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

Abstract

The invention belongs to the technical field of low-voltage switch cabinet operation control, and particularly discloses an intelligent low-voltage switch cabinet system based on a building intelligent function, which comprises an operation sensing module, a spatial topology module, a fusion diagnosis module, a fault positioning module and a dynamic protection module, current and insulation resistance are synchronously detected at a circuit wiring terminal of the low-voltage switch cabinet, temperature and humidity distribution data of the surrounding environment are collected, and then coupling characteristics between the current and temperature rise are fused. And carrying out visual display of coupling and time sequence correlation in the built three-dimensional heat-electricity correlation topological graph in the switch cabinet by utilizing the time sequence correlation between the insulation resistance and the humidity change, so as to realize comprehensive identification of potential faults. According to the method, the sensitivity and the accuracy of fault identification are effectively improved, early fault symptoms such as poor contact and insulation degradation can be found earlier, the early warning capability of the system is enhanced, and the safety and the stability of operation of the switch cabinet are guaranteed.

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 proposed 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 spatial topology module includes the following contents: establishing a three-dimensional rectangular coordinate system based on the physical structure of the switch cabinet, and marking the spatial coordinates of the circuit terminals in the cabinet.

[0017] The physical installation coordinates of the terminal are recorded with the terminal as the reference node, and the electrical topology matrix is ​​constructed based on the electrical connection relationship between the main bus and branch circuits.

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

[0019] A three-dimensional thermal-electric correlation topology map is generated on the electrical topology matrix based on a multi-dimensional attribute point set.

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

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

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

[0023] The thermal-electric degradation index is compared with a 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. When the thermal-electric degradation index deviates from the reference range, it is determined that a thermal-electric decoupling event has occurred.

[0024] The wet-electric asynchronous event recognition is performed as follows: circuit insulation resistance data is extracted in the switch cabinet according to a set acquisition cycle, and then an insulation resistance time series curve is drawn.

[0025] The humidity monitoring data is synchronously collected around the circuit terminals to construct a humidity timing curve.

[0026] The insulation resistance time series curve and the humidity time series curve are analyzed by point-by-point slope to obtain several insulation resistance drop points and several humidity increase points.

[0027] The identified insulation resistance drop points and humidity rise points are numbered in chronological order, and time points with the same number are grouped into a one-to-one corresponding time sequence matching point group.

[0028] For each timing matching point group, the time difference between the humidity rising point and the insulation resistance falling point is calculated and recorded as the time offset of the group.

[0029] All time offsets are compared with the thermal response time constant of the cabinet steel plate, and the percentage of point groups whose time offsets conform to the time constant is defined as the wet-electric synchronization index.

[0030] The wet-electric synchronization index is compared with a preset judgment threshold. If the synchronization index reaches or exceeds the judgment threshold, it is determined to be a wet-electric synchronous change state; otherwise, it is determined that a wet-electric asynchronous event has occurred.

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

[0032] Outputs the environmental intrusion fault type for only the wet-electrical asynchronous area in the 3D thermal-electrical correlation topology diagram.

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

[0034] The weighted average position of each volume unit is calculated based on the spatial coordinates and the volume weight it occupies, which is used as the centroid coordinate of the overlapping area.

[0035] The fault volume sphere is generated as follows: it expands outward along multiple radial paths with the centroid of the overlapping area as the center, and arranges a number of sampling points at a set interval on each path to form a temperature and humidity joint measurement grid covering the periphery of the overlapping area.

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

[0037] For the time series data of 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. 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 the typical humidity diffusion rate are weightedly fused, and combined with the duration of the monitoring time window to obtain the fused diffusion distance under the action of heat and humidity coupling.

[0039] The fault volume sphere in three-dimensional space is constructed with the centroid coordinate as the sphere center and the fusion diffusion distance as the sphere radius.

[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 present invention constructs a three-dimensional thermal-electric correlation topology map inside the switch cabinet and identifies the thermal-electric decoupling area by combining the current and temperature coupling relationship, and determines the wet-electric asynchronous area based on the insulation resistance and humidity delay relationship. The spatial overlap characteristics of the two are used to locate faults and dynamically protect the system, thereby realizing a visual expression of the fault evolution process, improving the intuitiveness of fault identification and decision-making efficiency, and being able to take control measures in time according to the fault diffusion trend, significantly enhancing the safety and stability of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 Schematic diagram of the system composition of the present invention.

[0050] Figure 2 This is a flow chart for identifying thermal-electrical decoupling events in the present invention.

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

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] The present invention proposes an intelligent low-voltage switchgear system based on building intelligent functions, including an operation perception module, a spatial topology module, a fusion diagnosis module, a fault location module, and a dynamic protection module.

[0054] See also Figure 1 As shown, the above modules are connected in sequence to form a closed-loop monitoring and intelligent response system, ensuring the automation and intelligence of the entire process from data collection to fault handling.

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

[0056] In a preferred embodiment of the present invention, the specific implementation process of the above module is as follows: the circuit terminal position is determined according to the internal circuit layout diagram of the switch cabinet, and the effective current value and insulation resistance value under the circuit operation state are obtained based on the data of the measuring instruments equipped at the terminal position.

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

[0058] A thermocouple sensor array and a humidity sensor unit are embedded in the space around the circuit terminal to obtain temperature distribution data and humidity distribution data.

[0059] It's important to understand that in low-voltage switchgear, terminals serve as the key physical interface for circuit connection and energy transmission. Their contact state directly affects current flow efficiency and localized heat generation. Furthermore, due to structural characteristics and environmental factors, moisture can easily infiltrate through wire channels or cabinet gaps and accumulate in the terminal area, degrading insulation performance and increasing the risk of short circuits or discharges. Therefore, the terminal area is a typical site for thermal-electrical coupling and moisture-electrical interactions.

[0060] Focusing on monitoring terminal blocks helps improve the sensitivity of early fault identification and significantly narrows the potential fault location. Furthermore, switchgear is typically equipped with measuring instruments for monitoring circuit operation, such as ammeters and insulation resistance testers. This allows for direct acquisition of key electrical parameters, providing reliable data support for fault diagnosis.

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

[0062] In another preferred embodiment of the present invention, the module includes the following contents: establishing a three-dimensional rectangular coordinate system based on the physical structure of the switch cabinet, and marking the spatial coordinates of the circuit terminals in the cabinet according to the equipment installation position.

[0063] For the specific implementation of the above solution, the process of establishing a three-dimensional rectangular coordinate system is as follows: the bottom of the lower left corner of the front of the switch cabinet is used as the reference base point and set as the origin of the coordinate system; the X-axis extends from left to right along the width of the cabinet; the Y-axis extends from bottom to top along the height of the cabinet; and the Z-axis extends from front to back along the depth of the cabinet.

[0064] After the three-dimensional rectangular coordinate system is established, the physical installation position of the terminal block is determined according to the internal structure drawing of the switch cabinet and the equipment layout drawing, and the three-dimensional spatial coordinates are marked to form its unique identification in the established coordinate system.

[0065] The above-mentioned unified three-dimensional coordinate system is established based on the actual structure of the switchgear, which 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 terminal are recorded with the terminal as the reference node, and the electrical topology matrix is ​​constructed based on the electrical connection relationship between the main bus and branch circuits.

[0067] The electrical topology matrix is ​​constructed to describe the power transmission path and the hierarchy and connection properties between devices.

[0068] The temperature sensors and humidity sensors deployed in the switch cabinet are spatially matched with their actual layout positions, and then the current, insulation resistance, temperature distribution, and humidity distribution data are mapped to the corresponding coordinate positions to generate a multi-dimensional sensing point set.

[0069] A three-dimensional thermal-electric correlation topology map is generated on the electrical topology matrix based on a multi-dimensional attribute point set.

[0070] The above-mentioned spatial topology model is used to achieve state synchronization between physical entities and virtual models, and to build a real-time and visual distribution equipment operation status perception system.

[0071] It should be pointed out that, given that electrical device faults inside switchgear usually exhibit localized characteristics and significant spatial dependence, the use of spatial topology modeling can more accurately locate the source of the fault.

[0072] The fusion diagnosis module is used to identify thermal-electrical decoupling events by analyzing the current change rate and temperature rise rate at the circuit terminals in the switch cabinet through covariance analysis, and to identify moisture-electrical asynchronous events by monitoring the time-shift relationship between insulation resistance drop and humidity increase.

[0073] In one possible implementation of the above module, see Figure 2As shown, the identification of thermal-electric decoupling events refers to the following operations: extracting the effective value of the current at the circuit terminal position according to the set acquisition cycle in the switch cabinet, and then drawing a current change curve based on the change of the effective value of the current over time, and then extracting the current change rate within the monitoring time window from the current change curve.

[0074] It should be noted that the aforementioned data collection period is set to a time interval consistent with the low-voltage switchgear operational fault diagnosis cycle and can be dynamically adjusted based on the typical operating conditions and load fluctuation characteristics of the terminal loads it serves. Specifically, based on the daily operating cycle of the load in the time dimension, for example, the data collection period is based on the peak period, stable period of daily load fluctuation, or a fixed time interval such as every two hours, thereby achieving efficient monitoring of key parameters and effective capture of fault characteristics.

[0075] It should be further added that the monitoring time window mentioned above refers to the time unit corresponding to the set data acquisition cycle, which is used to divide the analysis period during the continuous monitoring process in order to obtain the dynamic characteristics of the current in different time periods. Specifically, the time window can be set to a time interval of several minutes according to system requirements. For example, every ten minutes is used as an analysis unit. Within this time range, the trend of the change in the effective value of the current is locally fitted and the slope is calculated, thereby extracting the current change rate within this period as a current change indicator reflecting the dynamic characteristics of the load, which is used for subsequent thermal-electrical decoupling event identification and fault evolution trend judgment.

[0076] Temperature data is collected synchronously around the circuit terminals to draw a temperature rise curve. The temperature rise rate within the monitoring time window is extracted by performing a first-order differential operation on the curve to characterize the dynamic evolution characteristics of the local heating process.

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

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

[0079] It should be explained that under normal operating conditions, current changes in the circuit usually trigger corresponding temperature responses. This is due to the Joule heating effect generated when current flows through the conductor, which causes the local temperature to rise. Therefore, there is a clear physical correlation between current and temperature. In order to quantify the correlation between the dynamic changes of the two, the present invention introduces covariance as a statistical analysis tool to measure the degree of linear coordination between the current change rate and the temperature rise rate. Covariance reflects the consistency of changes in the two variables in the time series: if the change trends of the two are highly synchronized, it indicates that they are in a normal thermal-electric coupling state; on the contrary, 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 inside the low-voltage switch cabinet.

[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 there is good synchronization between the current change and the temperature rise response, and the thermal-electric coupling state is judged to be 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 current and temperature rise, which may be caused by faults such as poor contact, local overheating or conductor aging.

[0081] The reference range of the thermal-electrical degradation index mentioned above reflects the typical coupling characteristics between the current change and the temperature rise response of the low-voltage switchgear under normal operating conditions. It is an important reference for judging 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 within multiple cycles are collected under normal operating conditions of the low-voltage switchgear to calculate the corresponding thermal-electrical degradation index sequence, and statistical methods such as mean ± standard deviation are used to determine its fluctuation range under normal operating conditions. This statistical range is then used as the initial reference range to reflect the thermal-electrical response characteristics of the equipment in a healthy state.

[0082] In another way that the above modules can be implemented, see Figure 3 As shown, the wet-electric asynchronous event recognition operation is as follows: extracting circuit insulation resistance data in the switch cabinet according to the set acquisition cycle, and then drawing the insulation resistance timing curve.

[0083] The humidity monitoring data is synchronously collected around the circuit terminals to construct a humidity timing curve.

[0084] The insulation resistance time series curve and the humidity time series curve are analyzed by point-by-point slope to obtain several insulation resistance drop points and several humidity increase points.

[0085] The identified insulation resistance drop points and humidity rise points are numbered in chronological order, and time points with the same number are grouped into a one-to-one corresponding time sequence matching point group.

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

[0087] All time offsets are compared with the thermal response time constant of the cabinet steel plate. The percentage of point groups whose time offsets conform to this time constant is defined as the wet-electric synchronization index, which is used to quantitatively evaluate the degree of temporal consistency between humidity changes and insulation performance degradation.

[0088] It is important to understand that in high humidity environments, the insulation materials of circuits easily absorb moisture, causing their 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 changes and insulation performance. Specifically, rising humidity usually precedes the deterioration of insulation performance, forming a temporal sequence. This temporal sequence is reflected in the fact that the switchgear cabinet steel plate and internal components have a certain thermal inertia and response delay to changes in ambient temperature and humidity. This delay characteristic can be calculated using the specific heat capacity and thermal conductivity of the cabinet material and used to evaluate whether the timing relationship between rising humidity and insulation degradation is reasonable.

[0089] It's important to note that when evaluating the matching relationship between the time offset and the cabinet steel plate's thermal response time constant, the time offset isn't strictly required to be exactly equal to the time constant. Instead, a tolerance range is set, such as a certain percentage above or below the thermal response time constant. Time offsets within this range are considered consistent with the system's expected response characteristics and are therefore considered to meet the thermal response time constant.

[0090] Regarding the above operation, it is necessary to explain that if the time offset between the increase in humidity and the decrease in insulation resistance conforms to the thermal response time constant of the cabinet, it means that the changes between the two conform to the expected physical laws and are normal wet-electric coupling behavior. When the insulation material ages and is severely damp, its sensitivity to humidity changes increases. At this time, even a small increase in humidity may 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 point groups whose time offsets conform to the thermal response time constant is introduced to reflect the consistency of the overall trend, rather than accidental fluctuations based on individual points. If most time offsets are within a 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 is damaged and abnormally sensitive to humidity changes, indicating a potential failure risk.

[0091] The moisture-electricity synchronization index is compared with the preset judgment threshold. If the synchronization index reaches or exceeds the judgment threshold, it is determined to be a moisture-electricity synchronous change state, indicating that there is a reasonable time coupling relationship between the increase in humidity and the decrease in insulation resistance. Otherwise, it is determined that a moisture-electricity asynchronous event has occurred, indicating that there is an abnormal time misalignment between the two, which may be caused by fault factors such as local moisture, insulation degradation or environmental response delay.

[0092] In particular, the judgment threshold is a reference benchmark for determining whether the moisture-electricity synchronization index is within the normal range. It is usually expressed as a percentage or proportional value. It is the key criterion for distinguishing between moisture-electricity synchronous change states and moisture-electricity asynchronous events. The initial threshold can be set in combination with the insulation material humidity response curve or aging curve provided when the equipment leaves the factory.

[0093] Applicable to the above operating instructions, relying solely on a drop in insulation resistance or an increase in humidity as an alarm basis can easily lead to false alarms or missed alarms; however, introducing a timing consistency analysis of the two can effectively distinguish between normal environmental fluctuations and actual insulation degradation, thereby improving the accuracy of diagnosis.

[0094] The fault location module is used to mark the occurrence areas of thermal-electric decoupling events and moisture-electric asynchronous events in a three-dimensional thermal-electric correlation topology map. When the two areas do not overlap, the fault type of the corresponding area is output. When the two areas overlap, the center of mass coordinates of the overlapping area are calculated. With the center of mass as the center of the sphere, the thermal diffusion rate and the humidity diffusion rate are combined as the radius to generate a fault volume sphere, 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 diagram, for the thermal-electric decoupling area, it is judged that the electrical connection state is deteriorated, and the connector aging fault type is output, which is used to indicate that the area may have structural problems such as poor conductor contact, joint oxidation or loose fasteners.

[0096] In the three-dimensional thermal-electric correlation topology diagram, for the moisture-electricity asynchronous area only, it is judged that the insulation abnormality is caused by environmental factors, and the environmental intrusion fault type is output to indicate possible environmental interference problems such as reduced cabinet sealing, local moisture, or external water vapor penetration.

[0097] It should be pointed out that when the two areas do not overlap, it indicates that the two types of faults are caused by independent physical mechanisms and act in different areas respectively. By distinguishing these independent fault areas, it helps to locate the source of the fault more accurately, formulate an effective preventive maintenance plan, 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, Indicates time, represents the thermal diffusion rate, represents the Laplace operator, which represents the second-order derivative of temperature in space.

[0106] Similarly, the humidity diffusion rate characterizes the ability of moisture to migrate within a material or in the surrounding environment, and its diffusion behavior can be modeled by the humidity diffusion equation ,in Indicates humidity, Indicates time, represents the humidity diffusion coefficient.

[0107] In the present invention, the thermal and humidity diffusivities can be calculated by collecting temperature and humidity data from multiple spatial sampling points along each radial path, combining the physical distances between adjacent sampling points, and estimating their spatial gradients using the finite difference method. The corresponding diffusion coefficients are then obtained through inversion calculation. This calculation method falls within the scope of existing heat and mass transfer analysis techniques and will not be further described here.

[0108] Applied to the above scheme, the typical heat diffusion rate and the typical humidity diffusion rate are determined as follows: the heat diffusion rates on all radial paths are clustered and divided into several heat diffusion clusters. Similarly, the humidity diffusion rates on each path are clustered and divided into several humidity diffusion clusters.

[0109] For each heat diffusion cluster, the number of paths it contains is counted and its proportion to the total number of paths is calculated as the path number ratio. 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 heat diffusion distribution uniformity.

[0110] It's important to note that radial paths in a three-dimensional topology map 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, each path has an angle with 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, the distribution of the paths is relatively concentrated, meaning that diffusion is mainly concentrated in a specific direction. A large standard deviation indicates a more uniform distribution of the paths, indicating that heat diffusion has similar diffusion characteristics in all directions, indicating a wider heat diffusion coverage.

[0111] The product of the proportion of radial paths and the uniformity of heat diffusion distribution is defined as a comprehensive evaluation index. The average heat diffusion rate of the path in the cluster with the highest comprehensive evaluation index among all heat diffusion clusters is selected as the typical heat diffusion rate.

[0112] It is necessary to add that, since the value range of the proportion of the number of radial paths 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 clusters, and the average humidity diffusion rate of all paths in the cluster with the highest comprehensive evaluation index is selected as the typical humidity diffusion rate.

[0114] In the above scheme, the path number ratio is used to characterize the statistical significance of a cluster within its overall path distribution, reflecting the cluster's quantitative representativeness; while the heat diffusion distribution uniformity characterizes the degree of balance in the cluster's spatial angular distribution, reflecting the breadth of its spatial coverage. By multiplying these two parameters to construct a comprehensive evaluation index, it is possible to quantitatively assess the overall representativeness of each cluster while taking into account both the breadth of path number distribution and the balance of spatial directional coverage. This helps identify dominant mode clusters with broad statistical and spatial representativeness, ensuring that the ultimately extracted typical heat diffusion rate truly reflects the overall environmental conditions and material heat transfer properties of the system.

[0115] The typical heat diffusion rate and the typical humidity diffusion rate are weightedly fused, and combined with the duration of the monitoring time window to obtain the fused diffusion distance under the action of heat and humidity coupling.

[0116] It should be understood that the present invention adopts a linear weighted fusion method of heat diffusion rate and humidity diffusion rate, 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 fault evolution process of electrical equipment. If only a single 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 fault development trend under the synergistic effect of temperature and humidity can be more comprehensively reflected, and the weight distribution can be dynamically adjusted according to the degree of influence of environmental parameters on the probability of fault occurrence in different application scenarios, thereby improving the accuracy and adaptability of risk assessment.

[0117] Specifically, the weight setting can be determined based on the statistical analysis results of historical failure cases of low-voltage switchgear. By summarizing the temperature and humidity conditions when typical failures occur, the proportion of failures occurring under temperature conditions and the proportion of failures occurring under humidity conditions are obtained. Based on this, the relative importance of temperature and humidity in fault evolution can be quantified, and then their respective weighting coefficients can be reasonably configured.

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

[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 will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

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

[0127] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0128] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The intelligent low-voltage switchgear system based on building intelligent functions is characterized by: Includes the following modules: Operation perception module: collects real-time current RMS and insulation resistance data of the circuit terminals in the switch cabinet, as well as cabinet environmental data including temperature and humidity distribution around the circuit terminals; Spatial topology module: establishes the spatial mapping relationship between the physical coordinates of the circuit terminals and the temperature / humidity sensors, and generates a three-dimensional thermal-electric correlation topology map; Fusion Diagnosis Module: This module uses covariance analysis to identify thermal-electrical decoupling events based on the current change rate and temperature rise rate at the circuit terminals within the switchgear. It also identifies moisture-electrical asynchronous events by monitoring the time-shift relationship between insulation resistance drop and humidity increase. Fault location module: Marks the occurrence areas of thermal-electric decoupling events and moisture-electric asynchronous events in the three-dimensional thermal-electric correlation topology map. When the two areas do not overlap, the fault type of the corresponding area is output. When the two areas overlap, the center of mass coordinates of the overlapping area are calculated. With the center of mass as the center of the sphere, the thermal diffusion rate and the humidity diffusion rate are combined as the radius to generate a fault volume sphere, and the dynamic radius value of the fault volume sphere is output. Dynamic protection module: performs gradient protection according to the expansion rate of the fault volume sphere radius.

2. The intelligent low-voltage switchgear system based on building intelligent functions according to claim 1, characterized in that: The specific implementation process of the operation perception module: Determine the location of the circuit terminals according to the internal circuit layout of the switch cabinet, and obtain the effective current value and insulation resistance value of the circuit under the operating state based on the data of the measuring instruments equipped at the terminal locations; A thermocouple sensor array and a humidity sensor unit are embedded in the space around the circuit terminal to obtain temperature distribution data and humidity distribution data.

3. The intelligent low-voltage switchgear system based on building intelligent functions according to claim 1, characterized in that: The spatial topology module includes the following contents: Establish a three-dimensional rectangular coordinate system based on the physical structure of the switch cabinet, and mark the spatial coordinates of the circuit terminals in the cabinet; The physical installation coordinates of the terminal are recorded with the terminal as the reference node, and the electrical topology matrix is ​​constructed based on the electrical connection relationship between the main bus and branch circuits; The temperature sensors and humidity sensors deployed in the switch cabinet are spatially matched with their actual locations, and then the effective current value, insulation resistance, temperature distribution, and humidity distribution data are mapped to corresponding coordinate positions to generate a multi-dimensional sensor point set. A three-dimensional thermal-electric correlation topology map is generated on the electrical topology matrix based on a multi-dimensional attribute point set.

4. The intelligent low-voltage switchgear system based on building intelligent functions according to claim 3 is characterized in that: The identification of the thermal-electrical decoupling event refers to the following operations: In the switch cabinet, the effective value of the current is extracted at the circuit terminal according to the set acquisition cycle, and then a current change curve is drawn based on the change of the effective value of the current over time. Then, the current change rate within the monitoring time window is extracted from the current change curve; The temperature data is collected synchronously around the circuit terminals to draw a temperature rise curve, and the temperature rise rate within the monitoring time window is extracted by performing a first-order differential operation on the curve. The current change rate series and the temperature rise rate series are aligned in the time domain, and the covariance value of the two series is calculated as the thermal-electrical degradation index; The thermal-electric degradation index is compared with a 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. When the thermal-electric degradation index deviates from the reference range, it is determined that a thermal-electric decoupling event has occurred.

5. The intelligent low-voltage switchgear system based on building intelligent functions according to claim 3 is characterized in that: The wet-electric asynchronous event identifies the following operations: Extract circuit insulation resistance data in the switch cabinet according to the set acquisition cycle, and then draw the insulation resistance time series curve; Synchronously collect humidity monitoring data around the circuit terminals to construct a humidity time series curve; The insulation resistance time series curve and the humidity time series curve are analyzed by point-by-point slope to obtain several insulation resistance drop points and several humidity increase points respectively; The identified insulation resistance drop points and humidity rise points are numbered in chronological order, and the time points with the same number are grouped into a one-to-one corresponding time sequence matching point group; For each timing matching point group, the time difference between the humidity rising point and the insulation resistance falling point is calculated and recorded as the time offset of the group; Compare all time offsets with the thermal response time constant of the cabinet steel plate, and count the percentage of point groups whose time offsets conform to the time constant, which is defined as the wet-electric synchronization index; The wet-electric synchronization index is compared with a preset judgment threshold. If the synchronization index reaches or exceeds the judgment threshold, it is determined to be a wet-electric synchronous change state; otherwise, it is determined that a wet-electric asynchronous event has occurred.

6. The intelligent low-voltage switchgear system based on building intelligent functions according to claim 1, characterized in that: When the two areas do not overlap, the fault types of the corresponding areas are output as follows: Output the connector aging fault type for the thermal-electrical decoupling area in the 3D thermal-electrical correlation topology map; Outputs the environmental intrusion fault type for only the wet-electrical asynchronous area in the 3D thermal-electrical correlation topology diagram.

7. The intelligent low-voltage switchgear system based on building intelligent functions according to claim 1, characterized in that: When two areas overlap, the centroid coordinates of the overlapping area are calculated as follows: In the three-dimensional thermo-electric correlation topology, the spatial overlap area between the thermo-electric decoupling event area and the moisture-electric asynchronous event area is discretized into several small volume units. The weighted average position of each volume unit is calculated based on the spatial coordinates and the volume weight it occupies, which is used as the centroid coordinate of the overlapping area.

8. The intelligent low-voltage switchgear system based on building intelligent functions according to claim 1, characterized in that: The fault volume sphere is generated as follows: Expand outward along multiple radial paths with the centroid of the overlapping area as the center, and arrange a number of sampling points at set intervals on each path to form a temperature and humidity joint measurement grid covering the periphery of the overlapping area; At each sampling point on the radial path, real-time temperature data and humidity data of the corresponding position are collected respectively; For each radial path, the time series data of the sampling points are used to estimate the heat diffusion rate and humidity diffusion rate on the corresponding path based on heat conduction and moisture diffusion. The diffusion rate results of all paths are classified and aggregated according to their spatial distribution characteristics to determine the typical heat diffusion rate and typical humidity diffusion rate. The typical heat diffusion rate and the typical humidity diffusion rate are weightedly fused, and the fused diffusion distance under the effect of heat and humidity coupling is obtained by combining the duration of the monitoring time window. The fault volume sphere in three-dimensional space is constructed with the centroid coordinate as the sphere center and the fusion diffusion distance as the sphere radius.

9. The intelligent low-voltage switchgear system based on building intelligent functions according to claim 8, characterized in that: The typical heat diffusion rate and the typical humidity diffusion rate are determined as follows: The heat diffusion rates on all radial paths are clustered to form several heat diffusion clusters. Similarly, the humidity diffusion rates on each path are clustered to form several humidity diffusion clusters. For each heat diffusion cluster, the number of paths it contains is counted and its proportion to the total number of paths is calculated as the path number ratio. 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 heat diffusion distribution uniformity. The product of the proportion of radial paths and the uniformity of heat diffusion distribution is defined as a comprehensive evaluation index. The average heat diffusion rate of the paths in the cluster with the highest comprehensive evaluation index among all heat diffusion clusters is selected as the typical heat diffusion rate. Similarly, the same comprehensive evaluation process is performed on the humidity diffusion clusters, and the average humidity diffusion rate of all paths in the cluster with the highest comprehensive evaluation index is selected as the typical humidity diffusion rate.

10. The intelligent low-voltage switchgear system based on building intelligent functions according to claim 8, characterized in that: 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 initiated: the circuit breakers of all circuits covered by the fault volume sphere are disconnected, and the backup power supply incoming contactor is closed; If the warning threshold is not reached, the secondary response is initiated: when the heat diffusion rate is higher than the humidity diffusion rate, local temperature rise monitoring and current limiting protection are initiated; when the humidity diffusion rate is higher than the heat diffusion rate, insulation status enhanced monitoring and dehumidification linkage control are initiated.

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