A method and apparatus for analyzing and evaluating a test waveform of a superimposed voltage of a surge

Abstract

The application discloses a method and equipment for analyzing and evaluating a test waveform of superimposed impulse voltage. The method comprises the following steps: synchronously collecting and adaptively preprocessing multi-channel measurement data to obtain correction data; performing adaptive curve fitting-based voltage component separation and parameter extraction based on the correction data to obtain an impulse voltage waveform and standard parameters of the impulse voltage; and performing multi-scale time-frequency feature extraction-based waveform analysis and evaluation on the impulse voltage waveform based on the standard parameters of the impulse voltage to obtain an evaluation result.

Description

Technical Field

[0001] This invention relates to the field of high voltage testing technology, and more specifically, to a method and device for analyzing and evaluating waveforms of impulse superimposed voltage tests. Background Technology

[0002] With the rapid development of ultra-high voltage transmission technology and smart grids, power equipment faces increasingly complex voltage environments. In actual operation, equipment insulation not only has to withstand power frequency AC or DC voltages, but may also be subjected to the superimposed effects of transient overvoltages such as lightning strikes and switching impulses. To accurately evaluate the insulation performance of equipment under combined voltages, standards such as IEC 60060 have specified preliminary test methods for composite and combined voltages: by simultaneously applying the base voltage and impulse voltage to the same test specimen to form a superimposed waveform; or by applying the base voltage and impulse voltage separately from different locations and obtaining each voltage component through multi-channel synchronous measurement.

[0003] Current waveform analysis techniques for superimposed voltage testing mainly suffer from the following problems: Traditional methods often rely on manual waveform interpretation or simple peak detection, making it difficult to accurately separate the voltage components in the superimposed waveform, especially when the base voltage and impulse voltage highly overlap in the time domain, resulting in significant parameter extraction errors; existing analyses are mostly focused on time-domain parameter calculations, lacking evaluation of quality indicators such as waveform time-frequency characteristics, component coupling distortion, and channel synchronization errors; for multi-channel data, there is a lack of effective time alignment and consistency verification methods, and waveform quality assessment mainly relies on manual experience judgment, lacking standardized and intelligent evaluation methods, and failing to provide quantitative quality indicators and targeted improvement suggestions.

[0004] Based on the above situation, there is an urgent need to develop a superimposed voltage test waveform analysis technology that can adaptively identify test modes, separate voltage components, mine time-frequency characteristics, and perform comprehensive waveform quality evaluation, so as to improve the measurement accuracy and quality level of high voltage tests. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and equipment for analyzing and evaluating waveforms of impulse superimposed voltage tests.

[0006] According to one aspect of the present invention, a method for analyzing and evaluating the waveform of an impulse superimposed voltage test is provided, comprising: Simultaneous acquisition and adaptive preprocessing of multi-channel measurement data are performed to obtain correction data; Voltage component separation and parameter extraction based on adaptive curve fitting of the correction data yield the impulse voltage waveform and its standard parameters. Based on the standard parameters of impulse voltage, waveform analysis and evaluation are performed on the impulse voltage waveform by extracting multi-scale time-frequency features, and the evaluation results are obtained.

[0007] Optionally, the multi-channel measurement data are simultaneously acquired and adaptively preprocessed to obtain correction data, including: S101: The sampling rate is detected based on the number of channels of the acquired multi-channel measurement data, and the voltage type identifier is determined based on the detected sampling rate range to identify the type of measurement data; S102: Perform time axis alignment operation on the multi-channel measurement data after identifying the data type of the measurement data. Using the time axis of the impulse voltage channel as the reference time axis, the data of other channels are mapped to the reference time axis by interpolation to obtain time-synchronized multi-channel measurement data. S103: Adaptive downsampling of time-synchronized multi-channel measurement data; S104: Perform drift correction on the sampled multi-channel measurement data, determine the impact initiation point, calculate the baseline offset based on the pre-impact data, and use the baseline offset to correct all data to obtain the corrected data.

[0008] Optionally, the specific steps of step S101 include: Sampling rate detection is performed on multi-channel measurement data, and the sampling rate is calculated. ,in The average time interval between adjacent sampling points; when When the measured data is determined to contain lightning strike components, it is determined that the measured data contains lightning impact components. At that time, the measured data was determined to contain operational impact components.

[0009] Optionally, in step 102, for the three-channel measurement data, using the time axis of the impulse voltage channel as a reference, the expression for mapping the third channel data to the reference time axis through linear interpolation is as follows: In the formula, superscript Indicates the interpolated data, index j For the moment of impact, i Based on low-speed sampling time, k At the high-speed sampling point, the three-channel measurement data includes: low sampling rate data of the base voltage. High sampling rate data of impulse voltage High sampling rate synchronous data of base voltage .

[0010] Optionally, step S103 includes: When data points Calculate the downsampling factor. Data is retained using a uniform interval sampling method based on a downsampling factor. Nm Take 10 6 Before downsampling, an anti-aliasing filter is applied, and the filter cutoff frequency is set to [value missing]. It employs 4th to 8th order Butterworth low-pass filters.

[0011] Optionally, step 104 includes: Calculate the first-order voltage difference, record the moment when the voltage first exceeds the noise threshold, and mark it as the impulse initiation point. The noise threshold is calculated based on the statistical characteristics of the data before the impact. ,in and These are the noise mean and standard deviation, respectively. Determine the impact initiation point Then, the baseline offset was calculated using the data segment prior to the impact. And perform correction on all data, where the correction expression is: in, U c ( t i )for t i The voltage is constantly corrected. U ( t i )for t i The original measured voltage at time Δ U is the baseline bias, and is the average voltage of the sampling points before the impact.

[0012] Optionally, voltage component separation and parameter extraction are performed based on adaptive curve fitting of the correction data to obtain the impulse voltage waveform and its standard parameters, including: S201: Determine whether the base voltage type is AC or DC based on the calibration data; S202: Based on the identified base voltage type, the corresponding fitting model is used to separate and obtain the impulse voltage waveform; S203: After performing a double exponential function fit on the impulse voltage waveform, a test curve is constructed, and the standard parameters of the impulse voltage are calculated based on the test curve.

[0013] Optionally, in step S202: For AC base voltage, a weighted least squares fitting model containing harmonics is used to calculate the fundamental amplitude, effective value, and total harmonic distortion parameters, thus separating the impulse voltage waveform of the AC base voltage; or For the DC base voltage, the corrected voltage is used as the impulse voltage waveform.

[0014] Optionally, the impulse voltage waveform of the AC base voltage. U ms The expression is: In the formula, U a For the fitted function in t i The calculated value at time is determined by the fundamental amplitude, effective value, and total harmonic distortion parameter. U c ( t i )for t i The voltage is constantly corrected after adjustment; impulse voltage waveform of DC base voltage U ms The expression is: .

[0015] Optionally, the fitted double exponential function U mf The expression is: In the formula, Here, is the Heaviside step function, and is the amplitude coefficient. For virtual start time, subscript mf Represents the fitted curve; These are the parameters obtained iteratively using the Levenberg-Marquardt algorithm; Test curve U t The expression is: In the formula, U mf This serves as the baseline curve for the fitted function; To use an infinite impulse response filter to analyze the residual curve U ρ The filtered residual; For the residual curve, Represents the residual; The impact parameters calculated based on the test curve include: test voltage value. The maximum value of the test curve, wavefront time Half-peak time ,in , , The overshoot is calculated at the times corresponding to 30%, 90%, and 50% amplitude of the test curve, respectively: .

[0016] Optionally, based on the standard parameters of the impulse voltage, waveform analysis and evaluation are performed on the impulse wave voltage waveform using multi-scale time-frequency feature extraction, yielding evaluation results, including: S301: Perform wavelet decomposition on the impulse voltage waveform, extract time-frequency features, and calculate high-frequency noise index and mid-frequency oscillation index; S302: Based on the test curve, extract the wavefront slope uniformity and wave tail attenuation characteristics as local waveform features; S303: Calculate the coupling distortion between the ideal composite waveform and the actual measured waveform of the base voltage component and the impulse voltage component; S304: An indicator for evaluating the consistency of base voltage parameters between channels with different sampling rates.

[0017] S305: Determine the evaluation results based on high-frequency noise index, mid-frequency oscillation index, waveform local characteristics, coupling distortion, and consistency index.

[0018] Optionally, high-frequency noise specifications ,in E 1 represents the energy percentage of the first-layer wavelet coefficients. E 2 represents the energy proportion of the second-layer wavelet coefficients; Mid-frequency oscillation index It is the ratio of the maximum wavelet coefficient to the maximum scale coefficient of the decomposition layer corresponding to the wavefront; Wavefront slope uniformity ,in and These are the standard deviation and mean of the slope sequence, respectively; The normalized root mean square value of the fitting residuals for log-linear fitting of data with a tail decay characteristic of 50% to 10% amplitude range. ; Coupling distortion ,in, U id It is the ideal superposition of the base voltage and the impulse voltage. U c The corrected waveform; N This represents the number of waveform sampling points.

[0019] According to another aspect of the present invention, an impulse superimposed voltage test waveform analysis and evaluation device is provided, comprising: The preprocessing module is used to synchronously acquire and adaptively preprocess multi-channel measurement data to obtain correction data; The separation and extraction module is used for voltage component separation and parameter extraction based on adaptive curve fitting of the calibration data, to obtain the impulse voltage waveform and its standard parameters. The evaluation module is used to perform waveform analysis and evaluation based on the standard parameters of the impulse voltage, extracting multi-scale time-frequency features of the impulse wave voltage waveform, and obtaining the evaluation results.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing a computer program for performing the methods described in any of the above aspects of the present invention.

[0021] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the method described in any of the preceding aspects of the present invention.

[0022] Therefore, this invention provides a method for analyzing and evaluating the waveform of an impulse superimposed voltage test. By establishing an adaptive test pattern recognition and data preprocessing algorithm, the synchronization and correction of multi-channel data are achieved; a voltage component separation algorithm based on adaptive curve fitting is designed to reconstruct the base voltage and extract the impulse voltage, and calculate the parameters of each component; a multi-scale time-frequency feature extraction and fuzzy comprehensive evaluation model is constructed to realize the analysis of the test waveform. Attached Figure Description

[0023] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures: Figure 1 This is a schematic flowchart of an exemplary embodiment of the present invention for analyzing and evaluating the waveform of an impact superimposed voltage test. Figure 2 This is a schematic diagram of an impulse combined voltage test topology provided in an exemplary embodiment of the present invention; Figure 3 This is another schematic diagram of the waveform analysis and evaluation method for impact superimposed voltage test provided by an exemplary embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an impulse superimposed voltage test waveform analysis and evaluation device provided in an exemplary embodiment of the present invention; Figure 5 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. Detailed Implementation

[0024] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0025] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0026] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of the present invention are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0027] It should also be understood that in the embodiments of the present invention, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0028] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more unless explicitly defined or given contrary instructions in the context.

[0029] Furthermore, the term "and / or" in this invention is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this invention generally indicates that the preceding and following related objects have an "or" relationship.

[0030] It should also be understood that the description of the various embodiments in this invention emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0031] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0032] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0033] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0034] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0035] The embodiments of this invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate together with a wide range of other general-purpose or special-purpose computing system environments or configurations. Well-known examples of terminal devices, computing systems, environments, and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, and servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments including any of the above systems, etc.

[0036] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are executed by remote processing devices linked through communication networks. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.

[0037] Exemplary methods Figure 1 This is a schematic flowchart of an exemplary embodiment of the impact superimposed voltage test waveform analysis and evaluation method provided by the present invention. This embodiment can be applied to electronic devices, such as... Figure 1 As shown, the waveform analysis and evaluation method 100 for impulse superimposed voltage tests includes the following steps: Step 101: Synchronously acquire and adaptively preprocess the multi-channel measurement data to obtain the correction data; Step 102: Based on the correction data, perform adaptive curve fitting to separate the voltage components and extract parameters to obtain the impulse voltage waveform and its standard parameters. Step 103: Based on the standard parameters of the impulse voltage, perform waveform analysis and evaluation by extracting multi-scale time-frequency features of the impulse voltage waveform to obtain the evaluation results.

[0038] Specifically, this invention aims to solve the following technical problems in existing superimposed voltage test waveform analysis techniques: First, traditional waveform analysis methods struggle to accurately separate the base voltage component and impulse voltage component in superimposed waveforms. When multiple voltage components highly overlap in the time domain, the parameter extraction errors of peak detection and manual interpretation methods are large. Second, existing analyses lack assessments of waveform time-frequency characteristics and multi-channel synchronization errors, making it difficult to identify waveform distortions introduced by measurement system nonlinearity and digital noise, thus affecting the reliability of test results. Third, for data with different sampling rates, there is a lack of effective data fusion and consistency verification methods, which easily leads to systematic errors.

[0039] Therefore, this invention provides a method for analyzing and evaluating waveforms of impulse superimposed voltage tests. It establishes an adaptive test pattern recognition and data preprocessing algorithm to achieve synchronization and correction of multi-channel data; designs a voltage component separation algorithm based on adaptive curve fitting to reconstruct the base voltage and extract the impulse voltage, calculating the parameters of each component; and constructs a multi-scale time-frequency feature extraction and fuzzy comprehensive evaluation model to realize test waveform analysis. The specific impulse superimposed voltage test topology is as follows: Figure 2 As shown.

[0040] This invention provides a method for analyzing and evaluating waveforms of superimposed impulse voltage tests. First, accurate acquisition and preliminary correction of the combined voltage waveform are achieved through multi-channel synchronous measurement and data preprocessing. Second, based on an adaptive signal separation algorithm, the superimposed voltage waveform is decomposed into a base voltage component and an impulse voltage component, and the characteristic parameters of each component are extracted separately. Finally, waveform quality analysis and evaluation are achieved through multi-scale time-frequency feature extraction and fuzzy comprehensive evaluation. (Reference) Figure 3 As shown, it includes the following steps: Step 1: Synchronous Acquisition and Adaptive Preprocessing of Multi-channel Measurement Data This invention establishes a synchronous acquisition and adaptive preprocessing algorithm for combined voltage measurement data. Based on the number of input data channels, sampling rate characteristics, and voltage type identifier, the algorithm automatically identifies the test mode and performs corresponding data correction and normalization processing.

[0041] For single-channel measurement data ,in For the first Each sampling time, This represents the total number of sampling points. The algorithm first performs sampling rate detection and calculates the sampling rate. ,in This represents the average time interval between adjacent sampling points. The impact type is determined based on the sampling rate range: when… When, it is determined to contain lightning strike components; when When this occurs, it is determined to contain operational impact components.

[0042] The algorithm further examines the matching between data length and the impact timescale, defining the data duration. ,Require ,in The standard impact half-peak time is 50 μs for lightning impact and 2500 μs for switching impact.

[0043] For three-channel measurement data: base voltage low sampling rate data High sampling rate data of impulse voltage High sampling rate synchronous data of base voltage The algorithm performs a time axis alignment operation, using the time axis of the impulse voltage channel as a reference, and maps the data of the third channel to the reference time axis through linear interpolation: (1) in superscript This represents the interpolated data. After interpolation, the time alignment accuracy is checked, and the synchronization error is calculated.

[0044] Then adaptive downsampling is performed. When the number of data points... ( N m Take 10 6 When calculating the downsampling factor, Data is retained using a uniform interval decimation method. Anti-aliasing filtering is applied before downsampling, with the filter cutoff frequency set to [value missing]. It employs 4th to 8th order Butterworth low-pass filters.

[0045] Finally, drift correction is performed. The first-order voltage difference is calculated, and the moment when the noise threshold is first exceeded is recorded and marked as the impact initiation point. The threshold is calculated based on the statistical characteristics of the pre-impact data. ,in and These are the noise mean and standard deviation, respectively. Determine... Then, the baseline offset was calculated using the data segment prior to the impact. And perform corrections on all data: (2) Subscript This indicates the corrected data.

[0046] Step 2: Voltage component separation and parameter extraction based on adaptive curve fitting This invention establishes an adaptive component separation algorithm for superimposed voltage waveforms. Through iterative fitting and residual analysis, it effectively extracts the basic voltage component and the impulse voltage component, and calculates the standardized parameters of each component.

[0047] First, determine the type of the base voltage. When the dominant frequency component... When the base voltage is determined to be AC; when At that time, it was determined to be DC.

[0048] For the AC base voltage, a data segment of at least one complete cycle before the impact is selected to construct an AC fitting model containing harmonics: (3) in , The harmonic order is... This represents the DC component. The parameters are solved using the weighted least squares method, with the weighting function assigning higher weights to data points closer to the impact moment. After fitting, the AC voltage parameters are calculated: fundamental amplitude. , effective value Total Harmonic Distortion .

[0049] Fitting function Extending to the entire measurement period, the AC component was separated from the raw calibration data: (4) Subscript ms This represents the impulse voltage after separation.

[0050] For the DC base voltage, calculate the statistical characteristics of the data segment before the impact, where the DC voltage value is the arithmetic mean. Evaluate ripple characteristics, and calculate ripple amplitude and ripple coefficient. The separated impulse voltage is the correction data. .

[0051] The obtained impulse voltage waveform Perform a double exponential function fitting. First, estimate the initial parameters and identify the peak impact time. and peak Calculate the 30%, 90%, and 50% amplitude points of the waveform to estimate the initial time constant. Construct a double exponential fitting model: (5) in Here, is the Heaviside step function, and is the amplitude coefficient. For virtual start time, subscript mf The fitted curve is represented. The parameters are solved iteratively using the Levenberg-Marquardt algorithm. .

[0052] After fitting, the standard parameters of the impulse voltage are calculated. The baseline curve is defined as the fitting function. The residual curve is subscript Represents the residual. Using an infinite impulse response filter, the filtered residual is obtained. Build test curves: . (6) Calculation of impact parameters based on test curve: Test voltage value The maximum value of the test curve, wavefront time Half-peak time ,in , , These represent the times corresponding to 30%, 90%, and 50% amplitude of the test curve, respectively. Overshoot is calculated as follows: (7) Step 3: Waveform analysis and evaluation based on multi-scale time-frequency feature extraction This invention establishes an intelligent analysis and evaluation method for superimposed voltage test waveforms. By extracting the time-frequency features of the waveform through wavelet transform, quantitative analysis and evaluation of the waveform quality are achieved.

[0053] The system obtains the impulse voltage waveform from the separation. Perform wavelet decomposition. Select the Daubechies 4 (db4) wavelet as the mother wavelet function. Layered discrete wavelet transform. Calculate the energy proportion of wavelet coefficients at each layer. ( Define high-frequency noise metrics: (8) η 1 should be less than 0.05, mid-frequency oscillation index This is the ratio of the maximum wavelet coefficient to the maximum scale coefficient of the decomposition layer corresponding to the wavefront. η 2 should be less than 10%.

[0054] Extracting local waveform features and analyzing the impulse voltage test curve Calculate the wavefront slope uniformity within the 30% to 90% amplitude range. Divide this range into equal parts. part( ), calculate the average slope for each segment Define the coefficient of variation of the slope: (9) in and These are the standard deviation and mean of the slope sequence, respectively. .

[0055] The attenuation characteristics of the wave tail were evaluated by selecting the amplitude range of 50% to 10%. Log-linear fitting was performed on this data segment, and the normalized root mean square value of the fitting residuals was calculated. .

[0056] Ideal synthesized waveform As an ideal superposition of the base voltage and the impulse voltage, its coupling distortion is: (10) Calculate the consistency index of the basic voltage parameters between the low sampling rate channel and the high sampling rate synchronization channel. , It should be less than 1%.

[0057] The proposed method for analyzing and evaluating the waveform of superimposed voltage tests, through the innovative application of adaptive signal processing and intelligent algorithms, achieves accurate separation, parameter extraction, and quality evaluation of superimposed voltage waveforms, and has the following significant advantages: (1) High-precision voltage component separation: This invention achieves precise separation of base voltage and superimposed impulse voltage through an adaptive curve fitting algorithm. The base voltage reconstruction accuracy is better than 0.5%, and the parameter extraction error after impulse voltage separation is less than 1%, meeting the 3% measurement uncertainty requirement of IEC 60060-1 standard. Compared with traditional manual interpretation or simple peak detection methods, the parameter extraction accuracy is improved by more than 2 times.

[0058] (2) High-precision synchronization of multi-channel data: For joint voltage testing, the time axis alignment algorithm designed in this invention can control the synchronization error of channels with different sampling rates to within 0.1%, and achieve data fusion through linear interpolation. The consistency index of basic voltage parameters is better than 1%. This solves the problems of difficult time alignment of multi-channel data and large systematic errors in traditional methods.

[0059] (3) Waveform analysis and evaluation: This invention establishes a waveform analysis and evaluation model based on multi-scale time-frequency feature extraction, which can quantitatively calculate indicators such as high-frequency noise, mid-frequency oscillation, wavefront slope uniformity, wave tail attenuation characteristics, and component coupling distortion of waveforms. Compared with traditional methods that rely on human experience, the evaluation has higher objectivity and consistency. Exemplary device Figure 4 This is a schematic diagram of the structure of an impulse superimposed voltage test waveform analysis and evaluation device provided in an exemplary embodiment of the present invention. Figure 4 As shown, the device 400 includes: Preprocessing module 410 is used to synchronously acquire and adaptively preprocess multi-channel measurement data to obtain correction data; The separation and extraction module 420 is used for voltage component separation and parameter extraction based on adaptive curve fitting of the calibration data to obtain the impulse voltage waveform and its standard parameters. Evaluation module 430 is used to perform waveform analysis and evaluation based on the impulse voltage standard parameters, extracting multi-scale time-frequency features from the impulse wave voltage waveform, and obtaining evaluation results.

[0060] Optionally, the preprocessing module 410 includes: S101: The sampling rate is detected based on the number of channels of the acquired multi-channel measurement data, and the voltage type identifier is determined based on the detected sampling rate range to identify the type of measurement data; S102: Perform time axis alignment operation on the multi-channel measurement data after identifying the data type of the measurement data. Using the time axis of the impulse voltage channel as the reference time axis, the data of other channels are mapped to the reference time axis by interpolation to obtain time-synchronized multi-channel measurement data. S103: Adaptive downsampling of time-synchronized multi-channel measurement data; S104: Perform drift correction on the sampled multi-channel measurement data, determine the impact initiation point, calculate the baseline offset based on the pre-impact data, and use the baseline offset to correct all data to obtain the corrected data.

[0061] Optionally, the specific steps of step S101 include: Sampling rate detection is performed on multi-channel measurement data, and the sampling rate is calculated. ,in The average time interval between adjacent sampling points; when When the measured data is determined to contain lightning strike components, it is determined that the measured data contains lightning impact components. At that time, the measured data was determined to contain operational impact components.

[0062] Optionally, in step 102, for the three-channel measurement data, using the time axis of the impulse voltage channel as a reference, the expression for mapping the third channel data to the reference time axis through linear interpolation is as follows: In the formula, superscript Indicates the interpolated data, index j For the moment of impact, i Based on low-speed sampling time, k At the high-speed sampling point, the three-channel measurement data includes: low sampling rate data of the base voltage. High sampling rate data of impulse voltage High sampling rate synchronous data of base voltage .

[0063] Optionally, step S103 includes: When data points Calculate the downsampling factor. Data is retained using a uniform interval sampling method based on a downsampling factor. N m Take 10 6 Before downsampling, an anti-aliasing filter is applied, and the filter cutoff frequency is set to [value missing]. It employs 4th to 8th order Butterworth low-pass filters.

[0064] Optionally, step 104 includes: Calculate the first-order voltage difference, record the moment when the voltage first exceeds the noise threshold, and mark it as the impulse initiation point. The noise threshold is calculated based on the statistical characteristics of the data before the impact. ,in and These are the noise mean and standard deviation, respectively. Determine the impact initiation point Then, the baseline offset was calculated using the data segment prior to the impact. And perform correction on all data, where the correction expression is: in, U c ( t i )for t i The voltage is constantly corrected. U ( t i )for t i The original measured voltage at time Δ U is the baseline bias, and is the average voltage of the sampling points before the impact.

[0065] Optionally, the separation and extraction module 420 includes: S201: Determine whether the base voltage type is AC or DC based on the calibration data; S202: Based on the identified base voltage type, the corresponding fitting model is used to separate and obtain the impulse voltage waveform; S203: After performing a double exponential function fit on the impulse voltage waveform, a test curve is constructed, and the standard parameters of the impulse voltage are calculated based on the test curve.

[0066] Optionally, in step S202: For AC base voltage, a weighted least squares fitting model containing harmonics is used to calculate the fundamental amplitude, effective value, and total harmonic distortion parameters, thus separating the impulse voltage waveform of the AC base voltage; or For the DC base voltage, the corrected voltage is used as the impulse voltage waveform.

[0067] Optionally, the impulse voltage waveform of the AC base voltage. U ms The expression is: In the formula, U a For the fitted function in t i The calculated value at time is determined by the fundamental amplitude, effective value, and total harmonic distortion parameter. U c ( t i )for t i The voltage is constantly corrected after adjustment; impulse voltage waveform of DC base voltage U ms The expression is: .

[0068] Optionally, the fitted double exponential function U mf The expression is: In the formula, Here, is the Heaviside step function, and is the amplitude coefficient. For virtual start time, subscript mf Represents the fitted curve; These are the parameters obtained iteratively using the Levenberg-Marquardt algorithm; Test curve U t The expression is: In the formula, U mf This serves as the baseline curve for the fitted function; To use an infinite impulse response filter to analyze the residual curve U ρ The filtered residual; For the residual curve, Represents the residual; The impact parameters calculated based on the test curve include: test voltage value. The maximum value of the test curve, wavefront time Half-peak time ,in , , The overshoot is calculated at the times corresponding to 30%, 90%, and 50% amplitude of the test curve, respectively: .

[0069] Optionally, the evaluation module 430 includes: S301: Perform wavelet decomposition on the impulse voltage waveform, extract time-frequency features, and calculate high-frequency noise index and mid-frequency oscillation index; S302: Based on the test curve, extract the wavefront slope uniformity and wave tail attenuation characteristics as local waveform features; S303: Calculate the coupling distortion between the ideal composite waveform and the actual measured waveform of the base voltage component and the impulse voltage component; S304: An indicator for evaluating the consistency of base voltage parameters between channels with different sampling rates.

[0070] S305: Determine the evaluation results based on high-frequency noise index, mid-frequency oscillation index, waveform local characteristics, coupling distortion, and consistency index.

[0071] Optionally, high-frequency noise specifications ,in E 1 represents the energy percentage of the first-layer wavelet coefficients. E 2 represents the energy proportion of the second-layer wavelet coefficients; Mid-frequency oscillation index It is the ratio of the maximum wavelet coefficient to the maximum scale coefficient of the decomposition layer corresponding to the wavefront; Wavefront slope uniformity ,in and These are the standard deviation and mean of the slope sequence, respectively; The normalized root mean square value of the fitting residuals for log-linear fitting of data with a tail decay characteristic of 50% to 10% amplitude range. ; Coupling distortion ,in, U id It is the ideal superposition of the base voltage and the impulse voltage. U c The corrected waveform; N This represents the number of waveform sampling points.

[0072] Exemplary electronic devices Figure 5 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. For example... Figure 5 As shown, the electronic device 50 includes one or more processors 51 and a memory 52.

[0073] The processor 51 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0074] The memory 52 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 51 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above, and / or other desired functions. In one example, the electronic device may also include an input device 53 and an output device 54, these components being interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0075] In addition, the input device 53 may also include, for example, a keyboard, a mouse, etc.

[0076] The output device 54 can output various information to the outside. The output device 54 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0077] Of course, for the sake of simplicity, Figure 5 Only some of the components of this electronic device relevant to the present invention are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0078] Exemplary computer program products and computer-readable storage media In addition to the methods and apparatus described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0079] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0080] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0081] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0082] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0083] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0084] The block diagrams of devices, systems, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, systems, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0085] The methods and systems of the present invention may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of the present invention are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, the present invention may also be implemented as a program recorded on a recording medium, the program comprising machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers recording media storing programs for performing the methods according to the present invention.

[0086] It should also be noted that in the systems, apparatus, and methods of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered equivalents of the present invention. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0087] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for analyzing and evaluating the waveform of an impulse superimposed voltage test, characterized in that, include: Simultaneous acquisition and adaptive preprocessing of multi-channel measurement data are performed to obtain correction data; Based on the correction data, voltage component separation and parameter extraction are performed using adaptive curve fitting to obtain the impulse voltage waveform and its standard parameters. Based on the aforementioned impulse voltage standard parameters, waveform analysis and evaluation are performed on the impulse wave voltage waveform using multi-scale time-frequency feature extraction, and evaluation results are obtained.

2. The method according to claim 1, characterized in that, Synchronous acquisition and adaptive preprocessing of multi-channel measurement data yields calibration data, including: S101: The sampling rate is detected based on the number of channels of the acquired multi-channel measurement data, and the voltage type identifier is determined based on the detected sampling rate range to identify the type of measurement data; S102: Perform time axis alignment operation on the multi-channel measurement data after identifying the data type of the measurement data. Using the time axis of the impulse voltage channel as the reference time axis, the data of other channels are mapped to the reference time axis by interpolation to obtain time-synchronized multi-channel measurement data. S103: Adaptive downsampling of time-synchronized multi-channel measurement data; S104: Perform drift correction on the sampled multi-channel measurement data, determine the impact initiation point, calculate the baseline offset based on the pre-impact data, and use the baseline offset to correct all data to obtain the corrected data.

3. The method according to claim 2, characterized in that, The specific steps of step S101 include: The sampling rate is detected and calculated for the multi-channel measurement data. ,in The average time interval between adjacent sampling points; when When the measured data is determined to contain lightning strike components, it is determined that the measured data contains lightning impact components. At that time, the measured data was determined to contain operational impact components.

4. The method according to claim 2, characterized in that, In step 102, for the three-channel measurement data, using the time axis of the impulse voltage channel as a reference, the expression for mapping the third channel data to the reference time axis through linear interpolation is as follows: In the formula, superscript Indicates the interpolated data, index j For the moment of impact, i Based on low-speed sampling time, k At the base high-speed sampling time, the three-channel measurement data includes: base voltage low sampling rate data. High sampling rate data of impulse voltage High sampling rate synchronous data of base voltage .

5. The method according to claim 2, characterized in that, Step S103 includes: When data points Calculate the downsampling factor. And based on the downsampling factor, data is retained using a uniform interval extraction method, wherein N m Take 10 6 Before downsampling, an anti-aliasing filter is applied, and the filter cutoff frequency is set to [value missing]. It employs 4th to 8th order Butterworth low-pass filters.

6. The method according to claim 2, characterized in that, Step 104 includes: Calculate the first-order voltage difference, record the moment when the voltage first exceeds the noise threshold, and mark it as the impulse initiation point. The noise threshold is calculated based on the statistical characteristics of the data prior to the impact. ,in and These are the noise mean and standard deviation, respectively. Determine the impact initiation point Then, the baseline offset was calculated using the data segment prior to the impact. And perform correction on all data, where the correction expression is: in, U c ( t i )for t i The voltage is constantly corrected. U ( t i )for t i Original measured voltage at time Δ U is the baseline bias, and is the average voltage of the sampling points before the impact.

7. The method according to claim 1, characterized in that, Based on the corrected data, voltage component separation and parameter extraction are performed using adaptive curve fitting to obtain the impulse voltage waveform and its standard parameters, including: S201: Determine whether the base voltage type is AC or DC based on the correction data; S202: Based on the identified base voltage type, the corresponding fitting model is used to separate and obtain the impulse voltage waveform; S203: After performing a double exponential function fitting on the impulse voltage waveform, a test curve is constructed, and the standard parameters of the impulse voltage are calculated based on the test curve.

8. The method according to claim 7, characterized in that, In step S202: For the AC base voltage, a weighted least squares fitting is performed using an AC fitting model containing harmonics, and the fundamental amplitude, effective value, and total harmonic distortion parameters are calculated to separate the impulse voltage waveform of the AC base voltage; or For the DC base voltage, the corrected voltage is used as the impulse voltage waveform.

9. The method according to claim 8, characterized in that, The impulse voltage waveform of the AC base voltage U ms The expression is: In the formula, U a For the fitting function in t i The calculated value at time is determined by the fundamental amplitude, effective value, and total harmonic distortion parameter. U c ( t i )for t i The voltage is constantly corrected after adjustment; The impulse voltage waveform of the DC base voltage U ms The expression is: 。 10. The method according to claim 9, characterized in that, Fitted double exponential function U mf The expression is: In the formula, Here, is the Heaviside step function, and is the amplitude coefficient. For virtual start time, subscript mf Represents the fitted curve; These are the parameters obtained iteratively using the Levenberg-Marquardt algorithm; The test curve U t The expression is: In the formula, U mf This is the baseline curve for the fitted function; To use an infinite impulse response filter to analyze the residual curve U ρ The filtered residual; For the residual curve, Represents the residual; The impact parameters calculated based on the test curve include: test voltage value. The maximum value of the test curve, wavefront time Half-peak time ,in , , The overshoot is calculated at the times corresponding to 30%, 90%, and 50% amplitude of the test curve, respectively: 。 11. The method according to claim 10, characterized in that, Based on the aforementioned impulse voltage standard parameters, waveform analysis and evaluation are performed on the impulse wave voltage waveform using multi-scale time-frequency feature extraction, yielding evaluation results including: S301: Perform wavelet decomposition on the impulse voltage waveform, extract time-frequency features, and calculate high-frequency noise index and mid-frequency oscillation index; S302: Based on the test curve, extract the wavefront slope uniformity and wave tail attenuation characteristics as local waveform features; S303: Calculate the coupling distortion between the ideal composite waveform and the actual measured waveform of the base voltage component and the impulse voltage component; S304: An indicator for evaluating the consistency of base voltage parameters between channels with different sampling rates. S305: Determine the evaluation result based on the high-frequency noise index, the mid-frequency oscillation index, the waveform local characteristics, the coupling distortion, and the consistency index.

12. The method according to claim 11, characterized in that, The high-frequency noise index ,in E 1 represents the energy percentage of the first-layer wavelet coefficients. E 2 represents the energy proportion of the second-layer wavelet coefficients; The mid-frequency oscillation index It is the ratio of the maximum wavelet coefficient to the maximum scale coefficient of the decomposition layer corresponding to the wavefront; The wavefront slope uniformity ,in and These are the standard deviation and mean of the slope sequence, respectively; The tail decay characteristic is the normalized root mean square value of the fitting residuals from a log-linear fit performed on data within the 50% to 10% amplitude range. ; The coupling distortion ,in, U id It is the ideal superposition of the base voltage and the impulse voltage. U c The corrected waveform; N This represents the number of waveform sampling points.

13. A device for analyzing and evaluating waveforms of impulse superimposed voltage tests, characterized in that, include: The preprocessing module is used to synchronously acquire and adaptively preprocess multi-channel measurement data to obtain correction data; The separation and extraction module is used to separate the voltage components and extract parameters based on the correction data through adaptive curve fitting, so as to obtain the impulse voltage waveform and its standard impulse voltage parameters; The evaluation module is used to perform waveform analysis and evaluation of the shock wave voltage waveform by extracting multi-scale time-frequency features based on the standard parameters of the shock voltage, and to obtain the evaluation results.

14. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method described in any one of claims 1-12.

15. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1-12.

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