GIS sensor reliability true model test method and system

Abstract

The invention relates to the technical field of sensor reliability testing, in particular to a GIS sensor reliability real-model testing method and system, and the method comprises the steps: carrying out the performance and simulation source quantitative testing of a tested sensor before and after the real-model testing, connecting the tested sensor into a GIS operation simulation environment, and applying stress interference; the method comprises the following steps: carrying out a real-type test on a tested sensor to obtain a real-type test data set of the tested sensor, and evaluating the reliability of the tested sensor according to the performance of the tested sensor before and after the real-type test, the simulation source quantitative test and the real-type test result. According to the method, multiple stages of data of sensor performance change are formed in a mode of taking a real model test on the tested sensor as a main mode and taking a performance test and a simulation source quantitative test as auxiliary modes, so that the long-term reliability of the sensor in a complex operation environment can be evaluated more comprehensively; the problems that in the prior art, due to the fact that effective evaluation on the reliability of the sensor is lacked, the sensor fails to report and misreports are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor reliability testing, and specifically to a method and system for true reliability testing of GIS sensors, and more particularly to a method and system for true long-term operation reliability testing of GIS sensors. Background Art

[0002] Gas-insulated metal-enclosed switchgear (GIS) is a type of switchgear consisting of high-voltage electrical components, including high-voltage conductors, circuit breakers, disconnectors, earthing switches, transformers, lightning arresters, busbars, connectors, and outgoing line terminals. These components are fully or partially enclosed in a metal casing and filled with a certain pressure of SF6 or other insulating gas as the insulating medium. With the development of digital power grids, GIS, a crucial disconnecting device in the power transmission and transformation process, integrates ultra-high frequency partial discharge sensors and SF6 gas status sensors for real-time monitoring of the insulation and dielectric conditions and providing alarms for abnormalities. The ultra-high frequency partial discharge sensor detects partial discharges using ultra-high frequency electromagnetic waves, while the SF6 gas status sensor is a specialized sensor used to monitor and manage the SF6 gas state in high-voltage electrical equipment and assess the gas state. However, due to the lack of relevant testing standards for the reliability of the above-mentioned sensors and insufficient long-term operation test verification, the sensors may have false alarms and missed alarms during long-term operation in complex environments, resulting in insufficient ability to identify early defects of the main equipment and predict fault evolution, making it difficult to truly achieve "observability, measurability, and controllability". There is an urgent need to effectively evaluate the long-term operation reliability of sensors.

[0003] Existing GIS sensor testing and verification mainly involves conducting a full set of type tests on sensor components separately according to relevant standards, and this is separated from the type tests of the primary equipment itself. This fails to effectively test the long-term reliability and effectiveness of the sensors under actual working conditions. The main problems are: First, although various sensors have undergone complete type test verification, their long-term reliability cannot be verified; second, the test environment of the testing laboratory is far from the actual operating conditions, and it is difficult to simulate the fault conditions of primary equipment, making it impossible to verify the accuracy and sensitivity of the sensors in actual operation; third, there is little research on the cumulative impact of the strong electromagnetic environment, long-term periodic vibration and transient impact vibration of substation primary equipment on the performance of sensors; fourth, there is a lack of reliability evaluation methods for long-term operation of sensors.

[0004] Based on the above problems and research status, it is urgent to further study the reliability testing methods of sensors under actual working conditions for long-term operation. Before their large-scale application, the integrated operation scenarios of GIS and sensors should be simulated. Through real-type tests of large-scale and complex systems, their reliability can be verified and hidden defects can be discovered. This will provide empirical evidence for the long-term stable and reliable operation of sensors, and at the same time provide strong guarantees for the construction of smart grid sensor networks. Summary of the Invention

[0005] In order to solve the problem of sensor omission and false alarm caused by the lack of effective evaluation of sensor reliability in the prior art, the present invention provides a GIS sensor reliability true type testing method and system.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: The present invention provides a true type test method for reliability of a GIS sensor, comprising: S1: Perform the first performance test on the sensor under test to obtain the performance data set C1 of the sensor under test before the real test; S2: Use a known partial discharge signal source and a standard gas with known composition to perform quantitative testing on the sensor under test, and obtain a quantitative test result dataset M1 of the sensor under test before the real-type test; S3: Connect the sensor under test to the GIS operation simulation environment, apply stress interference, and perform a real-type test on the sensor under test to obtain a real-type test data set M2 of the sensor under test; S4: using a known partial discharge signal source and a standard gas with known composition to perform a quantitative test on the sensor after the real-type test, to obtain a quantitative test result data set M3 of the sensor after the real-type test; S5: Perform a second performance test on the sensor under test to obtain a performance data set C2 of the sensor under test after the real test; S6: Perform reliability assessment on the sensor under test based on C1, M1, M2, M3 and C2.

[0007] Optionally, the performance data set C1 of the sensor under test before the real-type test includes the test error, electromagnetic compatibility level and environmental adaptability of the sensor under test.

[0008] Optionally, the applied stress interference includes one or more of lightning impulse, operational overvoltage, temperature and humidity, and vibration.

[0009] Optionally, the real-type test data set M2 of the sensor under test includes the stress type of applied stress, the application time of applied stress, the stress magnitude of applied stress and measurement data of the sensor under test.

[0010] Optionally, the specific method of S6 includes M2 horizontal comparison, C1 and C2 performance comparison, M1 and M3 comparison and M2 trend analysis, wherein, M2 Horizontal Comparison: Compare and analyze the values ​​of the tested sensor and the standard test value during the execution of S3, and calculate the difference value ; C1 and C2 performance comparison: Compare the performance of the same sensor before and after the real test, and calculate the performance degradation value of the sensor before and after the real test If the electromagnetic compatibility level or environmental adaptability of the sensor under test does not meet the requirements of the relevant standards, ; Comparison between M1 and M3: Comparison of quantitative test result data sets before and after the real-type test for the same sensor under test. The absolute value of the difference between the quantitative test results before and after the real-type test of the sensor under test and the ratio of the quantitative test results before the real-type test are ; Analysis of missed alarm rate and false alarm rate in M1, M2 and M3: The false alarm rate of the tested sensor during the execution of S1, S2 and S3 is recorded as , the omission rate is recorded as ; M2 trend analysis: Based on the reliability analysis of accelerated degradation theory, the reliability of the sensor under test is calculated by using the accelerated degradation modeling method based on pseudo-failure life and the accelerated degradation modeling method based on quantitative distribution. The smaller value is taken and recorded as .

[0011] Optionally, the false alarm rate is calculated as follows:

[0012] in, is the number of false alarm failure events, The total number of fault events reported for the tested sensor.

[0013] Optionally, the omission rate calculation method is:

[0014] in, is the number of missed fault events, is the total number of failure events.

[0015] Optionally, in S6, the method for performing reliability assessment on the sensor under test is: ,

[0016] in, is the score of the overall reliability evaluation of the tested sensor, is the value obtained during the test. is the weight of the corresponding value in the test process, It is 1,2,3,4,5,6.

[0017] The present invention also provides a GIS sensor reliability real-type test system for the above-mentioned test method, comprising a GIS real-type test platform, a stress application device, a sensor to be tested, a standard sensor, and an acquisition and evaluation module; The GIS real-type test platform is used to simulate the actual operating conditions of the GIS. The tested sensors and standard sensors are used to collect the operating data of the GIS real-type test platform in real time and transmit the data to the collection and evaluation module; the collection and evaluation module is used to obtain the test data of the tested sensors and standard sensors and analyze them; The stress applying device is connected to the GIS real-type test platform and is used to apply stress to the GIS real-type test platform.

[0018] Optionally, a power supply device is further included, and the power supply device forms a primary circuit with the GIS real-type test platform.

[0019] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a reliability test method for a GIS sensor. The method performs quantitative performance and simulation source tests on the tested sensor before and after the real test, connects the tested sensor to a GIS operation simulation environment, and applies stress interference to perform real test on the tested sensor, obtains a real test data set of the tested sensor, and finally, based on the performance and quantitative test before and after the real test and the real test results of the tested sensor, realizes reliability evaluation of the tested sensor. Among them, comparing the performance of the tested sensor before and after the real test can intuitively reflect the performance change of the sensor in the entire test process, help to discover the performance degradation and drift of the sensor in long-term testing or specific environment, and provide basic data for evaluating the performance stability of the sensor at different stages; using a known partial discharge signal source and a standard gas with known composition to quantitatively test the tested sensor, obtain a quantitative test result data set before and after the real test of the tested sensor, can accurately evaluate the response capability of the tested sensor to specific signals and gases, assist in analyzing the difference between the tested sensor before and after the real test, and improve the accuracy and reliability of the reliability evaluation of the tested sensor. By simulating the actual operating environment of GIS, the actual performance of the tested sensors can be more realistically reflected. The reliability of sensors under the combined influence of complex electromagnetic environments, mechanical vibration, temperature changes, and other factors can be evaluated, thus avoiding the disconnect between simple laboratory testing and actual application scenarios. Through comprehensive analysis of multiple data sets, a correlation model between sensor performance changes and reliability can be established, predicting the reliability trends of sensors under different usage time and environmental conditions. This provides a scientific basis for sensor maintenance, replacement, and service life prediction, helping to improve the overall reliability and operational safety of GIS systems. Furthermore, test results can provide feedback for sensor design and manufacturing, helping manufacturers improve sensor structure and process, and enhance sensor reliability and stability.

[0020] The performance data set C1 of the sensor under test before the real-type test includes the test error, electromagnetic compatibility level, and environmental adaptability of the sensor under test. Among them, the test error is a key indicator for measuring the measurement accuracy of the sensor. Acquiring this data before the real-type test can accurately understand the measurement accuracy level of the sensor in the initial state, providing an accurate benchmark for the entire subsequent test process, and facilitating subsequent comparative analysis of whether the accuracy of the sensor changes in different test stages, and the degree of change, so as to more accurately evaluate the impact of the test on the sensor accuracy; GIS systems are usually in a complex electromagnetic environment with various electromagnetic interference sources. Testing the electromagnetic compatibility level of the sensor under test before and after the real-type test can evaluate the sensor in advance. The anti-interference ability of the sensor in this environment helps to predict whether the sensor will have performance abnormalities or failures due to electromagnetic interference in actual operation, so as to take corresponding protective measures in advance, such as adding shielding devices, optimizing grounding design, etc., to ensure that the sensor works stably and reliably in a complex electromagnetic environment; in addition, the GIS system may operate under various environmental conditions, such as different temperatures, humidity, air pressure, etc. Obtaining the environmental adaptability data of the sensor before and after the real test can understand the performance change law of the sensor under different environmental parameters, help predict the performance of the sensor in actual operation under different environmental conditions, and judge in advance whether it can meet the use requirements in a specific environment, providing a basis for the reasonable application of the sensor.

[0021] The stress interference described includes one or more of lightning surges, operational overvoltage, temperature and humidity fluctuations, and vibration. GIS systems encounter various stress interferences during actual operation, such as lightning surges, operational overvoltage, temperature and humidity fluctuations, and vibration. Applying these stress interferences during testing can simulate the actual operating environment of the sensor as realistically as possible, allowing the sensor under test to be tested under conditions close to those of real-world operation. The resulting test data better reflects its performance and reliability in real-world applications, avoiding the disconnect between testing under ideal laboratory conditions and actual usage, and significantly improving the accuracy and reliability of the evaluation results.

[0022] The real-world test dataset M2 for the sensor under test includes the type of applied stress, the duration of the applied stress, the magnitude of the applied stress, and the measured data of the sensor under test. By recording this data, we can clearly understand the mechanisms by which different types of stress affect sensor performance. The principles and extent of the effects of different stress types on the sensor under test vary. When subsequently analyzing changes in sensor performance, we can specifically study the effects of specific stress types and calculate the reliability of the sensor under various stress conditions. This allows us to analyze the sensor's response characteristics under different stress conditions, identify weaknesses and potential issues in sensor performance, and provide guidance for optimal sensor design.

[0023] The specific methods of S6 include M2 ​​horizontal comparison, C1 and C2 performance comparison, M1 and M3 comparison, and M2 trend analysis. Through M2 horizontal comparison, the gap between the actual performance of the sensor in a real-world test environment and the standard requirements can be intuitively understood. The false alarm rate and missed alarm rate of the tested sensor throughout the test process can be recorded, which can quantify the alarm accuracy of the sensor in the actual test environment, comprehensively evaluate the alarm performance of the tested sensor, and provide an important reference for sensor life prediction. C1 and C2 performance comparison, calculating the performance degradation difference of the tested sensor before and after the real-world test, can intuitively reflect the performance changes of the sensor throughout the test process. The performance degradation difference can cover multiple performance indicators, such as measurement accuracy, stability, response time, etc. By comprehensively analyzing the changes in these indicators, the degree of performance degradation of the sensor in the real-world test environment can be fully understood. By comparing M1 and M3, we can determine whether the tested sensor meets the relevant standard requirements before and after the real-type test. We can strictly control the standard compliance of the tested sensor before and after the real-type test, ensure that the sensor meets the specified technical requirements before and after being put into use and after testing, and timely screen out sensors that do not meet the standards. The trend analysis of M2 can more accurately identify possible problems that may arise in the subsequent actual application of the tested sensor and predict the stable operating life of the tested sensor. It can be seen that through the horizontal comparison of M2, the performance comparison of C1 and C2, the comparison of M1 and M3, and the trend analysis of M2, a comprehensive evaluation of the performance and reliability of the tested sensor during the real-type test is achieved from multiple dimensions. This comprehensive evaluation method can more accurately identify problems and potential risks in the tested sensor, and provide a comprehensive decision-making basis for the design, manufacture, selection and use of sensors.

[0024] In S6, the reliability level of the tested sensor is quantified into a specific value through the score of the overall reliability evaluation of the tested sensor. This can intuitively reflect the overall performance of the tested sensor in the real-type test, making it easier for users to make comparisons and decisions. By using the same evaluation method and indicator system to evaluate and compare sensors from different manufacturers and scientific research institutions, it helps promote technical exchanges and cooperation within the sensor industry and promote the continuous development and innovation of sensor technology.

[0025] The present invention also provides a GIS sensor reliability real-life testing system for the above-mentioned testing method, comprising a GIS real-life testing platform, a stress application device, a sensor under test, a standard sensor, and an acquisition and evaluation module. The GIS real-life testing platform is used to simulate the actual operating conditions of a GIS and provide a simulated working test environment for the sensor under test. The stress application device is used to apply stress to the GIS real-life testing platform to test the effects of different stress conditions on the stability of the sensor under test, comprehensively reflecting the effects of various factors on the sensor during GIS operation, and avoiding the problem of large deviations between test results in an idealized experimental environment and actual applications. The standard sensor serves as a reference benchmark with high precision and reliability. By comparing and analyzing the measurement data of the sensor under test with the data of the standard sensor, the measurement precision and accuracy of the sensor under test can be accurately evaluated. The acquisition and evaluation module is responsible for acquiring the test data of the sensor under test and the standard sensor, analyzing and drawing conclusions, and calculating the reliability evaluation score of the sensor, providing a scientific basis for the selection, use, and maintenance of the sensor.

[0026] It also includes a power supply device, which forms a primary circuit with the GIS real-type test platform to provide a stable power supply for the test platform. A stable power supply is the basis for ensuring the normal operation of the test platform and accurate measurement of the sensors. At the same time, the design of the primary circuit is also closer to the actual operation of the GIS, further improving the authenticity and reliability of the test. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The figure is a flow chart of a true type test method for reliability of a GIS sensor according to the present invention.

[0028] Figure 2 This is a structural diagram of a true-type test system for sensor reliability for GIS according to the present invention.

[0029] Figure 3 Schematic diagram of the module structure of the acquisition and evaluation module of the present invention.

[0030] Among them, 1-GIS real-type test platform, 2-stress application device, 3-sensor under test, 4-standard sensor, 5-acquisition and evaluation module, 6-power supply device. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] The present invention will be further described in detail below with reference to specific embodiments, which are intended to explain the present invention rather than to limit it.

[0034] GIS equipment, equipped with ultra-high frequency partial discharge sensors and SF6 gas status sensors, enables real-time monitoring of insulation faults and dielectric conditions, providing alarms for abnormalities. However, insufficient long-term operational stability testing and verification of these sensors in the complex electromagnetic field environments of substations has resulted in false alarms and missed alarms, seriously impacting the reliable operation and intelligent maintenance of power stations. Real-world testing is a comprehensive performance and reliability test of high-voltage electrical equipment conducted on actual equipment or in a realistic operating environment. Its purpose is to ensure the stability and reliability of the equipment under actual operating conditions, prevent potential failures, and ensure the safe operation of the power system.

[0035] Although existing technologies involve the evaluation of sensor reliability, they do not conduct systematic testing of long-term operational reliability, nor do they conduct research and verification on a real-world platform. It can be seen that existing similar technologies focus on reliability assessment methods based on reliability theory to establish assessment models, and there is little description of the relevant technical process of the test. To overcome the problem that the lack of real-world testing methods for GIS sensors leads to frequent false alarms and missed alarms during sensor operation, which makes it impossible to ensure the safe operation of the power system, see Figure 1 The present invention provides a GIS sensor reliability test method, comprising: S1: Perform the first performance test on the sensor under test to obtain the performance data set C1 of the sensor under test before the real test, specifically: According to industry standards, the first performance test is performed on the sensor under test to obtain a performance data set C1 of the sensor under test before the real test. The performance data set C1 of the sensor under test before the real test includes the test error, electromagnetic compatibility level, and environmental adaptability of the sensor under test.

[0036] S2: Use a known partial discharge signal source and a standard gas with known composition to perform quantitative testing on the sensor under test, and obtain a quantitative test result dataset M1 of the sensor under test before the real-type test, specifically: Use a quantitative partial discharge signal source to perform a quantitative test on the UHF partial discharge sensor under test, check the error of the UHF partial discharge sensor under test, and ensure that the response of the sensor under test is correct before the true type test; use a standard gas with known composition to perform a quantitative test on the SF6 gas state sensor to ensure that the response of the sensor under test is correct before the true type test, and record the quantitative test result as M1.

[0037] S3: Connect the sensor under test to the GIS operation simulation environment, apply stress interference, and perform a real-type test on the sensor under test to obtain a real-type test data set M2 of the sensor under test. Specifically: Within the target period (e.g. 100 days), the GIS operating environment is applied to the sensor under test, and multiple stresses (including one or more of lightning impulse, operational overvoltage, temperature and humidity, and vibration) are superimposed on it. The type of applied stress, the time of application of the stress, the magnitude of the applied stress, and the measurement data of the sensor under test are collected and recorded in real time to form a true test data set M2. In the process of applying stress interference, the above-mentioned multiple stresses can be applied alone or in combination, and the stress intensity level can be changed in an increasing or decreasing manner over time to make the acquired data more reliable.

[0038] S4: Using a known partial discharge signal source and a standard gas with known composition, a quantitative test is performed on the sensor after the real-type test to obtain a quantitative test result data set M3 of the sensor after the real-type test, specifically: For the sensor under test after the real-type test, a quantitative partial discharge signal source and a standard gas with known composition are used to verify again whether the test data of the sensor under test in the real-type test is accurate and whether the response is correct. The test results are recorded to form a quantitative test result data set M3.

[0039] S5: Perform a second performance test on the sensor under test to obtain a performance data set C2 of the sensor under test after the real test; perform a second performance test on the sensor under test after the real test according to industry standards to obtain S6: Based on C1, M1, M2, M3 and C2, the reliability of the tested sensor is evaluated. The specific methods include M2 ​​horizontal comparison, C1 and C2 performance comparison, M1 and M3 comparison and M2 trend analysis. M2 Horizontal Comparison: M2 horizontal comparison mainly compares the performance differences between the sensors under test and between the sensors under test and the standard values ​​(standard sensors can be designed) during the real-type test. Since the sensors under test are generally tested in batches in the same test project, the horizontal comparison mainly compares the measurement data, effectiveness, and other performance of sensors from different batches and brands to analyze and identify differences. Based on whether the sensor data meets the normal distribution and variance homogeneity, hypothesis testing methods such as analysis of variance (ANOVA) and Kruskal-Wallis H test can be used to measure whether there are significant differences in the performance of several batches of sensors and calculate the difference value. ; C1 and C2 performance comparison: After the performance test in the laboratory environment, the quantitative degradation of the sensor measurement error, electromagnetic compatibility level, and environmental adaptability is determined. Among them, the electromagnetic compatibility level and environmental adaptability are determined according to the relevant levels passed by the test; the degradation of the measurement error is determined by the ratio of the absolute value of the difference between the two tests and the first performance measurement error. The performance degradation error of the tested sensor before and after the real test is recorded as If the electromagnetic compatibility level or environmental adaptability does not meet the requirements of the relevant standards, ; Analysis of missed alarm rate and false alarm rate in M1, M2 and M3: The false alarm rate of the tested sensor during the execution of S1, S2 and S3 is recorded as , the omission rate is recorded as ; Among them, a false alarm refers to the sensor reporting a fault event when the measured parameters of the measured object are normal, and a missed alarm refers to the sensor refusing to report a fault event when the measured parameters of the measured object are abnormal. Both indicators are important indicators for measuring the reliability of sensors in actual production operations. The calculation method of the false alarm rate is:

[0040] in, is the number of false alarm failure events, The total number of fault events reported for the tested sensor.

[0041] The omission rate calculation method is:

[0042] in, is the number of missed fault events, is the total number of failure events.

[0043] Comparison between M1 and M3: This is a supplement to the performance comparison between C1 and C2. It compares the performance of the same sensor under test in two simulated source quantitative tests before and after the real platform live simulation test. Since the simulated source is close to the actual working conditions and the parameters are known, the change in the sensor measurement error after the real platform test under actual working conditions can be approximated. The change in measurement error is determined by the ratio of the absolute value of the difference between the two tests to the error of the first measurement. The output result is recorded as .

[0044] M2 trend analysis: Its main function is to evaluate the degradation of the performance of the individual sensors under test in the live simulation test on the real platform. Based on the accelerated degradation modeling of pseudo-failure life and the accelerated degradation modeling based on quantitative distribution, the reliability of the sensors under test in the real test is calculated and the minimum reliability value is taken as the Taking the sensor's parameters with degradation characteristics as the degradation quantity, the performance degradation of the sensor is systematically evaluated using accelerated degradation tests and degradation trajectory evaluation theory.

[0045] The method for reliability evaluation of the sensor under test is: ,

[0046] in, is the score of the overall reliability evaluation of the tested sensor, is the value obtained during the test. is the weight of the corresponding value in the test process, It is 1,2,3,4,5,6.

[0047] See also Figure 2 The present invention provides a GIS sensor reliability real-type test system for the above-mentioned test method, comprising a GIS real-type test platform 1, a stress applying device 2, a sensor to be tested 3, a standard sensor 4, an acquisition and evaluation module 5, and a power supply device 6; The GIS real-type test platform 1 is used to simulate the actual operating conditions of the GIS. The tested sensor 3 and the standard sensor 4 are used to collect the operating data of the GIS real-type test platform 1 in real time and transmit the data to the collection and evaluation module 5. The tested sensor 3 is installed on the GIS real-type test platform 1 and mainly includes an SF6 gas sensor and an ultra-high frequency partial discharge sensor. The standard sensor 4 is also installed on the GIS real-type test platform 1. A sensor that has passed the verification and has excellent performance is selected as the standard sensor 4 for data comparison with the tested sensor 3. The stress applying device 2 is connected to the GIS real-type test platform 1. The stress applying device 2 is mainly used to apply stress to the sensor 3 under test. The applied stress includes: impulse voltage, temperature and humidity, vibration, power frequency disturbance, etc. The main equipment includes: AC current boost device, power frequency test transformer, harmonic generator, temperature and humidity chamber, impact hammer, etc.

[0048] The power supply unit 6, which provides long-term energization capability under full current and full voltage conditions, primarily consists of a power-frequency test transformer, an AC current booster, and auxiliary cables. It forms a primary circuit with the GIS real-scale test platform 1. During quantitative testing, the primary circuit is disconnected and a fixed amount of partial discharge signal source and a standard gas of known composition are directly injected into the GIS real-scale test platform 1 to achieve quantitative testing of the sensor under test. During real-scale testing, the primary circuit is connected.

[0049] The GIS prototype test platform 1 is a complete GIS system consisting of incoming and outgoing bushings, arc extinguishing chambers, operating mechanisms, earth switches, disconnect switches, and other components. It is equipped with multiple test chambers that can simulate various partial discharge signals and provide a testing environment for SF6 gas sensors with varying pressures and composition ratios. It can operate at rated voltage, withstand surge voltage interference, and has a reserved physical interface for installing the sensor under test 3.

[0050] The acquisition and evaluation module 5 is used to obtain the test data of the tested sensor 3 and the standard sensor 4 for analysis; it is mainly used to collect the sensor data sent back by the tested sensor 3 and the standard sensor 4, and to judge the performance of the tested product based on the sent back data, and to evaluate the degradation of the tested product. Figure 3 The acquisition and evaluation module specifically includes an acquisition unit, a storage unit, a calculation unit and a reporting unit. The acquisition unit is responsible for collecting the real test data of the tested sensor 3 and the standard sensor 4 from the GIS real test platform 1, as well as the data of two performance tests and quantitative tests (including: M1, M2, M3, C1 and C2) and performing preliminary processing. The storage unit uses a time series database or a relational database to store the processed real test data. The reporting unit fills in the pre-set report template according to the results of the calculation unit and automatically generates a test report.

[0051] The calculation unit includes five processes, namely, M2 horizontal comparison process, C1 and C2 performance comparison process, M1 and M3 comparison process, false alarm rate and missed alarm rate calculation process and M2 trend analysis process.

[0052] The following describes five specific processes: The M2 horizontal comparison process mainly compares the performance differences between the tested sensors 3 and between the tested sensors 3 and the standard sensors 4 during the long-term testing of the GIS real-type test platform 1. Since the tested sensors 3 are generally tested in batches in the same test project, this unit mainly compares the measurement data, effectiveness and other performance of sensors of different batches and brands to analyze and point out the differences. According to whether the sensor data meets the normal distribution and variance homogeneity, hypothesis testing methods such as analysis of variance (ANOVA) and Kruskal-Wallis H test can be used to measure whether there are significant differences in the performance of several batches of sensors. The significant difference is output as the result, recorded as .

[0053] The C1 and C2 performance comparison process is to compare the performance difference of the same sensor before and after testing on the GIS real-type test platform 1. The performance test in the laboratory environment is used to determine the quantitative degradation of the sensor's measurement error, electromagnetic compatibility level, and environmental adaptability. Among them, the electromagnetic compatibility level and environmental adaptability are determined according to the relevant levels passed by the test; the degradation of the measurement error is determined by the ratio of the absolute value of the difference between the two tests to the first measurement error. The degradation of the measurement error is the output result, recorded as If the electromagnetic compatibility level or environmental adaptability does not meet the requirements of the relevant standards, .

[0054] The comparison process between M1 and M3 is a supplement to the performance comparison between C1 and C2. It compares the performance of the same sensor 3 under test in two simulated source quantitative tests before and after the live simulation test on the GIS real test platform 1. Because the simulated source is close to the actual working conditions and the parameters are known, this unit can approximate the change in sensor measurement error after the real platform test under actual working conditions. The change in measurement error is determined by the ratio of the absolute value of the difference between the two tests to the error of the first measurement, and the output result is recorded as .

[0055] The calculation process of false alarm rate and missed alarm rate is to calculate the false alarm rate of the measured sensor 3 ( ) and false negative rate ( A false alarm refers to a sensor reporting a fault event when the measured parameters of the measured object are normal. A missed alarm refers to a sensor refusing to report a fault event when the measured parameters of the measured object are abnormal. Both indicators are important indicators for measuring the reliability of sensors in actual production operations.

[0056] The main function of the M2 trend analysis process is to evaluate the degradation of the performance of the individual sensors under test in the live simulation test on the real platform. The parameters with degradation characteristics of the sensor are taken as the degradation quantity, and the performance degradation of the sensor is systematically evaluated using the accelerated degradation test and degradation trajectory evaluation theory. In particular, the M2 trend analysis unit adopts a reliability analysis based on the accelerated degradation theory, and uses two specific methods: accelerated degradation based on pseudo-failure life and accelerated degradation modeling based on quantitative distribution. The reliability R1(t) and R2(t) of the sensor under test in the real test are calculated respectively, and the smaller value is taken and recorded as .

[0057] The output results of the above modules are , the weight is recorded as , the overall reliability evaluation score is recorded as , the calculation formula is as follows: ,

[0058] in, is the score of the overall reliability evaluation of the tested sensor, is the value obtained during the test. is the weight of the corresponding value in the test process, It is 1,2,3,4,5,6.

[0059] In summary, the present invention provides a method and system for testing the reliability of GIS sensors. By combining performance testing, simulated source quantitative testing, and real-world testing, this method and system generates data on multiple stages of sensor performance changes, enabling statistical reliability analysis based on this data. Based on real-world testing, this method and system simulates the actual operating conditions of GIS (gas-insulated switchgear) sensors during actual operation, including multiple stress factors such as electrical, mechanical, and thermal stresses. Compared to traditional laboratory simulation testing, this method more accurately reflects the actual performance and reliability of sensors under long-term operating conditions, avoiding test result deviations caused by mismatches between test conditions and actual operating conditions. Furthermore, this method considers the multiple stress factors that affect sensor reliability, such as temperature, humidity, electromagnetic interference, and mechanical vibration. By comprehensively simulating the synergistic effects of these factors, a more comprehensive assessment of the long-term reliability of sensors in complex operating environments can be achieved, helping to identify potential failure modes and weaknesses, and providing a more accurate basis for sensor optimization and improvement. The reliability of the sensor is comprehensively evaluated through longitudinal comparison of performance before and after testing, horizontal comparison of multiple sensor samples, analysis of accelerated degradation trends, and sensor false alarm and missed alarm rates, avoiding the deviation caused by a single indicator. This provides an indicator basis for the long-term operational reliability of the sensor and is sufficient to provide comprehensive and detailed reliability data and feedback information for the research and development and production of GIS sensors, helping to ensure the stable operation of GIS equipment and, in turn, enhance the reliability and stability of the entire power system. Based on the test results, R&D personnel can optimize the design, material selection, and manufacturing process of the sensor to improve the quality and reliability of the product. At the same time, it also helps manufacturers develop more reasonable quality control standards and inspection processes to ensure that the product meets actual operational requirements. Before the large-scale application of the sensor, the present invention simulates the actual operating conditions of the GIS and the sensor, combines the accelerated degradation theory, and verifies its reliability through a true-type test with multiple stress superpositions to discover hidden defects, providing empirical evidence for the long-term stable and reliable operation of the sensor, while also providing a strong guarantee for the construction of the smart grid sensor network.

[0060] The above description is merely a preferred embodiment of the present invention and is not intended to impose any limitation on the technical solution of the present invention. Those skilled in the art should understand that, without departing from the spirit and principles of the present invention, the technical solution can also be subjected to several simple modifications and replacements, and these modifications and replacements are also within the scope of protection covered by the claims.

Claims

1. A GIS sensor reliability test method, characterized in that: include: S1: Perform the first performance test on the sensor under test to obtain the performance data set C1 of the sensor under test before the real test; S2: Use a known partial discharge signal source and a standard gas with known composition to perform quantitative testing on the sensor under test, and obtain a quantitative test result dataset M1 of the sensor under test before the real-type test; S3: Connect the sensor under test to the GIS operation simulation environment, apply stress interference, and perform a real-type test on the sensor under test to obtain a real-type test data set M2 of the sensor under test; S4: using a known partial discharge signal source and a standard gas with known composition to perform a quantitative test on the sensor after the real-type test, to obtain a quantitative test result data set M3 of the sensor after the real-type test; S5: Perform a second performance test on the sensor under test to obtain a performance data set C2 of the sensor under test after the real test; S6: Perform reliability assessment on the sensor under test based on C1, M1, M2, M3 and C2.

2. The reliability test method for GIS sensors according to claim 1, characterized in that: The performance data set C1 of the sensor under test before the real-type test includes the test error, electromagnetic compatibility level and environmental adaptability of the sensor under test.

3. The reliability test method for GIS sensors according to claim 1, characterized in that: The stress interference includes one or more of lightning impulse, operational overvoltage, temperature and humidity, and vibration.

4. The reliability test method for GIS sensors according to claim 1, characterized in that: The true type test data set M2 of the sensor under test includes the stress type of applied stress, the application time of applied stress, the stress magnitude of applied stress and the measurement data of the sensor under test.

5. The reliability test method for GIS sensors according to claim 1 is characterized in that: The specific methods of S6 include M2 ​​horizontal comparison, C1 and C2 performance comparison, M1 and M3 comparison and M2 trend analysis, among which, M2 Horizontal Comparison: Compare and analyze the values ​​of the tested sensor and the standard test value during the execution of S3, and calculate the difference value ; C1 and C2 performance comparison: Compare the performance of the same sensor before and after the real test, and calculate the performance degradation value of the sensor before and after the real test If the electromagnetic compatibility level or environmental adaptability of the sensor under test does not meet the requirements of the relevant standards, ; Comparison between M1 and M3: Comparison of quantitative test result data sets before and after the real-type test for the same sensor under test. The absolute value of the difference between the quantitative test results before and after the real-type test of the sensor under test and the ratio of the quantitative test results before the real-type test are ; Analysis of missed alarm rate and false alarm rate in M1, M2 and M3: The false alarm rate of the tested sensor during the execution of S1, S2 and S3 is recorded as , the omission rate is recorded as ; M2 trend analysis: Based on the reliability analysis of accelerated degradation theory, the reliability of the sensor under test is calculated by using the accelerated degradation modeling method based on pseudo-failure life and the accelerated degradation modeling method based on quantitative distribution. The smaller value is taken and recorded as .

6. The GIS sensor reliability true type testing method according to claim 5 is characterized in that: The calculation method of the false alarm rate is: in, is the number of false alarm failure events, The total number of fault events reported for the tested sensor.

7. The reliability test method for GIS sensors according to claim 5, characterized in that: The omission rate calculation method is: in, is the number of missed fault events, is the total number of failure events.

8. The reliability test method for GIS sensors according to claim 5, characterized in that: In S6, the method for reliability evaluation of the sensor under test is: ， in, is the score of the overall reliability evaluation of the tested sensor, is the value obtained during the test. is the weight of the corresponding value in the test process, It is 1,2,3,4,5,6.

9. A GIS sensor reliability test system for use in the test method according to any one of claims 1 to 8, characterized in that: It includes GIS real-type test platform, stress application device, tested sensor, standard sensor and acquisition and evaluation module; The GIS real-type test platform is used to simulate the actual operating conditions of the GIS. The tested sensors and standard sensors are used to collect the operating data of the GIS real-type test platform in real time and transmit the data to the collection and evaluation module; the collection and evaluation module is used to obtain the test data of the tested sensors and standard sensors and analyze them; The stress applying device is connected to the GIS real-type test platform and is used to apply stress to the GIS real-type test platform.

10. The GIS sensor reliability test system according to claim 9, characterized in that: It also includes a power supply device, which forms a primary circuit with the GIS real-type test platform.

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