Grid Security Operation and Maintenance Collaborative Control Method Based on Data Integration

Abstract

The present invention discloses a collaborative control method for power grid security operation and maintenance based on data integration, which relates to the technical field of power system operation and maintenance and intelligent control. It is used to solve the problem that the failure rate of electrical equipment and the operation risk coefficient are significantly increased due to single-sensor monitoring, reducing the safety and reliability of power grid operation. By detecting the operating internal resistance of the power grid materials to be measured and the electric charge passing through a preset cross-section, an insulation performance evaluation function is constructed in combination with historical data and the evaluation value is calculated; when the threshold is exceeded, the sulfur hexafluoride gas pressure is collected and the leakage risk coefficient is calculated in combination with the maintenance lag period. At the same time, the temperature-concentration ratio fluctuation is analyzed, the temperature calibration range is set and the real-time temperature data is collected; finally, it is judged whether there is a synergistic effect based on whether the leakage risk coefficient and the temperature exceed the limit, and the high-quality or simple processing mode is selected to execute, realizing the dynamic perception and classification response of the insulation state and the leakage risk, and improving the operation and maintenance efficiency and the power grid security.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation and maintenance and intelligent control. More specifically, the present invention relates to a grid security operation and maintenance collaborative control method based on data integration. Background Art

[0002] With the development of smart grids and new power systems, the operating states, insulation performances, and fault characteristics of grid equipment show a trend of multi-source, dynamic, and non-linear evolution. To ensure the safe and stable operation of the grid, traditional grid operation and maintenance mainly rely on regular inspections and post-fault response methods. However, such methods have certain limitations in practical applications.

[0003] The prior art has the following deficiencies:

[0004] Currently, especially in scenarios where high-voltage equipment such as circuit breakers and GIS combined electrical appliances use sulfur hexafluoride gas as an insulating medium, risks such as gas leakage and insulation performance degradation are potentially concealed and sudden, making it difficult to achieve effective early warning and collaborative control through single-sensor monitoring or decentralized data systems. This has led to a significant increase in the failure rate of electrical equipment and the operation risk coefficient, reducing the safety and reliability of grid operation. Therefore, a grid security operation and maintenance collaborative control method based on data integration is proposed.

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a grid security operation and maintenance collaborative control method based on data integration, which solves the problems raised in the above background art by applying multi-source data fusion analysis, historical trend modeling, dynamic insulation performance evaluation, and sulfur hexafluoride leakage risk linkage judgment mechanism.

[0007] To achieve the above object, the present invention provides the following technical solution, a grid security operation and maintenance collaborative control method based on data integration, including the following steps:

[0008] Step S1: Detect the material operation internal resistance of the power grid material to be tested, count the electric charge passing through the preset cross-section within the monitoring time, and collect the historical data of the power grid material to be tested;

[0009] Step S2: After processing the material operation internal resistance and the electric charge of the power grid material to be tested according to the historical data, analyze the degree of insulation performance degradation of the power grid material to be tested and classify it. When the classification result is a high degree of degradation, detect the gas pressure of sulfur hexafluoride in the power grid material to be tested;

[0010] Step S3: Call the maintenance date of the power grid material to be tested to determine the maintenance delay period, calculate the sulfur hexafluoride leakage risk coefficient in combination with the sulfur hexafluoride gas pressure detection result, set multiple monitoring time points, record the sulfur hexafluoride concentration and temperature in the power grid material to be tested, and set the calibration range according to the recording results;

[0011] Step S4: Monitor the temperature of the power grid material to be tested in real time and compare it with the calibration range. Use the comparison result and the sulfur hexafluoride leakage risk coefficient as inputs to judge the power grid material to be tested, and judge whether the insulation performance of the power grid material to be tested is synergistically affected by sulfur hexafluoride leakage. Select the sulfur hexafluoride leakage treatment mode according to the judgment result.

[0012] In a preferred embodiment, the ratio of the stable test voltage applied to the current power grid material to be tested and the instantaneous current passing through it is used as the internal resistance of the material during operation;

[0013] Calculate the integral of the current passing through the preset cross-section per unit time in the current monitoring time interval over time to obtain the charge passing through the preset cross-section.

[0014] In a preferred embodiment, retrieve and collect the internal resistance of the power grid material to be tested and the charge passing through the preset cross-section during the historical time period to form a historical data set;

[0015] Perform arithmetic mean processing on the historical data set respectively to obtain the historical internal resistance reference value and the historical charge reference value.

[0016] In a preferred embodiment, perform a difference operation on the internal resistance of the material during operation and the historical internal resistance reference value to obtain the change in internal resistance during operation;

[0017] Perform a difference operation on the charge passing through the preset cross-section and the historical charge reference value to obtain the change in charge.

[0018] In a preferred embodiment, perform normalization processing on the change in internal resistance during operation and the change in charge, and then perform weighted average to obtain the insulation performance degradation evaluation value;

[0019] Compare the insulation performance degradation evaluation value with the evaluation threshold;

[0020] If the insulation performance degradation evaluation value is less than the evaluation threshold, the classification result of the insulation performance degradation is a low degree of degradation; if the insulation performance degradation evaluation value is greater than or equal to the evaluation threshold, the classification result of the insulation performance degradation is a high degree of degradation.

[0021] In a preferred embodiment, when the classification result of the insulation performance degradation is a high degree of degradation, calculate the mean value of the pressure sampling values during the monitoring time interval to obtain the average gas pressure of sulfur hexafluoride.

[0022] In a preferred embodiment, the maintenance lag period is calculated by subtracting the date of completion of the most recent maintenance associated with the power grid material to be measured from the current time;

[0023] The calculation formula for the leakage risk coefficient is ;

[0024] where, is the leakage risk coefficient, γ is the adjustment coefficient, is the sulfur hexafluoride standard operating pressure reference, is the maintenance lag period.

[0025] In a preferred embodiment, within a preset analysis time interval, the ratio of the sulfur hexafluoride gas concentration to the internal temperature of the power grid material to be measured at each time point is calculated to obtain the concentration-temperature ratio;

[0026] The temperature corresponding to the maximum value among all the concentration-temperature ratios is selected as the reference temperature, and the standard deviation of all temperature samples within the analysis time interval is calculated;

[0027] The interval from the reference temperature minus the standard deviation to the reference temperature plus the standard deviation is defined as the temperature calibration range.

[0028] In a preferred embodiment, the real-time temperature of the power grid material to be measured is compared with the temperature calibration range, and in combination with the comparison between the leakage risk coefficient and the leakage determination threshold, it is determined whether the current power grid material to be measured is affected by sulfur hexafluoride leakage synergistically;

[0029] If the real-time temperature is within the temperature calibration range and the leakage risk coefficient is greater than or equal to the leakage determination threshold, it is determined that the insulation performance of the power grid material to be measured is affected by sulfur hexafluoride leakage synergistically;

[0030] Otherwise, it is determined that the insulation performance of the power grid material to be measured is not affected by sulfur hexafluoride leakage.

[0031] In a preferred embodiment, when it is determined that the insulation performance of the power grid material to be measured is affected by sulfur hexafluoride leakage synergistically, a high-quality treatment mode is executed, including leakage point detection and sealing repair, gas recovery and replenishment, and gas purification and circulation treatment;

[0032] When it is determined that the insulation performance of the power grid material to be measured is not affected by sulfur hexafluoride leakage, a simple treatment mode is executed, specifically including sulfur hexafluoride pressure compensation, off-site monitoring enhancement, and maintenance record keeping.

[0033] The technical effects and advantages of the present invention:

[0034] 1. The present invention detects the operating internal resistance of the power grid material to be measured and the electric charge passing through a preset cross-section, constructs an insulation performance degradation evaluation function in combination with historical reference data, and calculates the evaluation value. When the evaluation value exceeds the threshold, the absolute air pressure of sulfur hexafluoride gas is collected, and the sulfur hexafluoride leakage risk coefficient is calculated in combination with the maintenance lag period. In addition, the temperature-concentration ratio fluctuation is analyzed, the temperature calibration range is set, and the real-time temperature data is collected. Finally, based on whether the sulfur hexafluoride leakage risk coefficient and the real-time temperature exceed the limit, it is judged whether the power grid material to be measured is affected by leakage synergistically, and the high-quality processing mode or the simple processing mode is executed respectively. This method can realize the dynamic perception and classification response of the insulation state of the power grid material to be measured and the sulfur hexafluoride leakage risk, and improve the operation and maintenance efficiency and power grid security. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 FIG. is a flowchart for implementing the power grid security operation and maintenance collaborative control method based on data integration according to the present invention.

[0036] Figure 2 FIG. is a schematic diagram of the steps of the power grid security operation and maintenance collaborative control method based on data integration according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] Embodiment 1. Please refer to Figures 1 to 2 , the power grid security operation and maintenance collaborative control method based on data integration, and the specific operation process is as follows:

[0039] It includes the following steps:

[0040] Step S1: Detect the material operation internal resistance of the power grid material to be measured, count the electric charge passing through the preset cross-section during the monitoring time, and collect the historical data of the power grid material to be measured;

[0041] Step S2: After processing the material operation internal resistance and the electric charge of the power grid material to be measured according to the historical data, analyze the degree of insulation performance degradation of the power grid material to be measured and classify it. When the classification result is a high degree of degradation, detect the air pressure of sulfur hexafluoride in the power grid material to be measured;

[0042] Step S3: Call the maintenance date of the power grid material to be tested to determine the maintenance delay period, calculate the sulfur hexafluoride leakage risk coefficient in combination with the gas pressure detection result of sulfur hexafluoride, set multiple monitoring time points, record the sulfur hexafluoride concentration and temperature in the power grid material to be tested, and set the calibration range according to the recording results;

[0043] Step S4: Compare the calibration range by monitoring the temperature of the power grid material to be tested in real time, use the comparison result and the sulfur hexafluoride leakage risk coefficient as inputs to judge the power grid material to be tested, judge whether the insulation performance of the power grid material to be tested is affected by the synergistic effect caused by sulfur hexafluoride leakage, and select the sulfur hexafluoride leakage treatment mode according to the judgment result.

[0044] The specific implementation is as follows:

[0045] In step S1, detect the internal resistance of the power grid material to be tested during operation. The internal resistance of the material during operation is the resistance value encountered when current passes through the power grid material to be tested per unit time under the current power grid operation state, expressed as , with the unit of ohm. The internal resistance of the material during operation is obtained by applying a stable test voltage across both ends of the power grid material to be tested, and recording the instantaneous current passing through the power grid material to be tested, and calculating it according to Ohm's law .

[0046] Among them, is the value of the test voltage applied during measurement, with the unit of volt; is the actually measured current value in the power grid material to be tested during the same time period, with the unit of ampere.

[0047] Statistically, within the monitoring time interval with the set starting point at the current moment, the electric charge passing through the preset cross-section, and the electric charge is defined as the integral of the current passing through the preset cross-section per unit time over time, and the calculation formula is .

[0048] Among them, is the electric charge passing through the preset cross-section, with the unit of coulomb; I(t) is the instantaneous current measured at time t, with the unit of ampere.

[0049] Retrieve and collect the operation data of the power grid material to be tested in the historical time period, and construct a historical data set. The historical time period is recorded as , collect the internal resistance of the material during operation and the electric charge passing through the preset cross-section during this time period, and form a historical data set:

[0050] ;

[0051] Perform arithmetic mean processing on the historical data sets respectively to obtain the historical operating internal resistance reference value and the historical charge reference value , and the calculation formula is as follows:

[0052] ;

[0053] In step S2, perform a difference operation on the material operating internal resistance obtained in step S1 and the historical operating internal resistance reference value to obtain the change in operating internal resistance, and its calculation formula is , where is the change in operating internal resistance, is the material operating internal resistance, is the historical operating internal resistance reference value.

[0054] Similarly, perform a difference operation on the charge passing through the preset cross-section and the historical charge reference value to obtain the change in charge, and the calculation formula is as follows , where is the change in charge, is the charge passing through the preset cross-section during the current monitoring period, is the historical charge reference value.

[0055] After normalizing the above change in operating internal resistance and change in charge, construct an insulation performance degradation evaluation function, and the specific calculation expression is , where D is the insulation performance degradation evaluation value, and α and β are the weighting coefficients of the operating internal resistance and charge in the insulation performance evaluation respectively, and satisfy α + β = 1, which are preset by the statistical learning model, and both are real numbers between 0 and 1.

[0056] It should be noted that the statistical learning model refers to a mathematical model that establishes a mapping relationship between input variables and output variables based on statistical principles on the given data, which will not be elaborated here.

[0057] Compare the obtained insulation performance degradation evaluation value D with the evaluation threshold . If the insulation performance degradation evaluation value D is less than the evaluation threshold , then the insulation performance degradation classification result is a low degree of degradation; if the insulation performance degradation evaluation value D is greater than or equal to the evaluation threshold , then the insulation performance degradation classification result is a high degree of degradation. This evaluation threshold is obtained through experiments by professionals in this field and will not be elaborated here.

[0058] When the insulation performance degradation classification result is a high degree of degradation, use the high-precision pressure sensor unit integrated inside the material of the power grid under test to perform air pressure detection on the sulfur hexafluoride gas inside the material of the power grid under test in an absolute pressure measurement mode, and collect the absolute air pressure value of the sulfur hexafluoride gas at the current moment , in pascals.

[0059] It should be noted that the high-precision pressure sensor unit refers to a gas pressure sensing module with micro-pressure detection ability, fast response speed, high sensitivity, and a measurement error less than ±0.1%FS, which is used to detect the state of sulfur hexafluoride gas; the absolute pressure measurement method refers to the total pressure value measured after applying pressure to the gas inside the measured cavity with absolute vacuum as the zero reference, which is used for the standard state monitoring of gas in the enclosed environment inside power equipment ;

[0060] Sulfur hexafluoride gas is a colorless, odorless, non-toxic, and non-flammable gas with high insulation performance, which is used as an arc extinguishing medium and insulating gas in high-voltage electrical equipment, and will not be elaborated here.

[0061] To enhance data stability and anti-interference ability, the time window averaging mechanism is adopted in the acquisition process to calculate the mean value of the m pressure sampling values within the monitoring time interval to obtain the average gas pressure of sulfur hexafluoride gas at this stage , and the specific calculation formula is , where is the sulfur hexafluoride gas pressure value of the i-th sampling, and m is the total number of samplings within this monitoring interval.

[0062] In step S3, the historical maintenance data of the power grid material to be tested is called to determine the maintenance lag period. The most recent maintenance completion date associated with the power grid material to be tested is retrieved from the equipment operation and maintenance database, and the maintenance lag period is calculated in combination with the current time. The specific calculation formula is .

[0063] Where is the maintenance lag period, in days, which is used to reflect the actual operation cycle experienced by the power grid material to be tested since the last maintenance; is the current time; is the most recent maintenance completion date associated with the power grid material to be tested.

[0064] It should be noted that the equipment operation and maintenance database refers to a structured data set used to store the operation status, maintenance records, repair logs, and historical fault data of various electrical equipment in the power grid, providing data support for the monitoring and processing of the power grid material to be tested.

[0065] According to the average sulfur hexafluoride gas pressure obtained in step S2, compare it with the standard operating pressure of sulfur hexafluoride , and combine the maintenance lag period to construct the sulfur hexafluoride leakage risk coefficient , this coefficient is used to quantify the possibility and severity of sulfur hexafluoride leakage inside the power grid material to be tested. The calculation formula for the leakage risk coefficient is .

[0066] Where, is the leakage risk coefficient, and γ is the adjustment coefficient used to control the leakage risk weight; is the sulfur hexafluoride standard operating pressure reference provided by the power grid equipment manufacturer, with the unit of Pascal.

[0067] Within the preset analysis time interval , set n detection time points at the same interval. At each time point, synchronously collect the sulfur hexafluoride gas concentration and the internal temperature corresponding to the power grid material to be tested. Calculate the concentration-temperature ratio at each time point, and the calculation expression is as follows:

[0068] ;

[0069] Where, is the concentration-temperature ratio, is the sulfur hexafluoride gas concentration, is the internal temperature.

[0070] Select the temperature corresponding to the maximum value among all values as the reference temperature, and calculate the standard deviation of all temperature samples within the analysis time interval to construct the calibration range. The calculation formula for the standard deviation is , where, is the standard deviation, and is the mean value of the temperature, and its calculation formula is .

[0071] Finally, define the temperature calibration range as .

[0072] In step S4, collect the real-time temperature of the power grid material to be tested through the built-in temperature sensor, and compare it with the above temperature calibration range .

[0073] If the real-time temperature is within the temperature calibration range, it is considered that the temperature fluctuation of the power grid material to be tested is in a normal state;

[0074] If the real-time temperature is not within the temperature calibration range, it is considered that there is abnormal temperature fluctuation in the power grid material to be tested.

[0075] It should be noted that the built-in temperature sensor refers to the sensing unit embedded inside the power grid material to be tested, which is used to detect the internal environment temperature in real time.

[0076] Let the real-time temperature be , combined with the sulfur hexafluoride leakage risk coefficient , and introduce a preset leakage determination threshold , and determine whether the current power grid material to be tested is affected by sulfur hexafluoride leakage synergistically. The determination conditions are as follows:

[0077] If is satisfied, it is determined that the insulation performance of the power grid material to be tested has been affected by sulfur hexafluoride leakage synergistically;

[0078] Otherwise, it is determined that the insulation performance of the power grid material to be tested is not affected by sulfur hexafluoride leakage.

[0079] It should be noted that the above leakage determination threshold is obtained by professionals through experiments and will not be elaborated here.

[0080] When it is determined that the insulation performance of the power grid material to be tested is affected by sulfur hexafluoride leakage synergistically, a high-quality treatment mode is executed, which specifically includes leakage point detection and sealing repair, gas recovery and supplementation, and gas purification and circulation treatment. The specific implementation is as follows:

[0081] The leakage point detection and sealing repair locate the sulfur hexafluoride gas leakage site through local airtightness testing, and use special high-pressure sealing materials for on-site repair to ensure that the airtightness of the insulation system is fully restored.

[0082] The gas recovery and supplementation use a high-purity sulfur hexafluoride gas recovery device to extract some residual gas inside the power grid material to be tested, and after purity detection and filtration treatment, it is re-injected; and equal-pressure and high-purity new sulfur hexafluoride gas is supplemented to the standard operating pressure .

[0083] The gas purification and circulation treatment uses an external purification device to filter out micro-impurities and remove moisture from sulfur hexafluoride gas to improve the overall insulation performance.

[0084] It should be noted that the local airtightness testing refers to the airtightness inspection of the local structural area in the electrical equipment with potential leakage hazards by using infrared thermal imaging method, which is used to quickly locate the leakage point and judge the type of sealing defect; the special high-pressure sealing material refers to a sealing composite material with high insulation, high mechanical strength, corrosion resistance to sulfur hexafluoride gas, and excellent heat resistance performance, which is used for the repair and update of the gas sealing structure in high-voltage power equipment; the external purification device refers to a professional treatment device independent of the electrical equipment and connected to the equipment through pipelines or interfaces, which is used for the cyclic suction, filtration, dehumidification and purity restoration of sulfur hexafluoride gas, and is used for the regeneration and purification treatment of aged or contaminated sulfur hexafluoride gas in high-insulation requirement scenarios.

[0085] When it is determined that the insulation performance of the power grid material to be tested is not affected by the sulfur hexafluoride leakage, the simple treatment mode is executed, which specifically includes sulfur hexafluoride pressure compensation, off-site monitoring enhancement, and maintenance record keeping. The specific implementation is as follows:

[0086] For sulfur hexafluoride pressure compensation, a small amount of sulfur hexafluoride gas is directly replenished into the equipment to bring it back to the standard operating pressure range , where ΔP is the allowable deviation range.

[0087] For off-site monitoring enhancement, the monitoring of the pressure and temperature change trends of the power grid material to be tested during its future operation cycle is strengthened, and a dynamic alarm mechanism is set up.

[0088] For maintenance record keeping, the process and data of this treatment are recorded in the equipment operation and maintenance database, and the leakage point is not repaired.

[0089] Among them, the high-quality treatment mode can completely repair the leakage defect and ensure the long-term stable operation of the equipment. It is applicable to core or heavy-load power grid nodes, but has high operation costs, long treatment time, and requires professional equipment and operating personnel; the simple treatment mode is fast in operation, low in cost, and can achieve short-term maintenance. It is applicable to secondary or low-load power grid components, but does not eliminate the potential leakage risk, and may cause performance degradation or safety hazards during long-term operation.

[0090] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data and performing software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by technicians in this field according to the actual situation.

[0091] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more sets of available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0092] It should be understood that in various embodiments of the present application, the order numbers of the above processes do not indicate the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0093] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0094] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0095] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0096] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0097] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0098] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0099] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A power grid security operation and maintenance collaborative control method based on data integration, characterized in that: Including the following steps: Step S1: Detect the internal resistance of the material operation of the power grid material to be measured, count the amount of charge passing through the preset cross-section during the monitoring time, and collect the historical data of the power grid material to be measured; Step S2: Process the internal resistance of the material operation and the amount of charge of the power grid material to be measured according to the historical data, then analyze the degree of insulation performance degradation of the power grid material to be measured and classify it. When the classification result shows a high degree of degradation, detect the gas pressure of sulfur hexafluoride in the power grid material to be measured; Step S3: Call the maintenance date of the power grid material to be measured to determine the maintenance delay period, calculate the sulfur hexafluoride leakage risk coefficient in combination with the gas pressure detection result of sulfur hexafluoride, set multiple monitoring time points, record the sulfur hexafluoride concentration and temperature in the power grid material to be measured, and set the calibration range according to the recording results; Subtract the most recent maintenance completion date associated with the power grid material to be measured from the current time to calculate the maintenance delay period; The calculation formula for the leakage risk coefficient is ; Among them, is the average value of sulfur hexafluoride gas pressure, is the leakage risk coefficient, γ is the adjustment coefficient, is the sulfur hexafluoride standard operating pressure reference, is the maintenance delay period; Calculate the ratio of the sulfur hexafluoride gas concentration to the internal temperature of the power grid material to be measured at each time point within the preset analysis time interval to obtain the concentration-temperature ratio; Select the temperature corresponding to the maximum value among all the concentration-temperature ratios as the reference temperature, and calculate the standard deviation of all temperature samples within the analysis time interval; Define the interval from the reference temperature minus the standard deviation to the reference temperature plus the standard deviation as the temperature calibration range; Step S4: Monitor the temperature of the power grid material to be measured in real time and compare it with the calibration range. Use the comparison result and the sulfur hexafluoride leakage risk coefficient as inputs to judge the power grid material to be measured, and judge whether the insulation performance of the power grid material to be measured is affected by the synergistic effect caused by sulfur hexafluoride leakage. Select the sulfur hexafluoride leakage treatment mode according to the judgment result; When it is determined that the insulation performance of the power grid material to be measured is affected by the synergistic effect of sulfur hexafluoride leakage, execute the high-quality treatment mode, including leakage point detection and sealing repair, gas recovery and replenishment, and gas purification and circulation treatment; When it is determined that the insulation performance of the power grid material to be measured is not affected by sulfur hexafluoride leakage, execute the simple treatment mode, which specifically includes sulfur hexafluoride pressure compensation, off-site monitoring enhancement, and maintenance record keeping.

2. The method for collaborative control of power grid safety operation and maintenance based on data integration according to claim 1, characterized in that: Take the ratio of the stable test voltage applied to the current power grid material to be measured and the instantaneous current passing through it as the internal resistance of the material operation; Calculate the integral of the current passing through the preset cross-section over time per unit time within the current monitoring time interval to obtain the amount of charge passing through the preset cross-section.

3. The method for collaborative control of power grid safety operation and maintenance based on data integration according to claim 1, characterized in that: Retrieve and collect the internal resistance of the material operation and the amount of charge passing through the preset cross-section of the power grid material to be measured within the historical time period to form a historical data set; Perform arithmetic mean processing on the historical data set respectively to obtain the historical internal resistance reference value and the historical charge reference value.

4. The method for collaborative control of power grid safety operation and maintenance based on data integration according to claim 3, characterized in that: Perform a difference operation between the internal resistance of the material operation and the historical internal resistance reference value to obtain the change amount of the internal resistance of the operation; The difference operation is performed between the electric charge quantity passing through the preset cross-section and the historical electric charge quantity reference value to obtain the change value of the electric charge quantity.

5. The method for collaborative control of power grid security operation and maintenance based on data integration according to claim 4, wherein: The change amount of the operating internal resistance and the change value of the electric charge quantity are normalized and then weighted averaged to obtain an evaluation value of the insulation performance degradation; The evaluation value of the insulation performance degradation is compared with the evaluation threshold; If the evaluation value of the insulation performance degradation is less than the evaluation threshold, the classification result of the insulation performance degradation is a low degree of degradation; If the evaluation value of the insulation performance degradation is greater than or equal to the evaluation threshold, the classification result of the insulation performance degradation is a high degree of degradation.

6. The method for collaborative control of power grid security operation and maintenance based on data integration according to claim 5, wherein: When the classification result of the insulation performance degradation is a high degree of degradation, the mean value of the pressure sampling values within the monitoring time interval is calculated to obtain the average air pressure of the sulfur hexafluoride gas.

7. The method for collaborative control of power grid security operation and maintenance based on data integration according to claim 1, wherein: The real-time temperature of the power grid material to be measured is compared with the temperature calibration range, and combined with the comparison between the leakage risk coefficient and the leakage determination threshold, it is determined whether the current power grid material to be measured is affected by sulfur hexafluoride leakage synergistically; If the real-time temperature is within the temperature calibration range and the leakage risk coefficient is greater than or equal to the leakage determination threshold, it is determined that the insulation performance of the power grid material to be measured is affected by sulfur hexafluoride leakage synergistically; Otherwise, it is determined that the insulation performance of the power grid material to be measured is not affected by sulfur hexafluoride leakage.

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