Broadband phasor detection method based on two-point interpolation and discrete Fourier transform

Abstract

The invention relates to a broadband phasor detection method based on two-point interpolation and discrete Fourier transform. The method comprises the following steps: acquiring a sampling signal, and performing discrete Fourier transform analysis on the sampling signal to obtain a discrete Fourier transform analysis result; processing the sampling signal according to a discrete Fourier transform analysis result of the sampling signal and a two-point interpolation method to obtain a processed sampling signal; performing Hanning window-based discrete Fourier transform calculation on the processed sampling signal, determining a plurality of amplitude local extreme values, and eliminating noise interference by judging whether each amplitude local extreme value exceeds a threshold value of a fundamental wave; and calculating the amplitude, the frequency and the phase of each oscillation mode according to the plurality of amplitude local extreme values, discrete Fourier transform analysis and a two-point interpolation method. The frequency resolution capability and the detection precision of broadband phasor measurement are improved, and the near-power-frequency phasor measurement precision in a fundamental frequency offset scene is improved.

Description

Technical Field

[0001] The present application relates to the technical field of broadband phasor measurement, and in particular to a broadband phasor detection method based on two-point interpolation and discrete Fourier transform. Background Art

[0002] The large-scale grid connection of renewable energy power generation equipment and power electronic equipment has significantly changed the dynamic interaction characteristics of the power system, and the resulting broadband oscillation stability issues have become increasingly prominent. Broadband oscillations can not only damage equipment and cause renewable energy units to disconnect from the grid, but also seriously threaten the safe and stable operation of the system, becoming a key bottleneck restricting the efficient absorption of renewable energy. The frequency coverage of oscillation phenomena is wide, varies with operating conditions, and has multi-modal coupling characteristics, making early warning and prevention and control difficult. Therefore, in-depth understanding of the behavioral characteristics of broadband oscillations and the realization of online monitoring, early warning, and tracing of oscillation risks are key technologies for effectively preventing oscillation problems in "double-high" power systems.

[0003] However, existing wide-area measurement systems (WAMS) rely on synchronized phasor measurement units (PMUs) to achieve real-time dynamic monitoring, analysis, and control of large power grids. However, traditional PMUs are designed specifically for power-frequency phasor monitoring and do not cover the frequency range of broadband oscillations, such as subsynchronous and supersynchronous oscillations and medium- and high-frequency oscillations. Therefore, accurately and rapidly measuring broadband oscillation components is a crucial foundation for oscillation monitoring, early warning, and source tracing. Summary of the Invention

[0004] Based on this, it is necessary to provide a wide-band phasor detection method based on two-point interpolation and discrete Fourier transform to address the above technical problems. At least, it can estimate the frequency, amplitude and phase of the fundamental component and the wide-band phasor through the two-point interpolation method according to the spectral characteristics when the discrete Fourier transform spectrum leaks, reduce the degree of mutual interference between multiple oscillation components, improve the frequency resolution capability and detection accuracy of wide-band phasor measurement, and improve the near-industrial frequency phasor measurement accuracy in the fundamental frequency offset scenario.

[0005] In a first aspect, the present application provides a broadband phasor detection method based on two-point interpolation and discrete Fourier transform, comprising:

[0006] Acquire a sampling signal, and perform discrete Fourier transform analysis on the sampling signal to obtain a discrete Fourier transform analysis result;

[0007] Processing the sampled signal according to a discrete Fourier transform analysis result of the sampled signal and a two-point interpolation method to obtain a processed sampled signal;

[0008] Perform discrete Fourier transform calculation based on Hanning window on the processed sampled signal to determine multiple local extreme values ​​of amplitude, and eliminate noise interference by judging whether each local extreme value of amplitude exceeds the threshold of fundamental wave;

[0009] The amplitude, frequency and phase of each oscillation mode are calculated based on multiple local amplitude extrema, discrete Fourier transform analysis and two-point interpolation method.

[0010] In some embodiments, the sampled signal is processed according to a discrete Fourier transform analysis result of the sampled signal and a two-point interpolation method to obtain a processed sampled signal, including:

[0011] Calculate the fundamental component based on the two-point interpolation method;

[0012] Determine a fundamental wave time domain signal based on a discrete Fourier transform analysis result of the sampling signal and the fundamental wave component;

[0013] The fundamental time domain signal is removed from the sampled signal to obtain a processed sampled signal.

[0014] In some embodiments, the amplitude, frequency, and phase of each oscillation mode are calculated based on local amplitude extrema, discrete Fourier transform analysis, and two-point interpolation method, including:

[0015] For each extreme point, determine the two spectral lines with the largest frequency near the extreme point;

[0016] The amplitude, frequency and phase of the oscillation mode are determined based on the two spectral lines with the largest frequency near the extreme point, the discrete Fourier transform analysis results of the sampling signal and the two-point interpolation method.

[0017] In some embodiments, determining the fundamental time domain signal based on the discrete Fourier transform analysis result of the sampled signal and the fundamental component includes:

[0018] According to the discrete Fourier transform analysis results, the two target spectral lines with the largest amplitude near the fundamental frequency are determined;

[0019] According to the spectrum leakage mechanism and the pseudo-spectrum distribution characteristics, the corresponding relationship between the amplitude and phase of the two target spectrum lines and the amplitude-frequency and phase-frequency characteristics of the window function is determined;

[0020] The frequency deviation value is estimated according to the corresponding relationship between the amplitude of the two target spectral lines and the amplitude and phase of the two target spectral lines and the amplitude-frequency and phase-frequency characteristics of the window function;

[0021] Determine the true frequency of the oscillation component based on the frequency deviation value and the frequencies of the two target spectral lines;

[0022] Determining the true amplitude and true phase of the sampled signal according to the true frequency of the oscillation component, the amplitudes of the two target spectral lines, and the phases of the two target spectral lines;

[0023] The time domain waveform of the fundamental wave phasor is reconstructed according to the true frequency, true amplitude and true phase.

[0024] In some embodiments, estimating the frequency deviation value according to the corresponding relationship between the amplitudes of the two target spectral lines and the amplitudes and phases of the two target spectral lines and the amplitude-frequency and phase-frequency characteristics of the window function includes:

[0025] According to the amplitudes of the two target spectral lines, the range of the frequency deviation value is determined;

[0026] Determining a plurality of intermediate values ​​within a range of frequency deviation values ​​at preset intervals;

[0027] For each intermediate value, substitute the intermediate value into the corresponding relationship between the amplitude and phase of the two target spectral lines and the amplitude-frequency and phase-frequency characteristics of the window function to determine whether the intermediate value is a frequency deviation value.

[0028] In some embodiments, the corresponding relationship between the amplitude and phase of the two target spectral lines and the amplitude-frequency and phase-frequency characteristics of the window function includes a first relationship and a second relationship;

[0029] Substituting each intermediate value into the corresponding relationship between the amplitude and phase of the two target spectral lines and the amplitude-frequency and phase-frequency characteristics of the window function to determine whether the intermediate value is a frequency deviation value includes:

[0030] Substitute each intermediate value into the first and second equations respectively;

[0031] For each intermediate value, when the intermediate value is substituted into the first relational expression and the second relational expression, and the first relational expression and the second relational expression hold, the intermediate value is determined to be the frequency deviation value.

[0032] In a second aspect, the present application also provides a wideband phasor detection device based on two-point interpolation and discrete Fourier transform, including a discrete Fourier transform analysis module, a sampling signal processing module, a local extreme value determination module and an oscillation mode determination module, a discrete Fourier transform analysis module for acquiring a sampling signal and performing a discrete Fourier transform analysis on the sampling signal to obtain a discrete Fourier transform analysis result;

[0033] A sampling signal processing module, configured to process the sampling signal according to a discrete Fourier transform analysis result of the sampling signal and a two-point interpolation method to obtain a processed sampling signal;

[0034] The local extreme value determination module is used to perform discrete Fourier transform calculation based on the Hanning window on the processed sampled signal to determine multiple local extreme values ​​of the amplitude and eliminate noise interference by judging whether each local extreme value of the amplitude exceeds the threshold of the fundamental wave;

[0035] The oscillation mode determination module is used to calculate the amplitude, frequency and phase of each oscillation mode based on multiple local extreme values ​​of amplitude, discrete Fourier transform analysis and two-point interpolation method.

[0036] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0037] Acquire a sampling signal, and perform discrete Fourier transform analysis on the sampling signal to obtain a discrete Fourier transform analysis result;

[0038] Processing the sampled signal according to a discrete Fourier transform analysis result of the sampled signal and a two-point interpolation method to obtain a processed sampled signal;

[0039] Perform discrete Fourier transform calculation based on Hanning window on the processed sampled signal to determine multiple local extreme values ​​of amplitude, and eliminate noise interference by judging whether each local extreme value of amplitude exceeds the threshold of fundamental wave;

[0040] The amplitude, frequency and phase of each oscillation mode are calculated based on multiple local amplitude extrema, discrete Fourier transform analysis and two-point interpolation method.

[0041] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0042] Acquire a sampling signal, and perform discrete Fourier transform analysis on the sampling signal to obtain a discrete Fourier transform analysis result;

[0043] Processing the sampled signal according to a discrete Fourier transform analysis result of the sampled signal and a two-point interpolation method to obtain a processed sampled signal;

[0044] Perform discrete Fourier transform calculation based on Hanning window on the processed sampled signal to determine multiple local extreme values ​​of amplitude, and eliminate noise interference by judging whether each local extreme value of amplitude exceeds the threshold of fundamental wave;

[0045] The amplitude, frequency and phase of each oscillation mode are calculated based on multiple local amplitude extrema, discrete Fourier transform analysis and two-point interpolation method.

[0046] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0047] Acquire a sampling signal, and perform discrete Fourier transform analysis on the sampling signal to obtain a discrete Fourier transform analysis result;

[0048] Processing the sampled signal according to a discrete Fourier transform analysis result of the sampled signal and a two-point interpolation method to obtain a processed sampled signal;

[0049] Perform discrete Fourier transform calculation based on Hanning window on the processed sampled signal to determine multiple local extreme values ​​of amplitude, and eliminate noise interference by judging whether each local extreme value of amplitude exceeds the threshold of fundamental wave;

[0050] The amplitude, frequency and phase of each oscillation mode are calculated based on multiple local amplitude extrema, discrete Fourier transform analysis and two-point interpolation method.

[0051] The above-mentioned wideband phasor detection method based on two-point interpolation and discrete Fourier transform can at least estimate the frequency, amplitude and phase of the fundamental component and the wideband phasor through the two-point interpolation method according to the spectrum characteristics when the discrete Fourier transform spectrum leaks, thereby reducing the degree of mutual interference between multiple oscillation components, improving the frequency resolution capability and detection accuracy of wideband phasor measurement, and improving the near-power frequency phasor measurement accuracy in the fundamental frequency offset scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0053] Figure 1 1 is a flow chart of a wideband phasor detection method based on two-point interpolation and discrete Fourier transform in one embodiment;

[0054] Figure 2 Schematic diagram comparing the sidelobe attenuation characteristics of a rectangular window and a Hanning window in one embodiment;

[0055] Figure 3 Schematic diagram of the amplitudes of two spectral lines closest to the true frequency in one embodiment;

[0056] Figure 4 A schematic diagram of the phases of two spectral lines closest to the true frequency in one embodiment;

[0057] Figure 5: is a corresponding relationship diagram of X(M), X(M+1) and the amplitude-frequency characteristics of the window function in one embodiment;

[0058] Figure 6 : is a corresponding relationship diagram of P(M), P(M+1) and the phase-frequency characteristics of the window function in one embodiment;

[0059] Figure 7 Schematic diagram of HW-DFT results of the initial sampling signal in one embodiment

[0060] Figure 8 is the fundamental signal corresponding to an embodiment 、 Schematic diagram;

[0061] Figure 9 This is a time domain waveform diagram of the sampling signal after removing the fundamental signal in one embodiment.

[0062] Figure 10 This is a diagram showing the HW-DFT result of the sampled signal after removing the fundamental component in one embodiment.

[0063] Figure 11 A diagram showing the multi-mode oscillation phasor measurement results in one embodiment

[0064] Figure 12 A diagram showing the broadband phasor measurement results in one embodiment

[0065] Figure 13 1 is a block diagram of a wideband phasor detection device based on two-point interpolation and discrete Fourier transform in one embodiment;

[0066] Figure 14 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment.

[0067] Reference numerals and descriptions:

[0068] 1301, discrete Fourier transform analysis module; 1302, sampling signal processing module; 1303, local extreme value determination module; 1304, oscillation mode determination module. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0070] The broadband phasor detection method based on two-point interpolation and discrete Fourier transform provided in the embodiments of this application can be applied to various personal computers, laptops, smartphones, tablets, Internet of Things (IoT) devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart car devices, and projectors. Portable wearable devices can include smart watches, smart bracelets, and head-mounted devices. Head-mounted devices can include virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, and the like.

[0071] It is important to note that in the research on wideband phasor measurement algorithms, previous studies have proposed using the Taylor-Fourier transform (TFT), sinc interpolation function-based estimator (SIFE), and imaginary exponential function-based estimator to identify wideband signal characteristics. These three dynamic signal models are used to approximate the true signal using the least squares method. To improve the response speed of the measurement algorithm, relevant literature combines the Kalman filter with the Taylor signal model to propose the Taylor–Kalman–Fourier algorithm. To simplify the algorithm and improve its efficiency for online applications, relevant literature proposes a polynomial implementation of the TFT. To improve the ability to extract interharmonic components, relevant literature uses the Prony method to identify interharmonic components and then uses the Taylor signal model to model and estimate the signal parameters. However, the discrete Fourier transform (DFT) and fast Fourier transform (FFT) are currently highly mature in practical engineering applications due to their ease of hardware implementation, efficiency, and stability. However, DFT and FFT can exhibit significant measurement errors in situations such as frequency offset, time-varying harmonic / interharmonic amplitudes and frequencies, and harmonic / interharmonic interference. To address these issues, using window functions with low sidelobes and high sidelobe attenuation rates can effectively reduce the impact of frequency leakage and harmonic interference. Algorithms such as multi-point interpolation DFT, frequency-domain interpolation, and B-spline-based interpolation can also effectively mitigate the impact of spectrum leakage.

[0072] The main problem facing existing DFT detection algorithms is that when the DFT frequency resolution mismatches the oscillation frequency, spectrum leakage occurs, leading to inaccurate detection of the frequency, amplitude, and phase of the oscillation components. When multiple oscillation components with similar frequencies are present, the overlapping spectral lines caused by their frequency leakage will affect accurate detection of the oscillation components. Furthermore, fundamental frequency offset can cause fundamental spectrum leakage. Due to the large amplitude of the fundamental component, this fundamental spectrum leakage makes it difficult to accurately extract the near-power frequency oscillation component.

[0073] Please refer to Figure 1 In an exemplary embodiment, a broadband phasor detection method based on two-point interpolation and discrete Fourier transform is provided, comprising the following steps S101-S104.

[0074] S101 , acquiring a sampling signal, and performing discrete Fourier transform analysis on the sampling signal to obtain a discrete Fourier transform analysis result.

[0075] Here, the input voltage and current sampling signals x(n) are sampled at a sampling frequency fs in Hz. The sampling signal duration is td in seconds. The number of sampling points is Nd = td / fs. To perform DFT analysis on the sampled signals, a Hanning window (HW-DFT) is used to reduce mutual interference between multiple oscillating components and improve measurement accuracy.

[0076] like Figure 2 As shown, Figure 2 Shown Figure 2 (b) Rectangular window (RW) and Figure 2 The side lobe attenuation rate comparison of HW in (a) is shown in Figure 1. Figure 2 As can be seen, at ±2.35 Hz, the HW amplitude can be reduced to below 2.7%. In contrast, the RW amplitude at ±2.45 Hz is capped at 12.8%, approximately 4.74 times that of the HW. Furthermore, the RW amplitude at ±9.5 Hz is 3.3%, still slightly above 2.7%. Therefore, since the HW sidelobe decays the fastest, using the HW has the fastest spurious spectrum decay rate when spectral leakage occurs, thereby minimizing mutual interference between different oscillating components and improving the frequency resolution capability of wideband phasor measurements.

[0077] S102 , processing the sampled signal according to a discrete Fourier transform analysis result of the sampled signal and a two-point interpolation method to obtain a processed sampled signal.

[0078] Specifically, the sampling signal is processed according to the discrete Fourier transform analysis result of the sampling signal and the two-point interpolation method to obtain a processed sampling signal, including: calculating the fundamental component according to the two-point interpolation method; determining the fundamental time domain signal according to the discrete Fourier transform analysis result of the sampling signal and the fundamental component; and removing the fundamental time domain signal from the sampling signal to obtain a processed sampling signal.

[0079] Among them, the fundamental time domain signal is determined according to the discrete Fourier transform analysis result of the sampling signal and the fundamental component, including: determining the two target spectral lines with the largest amplitude near the fundamental frequency according to the discrete Fourier transform analysis result; determining the corresponding relationship between the amplitude and phase of the two target spectral lines and the amplitude-frequency and phase-frequency characteristics of the window function according to the mechanism of spectrum leakage and the pseudo-spectrum distribution characteristics; estimating the frequency deviation value according to the amplitude of the two target spectral lines and the corresponding relationship between the amplitude and phase of the two target spectral lines and the amplitude-frequency and phase-frequency characteristics of the window function; determining the true frequency of the oscillation component according to the frequency deviation value and the frequency of the two target spectral lines; determining the true amplitude and true phase of the sampling signal according to the true frequency of the oscillation component, the amplitude of the two target spectral lines and the phase of the two target spectral lines; and reconstructing the time domain waveform of the fundamental phasor according to the true frequency, true amplitude and true phase.

[0080] Among them, the frequency deviation value is estimated according to the amplitude of the two target spectral lines and the corresponding relationship between the amplitude and phase of the two target spectral lines and the amplitude-frequency and phase-frequency characteristics of the window function, including: determining the value range of the frequency deviation value according to the amplitude of the two target spectral lines; determining multiple intermediate values ​​within the value range of the frequency deviation value at preset intervals; for each intermediate value, substituting the intermediate value into the corresponding relationship between the amplitude and phase of the two target spectral lines and the amplitude-frequency and phase-frequency characteristics of the window function, and determining whether the intermediate value is a frequency deviation value.

[0081] The corresponding relationship expressions between the amplitudes and phases of the two target spectral lines and the amplitude-frequency and phase-frequency characteristics of the window function include a first relationship expression and a second relationship expression.

[0082] Specifically, each intermediate value is substituted into the corresponding relationship between the amplitude and phase of the two target spectral lines and the amplitude-frequency and phase-frequency characteristics of the window function to determine whether the intermediate value is a frequency deviation value, including: substituting each intermediate value into the first relationship and the second relationship respectively; for each intermediate value, when the intermediate value is substituted into the first relationship and the second relationship, the first relationship and the second relationship hold true, then the intermediate value is determined to be a frequency deviation value.

[0083] Here, according to the DFT results, the two spectral lines with the largest amplitude near the fundamental frequency are selected, and their frequency, amplitude and phase can be defined as , X(M), P(M) and , X(M+1), P(M+1), and fM+1-fM= fr, such as Figures 3-4 shown.

[0084] According to the spectrum leakage mechanism and the pseudo-spectrum distribution characteristics, the corresponding relationship between X(M), X(M+1), P(M), P(M+1) and the window function amplitude-frequency / phase-frequency characteristics is as follows: Figures 3-6 As shown in the figure, δ is the deviation between M and the true spectrum line, and the following mathematical relationship holds.

[0085] ;

[0086] Where |W( )| represents the magnitude of W( ).

[0087] ;

[0088] Here, θW( ) represents the phase of W( ).

[0089] It should be noted that This is the first relation, This is the second relation.

[0090] Specifically, according to the definition of the frequency deviation value δ, the value range of δ is [0, 1]. As an example, when X(M)>X(M+1)>0, δ∈(0, 0.5); when X(M+1)>X(M)>0, δ∈(0.5, 1); when X(M)=X(M+1), δ=0.5; according to the numerical relationship between X(M+1) and X(M), select δ from 0-0.5 or 0.5-1 in sequence with a certain interval Δδ , from which we can get 、 、 and Substitute the value of into the first and second equations. When the first and second equations hold, we can determine , and then the true frequency of the oscillation component can be calculated by the following formula.

[0091] ;

[0092] Thus, after determining ft, we can use the following formula:

[0093] ;

[0094] ;

[0095] The true amplitude and phase of the signal can be calculated, where Xr and Pr represent the true values ​​of the amplitude and phase respectively.

[0096] In theory, the real amplitude and phase can be obtained by the Mth or M+1th spectral line information, but considering the noise interference in the actual measurement process, the spectral line with the maximum amplitude can be selected to improve the anti-interference of the algorithm as much as possible and ensure the measurement accuracy.

[0097] The time-domain waveform of the fundamental phase quantity is reconstructed according to ft, Xr and Pr, and then removed from the original sampling signal x(n) to obtain , so as to eliminate the interference of the fundamental component on the wideband phasor measurement.

[0098] S103, the processed sampling signal is calculated based on the Hanning window discrete Fourier transform, a plurality of amplitude local extreme values are determined, and whether each amplitude local extreme value exceeds the threshold of the fundamental wave is judged to exclude noise interference.

[0099] Here, the signal after removing the fundamental wave is calculated by HW-DFT, and the analysis result amplitude is extracted. Based on the calculated amplitude-frequency characteristics, all amplitude local extreme values are found, and then whether the amplitude exceeds the threshold of the fundamental wave is judged to exclude noise and other interference factors.

[0100] S104, according to the plurality of amplitude local extreme values, the discrete Fourier transform analysis and the two-point interpolation method, the amplitude, frequency and phase of each oscillation mode are calculated.

[0101] Here, for all local extreme points exceeding the threshold of the fundamental wave , the two spectral lines with the maximum amplitude near the frequency of each extreme point are selected, and then the amplitude, frequency and phase of each oscillation mode are calculated by the same method as processing the sampling signal.

[0102] Optionally, all modes can be sent to the data storage and communication unit to complete the result display and upload.

[0103] Among them, according to the amplitude local extreme value, the discrete Fourier transform analysis and the two-point interpolation method, the amplitude, frequency and phase of each oscillation mode are calculated, including:

[0104] For each extreme point, determine the two spectral lines with the maximum frequency near the extreme point;

[0105] According to the two spectral lines with the maximum frequency near the extreme point, the discrete Fourier transform analysis result of the sampling signal and the two-point interpolation method, the amplitude, frequency and phase of the oscillation mode are determined.

[0106] In one possible embodiment, the test signal contains a fundamental component (50.1 Hz) with amplitude 500 V and multi-modal phasors with frequencies [8.2, 76.6, 167.3, 251.6, 432.5, 631.1, 992.8] Hz and amplitudes distributed between 5-20 V. First, the voltage current sampling signal x(n) is obtained, with sampling frequency fs=2000 Hz, in Hz. The sampling signal length is . The sampling signal point number is . The HW-DFT analysis of the sampling signal is performed, and the analysis result is shown in Figure 7 .

[0107] Secondly, according to the HW-DFT result of the sampling signal, the two-point interpolation method is used to estimate the fundamental component, and the fundamental time-domain signal is reconstructed and removed from the original sampling signal, wherein the fundamental signal corresponds to , As shown by the red dot in Figure 8 , X(M), P(M) and X(M+1), P(M+1) can be determined accordingly; through the two-point interpolation method, the fundamental phasor frequency is 50.1 Hz, the amplitude is 499.87, and the phase is 54.1°, which has a very small deviation from the true value 50.1 Hz, 500 V and 52.4°, indicating that the algorithm can accurately extract the fundamental component, and further reconstruct the fundamental time-domain signal and remove it from the original sampling signal. The waveform of the sampling signal after removing the fundamental signal is shown in Figure 9 .

[0108] Thirdly, the signal after removing the fundamental can be calculated by HW-DFT to extract the amplitude. Based on the calculated amplitude-frequency characteristics, all local amplitude extrema are found, and then by judging whether the amplitude exceeds the of the fundamental, noise and other interference factors are excluded, and the HW-DFT result of is shown in Figure 10 .

[0109] Here, based on the HW-DFT analysis result, the amplitude extrema are searched to determine the and corresponding to each oscillation component. The corresponding to each phasor are [5, 75, 165, 250, 430, 630, 990] Hz; and the corresponding to each phasor are [10, 80, 170, 255, 435, 635, 995] Hz.

[0110] For the detected and , the amplitude, frequency and phase of the broadband oscillation mode are calculated by the same method as that used to process the sampled signal, and then all modes are sent to the data storage and communication unit to complete the result display and upload.

[0111] Among them, the measurement errors of each mode obtained by using the proposed algorithm are shown in Table 1 below.

[0112] Table 1: Test results of multi-oscillation mode scenarios

[0113]

[0114] like Figure 11 The table shows a comparison between the measured results and the actual values, with the blue dots representing the measured results and the red dashed lines representing the actual amplitude and phase of the oscillation components. The data in the table and the results in the figure show that the broadband phasor measurements closely match the actual values ​​with minimal error, validating the effectiveness of the proposed broadband oscillation component measurement method.

[0115] In another optional embodiment, to further verify the effectiveness of the algorithm when the wide-band phasor frequencies are similar, the wide-band phasor frequencies are set to [5.2, 8.2, 11.2, 14.5, 17.3, 20.6, 23.3] Hz, the frequency interval is approximately 3 Hz, the amplitude of each phasor is 20 V, the sampling signal duration is 1 s, and fs = 1000 Hz. The phasor measurement errors obtained by the proposed sampling method are shown in Table 2.

[0116] Table 2 Wideband phasor measurement result error:

[0117]

[0118] like Figure 12 The table shows a comparison between the measured results and the actual values, with the blue dots representing the measured results and the red dashed line representing the actual amplitude and phase of the broadband phasor. The data in the table and the results in the figure show that the measured broadband phasor values ​​closely match the actual values, with very low error. This verifies that the proposed broadband oscillation component measurement method can still accurately extract broadband phasors when the frequency interval is only 3 Hz.

[0119] In another alternative embodiment, to test the algorithm's measurement accuracy for near-power-frequency phasors when fundamental spectrum leakage occurs, three sets of near-power-frequency components were tested. The near-power-frequency phasor amplitude was 20 V; the fundamental frequency was 50.1 Hz, the amplitude was 500 V, the sampling signal duration was 1 second, and fs = 1000 Hz. The detailed execution of the method is omitted, and the phasor measurement results are directly presented, as shown in Table 3.

[0120] Table 3 Near-power frequency phasor measurement error:

[0121]

[0122] The results in the table above show that the frequency, amplitude, and phase extraction errors for the three groups of near-power-frequency components are extremely small, indicating that the algorithm can accurately estimate the fundamental phasor and remove it from the original signal, thereby preventing fundamental spectrum leakage from interfering with the accurate extraction of near-power-frequency components.

[0123] Based on the spectrum characteristics when the DFT spectrum leaks, this application estimates the frequency, amplitude, and phase of the fundamental component and the wide-band phasor through the two-point interpolation method, which can reduce the degree of mutual interference between multiple oscillation components and improve the frequency resolution capability and detection accuracy of wide-band phasor measurement; and improve the near-power frequency phasor measurement accuracy in the fundamental frequency offset scenario.

[0124] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0125] Based on the same inventive concept, an embodiment of the present application further provides a wideband phasor detection device based on two-point interpolation and discrete Fourier transform for implementing the above-mentioned wideband phasor detection method based on two-point interpolation and discrete Fourier transform. The implementation solution provided by this device is similar to the implementation solution described in the above-mentioned method. Therefore, the specific limitations of one or more embodiments of the wideband phasor detection device based on two-point interpolation and discrete Fourier transform provided below can be found in the above-mentioned limitations of the wideband phasor detection method based on two-point interpolation and discrete Fourier transform, and will not be repeated here.

[0126] Please refer to Figure 13 In an exemplary embodiment, a wideband phasor detection device based on two-point interpolation and discrete Fourier transform is provided, comprising: a discrete Fourier transform analysis module 1301, a sampling signal processing module 1302, a local extreme value determination module 1303, and an oscillation mode determination module 1304. The discrete Fourier transform analysis module is configured to obtain a sampling signal and perform discrete Fourier transform analysis on the sampling signal to obtain a discrete Fourier transform analysis result.

[0127] A sampling signal processing module, configured to process the sampling signal according to a discrete Fourier transform analysis result of the sampling signal and a two-point interpolation method to obtain a processed sampling signal;

[0128] The local extreme value determination module is used to perform discrete Fourier transform calculation based on the Hanning window on the processed sampled signal to determine multiple local extreme values ​​of the amplitude and eliminate noise interference by judging whether each local extreme value of the amplitude exceeds the threshold of the fundamental wave;

[0129] The oscillation mode determination module is used to calculate the amplitude, frequency and phase of each oscillation mode based on multiple local extreme values ​​of amplitude, discrete Fourier transform analysis and two-point interpolation method.

[0130] Each module in the above-mentioned wideband phasor detection device based on two-point interpolation and discrete Fourier transform can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each of the above modules.

[0131] In an exemplary embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and computer program stored in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via wired or wireless communication, which may be achieved via Wi-Fi, a mobile cellular network, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements a wideband phasor detection method based on two-point interpolation and discrete Fourier transform. The display unit of the computer device forms a visually visible image and may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0132] Those skilled in the art will understand that Figure 14 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0133] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0134] Acquire a sampling signal, and perform discrete Fourier transform analysis on the sampling signal to obtain a discrete Fourier transform analysis result;

[0135] Processing the sampled signal according to a discrete Fourier transform analysis result of the sampled signal and a two-point interpolation method to obtain a processed sampled signal;

[0136] Perform discrete Fourier transform calculation based on Hanning window on the processed sampled signal to determine multiple local extreme values ​​of amplitude, and eliminate noise interference by judging whether each local extreme value of amplitude exceeds the threshold of fundamental wave;

[0137] The amplitude, frequency and phase of each oscillation mode are calculated based on multiple local amplitude extrema, discrete Fourier transform analysis and two-point interpolation method.

[0138] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0139] Acquire a sampling signal, and perform discrete Fourier transform analysis on the sampling signal to obtain a discrete Fourier transform analysis result;

[0140] Processing the sampled signal according to a discrete Fourier transform analysis result of the sampled signal and a two-point interpolation method to obtain a processed sampled signal;

[0141] Perform discrete Fourier transform calculation based on Hanning window on the processed sampled signal to determine multiple local extreme values ​​of amplitude, and eliminate noise interference by judging whether each local extreme value of amplitude exceeds the threshold of fundamental wave;

[0142] The amplitude, frequency and phase of each oscillation mode are calculated based on multiple local amplitude extrema, discrete Fourier transform analysis and two-point interpolation method.

[0143] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0144] Acquire a sampling signal, and perform discrete Fourier transform analysis on the sampling signal to obtain a discrete Fourier transform analysis result;

[0145] Processing the sampled signal according to a discrete Fourier transform analysis result of the sampled signal and a two-point interpolation method to obtain a processed sampled signal;

[0146] Perform discrete Fourier transform calculation based on Hanning window on the processed sampled signal to determine multiple local extreme values ​​of amplitude, and eliminate noise interference by judging whether each local extreme value of amplitude exceeds the threshold of fundamental wave;

[0147] The amplitude, frequency and phase of each oscillation mode are calculated based on multiple local amplitude extrema, discrete Fourier transform analysis and two-point interpolation method.

[0148] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0149] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0150] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A broadband phasor detection method based on two-point interpolation and discrete Fourier transform, characterized in that: The method comprises: Acquire a sampling signal, and perform discrete Fourier transform analysis on the sampling signal to obtain a discrete Fourier transform analysis result; Processing the sampled signal according to a discrete Fourier transform analysis result of the sampled signal and a two-point interpolation method to obtain a processed sampled signal; Perform discrete Fourier transform calculation based on Hanning window on the processed sampled signal to determine multiple local extreme values ​​of amplitude, and eliminate noise interference by judging whether each local extreme value of amplitude exceeds the threshold of fundamental wave; The amplitude, frequency and phase of each oscillation mode are calculated based on multiple local amplitude extrema, discrete Fourier transform analysis and two-point interpolation method.

2. The broadband phasor detection method according to claim 1, characterized in that: The sampling signal is processed according to a discrete Fourier transform analysis result of the sampling signal and a two-point interpolation method to obtain a processed sampling signal, including: Calculate the fundamental component based on the two-point interpolation method; Determine a fundamental wave time domain signal based on a discrete Fourier transform analysis result of the sampling signal and the fundamental wave component; The fundamental time domain signal is removed from the sampled signal to obtain a processed sampled signal.

3. The broadband phasor detection method according to claim 1, wherein: Based on the local extrema of the amplitude, discrete Fourier transform analysis and two-point interpolation method, the amplitude, frequency and phase of each oscillation mode are calculated, including: For each extreme point, determine the two spectral lines with the largest frequency near the extreme point; The amplitude, frequency and phase of the oscillation mode are determined based on the two spectral lines with the largest frequency near the extreme point, the discrete Fourier transform analysis results of the sampling signal and the two-point interpolation method.

4. The broadband phasor detection method according to claim 2, wherein: Determining a fundamental wave time domain signal according to a discrete Fourier transform analysis result of the sampling signal and the fundamental wave component includes: According to the discrete Fourier transform analysis results, the two target spectral lines with the largest amplitude near the fundamental frequency are determined; According to the spectrum leakage mechanism and the pseudo-spectrum distribution characteristics, the corresponding relationship between the amplitude and phase of the two target spectrum lines and the amplitude-frequency and phase-frequency characteristics of the window function is determined; The frequency deviation value is estimated according to the corresponding relationship between the amplitude of the two target spectral lines and the amplitude and phase of the two target spectral lines and the amplitude-frequency and phase-frequency characteristics of the window function; Determine the true frequency of the oscillation component based on the frequency deviation value and the frequencies of the two target spectral lines; Determining the true amplitude and true phase of the sampled signal according to the true frequency of the oscillation component, the amplitudes of the two target spectral lines, and the phases of the two target spectral lines; The time domain waveform of the fundamental wave phasor is reconstructed according to the true frequency, true amplitude and true phase.

5. The broadband phasor detection method according to claim 4, characterized in that: The frequency deviation value is estimated based on the corresponding relationship between the amplitudes of the two target spectral lines and the amplitudes and phases of the two target spectral lines and the amplitude-frequency and phase-frequency characteristics of the window function, including: According to the amplitudes of the two target spectral lines, the range of the frequency deviation value is determined; Determining a plurality of intermediate values ​​within a range of frequency deviation values ​​at preset intervals; For each intermediate value, substitute the intermediate value into the corresponding relationship between the amplitude and phase of the two target spectral lines and the amplitude-frequency and phase-frequency characteristics of the window function to determine whether the intermediate value is a frequency deviation value.

6. The broadband phasor detection method according to claim 5, characterized in that: The corresponding relationship between the amplitude and phase of the two target spectral lines and the amplitude-frequency and phase-frequency characteristics of the window function includes a first relationship and a second relationship; Substituting each intermediate value into the corresponding relationship between the amplitude and phase of the two target spectral lines and the amplitude-frequency and phase-frequency characteristics of the window function to determine whether the intermediate value is a frequency deviation value includes: Substitute each intermediate value into the first and second equations respectively; For each intermediate value, when the intermediate value is substituted into the first relational expression and the second relational expression, and the first relational expression and the second relational expression hold, the intermediate value is determined to be the frequency deviation value.

7. A broadband phasor detection device based on two-point interpolation and discrete Fourier transform, characterized in that: The device comprises: A discrete Fourier transform analysis module is used to obtain a sampled signal and perform discrete Fourier transform analysis on the sampled signal to obtain a discrete Fourier transform analysis result; A sampling signal processing module, configured to process the sampling signal according to a discrete Fourier transform analysis result of the sampling signal and a two-point interpolation method to obtain a processed sampling signal; The local extreme value determination module is used to perform discrete Fourier transform calculation based on the Hanning window on the processed sampled signal to determine multiple local extreme values ​​of the amplitude and eliminate noise interference by judging whether each local extreme value of the amplitude exceeds the threshold of the fundamental wave; The oscillation mode determination module is used to calculate the amplitude, frequency and phase of each oscillation mode based on multiple local extreme values ​​of amplitude, discrete Fourier transform analysis and two-point interpolation method.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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