Low-voltage power distribution cabinet monitoring method and system with electric leakage monitoring function

Abstract

The invention relates to the technical field of power distribution cabinet safety monitoring, in particular to a low-voltage power distribution cabinet monitoring method and system with an electric leakage monitoring function. According to the method, a distributed high-frequency sensor is utilized to integrate multiple detection technologies to acquire electric leakage monitoring indexes, and an electrical-environment database is established; adjusting electrical and environmental weights by adopting a dynamic game, and constructing an insulation degradation model in combination with the membership degree; generating an electric leakage time probability density function through diffusion simulation, obtaining an electric leakage time mean value and a confidence interval, calculating a risk priority level, and dividing the risk level of the power distribution cabinet part. And finally, monitoring frequency is distributed based on risk hierarchy, model parameters are calibrated by comparing actually measured and simulated data, and a monitoring strategy is iteratively updated. The electric leakage risk of the power distribution cabinet is accurately evaluated and graded by collecting multi-dimensional electric leakage monitoring indexes in real time and constructing an accurate insulation degradation model, a reasonable monitoring strategy is formulated according to the risk grade, and the operation safety and reliability of the low-voltage power distribution cabinet are improved.

Description

Technical Field

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

[0002] In the modern power supply system, low - voltage power distribution cabinets, as key equipment for power distribution and control, are widely used in many fields such as industrial production, commercial operation, and residential electricity consumption. Their stable and reliable operation is crucial for ensuring the normal operation of the power system and avoiding various economic losses and safety accidents caused by power failures. However, during the actual operation of current low - voltage power distribution cabinets, leakage problems occur frequently, posing a severe challenge to the safe and stable operation of the power system.

[0003] On the one hand, there are many defects in existing leakage monitoring means. Traditional monitoring is often limited to the detection of single or a few parameters, unable to comprehensively evaluate the leakage risk, and ignoring the coupling relationship between electrical and environmental factors, making it difficult to accurately evaluate the leakage risk in complex environments. At the same time, the fault warning is lagged, and it is difficult to detect in the initial stage of the leakage fault, resulting in equipment damage or even power outages.

[0004] On the other hand, the monitoring strategies are not entirely reasonable. They lack pertinence and flexibility, do not monitor differentially according to the risk level, resulting in waste or insufficiency of monitoring resources. Moreover, existing technologies lack a model calibration and strategy update mechanism, making it difficult to adapt to changes in the operating conditions of the equipment.

[0005] In summary, there are many deficiencies in the existing leakage monitoring and monitoring technologies for low - voltage power distribution cabinets, making it difficult to meet the strict requirements of modern power systems for safety and reliability. Developing an innovative technology that can comprehensively and accurately monitor leakage situations, fully consider the coupling relationship between electrical and environmental factors, give timely fault warnings, and can reasonably adjust monitoring strategies according to actual situations has an urgent practical need, which is also the key problem that the present invention is committed to solving. Summary of the Invention

[0006] The purpose of the invention is to provide a monitoring method and system for low - voltage power distribution cabinets including leakage monitoring, so as to solve the problems in the prior art such as incomplete leakage monitoring of low - voltage power distribution cabinets, inability to consider the coupling relationship between electrical and environmental factors, untimely fault warnings, and unreasonable monitoring strategies. By collecting multi - dimensional leakage monitoring indicators in real - time, constructing an accurate insulation degradation model, realizing accurate assessment and grading of the leakage risk of the power distribution cabinet, and formulating reasonable monitoring strategies according to the risk level, the safety and reliability of the operation of the low - voltage power distribution cabinet are improved.

[0007] The technical solution of the present invention is specifically as follows:

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

[0009] A monitoring method for low-voltage power distribution cabinets including leakage monitoring, characterized by comprising:

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

[0011] Adopt a dynamic game method to adjust the weights of electricity and environment in the leakage monitoring indicators, determine the membership degree for each monitoring indicator, and construct an insulation deterioration model based on multi-factor coupling by integrating the weights and membership degrees;

[0012] Adopt the insulation deterioration model based on multi-factor coupling and the electrical-environment database to conduct diffusion simulation on each part of the low-voltage power distribution cabinet, generate the probability density function of the leakage time, and extract the mean value and confidence interval of the leakage time;

[0013] According to the mean value and confidence interval of the leakage time, calculate the risk priority number, and divide the parts of the power distribution cabinet into high-risk layer, medium-risk layer and low-risk layer;

[0014] Based on the risk stratification results, allocate the initial monitoring frequency for each level; collect the leakage monitoring data in real time, calibrate the model parameters by comparing the on-site measured data with the simulation results, and periodically iterate and update the monitoring strategy.

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

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

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

[0018] As an optimization, the steps for adjusting the weights of electricity and environment in the leakage monitoring indicators include:

[0019] Introduce the dynamic game theory, calculate the dynamic game model of electrical and environmental parameters, and the objective function of the dynamic game model is:

[0020]

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

[0022] Use the game equilibrium algorithm to solve the optimal solution of the dynamic game model to obtain the new weights of electrical and environmental parameters;

[0023] The steps for determining the membership degree include:

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

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

[0026]

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

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

[0029]

[0030] Among them, D is the degradation index, n is the number of leakage monitoring indexes, ω i is the weight of electrical factors or environmental factors in the leakage monitoring indexes, and μ(x i ) is the membership degree of the leakage monitoring index.

[0031] As an optimization, the diffusion simulation steps for each part of the low-voltage power 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] Based on the insulation degradation model of multi-factor coupling, initialize each diffusion simulation;

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

[0035] Judge whether the insulation degradation degree of the current part reaches the leakage threshold, and record the time when leakage occurs in the simulation.

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

[0037]

[0038] Among them, f(t) is the probability density function of the leakage time t, M is the number of samples of the leakage occurrence time, h is the bandwidth of the kernel function, K(x) is the kernel function, and t j is the j-th leakage occurrence time;

[0039] The formula for calculating the mean value of the leakage time is as follows:

[0040]

[0041] Among them, m is the number of intervals, and Δt j is the length of the j-th leakage occurrence time interval, and Δt j = t j+1 - t j ;

[0042] The confidence interval CI selects a 95% confidence level, that is, the lower limit is the th sample value, and the upper limit is the th sample value, where represents rounding down the data inside it.

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

[0044] RPN = S × O × D;

[0045]

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

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

[0048] As an optimization, for the low-voltage distribution cabinet parts in the high-risk layer, high-frequency online monitoring is adopted; for the low-voltage distribution cabinet parts in the medium-risk layer, medium-frequency detection is adopted; for the low-voltage distribution cabinet parts in the low-risk layer, low-frequency sampling inspection is adopted

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

[0050] A database generation module, which fuses infrared thermal imaging and ultrasonic partial discharge detection through distributed high-frequency sensors to collect leakage monitoring indicators in real time and establish an electrical-environment database;

[0051] The weight and model construction module adjusts the weights of electricity and environment in the leakage monitoring indicators by using the dynamic game method, determines the membership degree for each monitoring indicator, and constructs an insulation degradation model based on multi-factor coupling by integrating the weights and membership degrees;

[0052] The simulation analysis module uses the insulation degradation model based on multi-factor coupling and the electrical-environment database to perform diffusion simulation on each part of the low-voltage power distribution cabinet, generates the probability density function of the leakage time, and extracts the mean value and confidence interval of the leakage time;

[0053] The risk assessment module calculates the risk priority number according to the mean value and confidence interval of the leakage time, and divides the parts of the power distribution cabinet into high-risk layer, medium-risk layer and low-risk layer;

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

[0055] The beneficial effects brought by the technical solution provided by the embodiment of the present application at least include the following beneficial effects:

[0056] In terms of the comprehensiveness and accuracy of monitoring, this method overcomes the defect of single traditional monitoring means by integrating a variety of detection technologies and collecting multi-dimensional indicators covering electrical parameters and environmental factors. The insulation degradation model constructed by using dynamic game and fuzzy membership degree fully considers the coupling relationship between electrical and environmental factors, can more accurately reflect the actual situation of the power distribution cabinet, avoid risk misjudgment caused by ignoring complex factors, and greatly improve the accuracy of leakage risk assessment.

[0057] In terms of the rationality of the monitoring strategy and resource optimization, the method of allocating the monitoring frequency based on risk stratification enables the rational use of monitoring resources. High-frequency online monitoring of the high-risk layer can timely discover potential fault hazards; moderate medium-frequency detection and low-frequency sampling inspection are respectively adopted for the medium-risk layer and the low-risk layer, which not only ensures the effective monitoring of the equipment operation status, but also avoids resource waste caused by over-monitoring, and realizes the balance between monitoring cost and efficiency.

[0058] In terms of safety guarantee and dynamic optimization of the strategy, the real-time monitoring and fault warning mechanism can quickly respond when the data is abnormal and automatically cut off the fault circuit to ensure the safety of personnel and equipment. At the same time, by comparing the on-site measured data with the simulation results to calibrate the model parameters and periodically iterating and updating the monitoring strategy, the monitoring system can adapt to the changes in the operation status of the power distribution cabinet and continuously maintain a highly reliable operation state, providing strong support for the stable operation of the low-voltage power distribution cabinet. Description of the Drawings

[0059] Figure 1Schematic diagram of the overall process of the low-voltage power distribution cabinet monitoring method including leakage monitoring;

[0060] Figure 2 Flow chart of step S100 of the low-voltage power distribution cabinet monitoring method including leakage monitoring;

[0061] Figure 3 Flow chart of step S200 of the low-voltage power distribution cabinet monitoring method including leakage monitoring;

[0062] Figure 4 Flow chart of step S300 of the low-voltage power distribution cabinet monitoring method including leakage monitoring;

[0063] Figure 5 Flow chart of step S400 of the low-voltage power distribution cabinet monitoring method including leakage monitoring;

[0064] Figure 6 Flow chart of step S500 of the low-voltage power distribution cabinet monitoring method including leakage monitoring. Detailed implementation manners

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0066] Embodiment 1

[0067] Traditional low-voltage power distribution cabinet monitoring methods often only monitor single parameters, such as only monitoring voltage or current, and cannot comprehensively and accurately evaluate the leakage risk of the power distribution cabinet. Moreover, traditional methods lack consideration of the coupling relationship between electrical parameters and environmental factors and are difficult to adapt to complex and changeable operating environments. In addition, traditional monitoring methods also have deficiencies in fault warning and monitoring strategy adjustment, and cannot timely detect potential leakage hazards and take effective measures to deal with them.

[0068] To solve the above problems, please refer to Figure 1 , which shows a low-voltage power distribution cabinet monitoring method including leakage monitoring provided by an embodiment of the present invention. The method includes:

[0069] S100: Through distributed high-frequency sensors integrating infrared thermal imaging and ultrasonic partial discharge detection, collect leakage monitoring indicators in real time and establish an electrical-environment database.

[0070] S200: Adjust the weights of electricity and environment in the leakage monitoring indicators using the dynamic game method, define the membership degree for each monitoring indicator, and construct an insulation deterioration model based on multi-factor coupling by integrating the weights and membership degrees.

[0071] S300: Use the insulation deterioration model based on multi-factor coupling and the electricity-environment database to perform diffusion simulation on each part of the low-voltage power distribution cabinet, generate the probability density function of the leakage time, and extract the mean time to failure (MTTF) and confidence interval of the leakage time.

[0072] S400: Calculate the risk priority number (RPN) according to the mean time to failure (MTTF) and confidence interval of the leakage time, and divide the parts of the power distribution cabinet into high-risk layer, medium-risk layer and low-risk layer.

[0073] S500: Based on the risk stratification results, allocate the initial monitoring frequency for each level; collect the leakage monitoring data in real time, calibrate the model parameters by comparing the on-site measured data with the simulation results, and periodically iterate and update the monitoring strategy.

[0074] In the monitoring of low-voltage power distribution cabinets, it is a crucial basic task to collect the leakage monitoring indicators in real time and accurately and establish a complete electricity-environment coupling database. This step mainly realizes data collection through distributed high-frequency sensors integrating a variety of detection technologies, and organizes and stores the collected data to provide support for subsequent analysis and model construction.

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

[0076] S110: Complete the sensor selection, layout planning, installation, data collection and preprocess the collected data.

[0077] Based on the structural characteristics, electrical parameter distribution and possible leakage hazard points of the low-voltage power distribution cabinet, conduct the selection and layout planning of sensors. For electrical parameter monitoring, select high-precision voltage sensors, current sensors and insulation resistance testers to ensure that the voltage, current and insulation resistance values can be accurately collected. For environmental factor monitoring, select temperature and humidity sensors, gas sensors to monitor the temperature, humidity and corrosive gas concentration of the environment, and at the same time use acceleration sensors and strain sensors to detect mechanical vibration and wear conditions.

[0078] Reasonably arrange sensors according to different regions and functional modules of the power distribution cabinet. Collect the data of each sensor and preprocess the collected raw data, such as removing noise and interference.

[0079] S120: Establish an electricity-environment database.

[0080] The electrical - environment database is stored using a relational database, and multiple data tables are established to store different types of data respectively.

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

[0082] Define environmental factors and formulate an environmental parameter table. List temperature and humidity, degree of mechanical wear, and environmental corrosivity as environmental factors. The environmental parameter table includes: timestamp, temperature and humidity, degree of mechanical wear, and environmental corrosivity.

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

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

[0085] Please refer to Figure 3 , which shows a flowchart of S200 of an exemplary low - voltage power distribution cabinet monitoring method including leakage monitoring in the present application. Its content includes:

[0086] S210: Set the initial weights of the leakage monitoring index and introduce dynamic game theory analysis.

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

[0088] Exemplarily, initially set the weight of electrical parameters to 0.6 and the weight of environmental parameters to 0.4.

[0089] Introduce the dynamic game theory, regarding electrical parameters and environmental parameters as two participants in the game. According to the real-time collected data and historical data, analyze the interaction and influence relationship between the two participants. Adjust the weight ratio of electrical parameters and environmental parameters through iterative calculation. In each iteration, calculate the dynamic game model of the two participants according to the current weight and data change situation. The objective function of the dynamic game model is:

[0090]

[0091] where ω e is the weight of electrical factors, ω c is the weight of environmental factors, U e is the electrical utility, such as the stability of insulation resistance, U c is the environmental utility, such as the contribution of temperature and humidity to deterioration.

[0092] Use the game equilibrium algorithm to solve the optimal solution of the dynamic game model and obtain the new weights of electrical parameters and environmental parameters. In the iterative process, continuously update the weights until the weights converge to a stable value.

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

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

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

[0096] Define the membership function for each index, and its formula is:

[0097]

[0098] where μ(x i ) is the membership degree of the leakage monitoring index x i , x mini , x opti , x maxi , x criti are respectively the minimum value of the leakage monitoring index x i , the best value of the leakage monitoring index x i in the fuzzy subset, the maximum value of the leakage monitoring index x i , and the critical value of the leakage monitoring index x i .

[0099] S230: Build an insulation degradation model.

[0100] Integrate the electrical factor weight ω e and the environmental factor weight ω c in the leakage monitoring indicators determined by dynamic game, as well as the membership degree μ(x i ) of each leakage monitoring indicator to build 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 indicator, and μ(x i ) is the membership degree of the leakage monitoring indicator.

[0103] S300 uses the insulation degradation model based on multi-factor coupling and the electrical-environment database to perform diffusion simulation on each key part of the power distribution cabinet, and then generates the probability density function of the leakage time, and extracts the mean value and confidence interval of the leakage time to provide key data support for subsequent risk assessment.

[0104] Please refer to Figure 4 , which shows the flowchart of S300 of an exemplary low-voltage power distribution cabinet monitoring method including leakage monitoring in this application. The content includes:

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

[0106] Extract the historical monitoring data related to each part of the low-voltage power distribution cabinet from the electrical-environment database, including insulation resistance value, temperature and humidity, voltage fluctuation, mechanical wear degree, and environmental corrosivity data. Analyze the change range and distribution characteristics of these data to determine the input parameter range of the diffusion simulation.

[0107] At the same time, determine the number of diffusion simulations. Preferably, the number of diffusion simulations is selected from 10,000 to 15,000 times. At the same time, determine the time step of each diffusion simulation. The time step should be selected according to the actual situation, which should not only ensure that the leakage development process can be accurately captured, but also not make the calculation amount too large.

[0108] Initialize each diffusion simulation for the insulation degradation model based on multi-factor coupling. Set the parameters in the insulation degradation model, such as the weights of electrical and environmental factors, the membership functions of each monitoring index, etc., according to the values determined in step S200. For each part of the power distribution cabinet, set the initial conditions of the simulation model according to its initial state, such as the initial insulation resistance value, the initial temperature and humidity, etc., to ensure that the starting state of the simulation conforms to the actual situation.

[0109] In each simulation, gradually advance the simulation process according to the determined time step. Within each time step, according to the insulation degradation model, combined with the current electrical-environmental conditions, calculate the insulation degradation degree of each part according to the insulation degradation model.

[0110] Judge whether the insulation degradation degree of the current part reaches the leakage threshold. If the leakage threshold is reached, record the time when leakage occurs in this simulation. If not, continue the simulation for the next time step until the simulation ends or reaches the preset maximum simulation time. Repeat the above process to obtain a sample of the leakage occurrence time.

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

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

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

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

[0115]

[0116] Taking the leakage occurrence time as the abscissa and the frequency as the ordinate, draw a histogram, which approximately represents the probability distribution of the leakage time.

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

[0118]

[0119] where tj is the j-th leakage occurrence time.

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

[0121] According to the generated probability density function f(t), calculate the mean time to failure MTTF. The calculation formula is:

[0122]

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

[0124] Calculate the confidence interval CI. Select a 95% confidence level. By sorting the leakage time samples, determine the upper and lower limits of the corresponding confidence interval, that is, the lower limit is the -th sample value, and the upper limit is the -th sample value, where represents rounding down the data inside it, that is

[0125] S400 Stratify the risks of the distribution cabinet parts by calculating the risk priority number (RPN) and based on this value.

[0126] Please refer to Figure 5 , which shows the flowchart of S400 of an exemplary low-voltage distribution cabinet monitoring method including leakage monitoring in this application. Its content includes:

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

[0128] The parameters of RPN include:

[0129] Severity S, divided into levels 1 - 10, determined by MTTF, that is

[0130] Occurrence probability O, determined by MTTF, that is

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

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

[0133] S420: Develop risk stratification criteria and stratify the risks of the low-voltage distribution cabinet parts.

[0134] Based on the calculated RPN value distribution, combined with actual operation experience and risk control requirements, formulate risk stratification criteria. Divide the RPN values 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 the key parts of each power distribution cabinet with the risk stratification criteria to determine the risk level it belongs to.

[0139] S500: Based on the risk stratification results, allocate reasonable initial monitoring frequencies for different levels of power distribution cabinet parts, and process data, trigger warnings, and cut off faulty circuits in real time during the monitoring process. At the same time, calibrate the model parameters by comparing the on-site measured data with the simulation results, and periodically iterate and update the monitoring strategy to achieve effective monitoring of the low-voltage power distribution cabinet and ensure its safe and stable operation.

[0140] Please refer to Figure 6 , which shows the flowchart of S500 of an exemplary low-voltage power distribution cabinet monitoring method including leakage monitoring in this application. Its content includes:

[0141] S510: Allocate monitoring frequencies.

[0142] For the power distribution cabinet parts in the high-risk layer, adopt high-frequency online monitoring.

[0143] Exemplarily, for the bus connection points in the high-risk layer, not only the conventional indicators such as insulation resistance value, temperature and humidity, and voltage fluctuation should be monitored in real time, but also infrared thermal imaging detection and ultrasonic partial discharge detection should be carried out every 15 minutes to timely discover potential fault hazards.

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

[0145] Exemplarily, collect the conventional leakage monitoring indicators every 2 hours, and conduct infrared thermal imaging detection and ultrasonic partial discharge detection every day. This can not only timely discover fault signs to a certain extent but also balance the monitoring cost and efficiency.

[0146] For the power distribution cabinet parts in the low-risk layer, adopt low-frequency spot checks.

[0147] Exemplarily, collect the conventional leakage monitoring indicators 2 - 3 times a week, and conduct infrared thermal imaging detection and ultrasonic partial discharge detection once a month. Through regular spot checks, maintain basic monitoring of the operation status of the low-risk layer parts, and at the same time avoid resource waste caused by over-monitoring.

[0148] S520: Compare the on-site measured data with the simulation results, and iteratively update the model parameters and monitoring strategies periodically.

[0149] Regularly compare the on-site measured data with the results obtained from the diffusion simulation in step S300. The comparison content includes the leakage monitoring index data, as well as the degree of insulation deterioration and leakage time predicted based on the leakage monitoring index data, etc. According to the error results obtained from the comparison, calibrate the parameters of the insulation deterioration model based on multi-factor coupling.

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

[0151] According to the results of model parameter calibration, newly obtained operation data, and changes in the risk assessment results, readjust the monitoring strategy parameters such as the monitoring frequency and warning threshold for each level of the distribution cabinet parts. To adapt to the changes in the actual operation state of the distribution cabinet and ensure the effectiveness and adaptability of the monitoring strategy.

[0152] Through steps S100 - S500, the leakage situation can be comprehensively monitored, and the coupling relationship between electrical and environmental factors is comprehensively considered. Through accurate risk assessment and grading, targeted monitoring is achieved, and resources are reasonably allocated. Timely fault warning and automatic circuit cutting ensure safety, and the model and monitoring strategy can be continuously optimized according to the actual operation situation, effectively improving the safety and reliability of the operation of low-voltage distribution cabinets.

[0153] Embodiment 2

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

[0155] A database generation module, which fuses infrared thermal imaging and ultrasonic partial discharge detection through distributed high-frequency sensors to collect leakage monitoring indicators in real time and establish an electrical - environment database;

[0156] A weight and model construction module, which uses the dynamic game method to adjust the weights of electricity and environment in the leakage monitoring indicators, determines the membership degree for each monitoring indicator, and constructs an insulation deterioration model based on multi-factor coupling by integrating the weights and membership degrees;

[0157] A simulation analysis module, which uses the insulation deterioration model based on multi-factor coupling and the electrical - environment database to perform diffusion simulation on each part of the low-voltage distribution cabinet, generate the probability density function of the leakage time, and extract the mean value and confidence interval of the leakage time;

[0158] A risk assessment module, which calculates the risk priority number according to the mean value and confidence interval of the leakage time, and divides the distribution cabinet parts into high-risk layers, medium-risk layers, and low-risk layers;

[0159] The monitoring strategy execution and adjustment module assigns an initial monitoring frequency to each level based on the risk stratification results; it collects leakage monitoring data in real time, calibrates the model parameters by comparing the on-site measured data with the simulation results, and periodically iteratively updates the monitoring strategy.

[0160] The basic principles of the present application have been described in conjunction with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are only examples and not limitations. It cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present application. Additionally, the specific details disclosed above are only for illustrative and facilitating understanding purposes, rather than limitations. The above details do not limit the present application to necessarily implement using the above specific details.

[0161] The block diagrams of the devices, apparatuses, equipment, and systems involved in the present application are only illustrative examples and do not intend to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any way. Words such as "including", "comprising", "having", etc. are open-ended terms meaning "including but not limited to" and can be used interchangeably with each other. The word "or" and "and" used herein refer to the word "and / or" and can be used interchangeably with it, 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 with it.

[0162] It should also be noted that in the devices, equipment, and methods of the present application, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present application.

[0163] The above description of the disclosed aspects enables any person skilled in the art to make or use the present application. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined herein can 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 the broadest scope consistent with the principles and novel features invented herein.

[0164] The above are only the preferred embodiments of the present application and are 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 within the protection scope of the present application.

Claims

1. A low voltage distribution cabinet monitoring method including leakage monitoring, characterized in that: include: Through the integration of infrared thermal imaging and ultrasonic partial discharge detection by distributed high-frequency sensors, leakage monitoring indicators are collected in real time to establish an electrical-environmental database; The dynamic game method is used to adjust the weights of electrical and environmental factors in the leakage monitoring index, and the membership degree is determined for each monitoring index. The insulation degradation model based on multi-factor coupling is constructed by combining the weights and membership degrees. Using the insulation degradation model based on multi-factor coupling and the electrical-environmental database, diffusion simulation is performed on various parts of the low-voltage distribution cabinet to generate the probability density function of the leakage time, and extract the mean value and confidence interval of the leakage time; According to the mean value and confidence interval of leakage time, the risk priority number is calculated, and the distribution cabinet parts are divided into high-risk layer, medium-risk layer and low-risk layer; Based on the risk stratification results, the initial monitoring frequency is allocated to each level. The leakage monitoring data is collected in real time, the model parameters are calibrated by comparing the field measured data with the simulation results, and the monitoring strategy is updated periodically.

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 value, temperature and humidity, voltage fluctuation, mechanical wear degree 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 steps of adjusting the weights of electricity and environment in the leakage monitoring index include: The 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: 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; Using the game equilibrium algorithm, the optimal solution of the dynamic game model is solved to obtain the weights of new electrical parameters and environmental parameters; The step of determining the membership degree comprises: Determine the fuzzy subsets and membership functions of each leakage monitoring index; Among them, the membership function of each leakage monitoring indicator is: 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 the leakage monitoring index x i The critical value of .

4. The low-voltage distribution cabinet monitoring method including leakage monitoring according to claim 3 is characterized in that: The insulation degradation model based on multi-factor coupling is: 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.

5. 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 range of input parameters for 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-environmental conditions, the insulation degradation degree of each part is calculated according to the insulation degradation model Determine whether the insulation degradation degree of the current part has reached the leakage threshold, and record the time when the leakage occurs in the simulation.

6. The low-voltage distribution cabinet monitoring method including leakage monitoring according to claim 5 is characterized in that: The probability density function is: 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 the kernel function, K(x) is the kernel function, t j is the time when the jth leakage occurs; The leakage time mean value calculation formula is: 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 ; The confidence interval CI selects 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.

7. The low-voltage distribution cabinet monitoring method including leakage monitoring according to claim 6 is characterized in that: The calculation formula for the risk priority number is: RPN = S × O × D; Among them, RPN is the risk priority number, S is the severity, which is divided into 1-10 levels, O is the probability of occurrence, and TD is the detection difficulty, which is divided into 1-10 levels.

8. The low-voltage distribution cabinet monitoring method including leakage monitoring according to claim 7 is characterized in that: 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.

9. The low-voltage distribution cabinet monitoring method including leakage monitoring according to claim 1 or 8, 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 random inspection.

10. A low voltage distribution cabinet monitoring system including leakage monitoring, 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 building module uses a dynamic game method to adjust the weights of electrical and environmental factors in the leakage monitoring index, and determines the membership degree for each monitoring index. The insulation degradation model based on multi-factor coupling is constructed by combining the weights and membership degrees. 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 value and confidence interval of leakage time, and divides the distribution cabinet into high-risk layer, medium-risk layer and low-risk layer; 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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