A method and device for monitoring corrosion of a steel structure of a substation
By installing sensors on the steel structure of substations to collect data, generating digital twin models, and using machine learning to predict corrosion, the problem of insufficient digitalization in corrosion monitoring of substation steel structures has been solved, enabling rapid and accurate corrosion monitoring and early warning.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ELECTRIC POWER SCI RES INST OF STATE GRID XINJIANG ELECTRIC POWER CO LTD
- Filing Date
- 2025-01-10
- Publication Date
- 2026-07-10
AI Technical Summary
At present, there is a lack of clear and intuitive digital display methods for simulating the corrosion status of substation steel structures, and digital twin technology has not been fully applied in this field.
By installing sensors on the steel structure of the substation to collect corrosion rate data, combining the three-dimensional model and environmental data to generate a digital twin model, and using a pre-trained machine learning model to predict future corrosion, real-time monitoring and early warning are provided.
It enables rapid and accurate monitoring and prediction of corrosion of substation steel structures, and can provide timely alarms to ensure the safety of the steel structures.
Smart Images
Figure CN122361253A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of risk early warning technology for substation steel structures, specifically to a method and device for monitoring corrosion of substation steel structures. Background Technology
[0002] The steel frame of a substation refers to its structural framework or skeleton, used to support and install electrical equipment and to provide positioning and connection for various components within the power system. During long-term operation, if a substation is exposed to a corrosive environment, the metal or coating materials of the steel frame are highly susceptible to corrosion, threatening its safety.
[0003] Currently, digital twin technology, as an emerging technology under the trend of digital development, is widely used in the field of power transmission and transformation engineering. Digital twin technology uses digital methods to construct virtual entities of physical entities in computer virtual space. Through data mapping and other means, the virtual entity can display the behavior of the physical entity in real time within the virtual space. Digital twins establish multi-dimensional dynamic virtual models of physical entities in a digital way to simulate the attributes and behaviors of physical entities in the real environment. At present, the corrosion state simulation technology of substation steel structures based on digital twin technology is in the theoretical research stage, while conventional corrosion diagnosis technology lacks clear and intuitive digital display methods. Summary of the Invention
[0004] To overcome the above-mentioned defects, this invention proposes a method and device for monitoring corrosion of steel structures in substations.
[0005] Firstly, a method for monitoring corrosion of steel structures in substations is provided, the method comprising:
[0006] The dynamic corrosion rate of the metal and coating materials of the substation steel structure is collected by sensors pre-installed on the steel structure.
[0007] The three-dimensional model of the substation steel structure, the environmental data of the substation location, and the dynamic corrosion rate are imported into the simulation platform to generate a digital twin model of the substation steel structure.
[0008] The digital twin model of the substation steel structure is used to display the current state of the substation steel structure and to predict the corrosion rate of the substation steel structure at future moments based on a pre-trained machine learning model.
[0009] Preferably, the process of building the three-dimensional model of the pre-built substation steel frame includes:
[0010] Obtain static data on the metal and coating materials of the substation steel structure;
[0011] The static data of the substation steel frame metal and coating materials are imported into a computer physics engine to obtain a three-dimensional model of the pre-built substation steel frame.
[0012] Furthermore, the static data includes at least one of the following: material, size, structure, corrosion rate.
[0013] Preferably, the environmental data includes at least one of the following: temperature, humidity, rainfall, chloride ion deposition rate, and sulfur dioxide deposition rate.
[0014] Preferably, the sensor is a resistance sensor or a probe sensor.
[0015] Preferably, the step of collecting the dynamic corrosion rate of the metal and coating materials of the substation steel structure through sensors pre-installed on the substation steel structure includes:
[0016] Electrical and current information is collected by sensors pre-installed on the steel structure of the substation;
[0017] The corrosion rate of the metal and coating materials of the substation steel structure is calculated based on the electrical and current information.
[0018] Furthermore, the corrosion rates of the metal and coating materials of the substation steel structure are as follows:
[0019] r = 0.8Q -0.25 ×I
[0020] In the above formula, r is the corrosion rate of the metal and coating materials of the substation steel structure, Q is the electrical quantity collected by the sensor, and I is the current collected by the sensor.
[0021] Furthermore, the prediction of the corrosion rate of the substation steel structure at future moments based on the pre-trained machine learning model includes:
[0022] The static data of the current steel structure metal and coating materials of the substation and the environmental data of the substation location are used as inputs to a pre-trained machine learning model to obtain the corrosion rate of the substation steel structure at future moments output by the pre-trained machine learning model.
[0023] Furthermore, the training process of the pre-trained machine learning model includes:
[0024] Training data was constructed using static data on the metal and coating materials of historical substation steel structures, environmental data of the substation locations, and corrosion rates of the substation steel structures.
[0025] The initial machine learning model is trained using the training data to obtain the pre-trained machine learning model.
[0026] Secondly, a corrosion monitoring device for substation steel structures is provided, the substation steel structure corrosion monitoring device comprising:
[0027] The data acquisition module is used to collect the dynamic corrosion rate of the metal and coating materials of the substation steel structure through sensors pre-set on the substation steel structure.
[0028] The generation module is used to import the pre-built three-dimensional model of the substation steel structure, the environmental data of the substation location, and the dynamic corrosion rate into the simulation platform to generate a digital twin model of the substation steel structure.
[0029] The digital twin model of the substation steel structure is used to display the current state of the substation steel structure and to predict the corrosion rate of the substation steel structure at future moments based on a pre-trained machine learning model.
[0030] Thirdly, a computer device is provided, comprising: one or more processors;
[0031] The processor is used to execute one or more programs;
[0032] When the one or more programs are executed by the one or more processors, the substation steel structure corrosion monitoring method is implemented.
[0033] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed, the substation steel structure corrosion monitoring method is implemented.
[0034] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects:
[0035] This invention provides a method and apparatus for monitoring corrosion of substation steel structures, comprising: collecting dynamic corrosion rates of the metal and coating materials of the substation steel structure using sensors pre-installed on the steel structure; importing a pre-built three-dimensional model of the substation steel structure, environmental data of the substation location, and the dynamic corrosion rates into a simulation platform to generate a digital twin model of the substation steel structure; wherein, the digital twin model of the substation steel structure is used to display the current state of the substation steel structure and predict the corrosion rate of the substation steel structure at future times based on a pre-trained machine learning model. The technical solution provided by this invention can quickly and accurately determine the current corrosion status of substation steel structures. Attached Figure Description
[0036] Figure 1 This is a schematic diagram of the main steps of the corrosion monitoring method for substation steel structures according to an embodiment of the present invention. Detailed Implementation
[0037] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.
[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0039] Example 1
[0040] See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a substation steel structure corrosion monitoring method according to an embodiment of the present invention. Figure 1 As shown, the substation steel frame corrosion monitoring method in this embodiment of the invention mainly includes the following steps:
[0041] Step S101: Collect the dynamic corrosion rate of the metal and coating materials of the substation steel structure by using sensors pre-installed on the substation steel structure;
[0042] Step S102: Import the pre-built three-dimensional model of the substation steel structure, the environmental data of the substation location, and the dynamic corrosion rate into the simulation platform to generate a digital twin model of the substation steel structure.
[0043] The digital twin model of the substation steel structure is used to display the current state of the substation steel structure and to predict the corrosion rate of the substation steel structure at future moments based on a pre-trained machine learning model.
[0044] In the application of digital twin models of substation steel structures, an early warning threshold can be set. When the corrosion rate of metal and coating materials at different locations of the steel structure exceeds the early warning threshold, the location of the area is determined and an alarm is triggered to notify the substation management and maintenance personnel.
[0045] In this embodiment, after the dynamic corrosion rate is collected, it needs to be preprocessed, including cleaning and noise reduction, missing data completion and format conversion, and then the preprocessed dynamic data is stored.
[0046] In this embodiment, the process of building the three-dimensional model of the pre-built substation steel frame includes:
[0047] Obtain static data on the metal and coating materials of the substation steel structure;
[0048] The static data of the substation steel frame metal and coating materials are imported into a computer physics engine to obtain a three-dimensional model of the pre-built substation steel frame.
[0049] In one embodiment, the static data includes at least one of the following: material, size, structure, and corrosion rate.
[0050] In this embodiment, the environmental data includes at least one of the following: temperature, humidity, rainfall, chloride ion deposition rate, and sulfur dioxide deposition rate.
[0051] In this embodiment, the sensor is a resistance sensor or a probe sensor.
[0052] In this embodiment, the step of collecting the dynamic corrosion rate of the metal and coating materials of the substation steel structure through sensors pre-installed on the substation steel structure includes:
[0053] Electrical and current information is collected by sensors pre-installed on the steel structure of the substation;
[0054] The corrosion rate of the metal and coating materials of the substation steel structure is calculated based on the electrical and current information.
[0055] In one embodiment, the corrosion rates of the metal and coating materials of the substation steel structure are as follows:
[0056] r = 0.8Q -0.25 ×I
[0057] In the above formula, r is the corrosion rate of the metal and coating materials of the substation steel structure, Q is the electrical quantity collected by the sensor, and I is the current collected by the sensor.
[0058] In one implementation, the prediction of the corrosion rate of the substation steel structure at future times based on the pre-trained machine learning model includes:
[0059] The static data of the current steel structure metal and coating materials of the substation and the environmental data of the substation location are used as inputs to a pre-trained machine learning model to obtain the corrosion rate of the substation steel structure at future moments output by the pre-trained machine learning model.
[0060] Specifically, when the current dynamic corrosion data in the corrosion digital twin model is updated, the pre-trained machine learning model is adjusted by comparing the predicted dynamic corrosion data with the current dynamic corrosion data until the difference is less than a set error threshold.
[0061] In one implementation, the training process of the pre-trained machine learning model includes:
[0062] Training data was constructed using static data on the metal and coating materials of historical substation steel structures, environmental data of the substation locations, and corrosion rates of the substation steel structures.
[0063] The initial machine learning model is trained using the training data to obtain the pre-trained machine learning model.
[0064] Example 2
[0065] Based on the same inventive concept, the present invention also provides a corrosion monitoring device for substation steel structures, the substation steel structure corrosion monitoring device comprising:
[0066] The data acquisition module is used to collect the dynamic corrosion rate of the metal and coating materials of the substation steel structure through sensors pre-set on the substation steel structure.
[0067] The generation module is used to import the pre-built three-dimensional model of the substation steel structure, the environmental data of the substation location, and the dynamic corrosion rate into the simulation platform to generate a digital twin model of the substation steel structure.
[0068] The digital twin model of the substation steel structure is used to display the current state of the substation steel structure and to predict the corrosion rate of the substation steel structure at future moments based on a pre-trained machine learning model.
[0069] Preferably, the process of building the three-dimensional model of the pre-built substation steel frame includes:
[0070] Obtain static data on the metal and coating materials of the substation steel structure;
[0071] The static data of the substation steel frame metal and coating materials are imported into a computer physics engine to obtain a three-dimensional model of the pre-built substation steel frame.
[0072] Furthermore, the static data includes at least one of the following: material, size, structure, corrosion rate.
[0073] Preferably, the environmental data includes at least one of the following: temperature, humidity, rainfall, chloride ion deposition rate, and sulfur dioxide deposition rate.
[0074] Preferably, the sensor is a resistance sensor or a probe sensor.
[0075] Preferably, the step of collecting the dynamic corrosion rate of the metal and coating materials of the substation steel structure through sensors pre-installed on the substation steel structure includes:
[0076] Electrical and current information is collected by sensors pre-installed on the steel structure of the substation;
[0077] The corrosion rate of the metal and coating materials of the substation steel structure is calculated based on the electrical and current information.
[0078] Furthermore, the corrosion rates of the metal and coating materials of the substation steel structure are as follows:
[0079] r = 0.8Q -0.25 ×I
[0080] In the above formula, r is the corrosion rate of the metal and coating materials of the substation steel structure, Q is the electrical quantity collected by the sensor, and I is the current collected by the sensor.
[0081] Furthermore, the prediction of the corrosion rate of the substation steel structure at future moments based on the pre-trained machine learning model includes:
[0082] The static data of the current steel structure metal and coating materials of the substation and the environmental data of the substation location are used as inputs to a pre-trained machine learning model to obtain the corrosion rate of the substation steel structure at future moments output by the pre-trained machine learning model.
[0083] Furthermore, the training process of the pre-trained machine learning model includes:
[0084] Training data was constructed using static data on the metal and coating materials of historical substation steel structures, environmental data of the substation locations, and corrosion rates of the substation steel structures.
[0085] The initial machine learning model is trained using the training data to obtain the pre-trained machine learning model.
[0086] Example 3
[0087] Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement corresponding method flows or corresponding functions, thereby realizing the steps of the substation steel structure corrosion monitoring method in the above embodiments.
[0088] Example 4
[0089] Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the substation steel structure corrosion monitoring method described in the above embodiments.
[0090] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0091] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0092] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0093] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A method for monitoring corrosion of steel structures in substations, characterized in that, The method includes: The dynamic corrosion rate of the metal and coating materials of the substation steel structure is collected by sensors pre-installed on the steel structure. The three-dimensional model of the substation steel structure, the environmental data of the substation location, and the dynamic corrosion rate are imported into the simulation platform to generate a digital twin model of the substation steel structure. The digital twin model of the substation steel structure is used to display the current state of the substation steel structure and to predict the corrosion rate of the substation steel structure at future moments based on a pre-trained machine learning model.
2. The method as described in claim 1, characterized in that, The process of building the three-dimensional model of the pre-built substation steel structure includes: Obtain static data on the metal and coating materials of the substation steel structure; The static data of the substation steel frame metal and coating materials are imported into a computer physics engine to obtain a three-dimensional model of the pre-built substation steel frame.
3. The method as described in claim 2, characterized in that, The static data includes at least one of the following: material, size, structure, and corrosion rate.
4. The method as described in claim 1, characterized in that, The environmental data includes at least one of the following: temperature, humidity, rainfall, chloride ion deposition rate, and sulfur dioxide deposition rate.
5. The method as described in claim 1, characterized in that, The sensor is a resistance sensor or a probe sensor.
6. The method as described in claim 1, characterized in that, The method of collecting the dynamic corrosion rate of the metal and coating materials of the substation steel structure through sensors pre-installed on the substation steel structure includes: Electrical and current information is collected by sensors pre-installed on the steel structure of the substation; The corrosion rate of the metal and coating materials of the substation steel structure is calculated based on the electrical and current information.
7. The method as described in claim 6, characterized in that, The corrosion rates of the metal and coating materials of the substation steel structure are as follows: r=0.8Q -0.25 ×I In the above formula, r is the corrosion rate of the metal and coating materials of the substation steel structure, Q is the electrical quantity collected by the sensor, and I is the current collected by the sensor.
8. The method as described in claim 2, characterized in that, The prediction of the corrosion rate of the substation steel structure at future moments based on the pre-trained machine learning model includes: The static data of the current steel structure metal and coating materials of the substation and the environmental data of the substation location are used as inputs to a pre-trained machine learning model to obtain the corrosion rate of the substation steel structure at future moments output by the pre-trained machine learning model.
9. The method as described in claim 8, characterized in that, The training process of the pre-trained machine learning model includes: Training data was constructed using static data on the metal and coating materials of historical substation steel structures, environmental data of the substation locations, and corrosion rates of the substation steel structures. The initial machine learning model is trained using the training data to obtain the pre-trained machine learning model.
10. An apparatus for monitoring corrosion of substation steel structures based on the method described in any one of claims 1-9, characterized in that, The device includes: The data acquisition module is used to collect the dynamic corrosion rate of the metal and coating materials of the substation steel structure through sensors pre-set on the substation steel structure. The generation module is used to import the pre-built three-dimensional model of the substation steel structure, the environmental data of the substation location, and the dynamic corrosion rate into the simulation platform to generate a digital twin model of the substation steel structure. The digital twin model of the substation steel structure is used to display the current state of the substation steel structure and to predict the corrosion rate of the substation steel structure at future moments based on a pre-trained machine learning model.
11. A computer device, characterized in that, include: One or more processors; The processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the method for monitoring corrosion of substation steel structures as described in any one of claims 1 to 9 is implemented.
12. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the substation steel frame corrosion monitoring method as described in any one of claims 1 to 9.