Knife switch residual life analysis method, system, equipment and medium
By building a digital twin model and combining actual monitoring data, the comprehensive quantification of tool switch mechanical and environmental losses is achieved, and the problem of insufficient accuracy of tool switch life analysis in the existing technology is solved, high-precision life prediction and early warning are achieved, and maintenance costs are reduced.
Patent Information
- Application Number
- CN202510502694.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-22
- Publication Date
- 2025-07-29
AI Technical Summary
In the prior art, the remaining life analysis of the knife switch lacks a comprehensive quantitative assessment of mechanical power loss and environmental factors, which leads to strong subjectivity of traditional manual judgments, which can easily lead to "over-repair" or "under-repair", resulting in waste of maintenance costs or safety hazards. The existing data-driven methods do not fully consider the synergistic impact of movement type and environmental factors, and the prediction accuracy is insufficient.
Build a digital twin model, integrate mechanical loss simulation and environmental loss simulation, quantify the loss impact based on the blade motion type and environmental parameters, combine historical operation data to perform simulation output, and calculate and predict the remaining life.
Real-time monitoring and accurate evaluation of the wear of the knife switch is achieved, avoiding the phenomenon of "over-repair" or "under-repair", improving the prediction accuracy and detection efficiency, and reducing maintenance costs.
Smart Images
Figure CN120387303A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of power equipment maintenance, and in particular, to a method, system, device and medium for analyzing the remaining life of a disconnecting switch. Background Art
[0002] As a key device for ensuring circuit isolation and maintenance safety in the power system, the reliability of a disconnecting switch directly affects the stable operation of the power grid. With the development of smart grid technology, the maintenance of power equipment is gradually transitioning from regular maintenance to condition-based maintenance. In the prior art, the life assessment of disconnecting switches mostly relies on manual inspections and historical experience judgments. Some advanced methods collect basic operation data (such as the number of operations, temperature, etc.) through sensors and combine simple statistical models for life prediction. However, these methods still have limitations in terms of data comprehensiveness and model accuracy, and it is difficult to dynamically reflect the actual wear state of the disconnecting switch.
[0003] The main problem in the current analysis of the remaining life of disconnecting switches lies in the lack of comprehensive quantitative assessment of mechanical power losses and environmental factors. Traditional manual judgments are highly subjective and prone to "over-maintenance" or "under-maintenance", resulting in waste of maintenance costs or potential safety hazards; while existing data-driven methods do not fully consider the differences in the motion types (rotation / linear) of disconnecting switches and the combined effects of complex environments (such as temperature, humidity, corrosive substances), leading to insufficient prediction accuracy. For example, the difference in the power loss models for rotational and linear motions is not distinguished, or the dynamic effect of environmental factors on material aging is not quantified, resulting in prediction results deviating from the actual life. Therefore, there is an urgent need for a high-precision life analysis method that can integrate multi-dimensional loss mechanisms and dynamically simulate the state of disconnecting switches. Summary of the Invention
[0004] In view of the above existing problems, the present invention is proposed.
[0005] Therefore, the present invention provides a method, system, device and medium for analyzing the remaining life of a disconnecting switch to solve the problem in the prior art that the subjective judgment of the remaining life of a disconnecting switch by traditional manual methods is strong, which is prone to "over-maintenance" or "under-maintenance", resulting in waste of maintenance costs or potential safety hazards.
[0006] To solve the above technical problems, the present invention provides the following technical solutions:
[0007] In a first aspect, the present invention provides a method for analyzing the remaining life of a disconnecting switch, including:
[0008] Establishing a digital twin model of the target disconnecting switch, wherein the digital twin model integrates mechanical loss simulation and environmental loss simulation, the mechanical loss simulation adapts a dynamic loss model according to the motion type of the blade, and the environmental loss simulation quantifies the impact of the environment on the life through the correlation between environmental parameters and the loss of the disconnecting switch;
[0009] Collect the historical operation data of the target disconnect switch, and input the historical operation data into the digital twin model to generate simulation output data;
[0010] Calculate the predicted remaining life of the target disconnect switch based on the simulation output data.
[0011] As a preferred solution of the disconnect switch remaining life analysis method of the present invention, wherein: establishing the digital twin model of the target disconnect switch includes:
[0012] Construct a three-dimensional geometric model according to the physical parameters of the target disconnect switch;
[0013] Determine the corresponding mechanical loss formula based on the movement mode of the blade, wherein the mechanical loss formula includes a rotational motion model and a linear motion model;
[0014] Determine the environmental loss formula according to the correlation between environmental parameters and disconnect switch loss;
[0015] Construct the digital twin model of the disconnect switch based on the three-dimensional geometric model, the mechanical loss formula and the environmental loss formula.
[0016] As a preferred solution of the disconnect switch remaining life analysis method of the present invention, wherein: the determining the corresponding mechanical loss formula based on the movement mode of the blade includes:
[0017] If the blade is in rotational motion, use the rotational power loss formula based on moment of inertia and angular acceleration;
[0018] If the blade is in linear motion, use the linear power loss formula based on mass and linear acceleration.
[0019] As a preferred solution of the disconnect switch remaining life analysis method of the present invention, wherein: determining the environmental loss formula according to the correlation between environmental parameters and disconnect switch loss includes:
[0020] By analyzing the influence of temperature, humidity and corrosive substance concentration on the disconnect switch loss, establish a temperature-loss relationship formula, a humidity-loss relationship formula and a corrosion concentration-loss relationship formula respectively;
[0021] Generate the environmental loss formula based on the temperature-loss relationship formula, the humidity-loss relationship formula and the corrosion concentration-loss relationship formula.
[0022] As a preferred solution of the disconnect switch remaining life analysis method of the present invention, wherein: the simulation output data includes:
[0023] The cumulative wear amount, temperature influence index, humidity influence index and corrosive substance concentration influence index of the target disconnect switch.
[0024] As a preferred solution of the knife switch remaining life analysis method of the present invention, wherein: calculating the predicted remaining life of the target knife switch includes:
[0025] Determining the remaining wearable amount according to the cumulative wear amount;
[0026] Based on the temperature influence index, humidity influence index and corrosive substance concentration influence index, correcting the theoretical wear rate to obtain the actual wear rate;
[0027] Dividing the remaining wearable amount by the actual wear rate to generate the predicted remaining life.
[0028] As a preferred solution of the knife switch remaining life analysis method of the present invention, wherein: the historical operation data includes:
[0029] The working parameters of the target knife switch and the average temperature, average humidity and average corrosive substance concentration of the environment where the target knife switch is located per unit time.
[0030] In a second aspect, the present invention provides a knife switch remaining life analysis system, including: a building module for building a digital twin model of a target knife switch, wherein the digital twin model integrates mechanical loss simulation and environmental loss simulation, the mechanical loss simulation adapts a dynamic loss model according to the motion type of the blade, and the environmental loss simulation quantifies the influence of the environment on the life through the correlation between environmental parameters and knife switch loss;
[0031] An output module that collects the historical operation data of the target knife switch and inputs the historical operation data into the digital twin model to generate simulation output data;
[0032] A calculation module for calculating the predicted remaining life of the target knife switch based on the simulation output data.
[0033] In a third aspect, the present invention provides an electronic device, including:
[0034] A memory and a processor;
[0035] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the knife switch remaining life analysis method are implemented.
[0036] In a fourth aspect, the present invention provides a computer-readable storage medium that stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the steps of the knife switch remaining life analysis method are implemented.
[0037] Compared with the prior art, the beneficial effects of the present invention are as follows: By constructing a digital twin model and combining it with actual monitoring data, the present invention can achieve a comprehensive quantification of the mechanical and environmental losses of the disconnecting switch; Based on the historical operation data and environmental parameters of the disconnecting switch, a model is dynamically constructed to accurately evaluate the cumulative wear amount and environmental impact index of the disconnecting switch, and to dynamically monitor the real-time wear condition of the disconnecting switch, and timely warn of abnormal wear rates; By optimizing algorithms and reference factors, a corresponding remaining life prediction model is constructed, and combined with damage accumulation, a time-varying remaining life prediction result is obtained to accurately predict the remaining life of the disconnecting switch. Improve detection efficiency, reduce maintenance costs, and at the same time achieve high-precision prediction and warning. BRIEF DESCRIPTION OF THE DRAWINGS
[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.
[0039] Figure 1 It is a schematic diagram of the overall process of the method for analyzing the remaining life of the disconnecting switch according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0040] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
[0041] Example 1, referring to Figure 1 , which is an embodiment of the present invention, provides a method for analyzing the remaining life of a disconnecting switch, including:
[0042] S100: Establish a digital twin model of the target disconnecting switch, where the digital twin model integrates mechanical loss simulation and environmental loss simulation. The mechanical loss simulation adapts a dynamic loss model according to the motion type of the blade, and the environmental loss simulation quantifies the impact of the environment on the life through the correlation between environmental parameters and the loss of the disconnecting switch;
[0043] It should be noted that the target disconnecting switch, that is, the disconnector, is a common device in the power system, mainly used for safely isolating the power supply. When the equipment is overhauled or maintained, the disconnecting switch can directly disconnect the circuit, forming an obvious air gap to ensure the safety of personnel.
[0044] It should be understood that the digital twin model of the target switch is a high-fidelity dynamic mapping of the target switch in virtual space, which can be used to achieve real-time simulation and optimization of the target switch throughout its life cycle.
[0045] It is understandable that the above-mentioned mechanical loss may refer to the loss caused by friction, collision or fatigue of mechanical parts during the opening and closing operations of the target knife switch, and the above-mentioned environmental loss may refer to the loss caused by external environmental factors of the target knife switch.
[0046] In the specific implementation, the digital twin model of the target knife switch can be constructed based on the mechanical loss simulation process and environmental loss simulation process of the target knife switch.
[0047] S200: Collect historical operating data of the target switch, and input the historical operating data into the digital twin model to generate simulation output data.
[0048] It should be noted that the above-mentioned historical operating data can be all data of the target switch from the time it was put into use to the time of collection (including operating parameters, ambient temperature, ambient humidity, ambient corrosive substance concentration, etc.). In addition, if the target switch is a newly put into use, its corresponding historical operating data can be the default factory data, for example, the ambient temperature is the factory test temperature, the ambient humidity is the factory test humidity, and the ambient corrosive substance concentration is the factory test concentration.
[0049] It should be understood that after collecting the historical operating data of the target switch, this historical operating data can be input into the digital twin model, thereby performing power loss simulation and environmental loss simulation of the target switch based on the historical operating data to obtain the simulation output data. In particular, the simulation output data can be used to indicate the wear condition of the target switch.
[0050] S300: Calculating the predicted remaining life of the target switch based on the simulation output data.
[0051] In a specific implementation, the wear condition of the target knife switch can be analyzed based on the above simulation output data, so as to determine the predicted remaining life corresponding to the target knife switch based on the wear condition.
[0052] It should be noted that during long-term operation, the mechanical components of knife switches will wear out due to frequent opening and closing operations. Furthermore, environmental conditions (such as temperature, humidity, and concentration of corrosive substances) can also accelerate the wear of knife switches. Traditional life prediction methods often rely on manual judgment, which is highly subjective and inaccurate.
[0053] Therefore, to address the problem of low accuracy in life prediction mentioned above, through steps S100 - S300, a digital twin model capable of simulating the actual operating state of the knife switch is constructed, realizing real-time monitoring and accurate evaluation of the wear condition of the knife switch. At the same time, based on the simulation output data of the digital twin model, the remaining life of the target knife switch is objectively and accurately predicted, thereby avoiding the phenomena of "over-maintenance" or "under-maintenance" of the knife switch in the prior art.
[0054] Example 2. Refer to Figure 1 , which is an embodiment of the present invention. Based on the above embodiment, a method for analyzing the remaining life of a knife switch is provided.
[0055] In the implementation manner of this application, establishing a digital twin model of the target knife switch in step S100 includes the following steps A1 - A4:
[0056] A1: Construct a three-dimensional geometric model according to the physical parameters of the target knife switch.
[0057] It should be noted that the above physical parameters may include, but are not limited to, the geometric dimensions, material properties, mechanical connection methods, etc. of the target knife switch, and this embodiment does not limit this.
[0058] In specific implementation, the above physical parameters can be input into software such as SolidWorks, AutoCAD, CATIA, etc. to generate a three-dimensional model corresponding to the target knife switch, and corresponding material properties are assigned to each part in the three-dimensional model to ensure that the physical characteristics of the target knife switch can be accurately reflected during simulation.
[0059] A2: Determine the corresponding mechanical loss formula based on the movement mode of the blade, where the mechanical loss formula includes a rotational motion model and a linear motion model.
[0060] A3: Determine the environmental loss formula according to the correlation between environmental parameters and the loss of the knife switch.
[0061] A4: Construct a digital twin model of the knife switch based on the three-dimensional geometric model, the mechanical loss formula, and the environmental loss formula.
[0062] Among them, in the implementation manner of this application, determining the corresponding mechanical loss formula based on the movement mode of the blade in step A2 includes the following steps A21 - A22:
[0063] A21: If the blade is in rotational motion, use a rotational power loss formula based on moment of inertia and angular acceleration.
[0064] A22: If the blade is in linear motion, use a linear power loss formula based on mass and linear acceleration.
[0065] Specifically, in A21, the specific form of the rotational power loss formula based on the moment of inertia and angular acceleration is as follows:
[0066]
[0067] In the formula: F1 represents the rotational power loss corresponding to the target disconnect switch, I represents the moment of inertia of the target disconnect switch, θ(t) represents the rotational angle of the blade in the target disconnect switch at time t, c represents the damping coefficient of the blade, and τ f represents the frictional force corresponding to the blade during rotational motion.
[0068] It should be noted that represents the rotational speed of the blade, represents the rotational acceleration of the blade.
[0069] Specifically, in A22, the specific form of the linear power loss formula based on the mass and linear acceleration is as follows:
[0070]
[0071] In the formula: F2 represents the linear power loss corresponding to the target disconnect switch, m represents the mass of the target disconnect switch, x(t) represents the linear displacement of the blade in the target disconnect switch at time t, c represents the damping coefficient of the blade, and F f represents the frictional force corresponding to the blade during linear motion.
[0072] It should be noted that represents the linear speed of the blade, represents the linear acceleration of the blade.
[0073] In the embodiment of the present application, in step A3, determining the environmental loss formula according to the correlation between the environmental parameters and the disconnect switch loss includes the following steps A31 - A32:
[0074] A31: By analyzing the influence of temperature, humidity, and corrosive substance concentration on the disconnect switch loss, establish a temperature - loss relationship formula, a humidity - loss relationship formula, and a corrosion concentration - loss relationship formula respectively.
[0075] A32: Based on the temperature - loss relationship formula, the humidity - loss relationship formula, and the corrosion concentration - loss relationship formula, generate the environmental loss formula.
[0076] Specifically, in A31, the specific form of the temperature - loss relationship formula is as follows:
[0077]
[0078] In the formula: D1 represents the relationship between temperature and the target disconnect switch, k T represents the relevant reaction rate constant (related to the material of the target disconnect switch), Ea It represents the activation energy of the target knife switch material (for example, the activation energy during copper oxidation is approximately equal to 40 kJ / mol). R is the gas constant, usually taken as 8.314 J / (mol·K), T represents temperature, and T + 273.15 represents the Kelvin value of temperature T.
[0079] The specific form of the humidity-loss relationship is as follows:
[0080] D2 = (1 + α·H) β
[0081] In the formula, D2 represents the relationship between humidity and the target knife switch, H represents humidity, and α and β represent humidity influence coefficients (for example, when the humidity is 90%, α can be taken as 0.05 and β can be taken as 2).
[0082] The specific form of the corrosion concentration-loss relationship is as follows:
[0083] D3 = 1 + γ·C
[0084] In the formula, D3 represents the relationship between the concentration of corrosive substances and the target knife switch, and γ represents the chemical corrosion sensitivity coefficient (for example, the chemical corrosion sensitivity coefficient of sulfur dioxide is 0.1 ppm -1 ), and C represents the concentration of corrosive substances.
[0085] Specifically, in A32, the environmental loss formula can be generated by summing up the above temperature-loss relationship, humidity-loss relationship, and corrosion concentration-loss relationship, that is, the environmental loss formula D = D1 + D2 + D3.
[0086] It should be noted that in this step, a three-dimensional geometric model is constructed based on the physical parameters of the target knife switch; if the blade is in rotational motion, a rotational power loss formula based on moment of inertia and angular acceleration is used; if the blade is in linear motion, a linear power loss formula based on mass and linear acceleration is used; by analyzing the influence of temperature, humidity, and the concentration of corrosive substances on the loss of the knife switch, the temperature-loss relationship, humidity-loss relationship, and corrosion concentration-loss relationship are established respectively; based on the temperature-loss relationship, humidity-loss relationship, and corrosion concentration-loss relationship, the environmental loss formula is generated; based on the three-dimensional geometric model, mechanical loss formula, and environmental loss formula, a digital twin model of the knife switch is constructed. In this step, the corresponding mechanical loss formula is determined based on the motion mode of the blade, thus realizing the true restoration of the mechanical loss in the target knife switch; at the same time, based on the relationships between the target knife switch and the temperature, humidity, and concentration of corrosive substances in the environment where the target knife switch is located, the loss formula of the target knife switch and its environment is accurately determined.
[0087] In an alternative embodiment, when establishing the digital twin model of the target disconnect switch in step S100, the digital twin model can also be constructed by using the finite element analysis method to accurately simulate the mechanical and thermal behaviors of the disconnect switch under different working conditions and predict the performance changes of the disconnect switch in a complex environment.
[0088] In another alternative embodiment, when establishing the digital twin model of the target disconnect switch in step S100, the multi-physics field coupling simulation technology can also be adopted to simultaneously consider the interactions of various physical phenomena such as electricity, heat, and mechanics, and construct the digital twin model to accurately reflect the actual operating state of the disconnect switch.
[0089] In the embodiment of the present application, the historical operation data in step S200 includes the working parameters of the target disconnect switch and the average temperature, average humidity, and average concentration of corrosive substances in the environment where the target disconnect switch is located per unit time.
[0090] It should be noted that when the movement mode of the above-mentioned target disconnect switch is rotational movement, the above-mentioned working parameters may include, but are not limited to, the moment of inertia of the target disconnect switch, the rotation angle within a certain time, the damping coefficient, the corresponding friction force during rotational movement, the wear coefficient, the material hardness, the cumulative sliding distance, etc.; when the movement mode of the above-mentioned target disconnect switch is linear movement, the above-mentioned working parameters may include, but are not limited to, the mass of the target disconnect switch, the linear displacement within a certain time, the damping coefficient, the corresponding friction force during linear movement, the wear coefficient, the material hardness, the cumulative sliding distance, etc. The above-mentioned unit time can be defined according to the actual usage scenario, such as 12 hours, 24 hours, etc., and this embodiment does not limit this.
[0091] In an alternative embodiment, when inputting the historical operation data into the digital twin model in step S200, the real-time data fusion technology can be adopted to fuse the real-time collected operation data with the model prediction data to improve the prediction accuracy of the model.
[0092] In another alternative embodiment, when inputting the historical operation data into the digital twin model in step S200, the historical data replay technology can also be used to input the historical operation data into the model according to the time series to simulate the long-term aging process of the disconnect switch and provide a more accurate basis for the remaining life prediction.
[0093] In the embodiment of the present application, the simulation output data in step S200 includes the cumulative wear amount, temperature influence index, humidity influence index, and corrosive substance concentration influence index of the target disconnect switch.
[0094] In a specific implementation, the above temperature influence index can be obtained by substituting the average temperature in step S200 into the temperature-loss relationship in the digital twin model. The above humidity influence index can be obtained by substituting the average humidity in step S200 into the humidity-loss relationship in the digital twin model. The above corrosive substance concentration influence index can be obtained by substituting the average corrosive substance concentration in step S200 into the corrosion concentration-loss relationship in the digital twin model. At the same time, the above working parameters can be substituted into the rotational power loss formula in step A21 to obtain the rotational power loss corresponding to the target disconnecting switch, or the above working parameters can be substituted into the linear power loss formula in step A22 to obtain the linear power loss corresponding to the target disconnecting switch. Specifically, assuming that the movement mode of the target disconnecting switch is rotational movement, the rotational power loss of the target disconnecting switch is F1, the temperature influence index is D1, the humidity influence index is D2, the corrosive substance concentration influence index is D3, the wear coefficient is K1, the material hardness is H1, and the cumulative sliding distance is S1, then the cumulative wear amount of the above target disconnecting switch can be expressed as (F1×K1×S1÷H1)×(D1 + D2 + D3); similarly, assuming that the movement mode of the target disconnecting switch is linear movement, the rotational power loss of the target disconnecting switch is F2, the temperature influence index is D1, the humidity influence index is D2, the corrosive substance concentration influence index is D3, the wear coefficient is K2, the material hardness is H2, and the cumulative sliding distance is S2, then the cumulative wear amount of the above target disconnecting switch can be expressed as (F2×K2×S2÷H2)×(D1 + D2 + D3).
[0095] In an alternative implementation, for obtaining the historical operation data in step S200, the operation data of the disconnecting switch can be collected in real time by using an Internet of Things sensor network.
[0096] In another alternative implementation, for obtaining the historical operation data in step S200, big data technology can also be used to preprocess the historical operation data, including data cleaning, normalization, and feature extraction, so as to improve the quality and usability of the data.
[0097] In the implementation manner of the present application, calculating the predicted remaining life of the target disconnecting switch in step S300 includes the following steps B1 - B3:
[0098] B1: Determine the remaining wearable amount according to the cumulative wear amount.
[0099] In a specific implementation, the total wearable amount of the target disconnecting switch (which can refer to the design specifications of the disconnecting switch and the technical documents provided by the manufacturer or follow the standards or specifications regarding the wear of disconnecting switches in the power industry or related fields, and these standards usually provide recommended total wearable amount values) is subtracted from the cumulative wear amount to obtain the remaining wearable amount of the above target disconnecting switch.
[0100] B2: Modify the theoretical wear rate based on the temperature influence index, humidity influence index, and corrosive substance concentration influence index to obtain the actual wear rate.
[0101] In a specific implementation, multiply the temperature influence index, humidity influence index, and corrosive substance concentration influence index to obtain the environmental influence index, and further multiply the environmental influence index by the theoretical wear rate of the target disconnect switch to obtain the actual wear rate of the target disconnect switch.
[0102] B3: Divide the remaining wearable amount by the actual wear rate to generate the predicted remaining life.
[0103] In a specific implementation, dividing the remaining wearable amount by the actual wear rate can obtain the predicted remaining life corresponding to the above-mentioned target disconnect switch.
[0104] It should be noted that in this step, the historical operation data of the target disconnect switch is collected. The historical operation data includes the working parameters of the target disconnect switch and the average temperature, average humidity, and average corrosive substance concentration of the environment where the target disconnect switch is located per unit time. The historical operation data is input into the digital twin model to obtain the simulation output data. The simulation output data includes the cumulative wear amount, temperature influence index, humidity influence index, and corrosive substance concentration influence index of the target disconnect switch. Based on the simulation output data, determine the remaining wearable amount and environmental influence index of the target disconnect switch. The environmental influence index is used to reflect the degree of wear aggravation of the environment where the target disconnect switch is located on the target disconnect switch. Analyze the target disconnect switch according to the remaining wearable amount and environmental influence index to obtain the predicted remaining life corresponding to the target disconnect switch. In this step, the cumulative wear amount, temperature influence index, humidity influence index, and corrosive substance concentration influence index of the target disconnect switch are determined through the digital twin model, and then the remaining wearable amount of the target disconnect switch and the degree of wear aggravation of the environment where the target disconnect switch is located on the target disconnect switch are determined based on these data, so as to analyze the target disconnect switch to more accurately obtain the predicted remaining life corresponding to the target disconnect switch.
[0105] In an alternative implementation, for predicting the remaining life in step S300, a life prediction method based on Monte Carlo simulation can also be used to consider various uncertainties and random factors to obtain the probability distribution of the remaining life of the disconnect switch, providing a more comprehensive reference for decision-making.
[0106] In another alternative implementation, for predicting the remaining life in step S300, a life prediction model based on damage mechanics can also be adopted to consider the damage evolution process of the material and accurately predict the remaining life of the disconnect switch.
[0107] In summary, by constructing a digital twin model and combining with actual monitoring data, the present invention can achieve a comprehensive quantification of the mechanical and environmental losses of the disconnect switch; dynamically construct a model based on the historical operation data and environmental parameters of the disconnect switch to achieve an accurate assessment of the cumulative wear amount and environmental impact index of the disconnect switch, and dynamically monitor the real-time wear condition of the disconnect switch to give an early warning of abnormal wear rates in a timely manner; by optimizing algorithms and reference factors, construct a corresponding remaining life prediction model, and combine damage accumulation to obtain a time-varying remaining life prediction result to accurately predict the remaining life of the disconnect switch. This improves the detection efficiency, reduces the maintenance cost, and at the same time realizes high-precision prediction and early warning.
[0108] Embodiment 3. The above is a schematic solution of a method for analyzing the remaining life of a disconnect switch. It should be noted that the technical solution of this disconnect switch remaining life analysis system belongs to the same concept as the technical solution of the above-mentioned method for analyzing the remaining life of a disconnect switch. For the details not described in detail in the technical solution of this disconnect switch remaining life analysis system in this embodiment, reference can be made to the description of the technical solution of the above-mentioned method for analyzing the remaining life of a disconnect switch.
[0109] This embodiment also provides a disconnect switch remaining life analysis system, including:
[0110] A building module, used to build a digital twin model of the target disconnect switch, where the digital twin model integrates mechanical loss simulation and environmental loss simulation. The mechanical loss simulation adapts a dynamic loss model according to the motion type of the blade, and the environmental loss simulation quantifies the impact of the environment on the life through the correlation between environmental parameters and the loss of the disconnect switch;
[0111] An output module, which collects the historical operation data of the target disconnect switch and inputs the historical operation data into the digital twin model to generate simulation output data;
[0112] A calculation module, used to calculate the predicted remaining life of the target disconnect switch based on the simulation output data.
[0113] This embodiment also provides an electronic device, applicable to the situation of analyzing the remaining life of a disconnect switch, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for analyzing the remaining life of a disconnect switch as proposed in the above embodiment.
[0114] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the method for analyzing the remaining life of a disconnect switch as proposed in the above embodiment.
[0115] The storage medium proposed in this embodiment and the method for analyzing the remaining life of a disconnect switch proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.
[0116] From the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and the necessary general-purpose hardware, and of course, it can also be implemented by hardware. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disc of a computer, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments of the present invention.
[0117] It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solution of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solution of the present invention, and all of them should be covered by the scope of the claims of the present invention.
Claims
1. A method for analyzing the remaining life of a knife switch, characterized in that, Including: Establish a digital twin model of the target disconnect switch, where the digital twin model integrates mechanical loss simulation and environmental loss simulation. The mechanical loss simulation adapts a dynamic loss model according to the motion type of the blade, and the environmental loss simulation quantifies the impact of the environment on the lifespan through the correlation between environmental parameters and the loss of the disconnect switch; Collect the historical operation data of the target disconnect switch and input the historical operation data into the digital twin model to generate simulation output data; Based on the simulation output data, calculate the predicted remaining lifespan of the target disconnect switch.
2. The residual life analysis method of the knife switch according to claim 1, characterized in that, Establishing the digital twin model of the target disconnect switch includes: Construct a three-dimensional geometric model according to the physical parameters of the target disconnect switch; Determine the corresponding mechanical loss formula based on the motion mode of the blade, where the mechanical loss formula includes a rotational motion model and a linear motion model; Determine the environmental loss formula according to the correlation between environmental parameters and the loss of the disconnect switch; Construct the digital twin model of the disconnect switch based on the three-dimensional geometric model, the mechanical loss formula, and the environmental loss formula.
3. The method for analyzing the remaining life of the knife switch according to claim 2, wherein The determining the corresponding mechanical loss formula based on the motion mode of the blade includes: If the blade is in rotational motion, use a rotational power loss formula based on moment of inertia and angular acceleration; If the blade is in linear motion, use a linear power loss formula based on mass and linear acceleration.
4. The method for analyzing the remaining life of the knife switch according to claim 3, characterized in that, The determining the environmental loss formula according to the correlation between environmental parameters and the loss of the disconnect switch includes: By analyzing the influence of temperature, humidity, and corrosive substance concentration on the loss of the disconnect switch, establish a temperature-loss relationship formula, a humidity-loss relationship formula, and a corrosion concentration-loss relationship formula respectively; Based on the temperature-loss relationship formula, the humidity-loss relationship formula, and the corrosion concentration-loss relationship formula, generate the environmental loss formula.
5. The method for analyzing the remaining life of the knife switch according to claim 4, characterized in that, The simulation output data includes: The cumulative wear amount, temperature influence index, humidity influence index, and corrosive substance concentration influence index of the target disconnect switch.
6. The method for analyzing the remaining life of the knife switch according to claim 5, characterized in that, The calculating the predicted remaining lifespan of the target disconnect switch includes, Determine the remaining wearable amount according to the cumulative wear amount; Based on the temperature influence index, humidity influence index, and corrosive substance concentration influence index, correct the theoretical wear rate to obtain the actual wear rate; Divide the remaining wearable amount by the actual wear rate to generate the predicted remaining lifespan.
7. The method for analyzing the remaining life of the knife switch according to claim 6, characterized in that, The historical operation data includes: The working parameters of the target disconnect switch and the average temperature, average humidity, and average corrosive substance concentration of the environment where the target disconnect switch is located per unit time.
8. A knife switch remaining life analysis system, applying the method according to any one of claims 1-7, characterized in that, Including: A building module for establishing a digital twin model of the target disconnect switch, where the digital twin model integrates mechanical loss simulation and environmental loss simulation. The mechanical loss simulation adapts a dynamic loss model according to the motion type of the blade, and the environmental loss simulation quantifies the impact of the environment on the lifespan through the correlation between environmental parameters and the loss of the disconnect switch; An output module that collects the historical operation data of the target disconnect switch and inputs the historical operation data into the digital twin model to generate simulation output data; A calculation module for calculating the predicted remaining lifespan of the target disconnect switch based on the simulation output data.
9. An electronic device, including: A memory and a processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the residual life analysis method of the disconnecting switch according to any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the steps of the residual life analysis method of the disconnecting switch according to any one of claims 1 to 7 are implemented.