Method, device and equipment for testing stability of power frequency electric field sensor and medium

Abstract

The embodiment of the invention discloses a stability test method, device and equipment for a power frequency electric field sensor and a medium, and the method comprises the steps: placing the sensor in a power frequency electric field generator, working in a power frequency electric field, and measuring the initial gain in real time; putting the sensor into an aging oven, starting a high-temperature aging oven and an ultraviolet radiation source, and starting a high-temperature and ultraviolet combined degradation process; performing real-time monitoring in the combined degradation process to obtain a current gain; respectively determining a gain change rate and a gain change rate of a current gain based on the monitoring data; and performing adaptive adjustment on a test parameter based on the gain change rate and the gain change rate of the current gain so as to maximize the gain drop rate, and stopping the test until the gain change rate reaches a preset threshold value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power frequency electric field sensor testing, and in particular to a stability testing method, device, equipment and medium for a power frequency electric field sensor. BACKGROUND

[0002] In the field of intelligent monitoring and fault diagnosis of power systems, power frequency electric field sensors, as a kind of core equipment, are widely used in key scenarios such as partial discharge monitoring and electric field strength measurement to improve the reliability and efficiency of power grid operation. In recent years, graphene nanocomposite sensors have become an important development direction in this field due to their high sensitivity, fast response characteristics, and excellent electrical properties such as high carrier mobility and low noise level, significantly improving monitoring accuracy and real-time performance. However, such sensors are susceptible to the synergistic effects of various environmental factors (such as temperature fluctuations and ultraviolet radiation) during long-term operation, especially in complex outdoor environments, leading to gradual performance degradation, including output gain attenuation, interface stability decline, and signal-to-noise ratio deterioration. These defects not only affect the measurement accuracy of the sensor, but also may cause system false alarms or monitoring failures, thereby threatening the safety of power infrastructure. Therefore, developing an efficient stability testing method to accurately evaluate the durability and performance evolution of sensors under real working conditions has become an urgent need in the current technical field.

[0003] Existing stability testing methods have fundamental limitations in simulating real complex environments, mainly manifested in single test conditions, rigid parameters, and insufficient control mechanisms. Single environmental aging tests usually only focus on individual factors (such as high temperature or ultraviolet radiation) for independent accelerated experiments, which cannot effectively simulate the synergistic effects of multiple environmental factors in actual working conditions, such as the coupling of high temperature and ultraviolet radiation or dynamic temperature and humidity cycles. This leads to significant deviations between test results and actual applications, making it difficult to reliably predict the long-term performance evolution of sensors. Fixed parameter tests rely on preset constant conditions (such as fixed temperature or ultraviolet intensity), with parameters remaining unchanged throughout the test period. This lack of adaptability to time-varying environmental factors fails to reflect the fluctuation characteristics in the real world (such as diurnal temperature difference or seasonal changes), thereby weakening the accuracy and universality of the test. In addition, existing testing procedures are mostly based on simple control units and data acquisition methods, which cannot achieve multi-variable synergistic optimization and real-time adjustment, such as dynamically coordinating temperature and ultraviolet intensity to simulate complex interactions. This not only prolongs the test period, but also may introduce errors due to human intervention or fixed parameters, ultimately affecting the efficiency and reliability of the test results. Overall, existing methods have obvious shortcomings in simulating multi-factor interactions, achieving dynamic parameter control, and optimizing test speed, limiting the scientificity and practicality of sensor stability evaluation. SUMMARY

[0004] Based on this, it is necessary to propose a stability testing method, device, equipment, and medium for power frequency electric field sensors to address the above problems.

[0005] A stability testing method for a power frequency electric field sensor, the method comprising: The sensor is placed in a power frequency electric field generator, operates under a power frequency electric field, and the initial gain is measured in real time. The sensor is installed in the aging chamber, the high-temperature aging chamber and the ultraviolet radiation source are turned on, and the high-temperature and ultraviolet combined degradation process is started. The current gain is obtained by real-time monitoring during the joint degradation process. Based on the monitoring data, the current gain change rate and gain change speed are determined respectively; The test parameters are adaptively adjusted based on the current gain change rate and gain change velocity to maximize the gain decrease rate until the gain change rate reaches a preset threshold, at which point the test stops.

[0006] Preferably, the step of placing the sensor in a power frequency electric field generator, operating under a power frequency electric field, and measuring the initial gain in real time specifically includes: The power frequency electric field generator is set with a power frequency electric field modulus E; Connect the signal output terminal of the sensor to a data acquisition device, and obtain the voltage value V generated by the sensor when the power frequency electric field generator is working; pass Determine the initial gain.

[0007] Preferably, during the combined degradation process, the initial temperature is set to 40°C to 60°C, and the initial ultraviolet intensity is set to 0.3 W / m² to 0.7 W / m².

[0008] Preferably, real-time monitoring is performed during the joint degradation process to obtain the current gain, specifically including: during the joint degradation process, keeping the power frequency electric field generator continuously working and maintaining its power frequency electric field magnitude E; The data acquisition device periodically acquires the real-time voltage value V generated by the sensor during the joint degradation process. c ; Through formula G c =V c / E determines the current gain G c .

[0009] Preferably, determining the rate of change of gain and the rate of change of gain based on the monitoring data specifically includes: Based on the current gain G c pass Determine the rate of change of gain R; where G0 is the initial gain; Based on the current gain G c pass Determine the rate of change of gain S; where G p Δt represents the gain at the previous sampling time, and Δt represents the sampling time interval.

[0010] Preferably, the adaptive adjustment of the test parameters based on the gain change rate and gain change percentage of the current gain specifically includes: The absolute values ​​|S| of the gain change rate R and the gain change rate S are used as inputs; Using the maximization of the absolute value of the gain change rate |S| as the objective function, an initial temperature adjustment ΔT is generated. fuzzy And the initial adjustment amount ΔUV of ultraviolet intensity fuzzy ; Based on the initial adjustment amount ΔT of the temperature. fuzzy And the initial adjustment amount ΔUV of ultraviolet intensity fuzzy Generate the corresponding temperature adjustment amount ΔT and ultraviolet intensity adjustment amount ΔUV; Based on the ΔT and ΔUV commands, the temperature setpoint of the aging chamber and the intensity setpoint of the ultraviolet radiation source are adjusted in real time.

[0011] Preferably, the initial adjustment amount ΔT based on the temperature... fuzzy And the initial adjustment amount ΔUV of ultraviolet intensity fuzzy The instructions for generating the corresponding temperature adjustment amount ΔT and ultraviolet intensity adjustment amount ΔUV specifically include: determining the initial temperature adjustment amount ΔT respectively. fuzzy And the initial adjustment amount ΔUV of ultraviolet intensity fuzzy The error e(t) between the current parameter adjustment amount and the corresponding current parameter adjustment amount; according to Determine the control output u1(t); where u1(t) represents the control output, e(t) represents the error (e.g., the difference between the target gain decrease rate and the actual value), and K p K i and K d These represent the proportional, integral, and differential gain coefficients, respectively. The control output u1(t) is used as the final temperature adjustment amount ΔT and ultraviolet intensity adjustment amount ΔUV command.

[0012] A stability testing device for a power frequency electric field sensor, the device comprising: The gain test module is used to place the sensor in the power frequency electric field generator, operate under the power frequency electric field, and measure the initial gain in real time. The combined degradation module is used to load the sensor into the aging chamber, turn on the high-temperature aging chamber and the ultraviolet radiation source, and start the high-temperature and ultraviolet combined degradation process. The real-time monitoring module is used to monitor the joint degradation process in real time and obtain the current gain. The data processing module is used to determine the gain change rate and gain change speed of the current gain based on the monitoring data. An adaptive control module is used to adaptively adjust the test parameters based on the current gain change rate and gain change speed to maximize the gain decrease rate until the gain change rate reaches a preset threshold, at which point the test stops.

[0013] A computer device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the following steps: The sensor is placed in a power frequency electric field generator, operates under a power frequency electric field, and the initial gain is measured in real time. The sensor is installed in the aging chamber, the high-temperature aging chamber and the ultraviolet radiation source are turned on, and the high-temperature and ultraviolet combined degradation process is started. The current gain is obtained by real-time monitoring during the joint degradation process. Based on the monitoring data, the current gain change rate and gain change speed are determined respectively; The test parameters are adaptively adjusted based on the current gain change rate and gain change velocity to maximize the gain decrease rate until the gain change rate reaches a preset threshold, at which point the test stops.

[0014] A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the following steps: The sensor is placed in a power frequency electric field generator, operates under a power frequency electric field, and the initial gain is measured in real time. The sensor is installed in the aging chamber, the high-temperature aging chamber and the ultraviolet radiation source are turned on, and the high-temperature and ultraviolet combined degradation process is started. The current gain is obtained by real-time monitoring during the joint degradation process. Based on the monitoring data, the current gain change rate and gain change speed are determined respectively; The test parameters are adaptively adjusted based on the current gain change rate and gain change velocity to maximize the gain decrease rate until the gain change rate reaches a preset threshold, at which point the test stops.

[0015] The embodiments of the present invention have the following beneficial effects: This invention optimizes the degradation rate by introducing a multi-factor joint aging process and employing an adaptive control mechanism with multi-dimensional parameters, thereby significantly improving testing efficiency. For example, testing time can be significantly shortened, while enhancing the stability and accuracy of data. This invention effectively solves the problems of neglecting multi-variable interactions, insufficient control precision, and inability to adapt to dynamic degradation processes in existing technologies by dynamically coordinating environmental variables through intelligent optimization methods. This makes it easy to extend to the stability assessment of similar graphene nanocomposite sensors, providing a more efficient and reliable testing solution for power system monitoring. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] in: Figure 1 This is a flowchart of a stability testing method for a power frequency electric field sensor in one embodiment. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0019] In one embodiment, a stability testing method for a power frequency electric field sensor is proposed, such as... Figure 1 As shown, the method includes: Step 101: Place the sensor in the power frequency electric field generator, operate it under the power frequency electric field, and measure the initial gain in real time; Specifically, a constant power frequency electric field magnitude E (e.g., 10 kV / m) is set using a power frequency electric field generator to eliminate the interference of external electric field fluctuations on the test results. Then, the sensor's signal output is connected to a high-precision data acquisition device (such as a data acquisition card or voltmeter) to acquire the output voltage value V generated by the sensor under the electric field in real time. This is achieved through the formula... Calculating the initial gain G0 not only simplifies the complex environmental variables but also normalizes the sensor's output, ensuring that the gain value objectively reflects the sensor's inherent performance characteristics rather than the influence of external electric field strength. The data acquisition module samples synchronously at fixed time intervals (e.g., 1 second), ensuring data real-time performance and continuity, providing high-quality input for dynamic monitoring of the subsequent degradation process.

[0020] This invention effectively captures the electrical characteristics of the sensor in a clean state by calculating the gain under a power frequency electric field, thus avoiding test errors caused by initial deviations.

[0021] Step 102: Install the sensor into the aging chamber, turn on the high temperature aging chamber and the ultraviolet radiation source, and start the high temperature and ultraviolet combined degradation process; Specifically, the sensor sample was properly placed in the working area of ​​the aging chamber, ensuring a relatively fixed spatial relationship between it and the heating element and ultraviolet radiation source. Subsequently, the temperature control system and the ultraviolet radiation system were simultaneously activated. The temperature control system stabilized the ambient temperature within an initial range of 40°C to 60°C, while the ultraviolet radiation system provided an initial radiation intensity of 0.3 W / m² to 0.7 W / m². This parameter setting, based on the thermal and photosensitivity properties of graphene nanocomposite materials, ensures both accelerated degradation and avoids atypical failures due to excessive stress. Throughout the entire combined degradation process, the power frequency electric field generator continuously operated, keeping the sensor in a simulated working state, thus realizing the key testing concept of aging under working conditions.

[0022] Step 103: Monitor the joint degradation process in real time to obtain the current gain; Specifically, after the joint degradation process is initiated, the power frequency electric field generator remains operational, and its output power frequency electric field modulus E is strictly maintained at the same level as when the initial gain was measured in step 101. This ensures that the comparability of the sensor gain calculation results is not affected by fluctuations in the excitation source throughout the entire aging test cycle. Data acquisition equipment (such as a high-precision data acquisition card) is configured to periodically read the real-time voltage reading V from the sensor's signal output terminal at fixed time intervals (i.e., the sampling period Δt, e.g., 1 second). c The synchronous acquisition mechanism ensures that at each sampling moment, the acquired voltage signal strictly corresponds to the currently applied aging stress conditions.

[0023] Obtaining the real-time voltage value V c Then, through formula G c =V c / E calculates the current gain G c The obtained G cIt is a dimensionless ratio that characterizes the instantaneous sensitivity of the sensor at a specific moment after a certain period of time under the combined effects of a deteriorating environment. The current gain value G_c, along with its corresponding timestamp, is recorded in real time and sent to the subsequent data processing module as the basic input data for calculating the gain change rate (R) and gain change speed (S), thus forming the feedback signal for closed-loop control. The entire monitoring and calculation process is automated until the test termination conditions are met.

[0024] Step 104: Based on the monitoring data, determine the current gain change rate and gain change speed respectively; Specifically, the current gain value G is extracted from the real-time monitoring data stream. c And compare it with the initial gain reference G0 determined in step 101, using the formula Calculate the gain change rate R. This ratio intuitively reflects the cumulative performance degradation of the sensor since the start of the test; it is a relative change.

[0025] At the same time, the rate of change of gain S is also calculated, and its formula is: Among them, G p Let Δt be the gain at the previous sampling time, and Δt be the sampling time interval. S represents the instantaneous rate of change of sensor performance per unit time; it is a dynamic rate of change indicator.

[0026] These two calculations are executed automatically once per sampling period (e.g., per second) to ensure that the latest state of sensor performance evolution is continuously obtained.

[0027] The calculated results of the gain change rate R and gain change speed S are updated in real time and temporarily stored in memory to form a time series dataset.

[0028] Step 105: Adaptively adjust the test parameters based on the current gain change rate and gain change speed to maximize the gain decrease rate until the gain change rate reaches a preset threshold, then stop the test.

[0029] Specifically, the gain change rate R and gain change speed S are received from step 104. The control algorithm first takes the absolute value of S to obtain |S|, i.e., the gain decrease rate, and sets it as the objective function to be maximized. Subsequently, a fuzzy logic control algorithm is used, with R and |S| as input variables, and generates an initial temperature adjustment ΔT by querying a predefined fuzzy rule base (e.g., "if R is moderate and |S| is slow, then increase the temperature change rate"). fuzzy And the initial adjustment amount ΔUV of ultraviolet intensity fuzzy .

[0030] Based on the initial adjustment amount ΔT of the temperature. fuzzyAnd the initial adjustment amount ΔUV of ultraviolet intensity fuzzy Generate the corresponding temperature adjustment amount ΔT and ultraviolet intensity adjustment amount ΔUV; Based on the ΔT and ΔUV commands, the temperature setpoint of the aging chamber and the intensity setpoint of the ultraviolet radiation source are adjusted in real time.

[0031] The initial adjustment amount ΔT based on the temperature fuzzy And the initial adjustment amount ΔUV of ultraviolet intensity fuzzy The instructions for generating the corresponding temperature adjustment ΔT and ultraviolet intensity adjustment ΔUV include: Determine the initial adjustment amount ΔT for the temperature respectively. fuzzy And the initial adjustment amount ΔUV of ultraviolet intensity fuzzy The error e(t) between the current parameter adjustment and the corresponding current parameter adjustment amount is ΔT. fuzzy ΔUV fuzzy .

[0032] according to Determine the control output u1(t); where u1(t) represents the control output, e(t) represents the error (e.g., the difference between the target gain decrease rate and the actual value), and K p K i and K d These represent the proportional, integral, and differential gain coefficients, respectively. To further enhance the technical clarity and algorithmic depth of the method, fuzzy reasoning is used for refinement. The process can be represented as follows: ,and The two outputs are fuzzy outputs derived from rule base and sensor information, respectively; the precise adjustment amount u(t) is obtained by defuzzifying using the centroid method. , z J To output discrete points in the universe of discourse, u(z) J ) represents the membership degree of the corresponding point.

[0033] The control quantity u(t) optimized by fuzzy inference will be used as the final parameter adjustment command, which will be used as the final temperature adjustment quantity ΔT and ultraviolet intensity adjustment quantity ΔUV command.

[0034] The parameter adjustment process is executed cyclically with a fixed control cycle, forming a complete closed-loop feedback control system. Within each control cycle, the optimal parameter combination is recalculated based on the latest sensor performance status, and the temperature setpoint of the aging chamber and the intensity setpoint of the ultraviolet radiation source are adjusted accordingly, ensuring that the test process always follows the fastest degradation path. Simultaneously, the gain change rate R is continuously monitored. When R drops to a preset threshold (e.g., 70% of the initial value), the test is considered to have reached its endpoint, the aging process is automatically stopped, and all key parameters are recorded. The entire control process requires no manual intervention, achieving a high degree of automation and significantly improving test efficiency and reliability.

[0035] The precise values ​​of gain change rate and gain change percentage are converted into fuzzy linguistic variables, such as classifying gain change rate as "slow," "medium," or "fast," and gain change percentage as "low," "medium," or "high," to simulate human decision-making processes. The fuzzification process, based on membership functions, maps input values ​​to corresponding fuzzy sets, thereby capturing the nonlinear characteristics of sensor performance evolution. Subsequently, a set of fuzzy rules is activated. These rules are predefined based on experimental data and theoretical analysis, such as "increase the temperature change rate if the gain decrease rate is slow and the gain change percentage is high" or "decrease the UV intensity adjustment if the gain change percentage is close to a preset threshold," to output the fuzzy adjustment amount for each test parameter. Rule evaluation employs fuzzy inference mechanisms, such as the Mamdani or Sugeno methods, synthesizing the results of all applicable rules to generate a unified fuzzy output. Finally, the defuzzification step converts these fuzzy outputs into precise adjustment values, for example, by calculating the specific changes in temperature, UV intensity, temperature change rate, and UV intensity change rate using the centroid method or maximum membership method, thereby achieving dynamic control of the test parameters. This process ensures that the algorithm can adaptively respond to sensor degradation, avoiding test bias caused by fixed parameters.

[0036] The optimization loop is continuously performed based on real-time feedback data. Gain data is collected at regular intervals (e.g., every second), the gain change rate and gain change percentage are updated, and the fuzzy logic and PID control steps are re-executed. During the loop, the algorithm continuously evaluates the objective function of the gain decrease rate, prioritizing adjustments to parameters that contribute most to accelerating degradation. For example, in the initial stage, the temperature change rate is prioritized to quickly initiate degradation, while in the later stage, the UV intensity is finely adjusted to avoid over-testing. The entire loop continues until the gain change rate reaches a preset threshold, such as when the gain change rate drops to a specific percentage of the initial value (e.g., 70%), at which point the test is considered complete, and key parameter data is recorded for subsequent analysis. This dynamic optimization method not only significantly shortens the testing time but also improves data consistency and repeatability, providing a reliable benchmark for the stability evaluation of similar sensors.

[0037] The degradation parameter optimization loop is continuously executed to achieve the fastest gain change rate, thereby shortening the testing cycle and improving efficiency. Finally, the gain change rate is compared during testing; when this value reaches a preset threshold, the test terminates, and key parameters at this point (such as temperature, UV intensity, temperature change rate, and UV intensity change rate) are recorded. This data is then extended to the stability testing of similar graphene nanocomposite sensors to ensure the method's universality and scalability. The entire design emphasizes dynamic adaptation and multivariate interaction, effectively solving the problems of long testing cycles and insufficient control precision in existing technologies.

[0038] Furthermore, prior to step 101, the method further includes: initializing test parameters.

[0039] Specifically, initializing the test parameters is the foundation for subsequent steps, involving preset values ​​for initial temperature, UV intensity, temperature change rate, and UV intensity change rate. These parameters are set based on the characteristics of the sensor material and typical aging conditions. For example, the initial temperature value can be 50℃, the initial UV intensity value can be 0.5 W / m², and the initial change rate value can be set to 0 to ensure the rationality and repeatability of the test starting point.

[0040] Example This example uses a typical graphene-based sensor, composed of a graphene and polymer composite, for power system monitoring. The testing system includes a high-temperature aging chamber (temperature range 0-150°C), an ultraviolet radiation source (wavelength 365 nm, intensity adjustable 0-1 W / m²), a power frequency electric field generator (frequency 50 Hz, electric field modulus adjustable), a data acquisition module (sampling frequency 1 Hz), and a control unit (based on an embedded system implementing fuzzy logic and PID control). The sensor sample is placed in the aging chamber and operates under a power frequency electric field, outputting gain data in real time. The control unit dynamically adjusts the aging parameters based on feedback. In this example, the initial test parameters are preset based on typical aging conditions of the sensor material: the initial temperature is set to 50°C, the ultraviolet intensity is set to 0.5 W / m², and both the temperature change rate and the ultraviolet intensity change rate are set to 0 to ensure the stability and repeatability of the test starting point. The sample is first placed in the power frequency electric field generator, and the gain is tested in real time.

[0041] The data acquisition module records the initial gain value (0.025 in the example) as the degradation baseline. Subsequently, the test setup is placed in an aging chamber, and heating and ultraviolet radiation sources are simultaneously activated to initiate a high-temperature and ultraviolet combined degradation process. The sensors operate in this combined environment, and the control unit monitors parameter changes in real time, acquiring gain data every second to calculate the gain change rate and gain change velocity. An optimization algorithm, aiming to maximize the gain decrease rate, adaptively adjusts the temperature, ultraviolet intensity, temperature change rate, and ultraviolet intensity change rate. In the example test, the initial gain was 0.025. After approximately 50 hours of combined degradation, the gain change rate decreased to 0.7, and the test was terminated. Key parameters at this point were recorded: temperature 85°C, ultraviolet intensity 0.8 W / m², temperature change rate 0.3°C / second, and ultraviolet intensity change rate 0.05 W / m² / second.

[0042] Compared to existing single aging methods (which typically require over 100 hours), this embodiment reduces the testing time by approximately 50%, while maintaining high data accuracy and a stable gain decay rate within the range of 0.002 units / second, without significant overshoot or oscillation. These results validate the effectiveness of this invention in accelerating sensor stability testing and can be extended to similar nanocomposite material sensors, such as components used in smart grid monitoring, improving testing efficiency and reliability.

[0043] This invention significantly shortens testing time. For example, in typical applications, the testing cycle can be reduced from over 100 hours in existing technologies to approximately 50 hours, improving efficiency by more than 50%. This is thanks to the optimization algorithm, which aims to maximize the rate of gain decline and dynamically adjusts parameters such as temperature and UV intensity, avoiding the rigidity and delays of traditional fixed-parameter methods. Simultaneously, data accuracy and stability are significantly enhanced. Through the synergistic application of fuzzy logic controllers and PID control principles, overshoot and oscillations are effectively reduced, ensuring smooth control of the gain change rate during testing. This provides more reliable and consistent degradation data, laying a solid foundation for sensor performance evaluation. Furthermore, this invention exhibits excellent control precision. The integration of proportional, integral, and derivative actions not only optimizes multi-variable collaborative behavior but also improves test robustness, preventing test deviations caused by environmental fluctuations, which is particularly important in practical applications such as power system monitoring. Moreover, this invention has a wide range of applications and can be flexibly adapted to various graphene nanocomposite material sensors and other similar components, such as smart grid or new energy vehicle sensing devices, enhancing the method's versatility and scalability, and overcoming the limitations of existing technologies that are restricted to specific sensor types. Overall, this invention not only reduces testing costs and time consumption, but also provides the industry with a repeatable and scalable testing solution through efficient and accurate degradation simulation, strongly supporting the reliability design and optimization of sensors in complex environments.

[0044] In one embodiment, a stability testing device for a power frequency electric field sensor is provided, the device comprising: The gain test module is used to place the sensor in the power frequency electric field generator, operate under the power frequency electric field, and measure the initial gain in real time. The combined degradation module is used to load the sensor into the aging chamber, turn on the high-temperature aging chamber and the ultraviolet radiation source, and start the high-temperature and ultraviolet combined degradation process. The real-time monitoring module is used to monitor the joint degradation process in real time and obtain the current gain. The data processing module is used to determine the gain change rate and gain change speed of the current gain based on the monitoring data. An adaptive control module is used to adaptively adjust the test parameters based on the current gain change rate and gain change speed to maximize the gain decrease rate until the gain change rate reaches a preset threshold, at which point the test stops.

[0045] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the following steps: The sensor is placed in a power frequency electric field generator, operates under a power frequency electric field, and the initial gain is measured in real time. The sensor is installed in the aging chamber, the high-temperature aging chamber and the ultraviolet radiation source are turned on, and the high-temperature and ultraviolet combined degradation process is started. The current gain is obtained by real-time monitoring during the joint degradation process. Based on the monitoring data, the current gain change rate and gain change speed are determined respectively; The test parameters are adaptively adjusted based on the current gain change rate and gain change velocity to maximize the gain decrease rate until the gain change rate reaches a preset threshold, at which point the test stops.

[0046] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform the following steps: The sensor is placed in a power frequency electric field generator, operates under a power frequency electric field, and the initial gain is measured in real time. The sensor is installed in the aging chamber, the high-temperature aging chamber and the ultraviolet radiation source are turned on, and the high-temperature and ultraviolet combined degradation process is started. The current gain is obtained by real-time monitoring during the joint degradation process. Based on the monitoring data, the current gain change rate and gain change speed are determined respectively; The test parameters are adaptively adjusted based on the current gain change rate and gain change velocity to maximize the gain decrease rate until the gain change rate reaches a preset threshold, at which point the test stops.

[0047] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0048] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0049] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for testing the stability of a power frequency electric field sensor, characterized in that, The method includes: The sensor is placed in a power frequency electric field generator, operates under a power frequency electric field, and the initial gain is measured in real time. The sensor is installed in the aging chamber, the high-temperature aging chamber and the ultraviolet radiation source are turned on, and the high-temperature and ultraviolet combined degradation process is started. The current gain is obtained by real-time monitoring during the joint degradation process. Based on the monitoring data, the current gain change rate and gain change speed are determined respectively; The test parameters are adaptively adjusted based on the current gain change rate and gain change velocity to maximize the gain decrease rate until the gain change rate reaches a preset threshold, at which point the test stops.

2. The stability testing method for a power frequency electric field sensor according to claim 1, characterized in that, The step of placing the sensor in a power frequency electric field generator, operating it under a power frequency electric field, and measuring the initial gain in real time specifically includes: The power frequency electric field generator is set with a power frequency electric field modulus E; Connect the signal output terminal of the sensor to a data acquisition device, and obtain the voltage value V generated by the sensor when the power frequency electric field generator is working; pass Determine the initial gain.

3. The stability testing method for a power frequency electric field sensor according to claim 1 or 2, characterized in that, During the combined degradation process, the initial temperature was set to 40°C to 60°C, and the initial UV intensity was set to 0.3 W / m² to 0.7 W / m².

4. The stability testing method for a power frequency electric field sensor according to claim 3, characterized in that, Real-time monitoring during the joint degradation process to obtain the current gain specifically includes: during the joint degradation process, keeping the power frequency electric field generator continuously working and maintaining its power frequency electric field magnitude E; The data acquisition device periodically acquires the real-time voltage value V generated by the sensor during the joint degradation process. c ; Through formula G c =V c / E determines the current gain G c .

5. The stability testing method for a power frequency electric field sensor according to claim 4, characterized in that, The determination of the gain change rate and gain change speed based on the monitoring data specifically includes: Based on the current gain G c pass Determine the rate of change of gain R; where G0 is the initial gain; Based on the current gain G c pass Determine the rate of change of gain S; where G p Δt represents the gain at the previous sampling time, and Δt represents the sampling time interval.

6. The stability testing method for a power frequency electric field sensor according to claim 5, characterized in that, The adaptive adjustment of the test parameters based on the gain change rate and gain change velocity of the current gain specifically includes: The absolute values ​​|S| of the gain change rate R and the gain change rate S are used as inputs; Using the maximization of the absolute value of the gain change rate |S| as the objective function, an initial temperature adjustment ΔT is generated. fuzzy And the initial adjustment amount ΔUV of ultraviolet intensity fuzzy ; Based on the initial adjustment amount ΔT of the temperature. fuzzy And the initial adjustment amount ΔUV of ultraviolet intensity fuzzy Generate the corresponding temperature adjustment amount ΔT and ultraviolet intensity adjustment amount ΔUV; Based on the ΔT and ΔUV commands, the temperature setpoint of the aging chamber and the intensity setpoint of the ultraviolet radiation source are adjusted in real time.

7. The stability testing method for a power frequency electric field sensor according to claim 6, characterized in that, The initial adjustment amount ΔT based on the temperature fuzzy And the initial adjustment amount ΔUV of ultraviolet intensity fuzzy The instructions for generating the corresponding temperature adjustment amount ΔT and ultraviolet intensity adjustment amount ΔUV specifically include: determining the initial temperature adjustment amount ΔT respectively. fuzzy And the initial adjustment amount ΔUV of ultraviolet intensity fuzzy The error e(t) between the current parameter adjustment amount and the corresponding current parameter adjustment amount; according to Determine the control output u1(t); where u1(t) represents the control output, e(t) represents the error (e.g., the difference between the target gain decrease rate and the actual value), and K p K i and K d These represent the proportional, integral, and differential gain coefficients, respectively. The control output u1(t) is used as the final temperature adjustment amount ΔT and ultraviolet intensity adjustment amount ΔUV command.

8. A stability testing device for a power frequency electric field sensor, characterized in that, The device includes: The gain test module is used to place the sensor in the power frequency electric field generator, operate under the power frequency electric field, and measure the initial gain in real time. The combined degradation module is used to load the sensor into the aging chamber, turn on the high-temperature aging chamber and the ultraviolet radiation source, and start the high-temperature and ultraviolet combined degradation process. The real-time monitoring module is used to monitor the joint degradation process in real time and obtain the current gain. The data processing module is used to determine the gain change rate and gain change speed of the current gain based on the monitoring data. An adaptive control module is used to adaptively adjust the test parameters based on the current gain change rate and gain change speed to maximize the gain decrease rate until the gain change rate reaches a preset threshold, at which point the test stops.

9. A computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of the method as claimed in any one of claims 1 to 7.

10. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as claimed in any one of claims 1 to 7.

Images