terminal in the established coordinate system.

[0065] The above-mentioned establishment of a unified three-dimensional coordinate system based on the actual structure of the switch cabinet digitizes the position information of the physical equipment, and provides a geometric basis for subsequent spatial mapping and visualization.

[0066] The physical installation coordinates of the wiring terminal are recorded as a reference node, and an electrical topology matrix is constructed in combination with the electrical connection relationship between the main bus and the branch circuit.

[0067] The above-mentioned construction of the electrical topology matrix is to describe the electrical energy transmission path and the hierarchical and connection attributes between the devices.

[0068] The temperature sensor and the humidity sensor deployed in the switch cabinet are matched with the actual layout position in the spatial coordinates, and then the current and the insulation resistance are combined with the temperature distribution and the humidity distribution data to be respectively mapped to the corresponding coordinate positions to generate a multi-dimensional attribute point set.

[0069] Based on the multi-dimensional attribute point set, a three-dimensional thermal-electric correlation topology graph is generated on the electrical topology matrix.

[0070] The above-mentioned use of the spatial topology model realizes the state synchronization between the physical entity and the virtual model, and constructs a power distribution equipment operation state perception system with real-time and visualization.

[0071] It should be noted that, since the internal electrical device failure of the switch cabinet usually shows localized characteristics and significant spatial dependence, the method of spatial topology modeling can more accurately locate the fault source.

[0072] The fusion diagnosis module is used for identifying thermal-electric decoupling events through covariance analysis of the current change rate and the temperature rise rate of the circuit wiring terminal in the switch cabinet, and identifying wet-electric asynchronous events by monitoring the time shift relationship between the insulation resistance drop and the humidity rise.

[0073] In one of the above-mentioned modules, referring to Figure 2As shown, the thermal-electric decoupling event identification refers to the following operations: extracting the current effective value at the circuit terminal position in the switch cabinet according to the set data acquisition period, and then drawing the current change curve based on the change of the current effective value over time, and then extracting the current change rate in the monitoring time window from the current change curve.

[0074] It needs to be added that the above-mentioned data acquisition period is set as a time interval compatible with the low-voltage switch cabinet operation fault diagnosis period, which can be dynamically adjusted according to the typical operation condition and load fluctuation characteristics of the terminal load it serves. Specifically, based on the daily operation cycle law exhibited by the load in the time dimension, for example, according to the peak segment, stable segment or fixed time interval such as every two hours of daily load change as the data acquisition period, thereby realizing efficient monitoring of key parameters and effective capture of fault characteristics.

[0075] It needs to be further added that the above-mentioned monitoring time window refers to a time unit corresponding to the set data acquisition period for dividing the analysis period in the continuous monitoring process, so as to obtain the current dynamic characteristics in different time periods. Specifically, the time window can be set to several minutes according to system requirements, for example, taking every ten minutes as an analysis unit, and the change trend of the current effective value is locally fitted and the slope is calculated in the time range, so as to extract the current change rate in the period as a current change index reflecting the dynamic characteristics of the load, which is used for subsequent thermal-electric decoupling event identification and fault evolution trend judgment.

[0076] Temperature data is synchronously collected around the circuit terminal to draw a temperature rise curve, and the temperature rise rate in the monitoring time window is extracted by first-order differential operation of the curve, which is used to represent the dynamic evolution characteristics of the local heating process.

[0077] The essence of the above-mentioned first-order differential operation of the temperature rise curve lies in quantifying the instantaneous rate of temperature change in the time dimension. The specific implementation is: the temperature rise curve drawn in the set data acquisition period is first segmented according to the preset monitoring time window to form several continuous local curve segments; then each segment of the curve is mathematically fitted to improve data smoothness and calculation stability; then the finite difference method is applied to perform first-order differential calculation on the fitted curve segment, so as to obtain the temperature rise rate value at each time node, and then the sliding time window mechanism is introduced to perform rolling analysis on the temperature rise rate at each time point in the monitoring time window, thereby obtaining the temperature rise rate in each monitoring time window.

[0078] The current change rate sequence and the temperature rise rate sequence are time-domain aligned to ensure that they have the same sampling starting point and time resolution, and the covariance value of the two sequences is calculated as the thermal-electric degradation index, which is used to represent the consistency degree between electrical parameters and thermal response.

[0079] It needs to be explained that the change of current in the circuit under normal operating conditions will usually trigger the corresponding temperature response, which is due to the Joule heating effect when the current flows through the conductor, causing the local temperature to rise accordingly. Therefore, there is a clear physical correlation between current and temperature. To quantify the correlation between the dynamic changes of the two, the covariance is introduced as a statistical analysis tool to measure the degree of linear cooperation between the current change rate and the temperature rise rate. Covariance reflects the consistency of the changes of two variables in time series: if the change trends of the two are highly synchronized, it indicates that they are in a normal thermal-electric coupling state; otherwise, if the covariance deviates significantly from the expected range, it may indicate that there is poor contact, local overheating or other abnormal fault phenomena in the circuit in the low-voltage switchgear.

[0080] The thermal-electric degradation index is compared with the preset reference range. When the thermal-electric degradation index is within the reference range, it indicates that the current change and the temperature rise response have good synchronization, and the thermal-electric coupling state is normal. When the thermal-electric degradation index deviates from the reference range, it is determined that a thermal-electric decoupling event has occurred, indicating that there is an abnormal decoupling phenomenon between the current and the temperature rise, which may be caused by poor contact, local overheating or conductor aging and other faults.

[0081] The reference range of the thermal-electric degradation index described above reflects the typical coupling characteristics between the current change and the temperature rise response of the low-voltage switchgear under normal operating conditions, and is an important reference for determining whether the system is in a healthy state. It can be obtained based on historical operating data. Specifically, the current change rate and temperature rise rate data in multiple periods under normal operating conditions of the low-voltage switchgear are collected to calculate the corresponding thermal-electric degradation index sequence, and statistical methods such as mean ± standard deviation are used to determine the fluctuation interval under normal operating conditions. Then, the statistical interval is used as the initial reference range, reflecting the thermal-electric response characteristics of the equipment in a healthy state.

[0082] In another implementation manner of the above module, referring to Figure 3 As shown in the figure, the wet-electric asynchronous event recognition is as follows: the circuit insulation resistance data is extracted in the switchgear according to the set collection period, and then the insulation resistance time series curve is drawn.

[0083] The humidity monitoring data around the circuit wiring end is collected synchronously to construct the humidity time series curve.

[0084] The insulation resistance time series curve and the humidity time series curve are analyzed by point-by-point slope to obtain a plurality of insulation resistance drop points and a plurality of humidity rise points, respectively.

[0085] The identified insulation resistance drop points and humidity rise points are numbered in time sequence, and time points with the same number are matched one by one to form a time series matching point group.

[0086] For each time sequence matching point group, the time difference between the humidity rising point and the insulation resistance falling point is calculated, denoted as the time offset of the group.

[0087] All time offsets are compared with the thermal response time constant of the cabinet steel plate, and the proportion of the number of point groups that meet the time constant is defined as the humidity-electricity synchronization degree index, which is used to quantify and evaluate the time sequence consistency between humidity change and insulation performance degradation.

[0088] It needs to be understood that in a high humidity environment, the insulation material of the circuit is easy to absorb water, which causes its volume resistivity to drop significantly, thereby increasing the risk of partial discharge and even insulation breakdown. Therefore, there is a clear physical coupling relationship between humidity change and insulation performance. Specifically, humidity rise usually precedes insulation performance degradation, forming a time sequence. This time sequence is reflected in the fact that the switch cabinet steel plate and internal components have certain thermal inertia and response delay to changes in environmental temperature and humidity. This delay characteristic can be calculated through the specific heat capacity and thermal conductivity of the cabinet material, and is used to evaluate whether the time sequence relationship between humidity rise and insulation degradation is reasonable.

[0089] It needs to be noted that when evaluating the matching relationship between the time offset and the thermal response time constant of the cabinet steel plate, the time offset is not strictly limited to being equal to the time constant, but a floating range is set as a tolerance interval, for example, the thermal response time constant is centered and a certain percentage is floated up and down. Time offsets within this range are considered to be consistent with the expected response characteristics of the system, i.e., they are determined to meet the thermal response time constant.

[0090] It needs to be explained that if the time offset between humidity rise and insulation resistance drop meets the thermal response time constant of the cabinet, it means that the changes of the two meet the expected physical law and belong to the normal humidity-electricity coupling behavior. When the insulation material ages or is severely damp, its sensitivity to humidity change increases, and even a small rise in humidity can cause the insulation resistance to drop rapidly. In such cases, the time offset will be significantly reduced, even close to zero, which is much smaller than the thermal response time constant. The proportion of the number of point groups whose time offset meets the thermal response time constant is introduced to reflect the consistency of the overall trend, rather than based on the occasional fluctuations of individual points. If most of the time offsets are within the reasonable range, it means that the system is in a healthy state. If a large number of time offsets are much smaller than the time constant, it may indicate that the insulation material has been damaged and is abnormally sensitive to humidity changes, indicating potential failure risks.

[0091] The wet-electric synchronization degree index is compared with a preset judgment threshold. If the synchronization degree index reaches or exceeds the judgment threshold, it is determined that the wet-electric synchronization change state exists, indicating that there is a reasonable time coupling relationship between the humidity rise and the insulation resistance drop. Otherwise, it is determined that a wet-electric asynchronous event occurs, indicating that there is an abnormal time misalignment between the two, which may be caused by local moisture, insulation deterioration, or environmental response delay.

[0092] In particular, the judgment threshold is a reference benchmark for determining whether the wet-electric synchronization degree index is within a normal range, usually expressed as a percentage or a ratio value, and is a key criterion for distinguishing between a wet-electric synchronization change state and a wet-electric asynchronous event. The initial threshold value can be set in combination with the insulation material humidity response curve or aging curve provided by the equipment at the time of factory delivery.

[0093] As applied to the above operation instructions, simply relying on insulation resistance drop or humidity rise as an alarm basis is prone to false positives or false negatives. However, the introduction of time sequence consistency analysis of the two can effectively distinguish between normal environmental fluctuations and real insulation deterioration, improving the accuracy of diagnosis.

[0094] The fault location module is used to mark the occurrence area of the thermal-electric decoupling event and the wet-electric asynchronous event in the three-dimensional thermal-electric correlation topology graph. When the two areas do not overlap, the fault type of the corresponding area is output. When the two areas overlap, the centroid coordinates of the overlapping area are calculated. With the centroid as the center of the sphere, the fault volume sphere is generated by merging the thermal diffusion speed and the humidity diffusion speed as the radius, and the dynamic radius value of the fault volume sphere is output.

[0095] As an optional embodiment of the above module, when the two areas do not overlap, the fault type of the corresponding area is output as follows: in the three-dimensional thermal-electric correlation topology graph, for the thermal-electric decoupling area, it is determined that the electrical connection state is deteriorated, and the connector aging fault type is output, indicating that there may be structural problems such as poor conductor contact, joint oxidation, or loose fasteners in this area.

[0096] In the three-dimensional thermal-electric correlation topology graph, for the wet-electric asynchronous area only, it is determined that the insulation abnormality is caused by environmental factors, and the environmental intrusion fault type is output, indicating that there may be environmental interference problems such as a decrease in cabinet sealing, local moisture, or external water vapor penetration.

[0097] It should be noted that when the two areas do not overlap, it indicates that the two types of faults are caused by independent physical mechanisms and act on different areas. By distinguishing these independent fault areas, it is helpful to more accurately locate the fault source, develop effective preventive maintenance plans, and reduce potential safety hazards.

[0098] As a further optional embodiment of the above module, the generating process of the fault volume sphere is as follows: determining the centroid coordinates of the spatial overlapping area of the thermal- electrical decoupling event area and the wet- electrical asynchronous event area in the three-dimensional thermal- electrical correlation topology map, which is implemented as follows: discretizing the spatial overlapping area of the thermal- electrical decoupling event area and the wet- electrical asynchronous event area in the three-dimensional thermal- electrical correlation topology map into a plurality of small volume units.

[0099] The weighted average position of each volume unit is calculated based on its spatial coordinates and its volume weight, as the centroid coordinates of the overlapping area.

[0100] It should be noted that the centroid coordinates of the above-mentioned overlapping area reflect the core position where the thermal- electrical decoupling event and the wet- electrical asynchronous event cooperate most intensively. In the dynamic fault evolution process, the centroid can be used as the starting point of the fault influence diffusion.

[0101] In the process of determining the centroid coordinates, the complex three-dimensional overlapping area is divided into a plurality of small volume units, each unit representing the temperature and humidity state in a local area, which can effectively convert the continuous space problem into discrete mathematical calculation, facilitating computer processing and analysis. At the same time, by subdividing the units, the accuracy of the calculation results is improved, and then based on the spatial coordinates of each volume unit and its volume weight, the weighted average position is calculated as the centroid coordinates, which takes into account the actual volume contribution of each volume unit, ensuring that the calculation of the centroid coordinates is more accurate. For irregularly shaped or non-uniformly distributed overlapping areas, this weighted average method can better reflect the actual center of gravity position than simple geometric center calculation.

[0102] Expanding outward along multiple radially distributed radial paths with the centroid as the center, arranging a plurality of sampling points on each path at a set interval to form a temperature and humidity joint measurement grid covering the periphery of the overlapping area, ensuring comprehensive perception of the local environmental state.

[0103] Real-time temperature data and humidity data at the corresponding positions are collected at the sampling points on each radial path.

[0104] The heat diffusion rate and humidity diffusion rate on each radial path are estimated based on the heat conduction and moisture diffusion of the time series data of the sampling points on each radial path, and the diffusion rate results of all paths are classified and aggregated according to the spatial distribution characteristics to determine the typical heat diffusion rate and typical humidity diffusion rate, thereby more accurately reflecting the heat- humidity propagation characteristics of the periphery of the overlapping area.

[0105] It should be noted that the heat diffusion velocity reflects the speed of heat propagation from a high temperature area to a low temperature area, which is usually described by the heat conduction equation, where represents the temperature, denotes time, denotes thermal diffusivity, denotes Laplacian, representing the second order derivative of temperature in space.

[0106] Similarly, the moisture diffusivity characterizes the ability of water to migrate within the material or the surrounding environment, and its diffusion behavior can be modeled by the moisture diffusion equation where denotes moisture, denotes time, denotes moisture diffusivity.

[0107] In the present invention, the thermal diffusivity and the moisture diffusivity can be estimated by collecting temperature and moisture data at multiple spatial sampling points on each radial path, combining the physical distance between adjacent sampling points, using finite difference method to estimate its spatial gradient, and obtaining the corresponding diffusivity through inversion calculation. This calculation method belongs to the existing heat and mass transfer analysis technology category, which will not be described here.

[0108] Applied to the above scheme, the determination of the typical thermal diffusivity and the typical moisture diffusivity is implemented as follows: the thermal diffusivities on all radial paths are clustered to divide them into several thermal diffusivity clusters, and similarly, the moisture diffusivities of each path are clustered to divide them into several moisture diffusivity clusters.

[0109] For each thermal diffusivity cluster, the number of paths it contains is counted and its proportion in the total number of paths is calculated as the path number proportion, and based on the spatial distribution position of each path, the angle difference between adjacent paths is obtained, and then the standard deviation of the angle difference between adjacent paths is calculated as the thermal diffusivity distribution uniformity.

[0110] It should be noted that the radial paths in the three-dimensional topological graph are distributed at different angles, and their directional information can be represented by the angle between them and a reference coordinate axis, such as the X-axis. Specifically, there is an angle between each path and the reference coordinate axis, and the angle difference between adjacent paths reflects the distribution characteristics of these paths in space. When the standard deviation of the angle difference between adjacent paths is small, it means that the distribution of the paths is concentrated, which means that the diffusion is mainly concentrated in a certain direction; a larger standard deviation indicates that the distribution of the paths is more uniform, indicating that the thermal diffusion has similar diffusion characteristics in all directions, showing a wider thermal diffusion coverage.

[0111] The product of the radial path number proportion and the thermal diffusivity distribution uniformity is defined as the comprehensive evaluation index, and thus the mean value of the path thermal diffusivity in the cluster with the highest comprehensive evaluation index among all thermal diffusivity clusters is selected as the typical thermal diffusivity.

[0112] It needs to be supplemented that, since the value range of the radial path quantity ratio is [0, 1], in order to ensure dimensional consistency, the standard deviation of the angle difference between adjacent paths needs to be normalized, so that it also falls within the interval [0, 1].

[0113] Similarly, the same comprehensive evaluation process is performed on the humidity diffusion clustering cluster to select the average humidity diffusion rate of all paths in the cluster with the highest comprehensive evaluation index as the typical humidity diffusion rate.

[0114] In the above scheme, the path quantity ratio is used to represent the statistical significance of a certain clustering cluster in its overall path distribution, reflecting the representativeness of the cluster in quantity; and the thermal diffusion distribution uniformity describes the balance degree of the cluster in the spatial angle distribution, embodying the universality of its spatial coverage. By multiplying these two parameters to construct a comprehensive evaluation index, the overall representativeness of each clustering cluster can be quantitatively evaluated while taking into account the breadth of path quantity distribution and the balance of spatial direction coverage, which helps to identify the dominant mode cluster with extensive statistical and spatial representativeness, ensuring that the typical thermal diffusion rate extracted can truly reflect the environmental conditions and material heat transfer characteristics of the system as a whole.

[0115] The typical thermal diffusion rate and the typical humidity diffusion rate are weighted and fused, and the fusion diffusion distance under the thermal-hygroscopic coupling is obtained by combining the length of the monitoring time window.

[0116] It should be understood that the present application adopts a linear weighted fusion method of thermal diffusion speed and humidity diffusion speed, rather than simply selecting the maximum value of the two as the expansion rate of the fault volume sphere. The main reason is that in the actual operating environment, temperature and humidity often act together on the aging and failure evolution process of electrical equipment. If only one factor is considered, the important influence of another parameter on insulation performance degradation or local overheating may be ignored. By introducing a weighted fusion mechanism, the failure development trend under the synergistic action of temperature and humidity can be more comprehensively reflected, and the weight distribution can be dynamically adjusted according to the influence of environmental parameters on the probability of failure occurrence in different application scenarios, thereby improving the accuracy and adaptability of risk assessment.

[0117] Specifically, the weight can be determined based on the statistical analysis results of historical failure cases of low-voltage switch cabinets. By summarizing the temperature and humidity conditions when the typical failure occurs, the proportion of failure occurrence under temperature conditions and the proportion of failure occurrence under humidity conditions are obtained, which quantifies the relative importance of temperature and humidity in failure evolution, and then reasonably allocates the respective weighting coefficients.

[0118] A fault volume sphere in a three-dimensional space is constructed with the centroid coordinates as the center of the sphere and the fusion diffusion distance as the radius of the sphere.

[0119] The present application focuses on the spatial intersection of thermal-electric decoupling and wet-electric asynchronous events, because poor electrical connection easily causes local temperature rise, and humidity rise may cause insulation deterioration. When both occur in the same area, it may cause serious composite faults. To accurately assess its impact, the dynamic diffusion characteristics of the fault need to be considered, and the fault volume sphere is constructed with the centroid of the overlapping area as the sphere center, combined with the thermal diffusion rate and humidity diffusion rate. Because the diffusion rate represents the migration ability of heat and moisture respectively, it directly determines the expansion speed of the fault influence range. By fusing the two as the sphere radius, accurate positioning and evolution trend prediction of composite electrical faults are realized, and the foresight and accuracy of fault response are improved.

[0120] The dynamic protection module is used to perform gradient protection according to the expansion rate of the fault volume sphere radius, and the specific implementation process is as follows: the expansion rate is calculated based on the dynamic radius of the fault volume sphere, that is, the growth speed of the sphere radius per unit time, and compared with the warning threshold. If the warning threshold is reached, it indicates that the fault influence range is expanding rapidly, and there is a risk of causing a chain fault, then the first level response is started: the circuit breaker that breaks all the circuits covered by the fault volume sphere is started, the fault propagation path is cut off, the standby power supply inlet contactor is closed, the load automatic switching is realized, and the power supply continuity is ensured.

[0121] The warning threshold reflects the safety tolerance of the system to the fault expansion speed, and a reasonable threshold can be set according to the design specifications and industry standards of related electrical equipment.

[0122] If the warning threshold is not reached, the second level response is started: when the thermal diffusion rate is higher than the humidity diffusion rate, it is determined that the heat source type fault evolution trend is dominated by temperature rise, and local temperature rise monitoring and current limiting protection are started; when the humidity diffusion rate is higher than the thermal diffusion rate, it indicates that the risk of environmental intrusion type fault increases, and insulation state strengthening monitoring and dehumidification linkage control are started.

[0123] Through the above operation, the response level and execution strategy of protection action are dynamically adjusted, realizing the intelligent transition from passive trip to active intervention.

[0124] The above embodiments can be realized all or partially by software, hardware, firmware or any combination thereof. When realized by software, the above embodiments can be realized in the form of a computer program product all or partially.

[0125] Those skilled in the art can understand that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0126] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0127] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0128] Finally, the above is only the preferred embodiments of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be covered in the protection scope of the present application.

Claims

1. A smart low-voltage switch cabinet system based on building intelligent functions, characterized in that, The application comprises the following modules: Running awareness module: real-time acquisition of circuit terminal current effective value, insulation resistance value and circuit terminal surrounding temperature and humidity distribution data in the switch cabinet; Spatial topology module: establish spatial mapping relationship between circuit terminal physical coordinates and temperature / humidity sensors, generate three-dimensional thermal-electric correlation topology graph; Fusion diagnosis module: identify thermal-electric decoupling events through covariance analysis of circuit terminal current change rate and temperature rise rate, and identify wet-electric asynchronous events through monitoring of insulation resistance drop and humidity rise time shift relationship; Fault location module: mark the occurrence area of thermal-electric decoupling events and wet-electric asynchronous events in the three-dimensional thermal-electric correlation topology graph, output the fault type of the corresponding area when the two areas do not overlap, calculate the centroid coordinates of the overlapping area when the two areas overlap, generate a fault volume sphere with the centroid as the sphere center and the thermal diffusion speed and humidity diffusion speed as the radius, and output the dynamic radius value of the fault volume sphere; Dynamic protection module: perform gradient protection according to the fault volume sphere radius expansion rate.

2. The intelligent low-voltage switch cabinet system based on building intelligent functions according to claim 1, characterized in that: The specific implementation process of the running awareness module is as follows: According to the internal circuit layout of the switch cabinet, the circuit terminal part is determined, and the current effective value and insulation resistance value under the circuit running state are obtained based on the data acquisition of the measuring instruments equipped at the terminal part; The thermocouple sensor array and humidity sensor unit are embedded in the space around the circuit terminal part to obtain temperature distribution data and humidity distribution data. 3.The intelligent low-voltage switch cabinet system based on building intelligent functions according to claim 1, characterized in that: The spatial topology module comprises the following contents: A three-dimensional rectangular coordinate system is established based on the physical structure of the switch cabinet, and the spatial coordinates of the circuit terminals in the cabinet are labeled; The physical installation coordinates of the terminals are recorded as reference nodes, and an electrical topology matrix is constructed based on the electrical connection relationship between the main bus and the branch circuit; The temperature sensors and humidity sensors deployed in the switch cabinet are matched with their actual layout positions in space coordinates, and then the current effective value, insulation resistance, temperature distribution and humidity distribution data are respectively mapped to the corresponding coordinate positions to generate a multi-dimensional sensing point set; Based on the multi-dimensional attribute point set, a three-dimensional thermal-electric correlation topology graph is generated on the electrical topology matrix. 4.The intelligent low-voltage switch cabinet system based on building intelligent functions according to claim 3, characterized in that: The thermal-electric decoupling event identification is as follows: In the switch cabinet, the current effective value is extracted at the circuit terminal position according to the set acquisition period, and then the current change curve is drawn based on the change of the current effective value with time, and then the current change rate in the monitoring time window is extracted from the current change curve; Temperature data is collected around the circuit terminal to draw a temperature rise curve, and the temperature rise rate in the monitoring time window is extracted by first-order differential operation on the curve; The current change rate sequence and the temperature rise rate sequence are time-domain aligned, and the covariance value of the two sequences is calculated as the thermal-electric degradation index; Compare the thermal-electric degradation index with the preset reference range. When the thermal-electric degradation index is within the reference range, it is judged that the thermal-electric coupling state is normal, and when the thermal-electric degradation index deviates from the reference range, it is judged that a thermal-electric decoupling event occurs. 5.The intelligent low-voltage switch cabinet system based on building intelligent functions according to claim 3, characterized in that: The wet-electric asynchronous event identification is as follows: Extracting circuit insulation resistance data according to a set acquisition period in the switch cabinet, and then drawing an insulation resistance time sequence curve; Synchronously collecting humidity monitoring data around the circuit wiring end to construct a humidity time sequence curve; Obtaining a plurality of insulation resistance drop points and a plurality of humidity rise points through point-by-point slope analysis of the insulation resistance time sequence curve and the humidity time sequence curve respectively; Numbering the identified insulation resistance drop points and humidity rise points in time sequence, and grouping the time points with the same number into one-to-one time sequence matching points; Calculating the time difference between the humidity rise point and the insulation resistance drop point in each time sequence matching point group as the time offset of the group; Comparing all time offsets with the thermal response time constant of the cabinet steel plate, and defining the proportion of the number of point groups whose time offsets meet the time constant as the humidity-electricity synchronization degree index; Comparing the humidity-electricity synchronization degree index with a preset judgment threshold, if the synchronization degree index reaches or exceeds the judgment threshold, it is determined that the humidity-electricity is in a synchronous change state, otherwise it is determined that a humidity-electricity asynchronous event occurs. 6.The intelligent low-voltage switch cabinet system based on building intelligent functions according to claim 1, wherein: The following content is output when the two regions do not overlap: In the three-dimensional thermal-electricity correlation topology graph, the connector aging failure type is output for the thermal-electricity decoupling region; In the three-dimensional thermal-electricity correlation topology graph, the environmental intrusion failure type is output for the only humidity-electricity asynchronous region. 7.The intelligent low-voltage switch cabinet system based on building intelligent functions according to claim 1, wherein: The following operation is performed when the two regions overlap: Discretize the spatial overlapping region of the thermal-electricity decoupling event region and the humidity-electricity asynchronous event region into a plurality of small volume units in the three-dimensional thermal-electricity correlation topology graph; Calculate the weighted average position of each volume unit based on its spatial coordinates and volume weight, as the centroid coordinates of the overlapping region. 8.The intelligent low-voltage switch cabinet system based on building intelligent functions according to claim 1, wherein: The following is the generation process of the fault volume sphere: Expand outward along multiple radially distributed radial paths with the centroid of the overlapping region as the center, arrange a plurality of sampling points on each path at a set interval to form a temperature and humidity joint measurement grid covering the periphery of the overlapping region; Collect real-time temperature data and humidity data at the sampling points on each radial path respectively; For the sampling point time sequence data of each radial path, estimate the thermal diffusion rate and humidity diffusion rate on the corresponding path based on heat conduction and moisture diffusion respectively, and classify and aggregate the diffusion rate results of all paths according to the spatial distribution characteristics to determine the typical thermal diffusion rate and the typical humidity diffusion rate; Weighted fusion of the typical thermal diffusion rate and the typical humidity diffusion rate, and combined with the length of the monitoring time window to obtain the fusion diffusion distance under the thermal-humidity coupling effect; Take the centroid coordinates as the center of the sphere and the fusion diffusion distance as the radius of the sphere to construct the fault volume sphere in three-dimensional space. 9.The intelligent low-voltage switch cabinet system based on building intelligent functions according to claim 8, characterized in that: The following operation is performed to determine the typical thermal diffusion rate and the typical humidity diffusion rate: Cluster the thermal diffusion rates on all radial paths to divide them into a plurality of thermal diffusion clustering clusters, and similarly cluster the humidity diffusion rates of each path to divide them into a plurality of humidity diffusion clustering clusters; The number of paths contained in each thermal diffusion clustering cluster is counted, and the proportion of the number of paths in the total number of paths is calculated as the proportion of the number of paths, and the angle difference between adjacent paths is obtained based on the spatial distribution position of each path, and then the standard deviation of the angle difference between adjacent paths is calculated as the uniformity of thermal diffusion distribution; The product of the radial path proportion and the thermal diffusion distribution uniformity is defined as the comprehensive evaluation index, and the mean value of the path thermal diffusion rate in the cluster with the highest comprehensive evaluation index among all thermal diffusion clustering clusters is selected as the typical thermal diffusion rate; Similarly, the same comprehensive evaluation process is performed on the humidity diffusion clustering cluster to select the mean value of the humidity diffusion rate of all paths in the cluster with the highest comprehensive evaluation index as the typical humidity diffusion rate. 10.The intelligent low-voltage switch cabinet system based on building intelligent functions according to claim 8, characterized in that: The implementation process of the dynamic protection module is as follows: Based on the dynamic radius of the fault volume sphere, the expansion rate is calculated, and compared with the warning threshold, if the warning threshold is reached, the first level response is started: the circuit breaker covering all circuits of the fault volume sphere is disconnected, and the standby power supply inlet contactor is closed; If the warning threshold is not reached, the second level response is started: when the thermal diffusion rate is higher than the humidity diffusion rate, local temperature rise monitoring and current limiting protection are started; when the humidity diffusion rate is higher than the thermal diffusion rate, insulation state strengthening monitoring and dehumidification linkage control are started.

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