Power distribution cabinet capable of intelligently detecting faults and detection method

Abstract

The present application belongs to the field of electric power engineering and intelligent power distribution technology, and discloses a power distribution cabinet capable of intelligently detecting faults and a detection method. The current current effective value is obtained, and a weighted moving average method is used to calculate the real-time load expected value, wherein the weight coefficient of recent sampling data is greater than that of long-term sampling data and decays according to an exponential law. The theoretical temperature of the current current effective value and the ambient temperature is calculated. The electrical quantity relative deviation and the temperature quantity relative deviation are calculated, and the electrical-thermal deviation index is obtained in combination with the confidence weight. The edge computing processing unit determines the health aging factor according to the cumulative running time of the equipment, and calculates the dynamic alarm threshold in combination with the basic safety threshold. When the electrical-thermal deviation index exceeds the dynamic alarm threshold, it is determined that the power distribution cabinet has a fault and an alarm signal is output. Through the electrical-thermal multi-source data fusion and dynamic threshold adjustment, the present application effectively improves the accuracy and timeliness of the fault early warning of the power distribution cabinet under complex working conditions.

Description

Technical Field

[0001] This invention belongs to the field of power engineering and intelligent power distribution technology, and relates to a power distribution cabinet capable of intelligently detecting faults and a detection method thereon. Background Technology

[0002] Distribution cabinets are critical equipment in power systems for distributing electrical energy, and their operational stability directly affects power supply security. Existing fault detection technologies for distribution cabinets primarily rely on preset fixed thresholds for judgment, which are ill-suited to dynamic conditions such as load fluctuations, ambient temperature changes, and equipment aging. False alarms or missed alarms are frequently caused by inappropriate threshold settings. For example, under high load conditions, the system may misinterpret normal current fluctuations as faults, while under low load conditions or in the early stages of a fault, it may miss subtle abnormal signals. Furthermore, existing monitoring systems often use single sensors to collect data, relying solely on current amplitude or node temperature for judgment, making them susceptible to harmonic interference or ambient temperature effects, and unable to effectively distinguish between load fluctuations and genuine contact failures. Existing detection schemes typically lack consideration for the entire equipment lifecycle and do not dynamically adjust detection benchmarks according to the equipment's aging process, resulting in the difficulty in timely detection of early localized temperature rises caused by increased contact resistance. These problems lead to low maintenance efficiency, delayed fault response, and seriously threaten power supply reliability.

[0003] Therefore, there is an urgent need for an intelligent detection technology that can integrate multi-dimensional data, dynamically adapt thresholds, and take into account the aging characteristics of equipment, in order to improve the accuracy and timeliness of power distribution cabinet fault early warning. Summary of the Invention

[0004] To address the problems existing in the background technology, this invention proposes a power distribution cabinet capable of intelligently detecting faults and a detection method thereon.

[0005] A first aspect of the present invention provides a power distribution cabinet capable of intelligently detecting faults, comprising: A multi-dimensional sensing layer is used to simultaneously collect three-phase current data, node temperature data, and ambient temperature data. An edge computing processing unit, connected to the multi-dimensional perception layer, includes a processor and a memory. The memory stores a computer program, and the processor executes the computer program to perform the following steps: The three-phase current data are processed based on a sliding time window to calculate the expected real-time load value; Based on the real-time load expectation, the node temperature data, and the ambient temperature data, an electrothermal coupling fault discrimination model is constructed, and the electrothermal deviation index is calculated. The health aging factor is determined based on the cumulative operating time of the equipment, and the basic safety threshold is corrected using the health aging factor to obtain the dynamic alarm threshold. The electrothermal deviation index is compared with the dynamic alarm threshold to generate a fault detection result.

[0006] Optionally, the multidimensional sensing layer includes: High-frequency current sensors are used to acquire high-frequency waveform data of three-phase current and zero-sequence current. A distributed temperature sensor array is deployed at busbar connection points and circuit breaker contacts to collect node temperature data. The environmental sensing unit is used to collect data on the ambient temperature, humidity, and cooling fan status inside the cabinet.

[0007] Optionally, the processor executes the computer program to implement the step of processing the three-phase current data based on a sliding time window and calculating the expected real-time load value, specifically including: Set the length of the sliding time window and the total number of sampling points; A weighting coefficient is assigned to each sampling point within the sliding time window, and the weighting coefficient decays exponentially with the time of the sampling point. The sum of the products of the sampled point values ​​and their corresponding weight coefficients is calculated and divided by the sum of the weight coefficients to obtain the expected real-time load value.

[0008] Optionally, the processor executes the computer program to implement the steps of constructing the electrothermal coupling fault discrimination model and calculating the electrothermal deviation index, specifically including: Calculate the electrical quantity deviation term, which represents the degree of difference between the current effective value and the real-time load expectation value at the current moment; Calculate the theoretical temperature, which is determined based on the ambient temperature, the heat dissipation coefficient, the square of the current effective value at the current moment, and the initial contact resistance of the circuit. Calculate the temperature deviation term, which characterizes the degree of difference between the measured node temperature and the theoretical temperature; The electrical quantity deviation term and the temperature quantity deviation term are weighted and summed to obtain the electrothermal deviation index.

[0009] Optionally, the processor executes the computer program to implement the step of correcting the basic safety threshold using the healthy aging factor to obtain a dynamic alarm threshold, specifically including: The ratio of the cumulative operating hours of the calculated equipment to the reference baseline operating time; Calculate the natural logarithm of the ratio plus one; Multiply the natural logarithm by the healthy aging factor, and add one to the product to obtain the correction coefficient; The dynamic alarm threshold is obtained by multiplying the basic safety threshold by the correction coefficient.

[0010] A second aspect of the present invention provides an intelligent detection method for a power distribution cabinet, comprising: Data acquisition steps: Simultaneously acquire three-phase current data, node temperature data, and ambient temperature data through a multi-dimensional sensing layer; Load identification steps: Using the edge computing processing unit, the real-time load expectation value is calculated based on the sliding time window to identify load fluctuation characteristics; Model building steps: Using the edge computing processing unit, based on the real-time load expectation value, measured node temperature and theoretical temperature calculated based on current thermal effect, an electrothermal coupling fault discrimination model is built, and the electrothermal deviation index is calculated; Threshold correction step: Using the edge computing processing unit, determine the health aging factor based on the cumulative operating time of the device, and calculate the dynamic alarm threshold in combination with the basic safety threshold; Fault determination steps: When the electrothermal deviation index exceeds the dynamic alarm threshold, the distribution cabinet is determined to have malfunctioned and an alarm signal is output.

[0011] Optionally, in the load identification step, the calculation of the real-time load expectation value adopts the weighted moving average method, wherein the weight coefficient of the recent sampled data is greater than the weight coefficient of the long-term sampled data, and the weight coefficient decays exponentially.

[0012] Optionally, in the model construction step, the calculation logic for the electrothermal deviation index is as follows: Calculate the absolute value of the difference between the current effective value and the expected real-time load value, and divide it by the expected real-time load value to obtain the relative deviation of the electrical quantity; Calculate the absolute value of the difference between the measured node temperature and the theoretical temperature, and divide it by the theoretical temperature to obtain the relative deviation of the temperature. The electrothermal deviation index is obtained by multiplying the relative deviation of the electrical quantity by the first confidence weight and adding the relative deviation of the temperature quantity by the second confidence weight.

[0013] Optionally, the formula for calculating the theoretical temperature is: Theoretical temperature = Ambient temperature + Heat dissipation coefficient × Effective current value 2 × Initial contact resistance of the circuit.

[0014] Optionally, the method for determining the operating status of the power distribution cabinet further includes: If the effective value of the current at the current moment increases and the measured node temperature does not increase with the increase of the effective value of the current at the current moment, it is determined to be a normal load fluctuation. If the effective value of the current at the current moment increases and the measured node temperature is higher than the theoretical temperature, then it is determined to be a poor contact fault and / or an overheating fault.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention discloses a power distribution cabinet and detection method capable of intelligent fault detection. It synchronously collects three-phase current data, node temperature data, and ambient temperature data through a multi-dimensional sensing layer. An electrothermal coupling fault discrimination model is constructed using an edge computing processing unit. This model comprehensively considers electrical quantity deviations and temperature deviations, effectively distinguishing between load fluctuations and actual faults, significantly reducing the false judgment rate of a single sensor under harmonic interference. This invention uses a sliding time window to calculate the real-time load expectation value and introduces a health aging factor to correct the basic safety threshold, resulting in a dynamic alarm threshold. This achieves adaptive adjustment of the detection benchmark as the equipment's cumulative operating time, solving the problem that traditional fixed thresholds are difficult to adapt to equipment aging and changes in operating conditions. By comparing the electrothermal deviation index with the dynamic alarm threshold, this invention can promptly detect early localized temperature rises caused by increased contact resistance, improving the accuracy and timeliness of fault warnings and ensuring the power supply reliability of the power distribution cabinet. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of a power distribution cabinet capable of intelligent fault detection in one embodiment of the present invention; Figure 2 This is a flowchart of an intelligent detection method for a power distribution cabinet according to an embodiment of the present invention. Detailed Implementation

[0017] 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.

[0018] In one embodiment, such as Figure 1 As shown, a power distribution cabinet capable of intelligent fault detection is provided. This power distribution cabinet with intelligent fault detection corresponds one-to-one with the intelligent fault detection method of the power distribution cabinet in the following embodiments. The power distribution cabinet with intelligent fault detection includes: a multi-dimensional perception layer and an edge computing processing unit. The functional modules are described in detail below: A multi-dimensional sensing layer is used to simultaneously collect three-phase current data, node temperature data, and ambient temperature data. An edge computing processing unit, comprising a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps: The three-phase current data are processed based on a sliding time window to calculate the expected real-time load value; Based on the real-time load expectation, the node temperature data, and the ambient temperature data, an electrothermal coupling fault discrimination model is constructed, and the electrothermal deviation index is calculated. The health aging factor is determined based on the cumulative operating time of the equipment, and the basic safety threshold is corrected using the health aging factor to obtain the dynamic alarm threshold. The electrothermal deviation index is compared with the dynamic alarm threshold to generate a fault detection result.

[0019] In one embodiment, the multi-dimensional sensing layer includes: a high-frequency current sensor for collecting high-frequency waveform data of three-phase current and zero-sequence current; a distributed temperature sensing array deployed at busbar connection points and circuit breaker contacts for collecting node temperature data; and an environmental sensing unit for collecting data on ambient temperature, humidity, and cooling fan status within the cabinet.

[0020] Specifically, the multi-dimensional sensing layer, serving as the data acquisition foundation for the intelligent fault detection distribution cabinet of this invention, has the following specific structure and functional implementation: The high-frequency current sensor employs a Rogowski coil or a high-precision Hall sensor. This sensor is mounted on the three-phase busbars and zero-sequence circuit conductors within the distribution cabinet to collect high-frequency waveform data of the three-phase and zero-sequence currents in real time. By capturing transient characteristics in the current signal, it provides accurate raw data support for the subsequent edge computing processing unit to identify load fluctuations and short-circuit faults. The distributed temperature sensing array employs a fiber optic grating temperature sensor or a wireless passive surface acoustic wave temperature sensor. This array is directly attached or installed at key nodes prone to heat generation, such as busbar connection points and circuit breaker contacts, to directly collect node temperature data. It can sensitively reflect local temperature rises caused by increased contact resistance, overcoming the monitoring blind zone problem inherent in traditional single-point temperature measurement. The environmental sensing unit integrates a temperature and humidity transmitter and a fan status monitoring circuit. The environmental sensing unit is deployed inside the cabinet to collect data on the ambient temperature, humidity and cooling fan status. By monitoring environmental parameters and heat dissipation conditions in real time, it provides necessary environmental compensation parameters for building an accurate electrothermal coupling fault discrimination model, thereby effectively eliminating the interference of ambient temperature changes on fault judgment and ensuring the safety and reliability of the power distribution cabinet under complex operating conditions.

[0021] In one embodiment, an edge computing processing unit includes a processor and a memory. The memory stores a computer program, and the processor executes the computer program to perform the following steps: processing the three-phase current data based on a sliding time window to calculate the expected real-time load; constructing an electrothermal coupling fault discrimination model based on the expected real-time load, the node temperature data, and the ambient temperature data, and calculating the electrothermal deviation index; determining a health aging factor based on the cumulative operating time of the equipment, and using the health aging factor to correct the basic safety threshold to obtain a dynamic alarm threshold; comparing the electrothermal deviation index with the dynamic alarm threshold to generate a fault detection result.

[0022] Specifically, the edge computing processing unit is the control center of this invention, which includes a processor and a memory. The memory stores a computer program, which, when executed by the processor, completes the intelligent fault detection logic of the power distribution cabinet. In specific implementation, the processor first processes the three-phase current data transmitted from the multi-dimensional sensing layer based on a sliding time window, and calculates the real-time load expectation value using a weighted moving average algorithm. This effectively filters out instantaneous interference signals, enabling the system to accurately identify the current normal load fluctuation characteristics. Subsequently, based on the calculated real-time load expectation value, node temperature data, and ambient temperature data, the processor constructs an electrothermal coupling fault discrimination model and further calculates the electrothermal deviation index. By introducing the principle of thermal inertia, this invention allows the model to comprehensively consider electrical quantity deviation and temperature quantity deviation, thereby effectively distinguishing between normal load impacts such as large motor startup and real overheating faults caused by poor contact. Simultaneously, the processor also reads the cumulative operating time of the equipment, determines the health aging factor based on it, and uses this factor to correct the basic safety threshold, ultimately obtaining the dynamic alarm threshold. This adaptive adjustment mechanism allows the detection benchmark to automatically fine-tune as the insulation performance of the equipment deteriorates or the contact resistance increases, avoiding missed alarms caused by parameter drift in older equipment. Finally, the processor compares the real-time calculated electrothermal deviation index with the dynamic alarm threshold. Once the threshold is exceeded, a fault detection result is generated and an alarm command is output. This invention realizes a shift from passive response to proactive prediction, significantly improving the accuracy and timeliness of fault early warning under complex operating conditions.

[0023] Furthermore, the processor executes the computer program to implement the step of processing the three-phase current data based on a sliding time window and calculating the expected real-time load value, specifically including: setting the length of the sliding time window and the total number of sampling points; assigning a weight coefficient to each sampling point within the sliding time window, wherein the weight coefficient decays exponentially with the time of the sampling point; calculating the sum of the products of the sampling point value and the corresponding weight coefficient, and dividing by the sum of the weight coefficients to obtain the expected real-time load value.

[0024] Specifically, the processor first sets the length of the sliding time window and the total number of sampling points according to the system's response requirements to extract continuous three-phase current data segments containing both current and historical data. Then, the processor assigns a weight coefficient to each sampling point within the sliding time window. This weight coefficient follows an exponential decay law over time, assigning higher weights to sampling points closer to the current time and lower weights to those farther away. Next, the processor iterates through all sampling points within the sliding time window, multiplying the value of each sampling point by its corresponding weight coefficient, and summing all the products to obtain the numerator sum. Simultaneously, the processor sums all the weight coefficients within the sliding time window to obtain the denominator sum. Finally, the processor divides the numerator sum by the denominator sum to accurately calculate the current real-time load expectation. Through the processing of the weighted moving average algorithm described above, the system can effectively smooth high-frequency noise and instantaneous interference signals, so that the output real-time load expectation value can not only quickly follow the changing trend of the actual load, but also reliably filter non-fault transient impacts, providing a stable and accurate benchmark reference for the subsequent construction of an electrothermal coupling fault discrimination model.

[0025] Further, the processor executes the computer program to implement the steps of constructing the electrothermal coupling fault discrimination model and calculating the electrothermal deviation index, specifically including: calculating an electrical quantity deviation term, which characterizes the degree of difference between the current effective value and the real-time load expectation value; calculating a theoretical temperature, which is determined based on the ambient temperature, heat dissipation coefficient, the square of the current effective value at the current moment, and the initial contact resistance of the loop; calculating a temperature quantity deviation term, which characterizes the degree of difference between the measured node temperature and the theoretical temperature; and performing a weighted summation of the electrical quantity deviation term and the temperature quantity deviation term to obtain the electrothermal deviation index.

[0026] Specifically, the processor first acquires the current effective value and the expected real-time load value, and normalizes the difference between the two to calculate the electrical quantity deviation term. This term accurately characterizes the degree to which the actual current deviates from the expected normal load. Next, the processor reads the ambient temperature data collected by the multi-dimensional sensing layer, and, combined with a preset heat dissipation coefficient, the square of the current effective value, and the initial contact resistance of the circuit, calculates the theoretical temperature using the Joule heating effect. This theoretical temperature reflects the heat generation level of the distribution cabinet under ideal healthy conditions. Subsequently, the processor acquires the measured node temperature collected by the distributed temperature sensor array, compares it with the theoretical temperature, and calculates the temperature deviation term. This term directly characterizes the degree to which the actual temperature rise of the equipment exceeds the theoretical normal heat generation level. Finally, the processor pre-sets a first confidence weight and a second confidence weight based on the reliability of each physical quantity. The product of the electrical quantity deviation term and the first confidence weight is added to the product of the temperature deviation term and the second confidence weight to obtain a comprehensive electrothermal deviation index. Through the above steps, the system achieves in-depth cross-verification of electrical and thermal parameters, effectively identifying local overheating caused by hidden defects such as poor contact, and significantly improving the accuracy of fault warning under complex operating conditions.

[0027] Furthermore, the processor executes the computer program to implement the step of correcting the basic safety threshold using the health aging factor to obtain the dynamic alarm threshold, specifically including: calculating the ratio of the cumulative operating hours of the equipment to the reference baseline operating time; calculating the natural logarithm of the ratio plus one; multiplying the natural logarithm by the health aging factor and adding one to the product to obtain the correction coefficient; and multiplying the basic safety threshold by the correction coefficient to obtain the dynamic alarm threshold.

[0028] Specifically, the processor first obtains the cumulative operating hours of the equipment from the system records and divides it by a preset reference baseline operating time to calculate the ratio, which reflects the overall service progress of the equipment. Next, the processor adds 1 to this ratio and performs a natural logarithmic operation. This logarithmic processing makes the threshold increase trend fast at the beginning and slow at the end, consistent with the rapid deterioration pattern in the early stages of equipment aging. Subsequently, the processor multiplies the calculated natural logarithmic value by a pre-calibrated health aging factor and adds 1 to the product to obtain the final correction coefficient. Finally, the processor multiplies the factory-set basic safety threshold by this correction coefficient to obtain a dynamic alarm threshold applicable to the current equipment state. Through this adaptive adjustment mechanism, the detection benchmark can automatically relax as the equipment's insulation performance deteriorates or contact resistance increases, effectively avoiding frequent false alarms caused by normal parameter drift in aging equipment, and ensuring the accuracy and timeliness of fault warnings for the distribution cabinet throughout its entire life cycle.

[0029] Specific limitations regarding the intelligent fault detection capability of the distribution cabinet can be found in the section below on the limitations of the intelligent fault detection method for distribution cabinets, and will not be repeated here. Each module in the aforementioned intelligent fault detection distribution cabinet can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0030] In one embodiment, such as Figure 2 As shown, an intelligent detection method for power distribution cabinets is provided, which is applied to... Figure 2 Taking China as an example, the following specific steps will be used: S10: Synchronously collect three-phase current data, node temperature data and ambient temperature data through a multi-dimensional sensing layer.

[0031] S20: Utilizes the edge computing processing unit to calculate the real-time load expectation based on a sliding time window in order to identify load fluctuation characteristics.

[0032] Specifically, the multi-dimensional sensing layer works collaboratively with high-frequency current sensors, a distributed temperature sensing array, and an environmental sensing unit to synchronously collect three-phase current data, node temperature data, and ambient temperature data during the operation of the distribution cabinet. This parallel acquisition mechanism of multi-source data ensures strict alignment of electrical and thermal quantities on the time axis, providing comprehensive and accurate basic data support for subsequent integrated analysis. After receiving the synchronously acquired data, the edge computing processing unit uses a sliding time window algorithm to process the three-phase current data in real time to calculate the current real-time load expectation. This step effectively smooths out instantaneous interference and high-frequency noise in the current signal, accurately extracting the true load fluctuation characteristics under the current operating conditions. Through these steps, the system can clearly define the boundary between normal load changes and abnormal faults, thereby effectively avoiding false alarms caused by transient impacts such as the starting of large motors, and significantly improving the system's accuracy in identifying load status.

[0033] S30: Using the edge computing processing unit, an electrothermal coupling fault discrimination model is constructed based on the real-time load expectation value, the measured node temperature, and the theoretical temperature calculated based on the current thermal effect, and the electrothermal deviation index is calculated.

[0034] Specifically, the edge computing processing unit first acquires the real-time expected load value and measured node temperature data synchronously transmitted from the multi-dimensional sensing layer. Then, based on the principle of current-thermal effect, and combining the square of the current's effective value, the initial contact resistance of the circuit, and the heat dissipation coefficient, the edge computing processing unit calculates the theoretical temperature of the equipment under ideal healthy conditions. Next, the edge computing processing unit compares the real-time expected load value with the current actual current, quantifying the electrical quantity deviation to characterize the degree of load fluctuation. Simultaneously, the edge computing processing unit compares the measured node temperature with the theoretical temperature, quantifying the temperature quantity deviation to characterize the degree of local abnormal heating. Finally, the edge computing processing unit performs weighted fusion of the electrical quantity deviation and temperature quantity deviation to accurately calculate a comprehensive electrothermal deviation index. Through the construction and calculation of the above electrothermal coupling fault discrimination model, the system achieves deep cross-validation of electrical and thermal parameters. This multi-source data fusion processing method effectively isolates interference from ambient temperature changes and normal load impacts, accurately identifies true overheating phenomena caused by hidden defects such as poor contact, and significantly improves the accuracy of fault warnings under complex operating conditions.

[0035] S40: Using the edge computing processing unit, determine the health aging factor based on the cumulative operating time of the device, and calculate the dynamic alarm threshold in combination with the basic safety threshold.

[0036] Specifically, the edge computing processing unit first obtains the cumulative operating time of the equipment from the system records and determines the health aging factor accordingly. This factor reflects the natural deterioration trend of the equipment as its service life increases, such as a decline in insulation performance or an increase in contact resistance. Next, the edge computing processing unit reads the factory-set basic safety threshold and combines this threshold with the determined health aging factor to calculate a dynamic alarm threshold suitable for the current equipment state. Through this adaptive adjustment mechanism, the system's detection benchmark can automatically fine-tune according to the aging degree of the equipment throughout its entire lifecycle. This processing method effectively avoids frequent false alarms caused by normal parameter drift in aging equipment, while also ensuring that real fault signals can still be accurately captured in the early stages of equipment aging, significantly improving the accuracy and timeliness of fault early warning for the distribution cabinet under complex operating conditions.

[0037] It is important to note that the edge computing processing unit determines the health aging factor based on the equipment's cumulative operating time as follows: First, the edge computing processing unit reads the commissioning date of the distribution cabinet from the equipment file database and calculates the equipment's cumulative operating time in hours, combining this with the current system time. Next, the edge computing processing unit reads a preset aging reference time based on the equipment type and insulation class. This aging reference time represents the point at which the insulation material performance begins to significantly decline under rated operating conditions. Subsequently, the edge computing processing unit calculates the aging progress ratio by comparing the cumulative operating time with the aging reference time. When the aging progress ratio is less than or equal to one, the equipment is in a healthy service period, and the health aging factor is set to the reference value of one. When the aging progress ratio is greater than one, the equipment has entered an accelerated aging period. In this case, the edge computing processing unit calculates the health aging factor according to a preset aging growth function based on the portion of the aging progress ratio exceeding the reference. This aging growth function uses an exponential growth form, causing the health aging factor to gradually increase with the increase in cumulative operating time, thus reflecting the natural deterioration trend of declining insulation performance and increasing contact resistance. Finally, the edge computing processing unit multiplies the calculated health aging factor with the factory-set basic safety threshold to obtain a dynamic alarm threshold applicable to the current aging state of the equipment.

[0038] S50: When the electrothermal deviation index exceeds the dynamic alarm threshold, the power distribution cabinet is determined to have malfunctioned and an alarm signal is output.

[0039] Specifically, the edge computing processing unit acquires the calculated electrothermal deviation index in real time and compares it with an adaptively generated dynamic alarm threshold. When the electrothermal deviation index exceeds the dynamic alarm threshold, it indicates that the electrical or temperature deviation inside the distribution cabinet has exceeded the safety tolerance limit of the equipment under its current aging state. At this point, the edge computing processing unit can accurately determine that a fault has occurred in the distribution cabinet. After confirming the fault, the edge computing processing unit immediately generates a corresponding alarm command and outputs an alarm signal to the external control system. Through this real-time comparison mechanism based on a dynamic benchmark, the system can accurately pinpoint the actual abnormal heating or short-circuit defects under complex and changing operating conditions. This processing method effectively filters out interference caused by normal load fluctuations, significantly improving the accuracy and reliability of fault detection, thereby providing timely and effective early warning protection for the safe and stable operation of the distribution cabinet.

[0040] In one embodiment, in step S20, the calculation of the expected real-time load value adopts the weighted moving average method, wherein the weight coefficient of recent sampled data is greater than the weight coefficient of long-term sampled data, and the weight coefficient decays exponentially.

[0041] Specifically, in this embodiment, the processor uses a weighted moving average method to calculate the expected real-time load. First, the processor sets the length of the sliding time window and the total number of sampling points to capture continuous three-phase current data segments containing both current and historical data points. Then, the processor assigns a weight coefficient to each sampling point within the sliding time window. This weight coefficient follows an exponential decay principle over time, assigning a larger weight coefficient to recent sampling data closer to the current time and a smaller weight coefficient to older sampling data farther from the current time. Next, the processor iterates through all sampling points within the sliding time window, multiplying the value of each sampling point by its corresponding weight coefficient, and summing all the products to obtain the numerator sum. Simultaneously, the processor sums all the weight coefficients within the sliding time window to obtain the denominator sum. Finally, the processor divides the numerator sum by the denominator sum to accurately calculate the current expected real-time load. Through the processing of the weighted moving average algorithm described above, the system can effectively smooth high-frequency noise and instantaneous interference signals, so that the output real-time load expectation value can not only quickly follow the changing trend of the actual load, but also reliably filter non-fault transient impacts, providing a stable and accurate benchmark reference for the subsequent construction of an electrothermal coupling fault discrimination model.

[0042] In one embodiment, in step S30, the calculation logic of the electrothermal deviation index is as follows: calculate the absolute value of the difference between the effective value of the current at the current moment and the expected value of the real-time load, and divide it by the expected value of the real-time load to obtain the relative deviation of the electrical quantity; calculate the absolute value of the difference between the measured node temperature and the theoretical temperature, and divide it by the theoretical temperature to obtain the relative deviation of the temperature quantity; add the product of the relative deviation of the electrical quantity and the first confidence weight to the product of the relative deviation of the temperature quantity and the second confidence weight to obtain the electrothermal deviation index.

[0043] Specifically, the processor obtains the current effective value and the real-time expected load value, subtracts the two and takes the absolute value, then divides this absolute value by the real-time expected load value to obtain the relative deviation of the electrical quantity. This normalization process can eliminate the influence of different load levels on deviation assessment and accurately characterize the relative degree of deviation of the actual current from the normal expected load. Next, the processor obtains the measured node temperature collected by the distributed temperature sensing array and the theoretical temperature calculated based on the current thermal effect, subtracts the two and takes the absolute value, then divides this absolute value by the theoretical temperature to obtain the relative deviation of the temperature quantity. This step intuitively reflects the relative degree of abnormality of the actual temperature rise of the equipment exceeding the theoretical normal heating level. Subsequently, the processor pre-sets a first confidence weight and a second confidence weight according to the reliability of each physical quantity, multiplies the relative deviation of the electrical quantity by the first confidence weight to obtain the electrical deviation component, and multiplies the relative deviation of the temperature quantity by the second confidence weight to obtain the temperature deviation component. Finally, the processor adds the electrical deviation component and the temperature deviation component together to obtain a comprehensive electrothermal deviation index. Through the above multi-source data fusion calculation logic, the system achieves deep cross-validation of electrical parameters and thermal parameters. This processing method can effectively eliminate interference from changes in ambient temperature and normal load impacts, accurately identify the real overheating phenomenon caused by hidden defects such as poor contact, and significantly improve the accuracy and reliability of fault warning under complex working conditions.

[0044] In one embodiment, in step S30, the formula for calculating the theoretical temperature is: Theoretical temperature = Ambient temperature + Heat dissipation coefficient × Effective current value 2 × Initial contact resistance of the circuit.

[0045] Specifically, the processor acquires the ambient temperature data of the distribution cabinet through a multi-dimensional sensing layer, reflecting the basic thermodynamic conditions outside the equipment. Next, the processor reads the pre-calibrated heat dissipation coefficient and the initial contact resistance of the circuit; these two parameters characterize the inherent thermal properties and electrical connection status of the equipment in a healthy state. Subsequently, the processor collects the current effective value in real time and squares this value using an internal calculation module to accurately reflect the basic heat generated by the Joule heating effect. Then, the processor multiplies the heat dissipation coefficient, the square of the current effective value, and the initial contact resistance of the circuit to calculate the temperature rise caused by the equipment's own heating. Finally, the processor sums this temperature rise with the ambient temperature to obtain the theoretical temperature of the equipment under current operating conditions. Through the above calculation logic based on the current heating effect, the system can accurately deduce the normal heating level of the distribution cabinet under ideal healthy conditions. The establishment of this benchmark temperature provides a scientific basis for subsequent comparison with measured node temperatures, enabling the system to effectively eliminate interference from ambient temperature fluctuations, accurately identify true overheating phenomena caused by hidden defects such as poor contact, and significantly improve the accuracy of fault warnings.

[0046] In one embodiment, step S50, i.e., the method for determining the operating status of the distribution cabinet, further includes: if the effective value of the current at the current moment increases and the measured node temperature does not increase with the increase of the effective value of the current at the current moment, it is determined to be a normal load fluctuation; if the effective value of the current at the current moment increases and the measured node temperature is higher than the theoretical temperature, it is determined to be a poor contact fault or an overheating fault.

[0047] Specifically, the processor acquires the current effective value and the measured node temperature in real time and analyzes their trends and numerical relationships. When the processor detects an increase in the current effective value, it further assesses the response of the measured node temperature. If the measured node temperature does not rise with the increase in the current effective value, it indicates that the temperature rise conforms to the normal Joule heating effect or is within the equipment's thermal inertia buffer period. In this case, the processor determines that the phenomenon is a normal load fluctuation, thus effectively avoiding false alarms. Conversely, if the processor detects an increase in the current effective value and simultaneously determines that the measured node temperature is higher than the theoretical temperature calculated based on the current heating effect, it indicates that the actual heat generation of the equipment has significantly exceeded the theoretical expectation under healthy conditions. In this case, the processor determines that the distribution cabinet has experienced a poor contact fault or overheating fault. Through the above-mentioned multi-dimensional parameter cross-validation judgment logic, the system can accurately distinguish between normal transient impacts and real internal defects. This processing method significantly improves the accuracy of fault diagnosis under complex operating conditions, providing a reliable guarantee for the safe and stable operation of the distribution cabinet.

[0048] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0049] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. 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 power distribution cabinet capable of intelligently detecting faults, characterized in that, include: A multi-dimensional sensing layer is used to simultaneously collect three-phase current data, node temperature data, and ambient temperature data. An edge computing processing unit, comprising a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps: Set the length of the sliding time window and the total number of sampling points; A weighting coefficient is assigned to each sampling point within the sliding time window, and the weighting coefficient decays exponentially with the time of the sampling point. Calculate the sum of the products of the sampled point values ​​and their corresponding weight coefficients, and divide by the sum of the weight coefficients to obtain the expected real-time load value; Based on the real-time load expectation, the node temperature data, and the ambient temperature data, an electrothermal coupling fault discrimination model is constructed, and the electrothermal deviation index is calculated. The health aging factor is determined based on the cumulative operating time of the equipment, and the basic safety threshold is corrected using the health aging factor to obtain the dynamic alarm threshold. The electrothermal deviation index is compared with the dynamic alarm threshold to generate a fault detection result.

2. The power distribution cabinet capable of intelligent fault detection according to claim 1, characterized in that, The multidimensional sensing layer includes: High-frequency current sensors are used to acquire high-frequency waveform data of three-phase current and zero-sequence current. A distributed temperature sensor array is deployed at busbar connection points and circuit breaker contacts to collect node temperature data. The environmental sensing unit is used to collect data on the ambient temperature, humidity, and cooling fan status inside the cabinet.

3. The power distribution cabinet capable of intelligent fault detection according to claim 1, characterized in that, The processor executes the computer program to implement the steps of constructing the electrothermal coupling fault discrimination model and calculating the electrothermal deviation index, specifically including: Calculate the electrical quantity deviation term, which represents the degree of difference between the current effective value and the real-time load expectation value at the current moment; Calculate the theoretical temperature, which is determined based on the ambient temperature, the heat dissipation coefficient, the square of the current effective value at the current moment, and the initial contact resistance of the circuit. Calculate the temperature deviation term, which characterizes the degree of difference between the measured node temperature and the theoretical temperature; The electrical quantity deviation term and the temperature quantity deviation term are weighted and summed to obtain the electrothermal deviation index.

4. The power distribution cabinet capable of intelligent fault detection according to claim 3, characterized in that, The processor executes the computer program to implement the step of correcting the basic safety threshold using the healthy aging factor to obtain the dynamic alarm threshold, specifically including: The ratio of the cumulative operating hours of the calculated equipment to the reference baseline operating time; Calculate the natural logarithm of the ratio plus one; Multiply the natural logarithm by the healthy aging factor, and add one to the product to obtain the correction coefficient; The dynamic alarm threshold is obtained by multiplying the basic safety threshold by the correction coefficient.

5. A method for intelligent detection of power distribution cabinets, characterized in that, include: Three-phase current data, node temperature data and ambient temperature data are collected synchronously through a multi-dimensional sensing layer; Using edge computing processing units, real-time load expectations are calculated based on a sliding time window to identify load fluctuation characteristics; Using the edge computing processing unit, an electrothermal coupling fault discrimination model is constructed based on the real-time load expectation, measured node temperature, and theoretical temperature calculated based on the current thermal effect, and the electrothermal deviation index is calculated. Using the edge computing processing unit, the health aging factor is determined based on the cumulative operating time of the device, and the dynamic alarm threshold is calculated in combination with the basic safety threshold. When the electrothermal deviation index exceeds the dynamic alarm threshold, it is determined that the power distribution cabinet has malfunctioned and an alarm signal is output. The steps for calculating the expected real-time load specifically include: Set the length of the sliding time window and the total number of sampling points; A weighting coefficient is assigned to each sampling point within the sliding time window, and the weighting coefficient decays exponentially with the time of the sampling point. The sum of the products of the sampled point values ​​and their corresponding weight coefficients is calculated and divided by the sum of the weight coefficients to obtain the expected real-time load value.

6. The intelligent detection method for power distribution cabinets according to claim 5, characterized in that, The calculation of the real-time load expectation value adopts the weighted moving average method, in which the weight coefficient of recent sampled data is greater than that of long-term sampled data, and the weight coefficient decays exponentially.

7. The intelligent detection method for power distribution cabinets according to claim 5, characterized in that, The calculation logic for the electrothermal deviation index is as follows: Calculate the absolute value of the difference between the current effective value and the expected real-time load value, and divide it by the expected real-time load value to obtain the relative deviation of the electrical quantity; Calculate the absolute value of the difference between the measured node temperature and the theoretical temperature, and divide it by the theoretical temperature to obtain the relative deviation of the temperature. The electrothermal deviation index is obtained by multiplying the relative deviation of the electrical quantity by the first confidence weight and adding the relative deviation of the temperature quantity by the second confidence weight.

8. The intelligent detection method for power distribution cabinets according to claim 5, characterized in that, The formula for calculating the theoretical temperature is: Theoretical temperature = Ambient temperature + Heat dissipation coefficient × Effective current value 2 × Initial contact resistance of the circuit.

9. The intelligent detection method for power distribution cabinets according to claim 7, characterized in that, Methods for determining the operating status of a power distribution cabinet also include: If the effective value of the current at the current moment increases and the measured node temperature does not increase with the increase of the effective value of the current at the current moment, it is determined to be a normal load fluctuation. If the effective value of the current at the current moment increases and the measured node temperature is higher than the theoretical temperature, then it is determined to be a poor contact fault and / or an overheating fault.

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