A DC Insulating Gas State Assessment Method and Related Equipment Based on Dark Current Measurement

Abstract

This invention relates to the field of DC high-voltage GIS insulation technology, and discloses a method and related equipment for assessing the state of DC insulating gas based on dark current measurement. The method includes acquiring dark current signals of the insulating gas under different gas pressures, conductor roughnesses, and electric field characteristics; processing the acquired dark current signals to obtain the average dark current value corresponding to each electric field strength; and using an SVM algorithm to assess the insulation margin of the insulating gas under different conditions based on the average dark current value, thus completing the state assessment of the insulating gas. This invention, by measuring and processing dark current signals in real time, enables real-time monitoring and dynamic assessment of the state of the insulating gas.

Description

Technical Field

[0001] This invention relates to the field of DC high-voltage GIS insulation technology, specifically to a DC insulation gas state assessment method and related equipment based on dark current measurement. Background Technology

[0002] With the vigorous development of my country's ultra-high voltage power grid and the further development of hydropower in the west and wind power resources in the southeast coastal areas, the construction of ultra-high voltage and extra-high voltage direct current (UHVDC) transmission projects will be further accelerated. DC field equipment is an important component of both offshore and onshore converter stations. Higher DC voltage levels require larger converter stations, especially for air-insulated equipment used for switching operations and system resets in the DC field, such as disconnect switches, grounding switches, current and voltage measurement systems, surge arresters, gas-air bushings, and cable connection equipment. Traditional open-type air-insulated DC field equipment occupies a large space, limiting the development of large-capacity, lightweight offshore platforms. my country's first flexible DC offshore wind power project, the Rudong Offshore Wind Power Station in Jiangsu Province, selected open-type DC field equipment. In contrast, gas-insulated switchgear (GIS) for DC not only helps reduce the footprint of onshore converter stations but also reduces the facility investment costs for large-capacity offshore wind farms located far from shore. Compared to DC fields using air-insulated equipment, DC fields using DC GIS can reduce space requirements by 70%-95% and offer higher reliability because high-voltage components are not affected by external environmental factors such as dust, salty air, rain, and snow. Therefore, the development of DC GIS is crucial for the future development of DC power transmission technology.

[0003] DC power equipment is a crucial component of DC systems. Higher DC voltage levels require larger converter stations, especially for air-insulated equipment used for switching operations and system resets, such as disconnectors, grounding switches, current and voltage measurement systems, surge arresters, gas-air bushings, and cable connection equipment. DC gas-insulated switchgear (GIS) not only helps reduce the footprint of converter stations but also lowers the investment costs of large-capacity renewable energy transmission equipment. Compared to DC power systems using air-insulated equipment, DC power systems using GIS can reduce space by 70%-95% and offer higher reliability because high-voltage components are unaffected by external dust, salty air, rain, snow, etc. Currently, international research is underway on the insulation issues of DC gas-insulated switchgear. For example, ABB has developed a ±320 kV DC GIS prototype, and Siemens has successively developed ±320 kV and ±500 kV DC GIS prototypes, providing important experience for the development of DC GIS. However, to date, no mature DC GIS product has been successfully applied in engineering, and a unified method for insulation condition detection and maintenance of DC GIS has not been established. Therefore, achieving gas insulation condition assessment of DC GIS under complex operating conditions is an important means of realizing DC GIS operation and maintenance, and relevant research is urgently needed to provide important support for the domestic substitution of high-voltage DC GIS. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a DC insulating gas state assessment method and related equipment based on dark current measurement, so as to solve the technical problem of how to quickly detect the state of insulating gas in the prior art.

[0005] This invention is achieved through the following technical solution:

[0006] In a first aspect, the present invention provides a method for assessing the state of DC insulating gas based on dark current measurement, comprising:

[0007] Acquire dark current signals of insulating gas under different gas pressures, different guide rod roughnesses, and different electric field characteristics;

[0008] The acquired dark current signal of the insulating gas is processed to obtain the average dark current value corresponding to each electric field strength.

[0009] The insulation margin of insulating gas under different conditions is evaluated based on the SVM algorithm, thus completing the state assessment of the insulating gas.

[0010] Preferably, the specific process for obtaining the dark current signal of the insulating gas under different gas pressures, different guide rod roughnesses, and different electric field characteristics is as follows:

[0011] An experimental setup is set up, which can adjust the gas pressure, guide rod roughness, and electric field characteristics;

[0012] Under multiple preset combinations of gas pressure, guide rod roughness, and electric field characteristics, the dark current signal of the insulating gas was measured using an experimental setup.

[0013] Preferably, in processing the acquired dark current signal of insulating gas, the dark current signal under each electric field strength is filtered and smoothed, and then the average value of the dark current signal under each electric field strength is calculated to obtain the average dark current value corresponding to each electric field strength.

[0014] Preferably, in the step of evaluating the insulation margin of the insulating gas under different conditions based on the SVM algorithm to complete the state assessment of the insulating gas, the specific process is as follows:

[0015] Adjusting the feature vector format and selecting training functions and setting training parameters;

[0016] To construct an SVM model, input the feature vector format into the SVM model, set the training function and training parameters, and then train the SVM model to obtain training samples. Perform back-substitution testing on the training samples within the SVM model to obtain the final trained SVM model.

[0017] The average dark current value corresponding to each electric field strength is input into the finally trained SVM model for testing to obtain the corresponding test training samples.

[0018] Each test training sample is compared with the training sample to obtain the comparison results, and the insulation margin of the insulating gas under different conditions is evaluated by the comparison results.

[0019] Furthermore, in the step of comparing each test training sample with the training sample to obtain the comparison result, if the test training sample is the same as the training sample, then the test training sample is qualified; otherwise, it is unqualified.

[0020] Secondly, the present invention also provides a DC insulating gas state assessment system based on dark current measurement, comprising:

[0021] The dark current signal acquisition module is used to acquire the dark current signal of insulating gas under different gas pressures, different guide rod roughnesses, and different electric field characteristics.

[0022] The signal processing module is used to process the acquired dark current signal of the insulating gas to obtain the average dark current value corresponding to each electric field strength.

[0023] The evaluation module is used to evaluate the insulation margin of insulating gas under different conditions based on the SVM algorithm, and to complete the state evaluation of insulating gas.

[0024] Preferably, the evaluation module includes a data setting module, a model training module, a model testing module, and a comparison module;

[0025] The data setting module is used to adjust the feature vector format and select training functions and set training parameters.

[0026] The model training module is used to build an SVM model. It inputs the feature vector format into the SVM model, sets the training function and training parameters, and then trains the SVM model to obtain training samples. The training samples are then back-tested within the SVM model to obtain the final trained SVM model.

[0027] The model testing module is used to input the average dark current value corresponding to each electric field strength into the finally trained SVM model to obtain the corresponding test training samples.

[0028] The comparison module is used to compare each test training sample with the training sample to obtain the comparison result, and to evaluate the insulation margin of the insulating gas under different conditions through the comparison result.

[0029] Thirdly, the present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the DC insulating gas state assessment method based on dark current measurement as described above.

[0030] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the DC insulating gas state assessment method based on dark current measurement as described above.

[0031] Fifthly, the present invention also provides a computer program product, including computer instructions that instruct a computing device to perform operations corresponding to the DC insulating gas state assessment method based on dark current measurement as described above.

[0032] Compared with the prior art, the present invention has the following beneficial technical effects:

[0033] This invention provides a DC insulating gas condition assessment method based on dark current measurement. By acquiring dark current signals of the insulating gas under different gas pressures, conductor roughnesses, and electric field characteristics, it comprehensively considers various factors affecting the insulating gas condition. This comprehensive data collection ensures the accuracy and reliability of the assessment results. The introduction of the SVM algorithm to assess the insulation margin using the average dark current value realizes the intelligent and automated assessment process. The SVM algorithm not only improves assessment efficiency but also reduces errors caused by human factors, making the assessment results more objective and accurate. This invention, through real-time measurement and processing of dark current signals, enables real-time monitoring and dynamic assessment of the insulating gas condition. This is of great significance for timely detection of abnormal insulating gas conditions and prevention of potential faults.

[0034] Furthermore, the application of the SVM algorithm makes the evaluation process more efficient, enabling the processing and analysis of large amounts of data in a short time, thus improving evaluation efficiency. This is particularly important for the insulation gas condition assessment of large-scale power systems, as it can reduce labor and time costs.

[0035] Furthermore, automated evaluation reduces errors caused by human factors. The SVM algorithm, based on data-driven principles, provides objective and accurate evaluation results, avoiding the subjectivity and uncertainty of human judgment. Attached Figure Description

[0036] Figure 1 This is a flowchart of the DC insulating gas state assessment method based on dark current measurement in an embodiment of the present invention;

[0037] Figure 2 This is a schematic diagram of the raw dark current data measured using a dark current measurement platform in an embodiment of the present invention;

[0038] Figure 3 This is a schematic diagram illustrating the processing of the acquired dark current signal of insulating gas in an embodiment of the present invention.

[0039] Figure 4 This is a schematic diagram of the average dark current value corresponding to each electric field strength in an embodiment of the present invention;

[0040] Figure 5 This is a flowchart of the SVM algorithm in an embodiment of the present invention;

[0041] Figure 6 This is a schematic diagram illustrating the test sample recognition performance and classification error of SVM in an embodiment of the present invention;

[0042] Figure 7 This is a schematic diagram of the DC insulating gas state assessment system based on dark current measurement in an embodiment of the present invention.

[0043] Figure 8 This is a schematic diagram of the internal structure of the evaluation module in an embodiment of the present invention;

[0044] In the diagram: 1. Dark current signal acquisition module; 2. Signal processing module; 3. Evaluation module; 31. Data setting module; 32. Model training module; 33. Model testing module; 34. Comparison module. Detailed Implementation

[0045] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0046] The purpose of this invention is to provide a DC insulating gas state assessment method and related equipment based on dark current measurement, so as to solve the technical problem of how to quickly detect the state of insulating gas in the prior art.

[0047] The present invention will now be described in further detail with reference to the accompanying drawings:

[0048] Example 1

[0049] See Figure 1 In one embodiment of the present invention, a method for assessing the state of DC insulating gas based on dark current measurement is provided, comprising:

[0050] Step 1: Obtain the dark current signal of the insulating gas under different gas pressures, different guide rod roughnesses, and different electric field characteristics;

[0051] Specifically, the steps for obtaining the dark current signal of the insulating gas under different gas pressures, different guide rod roughnesses, and different electric field characteristics are as follows:

[0052] An experimental setup is set up, which can adjust the gas pressure, guide rod roughness, and electric field characteristics;

[0053] Under multiple preset combinations of gas pressure, guide rod roughness, and electric field characteristics, the dark current signal of the insulating gas was measured using an experimental setup.

[0054] Step 2: Process the acquired dark current signal of the insulating gas to obtain the average dark current value corresponding to each electric field strength;

[0055] Specifically, in processing the acquired dark current signal of insulating gas, the dark current signal under each electric field strength is filtered and smoothed, and then the average value of the dark current signal under each electric field strength is calculated to obtain the average dark current value corresponding to each electric field strength.

[0056] Step 3: Based on the SVM algorithm, evaluate the insulation margin of the insulating gas under different conditions by assessing the average dark current value, and complete the state assessment of the insulating gas.

[0057] Specifically, in the step of evaluating the insulation margin of insulating gas under different conditions based on the SVM algorithm to complete the state assessment of insulating gas, the specific process is as follows:

[0058] S1, adjusting the feature vector format and selecting the training function and setting the training parameters;

[0059] S2, construct the SVM model by inputting the feature vector format into the SVM model, setting the training function and training parameters, and then training the SVM model to obtain training samples; perform back-testing on the training samples within the SVM model to obtain the final trained SVM model, such as... Figure 5 As shown;

[0060] S3. Input the average dark current value corresponding to each electric field strength into the finally trained SVM model to obtain the corresponding test training samples.

[0061] S4. Each test training sample is compared with the training sample to obtain the comparison result. The insulation margin of the insulating gas under different conditions is evaluated by the comparison result.

[0062] In the step of comparing each test training sample with the training sample to obtain the comparison result, if the test training sample is the same as the training sample, the test training sample is qualified; otherwise, it is unqualified.

[0063] Specifically, in this embodiment, the format of the feature sample vector is adjusted based on the specific input format of the SVM training and testing functions; the radial basis function is selected as the kernel function of the SVM program, which can solve the problem of linear inseparability of feature vectors in low-dimensional space through inner product.

[0064] Setting the g parameter to 1 and the c parameter to 2 ensures the SVM model's fitting ability and generalization capability. During SVM model creation and training, a specific format of test sample feature vectors is input, and the kernel function, g parameter, and c parameter are set. After training, the training samples are back-tested to ensure the training effect of the training samples on the SVM model. The trained SVM model is then used to test the test sample feature vectors; the LIBSVM-based prediction function directly provides the SVM model's recognition accuracy.

[0065] In this embodiment, LIBSVM is used in MATLAB software to realize pattern recognition, identify the gas insulation margin under different gas types, different guide rod roughness, and different electric field characteristics; through SVM pattern recognition, the final recognition accuracy reaches more than 90%, and the insulation margin assessment of insulating gas is finally realized.

[0066] In the method of the present invention, according to Figure 2 The image shows the raw dark current data measured using a dark current measurement platform. A boost method was used, with each boost followed by a period of stabilization before the dark current was allowed to stabilize. A Wiener filtering algorithm was employed, with a filter order of 4, a cutoff frequency of 1Hz, and a sampling frequency of 100Hz. The filtered data is shown below. Figure 3 As shown; after the dark current stabilizes, 200 data points are selected and averaged to obtain the dark current value under that electric field strength, as shown. Figure 4 As shown; the SVM has a g parameter of 1, a c parameter of 2, and 200 training feature vector samples.

[0067] according to Figure 6 The image shows the SVM's recognition performance and classification error on test samples. 200 (50x4) feature vector samples were selected for SVM training, and then applied to 100 (25x4) test samples to demonstrate the results. The final test accuracy for each pattern reached over 90%.

[0068] In summary, this invention provides a method for assessing the state of DC insulating gas based on dark current measurement. First, the dark current of the insulating gas is measured under different gas types, conductor roughnesses, and electric field characteristics using a coaxial dark current measurement platform. Then, the dark current signal is filtered and post-processed to obtain the average dark current value under the given electric field strength. Finally, pattern recognition is performed on the obtained dark current data using an SVM (Support Vector Machine) algorithm to evaluate the insulation margin of the insulating gas under different conditions, thus completing the state assessment of the insulating gas. This invention, by measuring the dark current of insulating gas in DC GIS and using the measured dark current signal for insulating gas state assessment, achieves an accuracy rate exceeding 90%, enabling rapid and accurate detection of the insulating gas state and providing an important tool for the detection and maintenance of DC GIS insulation performance.

[0069] This invention comprehensively considers various factors affecting the state of insulating gas by acquiring dark current signals of insulating gas under different gas pressures, conductor roughnesses, and electric field characteristics. This comprehensive data collection ensures the accuracy and reliability of the evaluation results. The introduction of the SVM algorithm to evaluate the insulation margin using the average dark current value realizes the intelligent and automated evaluation process. The SVM algorithm not only improves evaluation efficiency but also reduces errors caused by human factors, making the evaluation results more objective and accurate. This invention, through real-time measurement and processing of dark current signals, enables real-time monitoring and dynamic evaluation of the state of insulating gas. This is of great significance for timely detection of abnormal states of insulating gas and prevention of potential faults.

[0070] Example 2

[0071] according to Figure 7 As shown, the present invention also provides a DC insulating gas state assessment system based on dark current measurement, comprising:

[0072] Dark current signal acquisition module 1 is used to acquire the dark current signal of insulating gas under different gas pressures, different guide rod roughnesses and different electric field characteristics;

[0073] Signal processing module 2 is used to process the acquired dark current signal of insulating gas to obtain the average dark current value corresponding to each electric field strength.

[0074] Evaluation module 3 is used to evaluate the insulation margin of insulating gas under different conditions based on the average dark current value using the SVM algorithm, and to complete the state evaluation of insulating gas.

[0075] Among them, according to Figure 8 As shown, the evaluation module 1 includes a data setting module 31, a model training module 32, a model testing module 33, and a comparison module 34;

[0076] Data setting module 31 is used to adjust the feature vector format and select training functions and set training parameters;

[0077] The model training module 32 is used to build an SVM model. It inputs the feature vector format into the SVM model, sets the training function and training parameters, and then trains the SVM model to obtain training samples. The training samples are then back-tested in the SVM model to obtain the final trained SVM model.

[0078] The model testing module 33 is used to input the average dark current value corresponding to each electric field intensity into the finally trained SVM model to obtain the corresponding test training samples.

[0079] The comparison module 34 is used to compare each test training sample with the training sample to obtain the comparison result, and to evaluate the insulation margin of the insulating gas under different conditions through the comparison result.

[0080] Example 3

[0081] The present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, such as a DC insulating gas state assessment program based on dark current measurement.

[0082] When the processor executes the computer program, it implements the steps of the above-described DC insulating gas state assessment method based on dark current measurement, for example:

[0083] Acquire dark current signals of insulating gas under different gas pressures, different guide rod roughnesses, and different electric field characteristics;

[0084] The acquired dark current signal of the insulating gas is processed to obtain the average dark current value corresponding to each electric field strength.

[0085] The insulation margin of insulating gas under different conditions is evaluated based on the SVM algorithm, thus completing the state assessment of the insulating gas.

[0086] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above system, for example:

[0087] Dark current signal acquisition module 1 is used to acquire the dark current signal of insulating gas under different gas pressures, different guide rod roughnesses and different electric field characteristics;

[0088] Signal processing module 2 is used to process the acquired dark current signal of insulating gas to obtain the average dark current value corresponding to each electric field strength.

[0089] Evaluation module 3 is used to evaluate the insulation margin of insulating gas under different conditions based on the average dark current value using the SVM algorithm, and to complete the state evaluation of insulating gas.

[0090] The evaluation module 3 includes a data setting module 31, a model training module 32, a model testing module 33, and a comparison module 34.

[0091] Data setting module 31 is used to adjust the feature vector format and select training functions and set training parameters;

[0092] The model training module 32 is used to build an SVM model. It inputs the feature vector format into the SVM model, sets the training function and training parameters, and then trains the SVM model to obtain training samples. The training samples are then back-tested in the SVM model to obtain the final trained SVM model.

[0093] The model testing module 33 is used to input the average dark current value corresponding to each electric field intensity into the finally trained SVM model to obtain the corresponding test training samples.

[0094] The comparison module 34 is used to compare each test training sample with the training sample to obtain the comparison result, and to evaluate the insulation margin of the insulating gas under different conditions through the comparison result.

[0095] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the mobile terminal.

[0096] For example, the computer program can be divided into a dark current signal acquisition module 1, a signal processing module 2, an evaluation module 3, a data setting module 31, a model training module 32, a model testing module 33, and a comparison module 34.

[0097] The specific functions of each module are as follows:

[0098] Dark current signal acquisition module 1 is used to acquire the dark current signal of insulating gas under different gas pressures, different guide rod roughnesses and different electric field characteristics;

[0099] Signal processing module 2 is used to process the acquired dark current signal of insulating gas to obtain the average dark current value corresponding to each electric field strength.

[0100] Evaluation module 3 is used to evaluate the insulation margin of insulating gas under different conditions based on the average dark current value using the SVM algorithm, and to complete the state evaluation of insulating gas.

[0101] The evaluation module includes a data setting module 31, a model training module 32, a model testing module 33, and a comparison module 34.

[0102] Data setting module 31 is used to adjust the feature vector format and select training functions and set training parameters;

[0103] The model training module 32 is used to build an SVM model. It inputs the feature vector format into the SVM model, sets the training function and training parameters, and then trains the SVM model to obtain training samples. The training samples are then back-tested in the SVM model to obtain the final trained SVM model.

[0104] The model testing module 33 is used to input the average dark current value corresponding to each electric field intensity into the finally trained SVM model to obtain the corresponding test training samples.

[0105] The comparison module 34 is used to compare each test training sample with the training sample to obtain the comparison result, and to evaluate the insulation margin of the insulating gas under different conditions through the comparison result.

[0106] The mobile terminal can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The mobile terminal may include, but is not limited to, a processor and memory.

[0107] The processor can 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. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the mobile terminal, connecting various parts of the mobile terminal via various interfaces and lines.

[0108] The memory can be used to store the computer program and / or module. The processor implements various functions of the mobile terminal by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0109] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMediaCards (SMC), Secure Digital (SD) cards, FlashCards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0110] Example 4

[0111] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method for assessing the state of DC insulating gas based on dark current measurement.

[0112] If the modules / units integrated in the mobile terminal are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0113] Based on this understanding, all or part of the processes in the above method can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-described aggregated reinforcement learning resource scheduling method. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate form.

[0114] The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0115] It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0116] Example 5

[0117] A computer program product includes computer instructions that instruct a computing device to perform operations corresponding to the DC insulating gas state assessment method based on dark current measurement as described above.

[0118] 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 assessing the state of DC insulating gas based on dark current measurement, characterized in that, include: Acquire dark current signals of insulating gas under different gas pressures, different guide rod roughnesses, and different electric field characteristics; The acquired dark current signal of the insulating gas is processed to obtain the average dark current value corresponding to each electric field strength. The insulation margin of insulating gas under different conditions is evaluated based on the SVM algorithm, thus completing the state assessment of the insulating gas.

2. The method for assessing the state of DC insulating gas based on dark current measurement according to claim 1, characterized in that, The specific process for obtaining the dark current signal of the insulating gas under different gas pressures, different guide rod roughnesses, and different electric field characteristics is as follows: An experimental setup is set up that allows for adjustment of gas pressure, guide rod roughness, and electric field characteristics; Under multiple preset combinations of gas pressure, guide rod roughness, and electric field characteristics, the dark current signal of the insulating gas was measured using an experimental setup.

3. The DC insulating gas state assessment method based on dark current measurement according to claim 1, characterized in that, In processing the acquired dark current signal of insulating gas, the dark current signal under each electric field strength is filtered and smoothed, and then the average value of the dark current signal under each electric field strength is calculated to obtain the average dark current value corresponding to each electric field strength.

4. The DC insulating gas state assessment method based on dark current measurement according to claim 1, characterized in that, The specific process for evaluating the insulation margin of the insulating gas under different conditions based on the SVM algorithm to complete the state assessment of the insulating gas is as follows: Adjusting the feature vector format and selecting training functions and setting training parameters; To construct an SVM model, input the feature vector format into the SVM model, set the training function and training parameters, and then train the SVM model to obtain training samples. The training samples are back-tested within the SVM model to obtain the final trained SVM model. The average dark current value corresponding to each electric field strength is input into the finally trained SVM model for testing to obtain the corresponding test training samples. Each test training sample is compared with the training sample to obtain the comparison results, and the insulation margin of the insulating gas under different conditions is evaluated by the comparison results.

5. The DC insulating gas state assessment method based on dark current measurement according to claim 4, characterized in that, In the step of comparing each test training sample with the training sample to obtain the comparison result, if the test training sample is the same as the training sample, then the test training sample is qualified; otherwise, it is unqualified.

6. A DC insulating gas state assessment system based on dark current measurement, characterized in that, include: The dark current signal acquisition module is used to acquire the dark current signal of insulating gas under different gas pressures, different guide rod roughnesses, and different electric field characteristics. The signal processing module is used to process the acquired dark current signal of the insulating gas to obtain the average dark current value corresponding to each electric field strength. The evaluation module is used to evaluate the insulation margin of insulating gas under different conditions based on the SVM algorithm, and to complete the state evaluation of insulating gas.

7. The DC insulating gas state assessment system based on dark current measurement according to claim 6, characterized in that, The evaluation module includes a data setting module, a model training module, a model testing module, and a comparison module; The data setting module is used to adjust the feature vector format and select training functions and set training parameters. The model training module is used to build an SVM model. It inputs the feature vector format into the SVM model, sets the training function and training parameters, and then trains the SVM model to obtain training samples. The training samples are back-tested within the SVM model to obtain the final trained SVM model. The model testing module is used to input the average dark current value corresponding to each electric field strength into the finally trained SVM model to obtain the corresponding test training samples. The comparison module is used to compare each test training sample with the training sample to obtain the comparison result, and to evaluate the insulation margin of the insulating gas under different conditions through the comparison result.

8. A mobile terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the DC insulating gas state assessment method based on dark current measurement as described in any one of claims 1-5.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the DC insulating gas state assessment method based on dark current measurement as described in any one of claims 1-5.

10. A computer program product comprising computer instructions, characterized in that, The computer instructions instruct the computing device to perform the operation corresponding to the DC insulating gas state assessment method based on dark current measurement as described in any one of claims 1-5.

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