Oscillation characteristic parameter identification method and device, equipment and storage medium

Abstract

The invention provides an oscillation characteristic parameter identification method and device, equipment and a storage medium. The method comprises the steps of obtaining a frequency spectrum of a to-be-processed signal and screening out an oscillation mode set with the amplitude of a frequency spectrum peak value larger than a set threshold value; for each oscillation mode in the set, constructing a corresponding mode tracker for tracking according to the initial frequency of the oscillation mode, and updating the coefficient of the mode tracker according to the difference value between the to-be-processed signal and the reconstructed signal; and obtaining the frequency, the amplitude and the phase of the oscillation mode according to the updated coefficient of the corresponding mode tracker. Coarse granularity screening is carried out on the oscillation modes, then the corresponding mode trackers are used for tracking each oscillation mode, and the accurate frequency, amplitude and phase of the oscillation modes are obtained according to the coefficients updated by the corresponding mode trackers, so that the parameter identification capability of oscillation time-varying characteristics is improved; accurate power grid dynamic data are provided for power system operators, and safe and stable operation of a power system is guaranteed.

Description

Technical Field

[0001] The present application relates to the technical field of online monitoring of power systems, and in particular to a method, apparatus, device and storage medium for identifying oscillation characteristic parameters. Background Art

[0002] Broadband oscillations driven by power electronics are a major threat to the safe and stable operation of power systems. However, due to the influence of grid operation and the control parameters of power electronics, the oscillation patterns exhibit time-varying characteristics, particularly dynamic changes in amplitude and frequency. This makes rapid identification of oscillation characteristic parameters difficult, posing a significant challenge to the accurate and rapid identification of these parameters for each oscillation pattern. Summary of the Invention

[0003] In view of this, the present application provides an oscillation characteristic parameter identification method, device, equipment and storage medium to solve the above technical problems.

[0004] In a first aspect of the present 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 a set of oscillation modes whose spectrum peak amplitude is greater than a set threshold;

[0006] For each oscillation mode in the oscillation mode set, construct a corresponding mode tracker according to the initial frequency of the oscillation mode to track the oscillation mode, and update the coefficient 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 combination of output components of all mode trackers;

[0007] For each oscillation mode, 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.

[0008] According to one embodiment of the present 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, wherein 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 the present application, the step of constructing a corresponding mode tracker according to 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 the present application, the method further includes:

[0013] For each mode tracker, multiplying the state generated by the mode tracker by the current coefficient of the mode tracker;

[0014] The product results of all pattern trackers are summed up, the real part is taken, and normalized to obtain the reconstructed signal.

[0015] According to one embodiment of the present application, obtaining the oscillation characteristic parameter of the oscillation mode according to the updated coefficient of the corresponding mode tracker includes:

[0016] Adjusting the center frequency of the mode tracker according to the phase change of the updated coefficient of the corresponding mode tracker, and performing time smoothing on the adjusted center frequency to obtain the frequency of the oscillation mode;

[0017] Taking the updated coefficients of the mode tracker modulo to obtain the amplitude of the oscillation mode;

[0018] The argument of the updated coefficients of the mode tracker is calculated to obtain the oscillation mode phase.

[0019] According to one embodiment of the present 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, reacquiring the spectrum of the signal to be processed and re-screening a set of oscillation modes whose spectrum peak amplitudes are greater than the set threshold;

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

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

[0023] In the existing pattern tracker, if the amplitude corresponding to a pattern tracker is less than a fourth threshold and the duration is greater than a fifth threshold, the pattern tracker is deleted;

[0024] If the frequency corresponding to one mode tracker and the frequency corresponding to another mode 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, the frequencies, amplitudes and phases corresponding to the two mode trackers are merged respectively.

[0025] According to one embodiment of the present application, the mode tracker includes a main mode tracker and a sub-mode tracker, wherein the main mode tracker is used to track the oscillation mode of the harmonics whose initial frequency is the fundamental wave and the integer multiple frequency of the fundamental wave, and the sub-mode tracker is used to track the oscillation mode of the interharmonics whose initial frequency is the non-integer multiple frequency of the fundamental wave.

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

[0027] A preliminary screening unit is used to obtain the spectrum of the signal to be processed and screen out a set of oscillation modes whose spectrum peak amplitude is greater than a set threshold;

[0028] an updating unit, configured to construct, for each oscillation mode in the oscillation mode set, a corresponding pattern tracker according to an initial frequency of the oscillation mode for tracking, and update coefficients of the pattern tracker according to a difference between the signal to be processed and a reconstructed signal, wherein the reconstructed signal is obtained by weighted combination of output components of all pattern trackers;

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

[0030] In a third aspect of the present application, an electronic device is provided, comprising a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor is used to execute the machine-executable instructions to implement the steps of the method proposed in the above embodiment.

[0031] In a fourth aspect of the present application, a machine-readable storage medium is provided, wherein the machine-readable storage medium stores machine-executable instructions, and when the machine-executable instructions are executed by a processor, the steps of the method proposed in the above embodiment are implemented.

[0032] As can be seen from the above technical solution, by obtaining the spectrum of the signal to be processed and screening out the oscillation mode set whose spectrum peak amplitude is greater than the set threshold; for each oscillation mode in the oscillation mode set, a corresponding mode tracker is constructed according to the initial frequency of the oscillation mode for tracking, and the coefficient of the mode tracker is updated according to 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 according to the updated coefficients of the corresponding mode tracker. The spectrum of the signal to be processed is obtained to perform coarse-grained screening of the oscillation mode, preliminarily lock the potential oscillation mode, and then use the corresponding mode tracker to track each oscillation mode. According to the updated coefficients of the corresponding mode tracker, the accurate frequency, amplitude and phase of the oscillation mode are obtained, thereby improving the parameter identification capability of the time-varying characteristics of the oscillation, providing accurate grid dynamic data to power system operators, and ensuring the safe and stable operation of the power system.

[0033] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 1 is a flow chart of a method for identifying oscillation characteristic parameters provided in an embodiment of the present application;

[0035] Figure 2 1 is a flow chart of frequency, amplitude and phase updating of a mode tracker according to an embodiment of the present application;

[0036] Figure 3 This is a flow chart of a new mode tracker provided in an embodiment of the present application;

[0037] Figure 4 Schematic diagram of the structure of an oscillation characteristic parameter identification device provided in an embodiment of the present application;

[0038] Figure 5 It is a schematic diagram of the hardware structure of an electronic device shown in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0039] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0040] The terms used in this application are for the purpose of describing particular embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0041] In order to enable those skilled in the art to better understand the technical solutions provided by the embodiments of the present application, and to make the above-mentioned purposes, features and advantages of the embodiments of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application are further described in detail below with reference to the accompanying drawings.

[0042] Broadband oscillations driven by power electronics are a major threat to the safe and stable operation of power systems. However, due to the influence of grid operation and the control parameters of power electronics, the oscillation patterns exhibit time-varying characteristics, particularly dynamic changes in amplitude and frequency. This makes rapid identification of oscillation characteristic parameters difficult, posing a significant challenge to the accurate and rapid identification of these parameters for each oscillation pattern.

[0043] In view of this, an embodiment of the present application discloses a method for identifying oscillation characteristic parameters to solve the above technical problems.

[0044] like Figure 1 As shown, Figure 1 : is a flow chart of an oscillation characteristic parameter identification method provided by an embodiment of the present application. The oscillation characteristic parameter identification method may include the following steps:

[0045] S101: Acquire the spectrum of the signal to be processed, and screen out a set of oscillation modes whose spectrum peak amplitudes are greater than a set threshold.

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

[0047] Exemplarily, the signal to be processed may include a fundamental wave component, a harmonic component, an interharmonic component, and the like.

[0048] In some embodiments, the signal to be processed is a signal that has been preprocessed to meet the requirements of a broadband oscillation signal. The preprocessing step may include: processing the original signal (denoted as x[n]) using a low-pass filter with a set cutoff frequency to obtain a signal to be processed (denoted as s[n]) with high-frequency components suppressed, 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 may be set to 0.4f s , where f s is 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 the 12.8kHz sampling rate, 0.4 times the Nyquist frequency is taken, and the cutoff frequency of the filter can be pre-set to 3200Hz to meet the requirements of broadband oscillation signal monitoring.

[0051] For example, the low-pass filter may adopt a series structure of an infinite impulse response (IIR) filter and an all-pass filter, as shown in formula (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 formula (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 project needs.

[0054]

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

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

[0057] In this embodiment, the original signal is preprocessed to prevent high-frequency components from being aliased into low-frequency components, and a processed signal with required frequency components is obtained, that is, a broadband oscillation signal that meets the requirements, providing a clean signal base for subsequent pattern analysis.

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

[0059] Compare the amplitude of the spectrum peak with the set threshold, that is, the initial amplitude A in each mode information i Compare with the set threshold A0, from N raw The initial amplitude A is selected from the candidate oscillation modes i The oscillation mode set is greater than the set threshold A0, and the initial amplitude set and initial frequency set corresponding to the oscillation mode set are defined as A={A1,A2,…,A Nint} and f={f1,f2,…,f Nint}, thus achieving coarse-grained screening of oscillation modes.

[0060] For example, when performing spectrum analysis on the signal to be processed, non-parametric methods, parametric methods, and time-frequency analysis methods are used, including but not limited to non-parametric methods such as 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 using a Fast Fourier Transform (FFT) to quickly locate spectrum peaks. The FFT frequency resolution for the coarse-grained oscillation pattern screening process can be set to 1 Hz, for example, to improve analysis accuracy and precisely identify oscillation patterns.

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

[0063] In the embodiment of the present application, according to the method described in S101, the N int An oscillation mode set of oscillation modes is provided. For each oscillation mode in the set, a corresponding mode tracker is constructed according to the initial frequency of the oscillation mode to track the mode.

[0064] In some embodiments, the initial frequency of the oscillation mode may be used as the center frequency of the corresponding mode tracker, and the state of the mode tracker may be initialized according to the center frequency.

[0065] Exemplarily, the state of the mode tracker is a complex state, and the complex state of the mode tracker can be initialized to 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 an updated state of the mode tracker, as shown in formula (4).

[0067]

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

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

[0070] Regarding calculation of signal reconstruction: In the embodiment of the present application, the reconstructed signal is obtained by weighted combination of the output components of all pattern trackers.

[0071] In some embodiments, the output components of the pattern tracker include the state and coefficients of the pattern tracker. The current state of each pattern tracker is weighted by the current coefficient of the pattern tracker, 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 product results corresponding to all mode trackers are summed and the real part is taken, and then normalized to obtain the reconstructed signal, as shown in formula (5).

[0073]

[0074] In formula (5), y[n] represents the reconstructed signal; N int Indicates the number of pattern trackers, through Normalize; Re{} means taking the real part; X k [n] represents the coefficient of the pattern tracker at time n, that is, the current coefficient 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 calculating the difference between the signal to be processed and the reconstructed signal. In an embodiment of the present 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 a feedback error and is recorded as e[n].

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

[0077]

[0078] Regarding updating the coefficients of the pattern tracker: In the embodiment of the present application, the pattern trackers share the feedback error e[n] to achieve collaborative correction, 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., 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 formula (7).

[0081]

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

[0083] In formula (7), It is an adaptive update term, which drives the coefficient adjustment through the feedback error e[n], and the step size changes adaptively with the feedback error e[n]; Achieve normalized, complex conjugate gain Ensure that the error is correctly projected in the pattern direction; λX k[n] is a regularization term that penalizes excessively large coefficient values ​​and improves the robustness of the algorithm in noise.

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

[0085] After the coefficients of each mode tracker are updated, the frequency, amplitude and phase of the oscillation mode tracked by the mode tracker are obtained according to the updated coefficients of the mode tracker.

[0086] like Figure 2 As shown, Figure 2 This is a flow chart of the frequency, amplitude, and phase update process of the mode tracker provided in the embodiment of the present application. 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: adjusting the center frequency of the pattern tracker according to the phase change of the updated coefficient of the corresponding pattern tracker, and performing time smoothing on the adjusted center frequency to obtain the frequency of the oscillation mode.

[0088] In order to enable 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), represents the center frequency of the adjusted mode tracker; f k [n] represents the center frequency of the mode tracker before adjustment; angle is the phase calculation function; and Represent the coefficients of the pattern tracker before and after the update, G f Indicates the frequency adjustment gain, which is used to control the convergence speed.

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

[0092]

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

[0094] S202: Performing modulo operation on 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 coefficient of the pattern tracker k Take the modulus and get the amplitude of the oscillation mode tracked by the mode tracker k

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

[0098] φ[n+1]=∠X k [n+1] Formula (11)

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

[0100] In an embodiment of the present application, the spectrum of the signal to be processed is obtained, and a set of oscillation modes whose peak amplitudes are greater than a set threshold are screened out; for each oscillation mode in the oscillation mode 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. The spectrum of the signal to be processed is obtained to perform coarse-grained screening of the oscillation mode, preliminarily lock the potential oscillation mode, and then each oscillation mode is tracked using the corresponding mode tracker. The accurate frequency, amplitude, and phase of the oscillation mode are obtained based on the updated coefficients of the corresponding mode tracker, thereby improving the parameter identification capability of the time-varying characteristics of the oscillation, providing accurate grid dynamic data to power system operators, and ensuring the safe and stable operation of the power system.

[0101] In some embodiments, the iteration termination conditions can be pre-set according to actual engineering needs. The iteration termination conditions include but are not limited to the amplitude 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] According to the method described in S101, the N intThe oscillation pattern set of oscillation patterns is constructed. For each oscillation pattern in the set, the initial frequency of the oscillation pattern is used as the center frequency of the corresponding pattern tracker, and the state of the pattern tracker is initialized according to the center frequency. Subsequently, the first round of processing is performed according to the method described in formulas (5) to (11) to obtain the frequency, amplitude, and phase of the oscillation pattern tracked by each pattern tracker.

[0103] After the first round of processing, iterative processing can continue in the manner 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, thereby improving the parameter identification capability of broadband oscillation time-varying characteristics, providing accurate grid dynamic data for power system operators, and ensuring the safe and stable operation of the power system.

[0104] Specifically, in each round of iterative processing, for each mode tracker, the frequency of the oscillation mode obtained in the previous round of processing is used as the center frequency of the mode tracker, and the state of the mode tracker is updated in the manner shown in formula (4). The reconstructed signal is recalculated in the manner shown in formula (5). The feedback error between the signal to be processed and the reconstructed signal is calculated in the manner shown in formula (6). The coefficient of each mode tracker is updated according to the feedback error in the manner shown in formula (7). In the manner shown in formulas (8) to (11), for each oscillation mode, the frequency, amplitude and phase of the oscillation mode are updated according to the updated coefficients of the corresponding mode tracker, and the number of iterations n is added by 1 to record the number of iterations.

[0105] After a certain round of iteration, if the preset iteration termination condition is met, that is, the amplitude 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, it is considered that convergence has been reached or the computational budget has been exceeded, and the iteration is exited.

[0106] In this embodiment, by performing iterative processing in the manner described in Formula (4) to Formula (11) until the pre-set iteration termination condition is met, fine-grained identification is achieved, and more accurate oscillation characteristic parameters of each oscillation mode are obtained, thereby improving the parameter identification capability of broadband oscillation time-varying characteristics, providing accurate grid dynamic data for power system operators, and ensuring the safe and stable operation of the power system.

[0107] like Figure 3 As shown, Figure 3 This is a flow chart of a new mode tracker provided in an embodiment of the present application.

[0108] Since the oscillation signal has the characteristics of continuous frequency drift or cross-frequency hopping, new oscillation modes will 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, the spectrum of the signal to be processed is reacquired, and a set of oscillation modes whose spectrum peak amplitudes are greater than the set threshold are re-screened.

[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 a first threshold, which can be set according to actual engineering needs, the coarse-grained screening of the oscillation mode is re-triggered.

[0111] Specifically, if the feedback error e(n) satisfies formula (12), the coarse-grained screening of the oscillation mode is retriggered. Formula (12) is as follows.

[0112]

[0113] In formula (12), γ th is the first threshold value preset according to actual project needs, and [t, t+T] is the set time window.

[0114] The coarse-grained screening of retriggered oscillation modes specifically includes: re-analyzing the spectrum of the signal to be processed s[n] to obtain a spectrum and locating the spectrum peaks. Each spectrum peak corresponds to a candidate oscillation mode. The initial frequency and initial amplitude corresponding to each spectrum peak are obtained, and the initial amplitude of each spectrum peak is compared with the set threshold A0. The oscillation mode set whose spectrum peak amplitude is greater than the set threshold A0 is re-screened from multiple candidate oscillation modes.

[0115] S302: For the re-screened oscillation pattern set, if there is a target oscillation pattern whose frequency difference with the existing oscillation pattern is greater than the second threshold and whose duration is greater than the third threshold, a corresponding pattern tracker is created for the target oscillation pattern, and the state and coefficient of the pattern tracker corresponding to the target oscillation pattern are initialized.

[0116] For the re-screened oscillation mode set, if there is a frequency difference (denoted as Δf) between the set and the existing oscillation mode that is greater than the second threshold (denoted as f min ), and the duration is greater than the third threshold (denoted as T on ), a corresponding pattern tracker is created in the pattern tracker group for the target oscillation pattern. The state and coefficients of the newly created pattern tracker are initialized according to formula (13). Formula (13) is shown below.

[0117]

[0118] In formula (13), c new [0] represents 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 the 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 to realize dynamic management of mode trackers.

[0120] Since the oscillation signal has the characteristics of continuous frequency drift or cross-frequency hopping, the old oscillation mode may disappear, so the corresponding mode tracker needs to be dynamically deleted.

[0121] In some embodiments, in an existing pattern tracker, if the amplitude corresponding to a pattern tracker is smaller than a fourth threshold and the duration is greater than a fifth threshold, the pattern tracker is deleted.

[0122] Specifically, in the currently existing mode trackers, if the amplitude of the oscillation mode tracked by a mode tracker (denoted as A 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 ), the pattern tracker is deleted.

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

[0124] In some embodiments, in an existing mode tracker, if the frequency corresponding to one mode tracker and the frequency corresponding to another mode 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, the frequencies, amplitudes, and phases corresponding to the two mode trackers are merged respectively.

[0125] Specifically, in the currently existing mode trackers, if the frequency of the oscillation mode tracked by mode tracker i and the frequency of the oscillation mode tracked by mode tracker j converge to the first frequency (denoted as f i ) and the second frequency (denoted as f j ), and f i and f j Satisfy: |f i -f j|<Δf merge , where Δf merge is the fifth threshold value that can be set according to the actual project, and the frequency (f i and f j ), amplitude (A i and A j ) and phase (φ i and φ j ) to merge and obtain the merged 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 the frequency convergence judgment condition, intelligent merging of similar oscillation modes is achieved, and dynamic management of the mode tracker is realized.

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

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

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

[0131] The above content describes the method provided by this application. The following describes the device provided by this application:

[0132] See Figure 4 , which is a structural diagram of an oscillation characteristic parameter identification device provided in an embodiment of the present application.

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

[0134] A preliminary screening unit 410 is configured to obtain a spectrum of the signal to be processed and to screen out a set of oscillation patterns whose spectrum peak amplitudes are greater than a set threshold;

[0135] an updating unit 420 configured to construct, for each oscillation mode in the oscillation mode set, a corresponding pattern tracker according to the initial frequency of the oscillation mode for tracking, and update coefficients of the pattern tracker according to a difference between the signal to be processed and a reconstructed signal, wherein the reconstructed signal is obtained by weighted combination of output components of all pattern trackers;

[0136] The tracking and identifying unit 430 is configured to obtain, for each oscillation mode, oscillation characteristic parameters of the oscillation mode according to the updated coefficients of the corresponding mode tracker, wherein the oscillation characteristic parameters include frequency, amplitude, and phase.

[0137] Optionally, the device further includes a preprocessing unit, wherein the preprocessing unit is specifically configured to:

[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, wherein the low-pass filter adopts a series structure of an infinite impulse response filter and an all-pass filter.

[0139] Optionally, the updating unit 420 is specifically configured to:

[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 updating unit 420 is specifically configured to:

[0142] For each mode tracker, multiplying the state generated by the mode tracker by the current coefficient of the mode tracker;

[0143] The product results of all pattern trackers are summed up, the real part is taken, and normalized to obtain the reconstructed signal.

[0144] Optionally, the tracking and identifying unit 430 is specifically configured to:

[0145] Adjusting the center frequency of the mode tracker according to the phase change of the updated coefficient of the corresponding mode tracker, and performing time smoothing on the adjusted center frequency to obtain the frequency of the oscillation mode;

[0146] Taking the updated coefficients of the mode tracker modulo to obtain the amplitude of the oscillation mode;

[0147] The argument of the updated coefficients of the mode tracker is calculated to obtain the oscillation mode phase.

[0148] Optionally, the device further includes a new unit, and the new unit is specifically configured to:

[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, reacquiring the spectrum of the signal to be processed and re-screening a set of oscillation modes whose spectrum peak amplitudes are greater than the set threshold;

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

[0151] Optionally, the device further includes a deletion and merging unit, wherein the deletion and merging unit is specifically configured to:

[0152] In the existing pattern tracker, if the amplitude corresponding to a pattern tracker is less than a fourth threshold and the duration is greater than a fifth threshold, the pattern tracker is deleted;

[0153] If the frequency corresponding to one mode tracker and the frequency corresponding to another mode 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, 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, wherein the main mode tracker is used to track the oscillation mode of the harmonics whose initial frequency is the fundamental wave and the integer multiple frequency of the fundamental wave, and the sub-mode tracker is used to track the oscillation mode of the interharmonics whose initial frequency is the frequency that is not an integer multiple frequency of the fundamental wave.

[0155] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0156] The embodiment of the present application also provides a hardware structure. Figure 5 , Figure 5 This is a structural diagram of an electronic device provided in an embodiment of the present 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, an embodiment of the present application also provides a machine-readable storage medium, on which a number of computer instructions are stored. When the computer instructions are executed by a processor, the method disclosed in the above example of the present application can be implemented.

[0158] Exemplarily, the machine-readable storage medium may be any electronic, magnetic, optical, or other physical storage device that may contain or store information, such as executable instructions, data, and the like. For example, the machine-readable storage medium may be: RAM (Random Access Memory), volatile memory, non-volatile memory, flash memory, a storage drive (such as a hard disk drive), a solid-state drive, any type of storage disk (such as a CD, DVD, etc.), or similar storage media, or a combination thereof.

[0159] It should be noted that, in this document, relational terms such as target and objective are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element.

[0160] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present 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 a set of oscillation modes whose spectrum peak amplitude is greater than a set threshold; For each oscillation mode in the oscillation mode set, construct a corresponding mode tracker according to the initial frequency of the oscillation mode to track the oscillation mode, and update the coefficient 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 combination of output components of all mode trackers; For each oscillation mode, 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.

2. The method according to claim 1, characterized in that The signal to be processed is a pre-processed signal, and the pre-processing 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, wherein 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 according to 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, wherein The method further comprises: For each mode tracker, multiplying the state generated by the mode tracker by the current coefficient of the mode tracker; The product results of all pattern trackers are summed up, the real part is taken, and normalized to obtain the reconstructed signal.

5. The method according to claim 1, wherein Obtaining the oscillation characteristic parameters of the oscillation mode according to the updated coefficients of the corresponding mode tracker includes: Adjusting the center frequency of the mode tracker according to the phase change of the updated coefficient of the corresponding mode tracker, and performing time smoothing on the adjusted center frequency to obtain the frequency of the oscillation mode; Taking the updated coefficients of the mode tracker modulo to obtain the amplitude of the oscillation mode; The argument of the updated coefficients of the mode tracker is calculated to obtain the oscillation mode phase.

6. The method according to claim 1, wherein The method further comprises: 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, reacquiring the spectrum of the signal to be processed and re-screening a set of oscillation modes whose spectrum peak amplitudes are greater than the set threshold; For the re-screened oscillation pattern set, if there is a target oscillation pattern whose frequency difference with the existing oscillation pattern is greater than the second threshold and whose duration is greater than the third threshold, a corresponding pattern tracker is created for the target oscillation pattern, and the state and coefficient of the pattern tracker corresponding to the target oscillation pattern are initialized.

7. The method according to claim 1, characterized in that The method further comprises: In the existing pattern tracker, if the amplitude corresponding to a pattern tracker is less than a fourth threshold and the duration is greater than a fifth threshold, the pattern tracker is deleted; If the frequency corresponding to one mode tracker and the frequency corresponding to another mode 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, 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 mode tracker includes a main mode tracker and a sub-mode tracker. The main mode tracker is used to track the oscillation mode of the harmonics whose initial frequency is the fundamental wave and the integer multiple frequency of the fundamental wave, and the sub-mode tracker is used to track the oscillation mode of the interharmonics whose initial frequency is the non-integer multiple frequency of the fundamental wave.

9. An oscillation characteristic parameter identification device, characterized in that: The device includes: A preliminary screening unit is used to obtain the spectrum of the signal to be processed and screen out a set of oscillation modes whose spectrum peak amplitude is greater than a set threshold; an updating unit, configured to construct, for each oscillation mode in the oscillation mode set, a corresponding pattern tracker according to an initial frequency of the oscillation mode for tracking, and update coefficients of the pattern tracker according to a difference between the signal to be processed and a reconstructed signal, wherein the reconstructed signal is obtained by weighted combination of output components of all pattern trackers; The tracking and identifying unit is used to obtain, for each oscillation mode, oscillation characteristic parameters of the oscillation mode according to the updated coefficients of the corresponding mode tracker, wherein the oscillation characteristic parameters include frequency, amplitude and phase.

10. An electronic device, characterized in that: The system comprises a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor is configured to execute the machine-executable instructions to implement the method according to any one of claims 1 to 8.

11. A machine-readable storage medium, characterized in that The machine-readable storage medium stores machine-executable instructions, and when the machine-executable instructions are executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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