Power grid safety operation and maintenance cooperative control method based on data integration

Abstract

The invention discloses a power grid safety operation and maintenance cooperative control method based on data integration, relates to the technical field of power system operation and maintenance and intelligent control, and is used for solving the problems that the fault rate and the operation risk coefficient of electrical equipment are remarkably increased due to single sensor monitoring, and the safety and the reliability of power grid operation are reduced. The operation internal resistance of a to-be-detected power grid material and the electric charge quantity passing through a preset section are detected, an insulation performance evaluation function is constructed by combining historical data, and an evaluation value is calculated; when the pressure exceeds the threshold value, collecting the pressure of the sulfur hexafluoride gas, calculating a leakage risk coefficient in combination with a maintenance stagnation idle period, analyzing temperature-concentration ratio fluctuation, setting a temperature proofreading range, and collecting real-time temperature data; and finally, whether cooperative influence exists or not is judged according to whether the leakage risk coefficient and the temperature exceed the boundary, a high-quality or simple processing mode is selected to be executed, dynamic sensing and classified response of the insulation state and the leakage risk are achieved, and operation and maintenance efficiency and power grid safety are improved.

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 collaborative control method for grid security operation and maintenance based on data integration. Background Art

[0002] With the development of smart grids and new power systems, the operating status, insulation performance, 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 existing technologies have the following deficiencies: 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 have potential concealment and suddenness, making it difficult to achieve effective early warning and collaborative control through single sensors or decentralized data systems, resulting in a significant increase in the failure rate and operation risk coefficient of electrical equipment, and reducing the safety and reliability of grid operation. Therefore, a collaborative control method for grid security operation and maintenance based on data integration is proposed.

[0004] 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

[0005] To overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a collaborative control method for grid security operation and maintenance based on data integration, which solves the problems raised in the above background art by using multi-source data fusion analysis, historical trend modeling, dynamic insulation performance evaluation, and sulfur hexafluoride leakage risk linkage judgment mechanism.

[0006] To achieve the above object, the present invention provides the following technical solution, a collaborative control method for grid security operation and maintenance based on data integration, including the following steps: Step S1: Detect the internal resistance of the material operation of the power grid material to be tested, 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 tested; Step S2: Process and analyze the internal resistance of the material operation and the amount of charge of the power grid material to be tested according to the historical data, and classify the degree of insulation performance degradation of the power grid material to be tested. 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; 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 air 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; 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.

[0007] In a preferred embodiment, the ratio of the stable test voltage applied to the current power grid material to be tested and the instantaneous passing current is used as the internal resistance of the material during operation; 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 electric charge passing through the preset cross-section.

[0008] In a preferred embodiment, retrieve and collect the internal resistance of the power grid material to be tested and the electric charge passing through the preset cross-section in 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 electric charge reference value.

[0009] In a preferred embodiment, perform a difference operation between the internal resistance of the material during operation and the historical internal resistance reference value to obtain the change in internal resistance; Perform a difference operation between the electric charge passing through the preset cross-section and the historical electric charge reference value to obtain the change in electric charge.

[0010] In a preferred embodiment, perform normalization processing on the change in internal resistance and the change in electric charge and then perform weighted average to obtain the insulation performance degradation evaluation value; Compare the insulation performance degradation evaluation value with the evaluation threshold; 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.

[0011] 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 in the monitoring time interval to obtain the average air pressure of the sulfur hexafluoride gas.

[0012] In a preferred embodiment, calculate the maintenance delay period by subtracting the most recent maintenance completion date associated with the power grid material to be tested from the current time; The calculation formula for the leakage risk coefficient is ; wherein, is the leakage risk coefficient, γ is the adjustment coefficient, is the reference of the standard operating pressure of sulfur hexafluoride, is the maintenance delay period.

[0013] 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; 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; 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.

[0014] In a preferred embodiment, 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.

[0015] 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; 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.

[0016] The technical effects and advantages of the present invention: 1. The present invention detects the operating internal resistance of the power grid material to be tested 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 pressure of sulfur hexafluoride gas is collected and combined with the maintenance lag period to calculate the sulfur hexafluoride leakage risk coefficient. 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 boundary, it is determined whether the power grid material to be tested is affected by leakage synergy, and the high-quality processing mode or the simple processing mode is respectively executed. This method can realize the dynamic perception and classification response of the insulation state of the power grid material to be tested and the sulfur hexafluoride leakage risk, and improve the operation and maintenance efficiency and power grid security. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 FIG. is a flowchart of the implementation of the power grid security operation and maintenance collaborative control method based on data integration according to the present invention.

[0018] 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

[0019] 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.

[0020] 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: It includes the following steps: 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; 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 pressure of sulfur hexafluoride in the power grid material to be tested; Step S3: Call the maintenance date of the power grid material to be tested to determine the maintenance lag period, calculate the sulfur hexafluoride leakage risk coefficient in combination with the sulfur hexafluoride 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 result; Step S4: Monitor the temperature of the power grid material to be measured in real time, compare it with the calibration range, and use the comparison result and the sulfur hexafluoride leakage risk coefficient as inputs to judge the power grid material to be measured, determine whether the insulation performance of the power grid material to be measured is affected by the synergistic effect caused by sulfur hexafluoride leakage, and select the sulfur hexafluoride leakage treatment mode according to the judgment result.

[0021] The specific implementation is as follows: In step S1, detect the internal resistance of the power grid material to be measured 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 measured 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 the power grid material to be measured , and recording the instantaneous current passing through the power grid material to be measured , and calculating it according to Ohm's law .

[0022] 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 measured within the same time period, with the unit of ampere.

[0023] Statistically, within the monitoring time interval with the set starting point as the current moment, the electric charge passing through the preset cross-section. 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 .

[0024] 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.

[0025] Retrieve and collect the operation data of the power grid material to be measured within 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 within this time period, and form a historical data set: ; Perform arithmetic mean processing on the historical data set respectively to obtain the historical operation internal resistance reference value and the historical electric charge reference value , and the calculation formulas are as follows: ; In step S2, a difference operation is performed between the operating internal resistance of the material obtained in step S1 and the historical operating internal resistance reference value to obtain the change in operating internal resistance. Its calculation formula is , where is the change in operating internal resistance,[[]] is the operating internal resistance of the material,[[]] is the historical operating internal resistance reference value.[[]]

[0026] Similarly, a difference operation is performed between the electric charge passing through the preset cross-section and the historical electric charge reference value to obtain the change in electric charge. The calculation formula is as follows , where is the change in electric charge,[[]] is the electric charge passing through the preset cross-section during the current monitoring period,[[]] is the historical electric charge reference value.[[]]

[0027] After normalizing the above change in operating internal resistance and change in electric charge, an insulation performance degradation evaluation function is constructed. The specific calculation expression is , where D is the insulation performance degradation evaluation value, α and β are the weighting coefficients of the operating internal resistance and electric charge in the insulation performance evaluation respectively, and satisfy α + β = 1, which are preset by the statistical learning model. Both are real numbers between 0 and 1.[[]]

[0028] 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 for the given data, which will not be elaborated here.[[]]

[0029] 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 , 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 , 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.[[]]

[0030] When the insulation performance degradation classification result is a high degree of degradation, the absolute pressure measurement method is used to detect the air pressure of sulfur hexafluoride gas inside the material of the power grid to be measured through the high-precision pressure sensor unit integrated inside the material of the power grid to be measured, and the absolute air pressure value of sulfur hexafluoride gas at the current moment is collected , with the unit of Pascal.[[]]

[0031] 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. The standard state monitoring of gas; 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.

[0032] To enhance data stability and anti-interference ability, the time window averaging mechanism is adopted in the acquisition process to calculate the average value of m pressure sampling values within the monitoring time interval to obtain the average gas pressure of sulfur hexafluoride gas at this stage. 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.

[0033] In step S3, the historical maintenance data of the power grid material to be tested is called to determine the maintenance delay 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 delay period is calculated in combination with the current time. The specific calculation formula is .

[0034] where, is the maintenance delay period, with the unit of 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.

[0035] 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, inspection logs, and historical fault data of various electrical equipment in the power grid, which provides data support for the monitoring and processing of the power grid material to be tested.

[0036] 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 delay period to construct the sulfur hexafluoride leakage risk coefficient , which is used to quantify the possibility and severity of sulfur hexafluoride leakage inside the power grid material to be tested. The calculation formula of the leakage risk coefficient is .

[0037] 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 benchmark provided by the power grid equipment manufacturer, with the unit of Pascal.

[0038] Within the preset analysis time interval n detection time points at the same interval are set. At each time point, the sulfur hexafluoride gas concentration and the internal temperature corresponding to the power grid material to be measured are synchronously collected. Calculate the concentration-temperature ratio at each time point, and the calculation expression is as follows: ; where is the concentration-temperature ratio, is the sulfur hexafluoride gas concentration, is the internal temperature.

[0039] 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 for constructing the calibration range. The calculation formula for the standard deviation is , where is the standard deviation, and is the average value of the temperature, and its calculation formula is .

[0040] Finally, define the temperature calibration range as .

[0041] In step S4, the real-time temperature of the power grid material to be measured is collected through the built-in temperature sensor and compared with the above temperature calibration range .

[0042] 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 measured is in a normal state; If the real-time temperature is not within the temperature calibration range, it is considered that the power grid material to be measured has abnormal temperature fluctuations.

[0043] It should be noted that the built-in temperature sensor refers to the sensing unit embedded inside the power grid material to be measured for real-time detection of its internal environmental temperature.

[0044] Let the real-time temperature be , combined with the sulfur hexafluoride leakage risk coefficient , and introduce the preset leakage determination threshold , to determine whether the current power grid material to be measured is affected by sulfur hexafluoride leakage synergy. The determination conditions are as follows: If it satisfies , it is determined that the insulation performance of the power grid material to be tested has been synergistically affected by the sulfur hexafluoride leakage; Otherwise, it is determined that the insulation performance of the power grid material to be tested is not affected by the sulfur hexafluoride leakage.

[0045] It should be noted that the above leakage determination threshold is obtained by professionals through experiments and will not be elaborated here.

[0046] When it is determined that the insulation performance of the power grid material to be tested is synergistically affected by the sulfur hexafluoride leakage, the 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: Leakage point detection and sealing repair: Through local airtightness testing, locate the sulfur hexafluoride gas leakage site and use special high-pressure sealing materials for on-site repair to ensure that the airtightness of the insulation system is fully restored.

[0047] Gas recovery and supplementation: Use a high-purity sulfur hexafluoride gas recovery device to extract some of the residual gas inside the power grid material to be tested, conduct purity detection and filtration treatment on it, and then re-inject it; and supplement isobaric and high-purity new sulfur hexafluoride gas to the standard operating pressure .

[0048] Gas purification and circulation treatment: Use an external purification device to filter out micro-impurities and remove moisture from the sulfur hexafluoride gas to improve the overall insulation performance.

[0049] It should be noted that local airtightness testing refers to the airtightness inspection of local structural areas in electrical equipment that have potential leakage hazards, using infrared thermal imaging to quickly locate the leakage point and determine the type of sealing defect; special high-pressure sealing materials refer to sealing composite materials with high insulation, high mechanical strength, corrosion resistance to sulfur hexafluoride gas, and excellent heat resistance, which are used for the repair and update of gas sealing structures in high-voltage power equipment; external purification devices refer to professional treatment equipment that is independent of electrical equipment and is connected to the equipment through pipelines or interfaces, used for circulating suction, filtration, dehumidification, and purity recovery of sulfur hexafluoride gas, and is used for the regeneration and purification of aged or contaminated sulfur hexafluoride gas in scenarios with high insulation requirements.

[0050] 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: Sulfur hexafluoride pressure compensation: Directly supplement a small amount of sulfur hexafluoride gas into the equipment to make it return to the standard operating pressure range , where ΔP is the allowable deviation range.

[0051] Off-site monitoring enhances the monitoring of the air pressure and temperature change trends of the power grid materials to be measured during their future operation cycles, and a dynamic alarm mechanism is set up.

[0052] The maintenance record records the current processing process and data into the equipment operation and maintenance database, and no leak point repair is carried out.

[0053] Among them, the high-quality processing mode can completely repair leakage defects, ensure the long-term stable operation of the equipment, and is applicable to core or heavy-load power grid nodes, but it has high operation costs, long processing time, and requires professional equipment and operating personnel; the simple processing mode is fast in operation, low in cost, and can achieve short-term maintenance, and is applicable to secondary or low-load power grid components, but it fails to eliminate potential leakage risks, and long-term operation may cause performance degradation or safety hazards.

[0054] The above formulas are all dimensionless and take their numerical values for calculation. The formula is a formula obtained by software simulation of a large amount of collected data to approximate the real situation as closely as possible. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0055] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. 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 computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0056] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0057] Those of ordinary skill in the art will recognize that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled professionals may use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0058] Those skilled in the art can clearly understand that for the sake of 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.

[0059] In the several embodiments provided in this 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, and there can be other division methods in actual implementation. 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, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0060] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be 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.

[0061] In addition, the functional units in each embodiment of this application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0062] When the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this 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 may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0063] As described above, the above are only specific implementation manners of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A grid security operation and maintenance collaborative control method based on data integration, characterized in that: It includes 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 is 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; 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, determine whether the insulation performance of the power grid material to be measured is affected by the synergistic effect caused by sulfur hexafluoride leakage, and select the sulfur hexafluoride leakage treatment mode according to the judgment result.

2. The collaborative control method for power grid safety operation and maintenance based on data integration according to claim 1, wherein: The ratio of the stable test voltage applied to the current power grid material to be measured and the instantaneous passing current is used as the internal resistance of the material operation; 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 amount of charge passing through the preset cross-section.

3. The collaborative control method for power grid safety operation and maintenance based on data integration according to claim 1, wherein: 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 in 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 collaborative control method for power grid safety operation and maintenance based on data integration according to claim 3, wherein: 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; Perform a difference operation between the amount of charge passing through the preset cross-section and the historical charge reference value to obtain the change value of the amount of charge.

5. The collaborative control method for power grid safety operation and maintenance based on data integration according to claim 4, wherein: Perform normalization processing on the change amount of the internal resistance of the operation and the change value of the amount of charge, and then perform weighted average to obtain the insulation performance degradation evaluation value; Compare the insulation performance degradation evaluation value with the evaluation threshold; 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.

6. The collaborative control method for power grid safety 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, calculate the mean value of the pressure sampling values in the monitoring time interval to obtain the average gas pressure of sulfur hexafluoride gas.

7. The collaborative control method for grid security operation and maintenance based on data integration according to claim 6, characterized in that: Calculate the maintenance lag period by subtracting the completion date of the most recent maintenance associated with the grid material to be tested from the current time; 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 lag period.

8. The collaborative control method for grid security operation and maintenance based on data integration according to claim 1, characterized in that: Calculate the ratio of the sulfur hexafluoride gas concentration to the internal temperature of the grid material to be tested at each time point within a 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.

9. The collaborative control method for grid security operation and maintenance based on data integration according to claim 8, characterized in that: Compare the real-time temperature of the grid material to be tested with the temperature calibration range, and combine the comparison between the leakage risk coefficient and the leakage determination threshold to determine whether the current grid material to be tested is affected by sulfur hexafluoride leakage synergistically; If it satisfies that 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 grid material to be tested is affected by sulfur hexafluoride leakage synergistically; Otherwise, it is determined that the insulation performance of the grid material to be tested is not affected by sulfur hexafluoride leakage.

10. The collaborative control method for grid security operation and maintenance based on data integration according to claim 9, characterized in that: When it is determined that the insulation performance of the grid material to be tested is affected by sulfur hexafluoride leakage synergistically, execute the high-quality processing 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 grid material to be tested is not affected by sulfur hexafluoride leakage, execute the simple processing mode, specifically including sulfur hexafluoride pressure compensation, off-site monitoring enhancement, and maintenance record keeping.

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