A sensor reliability real model test method and system for GIS

Abstract

The application relates to the technical field of sensor reliability test, in particular to a GIS sensor reliability real model test method and system, which performs performance and simulation source quantitative test on a measured sensor before and after real model test, and applies stress interference by connecting the sensor into a GIS operation simulation environment; the measured sensor is subjected to real model test to obtain a real model test data set of the measured sensor; according to the performance and simulation source quantitative test and the real model test result before and after the real model test of the measured sensor, reliability evaluation of the measured sensor is realized. The method mainly takes the real model test of the measured sensor as the main part, and takes performance test and simulation source quantitative test as the auxiliary part, forms multiple stage data of sensor performance change, can more comprehensively evaluate the long-term reliability of the sensor in a complex operation environment, and solves the problems of sensor false negatives and false positives caused by the lack of effective evaluation of sensor reliability in the prior art.

Description

Technical Field

[0001] This invention relates to the field of sensor reliability testing technology, specifically to a true-model testing method and system for the reliability of GIS sensors, and more particularly to a true-model testing method and system for the long-term operational reliability of GIS sensors. Background Technology

[0002] Gas-insulated metal-enclosed switchgear (GIS) is a type of switchgear whose main components, such as high-voltage conductors, circuit breakers, disconnectors, grounding switches, instrument transformers, surge arresters, busbars, connectors, and outgoing terminals, are entirely or partially enclosed in a metal housing and filled with SF6 or other insulating gases at a certain pressure as the insulating medium. With the construction of digital power grids, GIS, as an important switching device in power transmission and transformation, integrates ultra-high frequency partial discharge sensors and SF6 gas state sensors for real-time monitoring and alarm of insulation and insulating medium conditions. The ultra-high frequency partial discharge sensor is a sensor that detects partial discharge based on ultra-high frequency electromagnetic waves, while the SF6 gas state sensor is a dedicated sensor for monitoring and managing the SF6 gas state in high-voltage electrical equipment, used to assess the gas state. However, due to the lack of relevant testing standards for the reliability of the aforementioned sensors and insufficient long-term operational testing and verification, the sensors may experience false alarms and missed alarms during long-term operation in complex environments. This results in insufficient ability to identify early defects in the main equipment and predict the evolution of faults, making it difficult to truly achieve "observable, measurable, and controllable" performance. Therefore, it is urgent to conduct an effective assessment of the long-term operational reliability of the sensors.

[0003] Current testing and verification of GIS sensors mainly involves conducting complete type tests on sensor components separately according to relevant standards, and these tests are separate from the type tests of the primary equipment itself. This fails to effectively test the reliability and effectiveness of the sensors under long-term operation in actual working conditions. The main problems are: First, although various sensors have undergone complete type testing and verification, their long-term operational reliability cannot be verified. Second, the testing environment in the testing laboratory differs significantly from 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 sensors in actual operation. Third, there is limited research on the cumulative impact of the strong electromagnetic environment, long-term periodic vibration, and instantaneous impact vibration of substation primary equipment on sensor performance. Fourth, there are insufficient methods for evaluating the long-term reliability of sensors.

[0004] Based on the above problems and the current research status, there is an urgent need to further study the reliability testing methods for sensors under actual working conditions for long-term operation. Before their large-scale application, we need to simulate the integrated operation scenario of GIS and sensors, verify their reliability through large-scale, complex system real-world testing, discover hidden defects, provide empirical evidence for the long-term stable and reliable grid-connected operation of sensors, and provide strong support for the construction of smart grid sensor networks. Summary of the Invention

[0005] To address the problems of missed and false alarms caused by the lack of effective assessment of sensor reliability in existing technologies, this invention provides a true-model testing method and system for the reliability of GIS sensors.

[0006] To achieve the above objectives, the present invention employs the following technical solution: This invention provides a method for real-world reliability testing of sensors used in GIS, comprising: S1: Perform the first performance test on the sensor under test and obtain the performance dataset C1 of the sensor under test before the real test. S2: Quantitatively test the sensor under test using a known partial discharge signal source and a standard gas with known composition, and obtain the quantitative test result dataset M1 of the sensor under test before the true test. S3: Connect the sensor under test to the GIS operation simulation environment, apply stress interference, and perform a true test on the sensor under test to obtain the true test dataset M2 of the sensor under test. S4: Quantitatively test the sensor under test after the full-scale test using a known partial discharge signal source and a standard gas with known composition, and obtain the quantitative test result dataset M3 of the sensor under test after the full-scale test. S5: Perform a second performance test on the sensor under test to obtain the performance dataset C2 of the sensor under test after the true test. S6: Based on C1, M1, M2, M3 and C2, perform a reliability assessment on the sensor under test.

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

[0008] Optionally, the applied stress disturbance includes one or more of lightning strikes, switching overvoltages, temperature and humidity fluctuations, and vibration.

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

[0010] Optionally, the specific methods of S6 include horizontal comparison of M2, performance comparison of C1 and C2, comparison of M1 and M3, and trend analysis of M2, among which, M2 Lateral Comparison: This involves analyzing the comparison between the measured sensor values ​​and standard test values ​​during the S3 execution process and calculating the differences. ; C1 vs. C2 Performance Comparison: A comparison of the performance of the same sensor before and after full-scale testing is conducted, calculating the performance degradation values ​​of the sensor before and after the full-scale testing. If either the electromagnetic compatibility level or environmental adaptability of the sensor under test does not meet the relevant standard requirements, ; Comparison of M1 and M3: A comparison of quantitative test result datasets before and after full-scale testing of the same sensor. The ratio of the absolute value of the difference between the quantitative test results before and after full-scale testing to the quantitative test result before full-scale testing is... ; Analysis of false alarm and false alarm rates in M1, M2, and M3: The false alarm rate of the measured sensor during the execution of S1, S2, and S3 is denoted as... The false negative rate is denoted as ; M2 Trend Analysis: Reliability analysis based on accelerated degradation theory. This analysis utilizes accelerated degradation modeling methods based on pseudo-failure lifetime and quantization distribution to calculate the reliability of the sensor under test, and then selects the smaller value as . .

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

[0012] in, This represents the number of false alarm fault events. Report the total number of fault events for the sensor under test.

[0013] Optionally, the method for calculating the false negative rate is as follows:

[0014] in, The number of unreported fault events, This represents the total number of failure events.

[0015] Optionally, in S6, the method for reliability assessment of the sensor under test is as follows: ,

[0016] in, This is the score for evaluating the overall reliability of the sensor under test. These are the values ​​obtained during the testing process. This represents the weight of the corresponding value during the testing process. The numbers are 1, 2, 3, 4, 5, 6.

[0017] The present invention also provides a GIS sensor reliability simulation test system for the above-mentioned test method, including a GIS simulation test platform, a stress application device, a sensor under test, a standard sensor, and a data acquisition and evaluation module; The GIS full-scale testing platform is used to simulate the actual operating conditions of GIS. The sensor under test and the standard sensor are used to collect the operating data of the GIS full-scale testing platform in real time and transmit the data to the acquisition and evaluation module. The acquisition and evaluation module is used to acquire and analyze the test data of the sensor under test and the standard sensor. The stress application device is connected to the GIS full-scale test platform and is used to apply stress to the GIS full-scale test platform.

[0018] Optionally, it also includes a power supply device, which forms a primary circuit with the GIS full-scale test platform.

[0019] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a method for full-scale reliability testing of GIS sensors. This method involves quantitative testing of the sensor's performance and simulated source before and after full-scale testing. The sensor is connected to a GIS operating simulation environment, and stress interference is applied to perform full-scale testing, obtaining a full-scale test dataset. Finally, based on the performance and quantitative test results before and after full-scale testing, a reliability assessment of the sensor is achieved. Comparing the sensor's performance before and after full-scale testing provides a clear picture of performance changes throughout the testing process, helping to identify performance degradation and drift issues during long-term testing or under specific environments. This provides fundamental data for evaluating the sensor's performance stability at different stages. Quantitative testing of the sensor using known partial discharge signal sources and standard gases with known compositions yields a dataset of quantitative test results before and after full-scale testing. This allows for accurate assessment of the sensor's response to specific signals and gases, aiding in the analysis of differences before and after full-scale testing, and improving the accuracy and reliability of the sensor's reliability assessment. By simulating the actual operating environment of GIS systems, the performance of the tested sensors can be more realistically reflected in actual work. This allows for the evaluation of sensor reliability under the combined effects of complex electromagnetic environments, mechanical vibrations, temperature changes, and other factors, avoiding the disconnect between simple laboratory testing and real-world application scenarios. Through comprehensive analysis of multiple datasets, a correlation model between sensor performance changes and reliability can be established, predicting the reliability trends of sensors under different usage times and environmental conditions. This provides a scientific basis for sensor maintenance, replacement, and lifespan prediction, contributing to improved overall reliability and operational safety of GIS systems. Furthermore, the test results can provide feedback for sensor design and manufacturing, helping manufacturers improve sensor structure and processes, thereby enhancing sensor reliability and stability.

[0020] The performance dataset C1 of the sensor under test before full-scale testing includes the sensor's test error, electromagnetic compatibility level, and environmental adaptability. Test error is a key indicator for measuring sensor accuracy. Acquiring this data before full-scale testing allows for an accurate understanding of the sensor's initial measurement accuracy level, providing a precise benchmark for the entire testing process. This facilitates subsequent comparative analysis of whether and to what extent the sensor's accuracy changes at different testing stages, thus enabling a more accurate assessment of the test's impact on sensor accuracy. GIS systems typically operate in complex electromagnetic environments with various electromagnetic interference sources. Testing the electromagnetic compatibility level of the sensor before and after full-scale testing allows for advance assessment of the sensor's performance in various environments. The sensor's anti-interference capability in this environment helps predict whether it will experience performance abnormalities or malfunctions due to electromagnetic interference during actual operation. This allows for the implementation of corresponding protective measures in advance, such as adding shielding devices and optimizing grounding design, to ensure the sensor operates stably and reliably in complex electromagnetic environments. Furthermore, GIS systems may operate under various environmental conditions, such as different temperatures, humidity levels, and air pressures. Obtaining the sensor's environmental adaptability data before and after real-world testing allows for an understanding of the sensor's performance changes under different environmental parameters. This helps predict the sensor's performance under different environmental conditions during actual operation, enabling an early assessment of whether it can meet the usage requirements of specific environments and providing a basis for the rational application of the sensor.

[0021] The applied stress interference includes one or more of lightning strikes, operational overvoltages, temperature and humidity fluctuations, and vibrations. GIS systems encounter various stress interferences during actual operation, such as lightning strikes, operational overvoltages, temperature and humidity changes, and vibrations. Applying these stress interferences during testing can simulate the actual working environment of the sensors as realistically as possible, subjecting the sensors under test to near-realistic operating conditions. The resulting test data better reflects their performance and reliability in practical applications, avoiding the disconnect between testing under ideal laboratory conditions and actual usage, and greatly improving the accuracy and reliability of the evaluation results.

[0022] The full-scale test dataset M2 of the sensor under test includes the type of applied stress, the application time of the applied stress, the magnitude of the applied stress, and the measurement data of the sensor under test. By recording this data, the influence mechanism of different types of stress on sensor performance can be clearly understood. Different stress types have different mechanisms of action and degrees of influence on the sensor under test. In subsequent analysis of sensor performance changes, the effects of specific stress types can be studied in a targeted manner, the reliability of the sensor under test under various stress conditions can be calculated, and the response characteristics of the sensor under different stress conditions can be analyzed. This allows for the identification of weak links and potential problems in sensor performance, providing direction for the optimized design of the sensor.

[0023] The specific methods of S6 include M2 ​​horizontal comparison, C1 and C2 performance comparison, M1 and M3 comparison, and M2 trend analysis. Through the M2 horizontal comparison, the gap between the sensor's actual performance in a real-world testing environment and standard requirements can be intuitively understood. Recording the false alarm rate and missed alarm rate of the sensor under test throughout the entire testing process allows for the quantification of the sensor's alarm accuracy in the actual testing environment, providing a comprehensive evaluation of the sensor's alarm performance and offering important reference for sensor lifespan prediction. The C1 and C2 performance comparison calculates the performance degradation difference of the sensor before and after real-world testing, intuitively reflecting the sensor's performance changes throughout the testing process. The performance degradation difference can cover multiple performance indicators, such as measurement accuracy, stability, and response time. By comprehensively analyzing the changes in these indicators, the degree of performance degradation of the sensor under a real-world testing environment can be fully understood. The comparison between M1 and M3 can determine whether the sensor under test meets the relevant standard requirements before and after the full-scale test. It can strictly control the standard compliance of the sensor before and after the full-scale test, ensuring that the sensor meets the specified technical requirements before and after use. It can also promptly screen out sensors that do not meet the standards. The trend analysis of M2 can more accurately identify potential problems of the sensor under test in subsequent actual application and predict the stable operating life of the sensor under test. 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 sensor under test in the full-scale test process is achieved from multiple dimensions. This comprehensive evaluation method can more accurately identify the problems and potential risks of the sensor under test, and provide a comprehensive decision-making basis for the design, manufacturing, selection and use of the sensor.

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

[0025] This invention also provides a GIS sensor reliability simulation testing system for the above-mentioned testing methods, comprising a GIS simulation testing platform, a stress application device, a sensor under test, a standard sensor, and a data acquisition and evaluation module. The GIS simulation testing platform simulates real GIS operating conditions, providing a simulated testing environment for the sensor under test. The stress application device applies stress to the GIS simulation testing platform to test the impact of different stress conditions on the stability of the sensor under test, comprehensively reflecting the influence of various factors on the sensor during GIS operation, and avoiding the problem of significant deviations between test results under idealized experimental environments and actual applications. The standard sensor serves as a reference benchmark, possessing high accuracy and reliability. By comparing and analyzing the measurement data of the sensor under test with that of the standard sensor, the measurement accuracy and precision of the sensor under test can be accurately evaluated. The data acquisition and evaluation module acquires the test data from the sensor under test and the standard sensor, analyzes it to draw conclusions, and calculates the sensor's reliability evaluation score, providing a scientific basis for sensor selection, use, and maintenance.

[0026] It also includes a power supply unit, which forms a primary loop with the GIS full-scale test platform to provide a stable power supply to the test platform. A stable power supply is the foundation for ensuring the normal operation of the test platform and the accurate measurement of the sensors. At the same time, the design of the primary loop is closer to the actual operation of GIS, further improving the authenticity and reliability of the test. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of a real-world reliability testing method for sensors used in GIS according to the present invention.

[0028] Figure 2 This is a structural diagram of a real-world reliability testing system for GIS sensors according to the present invention.

[0029] Figure 3 This is a schematic diagram of the module structure of the data acquisition and evaluation module of the present invention.

[0030] Among them, 1-GIS full-scale testing platform, 2-stress application device, 3-sensor under test, 4-standard sensor, 5-acquisition and evaluation module, and 6-power supply device. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] The present invention will be further described in detail below with reference to specific embodiments. These descriptions are for explanation purposes only and are not intended to limit the scope of the invention.

[0034] GIS equipment, equipped with UHF partial discharge sensors and SF6 gas state sensors, enables real-time monitoring and alarm of insulation faults and insulation medium conditions. However, insufficient long-term stability testing and verification of sensors in the complex electromagnetic environment of substations has led to false alarms and missed alarms, severely impacting the reliable operation and intelligent maintenance of substations. Real-world testing refers to comprehensive performance and reliability testing of high-voltage electrical equipment in actual equipment or real operating environments. Its purpose is to ensure the stability and reliability of equipment under actual operating conditions, prevent potential faults, and guarantee the safe operation of the power system.

[0035] While existing technologies involve sensor reliability assessment, they lack systematic testing for long-term operational reliability and have not been validated on a full-scale platform. It is evident that current technologies primarily rely on reliability theory to establish assessment models, with limited description of the testing process. To overcome the problem of frequent false alarms and missed alarms during sensor operation due to the lack of full-scale testing methods for GIS sensors, which fails to guarantee the safe operation of the power system, see [reference needed]. Figure 1 This invention provides a method for real-world reliability testing of sensors used in GIS, comprising: S1: Perform the first performance test on the sensor under test and obtain the performance dataset C1 of the sensor under test before the full-scale test, specifically: According to industry standards, the sensor under test is subjected to the first performance test to obtain the performance dataset C1 of the sensor under test before the real test. The performance dataset 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: Quantitative testing of the sensor under test is performed using a known partial discharge signal source and a standard gas with known composition to obtain the quantitative test result dataset M1 of the sensor under test before the full-scale test, specifically: A quantitative partial discharge signal source was used to perform quantitative testing on the UHF partial discharge sensor under test to check the error of the UHF partial discharge sensor under test, so as to ensure that the response of the sensor under test is correct before the full-scale test. A standard gas with known composition was used to perform quantitative testing on the SF6 gas state sensor to ensure that the response of the sensor under test is correct before the full-scale test. The quantitative test result was recorded as M1.

[0037] S3: Connect the sensor under test to the GIS simulation environment, apply stress interference, and perform a full-scale test on the sensor under test to obtain the full-scale test dataset M2 of the sensor under test, specifically: Within the target period (e.g., 100 days), the sensor under test is subjected to a GIS operating environment and subjected to multiple stresses (including one or more of lightning strikes, operational overvoltages, temperature and humidity, and vibration). The stress type, stress application time, stress magnitude, and sensor measurement data are collected and recorded in real time to form a true-type test dataset M2. During the stress interference process, the above-mentioned stresses can be applied individually or in combination, and the stress intensity level can be changed in a time-incrementing or decrementing manner to make the acquired data more reliable.

[0038] S4: Quantitative testing is performed on the sensor under test after the full-scale test using a known partial discharge signal source and a standard gas with known composition. The resulting quantitative test result dataset M3 is as follows: After the full-scale test, the accuracy of the test data and the correctness of the response of the sensor under test in the full-scale test are verified again by using a quantitative partial discharge signal source and a standard gas with known composition. The test results are recorded and a quantitative test result dataset M3 is formed.

[0039] S5: Perform a second performance test on the sensor under test to obtain the performance dataset C2 of the sensor under test after the full-scale test; according to industry standards, perform a second performance test on the sensor under test after the full-scale test to obtain... S6: Based on C1, M1, M2, M3, and C2, conduct a reliability assessment of the sensor under test. Specific methods include cross-sectional comparison of M2, performance comparison between C1 and C2, comparison between M1 and M3, and trend analysis of M2. M2 Horizontal Comparison: The M2 horizontal comparison primarily compares the performance differences between various tested sensors and between the tested sensors and standard values ​​(which can be achieved using a designed standard sensor) during full-scale testing. Since tested sensors are typically tested in batches within the same test project, the horizontal comparison mainly analyzes the measurement data and effectiveness of different batches and brands of sensors, identifying differences. Depending on whether the sensor data conforms to a normal distribution and homogeneity of variance, hypothesis testing methods such as Analysis of Variance (ANOVA) and the Kruskal-Wallis H test can be used to assess whether there are significant differences in the performance of several batches of sensors and calculate the numerical differences. ; C1 vs. C2 Performance Comparison: Performance testing in a laboratory environment was used to determine the quantitative degradation of sensor measurement error, electromagnetic compatibility (EMC) level, and environmental adaptability. EMC level and environmental adaptability were determined according to the relevant passing levels. Measurement error degradation was determined by the ratio of the absolute value of the difference between two tests to the error of the first performance measurement. The performance degradation error of the sensor before and after the actual test is denoted as... If either the electromagnetic compatibility level or environmental adaptability does not meet the relevant standard requirements, ; Analysis of false alarm and false alarm rates in M1, M2, and M3: The false alarm rate of the measured sensor during the execution of S1, S2, and S3 is denoted as... The false negative rate is denoted as False alarm refers to a sensor reporting a fault event when the measured parameter of the object being measured is normal, while false negative refers to a sensor refusing to report a fault event when the measured parameter of the object being measured is abnormal. Both indicators are important metrics for measuring the reliability of sensors in actual production operations. The false alarm rate is calculated as follows:

[0040] in, This represents the number of false alarm fault events. Report the total number of fault events for the sensor under test.

[0041] The method for calculating the false negative rate is as follows:

[0042] in, The number of unreported fault events, This represents the total number of failure events.

[0043] The comparison between M1 and M3 supplements the performance comparison between C1 and C2. It compares the performance of the same sensor under test in two quantitative tests using a simulated source before and after a live-line simulation test on a real-world platform. Since the simulated source closely approximates actual operating conditions and its parameters are known, the change in sensor measurement error after the live-line test under actual operating 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 denoted as... .

[0044] M2 trend analysis: Its main function is to evaluate the performance degradation of the individual sensor under test in live-line simulated testing. Based on accelerated degradation modeling of pseudo-failure lifetime and accelerated degradation modeling based on quantization distribution, it calculates the reliability of the sensor under test in live-line testing and takes the value with the minimum reliability as the reliability. The degradation parameters of the sensor are taken as degradation values, and the performance degradation of the sensor is systematically evaluated using accelerated degradation tests and degradation trajectory evaluation theory.

[0045] The method for reliability assessment of the sensor under test is as follows: ,

[0046] in, This is the score for the overall reliability evaluation of the sensor under test. These are the values ​​obtained during the testing process. This represents the weight of the corresponding value during the testing process. The numbers are 1, 2, 3, 4, 5, 6.

[0047] See Figure 2 The present invention provides a GIS sensor reliability model test system for the above-mentioned test method, including a GIS model test platform 1, a stress application device 2, a sensor under test 3, a standard sensor 4, an acquisition and evaluation module 5, and a power supply device 6. The GIS full-scale test platform 1 is used to simulate the actual operating conditions of GIS. The sensor under test 3 and the standard sensor 4 are used to collect the operating data of the GIS full-scale test platform 1 in real time and transmit the data to the acquisition and evaluation module 5. The sensor under test 3 is installed on the GIS full-scale 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 full-scale test platform 1. A sensor that has been calibrated and performs well is selected as the standard sensor 4 and is used to compare the data with the sensor under test 3. The stress application device 2 is connected to the GIS full-scale test platform 1. The stress application device 2 is mainly used to apply stress to the sensor 3 under test. The applied stress includes: impact voltage, temperature and humidity, vibration, power frequency disturbance, etc. The main equipment includes: AC current booster device, power frequency test transformer, harmonic generator, temperature and humidity chamber, impact hammer, etc.

[0048] The power supply unit 6 provides long-term energization capability under full current and full voltage conditions. It mainly consists of a power frequency test transformer, an AC current booster device, and auxiliary cables, forming a primary circuit with the GIS full-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 full-scale test platform 1 to achieve quantitative testing of the sensor under test; during full-scale testing, the primary circuit is connected.

[0049] The GIS full-scale testing platform 1 is a complete GIS device including inlet and outlet bushings, arc-extinguishing chambers, operating mechanisms, grounding switches, and disconnect switches. This device is equipped with multiple test chambers that can simulate various partial discharge signals and provide SF6 gas sensor testing environments with different pressures and component ratios. It can operate at rated voltage, withstand impulse 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 acquire and analyze the test data of the tested sensor 3 and the standard sensor 4; it is mainly used to acquire the sensing data returned by the tested sensor 3 and the standard sensor 4, evaluate the performance of the test sample based on the returned data, and evaluate the degradation of the test sample. See [link to module 5]. Figure 3 The acquisition and evaluation module specifically includes an acquisition unit, a storage unit, a computing unit, and a reporting unit. The acquisition unit is responsible for acquiring real-world test data from the tested sensor 3 and the standard sensor 4 from the GIS real-world test platform 1, as well as data from 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-world test data. The reporting unit fills in the pre-set report template based on the results of the computing unit and automatically generates a test report.

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

[0052] The following describes the five specific processes: The M2 horizontal comparison process primarily compares the performance differences between the tested sensors 3 and between the tested sensors 3 and the standard sensor 4 during long-term testing on the GIS full-scale test platform 1. Since the tested sensors 3 are generally tested in batches within the same test project, this unit mainly analyzes the measurement data and effectiveness of different batches and brands of sensors, pointing out the differences. Based on whether the sensor data satisfies normal distribution and homogeneity of variance, hypothesis testing methods such as analysis of variance (ANOVA) and the Kruskal-Wallis H test can be used to measure whether there are significant differences in the performance of several batches of sensors. Significant differences are recorded as output results. .

[0053] The performance comparison between C1 and C2 involves comparing the performance differences of the same sensor before and after testing on the GIS full-scale test platform 1. Performance testing in a laboratory environment determines the quantitative degradation of the sensor's measurement error, electromagnetic compatibility level, and environmental adaptability. The electromagnetic compatibility level and environmental adaptability are determined according to the relevant passing levels; the degradation of 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 degradation of measurement error is recorded as the output result. If either the electromagnetic compatibility level or environmental adaptability does not meet the relevant standard requirements, .

[0054] The comparison between M1 and M3 supplements the performance comparison between C1 and C2, comparing the performance of the same sensor 3 under test in two simulated source quantitative tests before and after the live-line simulation test on the GIS full-scale test platform 1. Since the simulated source closely approximates actual operating conditions and its parameters are known, this unit can approximately obtain the change in sensor measurement error after the live-line test under actual operating 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 denoted as... .

[0055] The calculation process for false alarm rate and missed alarm rate involves calculating the false alarm rate of the sensor under test 3. ) and underreporting rate ( False alarms refer to the sensor reporting a fault event when the measured parameters of the object being measured are normal, while false alarms refer to the sensor refusing to report a fault event when the measured parameters of the object being measured are abnormal. Both indicators are important metrics for measuring the reliability of sensors in actual production operations.

[0056] The primary function of the M2 trend analysis process is to assess the performance degradation of the individual sensor under test (SUT) during live-line simulated testing on a full-scale platform. Degradation parameters of the sensor are taken as degradation parameters, and accelerated degradation testing and degradation trajectory evaluation theory are used to systematically evaluate the sensor's performance degradation. Specifically, the M2 trend analysis unit employs reliability analysis based on accelerated degradation theory, using two specific methods: accelerated degradation based on pseudo-failure lifetime and accelerated degradation modeling based on quantization distribution. These methods calculate the reliability R1(t) and R2(t) of the SUT during live-line testing, and the smaller value is denoted as R2(t). .

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

[0058] in, This is the score for the overall reliability evaluation of the sensor under test. These are the values ​​obtained during the testing process. This represents the weight of the corresponding value during the testing process. The numbers are 1, 2, 3, 4, 5, 6.

[0059] In summary, this invention provides a method and system for full-scale reliability testing of GIS sensors. By combining performance testing, simulated source quantitative testing, and full-scale testing, it generates data on sensor performance changes across multiple stages, enabling data-driven reliability statistical analysis. This method and system, based on full-scale testing, can simulate the real-world operating conditions of GIS (Gas Insulated Switchgear) sensors, including various stress factors such as electrical, mechanical, and thermal stresses. Compared to traditional laboratory simulation testing, it more accurately reflects the actual performance and reliability of sensors under long-term operating conditions, avoiding test result deviations caused by discrepancies between test conditions and actual operating conditions. Furthermore, this method considers multiple factors that superimpose stresses on sensor reliability, such as temperature, humidity, electromagnetic interference, and mechanical vibration. By comprehensively simulating the synergistic effects of these factors, it can more comprehensively evaluate the long-term reliability of sensors under complex operating environments, helping to identify potential failure modes and weaknesses, and providing a more accurate basis for sensor optimization design and improvement. This invention comprehensively evaluates the reliability of sensors by comparing their performance before and after testing longitudinally, comparing multiple sensor samples laterally, analyzing accelerated degradation trends, and considering false alarm and false alarm rates. This avoids biases caused by single indicators and provides a basis for long-term sensor reliability. It offers comprehensive and detailed reliability data and feedback for the research and development and production of GIS sensors, helping to ensure the stable operation of GIS equipment and thus improving the reliability and stability of the entire power system. Researchers can optimize sensor design, material selection, and manufacturing processes based on test results, improving product quality and reliability. Simultaneously, it helps manufacturers develop more reasonable quality control standards and inspection procedures to ensure products meet actual operational requirements. Before large-scale sensor application, this invention simulates the actual operating conditions of GIS and sensors, combining accelerated degradation theory with multi-stress superposition simulation tests to verify reliability, discover hidden defects, and provide empirical evidence for long-term stable and reliable grid-connected operation of sensors. It also provides strong support for the construction of smart grid sensor networks.

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

Claims

1. A method for real-world reliability testing of sensors used in GIS, characterized in that, include: S1: Perform the first performance test on the sensor under test and obtain the performance dataset C1 of the sensor under test before the real test. S2: Quantitatively test the sensor under test using a known partial discharge signal source and a standard gas with known composition, and obtain the quantitative test result dataset M1 of the sensor under test before the true test. S3: Connect the sensor under test to the GIS operation simulation environment, apply stress interference, and perform a true test on the sensor under test to obtain the true test dataset M2 of the sensor under test. S4: Quantitatively test the sensor under test after the full-scale test using a known partial discharge signal source and a standard gas with known composition, and obtain the quantitative test result dataset M3 of the sensor under test after the full-scale test. S5: Perform a second performance test on the sensor under test to obtain the performance dataset C2 of the sensor under test after the true test. S6: Based on C1, M1, M2, M3, and C2, perform a reliability assessment of the sensor under test, specifically including a lateral comparison of M2, a performance comparison between C1 and C2, a comparison between M1 and M3, and a trend analysis of M2. M2 Lateral Comparison: This involves analyzing the comparison between the measured sensor values ​​and standard test values ​​during the S3 execution process and calculating the differences. ; C1 vs. C2 Performance Comparison: A comparison of the performance of the same sensor before and after full-scale testing is conducted, calculating the performance degradation values ​​of the sensor before and after the full-scale testing. If either the electromagnetic compatibility level or environmental adaptability of the sensor under test does not meet the relevant standard requirements, ; Comparison of M1 and M3: A comparison of quantitative test result datasets before and after full-scale testing of the same sensor under test. The ratio of the absolute value of the difference between the quantitative test results before and after full-scale testing to the quantitative test result before full-scale testing is... ; Analysis of false alarm and false alarm rates in M1, M2, and M3: The false alarm rate of the measured sensor during the execution of S1, S2, and S3 is denoted as... The false negative rate is denoted as ; M2 Trend Analysis: Reliability analysis based on accelerated degradation theory. This analysis utilizes accelerated degradation modeling methods based on pseudo-failure lifetime and quantization distribution to calculate the reliability of the sensor under test, and then selects the smaller value as . ; The method for reliability assessment of the sensor under test is as follows: ， in, This is the score for the overall reliability evaluation of the sensor under test. These are the values ​​obtained during the testing process. This represents the weight of the corresponding value during the testing process. The numbers are 1, 2, 3, 4, 5, 6.

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

3. The method for real-world reliability testing of GIS sensors according to claim 1, characterized in that, The applied stress disturbance includes one or more of lightning strikes, switching overvoltages, temperature and humidity fluctuations, and vibration.

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

5. The method for real-world reliability testing of GIS sensors according to claim 1, characterized in that, The false alarm rate is calculated as follows: in, This represents the number of false alarm fault events. Report the total number of fault events for the sensor under test.

6. The method for real-world reliability testing of GIS sensors according to claim 1, characterized in that, The method for calculating the false negative rate is as follows: in, The number of unreported fault events, This represents the total number of failure events.

7. A GIS sensor reliability simulation test system for the test method described in any one of claims 1-6, characterized in that, It includes a GIS full-scale testing platform, stress application device, sensor under test, standard sensor, and data acquisition and evaluation module; The GIS full-scale testing platform is used to simulate the actual operating conditions of GIS. The sensor under test and the standard sensor are used to collect the operating data of the GIS full-scale testing platform in real time and transmit the data to the acquisition and evaluation module. The acquisition and evaluation module is used to acquire the test data of the sensor under test and the standard sensor and analyze it. The stress application device is connected to the GIS full-scale test platform and is used to apply stress to the GIS full-scale test platform.

8. The GIS sensor reliability simulation test system according to claim 7, characterized in that, It also includes a power supply unit, which forms a primary circuit with the GIS full-scale test platform.

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