Method and apparatus for measuring signal harmonic and interharmonic components of a spectrum adaptive group

Abstract

In the method and device for measuring signal harmonic and inharmonic components of a spectrum adaptive group provided in the application, the spectrum of a signal is determined first; based on the determined harmonic analysis frequency range, all local maximum frequencies in the spectrum are determined, and minimum frequencies between the continuous local maximum frequencies are determined; initial spectrum segmentation boundaries are determined, new spectrum segmentation boundaries are constructed according to the number of minimum frequencies between the continuous harmonic frequencies, the initial spectrum segmentation boundaries between the adjacent two harmonic frequencies with the minimum frequencies are deleted, the new spectrum segmentation boundaries and the initial spectrum segmentation boundaries which are not deleted are taken as target spectrum segmentation boundaries; the spectrum is divided into multiple frequency bands according to the target spectrum segmentation boundaries, and wavelet band-pass filters are constructed in each frequency band to extract time domain discrete sequences; the effective values of the time domain discrete sequences, and the harmonic frequency times or the inharmonic frequency times are calculated to obtain the signal measurement results. In this way, the measurement accuracy of the signal can be improved.

Description

Technical Field

[0001] This application relates to the field of electrical measurement technology, and in particular to a method and apparatus for measuring the harmonic and interharmonic components of a signal using a spectrum adaptive grouping method. Background Technology

[0002] With the large-scale application of power electronic equipment such as new energy power generation, flexible DC transmission, electrified railways, electric vehicle charging stations (piles), and frequency converters in the power grid, the waveform distortion of voltage and current in the power grid is becoming increasingly severe. In addition to harmonic components, the distorted waveforms may also contain interharmonic components caused by fluctuating loads and unstable oscillations of power electronic equipment. At the same time, the strong volatility, intermittency, and uncertainty of new energy power generation and impulsive loads enhance the time-varying properties of interference injected into the power grid, making the voltage and current of the power grid more complex and variable in the time and frequency domain, and highlighting the non-stationary characteristics of the signal.

[0003] Traditional Fourier transform is used for harmonic and interharmonic measurements, but it suffers from spectral leakage and picket-fence effects when processing non-stationary signals, leading to measurement errors. IEC 61000-4-7 proposes a grouping method to suppress these errors. Harmonic subgroups and harmonic groups are two specific implementations of this method. Harmonic subgroups recover the spectral energy of the harmonic's left and right adjacent frequencies, while harmonic groups recover the spectral energy of the five adjacent frequencies to the left and right of each harmonic. However, the grouping method cannot adaptively adjust and can only select one fixed method. Currently, Class A power quality analyzers are specified to use harmonic subgroups, but they cannot recover as much spectral energy as possible, resulting in larger measurement errors compared to harmonic groups. However, when interharmonics are present, harmonic groups may mistakenly recover interharmonics into the harmonics, causing even greater errors; in this case, harmonic subgroups are actually more advantageous. Similarly, for interharmonic measurements, there are two spectral energy recovery methods: grouping and center subgrouping. These methods also suffer from the problem of recovering leaked harmonic energy into interharmonics, affecting measurement accuracy. Summary of the Invention

[0004] The purpose of this application is to at least address one of the aforementioned technical deficiencies, particularly the low accuracy of prior art in measuring harmonics and interharmonics in non-stationary signals.

[0005] In a first aspect, this application provides a method for measuring the harmonic and interharmonic components of a signal using a spectrum adaptive grouping method, the method comprising:

[0006] The signal is synchronously sampled to obtain a discrete signal sequence, and the discrete Fourier transform analysis is performed on the discrete signal sequence to obtain the spectrum of the signal. The signal is either a stationary signal or a non-stationary signal.

[0007] Determine the harmonic analysis frequency range, and based on the harmonic analysis frequency range, determine all local maxima frequencies in the spectrum, and determine the minimum frequencies between every two adjacent local maxima frequencies;

[0008] Based on each harmonic frequency in the spectrum, multiple initial spectrum segmentation boundaries of the spectrum are determined, and new spectrum segmentation boundaries are constructed based on the number of minimum frequencies between each pair of adjacent harmonic frequencies. Additionally, initial spectrum segmentation boundaries between adjacent harmonic frequencies that have minimum frequencies are deleted, and each new spectrum segmentation boundary and the undeleted initial spectrum segmentation boundaries are used as target spectrum segmentation boundaries.

[0009] Based on the target spectrum segmentation boundaries, the spectrum is divided into multiple frequency bands, and a wavelet bandpass filter is constructed in each frequency band to extract the time-domain discrete sequence in each frequency band;

[0010] The effective value corresponding to the time-domain discrete sequence in each frequency band is obtained, and the harmonic frequency order or interharmonic frequency order corresponding to each frequency band is determined to obtain the measurement result of the signal. The harmonic frequency order is the ratio between the harmonic frequency and the fundamental frequency of the signal, and the interharmonic frequency order is the ratio between the interharmonic frequency and the fundamental frequency of the signal.

[0011] In one embodiment, the step of determining all local maxima frequencies in the spectrum based on the harmonic analysis frequency range, and determining minimum frequencies between every two adjacent local maxima frequencies, includes:

[0012] Based on the harmonic analysis frequency range, for each spectral line in the spectrum, if the amplitude corresponding to the spectral line is greater than the amplitude corresponding to the adjacent spectral lines on its left and right sides, then the frequency corresponding to the spectral line is determined as the local maximum frequency, so as to obtain each of the local maximum frequencies of the spectrum.

[0013] Between each two adjacent local maxima frequencies in the spectrum, for each spectral line, if the amplitude corresponding to the spectral line is less than the amplitude corresponding to the adjacent spectral lines on its left and right sides, then the frequency corresponding to the spectral line is determined as the minimum frequency, so as to obtain each minimum frequency in the spectrum.

[0014] In one embodiment, the step of determining a plurality of initial spectral segmentation boundaries of the spectrum based on each harmonic frequency in the spectrum includes:

[0015] The frequency resolution of the spectrum is determined, wherein the frequency resolution is the ratio of the sampling frequency of the signal to the number of sampling points corresponding to the discrete signal sequence;

[0016] For each harmonic frequency, the first boundary frequency corresponding to the harmonic frequency is used as the initial spectrum segmentation boundary, where the first boundary frequency is the difference between the harmonic frequency and the five frequency resolutions.

[0017] In one embodiment, the steps of constructing new spectrum segmentation boundaries based on the number of minimum frequencies between every two adjacent harmonic frequencies, and deleting the initial spectrum segmentation boundaries between two adjacent harmonic frequencies containing minimum frequencies, and using each new spectrum segmentation boundary and the undeleted initial spectrum segmentation boundary as a target spectrum segmentation boundary, include:

[0018] For each pair of adjacent harmonic frequencies, the parity of the number of minimum frequencies between the two adjacent harmonic frequencies is determined. If the number of minimum frequencies between the two adjacent harmonic frequencies is odd, a second boundary frequency is determined, and the second boundary frequency and the minimum frequency between the two adjacent harmonic frequencies are used as the new frequency segmentation boundary. If the number of minimum frequencies between the two adjacent harmonic frequencies is even, the minimum frequency between the two adjacent harmonic frequencies is used as the new frequency segmentation boundary. The second boundary frequency is the difference between the larger harmonic frequency among the two adjacent harmonic frequencies and one frequency resolution.

[0019] The initial spectral segmentation boundary between two adjacent harmonic frequencies that have a minimum frequency is deleted, and each new spectral segmentation boundary and the undeleted initial spectral segmentation boundary are used as the target spectral segmentation boundary.

[0020] In one embodiment, the step of obtaining the effective value corresponding to the time-domain discrete sequence in each frequency band includes:

[0021] The effective value corresponding to the time-domain discrete sequence in each frequency band is obtained according to the following expression:

[0022]

[0023] In the formula, S rms,m Let RMS represent the root mean square (RMS) value corresponding to the discrete-time sequence in each frequency band, N be the number of sampling points corresponding to the discrete signal sequence, m be the m-th frequency band in the spectrum, and n be the n-th sampling point in the discrete signal sequence. m (n) is the time-domain discrete sequence corresponding to each frequency band.

[0024] In one embodiment, the step of determining the harmonic frequency order or interharmonic frequency order corresponding to each frequency band includes:

[0025] For each frequency band, if there are harmonic frequencies in the frequency band, the ratio of the harmonic frequencies in the frequency band to the fundamental frequency is determined to obtain the harmonic frequency order corresponding to the frequency band. If there are no harmonic frequencies in the frequency band, the intermediate frequency corresponding to the frequency band is taken as the interharmonic frequency, and the ratio of the interharmonic frequency corresponding to the frequency band to the fundamental frequency is determined to obtain the interharmonic frequency order corresponding to the frequency band.

[0026] Secondly, this application provides a spectrum adaptive grouping device for measuring signal harmonic and interharmonic components, the device comprising:

[0027] The spectrum acquisition module is used to synchronously sample the signal to obtain a discrete signal sequence of the signal, and perform discrete Fourier transform analysis on the discrete signal sequence to obtain the spectrum of the signal, wherein the signal is a stationary signal or a non-stationary signal;

[0028] The minimum frequency determination module is used to determine the harmonic analysis frequency range, and based on the harmonic analysis frequency range, determine all local maximum frequencies in the spectrum, and determine the minimum frequency between every two adjacent local maximum frequencies;

[0029] The target spectrum segmentation boundary determination module is used to determine multiple initial spectrum segmentation boundaries of the spectrum based on each harmonic frequency in the spectrum, construct new spectrum segmentation boundaries based on the number of minimum frequencies between each pair of adjacent harmonic frequencies, delete the initial spectrum segmentation boundaries between each pair of adjacent harmonic frequencies, and use each new spectrum segmentation boundary and the undeleted initial spectrum segmentation boundaries as the target spectrum segmentation boundary.

[0030] The time-domain discrete sequence extraction module is used to divide the spectrum into multiple frequency bands according to each target spectrum segmentation boundary, and to construct a wavelet bandpass filter in each frequency band to extract the time-domain discrete sequence in each frequency band.

[0031] The measurement result determination module is used to obtain the effective value corresponding to the time-domain discrete sequence in each frequency band, and determine the harmonic frequency order or interharmonic frequency order corresponding to each frequency band, so as to obtain the measurement result of the signal. The harmonic frequency order is the ratio between the harmonic frequency and the fundamental frequency of the signal, and the interharmonic frequency order is the ratio between the interharmonic frequency and the fundamental frequency of the signal.

[0032] In one embodiment, the minimum frequency determination module includes:

[0033] The local maximum frequency determination unit is used to determine the frequency corresponding to each spectral line in the spectrum according to the harmonic analysis frequency range. If the amplitude of the spectral line is greater than the amplitude of the adjacent spectral lines on its left and right sides, the frequency of the spectral line is determined as the local maximum frequency, so as to obtain the local maximum frequencies of the spectrum.

[0034] The minimum frequency determination unit is used to determine the frequency corresponding to each spectral line as a minimum frequency between every two adjacent local maximum frequencies in the spectrum, and for each spectral line, if the amplitude corresponding to the spectral line is less than the amplitude corresponding to the adjacent spectral lines on its left and right sides, so as to obtain each minimum frequency in the spectrum.

[0035] Thirdly, this application provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the signal harmonic and interharmonic component measurement method of the spectrum adaptive grouping described in any of the above embodiments.

[0036] Fourthly, this application provides a computer device, including: one or more processors, and a memory;

[0037] The memory stores computer-readable instructions, which, when executed by the one or more processors, perform the steps of the signal harmonic and interharmonic component measurement method of the spectrum adaptive grouping described in any of the above embodiments.

[0038] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0039] In the method and apparatus for measuring harmonic and interharmonic components of a signal using adaptive grouping provided in this application, the signal is first synchronously sampled to obtain a discrete signal sequence. Then, a discrete Fourier transform analysis is performed on the discrete signal sequence to obtain the signal's spectrum. This signal can be a stationary or non-stationary signal. A harmonic analysis frequency range is determined, and based on this range, all local maxima frequencies and minimum frequencies between each pair of adjacent local maxima frequencies are identified in the spectrum. This accurately determines the minimum points between harmonic frequencies and their adjacent frequencies, avoiding misclassification of noise or other frequencies as harmonics or interharmonics. Furthermore, multiple initial spectrum segmentation boundaries are determined based on each harmonic frequency in the spectrum. Then, new spectrum segmentation boundaries are constructed based on the number of minimum frequencies between each pair of adjacent harmonic frequencies, and frequencies containing minimum values ​​are deleted. The initial spectral segmentation boundary between two adjacent harmonic frequencies is used as the target spectral segmentation boundary, along with each new spectral segmentation boundary and the undeleted initial spectral segmentation boundary. This ensures that the spectrum can be better divided into multiple frequency bands, improving the accuracy of measurements of different frequency components. Then, based on each target spectral segmentation boundary, the spectrum is divided into multiple frequency bands. Wavelet bandpass filters can be constructed in each band to extract the discrete time-domain sequence, better preserving the effective information within the band and reducing interference from other bands. Finally, the effective value corresponding to the discrete time-domain sequence in each band is calculated, and the harmonic frequency order or interharmonic frequency order corresponding to each band is determined to obtain the signal measurement result. Here, the harmonic frequency order is the ratio between the harmonic frequency and the fundamental frequency of the signal, and the interharmonic frequency order is the ratio between the interharmonic frequency and the fundamental frequency. In this way, measurement errors caused by false spectrum recovery in the presence of interharmonics and poor spectrum leakage recovery in the absence of interharmonics can be avoided. At the same time, compared with the IEC method, the spectrum adaptive grouping signal harmonic and interharmonic component measurement method provided in this application can more accurately locate the interharmonic frequency. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 A schematic diagram illustrating the process of measuring signal harmonics and interharmonic components in a spectrum adaptive grouping system provided in this application embodiment;

[0042] Figure 2 A flowchart illustrating the steps for determining the target spectrum segmentation boundary provided in an embodiment of this application;

[0043] Figures 3(a) and (b) are schematic diagrams of the spectrum segmentation boundaries provided in the embodiments of this application;

[0044] Figure 4 A schematic diagram of a bandpass filter with a transition section based on spectrum segmentation boundary design provided for embodiments of this application;

[0045] Figure 5 A schematic diagram of the spectrum of a non-stationary current signal and the segmentation boundary frequency provided in the embodiments of this application;

[0046] Figure 6 A schematic diagram illustrating the extraction of time-domain discrete sequences within harmonic and interharmonic frequency bands by the filter provided in this embodiment of the application;

[0047] Figure 7 A schematic diagram of the structure of the signal harmonic and interharmonic component measurement device for spectrum adaptive grouping provided in an embodiment of this application;

[0048] Figure 8 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0049] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0050] This application provides a method for measuring the harmonic and interharmonic components of a signal using spectrum adaptive grouping. This method is applicable to both non-stationary and stationary signals. The following embodiments illustrate this method using a computer device as an example. It is understood that the computer device can be any device with data processing capabilities, including but not limited to a single server, server cluster, personal laptop, desktop computer, etc. Figure 1 As shown, this application provides a method for measuring the harmonic and interharmonic components of a signal using a spectrum adaptive grouping method, the method comprising:

[0051] S101: The signal is synchronously sampled to obtain a discrete signal sequence of the signal, and a discrete Fourier transform analysis is performed on the discrete signal sequence to obtain the spectrum of the signal. The signal is either a stationary signal or a non-stationary signal.

[0052] Wherein, a non-stationary signal refers to a signal with time-varying properties in the time domain, whose characteristics change with time. A stationary signal refers to a signal that does not change significantly with time in the time domain. A discrete signal sequence refers to a series of sampled values obtained by discretizing a continuous signal in time, which is usually represented by discrete time points. Discrete Fourier transform is a mathematical technique for converting discrete signals from the time domain to the frequency domain, which obtains the spectrum information of the signal by calculating the components of the signal at different frequencies. The discrete Fourier transform takes the discrete signal sequence as an input and outputs the corresponding spectrum amplitude and phase information. A spectrum refers to the representation of a signal in the frequency domain, which shows the component intensity or energy distribution of the signal at different frequencies. A spectrogram usually takes frequency as the horizontal axis and amplitude or energy as the vertical axis, and is used to analyze the frequency-domain characteristics of the signal, wherein harmonic and interharmonic components can be observed in the spectrum.

[0053] In this step, the time interval required for synchronous sampling of the signal, that is, the sampling frequency, may be determined first, then the signal is sampled at fixed time intervals according to the determined sampling frequency, and the obtained sampled values are used as a discrete signal sequence. Afterwards, the discrete Fourier transform algorithm is used to perform frequency-domain transformation on the discrete signal sequence, that is, converting the discrete signal from the time domain to the frequency domain to obtain the signal spectrum.

[0054] In one example, the fundamental frequency of the non-stationary signal is tracked by means of synchronous sampling, and a discrete signal sequence s(n) with a time length of 10 cycles is obtained, n=1,2,…,N, where N is the number of sampling points within 10 cycles; DFT analysis (Discrete Fourier Transform) is performed on the 10-cycle discrete signal sequence s(n) according to the following expression, and the amplitudes at different frequency components of the non-stationary signal, which may also be referred to as spectral lines, are obtained through decomposition, and the spectral lines are arranged from low to high in frequency to form the spectrum of the non-stationary signal:

[0055]

[0056] In the formula, |S(k)| refers to the k-th spectral line of the discrete signal sequence s(n), 0≤k<N-1; || denotes the modulo operation. The expression of the frequency corresponding to the k-th spectral line is as follows:

[0057]

[0058] In the formula, f s is the sampling frequency, Δf is the frequency resolution of the spectral line, For 50Hz voltage signals and current signals, the frequency resolution corresponding to their 10-cycle discrete data is 5Hz.

[0059] S102: Determine the harmonic analysis frequency range, and based on the harmonic analysis frequency range, determine all local maxima frequencies in the spectrum, and determine the minimum frequencies between every two adjacent local maxima frequencies.

[0060] In harmonic analysis, the frequency range refers to the frequency range used to detect and analyze harmonic components in spectrum analysis. This range typically contains the main harmonic components. Minimum frequencies are the frequencies corresponding to the minimum points in the spectrum. Local maximum frequencies are the frequencies corresponding to local maxima in the signal spectrum.

[0061] In this step, the harmonic analysis frequency range can be determined according to actual needs. In one embodiment, according to the Nyquist sampling theorem, ideally, the upper limit frequency of the harmonic analysis frequency range is half the sampling frequency, and the lower limit frequency of the harmonic analysis frequency range can be zero. Within the harmonic analysis frequency range, all local maxima in the spectrum are searched, and then the frequencies corresponding to minima are searched between the frequencies corresponding to consecutive local maxima, and all minima frequencies are recorded.

[0062] S103: Based on each harmonic frequency in the spectrum, determine multiple initial spectrum segmentation boundaries of the spectrum, construct new spectrum segmentation boundaries based on the number of minimum frequencies between each pair of adjacent harmonic frequencies, and delete the initial spectrum segmentation boundaries between two adjacent harmonic frequencies that have minimum frequencies, and use each new spectrum segmentation boundary and the undeleted initial spectrum segmentation boundaries as target spectrum segmentation boundaries.

[0063] Harmonic frequencies refer to integer multiples of the fundamental frequency in a signal. Spectrum segmentation boundaries are the boundaries that divide the spectrum into multiple sub-regions.

[0064] In this step, based on the number of harmonic frequencies, the position of each harmonic frequency in the spectrum is determined. Based on the position of each harmonic frequency, initial spectral segmentation boundaries are selected in the spectrogram, dividing the spectrum into multiple sub-regions. For adjacent harmonic frequencies, new spectral segmentation boundaries are constructed based on the number of minimum frequencies between adjacent harmonic frequencies, and the initial spectral segmentation boundaries between adjacent harmonic frequencies containing minimum frequencies are deleted. Each new spectral segmentation boundary and the remaining initial spectral segmentation boundaries are used as the target spectral segmentation boundaries.

[0065] S104: Based on the target spectrum segmentation boundaries, the spectrum is divided into multiple frequency bands, and a wavelet bandpass filter is constructed in each frequency band to extract the time-domain discrete sequence in each frequency band.

[0066] In this context, "frequency band" refers to a specific range of frequencies in the frequency domain. A wavelet bandpass filter is a filter that selectively passes or filters out different frequency components based on the frequency characteristics of a signal. A discrete-time sequence refers to a signal sequence represented in discrete form in the time domain.

[0067] In this step, the spectrum is divided into multiple frequency bands based on the target spectrum segmentation boundaries. A wavelet bandpass filter is constructed within each frequency band. The original signal is input into each wavelet bandpass filter to obtain the filtered result for each frequency band. The filtered result for each frequency band can be transformed to the time domain to obtain the corresponding discrete time-domain sequence. It can be understood that a wavelet bandpass filter is a filter bank where each filter selectively passes signals within its designated frequency band while suppressing signals in other frequency ranges.

[0068] S105: Obtain the effective value corresponding to the time-domain discrete sequence in each frequency band, and determine the harmonic frequency order or interharmonic frequency order corresponding to each frequency band to obtain the measurement result of the signal. The harmonic frequency order is the ratio between the harmonic frequency and the fundamental frequency of the signal, and the interharmonic frequency order is the ratio between the interharmonic frequency and the fundamental frequency.

[0069] In this step, based on the fundamental frequency of the signal, the harmonic frequency order or interharmonic frequency order can be calculated. The harmonic frequency order is the ratio between the harmonic frequency and the fundamental frequency, while the interharmonic frequency order is the ratio between the interharmonic frequency and the fundamental frequency. The harmonic and interharmonic frequency orders can be used to describe the relationship between harmonics and interharmonics. Finally, the effective value of the time-domain discrete sequence in each frequency band is calculated. The effective value represents the total energy of the signal in each frequency band. It can be understood that the signal measurement results are the effective value and the harmonic or interharmonic frequency order for each frequency band.

[0070] In the method and apparatus for measuring harmonic and interharmonic components of a signal using adaptive grouping provided in this application, the signal is first synchronously sampled to obtain a discrete signal sequence. Then, a discrete Fourier transform analysis is performed on the discrete signal sequence to obtain the signal's spectrum. This signal can be a stationary or non-stationary signal. A harmonic analysis frequency range is determined, and based on this range, all local maxima frequencies and minimum frequencies between each pair of adjacent local maxima frequencies are identified in the spectrum. This accurately determines the minimum points between harmonic frequencies and their adjacent frequencies, avoiding misclassification of noise or other frequencies as harmonics or interharmonics. Furthermore, multiple initial spectrum segmentation boundaries are determined based on each harmonic frequency in the spectrum. Then, new spectrum segmentation boundaries are constructed based on the number of minimum frequencies between each pair of adjacent harmonic frequencies, and frequencies containing minimum values ​​are deleted. The initial spectral segmentation boundary between two adjacent harmonic frequencies is used as the target spectral segmentation boundary, along with each new spectral segmentation boundary and the undeleted initial spectral segmentation boundary. This ensures that the spectrum can be better divided into multiple frequency bands, improving the accuracy of measurements of different frequency components. Then, based on each target spectral segmentation boundary, the spectrum is divided into multiple frequency bands. Wavelet bandpass filters can be constructed in each band to extract the discrete time-domain sequence, better preserving the effective information within the band and reducing interference from other bands. Finally, the effective value corresponding to the discrete time-domain sequence in each band is calculated, and the harmonic frequency order or interharmonic frequency order corresponding to each band is determined to obtain the signal measurement result. Here, the harmonic frequency order is the ratio between the harmonic frequency and the fundamental frequency of the signal, and the interharmonic frequency order is the ratio between the interharmonic frequency and the fundamental frequency. In this way, measurement errors caused by false spectrum recovery in the presence of interharmonics and poor spectrum leakage recovery in the absence of interharmonics can be avoided. At the same time, compared with the IEC method, the spectrum adaptive grouping signal harmonic and interharmonic component measurement method provided in this application can more accurately locate the interharmonic frequency.

[0071] In one embodiment, the step of determining all local maxima frequencies in the spectrum based on the harmonic analysis frequency range, and determining minimum frequencies between every two adjacent local maxima frequencies, includes:

[0072] Based on the harmonic analysis frequency range, for each spectral line in the spectrum, if the amplitude corresponding to the spectral line is greater than the amplitude corresponding to the adjacent spectral lines on its left and right sides, then the frequency corresponding to the spectral line is determined as the local maximum frequency, so as to obtain each of the local maximum frequencies of the spectrum.

[0073] Between each two adjacent local maxima frequencies in the spectrum, for each spectral line, if the amplitude corresponding to the spectral line is less than the amplitude corresponding to the adjacent spectral lines on its left and right sides, then the frequency corresponding to the spectral line is determined as the minimum frequency, so as to obtain each minimum frequency in the spectrum.

[0074] Specifically, based on the harmonic analysis frequency range, each spectral line in the spectrum is determined. For each spectral line, the amplitude corresponding to that line is compared with the amplitudes of its left and right adjacent spectral lines. If the amplitude of the spectral line is greater than the amplitudes of its left and right adjacent spectral lines, then the frequency corresponding to that spectral line is determined as a local maximum frequency. Between every two adjacent local maximum frequencies, for each spectral line, the amplitude corresponding to that line is compared with the amplitudes of its left and right adjacent spectral lines. If the amplitude of the spectral line is less than the amplitudes of its left and right adjacent spectral lines, then the frequency corresponding to that spectral line is determined as a minimum frequency.

[0075] In one example, using 0–2 kHz as the harmonic analysis frequency range, the following expression is used to search for all spectral lines within the 0–2 kHz range that are higher than their left and right adjacent spectral lines, in order to obtain all local maxima points in the spectrum:

[0076]

[0077] In the formula, S(k) i () represents a local maximum point, k i Let i be the spectral line position corresponding to the i-th maximum point, i = 1, 2, ..., i max The corresponding frequency is f H,i =k i ×Δf.

[0078] From each consecutive maximum point s(k) i ) and s(K i+1 Between corresponding frequencies, search for spectral lines smaller than their left and right adjacent spectral lines according to the following expression to obtain the minimum point s(k). j ), k j Let j be the spectral line position corresponding to the j-th local minimum point, where j = 1, 2, ..., j max The corresponding frequency is f L,j =k j ×Δf.

[0079]

[0080] In the formula, k j ∈[k i ,k i+1 ], i∈N + .

[0081] In this embodiment, by comparing the amplitudes of adjacent frequencies in the spectrum, local maxima frequencies can be identified. These local maxima frequencies typically correspond to harmonic components in the signal. Between each pair of adjacent local maxima frequencies, by further comparing the amplitudes of the adjacent frequencies, minimum frequencies can be found in the spectrum. These minimum frequencies represent interharmonic components or other frequency intervals in the signal. By determining the minimum frequencies, the spectrum can be decomposed into different frequency bands, making the frequency structure of the signal clearer.

[0082] In one embodiment, the step of determining a plurality of initial spectral segmentation boundaries of the spectrum based on each harmonic frequency in the spectrum includes:

[0083] The frequency resolution of the spectrum is determined, wherein the frequency resolution is the ratio of the sampling frequency of the signal to the number of sampling points corresponding to the discrete signal sequence;

[0084] For each harmonic frequency, the first boundary frequency corresponding to the harmonic frequency is used as the initial spectrum segmentation boundary, where the first boundary frequency is the difference between the harmonic frequency and the five frequency resolutions.

[0085] Specifically, first, the frequency resolution of the spectrum needs to be calculated. Frequency resolution represents the frequency interval corresponding to each discrete frequency point in the spectrum, and can be calculated by dividing the sampling frequency by the total number of samples in the discrete signal sequence. For each harmonic frequency, the first boundary frequency corresponding to that harmonic frequency is used as the initial spectrum segmentation boundary. The first boundary frequency is obtained by subtracting five times the frequency resolution from the harmonic frequency.

[0086] In one example, based on the definition of harmonic groups recovering energy from the corresponding harmonic spectral line and the five spectral lines to its left and right, the initial spectral segmentation boundary f is set according to the following expression. bs :

[0087] f bs,h =hf p -5Δf,h=1,2,…,40

[0088] In the formula, f p Given the fundamental frequency, under 10-cycle conditions, f p =10Δf, which is the spectral frequency at k=10; h is the harmonic order, which is a positive integer, referring to the harmonic frequency f. h The ratio of the fundamental frequency f1 to the frequency h, h = 1, 2, 3, ...

[0089] In this embodiment, harmonics refer to integer multiples of frequencies in the spectrum that are related to the fundamental frequency. By setting initial spectrum segmentation boundaries, harmonics can be segmented into corresponding frequency bands.

[0090] like Figure 2As shown, in one embodiment, the steps of constructing new spectrum segmentation boundaries based on the number of minimum frequencies between every two adjacent harmonic frequencies, and deleting the initial spectrum segmentation boundaries between two adjacent harmonic frequencies containing minimum frequencies, and using each new spectrum segmentation boundary and the undeleted initial spectrum segmentation boundary as the target spectrum segmentation boundary, include:

[0091] S201: For each pair of adjacent harmonic frequencies, determine the parity of the number of minimum frequencies between the two adjacent harmonic frequencies. If the number of minimum frequencies between the two adjacent harmonic frequencies is odd, determine a second boundary frequency and use the second boundary frequency and the minimum frequency between the two adjacent harmonic frequencies as the new frequency segmentation boundary. If the number of minimum frequencies between the two adjacent harmonic frequencies is even, use the minimum frequency between the two adjacent harmonic frequencies as the new frequency segmentation boundary. The second boundary frequency is the difference between the larger harmonic frequency among the two adjacent harmonic frequencies and one frequency resolution.

[0092] S202: Delete the initial spectrum segmentation boundary between two adjacent harmonic frequencies where a minimum frequency exists, and use each new spectrum segmentation boundary and the undeleted initial spectrum segmentation boundary as the target spectrum segmentation boundary.

[0093] Specifically, for each pair of adjacent harmonic frequencies, a minimum frequency is checked between them. If a minimum frequency exists, the initial spectral segmentation boundary between the two adjacent harmonic frequencies is deleted. When the number of minimum frequencies between them is odd, a second boundary frequency is determined. The second boundary frequency is the difference between the larger harmonic frequency among the two adjacent harmonic frequencies and a frequency resolution. The minimum frequency between each pair of adjacent harmonic frequencies and the second boundary frequency are used as new spectral segmentation boundaries. When the number of minimum frequencies between them is even, the minimum frequency between each pair of adjacent harmonic frequencies is used as a new spectral segmentation boundary. The original spectral segmentation boundary (not deleted) and each new spectral segmentation boundary are used as the target spectral segmentation boundary.

[0094] In one example, as shown in Figure 3, if there are q local minima S(k) between adjacent harmonic frequencies... j That is, it satisfies the following expression:

[0095] (h-1)f p +Δf≤f L,j+q-1 <hf p -Δf

[0096] Based on the number of minimum points and each harmonic frequency, a new spectral segmentation boundary f is constructed. bn304, and delete the initial spectral division boundary 303 between these adjacent harmonic frequencies, as follows:

[0097] As shown in Figure 3(a), when the number of local minima 301, q, is even, the specific spectrum segmentation boundary is expressed as follows:

[0098]

[0099] As shown in Figure 3(b), when the number of minimum points 301 q is odd, a final spectral division boundary 305 needs to be added between these adjacent harmonics according to the requirements of the harmonic subgroup. The specific spectral division boundary is expressed as follows:

[0100]

[0101] The remaining initial spectrum is divided into boundaries corresponding to frequencies f. bs 302 corresponds to the frequency f of the newly constructed spectrum segmentation boundary. bn 304, arranged in ascending order, constitute the target spectrum segmentation boundary.

[0102] In this embodiment, by deleting the initial spectral segmentation boundary between adjacent harmonic frequencies containing a minimum frequency, interference from interharmonics on harmonic measurements can be eliminated, thereby improving the accuracy of the measurement results. Using the existing minimum frequency and the second boundary frequency as new spectral segmentation boundaries allows for more precise division of the boundaries between harmonic frequencies and their interharmonic components, enabling independent analysis of each spectral segment. For cases with an odd number of minimum frequencies, determining the second boundary resolution ensures reasonable boundary positions for each spectral segment, avoiding uneven spectral segmentation and improving the completeness and consistency of spectral analysis. This allows for the identification of signal components and adaptive adjustment of spectral recovery capabilities, avoiding measurement errors caused by erroneous spectral recovery in the presence of interharmonics and insufficient spectral recovery due to spectral leakage in the absence of interharmonics, ultimately improving the measurement accuracy of harmonics and interharmonics in non-stationary states.

[0103] In one embodiment, the step of extracting the time-domain discrete sequence in each frequency band includes:

[0104] The detail coefficients for each frequency band are determined according to the following expression:

[0105] W(m,n)= <s(n),g m (n)>

[0106] In the formula, W(m,n) are the detail coefficients corresponding to each frequency band, s(n) is the discrete signal sequence, and g m(n) represents the empirical wavelet function corresponding to each frequency band, m represents the m-th frequency band in the spectrum, and n represents the n-th sampling point in the discrete signal sequence;

[0107] Extract the time-domain discrete sequence from each frequency band according to the following expression:

[0108] s m (n)=W(m,n)*g m (n)

[0109] In the formula, s m (n) is the time-domain discrete sequence corresponding to each frequency band.

[0110] In one example, such as Figure 4 As shown, the spectrum is adaptively divided into D consecutive parts by dividing the target spectrum boundary, where D is a positive integer greater than 1, and the bandwidth length of each part is:

[0111] Δf′ m =[f′ m ,f′ m+1 ],m=1,2,…,D

[0112] For each dividing boundary f′ m Define the width as T, centered on the center. m =2τ m The filter transition section, where τ m =γf′ m The value of the proportionality coefficient γ must satisfy the following requirement:

[0113]

[0114] Constructing an empirical wavelet function g using the Meyer wavelet basis m (f k To implement a bandpass filter considering each frequency band of the transition section, the filter parameters in the frequency domain are expressed as follows:

[0115]

[0116]

[0117] The discrete signal s(n) sampled in the time domain and the empirical wavelet function g in the time domain m The inner product of (n) yields the signal decomposition detail coefficients W(m,n) for different frequency bands, as shown in the following expression:

[0118] W(m,n)= <s(n),g m (n)>=F -1 [S(f k )G m (fk )]

[0119] In the formula, <> represents the inner product operation, F -1 This is the inverse discrete Fourier transform.

[0120] The discrete time-domain sequence s corresponding to each frequency band can then be extracted using the following expression. m (n).

[0121] s m (n)=W(m,n)*g m (n)

[0122] In this embodiment, by performing an inner product operation between the discrete signal sequence and the empirical wavelet function, detail coefficients corresponding to each frequency band can be obtained. These detail coefficients reflect the energy distribution of the signal in different frequency bands. The discrete time-domain sequence in each frequency band is then extracted using the detail coefficients and the corresponding empirical wavelet function. This discrete time-domain sequence contains the time-domain characteristics of the signal in different frequency bands. This improves the accuracy of signal measurement.

[0123] In one embodiment, the step of obtaining the effective value corresponding to the time-domain discrete sequence in each frequency band includes:

[0124] The effective value corresponding to the time-domain discrete sequence in each frequency band is obtained according to the following expression:

[0125]

[0126] In the formula, S rms,m RMS represents the effective value corresponding to the discrete time-domain sequence in each frequency band, and N is the number of sampling points corresponding to the discrete signal sequence.

[0127] In this embodiment, the effective value is an indicator for measuring signal energy. By calculating the effective value of the discrete time-domain sequence for each frequency band, we can understand the energy distribution of the signal at different frequency components, which helps to further analyze the frequency domain characteristics of the signal and the contribution of different frequency components.

[0128] In one embodiment, the step of determining the harmonic frequency order or interharmonic frequency order corresponding to each frequency band includes:

[0129] For each frequency band, if there are harmonic frequencies in the frequency band, the ratio of the harmonic frequencies in the frequency band to the fundamental frequency is determined to obtain the harmonic frequency order corresponding to the frequency band. If there are no harmonic frequencies in the frequency band, the intermediate frequency corresponding to the frequency band is taken as the interharmonic frequency, and the ratio of the interharmonic frequency corresponding to the frequency band to the fundamental frequency is determined to obtain the interharmonic frequency order corresponding to the frequency band.

[0130] In one example, the harmonic and interharmonic orders for each frequency band are determined based on whether the frequency range includes harmonic frequencies, as detailed below:

[0131] When the frequency corresponding to a certain h-th harmonic is within the frequency band, that is, when it satisfies the following expression, then the effective value calculated for this frequency band corresponds to the magnitude of that h-th harmonic:

[0132] f h ∈[f′ m ,f′ m+1 ]

[0133] When a certain frequency band does not contain any harmonic frequency, the interharmonic frequency f is taken as the frequency corresponding to the midpoint of the left and right boundary frequencies of the band, according to the following expression. ih The effective value calculated is the magnitude of that interharmonic:

[0134]

[0135] In this embodiment, determining the harmonic frequency number or interharmonic frequency number corresponding to each frequency band can provide more detailed spectrum information.

[0136] To facilitate understanding of the scheme in this application, a specific example is provided below.

[0137] Amplitude modulation is applied to a periodic steady-state signal to obtain a non-stationary signal, as expressed below:

[0138] S m (t)=S(t)×C(t)

[0139] The steady-state signal is:

[0140]

[0141] The specific parameters of the steady-state signal are shown in the table below:

[0142] <![CDATA[Interharmonic current I ih > 2A Interharmonic number ih 1.6，2.5 <![CDATA[Fundamental current I1]]> 100A Harmonic number h <![CDATA[6k±1,k≤6,k∈N + ]]> <![CDATA[Fundamental frequency f s > 50Hz

[0143] The amplitude modulation signal is:

[0144]

[0145] The specific parameters of this amplitude modulation signal are shown in the table below:

[0146] <![CDATA[10 frequency start time t s > 0 <![CDATA[Amplitude modulation start time t a > 5ms <![CDATA[Amplitude modulation end time t b > 15ms <![CDATA[10 cycle end time t e > 20ms Modulation depth α 0.5

[0147] After amplitude modulation, the ideal values ​​of the amplitudes of each harmonic and interharmonic component of the non-stationary current signal are the amplitudes of each component of the steady-state voltage signal multiplied by a reduction factor. get.

[0148] fs A non-steady-state current signal was synchronously sampled at a sampling frequency of 12.8 kHz. Within a 10-cycle time period, a discrete signal sequence with N = 2560 sampling points was obtained. DFT analysis was then performed on this sequence to obtain its frequency spectrum in the frequency domain. Figure 5 The solid line indicates the region. The harmonic frequency range for analysis was limited to 0–2kHz. Within this range, all local maxima were searched in the spectrum. Then, the frequencies corresponding to minima were searched between the frequencies corresponding to consecutive maxima. The results are shown in the table below:

[0149] 1 75 8 915 2 110 9 1095 3 130 10 1220 4 320 11 1400 5 500 12 1520 6 620 13 1705 7 795 14 1820

[0150] Determine the initial spectrum segmentation boundary f bs A new spectral segmentation boundary f is constructed based on the number of minimum points between adjacent harmonic frequencies. bn Simultaneously, the initial segmentation boundaries between adjacent harmonic frequencies containing the new spectral segmentation boundaries are deleted, and the final adaptive determination of the spectral segmentation boundaries is achieved, such as... Figure 5 As shown by the dashed lines, the results are shown in the table below:

[0151]

[0152] A wavelet bandpass filter is constructed by dividing the adjacent spectrum boundaries, and the time-domain discrete sequence within the corresponding frequency band is extracted. The result is as follows. Figure 6 As shown in the table below, the effective values ​​of the discrete time-domain sequences for each frequency band are obtained, and the harmonic and interharmonic orders corresponding to each frequency band are determined based on the segmentation boundary frequencies. The measurement results are shown in the table below. The results are compared with the measurement method required by the IEC standard for Class A power quality analyzers, which uses a "subgroup" approach for harmonics and a "center subgroup" approach for interharmonics, to construct a non-stationary current signal. The relative errors of the two methods relative to the ideal values ​​are shown in the table below.

[0153]

[0154]

[0155] It is easy to see that, under non-stationary conditions, the measurement results of harmonic and interharmonic components of non-stationary signals using the spectrum adaptive grouping method have a maximum error relative to the ideal value of no more than -4%. In contrast, the results of the fixed grouping method specified by the IEC standard, due to spectrum leakage into the interharmonic frequency band, are mistakenly recovered, resulting in a maximum error of up to 210% relative to the ideal value at the interharmonic level. Furthermore, at each harmonic and interharmonic level, the measurement results of the present invention are smaller than those of the IEC method, indicating that the adaptive nature of the present invention can recover more leaked energy and avoid erroneous spectrum recovery in the presence of interharmonics, resulting in higher final measurement accuracy. Moreover, the present method can more accurately locate the interharmonic frequency compared to the IEC method. These comparative results clearly demonstrate the superior measurement performance of the present method when the signal is in a non-stationary state.

[0156] The following describes the signal harmonic and interharmonic component measurement device for spectrum adaptive grouping provided in the embodiments of this application. The signal harmonic and interharmonic component measurement device for spectrum adaptive grouping described below can be referred to in correspondence with the signal harmonic and interharmonic component measurement method for spectrum adaptive grouping described above. Figure 7 As shown, this application provides a spectrum adaptive grouping device for measuring signal harmonics and interharmonic components, the device comprising:

[0157] The spectrum acquisition module 401 is used to synchronously sample the signal to obtain a discrete signal sequence of the signal, and perform discrete Fourier transform analysis on the discrete signal sequence to obtain the spectrum of the signal, wherein the signal is a stationary signal or a non-stationary signal.

[0158] The minimum frequency determination module 402 is used to determine the harmonic analysis frequency range, and based on the harmonic analysis frequency range, determine all local maximum frequencies in the spectrum, and determine the minimum frequency between every two adjacent local maximum frequencies.

[0159] The target spectrum segmentation boundary determination module 403 is used to determine multiple initial spectrum segmentation boundaries of the spectrum based on each harmonic frequency in the spectrum, construct new spectrum segmentation boundaries based on the number of minimum frequencies between each pair of adjacent harmonic frequencies, delete the initial spectrum segmentation boundaries between two adjacent harmonic frequencies that have minimum frequencies, and use each new spectrum segmentation boundary and the undeleted initial spectrum segmentation boundary as the target spectrum segmentation boundary.

[0160] The time-domain discrete sequence extraction module 404 is used to divide the spectrum into multiple frequency bands according to each of the target spectrum segmentation boundaries, and to construct a wavelet bandpass filter in each frequency band to extract the time-domain discrete sequence in each frequency band.

[0161] The measurement result determination module 405 is used to obtain the effective value corresponding to the time-domain discrete sequence in each frequency band, and determine the harmonic frequency order or interharmonic frequency order corresponding to each frequency band, so as to obtain the measurement result of the signal. The harmonic frequency order is the ratio between the harmonic frequency and the fundamental frequency of the signal, and the interharmonic frequency order is the ratio between the interharmonic frequency and the fundamental frequency of the signal.

[0162] In one embodiment, the minimum frequency determination module 402 includes:

[0163] The local maximum frequency determination unit is used to determine the frequency corresponding to each spectral line in the spectrum according to the harmonic analysis frequency range. If the amplitude of the spectral line is greater than the amplitude of the adjacent spectral lines on its left and right sides, the frequency of the spectral line is determined as the local maximum frequency, so as to obtain the local maximum frequencies of the spectrum.

[0164] The minimum frequency determination unit is used to determine the frequency corresponding to each spectral line as a minimum frequency between every two adjacent local maximum frequencies in the spectrum, and for each spectral line, if the amplitude corresponding to the spectral line is less than the amplitude corresponding to the adjacent spectral lines on its left and right sides, so as to obtain each minimum frequency in the spectrum.

[0165] In one embodiment, the target spectrum segmentation boundary determination module 403 includes:

[0166] A frequency resolution determination unit is used to determine the frequency resolution of the spectrum, wherein the frequency resolution is the ratio of the sampling frequency of the signal to the number of sampling points corresponding to the discrete signal sequence;

[0167] An initial spectrum segmentation boundary determination unit is used to determine, for each harmonic frequency, the first boundary frequency corresponding to the harmonic frequency as the initial spectrum segmentation boundary, wherein the first boundary frequency is the difference between the harmonic frequency and the five frequency resolutions.

[0168] In one embodiment, the target spectrum segmentation boundary determination module 403 includes:

[0169] The new spectrum segmentation boundary determination unit is used to determine the parity of the number of minimum frequencies between two adjacent harmonic frequencies for each pair of adjacent harmonic frequencies. If the number of minimum frequencies between two adjacent harmonic frequencies is odd, a second boundary frequency is determined, and the second boundary frequency and the minimum frequency between the two adjacent harmonic frequencies are used as the new frequency segmentation boundary. If the number of minimum frequencies between two adjacent harmonic frequencies is even, the minimum frequency between the two adjacent harmonic frequencies is used as the new frequency segmentation boundary. The second boundary frequency is the difference between the larger harmonic frequency among the two adjacent harmonic frequencies and one frequency resolution.

[0170] The target spectrum segmentation boundary determination unit is used to delete the initial spectrum segmentation boundary between two adjacent harmonic frequencies that have a minimum frequency, and to use each new spectrum segmentation boundary and the undeleted initial spectrum segmentation boundary as the target spectrum segmentation boundary.

[0171] In one embodiment, the measurement result determination module 405 includes:

[0172] The effective value determination unit is used to calculate the effective value corresponding to the time-domain discrete sequence in each frequency band according to the following expression:

[0173]

[0174] In the formula, S rms,m Let RMS represent the root mean square (RMS) value corresponding to the discrete-time sequence in each frequency band, N be the number of sampling points corresponding to the discrete signal sequence, m be the m-th frequency band in the spectrum, and n be the n-th sampling point in the discrete signal sequence. m (n) is the time-domain discrete sequence corresponding to each frequency band.

[0175] In one embodiment, the measurement result determination module 405 includes:

[0176] The frequency order determination unit is used to determine the ratio of the harmonic frequency to the fundamental frequency for each frequency band if there is a harmonic frequency in the frequency band, thereby obtaining the harmonic frequency order corresponding to the frequency band; if there is no harmonic frequency in the frequency band, the intermediate frequency corresponding to the frequency band is taken as the interharmonic frequency, and the ratio of the interharmonic frequency to the fundamental frequency is determined, thereby obtaining the interharmonic frequency order corresponding to the frequency band.

[0177] In one embodiment, this application also provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the signal harmonic and interharmonic component measurement method as described in any of the above embodiments.

[0178] In one embodiment, this application also provides a computer device storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the signal harmonic and interharmonic component measurement method as described in any of the above embodiments.

[0179] Indicatively, such as Figure 8 As shown, Figure 8 This is a schematic diagram of the internal structure of a computer device 500 provided in an embodiment of this application. The computer device 500 can be provided as a server. (Refer to...) Figure 8 The computer device 500 includes a processing component 502, which further includes one or more processors, and memory resources represented by memory 501 for storing instructions, such as application programs, that can be executed by the processing component 502. The application programs stored in memory 501 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 502 is configured to execute instructions to perform the signal harmonic and interharmonic component measurement method of any of the above embodiments of spectrum adaptive grouping.

[0180] The computer device 500 may also include a power supply component 503 configured to perform power management of the computer device 500, a wired or wireless network interface 504 configured to connect the computer device 500 to a network, and an input / output (I / O) interface 505. The computer device 500 may operate on an operating system stored in memory 501, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or similar.

[0181] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0182] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In this document, "a," "an," "the," "the," and "its" may also include plural forms unless the context clearly indicates otherwise. "Multiple" refers to at least two, such as 2, 3, 5, or 8, etc. "And / or" includes any and all combinations of the related listed items.

[0183] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0184] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for measuring the harmonic and interharmonic components of a signal using a spectrum-adaptive grouping method, characterized in that, The method includes: The signal is synchronously sampled to obtain a discrete signal sequence, and the discrete Fourier transform analysis is performed on the discrete signal sequence to obtain the spectrum of the signal. The signal is either a stationary signal or a non-stationary signal. Determine the harmonic analysis frequency range, and based on the harmonic analysis frequency range, determine all local maxima frequencies in the spectrum, and determine the minimum frequencies between every two adjacent local maxima frequencies; The frequency resolution of the spectrum is determined, which is the ratio of the sampling frequency of the signal to the number of sampling points corresponding to the discrete signal sequence. For each harmonic frequency in the spectrum, the first boundary frequency corresponding to the harmonic frequency is used as the initial spectrum segmentation boundary, and the first boundary frequency is the difference between the harmonic frequency and five of the frequency resolutions. For each pair of adjacent harmonic frequencies, the parity of the number of minimum frequencies between the two adjacent harmonic frequencies is determined. If the number of minimum frequencies between the two adjacent harmonic frequencies is odd, a second boundary frequency is determined, and the second boundary frequency and the minimum frequency between the two adjacent harmonic frequencies are used as new spectrum segmentation boundaries. If the number of minimum frequencies between the two adjacent harmonic frequencies is even, the minimum frequency between the two adjacent harmonic frequencies is used as new spectrum segmentation boundaries. The second boundary frequency is the difference between the larger harmonic frequency among the two adjacent harmonic frequencies and one frequency resolution. Delete the initial spectrum segmentation boundary between two adjacent harmonic frequencies that have a minimum frequency, and use each new spectrum segmentation boundary and the undeleted initial spectrum segmentation boundary as the target spectrum segmentation boundary; Based on the target spectrum segmentation boundaries, the spectrum is divided into multiple frequency bands, and a wavelet bandpass filter is constructed in each frequency band to extract the time-domain discrete sequence in each frequency band; The effective value corresponding to the time-domain discrete sequence in each frequency band is obtained, and the harmonic frequency order or interharmonic frequency order corresponding to each frequency band is determined to obtain the measurement result of the signal. The harmonic frequency order is the ratio between the harmonic frequency and the fundamental frequency of the signal, and the interharmonic frequency order is the ratio between the interharmonic frequency and the fundamental frequency of the signal.

2. The method for measuring signal harmonics and interharmonic components according to claim 1, characterized in that, The steps of determining all local maxima frequencies in the spectrum based on the harmonic analysis frequency range, and determining minimum frequencies between every two adjacent local maxima frequencies, include: Based on the harmonic analysis frequency range, for each spectral line in the spectrum, if the amplitude corresponding to the spectral line is greater than the amplitude corresponding to the adjacent spectral lines on its left and right sides, then the frequency corresponding to the spectral line is determined as the local maximum frequency, so as to obtain each of the local maximum frequencies of the spectrum. Between each two adjacent local maxima frequencies in the spectrum, for each spectral line, if the amplitude corresponding to the spectral line is less than the amplitude corresponding to the adjacent spectral lines on its left and right sides, then the frequency corresponding to the spectral line is determined as the minimum frequency, so as to obtain each minimum frequency in the spectrum.

3. The method for measuring signal harmonics and interharmonic components according to claim 1, characterized in that, The step of obtaining the effective value corresponding to the time-domain discrete sequence in each frequency band includes: The effective value corresponding to the time-domain discrete sequence in each frequency band is obtained according to the following expression: In the formula, The effective value corresponding to the discrete time-domain sequence in each frequency band. Represents the root mean square. The number of sampling points corresponding to the discrete signal sequence. For the first in the spectrum One frequency band, The first in the discrete signal sequence One sampling point, The time-domain discrete sequence corresponding to each frequency band.

4. The method for measuring signal harmonics and interharmonic components according to claim 1, characterized in that, The step of determining the harmonic frequency order or interharmonic frequency order corresponding to each frequency band includes: For each frequency band, if there are harmonic frequencies in the frequency band, the ratio of the harmonic frequencies in the frequency band to the fundamental frequency is determined to obtain the harmonic frequency order corresponding to the frequency band. If there are no harmonic frequencies in the frequency band, the intermediate frequency corresponding to the frequency band is taken as the interharmonic frequency, and the ratio of the interharmonic frequency corresponding to the frequency band to the fundamental frequency is determined to obtain the interharmonic frequency order corresponding to the frequency band.

5. A device for measuring the harmonic and interharmonic components of a signal with adaptive frequency grouping, characterized in that, The device includes: The spectrum acquisition module is used to synchronously sample the signal to obtain a discrete signal sequence of the signal, and perform discrete Fourier transform analysis on the discrete signal sequence to obtain the spectrum of the signal, wherein the signal is a stationary signal or a non-stationary signal; The minimum frequency determination module is used to determine the harmonic analysis frequency range, and based on the harmonic analysis frequency range, determine all local maximum frequencies in the spectrum, and determine the minimum frequency between every two adjacent local maximum frequencies; The target spectrum segmentation boundary determination module is used to determine the frequency resolution of the spectrum, which is the ratio of the sampling frequency of the signal to the number of sampling points corresponding to the discrete signal sequence. For each harmonic frequency in the spectrum, the first boundary frequency corresponding to that harmonic frequency is used as the initial spectrum segmentation boundary, where the first boundary frequency is the difference between the harmonic frequency and five of the frequency resolutions. For each pair of adjacent harmonic frequencies, the parity of the number of minimum frequencies between the two adjacent harmonic frequencies is determined. If the number of minimum frequencies between the two adjacent harmonic frequencies is odd, then a second boundary frequency is determined. The second boundary frequency and the minimum frequency between the two adjacent harmonic frequencies are used as new spectrum segmentation boundaries. If the number of minimum frequencies between the two adjacent harmonic frequencies is even, then the minimum frequency between the two adjacent harmonic frequencies is used as a new spectrum segmentation boundary. The second boundary frequency is the difference between the larger harmonic frequency among the two adjacent harmonic frequencies and one of the frequency resolutions. The initial spectrum segmentation boundaries between the two adjacent harmonic frequencies that have minimum frequencies are deleted, and each new spectrum segmentation boundary and the undeleted initial spectrum segmentation boundary are used as target spectrum segmentation boundaries. The time-domain discrete sequence extraction module is used to divide the spectrum into multiple frequency bands according to each target spectrum segmentation boundary, and to construct a wavelet bandpass filter in each frequency band to extract the time-domain discrete sequence in each frequency band. The measurement result determination module is used to obtain the effective value corresponding to the time-domain discrete sequence in each frequency band, and determine the harmonic frequency order or interharmonic frequency order corresponding to each frequency band, so as to obtain the measurement result of the signal. The harmonic frequency order is the ratio between the harmonic frequency and the fundamental frequency of the signal, and the interharmonic frequency order is the ratio between the interharmonic frequency and the fundamental frequency of the signal.

6. The signal harmonic and interharmonic component measurement device according to claim 5, characterized in that, The minimum frequency determination module includes: The local maximum frequency determination unit is used to determine the frequency corresponding to each spectral line in the spectrum according to the harmonic analysis frequency range. If the amplitude of the spectral line is greater than the amplitude of the adjacent spectral lines on its left and right sides, the frequency of the spectral line is determined as the local maximum frequency, so as to obtain the local maximum frequencies of the spectrum. The minimum frequency determination unit is used to determine the frequency corresponding to each spectral line as a minimum frequency between every two adjacent local maximum frequencies in the spectrum, and for each spectral line, if the amplitude corresponding to the spectral line is less than the amplitude corresponding to the adjacent spectral lines on its left and right sides, so as to obtain each minimum frequency in the spectrum.

7. A storage medium, characterized in that: The storage medium stores computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the signal harmonic and interharmonic component measurement method as described in any one of claims 1 to 4.

8. A computer device, characterized in that, include: One or more processors, and memory; The memory stores computer-readable instructions that, when executed by the one or more processors, perform the steps of the signal harmonic and interharmonic component measurement method as described in any one of claims 1 to 4.

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