A high-precision current sensor insulation state detection method

Abstract

The present application relates to the field of high-precision current sensor insulation state online detection method, and discloses a kind of high-precision current sensor withstand voltage detection method, including in the listening window of load current zero interval, the differential sampling synchronized with high-voltage bus on-off state is carried out to generate the differential current value of the leakage current, the present method passes through the on-off behavior of high-voltage bus as synchronous signal, the static working current of sensor is differentially sampled in very short time scale, it not only can eliminate the slow temperature drift caused by ambient temperature change, more can inhibit the asynchronous broadband noise universally existing in power electronic system from mechanism, so that the weak leakage current signal generated by high voltage previously submerged is identified as a differential value, so that the online evaluation of insulation performance is established on a certain physical cause and effect.

Description

TECHNICAL FIELD

[0001] The application relates to a high-precision current sensor insulation state detection method and belongs to the technical field of online detection of high-precision current sensor insulation states. BACKGROUND

[0002] Currently, ensuring the reliability of high-voltage insulation components throughout the entire life cycle is a core technical requirement. In order to achieve this goal, two completely different technical paths have been formed. One is to rely on offline high-voltage testing before the product leaves the factory. Although this method can provide a clear initial safety benchmark, it is powerless to the progressive insulation degradation process caused by electrical and thermal and environmental stress during the service life of the equipment, and cannot provide any online risk warning. The other approach attempts to conduct online monitoring, such as by adding independent temperature or leakage current sensors and using complex compensation models. However, this approach directly introduces additional hardware costs, system complexity, and new potential failure points, which is contrary to the trend of modern power electronic systems pursuing high integration and cost-effectiveness.

[0003] The problem with the above two paths lies in a key measurement physics problem. The microampere-level weak leakage current signal that can truly represent early insulation degradation is overwhelmed by the milliamper-level static working current of the sensor itself and its drift component at the measurement end. Not only are the two signals significantly different in amplitude, but the drift component caused by temperature changes and the leakage component caused by material chronic aging are also easily confused in the slowly changing time domain characteristics, resulting in low signal-to-noise ratio and high uncertainty.

[0004] The prior art has technical problems to be solved in realizing effective online monitoring of the insulation state of a high-precision current sensor, specifically: 1. The measurement method for separating the weak leakage signal associated with the high-voltage state from the working current background using the sensor's own working parameters has not been achieved. 2. In the actual operating environment full of various asynchronous electromagnetic noise, there is a lack of a mechanism that can extract the above-mentioned weak leakage signal with high reliability. Therefore, how to establish a measurement method that can effectively suppress the working current temperature drift and system noise interference, and then accurately extract the weak characteristic signal representing the insulation performance from the complex electrical signal background, without increasing the system hardware complexity and cost, has become a technical problem to be solved by the application. SUMMARY

[0005] The application provides a high-precision current sensor insulation state detection method, which mainly aims to solve the problem of accurately distinguishing and extracting the weak leakage current signal related to the insulation state while suppressing the combined interference of the sensor's own static working current temperature drift and system noise without additional hardware.

[0006] To achieve the above object, the application provides a high-precision current sensor insulation state detection method, comprising the following steps:

[0007] During the listening window in which the current sensor operates under zero load current, differential sampling synchronized with the on-off state of the high-voltage bus is performed to generate a differential current value representing the leakage current;

[0008] The synchronized differential sampling comprises: at a first time point under the off state of the high-voltage bus, measuring the static working current of the sensor to obtain an off reference current;

[0009] Immediately after the first time point, at a second time point under the on state of the high-voltage bus, the static working current of the sensor is measured again to obtain an on measured current; wherein the time interval between the second time point and the first time point is determined so that the temperature drift current increment determined by the thermal time constant of the sensor within the time interval is less than a current resolution value determined according to the predetermined signal-to-noise ratio requirement;

[0010] The difference between the on measured current and the off reference current is calculated to obtain the differential current value;

[0011] Based on the differential current value, the insulation state of the current sensor is diagnosed.

[0012] Preferably, the listening window is created by using the dead time of the pulse width modulation signal applied to the power electronic converter associated with the current sensor; during the dead time, the main load current is cut off, and the high-voltage bus potential is maintained stable.

[0013] Preferably, the step of diagnosing the insulation state of the current sensor is further used to identify the insulation degradation mode, and comprises: in the listening window, burst sampling is performed on the static working current to obtain a current time sequence; the variance and kurtosis of the current time sequence are calculated; the long-term mean value of the differential current value is combined with the variance and kurtosis for combined judgment to classify the insulation degradation mode: when the long-term mean value presents one-way growth and the variance and kurtosis are both lower than their respective reference thresholds, it is determined as a uniform insulation aging mode; when the variance or kurtosis is greater than its respective reference threshold, it is determined as an intermittent pulse leakage mode.

[0014] Preferably, the step of diagnosing the insulation state based on the statistical characteristics of the differential current value changing over time is further used to quantify the degradation trend of the insulation performance, and comprises: recording the differential current values obtained by multiple measurements over time to form a difference sequence; calculating the statistical mean value of the difference sequence within a predetermined evaluation period; calculating a degradation factor representing the insulation degradation rate by the following formula , wherein, is the statistical mean value of the current evaluation period, statistical mean of the previous evaluation period, duration of the predetermined evaluation period, reference differential current value measured by the sensor in a healthy state; when the degradation factor continuously exceeds a predetermined threshold, it is determined that the insulation performance has chronic degradation.

[0015] Preferably, the specific value of the time interval is calculated and determined during system initialization according to the thermal characteristic parameters of the sensor and the minimum detection sensitivity requirement of the detection method for the leakage current.

[0016] Preferably, the step of measuring the static operating current is realized by at least one of the following ways: monitoring the voltage across a detection resistor arranged in the power supply loop of the low-voltage side of the current sensor; or reading the power supply current data output by a power management chip.

[0017] Preferably, before the method is executed, it further includes the step of establishing a health state baseline: during the first power-on of the current sensor, under the condition that the high-voltage bus is not powered on and the load current is zero, the reference static operating current of the sensor is measured and stored, as well as the reference variance and reference kurtosis corresponding to the reference static operating current.

[0018] Preferably, the high-voltage bus on-off event relied on by the synchronous differential sampling is a high-voltage pulse operation periodically performed by the system without affecting the external load.

[0019] Preferably, the step of diagnosing the insulation state of the current sensor further includes transient abnormality determination: if the differential current value calculated at a single time exceeds a transient threshold defined by a predetermined percentage of the de-energized reference current, a transient insulation abnormality event is recorded.

[0020] Preferably, the creation of the listening window is performed when it is identified that the system in which the current sensor is located enters a predetermined standby working mode, in which the main load of the system is instructed to be turned off.

[0021] Compared with the prior art, the beneficial effects of the present application are:

[0022] 1、This method realizes the dimensional separation of signal and noise in the measurement mechanism through differential sampling synchronized with the on-off state of the high-voltage bus. This design can not only eliminate slow interference such as temperature drift, thereby suppressing the asynchronous broadband noise with extremely wide spectrum and random form that exists universally in power electronic systems; the principle is that the two time points of differential sampling are extremely close in time, and any random noise that is not synchronized with the high-voltage on-off, such as electromagnetic interference, can be regarded as a common-mode signal with highly correlated statistical characteristics in this time scale, and thus can be effectively canceled in the differential operation; in this way, only the leakage current signal that has a physical causal relationship with the high-voltage on-off can be retained, while all asynchronous interference is projected into an orthogonal noise dimension, thereby reducing the influence of asynchronous noise on the measurement result. This characteristic enables the method to avoid excessive reliance on additional hardware filtering or electromagnetic shielding, and to achieve high-precision weak leakage current detection in a real harsh electromagnetic environment.

[0023] 2、The present application provides a way to diagnose the insulation state of a high-precision current sensor online. By taking the on-off behavior of the high-voltage bus as a synchronization signal, the static working current of the sensor is differentially sampled within a very short time scale. This mechanism operates such that slow static working current drift caused by changes in ambient temperature is eliminated as a common-mode signal, thereby identifying the weak leakage current signal generated by the high voltage as a clear differential current value that was previously obscured or confused by temperature drift. In this way, the diagnostic uncertainty problem caused by the inability to distinguish between leakage and temperature drift in traditional online monitoring methods is avoided, and the online evaluation of insulation performance is based on a certain physical causal relationship.

[0024] 3、The method of the present application expands the diagnosis dimension from a single leakage amount to the recognition of leakage patterns. It acquires a micro time sequence by burst sampling the static working current in a listening window without load current interference, and analyzes the statistical characteristics such as variance and kurtosis of the sequence. Uniform material aging and local defect-induced pulse discharge will leave different statistical textures on this current sequence. By combining the differential current mean representing the total amount of leakage with the statistical characteristics representing the leakage texture for judgment, the system can not only determine whether the insulation performance has declined, but also further identify the mode of degradation, providing a basis for adopting a risk level maintenance strategy. BRIEF DESCRIPTION OF DRAWINGS

[0025] Fig. 1 Fig. 1 is a flowchart of the insulation state detection method of the high-precision current sensor of the present application.

[0026] Fig. 2 Fig. 3 is a comparison diagram of the noise suppression effect of the differential sampling method and the direct measurement method of the present application.

[0027] Fig. 3 Flowchart of the insulation condition diagnosis logic of the present application. DETAILED DESCRIPTION

[0028] In order to make the technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described in detail below. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0029] The application discloses a high-precision current sensor insulation state detection method, which mainly comprises a listening window creating step, a synchronous differential sampling step and an insulation state diagnosing step. The listening window creating step is used for identifying the interval in which the load current is zero or close to zero, so as to provide an operation environment for subsequent measurement. The synchronous differential sampling step is used for sampling the static working current of the sensor synchronously with the on-off state of the high-voltage bus in the listening window, and calculating the differential current value representing the high-voltage induced leakage current. The insulation state diagnosing step receives the differential current value, and evaluates and mode-recognizes the insulation health state of the current sensor based on the single size, long-term statistical trend or microscopic time sequence characteristics of the differential current value. In an application scenario of a power electronic converter driven by a pulse width modulation technology, the insulation state of the current sensor needs to be monitored online, and the problem of interference caused by the strong electromagnetic field and thermal effect of the main load current needs to be solved. Therefore, the listening window creating step of the method can be configured to create the listening window by using the dead time of the pulse width modulation signal applied to the power electronic converter. In the dead time, the power switch tube is in the off state, the main load current is cut off, but the direct current potential on the high-voltage bus is stable. This state constitutes a measurement window in which there is no main current interference and the leakage current can be generated. As another implementation, the creation of the listening window can also be performed when it is identified that the system in which the current sensor is located enters a predetermined standby working mode. In the working mode, the system main load is instructed to be turned off, and the environmental conditions for measurement are also met. In the listening window without the load current, the static working current of the sensor itself will still drift due to the change of the environmental temperature, and the amplitude and time domain characteristics thereof can be similar to the weak leakage current caused by material aging. The synchronous differential sampling step adopts a differential sampling procedure synchronized with the on-off state of the high-voltage bus. The procedure performs twice measurement of the static working current around the on-off event of the high-voltage bus in a determined time interval. Specifically, at the first time point in the off state of the high-voltage bus, the static working current of the sensor is measured to obtain an off reference current containing the inherent working current of the sensor and the temperature drift component thereof. Then, at the second time point in the on state of the high-voltage bus, the static working current is measured again to obtain an on measured current superimposed with the high-voltage induced leakage current. The off reference current and the on measured current can be realized by monitoring the voltage between the detection resistor arranged in the low-voltage side power supply circuit of the current sensor or by reading the power supply current data output by a power management chip. The determination of the time interval between the two measurement time points is calculated and set according to the thermal time constant of the sensor and the minimum detection sensitivity requirement of the leakage current during system initialization, and the constraint condition is to ensure that the time interval is greater than the thermal time constant of the sensor and less than the minimum detection sensitivity requirement of the leakage current. . . . The current increment caused by temperature drift due to thermal effect , which is less than the current resolution value ; finally, a differential current value representing the leakage current is obtained by calculating the difference between the measured current during energization and the reference current during de-energization ; this differential processing enables the weak leakage current signal associated with the high-voltage on-off state to be separated from the static working current background as an identifiable differential value, thereby establishing the insulation state evaluation on the basis of the physical causal relationship between the leakage current and the high-voltage on-off.

[0030] To realize the creation and confirmation of the listening window, the system performs a noise floor calibration procedure in the initialization phase, which obtains a statistical distribution representing the inherent noise level of the current sensor by performing multiple micro-time series sampling of the static working current under the reference condition of no high-voltage excitation and the main load turned off, and calculating the standard deviation of each sequence; the system determines and stores a noise threshold value based on the 99th percentile point of this distribution; in the subsequent online diagnosis process, whenever the system identifies a potential listening window, it will first perform a pre-sampling, calculate the current standard deviation in real time within the window, and only when the condition is met, will it confirm the window as a valid measurement window and start the subsequent synchronous differential sampling, otherwise it will abandon this measurement to avoid abnormal noise interference; to realize the burst sampling and its analysis in insulation degradation mode identification, the core parameters of the sampling, i.e. the sampling frequency and the sample number , are determined through a pre-calibration procedure, wherein the value of the sampling frequency is determined according to the key characteristic frequency obtained by spectral analysis of a typical intermittent pulse leakage signal, and is set to to capture the transient characteristics of the pulse signal; the value of the sample number is determined by calculating the kurtosis value corresponding to different sample numbers at the above-mentioned sampling frequency , and finding the minimum that satisfies the following kurtosis convergence criterion as the final , wherein is the incremental step size of the sample number, and is a preset convergence tolerance, e.g. 0.01; this procedure establishes the effectiveness of statistical analysis on the basis of quantifiable signal integrity and statistical convergence, thereby providing a deterministic identification basis for the early insulation degradation caused by partial discharge commonly found in high-voltage power electronic systems; the differential current value Subsequently, the insulation condition diagnosis step executes multi-dimensional diagnostic logic; to cope with sudden changes in insulation condition, the module is configured with instantaneous anomaly judgment rules, if the single calculation yields... If the instantaneous threshold is exceeded by a predetermined percentage of the power outage reference current, a transient insulation anomaly event is recorded. To further identify the specific pattern of insulation degradation, this step can be configured to burst-sample the quiescent operating current within the listening window to obtain a current time series; subsequently, the variance of this current time series is calculated. with kurtosis And combined with the long-term average of the differential current values ​​obtained from multiple measurements A combined judgment is performed; when the long-term mean shows a unidirectional increase while both variance and kurtosis are below their respective benchmark thresholds, it is determined to be a uniform insulation aging mode; when the variance or kurtosis is greater than their respective benchmark thresholds, it is determined to be an intermittent pulse leakage mode with partial discharge risk; the benchmark variance and benchmark kurtosis used for comparison are a portion of the health status baseline measured and stored during the initial power-on of the current sensor, under the condition that the high-voltage bus is not energized and the load current is zero; the result of this combined judgment is used to classify insulation degradation modes, providing a status classification basis for risk assessment and maintenance decisions; in addition, to quantify the chronic degradation trend of insulation performance, the diagnostic step also records the differential current values ​​obtained from multiple measurements over time, forming a difference sequence, and calculates the sequence at a predetermined assessment period. Statistical mean within ; through formula Calculate the degradation factor characterizing the rate of insulation degradation. ,in, This is the statistical mean of the previous evaluation period. The reference differential current value is measured by the sensor in a healthy state. When this degradation factor continues to exceed a predetermined threshold, the system determines that the insulation performance has undergone chronic degradation and uses this determination result as one of the triggering conditions for predictive maintenance.

[0031] Example 1: In a continuously running electric vehicle battery management system, the high-precision current sensor inside works in a high-voltage electric stress and large temperature change environment for a long time, and its insulation components are therefore at risk of progressive degradation due to electrothermal fatigue. This slow degradation does not cause any observable functional abnormalities at the beginning; during a routine charging process, when the battery enters a preset stationary observation stage, the main charging current is interrupted, and the system creates a listening window without load current disturbance accordingly; in this window, the system performs a preset insulation self-checking program, the core of which is to control the high-voltage bus to perform a turn-on and turn-off operation. The creation of the listening window provides a condition without main current noise for subsequent measurement, and the turn-on and turn-off event of the high-voltage bus provides a synchronous reference for differential sampling. The combination of the two enables an effective measurement sequence to be started; the system sequentially collects the off-reference current and the on-measured current in a very short time interval before and after the high-voltage bus turn-on and turn-off event, and through differential operation, a differential current value is obtained, which has offset the static working current drift component caused by environmental temperature changes in the operation . This method converts the physical basis of diagnosis from an observation of the absolute value of the static working current to an identification of the current increment that is physically and causally related to the high-voltage turn-on and turn-off, so that the weak leakage signal originally submerged in the temperature drift background can be revealed.

[0032] In continuous months of vehicle operation and charging cycles, the system accumulates a sequence of differential current values generated by each measurement, and based on the preset diagnostic logic, continuously calculates a degradation factor representing the insulation degradation rate . When the statistical value of this degradation factor shows a consistent one-way growth trend and eventually exceeds the preset threshold, the vehicle diagnostic system generates a warning information about the insulation state of the current sensor; this information is output before the sensor fails, so that maintenance activities for the potential risk can be performed in advance; the application of this detection method enables the operating state parameters of the current sensor to be used to assess its own insulation health, and the maintenance of the vehicle is thus changed from a response to the failure that has occurred to a tracking of the performance evolution trend of the component.

[0033] Example 2: To verify the ability of the method of the present invention to extract weak leakage current signals under temperature drift conditions, the following test platform was built; the platform consists of a temperature control chamber, a high-precision current sensor under test, a programmable high-voltage DC power supply, a simulated leakage load module, and a main control unit; the sensor under test is placed in the temperature control chamber, and the operating temperature can vary from 25°C to 85°C; the programmable high-voltage power supply is used to apply an 800V bus voltage; the simulated leakage load module includes a 160MΩ resistor connected in series with a relay, which can generate an injection leakage current of 5.0µA at 800V voltage upon instruction; the main control unit is used to execute the detection logic and record data; during the experiment, the time interval of synchronous differential sampling is... The settings need to balance suppressing temperature drift interference with meeting system timing constraints; given the thermal time constant of the sensor under test... The time is on the order of minutes, while the high-voltage switching time is on the order of hundreds of microseconds. In order to reduce the impact of temperature drift while ensuring the completion of the switching action, this experiment will... The value is set to 1.0ms.

[0034] The experiment included a control group and an experimental group based on the present invention. The former used a direct measurement method, while the latter used the synchronous differential sampling method described in the specific implementation. Initially, the temperature of the temperature control chamber was stabilized at 25°C. When a leakage current of 5.0µA was injected, the current increment measured in the control group was 5.2µA, while the differential current value output by the experimental group of the present invention was... Both the 5.1µA and 5.0µA leakage currents reflect the injected leakage current. Subsequently, the temperature of the control chamber was raised to 85°C and maintained stable. The same 5.0µA leakage current was injected again. At this point, the static operating current measured by the control group showed an increase of over 560µA compared to its reference value at 25°C. This increase was dominated by temperature drift, completely drowning out the 5.0µA leakage signal. In contrast, the differential current value measured by the experimental group of this invention at 85°C... The value was 5.1µA, which was not significantly different from the measurement result at 25°C. In this test result, the stability of the output value of the test group of the present invention is a direct manifestation of its synchronous differential sampling mechanism. The temporal proximity of the two samplings makes the slow drift component caused by temperature change a common-mode signal that is canceled out in the differential operation. The test data confirms that the present technical solution can separate and measure the weak leakage current signal that is synchronized with the high-voltage state from the static operating current background affected by temperature drift within a wide temperature range.

[0035] Example 3: This example combines Figs. 1 to 3 This document describes a method for detecting the insulation status of a high-precision current sensor, as follows: Fig. 1As shown, the method starts with two initial steps performed in parallel, i.e. identifying the interval where the load current is zero by creating a listening window, and taking the high-voltage bus on-off event as the synchronization signal for subsequent differential sampling, on this basis, synchronous differential sampling synchronized with the high-voltage on-off event is performed within the listening window, which contains two core actions, i.e. measuring the static operating current at the first time to obtain the off power reference current when the high-voltage bus is powered off, and then measuring again at the second time to obtain the on power measured current when the high-voltage bus is powered on, and performing difference calculation on the two current values to generate a differential current value that can represent the leakage current Finally, the differential current value is used for three parallel diagnostic paths, one is instantaneous abnormality judgment, when the single differential current value exceeds the threshold value, an instantaneous insulation abnormality event is recorded, the second is insulation deterioration mode identification, the variance and kurtosis of the current time series obtained by burst sampling are calculated, and the reference variance and kurtosis obtained from the health state baseline establishment step are combined to distinguish the deterioration mode into uniform insulation aging mode or intermittent pulse leakage mode, and the third is chronic deterioration trend quantification, by calculating the deterioration factor of the long-term recorded difference sequence and comparing it with the reference differential current value from the health state baseline, to trigger a predictive maintenance warning to determine the chronic deterioration of insulation performance.

[0036] As Fig. 2 shown, the figure shows the performance difference between the two methods by comparing the noise amplitude dB of the direct measurement method and the differential sampling method at different noise frequencies Hz, in the figure, the direct measurement method identified by the dashed line maintains a high amplitude in the entire frequency range from 0.1 Hz to 100 kHz, while the differential sampling method adopted by the present application has a significantly lower noise level than the former, and shows noise suppression effect in the entire frequency band, thereby proving that the present method can realize high signal-to-noise ratio extraction of weak leakage current signal without excessive additional hardware filtering.

[0037] As Fig. 3 shown, during normal operation of the system, first, synchronous differential sampling is performed through the listening and sampling steps, if the measurement is completed and the differential current value is normal, the system enters the health monitoring state, at this time the differential current value is at the baseline level, from this central state, the system performs multi-dimensional diagnosis according to different trigger conditions, if the single differential current value > instantaneous threshold value, it is determined as instantaneous abnormality and an abnormal event is recorded, if the variance or kurtosis > threshold value, it is determined as intermittent pulse leakage mode, and its risk level is high; if the long-term mean value increases and the variance / kurtosis < threshold value, it is determined as uniform insulation aging mode, and its risk level is medium, if the finally calculated deterioration factor continues to > threshold value, it is determined to enter the chronic deterioration trend stage, and a predictive maintenance warning is triggered.

[0038] In the design and development stage of a high-reliability power supply system, in order to make the high-precision current sensor integrated and used for monitoring the busbar condition have a certain performance index for the insulation online diagnosis function, the core algorithm parameters in the diagnosis method need to be systematically calibrated, which associates the algorithm threshold with the physical characteristics of the sensor and the constraint conditions of the application environment; the calibration process is carried out in a controlled experimental environment, and first, the time interval of synchronous differential sampling is determined ; the determination of this parameter takes the thermal characteristic parameters of the sensor and the maximum expected environmental temperature change rate of the target application as inputs; in this calibration, it is known that the static working current temperature coefficient of the sensor is 20 µA / °C, and the maximum environmental temperature change rate of the target application is 0.1 °C / s; in order to suppress the current error introduced by temperature drift during the two differential sampling periods to be not greater than 0.2 µA, the inequality must be satisfied, and accordingly, the upper limit of the time interval is calculated to be 100 ms, and finally, the value is set to 80 ms; secondly, the threshold of the statistical quantity used to distinguish the insulation deterioration mode is calibrated; a sensor to be tested in a brand-new state is placed on the test bench, and under the condition of not applying high voltage, the static working current is sampled multiple times at multiple environmental temperature points, the baseline variance and baseline kurtosis of each current time sequence are calculated and recorded to form a healthy state statistical sample library; by analyzing the data distribution of the sample library, the diagnosis threshold for judging the intermittent pulse leakage mode is set to be six times the standard deviation higher than the statistical mean of the healthy samples.

[0039] Finally, in order to determine the deterioration factor threshold for judging chronic deterioration, another group of sensor samples are subjected to accelerated aging test; the samples are placed in a high-temperature and high-humidity environment and continuously subjected to rated high voltage, while the deterioration factor is continuously calculated by the method of the present application, and the actual insulation resistance value is monitored by a high-precision insulation resistance tester; when the reading of the insulation resistance tester drops to 70% of the initial value, the series of values calculated by the method of the present application at this moment are recorded, and the statistical average of these values is taken as the deterioration factor threshold ; through the above calibration procedures, each key parameter in the diagnosis algorithm is given a certain numerical value based on the physical characteristics of the sensor and the application requirements, and these numerical values are fixed in the firmware of the device, so that the insulation diagnosis function has consistent performance after deployment.

[0040] Example 5: In order to ensure the reliability of the insulation diagnosis function integrated in the power electronic converter system, a field calibration procedure for the core measurement condition needs to be performed in the pre-deployment verification phase of the system; the procedure aims to provide a reference for the system to identify valid listening windows and perform synchronous differential sampling in actual operation; the verification personnel use a high-bandwidth oscilloscope as an external reference to monitor the main load current of the converter and the static working current loop of the sensor to be tested simultaneously; the system is driven to operate under various conditions, including rated load, light load, and load step changes, and the main control unit continuously identifies and outputs valid signals of the listening window according to its internal logic during this period; the reference oscilloscope records the standard deviation of the actual noise floor of the static working current loop in the identified listening window, and multiplies the maximum standard deviation value by a preset safety factor, which is 3 in this calibration, and the resulting product is solidified into the system software as a noise threshold; in subsequent actual operation, the diagnosis system will first evaluate the noise level in the current listening window before each differential sampling is performed, and only when the level is lower than the calibrated noise threshold will the window be confirmed as valid and the subsequent sampling steps will be started.

[0041] In the next step of the verification procedure, the system is instructed to periodically perform on-off operations on the high-voltage bus for triggering differential sampling; the reference oscilloscope now focuses on monitoring the transient response of the sensor static working current loop after each high-voltage on-off event occurs, and measures the longest stabilization time required from fluctuation to recovery to stability ; this stabilization time is recorded and written into the system firmware as a configuration parameter; in actual operation, when synchronous differential sampling is triggered, the main control unit waits for a delay time equal to after the high-voltage bus completes the power-off action before performing sampling on the power-off reference current, similarly, after the bus completes the power-on action, it also waits for the same delay time before performing sampling on the power-on measured current; this timing calibration is used to avoid the transient electromagnetic interference that may be introduced by high-voltage switch action, through this procedure, the sampling time can be set in the steady state interval after the end of the circuit transient response.

[0042] Example 6: The accuracy of long-term analysis of the insulation degradation trend diagnosis of the present application depends on the stability of a reference model that can represent the health status of the sensor itself, a core parameter of the model is the static working current reference value of the sensor under no leakage condition, in order to cope with the long-term slow drift of this reference value that may be caused by the aging of low-voltage side electronic components, a periodic reference model reconstruction procedure needs to be performed.

[0043] During a planned maintenance shutdown of a data center power system that has been continuously operating for more than ten thousand hours, the system's master control unit initiates the reference model reconstruction procedure after the high voltage bus inside the system is confirmed to be in a fully de-energized state; under this specific condition of zero high voltage and zero load current, the system measures the static operating current of the sensors, which in this case does not physically contain any insulation leakage component induced by the high voltage, and thus the measured value is confirmed by the system as a new static operating current reference value that characterizes the current state of the aging of the components ; this newly measured reference value is used to update the health state reference model stored in the system, replacing the original parameter values; by periodically reconstructing the reference model in this way, the current changes caused by the two different physical processes of high voltage insulation material degradation and low voltage component aging can be separated at the source of the data analysis, and thus the long-term trend calculation of the degradation factor can continue to maintain its effectiveness.

[0044] It is apparent to those skilled in the art that the present application is not limited to the details of the foregoing exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0045] Finally, it should be noted that the above examples are only used to illustrate the technical solutions of the present application and are not limiting, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A high-precision current sensor insulation state detection method, characterized by, The method comprises the following steps: During a listening window in which the current sensor operates with zero load current, performing a synchronized differential sampling with the on-off state of the high-voltage bus to generate a differential current value representing the leakage current; The synchronized differential sampling comprises: at a first time point in the off state of the high-voltage bus, measuring the static operating current of the sensor to obtain an off-state reference current; Immediately after the first time point, at a second time point in the on state of the high-voltage bus, the static operating current of the sensor is measured again to obtain an on-state measured current; wherein the time interval between the second time point and the first time point is determined so that the temperature drift current increment determined by the thermal time constant of the sensor within the time interval is less than a current resolution value determined according to the predetermined signal-to-noise ratio requirement; Calculating the difference between the on-state measured current and the off-state reference current to obtain the differential current value; Diagnosing the insulation state of the current sensor based on the differential current value.

2. The high-precision current sensor insulation state detection method according to claim 1, characterized in that, The listening window is created by utilizing the dead time of the pulse width modulation signal applied to the power electronic converter associated with the current sensor; during the dead time, the main load current is cut off, and the high-voltage bus potential is maintained stable.

3. The high-precision current sensor insulation state detection method according to claim 1, characterized in that, The step of diagnosing the insulation state of the current sensor is further used to identify the insulation degradation mode, and comprises: during the listening window, burst sampling the static operating current to obtain a current time series; calculating the variance and kurtosis of the current time series; based on the long-term mean, variance and kurtosis of the differential current value, combined judgment is made to classify the insulation degradation mode: when the long-term mean shows one-way growth and the variance and kurtosis are both lower than their respective reference thresholds, it is determined as a uniform insulation aging mode; when the variance or kurtosis is greater than its respective reference threshold, it is determined as an intermittent pulse leakage mode.

4. The high-precision current sensor insulation state detection method according to claim 1, characterized in that, The step of diagnosing the insulation state based on the statistical characteristics of the differential current value changing over time is further used for quantifying the degradation trend of the insulation performance, and comprises: recording the differential current values obtained by multiple measurements over time to form a differential value sequence; calculating the statistical mean of the differential value sequence in a predetermined evaluation period; calculating a degradation factor representing the insulation degradation rate by the following formula , , is the statistical mean of the current evaluation period, is the statistical mean of the previous evaluation period, is the length of the predetermined evaluation period, is the reference differential current value measured by the sensor in a healthy state; when the degradation factor continuously exceeds a predetermined threshold value, it is determined that the insulation performance has undergone chronic degradation.

5. The high-precision current sensor insulation state detection method according to claim 1, characterized in that, The specific value of the time interval is calculated and determined during system initialization according to the thermal characteristic parameters of the sensor and the minimum detection sensitivity requirement of the detection method for the leakage current.

6. The high-precision current sensor insulation state detection method according to claim 1, characterized in that, The step of measuring the static operating current is realized by at least one of the following ways: monitoring the voltage across a detection resistor arranged in the low-voltage side power supply circuit of the current sensor; or reading the power supply current data output by a power management chip.

7. The high-precision current sensor insulation state detection method according to claim 1, characterized by, Before the method is executed, further comprising the step of establishing a health state baseline: during the first power-on of the current sensor, under the condition that the high-voltage bus is not powered on and the load current is zero, measuring and storing the reference static operating current of the sensor, as well as the reference variance and reference kurtosis corresponding to the reference static operating current.

8. The high-precision current sensor insulation state detection method according to claim 1, characterized in that, The high-voltage bus on-off event relied on by the synchronized differential sampling is a high-voltage pulse operation periodically performed by the system without affecting the external load.

9. The high-precision current sensor insulation state detection method according to claim 1, characterized by, The step of diagnosing the insulation state of the current sensor further comprises instantaneous abnormality judgment: if the differential current value obtained by single calculation exceeds an instantaneous threshold defined by a predetermined percentage of the off-state reference current, an instantaneous insulation abnormality event is recorded.

10. The high-precision current sensor insulation state detection method according to claim 1, characterized in that, The creation of the listening window is performed when it is identified that the system in which the current sensor is located enters a predetermined standby working mode in which the system main load is instructed to be turned off.

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