Method for detecting electrical performance of high-precision current sensor

Abstract

The invention relates to the technical field of electrical engineering and sensors, and provides a high-precision current sensor electrical performance detection method, which comprises the steps of collecting an original output signal of a current sensor and performing multi-dimensional signal preprocessing; extracting multi-dimensional feature parameters from the preprocessed signals based on a dynamic feature extraction algorithm; establishing a dynamic compensation model according to the multi-dimensional characteristic parameters, and generating a compensation control signal; a compensation control signal is injected into the detected current sensor to form closed-loop feedback, and a compensated output signal is collected; calculating a sensor performance degradation parameter according to the signal difference before and after compensation; an electrical performance assessment report is generated based on the performance degradation parameter. According to the invention, the measurement precision and reliability of the sensor can be improved, the service life is prolonged, the maintenance cost is reduced, and a powerful guarantee is provided for safe and stable operation of a power system and electronic equipment.

Description

Technical Field

[0001] The present invention relates to the field of electrical engineering and sensor technology, and more specifically, to a method for detecting electrical performance of a high-precision current sensor. Background Art

[0002] In modern power systems and electronic equipment, high-precision current sensors are widely used in key links such as current measurement, fault detection, and system control. Existing current sensor detection methods mainly rely on static performance tests, evaluating the performance by comparing the difference between the sensor output and the standard signal. These methods usually include simple signal acquisition and preliminary error analysis, but often ignore the complex characteristics of sensors in dynamic environments, such as nonlinear errors, temperature drift, and performance degradation during long-term use. When processing dynamic signals, traditional detection technologies have difficulty accurately extracting multi-dimensional feature parameters, and cannot adjust compensation models in real time to adapt to dynamic changes in sensors. In addition, when evaluating sensor performance degradation, existing technologies lack effective multi-physics field coupling models and cannot accurately predict the remaining service life of sensors.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the existing detection method cannot fully reflect the true performance of the current sensor under dynamic conditions, and lacks in-depth analysis and real-time compensation capabilities for the dynamic characteristics of the sensor; at the same time, the evaluation of sensor performance degradation is not accurate enough, and its failure time cannot be effectively predicted, resulting in difficulty in taking maintenance measures in advance in actual applications, affecting the stability and reliability of the power system. Summary of the invention

[0004] The present invention provides a high-precision current sensor electrical performance detection method, comprising: S1, collecting the original output signal of the current sensor and performing multi-dimensional signal preprocessing; S2, extracting multidimensional feature parameters from the preprocessed signal based on a dynamic feature extraction algorithm; S3, establishing a dynamic compensation model according to the multi-dimensional characteristic parameters and generating a compensation control signal; S4, injecting the compensation control signal into the measured current sensor to form a closed-loop feedback, and collecting the output signal after compensation; S5, calculating the sensor performance degradation parameter according to the signal difference before and after compensation; S6. Generate an electrical performance evaluation report based on the performance degradation parameters.

[0005] Further, step S2 includes: S21, use the improved time-frequency joint decomposition algorithm to decompose the preprocessed signal and obtain the time domain feature vector and frequency domain eigenvector ,in represents the i-th time domain feature parameter, represents the jth frequency domain feature parameter; S22. Calculate feature fusion weight matrix based on dynamic correlation analysis algorithm ,in Representing time domain features Frequency domain characteristics The correlation coefficient of S23. Calculate the multidimensional feature parameter matrix through the nonlinear feature fusion formula .

[0006] Further, step S3 includes: S31. Construct a compensation function with memory effect: ; in, is the compensation control signal at time t, K is the dynamic gain coefficient, is the historical compensation impact factor, is the time interval, is the modulation angular frequency, is the phase compensation amount; S32, real-time adjustment of parameter groups through online learning algorithms , so that the compensation signal matches the spectral characteristics of the sensor nonlinear error.

[0007] Furthermore, the calculation of the performance degradation parameter in step S5 includes constructing an error evolution model of multi-physics field coupling: S51. Define dynamic error parameters: ; in, is the composite error parameter at time t, is the compensated signal, is the original signal, for The rate of change of multidimensional characteristic parameters at each moment, is the instantaneous error weight, is the cumulative error weight; S52. Establish the degradation equation of multi-factor coupling: ; Where D is the performance degradation parameter, is the aging attenuation coefficient, is the time decay factor, is the mutation sensitivity coefficient, is the temperature coupling coefficient, is the change in ambient temperature; S53, solve the degradation equation by the fourth-order Runge-Kutta method to obtain the time-varying performance degradation curve .

[0008] Furthermore, step S53 further includes: S531. Construct a temperature compensation model, as shown in the following formula: ; Where T is the real-time temperature value, is the thermal characteristic coefficient of the material; S532. Define the degradation stage division rules: when It is judged as healthy state; when When the warning state is triggered; when It is judged as failure state.

[0009] Furthermore, the dynamic correlation analysis algorithm of step S22 includes: S221. Calculate the mutual information entropy of time-frequency features: ; in is the probability distribution function; S222. Calculate dynamic weights through a sliding time window: ; in, is the weight update rate coefficient, is the update interval.

[0010] Further, step S1 includes: S11. Implement multimodal signal enhancement: Adopt adaptive notch filter to eliminate power frequency interference; Separate signal eigenmodes using the empirical mode decomposition algorithm; S12. Execution environment disturbance compensation: Synchronous acquisition of three-dimensional electromagnetic field intensity vector Correct the original signal using the field strength compensation formula: ; in, is the axial compensation coefficient, is the reference field strength value.

[0011] Furthermore, the online learning algorithm of step S32 includes: S321. Construct a dual-objective optimization function as shown in the following formula: ; ; in, is the weight coefficient, is the average tracking error, To compensate for the maximum fluctuation of the signal; S322. Use the constrained particle swarm optimization algorithm to perform a directional search in the parameter space, and update the parameter group every 5 seconds.

[0012] Further, step S4 includes: S41. Design anti-aliasing injection circuit, including: Programmable gain instrumentation amplifier, gain range 60dB ~ 100dB; Digital isolation device, isolation voltage ≥ 2500Vrms; S42, implement multi-rate signal synchronization: The original signal was collected at a sampling rate of 200kHz; Inject compensation signal at 50kHz update rate; Time alignment of signals of different rates is achieved through interpolation algorithm.

[0013] Further, step S6 includes: S61, constructing a three-dimensional health status map, wherein the three-dimensional health status map includes a time axis, a performance axis, and an environment axis; The time axis is the cumulative working time; The performance axis is the degradation parameter; The environmental axis is a composite index of temperature, humidity and vibration; S62. Predict the remaining lifespan through a deep residual network, as shown in the following formula: ; in For the trained neural network model, is the failure threshold, is the degradation acceleration.

[0014] The above-mentioned embodiments of the present invention have at least the following beneficial effects: The high-precision current sensor electrical performance detection method of the present invention can realize a comprehensive evaluation of the dynamic performance of the current sensor. Through multi-dimensional signal preprocessing and dynamic feature extraction algorithms, time domain and frequency domain feature parameters can be accurately extracted to provide richer information for subsequent performance analysis. Combined with the dynamic compensation model and closed-loop feedback mechanism, the compensation signal can be adjusted in real time to effectively reduce the nonlinear error and dynamic deviation of the sensor and improve the measurement accuracy. In addition, based on the error evolution model and performance degradation parameter calculation of multi-physical field coupling, the degree of performance degradation of the sensor can be accurately evaluated, and its health status can be intuitively displayed through a three-dimensional health status map, providing a scientific basis for equipment maintenance.

[0015] At the same time, the present invention can also predict the remaining service life of the sensor through the deep residual network, providing strong support for the preventive maintenance of power systems and electronic equipment. This method can not only improve the reliability of current sensors, but also reduce system downtime and maintenance costs caused by sensor failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which: Figure 1 A schematic flow chart of a method for detecting electrical performance of a high-precision current sensor provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0018] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0019] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0020] Reference below Figure 1 , Figure 1 The following is a flow chart of a method for detecting electrical performance of a high-precision current sensor provided by an embodiment of the present invention. Figure 1 As shown, a high-precision current sensor electrical performance detection method 100 includes: S1, collecting the original output signal of the current sensor and performing multi-dimensional signal preprocessing; S2, extracting multidimensional feature parameters from the preprocessed signal based on a dynamic feature extraction algorithm; S3, establishing a dynamic compensation model according to the multi-dimensional characteristic parameters and generating a compensation control signal; S4, injecting the compensation control signal into the measured current sensor to form a closed-loop feedback, and collecting the output signal after compensation; S5, calculating the sensor performance degradation parameter according to the signal difference before and after compensation; S6. Generate an electrical performance evaluation report based on the performance degradation parameters.

[0021] It should be noted that the high-precision current sensor electrical performance detection method of the present invention first involves collecting the original output signal of the current sensor and performing multi-dimensional signal preprocessing. The multi-dimensional signal preprocessing here refers to a series of processing operations on the collected original signal to remove noise, interference and other factors that affect the signal quality, while enhancing the useful characteristics of the signal. For example, the original output signal refers to the electrical signal generated by the current sensor in the actual working environment, which may be affected by various factors such as electromagnetic interference and temperature changes. The multi-dimensional signal preprocessing is to improve the signal-to-noise ratio of the signal, making it more suitable for subsequent feature extraction and analysis.

[0022] Specifically, multi-dimensional signal preprocessing may include implementing multi-modal signal enhancement and performing environmental disturbance compensation. Multi-modal signal enhancement refers to the use of an adaptive notch filter to eliminate power frequency interference and the use of an empirical mode decomposition algorithm to separate the signal eigenmode. Power frequency interference usually refers to 50Hz or 60Hz AC interference, which is more common in power systems. The adaptive notch filter can dynamically adjust the filter parameters according to the frequency characteristics of the signal, thereby effectively removing the power frequency interference. The empirical mode decomposition algorithm is an adaptive signal processing method that can decompose complex signals into several eigenmode functions that can better reflect the local characteristics of the signal. Environmental disturbance compensation is achieved by synchronously collecting three-dimensional electromagnetic field intensity vectors and using field strength compensation formulas to correct the original signal. The three-dimensional electromagnetic field intensity vector includes electromagnetic field intensity components in three directions. By measuring these components and combining the compensation formula, the original signal can be corrected to reduce the impact of environmental electromagnetic field changes on the signal.

[0023] Preferably, the notch frequency of the adaptive notch filter in the multimodal signal enhancement can be adjusted according to the frequency of the actual power frequency interference. For example, in an environment with 50Hz power frequency interference, the notch frequency can be set to 50Hz. The decomposition process of the empirical mode decomposition algorithm can be optimized according to the complexity of the signal to improve the decomposition efficiency and accuracy. In environmental disturbance compensation, each axial compensation coefficient can be calibrated according to the installation position of the sensor and the characteristics of the environmental electromagnetic field to ensure the accuracy of compensation. In addition, other signal enhancement methods, such as wavelet transform, can also be used as a supplement to the adaptive notch filter to further improve the anti-interference ability of the signal.

[0024] In some embodiments, step S2 includes: S21, use the improved time-frequency joint decomposition algorithm to decompose the preprocessed signal and obtain the time domain feature vector and frequency domain eigenvector ,in represents the i-th time domain feature parameter, represents the jth frequency domain feature parameter; S22. Calculate feature fusion weight matrix based on dynamic correlation analysis algorithm ,in Representing time domain features Frequency domain characteristics The correlation coefficient of S23. Calculate the multidimensional feature parameter matrix through the nonlinear feature fusion formula .

[0025] It should be noted that step S2 in the present invention includes decomposing the preprocessed signal using an improved time-frequency joint decomposition algorithm, obtaining time-domain eigenvectors and frequency-domain eigenvectors, and calculating the feature fusion weight matrix based on the dynamic correlation analysis algorithm, and finally calculating the multidimensional feature parameter matrix through the nonlinear feature fusion formula. The time-frequency joint decomposition algorithm here is a signal processing method, which can analyze the signal in the time domain and frequency domain at the same time, so as to extract the characteristics of the signal more comprehensively. The time-domain eigenvector and the frequency-domain eigenvector describe the characteristics of the signal in time and frequency respectively, and the dynamic correlation analysis algorithm is used to calculate the correlation between these features to determine their weights in the fusion process. The calculation of this weight matrix is ​​crucial for subsequent feature fusion because it determines the importance of different features in the final feature parameter matrix.

[0026] Specifically, the improved time-frequency joint decomposition algorithm is optimized on the basis of the traditional time-frequency analysis method to better adapt to the characteristics of the current sensor signal. For example, the time domain feature vector can include parameters such as the amplitude, mean, and variance of the signal, which reflect the temporal variation of the signal. The frequency domain feature vector can include parameters such as the frequency distribution and power spectrum density of the signal, which reveal the characteristics of the signal in frequency. The dynamic correlation analysis algorithm determines the correlation coefficient of the time domain features and the frequency domain features by calculating the mutual information entropy between them. Mutual information entropy is an indicator of the degree of mutual dependence between two variables, which can help determine which features have a strong correlation. When calculating the feature fusion weight matrix, weights can be assigned according to these correlation coefficients, so that the fused feature parameter matrix can more accurately reflect the comprehensive characteristics of the signal.

[0027] Preferably, the time-frequency joint decomposition algorithm can adopt a combination of wavelet transform and Hilbert-Huang transform. Wavelet transform can provide good time-frequency resolution, while Hilbert-Huang transform is more suitable for processing non-stationary signals. The combination of the two can better extract the characteristics of the current sensor signal. When calculating the dynamic correlation, the concept of sliding time window can be introduced to adapt to the dynamic changes of the signal. The size of the sliding time window can be adjusted according to the frequency characteristics of the signal. For example, for high-frequency signals, the time window can be set smaller to capture the changes in the signal more quickly. In addition, other dynamic correlation analysis methods, such as correlation analysis based on neural networks, can also be used to further improve the accuracy and adaptability of weight calculation.

[0028] In some embodiments, step S3 includes: S31. Construct a compensation function with memory effect: ; in, is the compensation control signal at time t, K is the dynamic gain coefficient, is the historical compensation impact factor, is the time interval, is the modulation angular frequency, is the phase compensation amount; S32, real-time adjustment of parameter groups through online learning algorithms , so that the compensation signal matches the spectral characteristics of the sensor nonlinear error.

[0029] It should be noted that step S3 of the present invention involves constructing a compensation function with a memory effect, and adjusting the parameter group in real time through an online learning algorithm so that the compensation signal matches the spectral characteristics of the sensor nonlinear error. The compensation function here is a mathematical model used to generate a compensation control signal to correct the measurement error of the current sensor. The memory effect means that the compensation function can take into account historical compensation information to better adapt to the dynamic changes of the sensor. The online learning algorithm is an adaptive algorithm that can dynamically adjust the parameters of the compensation function according to real-time data to ensure the matching degree of the compensation signal with the sensor error characteristics.

[0030] Specifically, the construction of the compensation function takes into account multiple parameters, including dynamic gain coefficient, historical compensation influence factor, time interval, modulation angular frequency and phase compensation amount. The dynamic gain coefficient is used to adjust the amplitude of the compensation signal to adapt to different error sizes; the historical compensation influence factor reflects the impact of past compensation on the current compensation, which helps the compensation function to better remember the historical state; the time interval and modulation angular frequency are used to control the time and frequency characteristics of the compensation signal; the phase compensation amount is used to correct the phase deviation of the signal. The online learning algorithm adjusts these parameters by constructing a dual-objective optimization function. The optimization objectives usually include minimizing the average tracking error and limiting the maximum fluctuation of the compensation signal. The constrained particle swarm optimization algorithm is a commonly used online learning algorithm that can efficiently search in the parameter space to find the optimal parameter combination.

[0031] Preferably, the dynamic gain coefficient can be dynamically adjusted according to the error range of the sensor, for example, the gain is increased when the error is large to speed up the compensation. The historical compensation influencing factor can be determined experimentally to ensure that the compensation function can effectively utilize historical information. The time interval can be set according to the sampling rate of the sensor. For example, when the sampling rate is 1kHz, the time interval can be set to 1ms. The modulation angular frequency can be selected according to the operating frequency range of the sensor to ensure that the compensation signal matches the frequency characteristics of the error signal. The phase compensation amount can be determined by a phase analysis algorithm to correct the phase deviation of the sensor output signal. In addition, other online learning algorithms, such as adaptive filtering algorithms or deep learning algorithms, can also be used to further improve the accuracy and adaptability of parameter adjustment.

[0032] In some embodiments, the calculation of the performance degradation parameter in step S5 includes constructing a multi-physics field coupled error evolution model: S51. Define dynamic error parameters: ; in, is the composite error parameter at time t, is the compensated signal, is the original signal, for The rate of change of multidimensional characteristic parameters at each moment, is the instantaneous error weight, is the cumulative error weight; S52. Establish the degradation equation of multi-factor coupling: ; Where D is the performance degradation parameter, is the aging attenuation coefficient, is the time decay factor, is the mutation sensitivity coefficient, is the temperature coupling coefficient, is the change in ambient temperature; S53, solve the degradation equation by the fourth-order Runge-Kutta method to obtain the time-varying performance degradation curve .

[0033] It should be noted that step S5 of the present invention involves constructing a multi-physics field coupling error evolution model for calculating performance degradation parameters. Multi-physics field coupling here refers to the consideration of the performance changes of the sensor under the combined action of multiple physical factors (such as temperature, time, mutation factors, etc.). The performance degradation parameter is an indicator to measure the decline in sensor performance over time. By establishing an error evolution model, the dynamic error changes of the sensor can be quantitatively analyzed, thereby providing a basis for evaluating its health status.

[0034] Specifically, the definition of the dynamic error parameter combines the instantaneous error and the cumulative error, where the instantaneous error weight and the cumulative error weight are used to balance the importance of the two. The instantaneous error weight reflects the impact of the error at the current moment, while the cumulative error weight takes into account the cumulative effect of the error over time. The multi-factor coupled degradation equation further considers factors such as the aging attenuation coefficient, time attenuation factor, mutation sensitivity coefficient, and temperature coupling coefficient, which correspond to the aging, time dependence, sensitivity to mutations, and the impact of temperature changes on the performance of the sensor. By solving the degradation equation using the fourth-order Runge-Kutta method, a time-varying performance degradation curve can be obtained, which can intuitively show the trend of sensor performance over time.

[0035] Preferably, the temperature compensation model can be adjusted according to the working environment temperature of the sensor, and the thermal property coefficient of the material can be obtained by experimental measurement to ensure the accuracy of the model. The degradation stage division rules can be set according to the actual application requirements. For example, the thresholds of the healthy state, warning state and failure state can be determined according to the performance requirements and safety standards of the sensor. In addition, other physical factors (such as humidity, vibration, etc.) can be introduced to further improve the multi-physics field coupling model to more comprehensively evaluate the performance degradation of the sensor.

[0036] In some embodiments, step S53 further includes: S531. Construct a temperature compensation model, as shown in the following formula: ; Where T is the real-time temperature value, is the thermal characteristic coefficient of the material; S532. Define the degradation stage division rules: when It is judged as healthy state; when When the warning state is triggered; when It is judged as failure state.

[0037] It should be noted that step S53 of the present invention further refines the calculation process of the performance degradation parameters, especially introduces the temperature compensation model and the degradation stage division rule. The temperature compensation model is used to correct the influence of temperature change on the performance degradation parameters to ensure the accuracy of performance evaluation under different ambient temperatures. The degradation stage division rule divides the health status of the sensor into three stages: healthy, early warning and failure according to the numerical range of the performance degradation parameters, so as to take corresponding maintenance measures in time.

[0038] Specifically, the material thermal characteristic coefficients in the temperature compensation model are parameters determined based on the thermophysical properties of the sensor material. These coefficients can be obtained through experimental measurements and are used to describe the impact of temperature changes on sensor performance. The real-time temperature value refers to the actual temperature in the sensor working environment, which is measured by a temperature sensor. The thresholds in the degradation stage division rules (such as and ) is pre-set according to the performance indicators and reliability requirements of the sensor and is used to determine the health status of the sensor. For example, when the performance degradation parameter D is less than D1, the sensor is in a healthy state; when D is between and When D is greater than or equal to The sensor is considered to be failed.

[0039] Preferably, the thermal characteristic coefficients of the material can be determined by performing a temperature cycle experiment on the sensor, recording the performance changes of the sensor at different temperatures in the experiment, and then obtaining these coefficients by fitting. The real-time temperature value can be collected by a high-precision temperature sensor, and its accuracy can be ensured by data calibration. The thresholds in the degradation stage division rules can be adjusted according to the actual application scenarios of the sensor. For example, in applications with high reliability requirements, the thresholds can be appropriately reduced. In addition, compensation models for other environmental factors (such as humidity, vibration, etc.) can be introduced to further improve the comprehensiveness and accuracy of performance evaluation.

[0040] In some embodiments, the dynamic correlation analysis algorithm of step S22 includes: S221. Calculate the mutual information entropy of time-frequency features: ; in is the probability distribution function; S222. Calculate dynamic weights through a sliding time window: ; in, is the weight update rate coefficient, is the update interval.

[0041] It should be noted that the dynamic correlation analysis algorithm of step S22 in the present invention includes calculating the mutual information entropy of time-frequency features and calculating dynamic weights through a sliding time window. The mutual information entropy of time-frequency features here is an indicator of the degree of mutual dependence between time domain features and frequency domain features, which can help determine which features have a strong correlation. The sliding time window is a dynamic update mechanism for adjusting weights according to real-time changes in the signal to adapt to the dynamic characteristics of the signal.

[0042] Specifically, the calculation of the mutual information entropy of time-frequency features is based on the probability distribution function, which reflects the degree of information sharing between time domain features and frequency domain features. The probability distribution function describes the probability distribution of the occurrence of feature values. By calculating the mutual information entropy, the degree of association between two features can be quantified. The size of the sliding time window and the weight update rate coefficient are key parameters in the dynamic correlation analysis algorithm. The size of the sliding time window determines the speed at which the algorithm responds to signal changes, while the weight update rate coefficient controls the degree of influence of new data on the weight. These parameters can be adjusted according to the characteristics of the signal and the application scenario to optimize the performance of the algorithm.

[0043] Preferably, the size of the sliding time window can be selected according to the frequency characteristics of the signal. For high-frequency signals, a smaller time window can capture the dynamic changes of the signal more quickly; while for low-frequency signals, a larger time window can reduce the impact of noise. The weight update rate coefficient can be adjusted according to the stability of the signal. For example, in an environment where the signal changes rapidly, the update rate can be increased to improve the adaptability of the algorithm. In addition, other dynamic weight update mechanisms, such as weight update methods based on adaptive filters, can be used to further improve the flexibility and accuracy of the algorithm.

[0044] In some embodiments, step S1 includes: S11. Implement multimodal signal enhancement: Adopt adaptive notch filter to eliminate power frequency interference; Separate signal eigenmodes using the empirical mode decomposition algorithm; S12. Execution environment disturbance compensation: Synchronous acquisition of three-dimensional electromagnetic field intensity vector Correct the original signal using the field strength compensation formula: ; in, is the axial compensation coefficient, is the reference field strength value.

[0045] It should be noted that in the present invention, step S1 involves implementing multimodal signal enhancement and performing environmental disturbance compensation. Multimodal signal enhancement here refers to improving the quality and reliability of the signal through a variety of signal processing technologies, while environmental disturbance compensation refers to measuring and correcting the impact of external environmental factors on the signal to reduce the interference of these factors on the performance detection of the current sensor. These operations are the basis for ensuring the accuracy of subsequent signal processing and performance evaluation.

[0046] Specifically, multimodal signal enhancement includes using an adaptive notch filter to eliminate power frequency interference and using an empirical mode decomposition algorithm to separate signal eigenmodes. Power frequency interference usually refers to interference generated by alternating current in the power system, and its frequency is generally 50Hz or 60Hz. The adaptive notch filter can dynamically adjust the filter parameters according to the characteristics of the signal to effectively remove the interference of this specific frequency. The empirical mode decomposition algorithm is an adaptive signal decomposition method that can decompose complex signals into several eigenmode functions, which can better reflect the local characteristics of the signal. In environmental disturbance compensation, the three-dimensional electromagnetic field intensity vector is synchronously collected and the original signal is corrected using the field strength compensation formula. The three-dimensional electromagnetic field intensity vector includes electromagnetic field intensity components in three directions. By measuring these components and combining the compensation formula, the original signal can be corrected to reduce the impact of environmental electromagnetic field changes on the signal. Each axial compensation coefficient is a parameter pre-calibrated according to the installation position of the sensor and the characteristics of the environmental electromagnetic field, and is used to adjust the correction amplitude.

[0047] Preferably, the notch frequency of the adaptive notch filter can be accurately adjusted according to the frequency of the actual power frequency interference. For example, in an environment with 50Hz power frequency interference, the notch frequency can be set to 50Hz, and the notch depth can be adjusted in real time according to the dynamic changes of the signal. The decomposition process of the empirical mode decomposition algorithm can be optimized according to the complexity of the signal, for example, by adjusting the number of decomposition iterations to improve the decomposition efficiency and accuracy. In environmental disturbance compensation, each axial compensation coefficient can be determined by experimental methods, such as measuring the output signal of the sensor under different environmental electromagnetic field conditions, and obtaining the optimal compensation coefficient by least squares fitting. In addition, other signal enhancement methods, such as wavelet transform, etc., can be used as a supplement to the adaptive notch filter to further improve the anti-interference ability of the signal.

[0048] In some embodiments, the online learning algorithm of step S32 includes: S321. Construct a dual-objective optimization function as shown in the following formula: ; ; in, is the weight coefficient, is the average tracking error, To compensate for the maximum fluctuation of the signal; S322. Use the constrained particle swarm optimization algorithm to perform a directional search in the parameter space, and update the parameter group every 5 seconds.

[0049] It should be noted that the online learning algorithm of step S32 in the present invention includes constructing a dual-objective optimization function and using a constrained particle swarm optimization algorithm to perform a directional search in the parameter space. The dual-objective optimization function here is a mathematical model for optimizing multiple objectives at the same time, such as minimizing the average tracking error and limiting the maximum fluctuation of the compensation signal. The constrained particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence, which searches for the optimal solution by simulating the behavior of a particle swarm. This algorithm can efficiently find the optimal parameter combination that meets the constraint conditions in the parameter space, thereby realizing dynamic adjustment of the compensation model parameters.

[0050] Specifically, the weight coefficients (such as weight coefficient 1 and weight coefficient 2) in the dual-objective optimization function are used to balance the importance of different optimization objectives. The average tracking error refers to the difference between the compensation signal and the actual error. By minimizing this error, the accuracy of compensation can be improved. The maximum fluctuation of the compensation signal reflects the stability of the compensation signal. Limiting this fluctuation can avoid the excessive fluctuation of the compensation signal from affecting the system. The constrained particle swarm optimization algorithm searches for the optimal solution by defining the speed and position update rules of the particles. In the parameter space, each particle represents a potential solution, and the particle swarm gradually approaches the optimal solution through information sharing and individual learning. The parameters of the algorithm, such as the inertia weight and learning factor in the particle speed and position update formula, can be adjusted according to the specific problem to improve the search efficiency and convergence speed.

[0051] Preferably, the weight coefficient can be adjusted according to the needs in practical applications. For example, in scenarios where high compensation accuracy is required, the weight of the average tracking error can be increased; and in scenarios where high system stability is required, the weight of the maximum fluctuation of the compensation signal can be increased. The inertia weight of the constrained particle swarm optimization algorithm can be adjusted dynamically, for example, a larger inertia weight is used in the early stage of the search to speed up the search, and the inertia weight is reduced in the later stage of the search to improve the search accuracy. The learning factor can be dynamically adjusted according to the diversity and convergence of the group to balance the global search capability and the local search capability. In addition, other optimization algorithms, such as genetic algorithms or simulated annealing algorithms, can be used as alternatives to the constrained particle swarm optimization algorithm to further improve the performance and adaptability of parameter optimization.

[0052] In some embodiments, step S4 includes: S41. Design anti-aliasing injection circuit, including: Programmable gain instrumentation amplifier, gain range 60dB ~ 100dB; Digital isolation device, isolation voltage ≥ 2500Vrms; S42, implement multi-rate signal synchronization: The original signal was collected at a sampling rate of 200kHz; Inject compensation signal at 50kHz update rate; Time alignment of signals of different rates is achieved through interpolation algorithm.

[0053] It should be noted that in the present invention, step S4 involves designing an anti-aliasing injection circuit and implementing multi-rate signal synchronization. The anti-aliasing injection circuit here is a circuit design for preventing aliasing of the signal during the sampling process, which can ensure the accurate injection and acquisition of the compensation signal. Multi-rate signal synchronization refers to time alignment of the signal at different sampling rates to ensure the temporal consistency of the compensation signal and the original signal. These two operations are key steps to achieve closed-loop feedback and accurate performance evaluation.

[0054] Specifically, the anti-aliasing injection circuit includes a programmable gain instrumentation amplifier and a digital isolation device. The programmable gain instrumentation amplifier is used to adjust the amplitude of the compensation signal, and its gain range can be set according to the actual application requirements. For example, in the present invention, the gain range is 60dB to 100dB. The digital isolation device is used to isolate the electrical connection between the compensation signal and the original signal to prevent mutual interference between the signals. Its isolation voltage should meet the system safety requirements, such as the isolation voltage ≥ 2500Vrms. In multi-rate signal synchronization, the sampling rate of the original signal and the update rate of the compensation signal can be selected according to the dynamic characteristics of the sensor and the system requirements. For example, the original signal is collected at a sampling rate of 200kHz, and the compensation signal is injected at an update rate of 50kHz. The time alignment of signals of different rates can be achieved through the interpolation algorithm to ensure that the compensation signal can accurately act on the original signal.

[0055] Preferably, the gain of the programmable gain instrumentation amplifier can be dynamically adjusted according to the error range of the sensor, for example, the gain can be increased when the error is large to improve the compensation effect. The selection of digital isolation devices should take into account their isolation performance and reliability, such as the use of high-voltage digital isolation chips. In multi-rate signal synchronization, the interpolation algorithm can use methods such as linear interpolation or spline interpolation, and the specific choice depends on the characteristics of the signal and the accuracy requirements. For example, for smoothly changing signals, linear interpolation may be sufficient; while for rapidly changing signals, spline interpolation can provide higher accuracy. In addition, other synchronization technologies, such as phase-locked loop synchronization, can also be used to further improve the accuracy and stability of signal synchronization.

[0056] In some embodiments, step S6 includes: S61, constructing a three-dimensional health status map, wherein the three-dimensional health status map includes a time axis, a performance axis, and an environment axis, The time axis is the cumulative working time; The performance axis is the degradation parameter; The environmental axis is a composite index of temperature, humidity and vibration; S62. Predict the remaining lifespan through a deep residual network, as shown in the following formula: ; in For the trained neural network model, is the failure threshold, is the degradation acceleration.

[0057] It should be noted that in the present invention, step S6 involves constructing a three-dimensional health status map and predicting the remaining life through a deep residual network. The three-dimensional health status map here is a visualization tool for displaying the health status of the current sensor. It includes a time axis, a performance axis, and an environmental axis, which can intuitively reflect the state of the sensor at different times, performance levels, and environmental conditions. The deep residual network is a deep learning model for predicting the remaining service life of the sensor based on its performance degradation parameters. This model can accurately predict the failure time of the sensor by learning a large amount of sensor data, providing a scientific basis for maintenance and replacement.

[0058] Specifically, the time axis in the three-dimensional health status map represents the cumulative working time of the sensor, the performance axis represents the performance degradation parameters, and the environment axis is a composite indicator, including factors such as temperature, humidity, and vibration. The settings of these axes can be adjusted according to actual application requirements. For example, the unit of the time axis can be hours, days, or months, the range of the performance axis can be set according to the performance indicators of the sensor, and the composite indicator of the environment axis can be calculated by weighted summation. The input of the deep residual network includes performance degradation parameters, degradation acceleration, and failure threshold, etc. These parameters can be obtained through the actual operation data of the sensor. The output of the network is the remaining service life of the sensor, and the new sensor data can be predicted through the trained model.

[0059] Preferably, the three-dimensional health status map can be implemented by visualization software, such as using Python's Matplotlib library or JavaScript's D3.js library. In the calculation of the composite index of the environmental axis, weights can be assigned according to the degree of influence of different environmental factors on sensor performance. For example, temperature may have a greater impact on sensor performance and a higher weight can be assigned. The training of the deep residual network can be carried out by supervised learning using a large amount of historical sensor data. When predicting the remaining life, uncertainty analysis can be introduced, such as estimating the confidence interval of the prediction through the Monte Carlo method. In addition, other deep learning models, such as recurrent neural networks (RNNs) or long short-term memory networks (LSTMs), can be used as alternatives to deep residual networks to further improve the accuracy and adaptability of the prediction.

[0060] The above-mentioned embodiments of the present invention have the following beneficial effects: by collecting the original output signal of the current sensor and performing multi-dimensional signal preprocessing, the power frequency interference and environmental disturbance can be effectively eliminated and the signal quality can be improved. The improved time-frequency joint decomposition algorithm is used to extract multi-dimensional feature parameters, which can more comprehensively reflect the dynamic characteristics of the sensor. The feature fusion weight matrix is ​​calculated based on the dynamic correlation analysis algorithm, which can further optimize the fusion effect of the feature parameters and provide more accurate input for the subsequent establishment of the compensation model. Constructing a compensation function with a memory effect and adjusting the parameters in real time through an online learning algorithm can make the compensation signal accurately match the spectral characteristics of the sensor's nonlinear error, thereby improving the measurement accuracy and stability of the sensor. In addition, by calculating the performance degradation parameters through the error evolution model coupled with multiple physical fields, the performance changes of the sensor can be accurately evaluated, providing a scientific basis for the maintenance and replacement of the equipment.

[0061] In the closed-loop feedback process, designing an anti-aliasing injection circuit and implementing multi-rate signal synchronization can ensure the accurate injection and acquisition of compensation signals, further improving the reliability of the system. By solving the degradation equation using the fourth-order Runge-Kutta method, the time-varying performance degradation curve can be obtained, providing a more intuitive reference for the health status monitoring of the sensor. Constructing a three-dimensional health status map and a remaining life prediction model based on a deep residual network can realize the visualization of the health status of the sensor and life prediction, providing strong support for the preventive maintenance of power systems and electronic equipment. These improvement measures can jointly improve the performance evaluation accuracy and reliability of current sensors, and reduce system downtime and maintenance costs caused by sensor failures.

[0062] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0063] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A high-precision current sensor electrical performance detection method, characterized in that: The following steps are involved: S1, collecting the original output signal of the current sensor and performing multi-dimensional signal preprocessing; S2, extracting multidimensional feature parameters from the preprocessed signal based on a dynamic feature extraction algorithm; S3, establishing a dynamic compensation model according to the multi-dimensional characteristic parameters and generating a compensation control signal; S4, injecting the compensation control signal into the measured current sensor to form a closed-loop feedback, and collecting the output signal after compensation; S5, calculating the sensor performance degradation parameter according to the signal difference before and after compensation; S6. Generate an electrical performance evaluation report based on the performance degradation parameters.

2. The method according to claim 1, characterized in that Step S2 includes: S21, use the improved time-frequency joint decomposition algorithm to decompose the preprocessed signal and obtain the time domain feature vector and frequency domain eigenvector ,in represents the i-th time domain feature parameter, represents the jth frequency domain feature parameter; S22. Calculate feature fusion weight matrix based on dynamic correlation analysis algorithm ,in Representing time domain features Frequency domain characteristics The correlation coefficient of S23. Calculate the multi-dimensional feature parameter matrix through the nonlinear feature fusion formula.

3. The method according to claim 1, characterized in that Step S3 includes: S31. Construct a compensation function with memory effect, as shown in the following formula: ； in, is the compensation control signal at time t, K is the dynamic gain coefficient, is the historical compensation impact factor, is the time interval, is the modulation angular frequency, is the phase compensation amount; S32, real-time adjustment of parameter groups through online learning algorithms , so that the compensation signal matches the spectral characteristics of the sensor nonlinear error.

4. The method according to claim 1, characterized in that: The calculation of the performance degradation parameters in step S5 includes constructing an error evolution model of multi-physics field coupling: S51. Define dynamic error parameters, as shown in the following formula: ； in, is the composite error parameter at time t, is the compensated signal, is the original signal, for The rate of change of multidimensional characteristic parameters at each moment, is the instantaneous error weight, is the cumulative error weight; S52. Establish a multi-factor coupled degradation equation, as shown in the following formula: ； Where D is the performance degradation parameter, is the aging attenuation coefficient, is the time decay factor, is the mutation sensitivity coefficient, is the temperature coupling coefficient, is the change in ambient temperature; S53, solve the degradation equation by the fourth-order Runge-Kutta method to obtain the time-varying performance degradation curve .

5. The method according to claim 4, characterized in that Step S53 also includes: S531. Construct a temperature compensation model, as shown in the following formula: ； Where T is the real-time temperature value, is the thermal characteristic coefficient of the material; S532. Define the degradation stage division rules: when It is judged as healthy state; when When the warning state is triggered; when It is judged as failure state.

6. The method according to claim 2, characterized in that The dynamic correlation analysis algorithm of step S22 includes: S221. Calculate the mutual information entropy of time-frequency features, as shown in the following formula: ； in, is the probability distribution function; Time domain features Frequency domain characteristics The joint probability distribution of Time domain features The marginal probability distribution of is the frequency domain feature The marginal probability distribution of S222. Calculate dynamic weights through a sliding time window: ； in, is the weight update rate coefficient, is the update interval.

7. The method according to claim 1, characterized in that Step S1 includes: S11. Implement multimodal signal enhancement, including: Adopt adaptive notch filter to eliminate power frequency interference; Separate signal eigenmodes using the empirical mode decomposition algorithm; S12, performing environmental disturbance compensation, including: Synchronous acquisition of three-dimensional electromagnetic field intensity vector And the original signal is corrected by the field strength compensation formula, as shown in the following formula: ； in, is the axial compensation coefficient, is the reference field strength value, is the correction signal after environmental compensation, is the original signal and is the compensated signal. is the axial field strength value.

8. The method according to claim 3, characterized in that The online learning algorithm of step S32 includes: S321. Construct a dual-objective optimization function, as shown in the following formula: ； ； in, is the weight coefficient, is the average tracking error, To compensate for the maximum fluctuation of the signal; S322. Use the constrained particle swarm optimization algorithm to perform a directional search in the parameter space, and update the parameter group every 5 seconds.

9. The method according to claim 1, characterized in that: Step S4 includes: S41. Design an anti-aliasing injection circuit, the anti-aliasing injection circuit includes: Programmable gain instrumentation amplifier, gain range 60dB ~ 100dB; Digital isolation device, isolation voltage ≥ 2500Vrms; S42, implement multi-rate signal synchronization: The original signal was collected at a sampling rate of 200kHz; Inject compensation signal at 50kHz update rate; Time alignment of signals of different rates is achieved through interpolation algorithm.

10. The method according to claim 1, characterized in that Step S6 includes: S61, constructing a three-dimensional health status map, wherein the three-dimensional health status map includes a time axis, a performance axis, and an environment axis; Among them, the time axis is the accumulated working time; the performance axis is the degradation parameter; the environment axis is the temperature, humidity and vibration composite index; S62. Predict the remaining lifespan through a deep residual network, as shown in the following formula: ； in, For the trained neural network model, is the failure threshold, is the degradation acceleration, To predict remaining life.

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