A low-voltage power distribution cabinet emergency power-off management method and system based on the Internet of Things

Abstract

The application belongs to the technical field of power equipment safety management, and discloses a low-voltage power distribution cabinet emergency power-off management method and system based on the Internet of Things; the method comprises the following steps: constructing an energy flow topology network based on a physical electrical connection structure and analyzing the network to determine the real-time energy residual error of the low-voltage power distribution cabinet; comparing a virtual temperature rise mapping curve with measured temperature data of the low-voltage power distribution cabinet to generate a blind area fault early warning signal; starting a power-off deduction process and a power preservation deduction process in parallel based on the blind area fault early warning signal and an original operating parameter set to obtain a power-off comprehensive cost index and a thermal runaway probability parameter, respectively; comparing the power supply comprehensive cost index with the power-off comprehensive cost index to determine whether to generate an emergency power-off execution instruction or an artificial maintenance prompt signal; and driving the low-voltage power distribution cabinet to perform a physical power-off operation in response to the emergency power-off execution instruction, and synchronously generating an accident traceability voucher, thereby significantly improving the early warning accuracy of the low-voltage power distribution cabinet.

Description

Technical Field

[0001] This invention relates to the field of power equipment safety management technology, and more specifically, to an emergency power outage management method and system for low-voltage distribution cabinets based on the Internet of Things. Background Technology

[0002] As a key node in power distribution, low-voltage distribution cabinets are prone to abnormal heating at electrical connection points during long-term high-load operation due to loose joints, oxidation of contact surfaces, or intrusion of environmental dust and oil. If not detected and blocked in time, this can often lead to serious electrical fire accidents, posing a serious threat to personnel safety and continuous production. Therefore, real-time early warning of overheating hazards is necessary.

[0003] Existing technologies for overheating warning of low-voltage switchgear mainly rely on online monitoring technology based on physical temperature sensors. This involves placing sensors at different locations on the same monitored object to achieve cross-validation and improve monitoring reliability. However, when sensor probes are exposed to high concentrations of oil or conductive dust for extended periods, their sensing surfaces easily absorb and accumulate a thick layer of insulating sludge. This causes the sensors to become extremely sluggish in response to temperature changes or even completely fail, creating data blind spots. For example, in a metal processing workshop, the actual temperature of the circuit breaker's moving and stationary contacts has soared to a dangerous critical value of 120°C due to increased contact resistance caused by oxidation. However, the sensor probes attached to the contact surfaces are tightly covered by accumulated oil and dust, resulting in a reading of only 65°C, far below the alarm threshold of 80°C. Meanwhile, redundant sensors placed near the heat dissipation vents have even lower readings (e.g., 45°C) due to good ventilation. In this situation, the system mistakenly judges the low-voltage switchgear to be operating healthily based on these two normal readings, failing to trigger any warning or power-off operation. Ultimately, this leads to the contacts melting under sustained high temperatures and causing a fire.

[0004] In view of this, the present invention proposes an emergency power outage management method and system for low-voltage distribution cabinets based on the Internet of Things to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: an emergency power outage management method for low-voltage distribution cabinets based on the Internet of Things, comprising:

[0006] S1. Obtain the physical electrical connection structure and original operating parameter set of the low-voltage distribution cabinet, construct an energy flow topology network based on the physical electrical connection structure, and perform coupling analysis on the original operating parameter set to determine the real-time energy residual of the low-voltage distribution cabinet.

[0007] S2. Construct a virtual temperature rise mapping curve based on real-time energy residual, compare the virtual temperature rise mapping curve with the measured temperature data of the low-voltage distribution cabinet, perform deviation level analysis on the temperature sensors judged to be in the failure blind zone state, and generate a blind zone fault early warning signal.

[0008] S3. Based on the blind zone fault warning signal and the original operating parameter set, the power outage simulation process and the power supply simulation process are started in parallel to obtain the comprehensive power outage cost index and thermal runaway probability parameters, respectively.

[0009] S4. Determine the comprehensive power supply cost index based on the thermal runaway probability parameter, compare the comprehensive power supply cost index with the comprehensive power outage cost index, and determine whether to generate an emergency power outage execution command or a manual maintenance prompt signal.

[0010] S5. In response to the emergency power outage execution command, drive the low-voltage distribution cabinet to perform a physical power outage operation, and simultaneously acquire the accident operation parameter set and simulation record before the power outage, and generate accident tracing evidence.

[0011] Furthermore, methods for constructing energy flow topology networks based on physical electrical connection structures include:

[0012] The physical and electrical connection structure of the low-voltage distribution cabinet is analyzed and an energy flow topology network is constructed. The upper end of the main incoming circuit breaker in the physical and electrical connection structure is marked as the energy injection node, and each branch outgoing terminal is marked as the energy output node. The copper busbar and conductive cable connecting the energy injection node and the energy output node are marked as directed transmission impedance sides, and the arrow of the directed transmission impedance side points to the energy output node. Each electrical connection point distributed on the directed transmission impedance side is marked as a contact loss node, thus completing the energy flow topology network.

[0013] Furthermore, methods for determining the real-time energy residual of low-voltage switchgear include:

[0014] Real-time acquisition of input voltage and total input current of energy injection nodes, output voltage and branch current of energy output nodes, as well as ambient temperature data inside the cabinet and surface temperature data of each contact loss node, and summarization to form a set of original operating parameters with timestamps.

[0015] Based on the ambient temperature data inside the cabinet, the standard reference resistor of the directed transmission impedance side is drift compensated in real time. Based on the dynamic resistance value after drift compensation and the total input current, Joule thermal integration is performed to obtain the theoretical heat flux input value.

[0016] The total inflow power is determined based on the input terminal voltage and the total input current; the total outflow power is determined based on the output terminal voltage and branch current of all energy output nodes.

[0017] Within a preset sampling period, the difference between the total inflow power and the total outflow power is integrated and accumulated to obtain the actual energy loss value; the difference between the actual energy loss value and the theoretical heat flow input value is calculated to determine the real-time energy residual inside the low-voltage distribution cabinet.

[0018] Furthermore, methods for constructing virtual temperature rise mapping curves based on real-time energy residuals include:

[0019] Based on the inherent equivalent heat capacity coefficient and thermal resistance coefficient of the low-voltage distribution cabinet, the real-time energy residual is converted into a point-by-point thermal balance to obtain the theoretical temperature rise numerical sequence; based on the timestamp, the theoretical temperature rise numerical sequence is aligned and superimposed with the ambient temperature data inside the cabinet to determine the virtual temperature rise mapping curve.

[0020] The surface temperature data of the contact loss node and the virtual temperature value on the virtual temperature rise mapping curve are compared with the preset normal operating temperature threshold and overheat alarm threshold, respectively. If the surface temperature data is less than the normal operating temperature threshold and the virtual temperature value is greater than the overheat alarm threshold, the temperature sensor responsible for collecting the surface temperature data is determined to be in the failure blind zone.

[0021] Furthermore, methods for analyzing the deviation level of temperature sensors determined to be in a failure blind zone include:

[0022] Set up a fault level association table that includes several temperature deviation ranges and their corresponding fault probability levels.

[0023] Calculate the maximum absolute value of the deviation between the virtual temperature rise mapping curve and the surface temperature data; map the maximum absolute value of the deviation to a pre-built fault level association table, and generate a blind zone fault warning signal containing the fault probability level;

[0024] If the generated fault probability level is an observation-level risk level, only manual cleaning and inspection instructions will be triggered; if the fault probability level is an action-level risk level, the dual-path evolution simulation engine will be triggered immediately.

[0025] Furthermore, methods for initiating power outage simulation and power restoration simulation processes in parallel include:

[0026] A dual-path evolution simulation engine is constructed, comprising a first virtual simulation environment and a second virtual simulation environment, and the original set of operating parameters is injected into the first virtual simulation environment and the second virtual simulation environment in parallel.

[0027] The first virtual simulation environment simulates the circuit breaker tripping action and retrieves the production value index of downstream load equipment electrically connected to the low-voltage distribution cabinet based on the virtual tripping status; the production value index is multiplied by the preset average time for fault diagnosis and repair to determine the comprehensive cost index of power outage.

[0028] The second virtual simulation environment maintains the current circuit breaker closed state and uses the total input current and real-time energy residual in the original operating parameter set to extrapolate the thermal accumulation trend, predicting the core component temperature values ​​of the low-voltage distribution cabinet contact loss node in the future time window.

[0029] By comparing the insulation material tolerance limit temperature curve of the contact loss node with the core component temperature value, the probability of the core component temperature value exceeding the insulation material tolerance limit is calculated, and the thermal runaway probability parameter under the power supply state is determined.

[0030] Furthermore, the methods for determining the generation of emergency power outage execution commands or manual maintenance prompt signals include:

[0031] The comprehensive power supply cost index is determined by multiplying the estimated safety accident losses obtained based on manual assessment with the thermal runaway probability parameter.

[0032] The comprehensive cost index of power supply is compared with the comprehensive cost index of power outage. If the comprehensive cost index of power supply is greater than the comprehensive cost index of power outage, an emergency power outage execution command is generated; if the comprehensive cost index of power supply is less than the comprehensive cost index of power outage, a manual maintenance prompt signal is generated.

[0033] Furthermore, methods for driving the low-voltage distribution cabinet to perform a physical power-off operation include:

[0034] In response to an emergency power outage command, a high-level pulse drive signal is injected into the shunt trip mechanism of the low-voltage distribution cabinet through the hardware control port. Based on the high-level pulse drive signal, the main circuit breaker is driven to perform a mechanical tripping action, thus completing the physical power outage operation.

[0035] Furthermore, methods for generating accident tracing evidence include:

[0036] The continuous set of original operating parameters prior to the mechanical tripping action is extracted to determine the accident operating parameter set; the comprehensive cost index of power outage, thermal runaway probability parameters, and comprehensive cost index of power supply that trigger the emergency power outage execution command are packaged into a simulation record; the accident operating parameter set and the simulation record are merged and marked as accident tracing evidence.

[0037] Furthermore, an IoT-based emergency power outage management system for low-voltage distribution cabinets is characterized by comprising:

[0038] The sensing access module is used to construct an energy flow topology network based on the physical electrical connection structure;

[0039] The virtual monitoring module is used to perform coupled analysis on the original set of operating parameters to determine the real-time energy residual of the low-voltage distribution cabinet;

[0040] The fault identification module is used to compare the constructed virtual temperature rise mapping curve with the measured temperature data, perform deviation level analysis on the temperature sensors judged to be in the failure blind zone state, and generate blind zone fault warning signals.

[0041] The simulation module is used to initiate the power outage simulation process and the power preservation simulation process in parallel, and to obtain the comprehensive cost index of power outage and the thermal runaway probability parameters, respectively.

[0042] The decision execution module is used to compare the determined comprehensive power supply cost index with the comprehensive power outage cost index to determine whether to generate an emergency power outage execution command or a manual maintenance prompt signal; in response to the emergency power outage execution command, it drives the low-voltage distribution cabinet to perform a physical power outage operation and generates an accident tracing certificate.

[0043] The technical effects and advantages of the present invention, a method and system for emergency power outage management of low-voltage distribution cabinets based on the Internet of Things, are as follows:

[0044] 1. This invention analyzes the original set of operating parameters by constructing an energy flow topology network, uses the acquired real-time energy residual as the core criterion, and combines a cross-validation mechanism of virtual temperature rise mapping curve and measured temperature to create a virtual temperature sensor for each key connection point in the low-voltage distribution cabinet that is unaffected by dust and oil, thus realizing active detection and compensation for the blind spots of physical sensor failure; by dynamically comparing the virtual temperature rise mapping curve with the measured temperature data, the risk of missed thermal runaway due to the failure of a single sensor is eliminated, significantly improving the early warning accuracy and intrinsic safety level of the low-voltage distribution cabinet, and realizing bidirectional and intelligent diagnosis of sensor health status.

[0045] 2. This invention constructs a dual-path evolution simulation engine comprising a first virtual simulation environment and a second virtual simulation environment. It simultaneously assesses the economic cost of immediate power outages and the expected losses from thermal runaway accidents caused by continued power supply. Based on the quantitative comparison of these two factors, it automatically generates emergency power outage commands or manual maintenance prompts. This is further supplemented by a high-level pulse hard-wired direct drive of the shunt trip mechanism to achieve reliable physical power outages. This achieves a dynamic optimal balance between safety and economy, avoiding two extreme misjudgments: excessive power outages and risky power preservation, thereby reducing power outage losses. By generating accident traceability evidence including accident operating parameters, dual-path simulation records, and cost indices, the decision-making process becomes transparent and auditable, providing tamper-proof data for post-event analysis and liability determination. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating an emergency power outage management method for low-voltage distribution cabinets based on the Internet of Things according to the present invention.

[0047] Figure 2 This is a schematic diagram illustrating the principle of an Internet of Things-based emergency power outage management system for low-voltage distribution cabinets according to the present invention.

[0048] Figure 3 This is a schematic diagram of the simulation and deduction module of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Example 1

[0051] Please see Figure 1 and Figure 3 As shown in this embodiment, the main design contents of the emergency power outage management method and system for low-voltage distribution cabinets based on the Internet of Things are as follows:

[0052] Existing emergency management methods for low-voltage distribution cabinets typically involve attaching thermistors or infrared temperature probes to the surface of key heat-generating nodes for monitoring. Sensors are placed at different locations on the same monitored object (such as the base of the contact and near the heat dissipation vent) to achieve cross-verification, and a fixed high-temperature alarm threshold (such as 80°C) is set.

[0053] However, this monitoring method, which relies on "absolute temperature readings," has significant drawbacks in harsh environments: when the sensor probe is exposed to high concentrations of oil or conductive dust for a long time, a thick layer of "insulating sludge" easily forms on the probe surface, causing the sensor to respond extremely slowly to temperature changes or even fail completely, creating a "data blind spot."

[0054] Based on this, an emergency power outage management method for low-voltage distribution cabinets based on the Internet of Things is designed, including:

[0055] S1. Obtain the physical electrical connection structure and original operating parameter set of the low-voltage distribution cabinet, construct an energy flow topology network based on the physical electrical connection structure, and perform coupling analysis on the original operating parameter set to determine the real-time energy residual of the low-voltage distribution cabinet.

[0056] Methods for constructing energy flow topology networks based on physical electrical connection structures include:

[0057] This paper analyzes the physical and electrical connection structure of a low-voltage distribution cabinet and constructs an energy flow topology network. The upper terminal of the main incoming circuit breaker in the physical and electrical connection structure is marked as the energy injection node, and each branch outgoing terminal is marked as the energy output node. The physical and electrical connection structure represents the physical layout relationship of all electrical components within the low-voltage distribution cabinet. The upper terminal of the main incoming circuit breaker is located at the incoming terminal of the main circuit breaker (main switch) in the low-voltage distribution cabinet where external power (such as transformer cables) is connected, representing the starting point of electrical energy in the energy flow topology network. The branch outgoing terminals are located at the terminal blocks at the lower ends of each branch circuit breaker (opening switch) in the low-voltage distribution cabinet, representing the endpoint where electrical energy flows out of the energy flow topology network to drive loads such as motors and lighting fixtures.

[0058] The copper busbars and conductive cables connecting the energy injection and energy output nodes are marked as directed transmission impedance sides, with the arrows on these sides pointing towards the energy output nodes (i.e., the arrows point from the current inflow direction to the current outflow direction). Each electrical connection point distributed along the directed transmission impedance sides is marked as a contact loss node, completing the energy flow topology network. Contact loss nodes represent connection points between copper busbars, or between copper busbars and circuit breakers, connected by bolts, crimping, or welding. If screws become loose or oxidized at these points, the contact resistance will increase, resulting in abnormal heating. Therefore, these connection points are marked as contact loss nodes (i.e., "potential hazard points" requiring close monitoring).

[0059] Methods for determining the real-time energy residual of low-voltage switchgear include:

[0060] Real-time acquisition of the input voltage and total input current of the energy injection node. Using three-phase voltage transformers and current transformers installed at the energy injection node, the analog voltage and current signals are converted into digital signals at a certain high-frequency sampling rate (e.g., 10kHz) to obtain the input voltage and total input current.

[0061] The output terminal voltage and branch current of the energy output nodes, as well as the ambient temperature data inside the cabinet and the surface temperature data of each contact loss node, are collected and summarized to form a raw operating parameter set with timestamps. The branch current is collected using current sensors (such as open-type Hall effect current sensors) installed on the three-phase conductors of each energy output node. A set of voltage sampling terminals is connected to the three-phase terminals of each energy output node to collect the output terminal voltage. The average temperature of the air inside the cabinet is read using an air temperature sensor suspended in the middle space of the low-voltage distribution cabinet to determine the ambient temperature data inside the cabinet. At the same time, the surface temperature of the metal surface of each node is read using temperature sensors attached to the surface of each contact loss node to obtain the surface temperature data of each contact loss node. Then, the input terminal voltage, total input current, output terminal voltage, branch current, ambient temperature data inside the cabinet, and surface temperature data at the same sampling time are uniformly tagged with timestamps using a clock synchronization protocol, and all data are packaged and stored according to the timestamps to form the raw operating parameter set.

[0062] Real-time drift compensation is performed on the standard reference resistor of the directed transmission impedance side based on the ambient temperature data inside the cabinet.

[0063] Dynamic resistance value The formula for obtaining it is: ;

[0064] in, The standard reference resistor represents the directed transmission impedance side of a low-voltage distribution cabinet; This indicates the material resistance temperature coefficient corresponding to the standard reference resistor; both the standard reference resistor and the material resistance temperature coefficient are read from the factory parameters of the corresponding low-voltage distribution cabinet.

[0065] This indicates the ambient temperature data inside the cabinet; This is the standard reference temperature for the low-voltage distribution cabinet, representing the ambient temperature reference point when measuring the standard reference resistance. The standard reference temperature can be preset based on the typical ambient temperature of the location of the low-voltage distribution cabinet (e.g., 20℃).

[0066] For example, the standard reference resistance read by this low-voltage distribution cabinet is 0.001 ohms (at 20℃), corresponding to a resistance temperature coefficient of 0.004 / ℃, and the standard reference temperature is usually 20℃; the current ambient temperature inside the cabinet is 45℃.

[0067] Substituting into the formula, the dynamic resistance value = 0.001 × [1 + 0.004 × (45 - 20)] = 0.0011 ohms.

[0068] Based on the drift-compensated dynamic resistance value and the total input current, Joule thermal integration is performed to obtain the theoretical heat flux input value. The total input current is extracted from the original operating parameter set, squared, and the result is multiplied by the drift-compensated dynamic resistance value to obtain the instantaneous theoretical heating power. The instantaneous theoretical heating power is integrated over a preset sampling period (the sampling period can be between 1s and 10s, for example, set to 5s), and the accumulated energy value is marked as the theoretical heat flux input value.

[0069] The total inflow power is determined based on the incoming line voltage and total input current; the total outflow power is determined based on the outflow line voltage and branch current of all energy output nodes. Total inflow power represents the total energy rate delivered by the external power grid to this low-voltage distribution cabinet; total outflow power represents the effective energy rate delivered by the low-voltage distribution cabinet to all downstream loads.

[0070] The difference between the total inflow power and the total outflow power is integrated and accumulated within a preset sampling period to obtain the actual energy loss value. The total inflow power is subtracted from the total outflow power at the same sampling moment to obtain the power difference at the current moment. The power difference is multiplied by the time step of the preset sampling period (which is consistent with the sampling period of the instantaneous theoretical heating power) by the discrete time integration algorithm, and then accumulated within a time window to obtain the actual energy loss value.

[0071] Calculate the difference between the actual energy loss value and the theoretical heat flux input value to determine the real-time energy residual inside the low-voltage distribution cabinet. The actual energy loss value represents the specific electrical energy consumed inside the low-voltage distribution cabinet (including normal transmission losses and additional heat generated by faults); the theoretical heat flux input value represents how much electrical energy the low-voltage distribution cabinet should consume under "perfect conditions".

[0072] S2. Construct a virtual temperature rise mapping curve based on real-time energy residual, compare the virtual temperature rise mapping curve with the measured temperature data of the low-voltage distribution cabinet, perform deviation level analysis on the temperature sensors judged to be in the failure blind zone state, and generate a blind zone fault early warning signal.

[0073] Methods for constructing virtual temperature rise mapping curves based on real-time energy residuals include:

[0074] Based on the inherent equivalent heat capacity coefficient and thermal resistance coefficient of the low-voltage distribution cabinet, a point-by-point thermal balance conversion is performed on the real-time energy residual to obtain a theoretical temperature rise numerical sequence. The equivalent heat capacity coefficient and thermal resistance coefficient are read from the factory parameters of the low-voltage distribution cabinet. For each sampling point in the sampling period, the theoretical temperature rise value at the previous sampling moment is divided by the thermal resistance coefficient and multiplied by the sampling period duration to obtain the heat dissipation loss. The heat dissipation loss is subtracted from the current real-time energy residual to obtain the net accumulated heat. The net accumulated heat is divided by the equivalent heat capacity coefficient to calculate the theoretical temperature rise value at the current moment. The theoretical temperature rise values ​​calculated point-by-point are assembled into a theoretical temperature rise numerical sequence according to the time sampling order.

[0075] Based on timestamps, the theoretical temperature rise numerical sequence is aligned and superimposed with the ambient temperature data inside the cabinet to determine the virtual temperature rise mapping curve. Each theoretical temperature rise value in the theoretical temperature rise numerical sequence is added to the corresponding ambient temperature data inside the cabinet to obtain a virtual temperature numerical sequence that reflects the superposition effect of fault temperature rise and ambient temperature. This virtual temperature numerical sequence is then used to construct a virtual temperature rise mapping curve that dynamically changes over time.

[0076] For example, if the theoretical temperature rise at a certain moment is 20°C and the corresponding ambient temperature inside the cabinet is 30°C, then the superimposed virtual temperature value is 50°C, which is the coordinate point of the virtual temperature rise mapping curve at that moment.

[0077] The surface temperature data of the contact loss node and the virtual temperature value on the virtual temperature rise mapping curve are compared with the preset normal operating temperature threshold and overheat alarm threshold, respectively. If the surface temperature data is less than the normal operating temperature threshold and the virtual temperature value is greater than the overheat alarm threshold, the temperature sensor responsible for collecting the surface temperature data is determined to be in the failure blind zone.

[0078] If the surface temperature data is greater than the normal operating temperature threshold and the virtual temperature value is greater than the overheat alarm threshold, it indicates that the low-voltage distribution cabinet is overheating, which is a normal thermal fault (temperature sensor is not faulty), thus triggering the normal overheat protection logic.

[0079] If the surface temperature data is less than the normal operating temperature threshold and the virtual temperature value is less than the overheat alarm threshold, it indicates that the low-voltage distribution cabinet is in normal operating condition (the temperature sensor is not faulty).

[0080] If the surface temperature data is greater than the normal operating temperature threshold and the virtual temperature value is less than the overheat alarm threshold, it indicates that the sensor has a positive drift fault or is interfered with by a non-load heat source, thus triggering a sensor anomaly check and warning.

[0081] It should be explained that the normal operating temperature threshold is set based on the rated load temperature rise standard of the low-voltage distribution cabinet and the statistical upper limit of historical operating data; if the temperature sensor reading is below the normal operating temperature threshold, it is usually considered "safe", for example, set to 70℃.

[0082] It should be explained that the overheat alarm threshold is based on the heat resistance limit temperature and safety margin of the insulation material (such as PVC or XLPE) inside the distribution cabinet. This means that if the virtual temperature value exceeds this threshold, the insulation will be damaged, for example, it is set to 85℃.

[0083] Methods for deviation level analysis of temperature sensors identified as being in a failure blind zone include:

[0084] Set up a fault level association table that includes several temperature deviation ranges and their corresponding fault probability levels.

[0085] It should be explained that the low temperature deviation range (e.g., 10-30℃) is set based on the measurement error limit and the slight dust accumulation test. Being in the low temperature deviation range means that the maximum deviation is caused by calculation error or a thin layer of dust, and has not yet posed an immediate threat to safety. The high temperature deviation range (e.g., >30℃) is set based on the oil stain half-coverage test and the critical temperature rise of the insulation material. Being in the high temperature deviation range means that the temperature sensor has seriously failed, and the actual internal temperature is very likely to have exceeded the insulation tolerance limit.

[0086] Calculate the maximum absolute value of the deviation between the virtual temperature rise mapping curve and the surface temperature data. Extract the virtual temperature value of the virtual temperature rise mapping curve at the current moment, and subtract it from the surface temperature data of each contact loss node collected at the same moment to obtain several temperature difference values. Perform absolute value processing on several temperature difference values, and select the one with the largest value as the maximum absolute value of deviation.

[0087] For example, if the virtual temperature value is 90 degrees Celsius, the surface temperature of node A is 80 degrees Celsius, and the surface temperature of node B is 40 degrees Celsius, then the maximum absolute value of the deviation is 50 degrees Celsius.

[0088] The absolute value of the maximum deviation is mapped to a pre-built fault level association table, and a blind zone fault warning signal containing the fault probability level is generated. If the generated fault probability level is an observation-level risk level, only manual cleaning and inspection instructions are triggered. If the fault probability level is an action-level risk level, the dual-path evolution simulation engine is triggered immediately.

[0089] S3. Based on the blind zone fault warning signal and the original operating parameter set, the power outage simulation process and the power supply simulation process are started in parallel to obtain the power outage comprehensive cost index and thermal runaway probability parameters, respectively.

[0090] Methods for initiating power outage simulation and power preservation simulation processes in parallel include:

[0091] A dual-path evolutionary simulation engine is constructed, comprising a first virtual simulation environment and a second virtual simulation environment. The original set of operating parameters is injected in parallel into both the first and second virtual simulation environments. Two independent computing spaces are created in the computing memory of the edge computing gateway, named the first virtual simulation environment and the second virtual simulation environment, respectively, and both the first and second virtual simulation environments have the same initial state data structure.

[0092] The first virtual simulation environment simulates the circuit breaker tripping action and retrieves the production value indicators of downstream load equipment electrically connected to the low-voltage distribution cabinet based on the virtual tripping status. The first virtual simulation environment changes the status flag of the virtual circuit breaker from closed to open, triggering virtual shutdown logic to simulate the electrical state where the current disappears immediately after a physical power outage. Based on the outgoing circuit number of the low-voltage distribution cabinet, it retrieves the production value indicators of downstream load equipment connected to each outgoing circuit (e.g., the production value indicator of the downstream injection molding machine production line is 5000 yuan per hour). All extracted production value indicators are summed to obtain a total production value indicator.

[0093] The comprehensive cost index of power outage is determined by multiplying the production value index by the preset average time for fault diagnosis and repair. From the historical equipment maintenance management database, all maintenance records related to "hidden overheating faults in low-voltage distribution cabinets" within a past period (e.g., the past two years) are retrieved. The total time difference between the fault alarm time and the power restoration time in each record is extracted and averaged to determine the preset average time for fault diagnosis and repair.

[0094] For example, by retrieving the production value indicators of three downstream load devices, which are 5000 yuan / hour, 3000 yuan / hour, and 8000 yuan / hour respectively; the total production value indicator = 5000 + 3000 + 8000 = 16000 yuan / hour; and the preset average time for fault diagnosis and repair is set to 2.5 hours. Then the comprehensive cost index of power outage = 16000 yuan / hour × 2.5 hours = 40000 yuan.

[0095] The second virtual simulation environment maintains the current closed state of the circuit breaker and uses the total input current and real-time energy residual from the original operating parameter set to extrapolate the thermal accumulation trend, predicting the core component temperature values ​​of the low-voltage distribution cabinet contact loss node in the future time window. In the second virtual simulation environment, the simulated virtual circuit breaker's status flag is set to closed, and the total input current and real-time energy residual values ​​are maintained. Using a thermodynamic linear extrapolation algorithm, based on the Joule heating effect generated by the total input current and the hidden thermal effect represented by the real-time energy residual, the comprehensive temperature rise rate at the current moment is calculated. Based on the comprehensive temperature rise rate, the temperature of the contact loss node is extrapolated over time to predict the core component temperature values ​​that the corresponding contact loss node may reach at the end of the future time window.

[0096] For example, if the current temperature is 100°C, the overall temperature rise rate is 10°C per minute, and the preset future time window is 10 minutes, then the predicted core component temperature value is 200°C.

[0097] By comparing the insulation material tolerance limit temperature curve of the contact loss node with the core component temperature value, the probability of the core component temperature exceeding the insulation material tolerance limit is calculated, and the thermal runaway probability parameter under sustained power supply conditions is determined. The insulation material tolerance limit temperature curve, with temperature as the horizontal axis and failure probability as the vertical axis, defines the statistical probability of insulation performance failure of the corresponding insulation material at different temperature levels. Based on the factory parameters, the specific insulation material used in the contact loss node and its tolerance limit temperature curve are identified (for example, if the contact loss node is a busbar overlap, and its insulation material is identified as DMC unsaturated polyester glass fiber, then the tolerance limit temperature curve of DMC material is used). The predicted core component temperature value is used as an input variable, and a mapping search is performed on the insulation material tolerance limit temperature curve to locate the vertical axis value corresponding to the core component temperature value. This failure probability coordinate point is defined as the statistical probability value of thermal breakdown failure of the insulation material within a future time window.

[0098] S4. Determine the comprehensive power supply cost index based on the thermal runaway probability parameter, compare the comprehensive power supply cost index with the comprehensive power outage cost index, and determine whether to generate an emergency power outage execution command or a manual maintenance prompt signal.

[0099] The methods for determining the generation of emergency power outage execution commands or manual maintenance prompt signals include:

[0100] The comprehensive power supply cost index is determined by multiplying the safety accident loss estimate obtained from manual assessment with the thermal runaway probability parameter. The safety accident loss estimate represents the estimated total direct and indirect economic losses that may be caused by a fire or serious accident caused by a low-voltage distribution cabinet failure. The safety accident loss estimate includes at least direct property losses (e.g., the value of the low-voltage distribution cabinet itself is 50,000 yuan; damage to the core equipment of the production line it supplies is 500,000 yuan), production interruption losses (e.g., a daily output value loss of 1 million yuan due to production line shutdown), and indirect and derivative losses (e.g., a penalty of 100,000 yuan for delayed product delivery; 50,000 yuan for environmental cleanup and penalties; and 100,000 yuan for negative impact on corporate reputation).

[0101] The comprehensive cost index for power supply is compared with the comprehensive cost index for power outage. If the comprehensive cost index for power supply is greater than the comprehensive cost index for power outage, it indicates that the expected risk loss incurred in maintaining power supply exceeds the certain economic loss caused by an immediate power outage. In this case, an emergency power outage execution order is generated. The emergency power outage execution order includes the target low-voltage distribution cabinet identifier, the order generation timestamp, and the decision-making basis data.

[0102] If the comprehensive cost index of power supply is less than the comprehensive cost index of power outage, and the expected risk loss borne by maintaining power supply is lower than the certain economic loss caused by immediate power outage, then a manual maintenance prompt signal is generated.

[0103] S5. In response to the emergency power outage execution command, drive the low-voltage distribution cabinet to perform a physical power outage operation, and simultaneously acquire the accident operation parameter set and simulation record before the power outage, and generate accident tracing evidence.

[0104] Methods for driving low-voltage distribution cabinets to perform physical power-off operations include:

[0105] In response to the emergency power outage execution command, the command is parsed to confirm whether the identifier of the target low-voltage distribution cabinet is consistent with the identifier of the cabinet it manages. After verification, a high-level pulse drive signal is injected into the shunt trip mechanism of the low-voltage distribution cabinet through the hardware control port. Based on the high-level pulse drive signal, the main circuit breaker is driven to perform a mechanical tripping action to complete the physical power outage operation.

[0106] Methods for generating accident tracing evidence include:

[0107] The continuous set of raw operating parameters prior to the mechanical tripping action is extracted to determine the fault operating parameter set. The extracted raw operating parameter set can cover the entire process from the obvious appearance of the anomaly to the stabilization of the state after the action is executed.

[0108] The comprehensive cost index of power outage, thermal runaway probability parameters, and comprehensive cost index of power supply that trigger the emergency power outage execution command are packaged into a simulation record; the accident operation parameter set is merged with the simulation record and marked as accident tracing evidence.

[0109] In this embodiment, the original operating parameter set is analyzed by constructing an energy flow topology network. The obtained real-time energy residual is used as the core criterion. Combined with the cross-validation mechanism of virtual temperature rise mapping curve and measured temperature, a virtual temperature sensor unaffected by dust and oil is created for each key connection point in the low-voltage distribution cabinet. This enables active detection and compensation for blind spots caused by physical sensor failure. By dynamically comparing the virtual temperature rise mapping curve with the measured temperature data, the risk of missed thermal runaway due to the failure of a single sensor is eliminated. This significantly improves the accuracy of early warning and the intrinsic safety level of the low-voltage distribution cabinet, and realizes bidirectional and intelligent diagnosis of sensor health status.

[0110] By constructing a dual-path evolution simulation engine that includes a first virtual simulation environment and a second virtual simulation environment, the economic cost of immediate power outage and the expected accident loss from thermal runaway while continuing to supply power are simultaneously assessed. Based on the quantitative comparison of the two, an emergency power outage command or manual maintenance prompt is automatically generated. This is supplemented by a high-level pulse hard-wired direct drive of the shunt trip mechanism to achieve a reliable physical power outage. This achieves a dynamic optimal balance between safety and economy, avoiding two extreme misjudgments: excessive power outage and risky power preservation, thereby reducing power outage losses. By generating accident traceability evidence including accident operation parameters, dual-path simulation records, and cost indexes, the decision-making process becomes transparent and auditable, providing tamper-proof data for post-event analysis and responsibility determination.

[0111] Example 2

[0112] Please see Figure 2 As shown in this embodiment, an emergency power outage management system for low-voltage distribution cabinets based on the Internet of Things includes:

[0113] The system comprises the following modules: a perception access module for constructing an energy flow topology network based on physical electrical connections; a virtual monitoring module for performing coupled analysis on the original operating parameter set to determine the real-time energy residual of the low-voltage distribution cabinet; a fault identification module for comparing the constructed virtual temperature rise mapping curve with measured temperature data, performing deviation level analysis on temperature sensors identified as being in a failure blind zone, and generating a blind zone fault warning signal; a simulation and deduction module for initiating power outage and power supply simulation processes in parallel, obtaining the comprehensive cost index of power outage and thermal runaway probability parameters respectively; and a decision execution module for comparing the determined comprehensive cost index of power supply with the comprehensive cost index of power outage to determine and generate an emergency power outage execution command or a manual maintenance prompt signal. In response to the emergency power outage execution command, the module drives the low-voltage distribution cabinet to perform a physical power outage operation and generates an accident tracing certificate.

[0114] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented 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 implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0115] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0116] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

[0117] In conclusion, 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 within the protection scope of the present invention.

Claims

1. A method for emergency power outage management of low-voltage distribution cabinets based on the Internet of Things, characterized in that, include: S1. Obtain the physical electrical connection structure and original operating parameter set of the low-voltage distribution cabinet, construct an energy flow topology network based on the physical electrical connection structure, and perform coupling analysis on the original operating parameter set to determine the real-time energy residual of the low-voltage distribution cabinet. S2. Construct a virtual temperature rise mapping curve based on real-time energy residual, compare the virtual temperature rise mapping curve with the measured temperature data of the low-voltage distribution cabinet, perform deviation level analysis on the temperature sensors judged to be in the failure blind zone state, and generate a blind zone fault early warning signal. S3. Based on the blind zone fault warning signal and the original operating parameter set, the power outage simulation process and the power supply simulation process are started in parallel to obtain the comprehensive power outage cost index and thermal runaway probability parameters, respectively. S4. Determine the comprehensive power supply cost index based on the thermal runaway probability parameter, compare the comprehensive power supply cost index with the comprehensive power outage cost index, and determine whether to generate an emergency power outage execution command or a manual maintenance prompt signal. S5. In response to the emergency power outage execution command, drive the low-voltage distribution cabinet to perform a physical power outage operation, and simultaneously acquire the accident operation parameter set and simulation record before the power outage, and generate accident tracing evidence.

2. The method for emergency power outage management of low-voltage distribution cabinets based on the Internet of Things according to claim 1, characterized in that, The method for constructing an energy flow topology network based on physical electrical connection structures includes: The physical and electrical connection structure of the low-voltage distribution cabinet is analyzed and an energy flow topology network is constructed. The upper end of the main incoming circuit breaker in the physical and electrical connection structure is marked as the energy injection node, and each branch outgoing terminal is marked as the energy output node. The copper busbar and conductive cable connecting the energy injection node and the energy output node are marked as directed transmission impedance sides, and the arrow of the directed transmission impedance side points to the energy output node. Each electrical connection point distributed on the directed transmission impedance side is marked as a contact loss node, thus completing the energy flow topology network.

3. The method for emergency power outage management of low-voltage distribution cabinets based on the Internet of Things according to claim 2, characterized in that, The method for determining the real-time energy residual of the low-voltage distribution cabinet includes: Real-time acquisition of input voltage and total input current of energy injection nodes, output voltage and branch current of energy output nodes, as well as ambient temperature data inside the cabinet and surface temperature data of each contact loss node, and summarization to form a set of original operating parameters with timestamps. Based on the ambient temperature data inside the cabinet, the standard reference resistor of the directed transmission impedance side is drift compensated in real time. Based on the dynamic resistance value after drift compensation and the total input current, Joule thermal integration is performed to obtain the theoretical heat flux input value. The total inflow power is determined based on the input terminal voltage and the total input current; the total outflow power is determined based on the output terminal voltage and branch current of all energy output nodes. Within a preset sampling period, the difference between the total inflow power and the total outflow power is integrated and accumulated to obtain the actual energy loss value; the difference between the actual energy loss value and the theoretical heat flow input value is calculated to determine the real-time energy residual inside the low-voltage distribution cabinet.

4. The method for emergency power outage management of low-voltage distribution cabinets based on the Internet of Things according to claim 3, characterized in that, The method for constructing a virtual temperature rise mapping curve based on real-time energy residuals includes: Based on the inherent equivalent heat capacity coefficient and thermal resistance coefficient of the low-voltage distribution cabinet, the real-time energy residual is converted into a point-by-point thermal balance to obtain the theoretical temperature rise numerical sequence; based on the timestamp, the theoretical temperature rise numerical sequence is aligned and superimposed with the ambient temperature data inside the cabinet to determine the virtual temperature rise mapping curve. The surface temperature data of the contact loss node and the virtual temperature value on the virtual temperature rise mapping curve are compared with the preset normal operating temperature threshold and overheat alarm threshold, respectively. If the surface temperature data is less than the normal operating temperature threshold and the virtual temperature value is greater than the overheat alarm threshold, the temperature sensor responsible for collecting the surface temperature data is determined to be in the failure blind zone.

5. The method for emergency power outage management of low-voltage distribution cabinets based on the Internet of Things according to claim 4, characterized in that, The method for analyzing the deviation level of temperature sensors determined to be in a failure blind zone includes: Set up a fault level association table that includes several temperature deviation ranges and their corresponding fault probability levels. Calculate the maximum absolute value of the deviation between the virtual temperature rise mapping curve and the surface temperature data; map the maximum absolute value of the deviation to a pre-built fault level association table, and generate a blind zone fault warning signal containing the fault probability level; If the generated fault probability level is an observation-level risk level, only manual cleaning and inspection instructions will be triggered; if the fault probability level is an action-level risk level, the dual-path evolution simulation engine will be triggered immediately.

6. The method for emergency power outage management of low-voltage distribution cabinets based on the Internet of Things according to claim 5, characterized in that, The method for parallel initiation of the power outage simulation process and the power preservation simulation process includes: A dual-path evolution simulation engine is constructed, comprising a first virtual simulation environment and a second virtual simulation environment, and the original set of operating parameters is injected into the first virtual simulation environment and the second virtual simulation environment in parallel. The first virtual simulation environment simulates the circuit breaker tripping action and retrieves the production value index of downstream load equipment electrically connected to the low-voltage distribution cabinet based on the virtual tripping status; the production value index is multiplied by the preset average time for fault diagnosis and repair to determine the comprehensive cost index of power outage. The second virtual simulation environment maintains the current circuit breaker closed state and uses the total input current and real-time energy residual in the original operating parameter set to extrapolate the thermal accumulation trend, predicting the core component temperature values ​​of the low-voltage distribution cabinet contact loss node in the future time window. By comparing the insulation material tolerance limit temperature curve of the contact loss node with the core component temperature value, the probability of the core component temperature value exceeding the insulation material tolerance limit is calculated, and the thermal runaway probability parameter under the power supply state is determined.

7. The method for emergency power outage management of low-voltage distribution cabinets based on the Internet of Things according to claim 6, characterized in that, The method for determining whether to generate an emergency power outage execution command or a manual maintenance prompt signal includes: The comprehensive power supply cost index is determined by multiplying the estimated safety accident losses obtained based on manual assessment with the thermal runaway probability parameter. The comprehensive cost index of power supply is compared with the comprehensive cost index of power outage. If the comprehensive cost index of power supply is greater than the comprehensive cost index of power outage, an emergency power outage execution command is generated; if the comprehensive cost index of power supply is less than the comprehensive cost index of power outage, a manual maintenance prompt signal is generated.

8. The method for emergency power outage management of low-voltage distribution cabinets based on the Internet of Things according to claim 7, characterized in that, The method for driving the low-voltage distribution cabinet to perform a physical power-off operation includes: In response to an emergency power outage command, a high-level pulse drive signal is injected into the shunt trip mechanism of the low-voltage distribution cabinet through the hardware control port. Based on the high-level pulse drive signal, the main circuit breaker is driven to perform a mechanical tripping action, thus completing the physical power outage operation.

9. The method for emergency power outage management of low-voltage distribution cabinets based on the Internet of Things according to claim 8, characterized in that, The method for generating accident tracing credentials includes: The continuous set of original operating parameters prior to the mechanical tripping action is extracted to determine the accident operating parameter set; the comprehensive cost index of power outage, thermal runaway probability parameters, and comprehensive cost index of power supply that trigger the emergency power outage execution command are packaged into a simulation record; the accident operating parameter set and the simulation record are merged and marked as accident tracing evidence.

10. An Internet of Things (IoT)-based emergency power outage management system for low-voltage distribution cabinets, applied to the IoT-based emergency power outage management method for low-voltage distribution cabinets described in any one of claims 1-9, characterized in that, include: The sensing access module is used to construct an energy flow topology network based on the physical electrical connection structure; The virtual monitoring module is used to perform coupled analysis on the original set of operating parameters to determine the real-time energy residual of the low-voltage distribution cabinet; The fault identification module is used to compare the constructed virtual temperature rise mapping curve with the measured temperature data, perform deviation level analysis on the temperature sensors judged to be in the failure blind zone state, and generate blind zone fault warning signals. The simulation module is used to initiate the power outage simulation process and the power preservation simulation process in parallel, and to obtain the comprehensive cost index of power outage and the thermal runaway probability parameters, respectively. The decision execution module is used to compare the determined comprehensive power supply cost index with the comprehensive power outage cost index to determine whether to generate an emergency power outage execution command or a manual maintenance prompt signal. In response to an emergency power outage command, the low-voltage distribution cabinet is driven to perform a physical power outage operation, and an accident tracing certificate is generated.

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