An oscillation characteristic parameter identification method, device and equipment and a storage medium

Abstract

The application provides an oscillation characteristic parameter identification method and device, equipment and a storage medium. The method comprises the following steps: obtaining a frequency spectrum of a to-be-processed signal, and screening an oscillation mode set with an amplitude of a frequency spectrum peak greater than a set threshold; for each oscillation mode in the set, constructing a corresponding mode tracker according to an initial frequency of the oscillation mode to perform tracking, and updating a coefficient of the mode tracker according to a difference between the to-be-processed signal and a reconstructed signal; and obtaining a frequency, an amplitude and a phase of the oscillation mode according to the updated coefficient of the corresponding mode tracker. The oscillation mode is coarsely screened, and then each oscillation mode is tracked by using the corresponding mode tracker. The accurate frequency, amplitude and phase of the oscillation mode are obtained according to the updated coefficient of the corresponding mode tracker, the parameter identification capability of the oscillation time-varying characteristic is improved, accurate power grid dynamic data is provided for power system operators, and the safe and stable operation of the power system is ensured.

Description

Technical Field

[0001] This application relates to the field of power system online monitoring technology, and in particular to a method, apparatus, device and storage medium for identifying oscillation characteristic parameters. Background Technology

[0002] Broadband oscillations dominated by power electronic devices are one of the important factors threatening the safe and stable operation of power systems. However, due to the influence of grid operation mode and power electronic device control parameters, the oscillation modes have time-varying characteristics, especially the dynamic changes in amplitude and frequency, which makes it difficult to quickly identify the oscillation characteristic parameters, posing a serious challenge to the accurate and rapid identification of the oscillation characteristic parameters of each oscillation mode. Summary of the Invention

[0003] In view of this, this application provides a method, apparatus, device and storage medium for identifying oscillation characteristic parameters to solve the above-mentioned technical problems.

[0004] In a first aspect of this application, a method for identifying oscillation characteristic parameters is provided, the method comprising:

[0005] Obtain the spectrum of the signal to be processed and filter out the set of oscillation modes whose peak amplitude is greater than a set threshold;

[0006] For each oscillation mode in the set of oscillation modes, a corresponding mode tracker is constructed based on the initial frequency of the oscillation mode for tracking, and the coefficients of the mode tracker are updated based on the difference between the signal to be processed and the reconstructed signal, wherein the reconstructed signal is obtained by weighted merging of the output components of all mode trackers;

[0007] For each oscillation mode, the oscillation characteristic parameters of the oscillation mode are obtained based on the updated coefficients of the corresponding mode tracker, wherein the oscillation characteristic parameters include frequency, amplitude and phase.

[0008] According to one embodiment of this application, the signal to be processed is a preprocessed signal, and the preprocessing includes:

[0009] The original signal is processed by a low-pass filter with a set cutoff frequency to obtain the signal to be processed after suppressing high-frequency components. The low-pass filter adopts a series structure of an infinite impulse response filter and an all-pass filter.

[0010] According to one embodiment of this application, the step of constructing a corresponding mode tracker based on the initial frequency of the oscillation mode for tracking includes:

[0011] The initial frequency of the oscillation mode is used as the center frequency of the mode tracker, and the state of the mode tracker is initialized according to the center frequency.

[0012] According to one embodiment of this application, the method further includes:

[0013] For each pattern tracker, the state generated by the pattern tracker is multiplied by the current coefficient of the pattern tracker.

[0014] The reconstructed signal is obtained by summing the product results of all mode trackers, taking the real part, and normalizing it.

[0015] According to one embodiment of this application, obtaining the oscillation characteristic parameters of the oscillation mode based on the updated coefficients of the corresponding mode tracker includes:

[0016] Based on the phase change of the updated coefficients of the corresponding mode tracker, the center frequency of the mode tracker is adjusted, and the adjusted center frequency is time-smoothed to obtain the frequency of the oscillation mode.

[0017] The amplitude of the oscillation mode is obtained by taking the modulus of the updated coefficients of the mode tracker.

[0018] The phase of the oscillation mode is obtained by calculating the amplitude of the updated coefficients of the mode tracker.

[0019] According to one embodiment of this application, the method further includes:

[0020] If the energy integral of the difference between the signal to be processed and the reconstructed signal within a set time window is greater than a first threshold, then the spectrum of the signal to be processed is reacquired, and the set of oscillation modes whose peak amplitude is greater than the set threshold is re-selected.

[0021] For the re-selected set of oscillation modes, if there is a target oscillation mode whose frequency difference with the existing oscillation mode is greater than the second threshold and whose duration is greater than the third threshold, then a corresponding mode tracker is created for the target oscillation mode, and the state and coefficients of the mode tracker corresponding to the target oscillation mode are initialized.

[0022] According to one embodiment of this application, the method further includes:

[0023] In existing pattern trackers, if the amplitude of a pattern tracker is less than the fourth threshold and the duration is greater than the fifth threshold, then the pattern tracker is deleted.

[0024] If the frequency corresponding to one mode tracker and the frequency corresponding to another mode tracker converge to the first frequency and the second frequency respectively, and the difference between the first frequency and the second frequency is less than the fifth threshold, then the frequencies, amplitudes and phases corresponding to the two mode trackers are merged respectively.

[0025] According to one embodiment of this application, the mode tracker includes a main mode tracker and a sub-mode tracker. The main mode tracker is used to track oscillation modes with initial frequencies of the fundamental frequency and harmonics that are integer multiples of the fundamental frequency. The sub-mode tracker is used to track oscillation modes with initial frequencies of interharmonics that are not integer multiples of the fundamental frequency.

[0026] In a second aspect of this application, an oscillation characteristic parameter identification device is provided, the device comprising:

[0027] The preliminary screening unit is used to acquire the spectrum of the signal to be processed and to screen out the set of oscillation modes whose peak amplitudes are greater than a set threshold.

[0028] The update unit is used to construct a corresponding mode tracker for each oscillation mode in the oscillation mode set according to the initial frequency of the oscillation mode, and update the coefficients of the mode tracker according to the difference between the signal to be processed and the reconstructed signal, wherein the reconstructed signal is obtained by weighted merging of the output components of all mode trackers.

[0029] The tracking and identification unit is used to obtain the oscillation characteristic parameters of each oscillation mode based on the updated coefficients of the corresponding mode tracker. The oscillation characteristic parameters include frequency, amplitude, and phase.

[0030] In a third aspect of this application, an electronic device is provided, including a processor and a memory, the memory storing machine-executable instructions executable by the processor, the processor executing the machine-executable instructions to implement the steps of the method proposed in the above embodiments.

[0031] In a fourth aspect of this application, a machine-readable storage medium is provided, wherein machine-executable instructions are stored therein, and when executed by a processor, the machine-executable instructions implement the steps of the method proposed in the above embodiments.

[0032] As can be seen from the above technical solution, by acquiring the spectrum of the signal to be processed and filtering out a set of oscillation modes whose peak amplitudes are greater than a set threshold; for each oscillation mode in the set, a corresponding mode tracker is constructed based on the initial frequency of the oscillation mode for tracking, and the coefficients of the mode tracker are updated based on the difference between the signal to be processed and the reconstructed signal; for each oscillation mode, the oscillation characteristic parameters of the oscillation mode, including frequency, amplitude, and phase, are obtained based on the updated coefficients of the corresponding mode tracker. Acquiring the spectrum of the signal to be processed for coarse-grained screening of oscillation modes initially identifies potential oscillation modes, and then using the corresponding mode tracker to track each oscillation mode, the precise frequency, amplitude, and phase of the oscillation mode are obtained based on the updated coefficients of the corresponding mode tracker. This improves the parameter identification capability of the time-varying characteristics of oscillations, provides accurate power grid dynamic data for power system operators, and ensures the safe and stable operation of the power system.

[0033] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0034] Figure 1 This is a flowchart illustrating an oscillation characteristic parameter identification method provided in an embodiment of this application;

[0035] Figure 2 This is a schematic diagram of the process for updating the frequency, amplitude, and phase of the mode tracker provided in the embodiments of this application;

[0036] Figure 3 This is a flowchart illustrating a new pattern tracker provided in an embodiment of this application;

[0037] Figure 4 This is a schematic diagram of the structure of an oscillation characteristic parameter identification device provided in an embodiment of this application;

[0038] Figure 5 This is a schematic diagram of the hardware structure of an electronic device illustrated in an exemplary embodiment of this application. Detailed Implementation

[0039] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0040] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0041] To enable those skilled in the art to better understand the technical solutions provided in the embodiments of this application, and to make the above-mentioned objectives, features and advantages of the embodiments of this application more apparent and understandable, the technical solutions in the embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0042] Broadband oscillations dominated by power electronic devices are one of the important factors threatening the safe and stable operation of power systems. However, due to the influence of grid operation mode and power electronic device control parameters, the oscillation modes have time-varying characteristics, especially the dynamic changes in amplitude and frequency, which makes it difficult to quickly identify the oscillation characteristic parameters, posing a serious challenge to the accurate and rapid identification of the oscillation characteristic parameters of each oscillation mode.

[0043] In view of this, embodiments of this application disclose a method for identifying oscillation characteristic parameters to solve the above-mentioned technical problems.

[0044] like Figure 1 As shown, Figure 1 This is a flowchart illustrating an oscillation feature parameter identification method provided in an embodiment of this application. The oscillation feature parameter identification method may include the following steps:

[0045] S101: Obtain the spectrum of the signal to be processed and filter out the set of oscillation modes whose peak amplitude is greater than a set threshold.

[0046] For example, the signal to be processed can be a voltage or current signal in a power system.

[0047] For example, the signal to be processed may include fundamental components, harmonic components, and interharmonic components.

[0048] In some embodiments, the signal to be processed is a preprocessed signal to meet the requirements of a wideband oscillation signal. The preprocessing steps may include: processing the original signal (denoted as x[n]) with a low-pass filter of a set cutoff frequency to obtain the signal to be processed after suppressing high-frequency components (denoted as s[n]), wherein the low-pass filter may be a series structure of an infinite impulse response filter and an all-pass filter.

[0049] For example, the cutoff frequency can be set to 0.4f. s , where f s The sampling frequency.

[0050] Currently, the hardware sampling rate of commonly used monitoring devices in power systems is no less than 12.8kHz. Based on the anti-aliasing criterion of 12.8kHz sampling rate, taking 0.4 times the Nyquist frequency, the cutoff frequency of the filter can be preset to 3200Hz to meet the monitoring requirements of broadband oscillation signals.

[0051] For example, the low-pass filter can be a series structure of an Infinite Impulse Response (IIR) filter and an all-pass filter, as shown in Equation (1), to ensure the real-time performance and phase adjustment capability of the low-pass filter.

[0052]

[0053] In formula (1), H IIR (z) represents an IIR filter, whose transfer function is shown in equation (2); H AP (z) represents a second-order all-pass filter, whose transfer function is shown in formula (3); L represents the number of cascaded all-pass filters, which can be set according to actual engineering needs.

[0054]

[0055] In formula (2), a k b k These are the filter coefficients; M and N are the orders of the numerator and denominator, respectively; z -k This indicates a delay unit.

[0056] In formula (3), z -1 z -2 This represents a delay unit; α1 and α2 are adjustable compensation parameters.

[0057] In this embodiment, by preprocessing the original signal, high-frequency components are prevented from aliasing into low-frequency components, resulting in a signal to be processed with frequency components that meet the requirements, i.e., a broadband oscillation signal that meets the requirements, providing a clean signal substrate for subsequent mode analysis.

[0058] For the signal s[n] to be processed, spectral analysis can be performed to obtain the spectrum S(f), and the spectral peaks can be located. Each spectral peak corresponds to a candidate oscillation mode. The mode information (f) corresponding to each spectral peak is then obtained. i A i ), in pattern information (f i A i In ), f i Let A represent the initial frequency corresponding to the i-th spectral peak. i This represents the initial amplitude corresponding to the i-th spectral peak, where i = 1, 2, 3, ..., N raw .

[0059] The amplitude of the spectral peak is compared with a set threshold, that is, the initial amplitude A in each mode information is compared. i Compare with the set threshold A0, from N raw The initial amplitude A was selected from the candidate oscillation modes. i The set of oscillation modes exceeding a set threshold A0 is defined as follows: the initial amplitude set and the initial frequency set corresponding to the oscillation mode set are defined as A = {A1, A2, ..., A0}. Nint} and f = {f1, f2, ..., f Nint This enables coarse-grained screening of oscillation modes.

[0060] For example, when performing spectral analysis on the signal to be processed, methods include, but are not limited to, non-parametric methods, parametric methods, and time-frequency analysis methods. Non-parametric methods include, but are not limited to, Fast Fourier Transform (FFT) and Discrete Fourier Transform (DFT); parametric methods include, but are not limited to, autoregressive models and multiple signal classification; and time-frequency analysis methods include, but are not limited to, Short-Time Fourier Transform (STFT) and wavelet transform.

[0061] In some embodiments, the signal to be processed, s[n], can be scanned based on Fast Fourier Transform (FFT) to achieve rapid location of spectral peaks. The frequency resolution of the coarse-grained screening process FFT for oscillation modes can be set according to actual engineering needs, such as setting it to 1Hz, thereby improving analysis accuracy and accurately identifying oscillation modes.

[0062] S102: For each oscillation mode in the set of oscillation modes, a corresponding mode tracker is constructed based on the initial frequency of the oscillation mode for tracking, and the coefficients of the mode tracker are updated based on the difference between the signal to be processed and the reconstructed signal, wherein the reconstructed signal is obtained by weighted merging of the output components of all mode trackers.

[0063] In the embodiments of this application, as described in S101, it is possible to obtain a result containing N. int A set of oscillation modes is provided. For each oscillation mode in the set, a corresponding mode tracker is constructed based on the initial frequency of the oscillation mode for tracking.

[0064] In some embodiments, the initial frequency of the oscillation mode can be used as the center frequency of the corresponding mode tracker, and the state of the mode tracker can be initialized based on the center frequency.

[0065] For example, the state of the pattern tracker is a complex state, which can be initialized as a complex exponential signal with the center frequency as a parameter.

[0066] In some embodiments, the state of the mode tracker can be updated. That is, for each mode tracker corresponding to an oscillation mode, the state of the mode tracker can be updated according to the current center frequency of the mode tracker to obtain the updated state of the mode tracker, as shown in formula (4).

[0067]

[0068] In formula (4), c k [n] represents the complex state of pattern tracker k at time n, which is also the complex state of pattern tracker k before the update; f k [n] represents the center frequency of the pattern tracker k at time n; c k [n+1] represents the complex state of the updated pattern tracker k; e is a natural number; j is the imaginary unit.

[0069] Equation (4) indicates that the current complex state of the pattern tracker k is phase-rotated using the current center frequency of the pattern tracker k to obtain the updated complex state of the pattern tracker k. That is, the state update adopts the form of complex exponential rotation.

[0070] Regarding signal reconstruction, in the embodiments of this application, the reconstructed signal is obtained by weighted merging of the output components of all mode trackers.

[0071] In some embodiments, the output components of the pattern tracker include the state of the pattern tracker and its coefficients. The current state of each pattern tracker is weighted by its current coefficients, and the weighted results of all pattern trackers are combined to obtain the reconstructed signal.

[0072] Specifically, for each mode tracker, the current state of the mode tracker is multiplied by the current coefficient of the mode tracker; the real part of the sum of the product results of all mode trackers is taken and normalized to obtain the reconstructed signal, as shown in formula (5).

[0073]

[0074] In formula (5), y[n] represents the reconstructed signal; N int The number of pattern trackers is indicated by... Normalization is performed; Re{} denotes taking the real part; X k [n] represents the coefficients of the pattern tracker at time n, i.e., the current coefficients of the pattern tracker; c k[n] represents the state of the pattern tracker at time n, that is, the current state of the pattern tracker.

[0075] Regarding the calculation of the difference between the signal to be processed and the reconstructed signal. In the embodiments of this application, after obtaining the reconstructed signal y[n], the difference between the signal to be processed s[n] and the reconstructed signal y[n] is calculated. This difference can be called the feedback error, denoted as e[n].

[0076] Specifically, the formula for calculating the feedback error e[n] is shown in formula (6).

[0077]

[0078] Regarding the updating of the coefficients of the pattern trackers: In the embodiments of this application, the pattern trackers achieve collaborative correction by sharing the feedback error e[n], and the coefficients of each pattern tracker can be adaptively updated according to the feedback error e[n].

[0079] In some embodiments, the step size parameter can be adaptively adjusted according to the local error of the signal (i.e., the feedback error e[n]), and a penalty term can be introduced in the update of the coefficients of the pattern tracker to suppress excessive changes or oscillations of the parameters in a noisy environment.

[0080] Specifically, the coefficients of the pattern tracker are updated as shown in Equation (7).

[0081]

[0082] In formula (7), X k [n] represents the coefficients of the pattern tracker before the update; X k [n+1] represents the coefficients of the updated pattern tracker; e[n] represents the feedback error, i.e., the difference between the signal to be processed s[n] and the reconstructed signal y[n]; g k [n] represents the coefficient X k The update gain [n] is mainly used to control the update speed and accuracy, and is usually a complex state c of the pattern tracker. k The complex conjugate of [n]; g0 represents the initial step size factor, used to control the overall update magnitude; ε is a very small constant used to prevent division by zero errors; λ represents the regularization coefficient, used to suppress coefficient overfitting or noise amplification.

[0083] In formula (7), For the adaptive update term, the coefficients are adjusted by the feedback error e[n], and the step size changes adaptively with the magnitude of the feedback error e[n]. Achieving normalization, complex conjugate gain Ensure the error is correctly projected in the mode direction; λX k[n] is the regularization term, which penalizes excessively large coefficient values ​​and improves the algorithm's robustness to noise.

[0084] S103: For each oscillation mode, the oscillation characteristic parameters of the oscillation mode are obtained according to the updated coefficients of the corresponding mode tracker, wherein the oscillation characteristic parameters include frequency, amplitude and phase.

[0085] After updating the coefficients of each mode tracker, the frequency, amplitude, and phase of the oscillation mode tracked by that mode tracker are obtained based on the updated coefficients.

[0086] like Figure 2 As shown, Figure 2 This is a schematic flowchart illustrating the frequency, amplitude, and phase updates of the mode tracker provided in this application embodiment. Obtaining the oscillation characteristic parameters of the oscillation mode based on the updated coefficients of the corresponding mode tracker may include the following steps:

[0087] S201: Based on the phase change of the updated coefficients of the corresponding mode tracker, adjust the center frequency of the mode tracker, and perform time smoothing on the adjusted center frequency to obtain the frequency of the oscillation mode.

[0088] In order for each pattern tracker to independently track the frequency components in the signal, its center frequency can be adjusted according to the phase change of the updated coefficients of the pattern tracker, as shown in formula (8).

[0089]

[0090] In formula (8), This indicates the center frequency of the adjusted pattern tracker; f k [n] represents the center frequency of the pattern tracker before adjustment; angle is the phase calculation function; and G represents the coefficients of the pattern tracker before and after the update, respectively. f This indicates the frequency adjustment gain, used to control the convergence speed.

[0091] Further adjustments to the center frequency of the adjusted pattern tracker Time smoothing is performed to prevent frequency jumps caused by short-term noise, ultimately yielding the precise frequency f of the oscillation mode tracked by the mode tracker. k [n+1], as shown in formula (9).

[0092]

[0093] In formula (9), α is the smoothing factor, and 0 < α < 1.

[0094] S202: Take the modulus of the updated coefficients of the mode tracker to obtain the amplitude of the oscillation mode, as shown in formula (10).

[0095]

[0096] Formula (10) represents the updated coefficients of the pattern tracker k. The amplitude of the oscillation mode tracked by the mode tracker k is obtained by taking the modulus.

[0097] S203: Calculate the phase angle of the updated coefficients of the mode tracker to obtain the phase of the oscillation mode, as shown in formula (11).

[0098] φ[n+1]=∠X k [n+1] Formula (11)

[0099] In formula (11), ∠ is the operator for calculating the argument. Formula (11) represents the calculation of the updated coefficients of the pattern tracker k. The argument is used to obtain the phase φ[n+1] of the oscillation mode tracked by the mode tracker k.

[0100] In the embodiments of this application, the spectrum of the signal to be processed is acquired, and a set of oscillation modes with peak amplitudes greater than a set threshold is selected. For each oscillation mode in the set, a corresponding mode tracker is constructed based on the initial frequency of the oscillation mode for tracking, and the coefficients of the mode tracker are updated based on the difference between the signal to be processed and the reconstructed signal. For each oscillation mode, the oscillation characteristic parameters of the oscillation mode, including frequency, amplitude, and phase, are obtained based on the updated coefficients of the corresponding mode tracker. Acquiring the spectrum of the signal to be processed for coarse-grained screening of oscillation modes initially identifies potential oscillation modes. Then, the corresponding mode tracker is used to track each oscillation mode, and the precise frequency, amplitude, and phase of the oscillation mode are obtained based on the updated coefficients of the corresponding mode tracker. This improves the parameter identification capability of the time-varying characteristics of oscillations, provides accurate power grid dynamic data for power system operators, and ensures the safe and stable operation of the power system.

[0101] In some embodiments, an iteration termination condition can be preset according to actual engineering needs. The iteration termination condition includes, but is not limited to, the magnitude of the feedback error e[n]|e[n]| being less than a preset threshold (denoted as tol), or the number of iterations n being greater than the maximum number of iterations (i.e., max_iter).

[0102] Following the method described in S101, we can obtain N. intA set of oscillation modes is provided. For each oscillation mode in the set, the initial frequency of the oscillation mode is used as the center frequency of the corresponding mode tracker, and the state of the mode tracker is initialized according to the center frequency. Then, the first round of processing is performed in the manner described by formulas (5) to (11) to obtain the frequency, amplitude and phase of the oscillation mode tracked by each mode tracker.

[0103] After the first round of processing, iterative processing can continue in accordance with the methods described by formulas (4) to (11) until the pre-set iteration termination condition is met, thereby achieving fine-grained identification and obtaining more accurate oscillation characteristic parameters for each oscillation mode. This improves the parameter identification capability for broadband oscillation time-varying characteristics, provides accurate power grid dynamic data for power system operators, and ensures the safe and stable operation of the power system.

[0104] Specifically, in each iteration, for each mode tracker, the frequency of the oscillation mode obtained in the previous iteration is used as the center frequency of the mode tracker, and the state of the mode tracker is updated according to Equation (4). The reconstructed signal is recalculated according to Equation (5). The feedback error between the signal to be processed and the reconstructed signal is calculated according to Equation (6). The coefficients of each mode tracker are updated according to the feedback error according to Equation (7). For each oscillation mode, the frequency, amplitude, and phase are updated according to the updated coefficients of the corresponding mode tracker according to Equations (8) to (11), and the iteration number n is incremented by 1 to record the number of iterations.

[0105] After a certain round of iterations, if the pre-set iteration termination condition is met, that is, the magnitude of the feedback error is less than the preset threshold tol, or the number of iterations n is greater than the maximum number of iterations max_iter, then it is considered that the convergence state has been reached or the computation budget has been exceeded, and the iteration is terminated.

[0106] In this embodiment, iterative processing is performed according to the methods described in formulas (4) to (11) until the pre-set iteration termination condition is met, thereby achieving fine-grained identification and obtaining more accurate oscillation characteristic parameters for each oscillation mode. This improves the parameter identification capability for broadband oscillation time-varying characteristics, provides accurate power grid dynamic data for power system operators, and ensures the safe and stable operation of the power system.

[0107] like Figure 3 As shown, Figure 3 This is a flowchart illustrating a new pattern tracker provided in an embodiment of this application.

[0108] Because oscillation signals have the characteristic of continuous frequency drift or frequency jump, new oscillation modes may appear. Therefore, it is necessary to dynamically add a mode tracker and initialize its parameters.

[0109] S301: If the energy integral of the difference between the signal to be processed and the reconstructed signal within a set time window is greater than a first threshold, then the spectrum of the signal to be processed is reacquired, and the set of oscillation modes whose peak amplitude is greater than the set threshold is re-selected.

[0110] If the energy integral of the feedback error (i.e. the difference between the signal to be processed and the reconstructed signal) within the set time window is greater than the first threshold, the first threshold can be set according to actual engineering needs, then the coarse-grained screening of the oscillation mode is retried.

[0111] Specifically, if the feedback error e(n) satisfies formula (12), then the coarse-grained screening of the oscillation mode is retried. Formula (12) is shown below.

[0112]

[0113] In formula (12), γ th The first threshold is set in advance according to the actual engineering needs, and [t, t+T] is the set time window.

[0114] The coarse-grained screening of oscillation modes is re-triggered, specifically including: for the signal to be processed s[n], re-analyzing its spectrum to obtain the spectrum and locating the spectral peaks, with each spectral peak corresponding to a candidate oscillation mode. The initial frequency and initial amplitude corresponding to each spectral peak are obtained, and the initial amplitude of each spectral peak is compared with a set threshold A0. From multiple candidate oscillation modes, a set of oscillation modes with spectral peak amplitudes greater than the set threshold A0 is re-screened.

[0115] S302: For the re-selected set of oscillation modes, if there is a target oscillation mode whose frequency difference with the existing oscillation mode is greater than the second threshold and whose duration is greater than the third threshold, then create a corresponding mode tracker for the target oscillation mode and initialize the state and coefficients of the mode tracker corresponding to the target oscillation mode.

[0116] For the newly selected set of oscillation modes, if there exists a frequency difference (denoted as Δf) between the set and an existing oscillation mode that is greater than the second threshold (denoted as f), then... min And the duration is greater than the third threshold (denoted as T). on If the target oscillation mode is determined, a corresponding mode tracker is created in the mode tracker group for that target oscillation mode. For the newly created mode tracker, its state and coefficients are initialized in the manner shown in formula (13). Formula (13) is shown below.

[0117]

[0118] In formula (13), c new [0] indicates the initial state of the newly created pattern tracker, X new [0] represents the initial coefficients of the newly created pattern tracker.

[0119] In this embodiment, by setting dual criteria of frequency difference threshold and duration threshold, it is possible to effectively distinguish between real oscillations and transient interference, achieve accurate screening of new oscillation modes, and automatically create corresponding mode trackers for new oscillation modes, thereby realizing dynamic management of mode trackers.

[0120] Because oscillation signals have the characteristic of continuous frequency drift or frequency jump, old oscillation modes may disappear, so it is necessary to dynamically delete the corresponding mode trackers.

[0121] In some embodiments, in existing pattern trackers, if the amplitude of a pattern tracker is less than a fourth threshold and the duration is greater than a fifth threshold, then the pattern tracker is deleted.

[0122] Specifically, among the existing pattern trackers, if the amplitude of the oscillation mode tracked by a pattern tracker (denoted as A) is... k (t) is less than the fourth threshold (denoted as A). min And the duration is greater than the fifth threshold (denoted as T). off If the pattern is not specified, then the pattern tracker will be deleted.

[0123] In this embodiment, by setting dual criteria of amplitude threshold and duration threshold, intelligent cleaning of the mode tracker corresponding to the failed oscillation mode is achieved, realizing dynamic management of the mode tracker.

[0124] In some embodiments, in existing pattern trackers, if the frequency corresponding to one pattern tracker and the frequency corresponding to another pattern tracker converge to a first frequency and a second frequency respectively, and the difference between the first frequency and the second frequency is less than a fifth threshold, then the frequencies, amplitudes, and phases corresponding to the two pattern trackers are merged respectively.

[0125] Specifically, in the existing pattern trackers, if the frequencies of the oscillation modes tracked by pattern tracker i and the frequencies of the oscillation modes tracked by pattern tracker j converge to a first frequency (denoted as f), then... i ) and second frequency (denoted as f) j ), and f i and f j Satisfy: |f i -f j|<Δf merge , where Δf merge To set a fifth threshold according to actual engineering requirements, the frequency (f) of the oscillation mode tracked by the two mode trackers is... i and f j ), Amplitude (A) i and A j ) and phase (φ) i and φ j The frequencies are combined to obtain the combined frequency f. new Amplitude A new and phase φ new The merging method is shown in formula (14).

[0126]

[0127] In this embodiment, by setting frequency convergence judgment conditions, intelligent merging of similar oscillation modes is realized, thereby achieving dynamic management of the mode tracker.

[0128] In some embodiments, the pattern tracker adopts a master-slave distributed architecture, that is, the pattern tracker includes a master pattern tracker and a sub-pattern tracker, wherein the master pattern tracker is used to track the fundamental and harmonic components of the signal, while the sub-pattern tracker is independently configured to track specific interharmonic components.

[0129] For example, if the initial frequency of an oscillation mode is the fundamental frequency (e.g., 50Hz) or its integer multiple harmonic components (i.e., 50×n Hz, where n is a positive integer, such as 100Hz for the second harmonic, 150Hz for the third harmonic, etc.), then a master mode tracker is configured for tracking the oscillation mode; if the initial frequency of an oscillation mode is a non-integer multiple interharmonic component (e.g., 50×1.3=65Hz or 50×2.7=135Hz, etc.), then an independent sub-mode tracker is configured for tracking it.

[0130] In this embodiment, the mode tracker adopts a master-slave distributed architecture, which enables synchronous measurement of fundamental, harmonic, and interharmonic modes.

[0131] The above description describes the method provided in this application. The following description describes the apparatus provided in this application:

[0132] Please see Figure 4 This is a schematic diagram of the structure of an oscillation characteristic parameter identification device provided in an embodiment of this application.

[0133] like Figure 4 As shown, the device may include:

[0134] The preliminary screening unit 410 is used to acquire the spectrum of the signal to be processed and to screen out the set of oscillation modes whose peak amplitudes are greater than a set threshold.

[0135] The update unit 420 is used to construct a corresponding mode tracker for each oscillation mode in the oscillation mode set according to the initial frequency of the oscillation mode, and update the coefficients of the mode tracker according to the difference between the signal to be processed and the reconstructed signal, wherein the reconstructed signal is obtained by weighted merging of the output components of all mode trackers.

[0136] The tracking and identification unit 430 is used to obtain the oscillation characteristic parameters of each oscillation mode based on the updated coefficients of the corresponding mode tracker, wherein the oscillation characteristic parameters include frequency, amplitude and phase.

[0137] Optionally, the apparatus further includes a preprocessing unit, which is specifically used for:

[0138] The original signal is processed by a low-pass filter with a set cutoff frequency to obtain the signal to be processed after suppressing high-frequency components. The low-pass filter adopts a series structure of an infinite impulse response filter and an all-pass filter.

[0139] Optionally, the update unit 420 is specifically used for:

[0140] The initial frequency of the oscillation mode is used as the center frequency of the mode tracker, and the state of the mode tracker is initialized according to the center frequency.

[0141] Optionally, the update unit 420 is specifically used for:

[0142] For each pattern tracker, the state generated by the pattern tracker is multiplied by the current coefficient of the pattern tracker.

[0143] The reconstructed signal is obtained by summing the product results of all mode trackers, taking the real part, and normalizing it.

[0144] Optionally, the tracking and identification unit 430 is specifically used for:

[0145] Based on the phase change of the updated coefficients of the corresponding mode tracker, the center frequency of the mode tracker is adjusted, and the adjusted center frequency is time-smoothed to obtain the frequency of the oscillation mode.

[0146] The amplitude of the oscillation mode is obtained by taking the modulus of the updated coefficients of the mode tracker.

[0147] The phase of the oscillation mode is obtained by calculating the amplitude of the updated coefficients of the mode tracker.

[0148] Optionally, the device further includes a new unit, which is specifically used for:

[0149] If the energy integral of the difference between the signal to be processed and the reconstructed signal within a set time window is greater than a first threshold, then the spectrum of the signal to be processed is reacquired, and the set of oscillation modes whose peak amplitude is greater than the set threshold is re-selected.

[0150] For the re-selected set of oscillation modes, if there is a target oscillation mode whose frequency difference with the existing oscillation mode is greater than the second threshold and whose duration is greater than the third threshold, then a corresponding mode tracker is created for the target oscillation mode, and the state and coefficients of the mode tracker corresponding to the target oscillation mode are initialized.

[0151] Optionally, the apparatus further includes a deletion and merging unit, which is specifically used for:

[0152] In existing pattern trackers, if the amplitude of a pattern tracker is less than the fourth threshold and the duration is greater than the fifth threshold, then the pattern tracker is deleted.

[0153] If the frequency corresponding to one mode tracker and the frequency corresponding to another mode tracker converge to the first frequency and the second frequency respectively, and the difference between the first frequency and the second frequency is less than the fifth threshold, then the frequencies, amplitudes and phases corresponding to the two mode trackers are merged respectively.

[0154] Optionally, the mode tracker includes a main mode tracker and a sub-mode tracker. The main mode tracker is used to track oscillation modes with an initial frequency of the fundamental frequency and harmonics that are integer multiples of the fundamental frequency. The sub-mode tracker is used to track oscillation modes with an initial frequency of interharmonics that are not integer multiples of the fundamental frequency.

[0155] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0156] This application also provides a hardware structure. See [link to relevant documentation]. Figure 5 , Figure 5 This is a structural diagram of an electronic device provided in an embodiment of this application. Figure 5 As shown, the hardware structure may include: a processor and a machine-readable storage medium, the machine-readable storage medium storing machine-executable instructions that can be executed by the processor; the processor is used to execute the machine-executable instructions to implement the method disclosed in the above example of this application.

[0157] Based on the same application concept as the above method, this application embodiment also provides a machine-readable storage medium storing a plurality of computer instructions, which, when executed by a processor, can implement the method disclosed in the above examples of this application.

[0158] For example, the aforementioned machine-readable storage medium can be any electronic, magnetic, optical, or other physical storage device that can contain or store information such as executable instructions, data, etc. For instance, machine-readable storage media can be: RAM (Random Access Memory), volatile memory, non-volatile memory, flash memory, storage drives (such as hard disk drives), solid-state drives, any type of storage disk (such as optical discs, DVDs, etc.), or similar storage media, or combinations thereof.

[0159] It should be noted that, in this document, relational terms such as "objective" and "target" are used merely 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. Without further limitations, 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.

[0160] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for identifying oscillation characteristic parameters, characterized in that, The method includes: Obtain the spectrum of the signal to be processed and filter out the set of oscillation modes whose peak amplitude is greater than a set threshold; For each oscillation mode in the set of oscillation modes, a corresponding mode tracker is constructed based on the initial frequency of the oscillation mode for tracking. The output components of the mode tracker include the state and coefficients of the mode tracker. The state of the mode tracker is updated based on the current center frequency of the mode tracker. The current state of each mode tracker is weighted by the current coefficients of that mode tracker, and the weighted results of all mode trackers are combined to obtain the reconstructed signal. The coefficients of the mode tracker are updated based on the difference between the signal to be processed and the reconstructed signal. For each oscillation mode, the oscillation characteristic parameters of the oscillation mode are obtained based on the updated coefficients of the corresponding mode tracker, wherein the oscillation characteristic parameters include frequency, amplitude and phase.

2. The method according to claim 1, characterized in that, The signal to be processed is a preprocessed signal, and the preprocessing includes: The original signal is processed by a low-pass filter with a set cutoff frequency to obtain the signal to be processed after suppressing high-frequency components. The low-pass filter adopts a series structure of an infinite impulse response filter and an all-pass filter.

3. The method according to claim 1, characterized in that, The step of constructing a corresponding mode tracker based on the initial frequency of the oscillation mode for tracking includes: The initial frequency of the oscillation mode is used as the center frequency of the mode tracker, and the state of the mode tracker is initialized according to the center frequency.

4. The method according to claim 1, characterized in that, The step of weighting the current state of each mode tracker by its current coefficients and merging the weighted results of all mode trackers to obtain the reconstructed signal includes: For each pattern tracker, multiply the current state of the pattern tracker with the current coefficient of the pattern tracker. The reconstructed signal is obtained by summing the product results of all mode trackers, taking the real part, and normalizing it.

5. The method according to claim 1, characterized in that, The step of obtaining the oscillation characteristic parameters of the oscillation mode based on the updated coefficients of the corresponding mode tracker includes: Based on the phase change of the updated coefficients of the corresponding mode tracker, the center frequency of the mode tracker is adjusted, and the adjusted center frequency is time-smoothed to obtain the frequency of the oscillation mode. The amplitude of the oscillation mode is obtained by taking the modulus of the updated coefficients of the mode tracker. The phase of the oscillation mode is obtained by calculating the amplitude of the updated coefficients of the mode tracker.

6. The method according to claim 1, characterized in that, The method further includes: If the energy integral of the difference between the signal to be processed and the reconstructed signal within a set time window is greater than a first threshold, then the spectrum of the signal to be processed is reacquired, and the set of oscillation modes whose peak amplitude is greater than the set threshold is re-selected. For the re-selected set of oscillation modes, if there is a target oscillation mode whose frequency difference with the existing oscillation mode is greater than the second threshold and whose duration is greater than the third threshold, then a corresponding mode tracker is created for the target oscillation mode, and the state and coefficients of the mode tracker corresponding to the target oscillation mode are initialized.

7. The method according to claim 1, characterized in that, The method further includes: In existing pattern trackers, if the amplitude of a pattern tracker is less than the fourth threshold and the duration is greater than the fifth threshold, then the pattern tracker is deleted. If the frequency corresponding to one mode tracker and the frequency corresponding to another mode tracker converge to the first frequency and the second frequency respectively, and the difference between the first frequency and the second frequency is less than the fifth threshold, then the frequencies, amplitudes and phases corresponding to the two mode trackers are merged respectively.

8. The method according to claim 1, characterized in that, The pattern tracker includes a main pattern tracker and a sub-pattern tracker. The main pattern tracker is used to track oscillation modes with initial frequencies of the fundamental frequency and harmonics that are integer multiples of the fundamental frequency. The sub-pattern tracker is used to track oscillation modes with initial frequencies of interharmonics that are not integer multiples of the fundamental frequency.

9. A device for identifying oscillation characteristic parameters, characterized in that, The device includes: The preliminary screening unit is used to acquire the spectrum of the signal to be processed and to screen out the set of oscillation modes whose peak amplitudes are greater than a set threshold. An update unit is configured to, for each oscillation mode in the oscillation mode set, construct a corresponding mode tracker for tracking based on the initial frequency of the oscillation mode, wherein the output component of the mode tracker includes the state and coefficients of the mode tracker; update the state of the mode tracker based on the current center frequency of the mode tracker; weight the current state of each mode tracker by the current coefficients of the mode tracker, merge the weighted results of all mode trackers to obtain a reconstructed signal, and update the coefficients of the mode tracker based on the difference between the signal to be processed and the reconstructed signal; The tracking and identification unit is used to obtain the oscillation characteristic parameters of each oscillation mode based on the updated coefficients of the corresponding mode tracker. The oscillation characteristic parameters include frequency, amplitude, and phase.

10. An electronic device, characterized in that, It includes a processor and a memory, the memory storing machine-executable instructions that can be executed by the processor, the processor being used to execute the machine-executable instructions to implement the method as described in any one of claims 1-8.

11. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores machine-executable instructions, which, when executed by a processor, implement the method as described in any one of claims 1-8.

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