Low-voltage distribution cabinet monitoring method and system including leakage monitoring

Abstract

The present invention relates to the technical field of safety monitoring of distribution cabinets, and in particular to a monitoring method and system for low-voltage distribution cabinets including leakage monitoring. The present invention first utilizes distributed high-frequency sensors to fuse multiple detection technologies to collect leakage monitoring indicators and establish an electrical-environmental database. Then, dynamic game is used to adjust the electrical and environmental weights, and an insulation degradation model is constructed in combination with the degree of membership. Then, the leakage time probability density function is generated through diffusion simulation, the leakage time mean and confidence interval are obtained, and the risk priority number is calculated to divide the risk level of the distribution cabinet parts. Finally, the monitoring frequency is allocated based on the risk stratification, the model parameters are calibrated by comparing the measured and simulated data, and the monitoring strategy is iteratively updated. The strategy collects multi-dimensional leakage monitoring indicators in real time, constructs an accurate insulation degradation model, realizes accurate assessment and classification of the leakage risk of the distribution cabinet, and formulates a reasonable monitoring strategy according to the risk level, thereby improving the safety and reliability of the operation of the low-voltage distribution cabinet.

Description

Technical Field

[0001] The present invention relates to the technical field of low-voltage distribution cabinet monitoring, and in particular to a low-voltage distribution cabinet monitoring method and system including leakage monitoring. Background Art

[0002] In modern power supply systems, low-voltage distribution cabinets (LVDCs), as key equipment for power distribution and control, are widely used in numerous fields, including industrial production, commercial operations, and residential electricity consumption. Their stable and reliable operation is crucial to ensuring the normal operation of power systems and preventing economic losses and safety incidents caused by power failures. However, current low-voltage distribution cabinets frequently suffer from leakage problems during actual operation, posing a severe challenge to the safe and stable operation of power systems.

[0003] On the one hand, existing leakage monitoring methods have numerous flaws. Traditional monitoring methods are often limited to detecting a single or a few parameters, failing to comprehensively assess leakage risks. They also ignore the coupling between electrical and environmental factors, making it difficult to accurately assess leakage risks in complex environments. Furthermore, fault warnings are delayed, making it difficult to detect leakage faults in their early stages, leading to equipment damage and even power outages.

[0004] On the other hand, monitoring strategies are not entirely rational. They lack specificity and flexibility, and fail to differentiate monitoring based on risk levels, resulting in wasted or insufficient monitoring resources. Furthermore, existing technologies lack model calibration and policy update mechanisms, making them difficult to adapt to changes in equipment operating conditions.

[0005] In summary, existing leakage monitoring and surveillance technologies for low-voltage distribution cabinets have numerous shortcomings, making them difficult to meet the stringent safety and reliability requirements of modern power systems. There is an urgent need to develop innovative technologies that can comprehensively and accurately monitor leakage, fully consider the coupling relationship between electrical and environmental factors, provide timely fault warnings, and rationally adjust monitoring strategies based on actual conditions. This is the key issue that this invention aims to address. Summary of the Invention

[0006] The purpose of the invention is to provide a low-voltage distribution cabinet monitoring method and system, including leakage monitoring, to address existing issues such as incomplete leakage monitoring, failure to consider the coupling relationship between electrical and environmental factors, delayed fault warnings, and irrational monitoring strategies. By collecting multi-dimensional leakage monitoring indicators in real time and constructing a precise insulation degradation model, the leakage risk of distribution cabinets can be accurately assessed and classified. A reasonable monitoring strategy can be formulated based on the risk level, thereby improving the safety and reliability of low-voltage distribution cabinet operations.

[0007] The technical solutions of the present invention are as follows:

[0008] One of the solutions of the present invention is to provide a low-voltage distribution cabinet monitoring method and system including leakage monitoring, comprising:

[0009] A low-voltage distribution cabinet monitoring method including leakage monitoring is characterized by comprising:

[0010] By integrating infrared thermal imaging and ultrasonic partial discharge detection with distributed high-frequency sensors, leakage monitoring indicators are collected in real time to establish an electrical-environmental database.

[0011] A dynamic game theory method is used to adjust the weights of electrical and environmental factors in leakage monitoring indicators, and the membership degree is determined for each monitoring indicator. The weights and membership degrees are then combined to construct an insulation degradation model based on multi-factor coupling.

[0012] Using an insulation degradation model based on multi-factor coupling and an electrical-environmental database, diffusion simulation is performed on various parts of the low-voltage distribution cabinet to generate a probability density function of leakage time and extract the mean value and confidence interval of leakage time.

[0013] Based on the mean value and confidence interval of leakage time, the risk priority number is calculated, and the distribution cabinet area is divided into high-risk layer, medium-risk layer and low-risk layer;

[0014] Based on the risk stratification results, an initial monitoring frequency is assigned to each level. Leakage monitoring data is collected in real time, and model parameters are calibrated by comparing field measured data with simulation results. The monitoring strategy is then updated periodically and iteratively.

[0015] As an optimization, the leakage monitoring indicators include insulation resistance value, temperature and humidity, voltage fluctuation, mechanical wear degree and environmental corrosiveness;

[0016] Among them, insulation resistance, leakage current, and voltage fluctuation rate are classified as electrical factors;

[0017] Temperature and humidity, degree of mechanical wear, and environmental corrosiveness are listed as environmental factors.

[0018] As an optimization, the steps for adjusting the weights of electrical and environmental factors in the leakage monitoring index include:

[0019] Dynamic game theory is introduced to calculate the dynamic game model of electrical and environmental parameters. The objective function of the dynamic game model is:

[0020]

[0021] Among them, ω e is the electrical factor weight, ω c is the weight of environmental factors, U e For electrical utility, for example U c For environmental utility;

[0022] Using the game equilibrium algorithm, the optimal solution of the dynamic game model is solved to obtain the new weights of electrical parameters and environmental parameters;

[0023] The step of determining the membership degree includes:

[0024] Determine the fuzzy subsets and membership functions of each leakage monitoring indicator;

[0025] Among them, the membership function of each leakage monitoring indicator is:

[0026]

[0027] Among them, μ(x i ) is the leakage monitoring index x i The membership degree, x mini ,x opti ,x maxi ,x criti They are leakage monitoring indicators x i The minimum value of leakage monitoring index x i In the optimal value of the fuzzy subset, the leakage monitoring index x i The maximum value of leakage monitoring index x i critical value.

[0028] As an optimization, the insulation degradation model based on multi-factor coupling is

[0029]

[0030] Where D is the degradation index, n is the number of leakage monitoring indicators, ω i is the electrical factor weight or environmental factor weight in the leakage monitoring index, μ(x i ) is the membership degree of leakage monitoring index.

[0031] As an optimization, the diffusion simulation steps of each part of the low-voltage distribution cabinet include:

[0032] Determine the input parameter range of the diffusion simulation, the number of diffusion simulations and the time step of each diffusion simulation;

[0033] Initialize each diffusion simulation based on the insulation degradation model with multi-factor coupling;

[0034] According to the insulation degradation model, combined with the current electrical and environmental conditions, the insulation degradation degree of each part is calculated according to the insulation degradation model

[0035] Determine whether the insulation degradation level of the current location has reached the leakage threshold and record the time when leakage occurs in the simulation.

[0036] As an optimization, the probability density function is:

[0037]

[0038] Where f(t) is the probability density function of leakage time t, M is the number of samples of leakage occurrence time, h is the bandwidth of kernel function, K(x) is kernel function, t j is the time when the jth leakage occurs;

[0039] The formula for calculating the mean leakage time is:

[0040]

[0041] Where m is the number of intervals, Δt j is the length of the jth leakage occurrence time interval, Δt j =t j+1 -t j ;

[0042] The confidence interval CI is selected at the 95% confidence level. That is, the lower limit is sample values, with an upper limit of sample values, where Indicates that the data is rounded down.

[0043] As an optimization, the calculation formula of the risk priority number is:

[0044] RPN = S × O × D;

[0045]

[0046] Among them, RPN is the risk priority number, S is the severity, which is divided into levels 1-10, O is the probability of occurrence, and TD is the detection difficulty, which is divided into levels 1-10.

[0047] As an optimization, the high-risk layer classification standard is RPN≥200; the medium-risk layer classification standard is 50≤RPN≤200; and the low-risk layer classification standard is RPN<50.

[0048] As an optimization, the low-voltage distribution cabinet parts of the high-risk floor adopt high-frequency online monitoring; the low-voltage distribution cabinet parts of the medium-risk floor adopt medium-frequency detection; the low-voltage distribution cabinet parts of the low-risk floor adopt low-frequency spot inspection

[0049] A second solution of the present invention is to provide a low-voltage distribution cabinet monitoring system including leakage monitoring, comprising:

[0050] The database generation module uses distributed high-frequency sensors to fuse infrared thermal imaging and ultrasonic partial discharge detection to collect leakage monitoring indicators in real time and establish an electrical-environmental database;

[0051] The weight and model construction module uses a dynamic game method to adjust the weights of electrical and environmental factors in leakage monitoring indicators, and determines the membership degree for each monitoring indicator. The weight and membership degree are integrated to construct an insulation degradation model based on multi-factor coupling.

[0052] The simulation analysis module uses an insulation degradation model based on multi-factor coupling and an electrical-environmental database to perform diffusion simulation on various parts of the low-voltage distribution cabinet, generate a probability density function of leakage time, and extract the mean value and confidence interval of leakage time;

[0053] The risk assessment module calculates the risk priority number based on the mean leakage time and confidence interval, and divides the distribution cabinet area into high-risk, medium-risk, and low-risk layers;

[0054] The monitoring strategy execution and adjustment module allocates initial monitoring frequencies to each level based on risk stratification results; collects leakage monitoring data in real time, calibrates model parameters by comparing field measured data with simulation results, and periodically iterates and updates the monitoring strategy.

[0055] The beneficial effects brought about by the technical solutions provided in the embodiments of the present application include at least the following beneficial effects:

[0056] In terms of comprehensiveness and accuracy, this method overcomes the limitations of traditional monitoring methods, which rely solely on single-dimensional metrics, by integrating multiple detection technologies to collect multi-dimensional indicators covering electrical parameters and environmental factors. The insulation degradation model, constructed using dynamic game theory and fuzzy membership, fully considers the coupling relationship between electrical and environmental factors. This model can more accurately reflect the actual condition of the distribution cabinet, avoid risk misjudgments caused by ignoring complex factors, and significantly improve the accuracy of leakage risk assessment.

[0057] In terms of monitoring strategy rationality and resource optimization, the risk-based allocation of monitoring frequency ensures the rational use of monitoring resources. High-frequency online monitoring of high-risk layers can promptly identify potential fault hazards. Moderate- and low-frequency spot checks are used for medium- and low-risk layers, respectively. This ensures effective monitoring of equipment operating status while avoiding resource waste caused by excessive monitoring, achieving a balance between monitoring cost and efficiency.

[0058] In terms of safety assurance and dynamic strategy optimization, the real-time monitoring and fault warning mechanism can quickly respond to data anomalies, automatically disconnecting the faulty circuit and ensuring the safety of personnel and equipment. Furthermore, by comparing field-measured data with simulation results to calibrate model parameters and periodically iteratively update the monitoring strategy, the monitoring system can adapt to changes in the operating status of the distribution cabinet, maintaining efficient and reliable operation, and providing strong support for the stable operation of the low-voltage distribution cabinet. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1The figure is a schematic diagram of the overall process of the low-voltage distribution cabinet monitoring method including leakage monitoring;

[0060] Figure 2 Flowchart of the steps S100 of the low-voltage distribution cabinet monitoring method including leakage monitoring;

[0061] Figure 3 A flow chart of the steps of the low-voltage distribution cabinet monitoring method S200 including leakage monitoring;

[0062] Figure 4 A flow chart of the steps of the low-voltage distribution cabinet monitoring method S300 including leakage monitoring;

[0063] Figure 5 A flow chart of the steps of the low-voltage distribution cabinet monitoring method S400 including leakage monitoring;

[0064] Figure 6 The figure is a flowchart of the low-voltage distribution cabinet monitoring method S500 including leakage monitoring. DETAILED DESCRIPTION

[0065] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0066] Example 1

[0067] Traditional monitoring methods for low-voltage distribution cabinets often focus solely on a single parameter, such as voltage or current, and are unable to comprehensively and accurately assess leakage risks within the distribution cabinet. Furthermore, these methods fail to consider the coupling between electrical parameters and environmental factors, making them inadequate for complex and changing operating environments. Furthermore, these methods also lack sufficient fault warning and monitoring strategy adjustments, hindering timely detection of potential leakage hazards and the implementation of effective measures to address them.

[0068] To resolve the above issues, please refer to Figure 1 , which shows a low-voltage distribution cabinet monitoring method including leakage monitoring provided by an embodiment of the present invention, the method comprising:

[0069] S100: Through the integration of distributed high-frequency sensors with infrared thermal imaging and ultrasonic partial discharge detection, leakage monitoring indicators are collected in real time to establish an electrical-environmental database.

[0070] S200: A dynamic game theory method is used to adjust the weights of electrical and environmental factors in leakage monitoring indicators, and a membership degree is defined for each monitoring indicator. The weights and membership degrees are combined to construct an insulation degradation model based on multi-factor coupling.

[0071] S300: Using an insulation degradation model based on multi-factor coupling and an electrical-environmental database, this system performs diffusion simulation on various parts of a low-voltage distribution cabinet, generates a probability density function of leakage time, and extracts the mean time to failure (MTTF) and confidence interval.

[0072] S400: Calculate the risk priority number (RPN) based on the mean time to failure (MTTF) and confidence interval, and divide the distribution cabinet into high-risk, medium-risk, and low-risk layers.

[0073] S500: Based on the risk stratification results, the initial monitoring frequency is assigned to each level; leakage monitoring data is collected in real time, model parameters are calibrated by comparing field measured data with simulation results, and the monitoring strategy is updated periodically and iteratively.

[0074] For low-voltage distribution cabinet monitoring, the S100's critical foundational work is to accurately and real-timely collect leakage monitoring indicators and establish a comprehensive electrical-environmental coupling database. This step primarily involves integrating multiple detection technologies through distributed high-frequency sensors to collect data. The collected data is then organized and stored to support subsequent analysis and model building.

[0075] Please refer to Figure 2 , which shows a flowchart of S100 of an exemplary low-voltage distribution cabinet monitoring method including leakage monitoring of the present application, and its contents include:

[0076] S110: Complete sensor selection and layout planning, installation, data collection, and pre-processing of collected data.

[0077] Sensor selection and layout planning are based on the structural characteristics of the low-voltage distribution cabinet, the distribution of electrical parameters, and potential leakage hazards. For electrical parameter monitoring, high-precision voltage sensors, current sensors, and insulation resistance testers are used to ensure accurate acquisition of voltage, current, and insulation resistance values. For environmental monitoring, temperature and humidity sensors and gas sensors are used to monitor ambient temperature, humidity, and corrosive gas concentrations. Accelerometers and strain sensors are also used to detect mechanical vibration and wear.

[0078] Arrange sensors appropriately according to the different areas and functional modules of the power distribution cabinet. Collect data from each sensor and perform pre-processing such as cleaning the collected raw data to remove noise and interference.

[0079] S120: Establish electrical-environmental database.

[0080] The electrical-environmental database uses a relational database for storage, and establishes multiple data tables to store different types of data.

[0081] Identify electrical factors and create an electrical parameter table. Classify insulation resistance, leakage current, and voltage fluctuation as electrical factors. The electrical parameter table fields include timestamp, insulation resistance, leakage current, and voltage fluctuation.

[0082] Define environmental factors and create an environmental parameter table. Include temperature and humidity, mechanical wear, and environmental corrosiveness as environmental factors. The environmental parameter table includes: timestamp, temperature and humidity, mechanical wear, and environmental corrosiveness.

[0083] For example, create an "equipment information table" to record the basic information of the low-voltage distribution cabinet, such as equipment model, manufacturer, installation location, commissioning time, etc.; a "sensor information table" to record the number, type, installation location, measurement range, accuracy and other parameters of each sensor; a "monitoring data table" to store leakage monitoring index data collected in real time and pre-processed, including insulation resistance value, temperature and humidity, voltage fluctuation, mechanical wear degree, environmental corrosion data, and corresponding collection time, sensor number and other information; an "infrared thermal imaging data table" to store relevant data of infrared thermal imaging detection, such as thermal image file path, temperature values ​​of key parts, temperature anomaly marks, etc.; an "ultrasonic partial discharge data table" to store signal data, spectrum analysis results, partial discharge location information, etc. of ultrasonic partial discharge detection.

[0084] S200 aims to determine the reasonable weight ratio of electrical and environmental factors in leakage monitoring indicators through a dynamic game method, and define a membership function for each indicator. The weight ratio and membership are combined to construct an accurate insulation degradation model based on multi-factor coupling, outputting a degradation index to provide strong support for subsequent leakage analysis.

[0085] Please refer to Figure 3 , which shows a flowchart of S200 of an exemplary low-voltage distribution cabinet monitoring method including leakage monitoring of the present application, and its contents include:

[0086] S210: Initial weights of leakage monitoring indicators are set and dynamic game theory analysis is introduced.

[0087] According to historical experience and theoretical analysis, the initial weights of electrical parameters and environmental parameters in leakage monitoring indicators are set, namely, the electrical factor weight ω e and environmental factor weight ω c The electrical parameters mainly include voltage fluctuation, current change and insulation resistance value, while the environmental parameters mainly include temperature and humidity, mechanical wear degree and environmental corrosiveness.

[0088] For example, initially, the weight of the electrical parameter is set to 0.6, and the weight of the environmental parameter is set to 0.4.

[0089] Dynamic game theory is introduced, treating electrical parameters and environmental parameters as two participants in the game. The interaction and influence between the two participants is analyzed based on real-time and historical data. The weighting of electrical and environmental parameters is adjusted through iterative calculations. In each iteration, the dynamic game model of the two participants is calculated based on the current weights and data changes. The objective function of the dynamic game model is:

[0090]

[0091] Among them, ω e is the electrical factor weight, ω c is the weight of environmental factors, U e For electrical utilities, such as insulation resistance stability, U c Environmental effects, such as the contribution of temperature and humidity to degradation.

[0092] Using a game equilibrium algorithm, the optimal solution of the dynamic game model is found, and new weights of electrical and environmental parameters are obtained. During the iterative process, the weights are continuously updated until they converge to a stable value.

[0093] S220: Determine the fuzzy membership of each leakage monitoring indicator.

[0094] Determine the fuzzy subsets and membership functions of each leakage monitoring indicator. For each indicator, divide it into several fuzzy subsets according to its value range and the degree of influence on insulation degradation.

[0095] Exemplarily, the insulation resistance value is divided into three fuzzy subsets: "low insulation resistance", "medium insulation resistance" and "high insulation resistance".

[0096] The membership function is defined for each indicator, and its formula is:

[0097]

[0098] Among them, μ(x i ) is the leakage monitoring index x i The membership degree, x mini ,x opti ,x maxi ,x criti They are leakage monitoring indicators x i The minimum value of leakage monitoring index x i In the optimal value of the fuzzy subset, the leakage monitoring index x i The maximum value of leakage monitoring index x i critical value.

[0099] S230: Construct an insulation degradation model.

[0100] The electrical factor weight ω in the leakage monitoring index determined by dynamic game e and environmental factor weight ω c And the membership degree μ(x i ) are integrated to construct an insulation degradation model based on multi-factor coupling. The insulation degradation model is:

[0101]

[0102] Where D is the degradation index, n is the number of leakage monitoring indicators, ω i is the electrical factor weight or environmental factor weight in the leakage monitoring index, μ(x i ) is the membership degree of leakage monitoring index.

[0103] S300 uses an insulation degradation model based on multi-factor coupling and an electrical-environmental database to perform diffusion simulation on key parts of the distribution cabinet, thereby generating a probability density function of leakage time and extracting the mean and confidence interval of leakage time, providing key data support for subsequent risk assessment.

[0104] Please refer to Figure 4 , which shows a flowchart of S300 of an exemplary low-voltage distribution cabinet monitoring method including leakage monitoring of the present application, and its contents include:

[0105] S310: Determine diffusion simulation parameters, initialize an insulation degradation model based on multi-factor coupling, and then perform diffusion simulation according to the model.

[0106] Historical monitoring data related to various parts of the low-voltage distribution cabinet was extracted from the electrical-environmental database, including insulation resistance, temperature and humidity, voltage fluctuations, mechanical wear, and environmental corrosiveness. The variation range and distribution characteristics of this data were analyzed to determine the input parameter range for the diffusion simulation.

[0107] The number of diffusion simulations is also determined. Preferably, 10,000 to 15,000 diffusion simulations are performed. The time step for each diffusion simulation is also determined. This time step should be selected based on the actual situation, ensuring that the leakage development process can be accurately captured without excessive computational effort.

[0108] Initialize each diffusion simulation based on the multi-factor coupled insulation degradation model. Parameters in the insulation degradation model, such as the weights of electrical and environmental factors and the membership functions of each monitoring indicator, are set according to the values ​​determined in step S200. Initial conditions for the simulation model are set for each component of the distribution cabinet based on its initial state, such as initial insulation resistance, initial temperature and humidity, to ensure that the initial state of the simulation matches the actual situation.

[0109] In each simulation, the simulation process is advanced step by step according to the specified time step. In each time step, the insulation degradation model is used to calculate the insulation degradation degree of each part in combination with the current electrical and environmental conditions.

[0110] Determine whether the insulation degradation level at the current location has reached the leakage threshold. If so, record the time at which leakage occurred during that simulation. If not, continue the simulation at the next time step until the simulation ends or the preset maximum simulation time is reached. Repeat this process to obtain a sample of leakage occurrence times.

[0111] S320: Generate a probability density function of leakage time.

[0112] Perform statistical analysis on M samples of leakage occurrence time. First, divide the range of leakage occurrence time into several small intervals. The interval division should be reasonably determined based on the distribution of sample data to ensure that there are a certain number of samples in each interval.

[0113] For example, if the leakage occurrence time is between 0 and 1000 hours, it can be divided into 10 intervals such as 0-100 hours and 100-200 hours.

[0114] Count the number of samples in each interval m k , where k represents the kth interval. Calculate the frequency f of samples in each interval k , the frequency calculation formula is:

[0115]

[0116] A histogram is drawn with the leakage occurrence time as the horizontal axis and the frequency as the vertical axis. The histogram approximately represents the probability distribution of the leakage time.

[0117] In an optional embodiment, in order to obtain a more accurate probability density function, kernel density estimation is used to smooth the histogram. Assuming the kernel function is K(x) and the bandwidth is h, the kernel density estimation formula for the probability density function f(t) of the leakage time t is:

[0118]

[0119] Among them, tj is the time when the jth leakage occurs.

[0120] S330: Extracting the mean time to failure (MTTF) and the confidence interval.

[0121] According to the generated probability density function f(t), the mean leakage time MTTF is calculated using the following formula:

[0122]

[0123] Where m is the number of intervals, Δt j is the length of the jth leakage occurrence time interval, Δt j =t j+1 -t j .

[0124] Calculate the confidence interval CI, select the 95% confidence level, and determine the corresponding upper and lower limits of the confidence interval by sorting the leakage time samples, that is, the lower limit is the sample values, with an upper limit of sample values, where Indicates that the data is rounded down, that is,

[0125] S400 calculates the risk priority number (RPN) and stratifies the risk of distribution cabinet locations based on this value.

[0126] Please refer to Figure 5 , which shows a flowchart of S400 of an exemplary low-voltage distribution cabinet monitoring method including leakage monitoring of the present application, and its contents include:

[0127] S410: Define RPN parameters and calculate RPN.

[0128] The parameters of RPN include:

[0129] Severity S is divided into 1-10 levels and is determined by MTTF, i.e.

[0130] The probability of occurrence O is determined by MTTF, that is,

[0131] The detection difficulty TD is divided into 1-10 levels, which is determined by the confidence interval, that is,

[0132] The calculation formula for risk priority number (RPN) is: RPN = S × O × D.

[0133] S420: Risk stratification standards are established and the risks of low-voltage distribution cabinets are stratified.

[0134] Based on the calculated RPN value distribution, combined with actual operating experience and risk management needs, a risk stratification standard is formulated. The RPN value is divided into three intervals, corresponding to different risk levels:

[0135] High-risk layer: RPN ≥ 200;

[0136] Medium-risk layer: 50≤RPN≤200;

[0137] Low-risk layer: RPN<50.

[0138] Compare the RPN values ​​calculated for key parts of each distribution cabinet with the risk stratification standards to determine the risk level to which it belongs.

[0139] S500: Based on risk stratification results, it assigns reasonable initial monitoring frequencies to distribution cabinet locations at different levels. During the monitoring process, it processes data in real time, triggers warnings, and disconnects fault circuits. Simultaneously, it calibrates model parameters by comparing field measured data with simulation results, and periodically iterates and updates monitoring strategies to achieve effective monitoring of low-voltage distribution cabinets and ensure their safe and stable operation.

[0140] Please refer to Figure 6 , which shows a flowchart of S500 of an exemplary low-voltage distribution cabinet monitoring method including leakage monitoring of the present application, and its contents include:

[0141] S510: Allocate monitoring frequency.

[0142] High-frequency online monitoring is used for distribution cabinets in high-risk floors.

[0143] For example, for busbar connection points in high-risk layers, not only conventional indicators such as insulation resistance, temperature and humidity, and voltage fluctuations should be monitored in real time, but infrared thermal imaging detection and ultrasonic partial discharge detection should also be carried out every 15 minutes to promptly detect potential fault hazards.

[0144] For the distribution cabinet parts in the medium-risk layer, medium-frequency detection is adopted.

[0145] For example, routine leakage monitoring indicators are collected every two hours, and infrared thermal imaging and ultrasonic partial discharge testing are performed daily. This can detect fault signs in a timely manner to a certain extent while balancing monitoring costs and efficiency.

[0146] Low-frequency spot inspections are adopted for distribution cabinets in low-risk floors.

[0147] For example, routine leakage monitoring indicators are collected 2-3 times a week, and infrared thermal imaging and ultrasonic partial discharge testing are conducted once a month. Through regular spot checks, basic monitoring of the operating status of low-risk areas is maintained, while avoiding excessive monitoring and waste of resources.

[0148] S520: Compare the field measured data with the simulation results, and calibrate the model parameters and perform periodic iterative updates on the monitoring strategy.

[0149] Regularly compare field measurement data with the results of the diffusion simulation in step S300. This comparison includes leakage monitoring indicator data and the insulation degradation degree and leakage duration predicted based on this data. Based on the error results from this comparison, calibrate the parameters of the insulation degradation model based on multi-factor coupling.

[0150] Set the iterative update cycle of the monitoring strategy. In each iterative cycle, comprehensively consider factors such as new situations during the operation of the distribution cabinet and historical fault data.

[0151] Based on the results of model parameter calibration, newly acquired operating data, and changes in risk assessment results, monitoring strategy parameters such as monitoring frequency and warning thresholds for distribution cabinets at each level are readjusted to accommodate changes in the actual operating status of distribution cabinets and ensure the effectiveness and adaptability of the monitoring strategy.

[0152] Through steps S100-S500, comprehensive leakage monitoring is achieved, comprehensively considering the coupling relationship between electrical and environmental factors. Accurate risk assessment and grading enable targeted monitoring and rational resource allocation. Timely fault warnings and automatic circuit disconnection ensure safety, and models and monitoring strategies can be continuously optimized based on actual operating conditions, effectively improving the safety and reliability of low-voltage distribution cabinet operations.

[0153] Example 2

[0154] According to embodiment 1 of the present application, a low-voltage distribution cabinet monitoring system including leakage monitoring is provided, including:

[0155] The database generation module uses distributed high-frequency sensors to fuse infrared thermal imaging and ultrasonic partial discharge detection to collect leakage monitoring indicators in real time and establish an electrical-environmental database;

[0156] The weight and model construction module uses a dynamic game method to adjust the weights of electrical and environmental factors in leakage monitoring indicators, and determines the membership degree for each monitoring indicator. The weight and membership degree are integrated to construct an insulation degradation model based on multi-factor coupling.

[0157] The simulation analysis module uses an insulation degradation model based on multi-factor coupling and an electrical-environmental database to perform diffusion simulation on various parts of the low-voltage distribution cabinet, generate a probability density function of leakage time, and extract the mean value and confidence interval of leakage time;

[0158] The risk assessment module calculates the risk priority number based on the mean leakage time and confidence interval, and divides the distribution cabinet area into high-risk, medium-risk, and low-risk layers;

[0159] The monitoring strategy execution and adjustment module allocates initial monitoring frequencies to each level based on risk stratification results; collects leakage monitoring data in real time, calibrates model parameters by comparing field measured data with simulation results, and periodically iterates and updates the monitoring strategy.

[0160] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to necessarily being implemented using the above specific details.

[0161] The block diagrams of the devices, devices, equipment, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0162] It should also be noted that in the apparatus, device, and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0163] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to be applied in the widest sense consistent with the principles and novel features of the present invention.

[0164] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A low-voltage distribution cabinet monitoring method including leakage monitoring, characterized in that: include: By integrating infrared thermal imaging and ultrasonic partial discharge detection with distributed high-frequency sensors, leakage monitoring indicators are collected in real time to establish an electrical-environmental database. A dynamic game theory method is used to adjust the weights of electrical and environmental factors in leakage monitoring indicators, and the membership degree is determined for each monitoring indicator. The weights and membership degrees are then combined to construct an insulation degradation model based on multi-factor coupling. Using an insulation degradation model based on multi-factor coupling and an electrical-environmental database, diffusion simulation is performed on various parts of the low-voltage distribution cabinet to generate a probability density function of leakage time and extract the mean value and confidence interval of leakage time. Based on the mean value and confidence interval of leakage time, the risk priority number is calculated, and the distribution cabinet area is divided into high-risk layer, medium-risk layer and low-risk layer; Based on the risk stratification results, initial monitoring frequencies are assigned to each level. Leakage monitoring data is collected in real time, and model parameters are calibrated by comparing field measured data with simulation results. Monitoring strategies are then updated periodically and iteratively. The steps for adjusting the weights of electrical and environmental factors in the leakage monitoring index include: Dynamic game theory is introduced to calculate the dynamic game model of electrical and environmental parameters. The objective function of the dynamic game model is: ； in, is the electrical factor weight, is the weight of environmental factors, For electrical utility, For environmental utility; Using the game equilibrium algorithm, the optimal solution of the dynamic game model is solved to obtain the new weights of electrical parameters and environmental parameters; The step of determining the membership degree includes: Determine the fuzzy subsets and membership functions of each leakage monitoring indicator; Among them, the membership function of each leakage monitoring indicator is: ； in, Leakage monitoring indicator The membership degree, Leakage monitoring indicators Minimum value of leakage monitoring indicator In the optimal value of fuzzy subset, leakage monitoring index The maximum value of leakage monitoring indicators The critical value of The insulation degradation model based on multi-factor coupling is: ； in, is the degradation index, is the number of leakage monitoring indicators, is the electrical factor weight or environmental factor weight in the leakage monitoring index, is the membership degree of leakage monitoring indicator.

2. The low-voltage distribution cabinet monitoring method including leakage monitoring according to claim 1 is characterized in that: The leakage monitoring indicators include insulation resistance, temperature and humidity, voltage fluctuation, mechanical wear and tear, and environmental corrosiveness; Among them, insulation resistance, leakage current, and voltage fluctuation rate are classified as electrical factors; Temperature and humidity, degree of mechanical wear, and environmental corrosiveness are listed as environmental factors.

3. The low-voltage distribution cabinet monitoring method including leakage monitoring according to claim 1 is characterized in that: The diffusion simulation steps of various parts of the low-voltage distribution cabinet include: Determine the input parameter range of the diffusion simulation, the number of diffusion simulations and the time step of each diffusion simulation; Initialize each diffusion simulation based on the insulation degradation model with multi-factor coupling; According to the insulation degradation model, combined with the current electrical and environmental conditions, the insulation degradation degree of each part is calculated according to the insulation degradation model Determine whether the insulation degradation level of the current location has reached the leakage threshold and record the time when leakage occurs in the simulation.

4. The low-voltage distribution cabinet monitoring method including leakage monitoring according to claim 3 is characterized in that: The probability density function is: ； in, Leakage time The probability density function of is the number of samples of leakage occurrence time, is the bandwidth of the kernel function, is the kernel function, For the The time when leakage occurs; The formula for calculating the mean leakage time is: ； in, is the number of intervals, For the The length of the leakage time interval, ; The confidence interval Select a 95% confidence level. , that is, the lower limit is sample values, with an upper limit of sample values, where Indicates that the data is rounded down.

5. The low-voltage distribution cabinet monitoring method including leakage monitoring according to claim 4 is characterized in that: The calculation formula for the risk priority number is: ； ； ； ； in, is the risk priority number, The severity is graded from 1 to 10. is the probability of occurrence, The difficulty of the test is divided into levels 1-10.

6. The low-voltage distribution cabinet monitoring method including leakage monitoring according to claim 5, characterized in that: The high-risk layer classification standard is: The medium-risk layer classification standard is ; The classification standard for low-risk layers is .

7. The low-voltage distribution cabinet monitoring method including leakage monitoring according to claim 1 or 6, characterized in that: The low-voltage distribution cabinet parts on the high-risk floor adopt high-frequency online monitoring; the low-voltage distribution cabinet parts on the medium-risk floor adopt medium-frequency detection; the low-voltage distribution cabinet parts on the low-risk floor adopt low-frequency spot inspection.

8. A system for monitoring a low-voltage distribution cabinet including leakage monitoring according to any one of claims 1 to 7, characterized in that: include: The database generation module uses distributed high-frequency sensors to fuse infrared thermal imaging and ultrasonic partial discharge detection to collect leakage monitoring indicators in real time and establish an electrical-environmental database; The weight and model construction module uses a dynamic game method to adjust the weights of electrical and environmental factors in leakage monitoring indicators, and determines the membership degree for each monitoring indicator. The weight and membership degree are integrated to construct an insulation degradation model based on multi-factor coupling. The simulation analysis module uses an insulation degradation model based on multi-factor coupling and an electrical-environmental database to perform diffusion simulation on various parts of the low-voltage distribution cabinet, generate a probability density function of leakage time, and extract the mean value and confidence interval of leakage time; The risk assessment module calculates the risk priority number based on the mean leakage time and confidence interval, and divides the distribution cabinet area into high-risk, medium-risk, and low-risk layers; The monitoring strategy execution and adjustment module allocates initial monitoring frequencies to each level based on risk stratification results; collects leakage monitoring data in real time, calibrates model parameters by comparing field measured data with simulation results, and periodically iterates and updates the monitoring strategy.

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