Broadband oscillation signal adaptive analysis method based on Taylor Fourier transform

Abstract

The invention discloses a broadband oscillation signal adaptive analysis method based on Taylor Fourier transform, and the method comprises the steps: carrying out the adaptive noise reduction of a broadband oscillation signal of a signal source through employing a normalized least mean square root algorithm, and outputting a noise signal; determining an expected broadband oscillation signal after noise reduction according to the broadband oscillation signal and the noise signal; performing signal analysis on the expected broadband oscillation signal to obtain the amplitude and frequency of each component of the expected broadband oscillation signal; calculating the damping ratio of each signal component of the expected broadband oscillation signal according to the amplitude and frequency of each component of the expected broadband oscillation signal; and determining the oscillation risk of each signal component of the broadband oscillation signal according to the damping ratio of each signal component.

Description

Technical Field

[0001] The present invention relates to the field of new energy access and control technology, and more specifically, to a Taylor Fourier transform-based broadband oscillation signal adaptive analysis method. Background Art

[0002] In recent years, the proportion of renewable energy such as wind power and photovoltaics connected to the power grid with power electronic equipment as the core technology has been increasing. The power system with a high proportion of power electronic equipment connected will show low inertia and low damping characteristics. The system is prone to problems such as rapid frequency changes and large deviations, which affect the stability of the power grid; at the same time, the large-scale application of power electronic equipment has injected a large number of low-frequency and high-frequency components into the power grid, making the power grid signal present broadband characteristics. If a broadband oscillation accident occurs in the system, it may cause equipment damage, renewable energy units to be disconnected from the grid, and even be transmitted to the user side to cause large-scale power outages. With the proposal of China's "dual carbon" goals, the penetration rate of renewable energy will be further improved, and the problem of broadband oscillation in the system will become more prominent. Accurate detection of broadband oscillation signals is of great significance to ensure the safe and reliable operation of the power grid. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a method for adaptively analyzing broadband oscillation signals based on Taylor Fourier transform.

[0004] According to one aspect of the present invention, there is provided a method for adaptively analyzing a broadband oscillation signal based on Taylor Fourier transform, comprising:

[0005] The normalized minimum mean square root algorithm is used to perform adaptive noise reduction on the broadband oscillation signal of the signal source and output a noise signal;

[0006] Determine a desired broadband oscillation signal after noise reduction according to the broadband oscillation signal and the noise signal;

[0007] Performing signal analysis on the expected broadband oscillation signal to obtain the amplitude and frequency of each component of the expected broadband oscillation signal;

[0008] Calculating the damping ratio of each signal component of the desired broadband oscillation signal according to the amplitude and frequency of each component of the desired broadband oscillation signal;

[0009] The oscillation risk of each signal component of the broadband oscillating signal is determined based on the damping ratio of each signal component.

[0010] Optionally, a normalized minimum root mean square algorithm is used to perform adaptive noise reduction processing on the broadband oscillation signal of the signal source to output a sampled noise signal, including:

[0011] The collected broadband oscillation signal is input into a digital high-pass filter to extract the high-frequency noise signal of the broadband oscillation signal;

[0012] The high-frequency noise signal is input into the normalized minimum mean square algorithm for weight vector adjustment, and the sampled noise signal is output.

[0013] Optionally, the weight vector update formula of the normalized least mean square algorithm is:

[0014]

[0015] Where W(n)=[w0(n)...w L-1 (n)] is the weight vector composed of weight parameters, L is the order of the normalized least mean square algorithm, X(n) = [x(n)...x(n-L+1)] is the input vector composed of the broadband oscillation signal x(n), μ∈(0,1] is the normalized weight update step, and a is a constant tending to 0.

[0016] Optionally, the optimization objective of the normalized least mean square algorithm is:

[0017] Min E{|x(n)-y(n)| 2}=E{|S(n)| 2}+Min E{|e0(n)-y(n)| 2}

[0018] Wherein, x(n) is the broadband oscillation signal, y(n) is the noise signal, x(n)=S(n)+e0(n), S(n) is the desired broadband oscillation signal, and e0(n) is the sampled noise signal.

[0019] Optionally, performing signal analysis on the expected broadband oscillation signal to obtain the amplitude and frequency corresponding to each component of the expected broadband oscillation signal includes:

[0020] Construct a sampling sequence according to the expected broadband oscillation signal;

[0021] According to the sampling sequence, the initial frequency of Taylor Fourier transform is determined;

[0022] According to the initial frequency, construct the Taylor Fourier basis vector matrix;

[0023] According to the sampling sequence and Taylor Fourier basis vector matrix, the derivative vectors of each order of the phasor are solved;

[0024] Iteratively update the initial frequency according to each order derivative vector of the phasor to obtain the update frequency;

[0025] When the maximum number of iterations is reached, the derivative vectors of each order of the final phasor are obtained;

[0026] According to the derivative vectors of each order of the final phasor, the amplitude, phase and frequency of each component of the expected broadband oscillation signal are calculated.

[0027] Alternatively, the formula for the Taylor Fourier basis vector matrix is:

[0028] B=[B1...B m ...B M ]

[0029]

[0030] In the formula, is the conjugate operator; B is the Taylor Fourier basis vector matrix with dimension (2N+1)×(2k+2)M; 2N+1 is the number of sampling points; k is the order of Taylor expansion; M is the total number of signal components; Ts is the sampling period; f m is the frequency of the mth signal component; i is the complex number sign.

[0031] Optionally, the formulas for solving the derivative vectors of each order of the phasor and updating the frequency are:

[0032]

[0033] In the formula, is a column vector consisting of the derivatives of each order of the phasor and its conjugate terms with a dimension of (2k+2)M×1; H is the Hermitian operator; are the amplitude, phase and frequency of the mth component of the broadband oscillation signal respectively; f m0 is the initial value of the frequency of the mth component.

[0034] Optionally, determining the oscillation risk of each signal component of the broadband oscillation signal according to the damping ratio of each signal component comprises:

[0035] Calculate the damping ratio of each signal component according to the amplitude and frequency of each signal component of the expected broadband oscillation signal;

[0036] If the damping ratio of the signal component is less than a preset damping ratio threshold, it is determined that the signal component has an oscillation risk; otherwise, it is determined that the signal component has no oscillation risk.

[0037] Optionally, the damping ratio is calculated as:

[0038]

[0039] in,

[0040]

[0041] In the formula, A m is the amplitude of the mth signal component, ζ m is the damping ratio of the mth signal component, is the undamped oscillation frequency of the mth signal component, ω m =2πf m is the angular frequency of the mth component, and remember An intermediate amount.

[0042] According to another aspect of the present invention, there is provided a wide-band oscillation signal adaptive analysis device based on Taylor Fourier transform, comprising:

[0043] An output module, used for performing adaptive noise reduction processing on the broadband oscillation signal of the signal source by using a normalized minimum mean square root algorithm, and outputting a noise signal;

[0044] A first determination module, configured to determine a desired broadband oscillation signal after noise reduction according to the broadband oscillation signal and the noise signal;

[0045] An acquisition module is used to perform signal analysis on the expected broadband oscillation signal to obtain the amplitude and frequency of each component of the expected broadband oscillation signal;

[0046] A calculation module, used for calculating the damping ratio of each signal component of the expected broadband oscillation signal according to the amplitude and frequency of each component of the expected broadband oscillation signal;

[0047] The second determination module is used to determine the oscillation risk of each signal component of the broadband oscillation signal according to the damping ratio of each signal component.

[0048] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method described in any one of the above aspects of the present invention.

[0049] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of the above aspects of the present invention.

[0050] Therefore, the present invention uses a normalized minimum root mean square algorithm to perform adaptive filtering and noise reduction on the broadband oscillation sampling signal, uses a TFT algorithm to analyze the oscillation frequency, amplitude and other information of the broadband oscillation signal after noise reduction, further calculates the damping ratio of each frequency component, performs oscillation judgment based on the damping ratio, and realizes adaptive analysis of the broadband acquisition signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0052] Figure 1It is a flow chart of a method for adaptively analyzing a broadband oscillation signal based on Taylor Fourier transform provided by an exemplary embodiment of the present invention;

[0053] Figure 2 is another flow chart of a method for adaptively analyzing a broadband oscillation signal based on Taylor Fourier transform provided by an exemplary embodiment of the present invention;

[0054] Figure 3 is a schematic diagram of a commonly used adaptive filter structure provided by an exemplary embodiment of the present invention;

[0055] Figure 4 is a schematic diagram of an adaptive noise cancellation system provided by an exemplary embodiment of the present invention;

[0056] Figure 5 It is a schematic structural diagram of a wide-band oscillation signal adaptive analysis device based on Taylor Fourier transform provided by an exemplary embodiment of the present invention;

[0057] Figure 6 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0058] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0059] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0060] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0061] It should also be understood that, in the embodiments of the present invention, “plurality” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0062] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0063] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0064] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0065] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0066] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0067] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0068] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0069] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0070] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0071] Exemplary Methods

[0072] Figure 1 FIG. 1 is a flow chart of a method for adaptively analyzing a broadband oscillation signal based on Taylor Fourier transform provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the adaptive analysis method 100 of broadband oscillation signal based on Taylor Fourier transform includes the following steps:

[0073] Step 101, using a normalized least mean square root algorithm to perform adaptive noise reduction processing on a broadband oscillation signal of a signal source, and outputting a noise signal;

[0074] Step 102, determining a desired broadband oscillation signal after noise reduction according to the broadband oscillation signal and the noise signal;

[0075] Step 103, performing signal analysis on the expected broadband oscillation signal to obtain the amplitude and frequency of each component of the expected broadband oscillation signal;

[0076] Step 104, calculating the damping ratio of each signal component of the desired broadband oscillation signal according to the amplitude and frequency of each component of the desired broadband oscillation signal;

[0077] Step 105: determining the oscillation risk of each signal component of the broadband oscillation signal according to the damping ratio of each signal component.

[0078] Specifically, the Taylor-Fourier Transform (TFT) algorithm has superior dynamic performance because it uses a dynamic phasor model and is suitable for the detection of broadband oscillation signals. In order to accurately collect broadband oscillation signals, broadband measurement devices usually use sensors such as Hall current sensors and isolation operational amplifiers. Compared with traditional electromagnetic sensors, broadband sensors are more sensitive to electromagnetic interference, which will cause large noise in the sampled signal. However, the TFT algorithm is easily interfered by signal noise, so it is necessary to filter and reduce noise on the signal to minimize interference. The filtering algorithm based on the Normalized Least Mean Square (NLMS) does not require prior knowledge of the signal, has a strong adaptive noise reduction capability, and is suitable for adaptive noise reduction processing of broadband acquisition signals.

[0079] In view of this, in order to accurately estimate the broadband oscillation signal of the power system, the present invention proposes an adaptive analysis method of broadband oscillation signals based on Taylor-Fourier Transform (TFT). In view of the problem that the TFT algorithm is easily affected by noise interference, the Normalized Least Mean Square (NLMS) algorithm is used to adaptively filter and reduce noise on the broadband oscillation sampling signal. The TFT algorithm can be used to analyze the frequency, amplitude and other information of each component of the broadband oscillation signal after noise reduction, and the damping ratio of each frequency component can be further calculated to make oscillation judgments based on the damping ratio. Figure 2 The specific contents are as follows:

[0080] 1. NLMS algorithm

[0081] aAlgorithm Description

[0082] The use of broadband voltage and current sensors can effectively improve the signal acquisition and analysis bandwidth of broadband measurement devices, but it will also introduce some high-frequency electromagnetic interference. At this time, the signal collected by the broadband measurement device is:

[0083]

[0084] In the formula, A m (t), f m , are the peak value, frequency and phase of the mth signal component respectively; e(t) is the sampling noise; M is the total number of signal components; t is time.

[0085] Adaptive noise reduction processing of broadband acquisition signal x(t) is the prerequisite for accurate broadband oscillation detection. The NLMS-based filtering algorithm can realize adaptive filtering of signals without prior knowledge, so this scheme chooses NLMS to perform adaptive noise reduction processing on broadband acquisition signal.

[0086] In an adaptive filter, the output signal y(n) is obtained by linearly weighted superposition of a set of L input elements. The most commonly used transversal filter structure is Figure 4 The core idea of ​​NLMS filter is to adjust the parameters W0~W L-1 To minimize the square expectation of the error err(n), the update iterative formula of the weight vector is:

[0087]

[0088] Where W(n)=[w0(n)...w L-1 (n)] is the weight vector composed of weight parameters, X(n) = [x(n) ... x(n-L+1)] is the input vector composed of input signals, μ∈(0,1] is the normalized weight update step, and a is a very small number to avoid the denominator being 0.

[0089] The core idea of ​​NLMS filter is to minimize the square expectation of the difference between the filter output and the expected output, but in practical engineering applications we cannot know the expected output d(n) of the signal, and the algorithm needs to be further improved. Based on the core idea of ​​NLMS filter, the following adaptive noise cancellation system can be used to estimate the noise signal to filter out the noise:

[0090] Input signal x(n)=S(n)+e0(n), S(n) is the expected signal, e0(n) is the sampling noise signal; the sampling noise e0(n) is mostly high-frequency signals generated by external electromagnetic interference, so the noise estimation signal e1(n) can be obtained by signal filtering (such as using a high-pass filter, etc.); the noise estimation signal e1(n) is output as y(n) after passing through the NLMS filter, and E{|x(n)-y(n)| 2}minimum as the optimization target of NLMS filter, then:

[0091] Min E{|x(n)-y(n)| 2}=Min E{|S(n)+e0(n)-y(n)| 2}

[0092] Since S(n) is unrelated to e0(n) and y(n), and S(n) is independent of the adjustment of the NLMS filter, the above formula can be expanded as follows:

[0093] Min E{|x(n)-y(n)| 2}=E{|S(n)| 2}+Min E{|e0(n)-y(n)| 2}

[0094] That is, minimize E{|x(n)-y(n)| 2} is equivalent to minimizing E{|e0(n)-y(n)| 2}, that is, the output y(n) of the NLMS filter approaches the sampled noise signal e0(n). At this time, the output x(n)-y(n) of the adaptive noise cancellation system approaches the expected signal S(n).

[0095] b. Implementation steps

[0096] 1) Collect the broadband oscillation signal x(n) and use it as the input of the digital high-pass filter and the adaptive noise cancellation system;

[0097] 2) Use a digital high-pass filter to extract the high-frequency noise signal e1(n) from x(n) as the input of the NLMS filter;

[0098] 3) The NLMS algorithm adaptively adjusts its weight vector W(n) so that the NLMS filter output y(n) approaches the sampled noise signal e0(n). The output x(n)-y(n) of the adaptive noise cancellation system is the broadband oscillation signal with noise reduction completed.

[0099] 4) Repeat 1) to 3) until the end.

[0100] 2. TFT algorithm

[0101] aAlgorithm Description

[0102] The time domain model of the system broadband oscillation is:

[0103]

[0104] Among them, m is the oscillation damping ratio of the mth component, is the natural frequency of the mth component. Then we have:

[0105]

[0106] In the formula, A m (t), f m , are the peak value, frequency and phase of the mth signal component respectively; e(t) is the sampling noise; M is the total number of signal components; t is the time. Ignoring the noise, the sampling period is T sSample x(t), and the expression of the nth sampling point is:

[0107]

[0108] make

[0109]

[0110] p m (nT s ) is the mth signal component phasor. m (nT s ) Perform a k-order Taylor series expansion at zero:

[0111]

[0112] is the value of the i-th derivative of the m-th signal component phasor at zero point; assuming that the sampling point n = [-N, -N+1, ..., -1, 0, 1, ... N-1, N]; the sampling sequence of the broadband oscillation signal is X = [x (-NT s )...x(0)...x(NT s )] T , X is a column vector with dimension 2N+1; Combining equations (4), (5), and (6), we can obtain:

[0113]

[0114] in,

[0115] B=[B1...B m ...B M ](9)

[0116]

[0117] P=[P1...P m ...P M ] T (11)

[0118]

[0119] is the conjugate operator. B is a Taylor Fourier basis vector matrix with a dimension of (2N+1)×(2k+2)M, and P is a column vector of phasor derivatives of various orders and their conjugate terms with a dimension of (2k+2)M×1.

[0120] The least squares method can be used to solve P:

[0121]

[0122] Where H is the Hermitian operator. The peak value, phase and frequency of the broadband oscillation signal are:

[0123]

[0124] f m0 is the initial value of the frequency of the mth component.

[0125] From the composition of the basis vector matrix B, it can be seen that the TFT algorithm is affected by the initial substitution frequency. The fast Fourier transform (FFT) is used to determine the initial frequency. Specifically, FFT is performed on the broadband oscillation signal after noise reduction, and the frequency with a larger corresponding amplitude is extracted as the initial frequency. The amplitude is determined according to the proportion of the fundamental wave amplitude.

[0126] b. Implementation steps

[0127] 1) Construct a sampling sequence X based on the broadband oscillation signal after noise reduction, and determine the initial frequency based on FFT;

[0128] 2) Substitute the initial frequency into equations (9) and (10) to obtain the matrix B;

[0129] 3) Update the frequency according to equations (13) and (14);

[0130] 4) Repeat 2) to 3) until the maximum number of iterations is reached;

[0131] 5) According to formula (14), the amplitude, phase and frequency of each component of the broadband oscillation signal are calculated.

[0132] 3. Oscillation judgment

[0133] aAlgorithm Description

[0134] The time domain expression of the mth component is:

[0135]

[0136] Among them, A m is the initial amplitude of the mth signal component, ζ m is the damping ratio of the mth signal component, is the undamped oscillation frequency of the mth signal component, ω m =2πf m is the angular frequency of the mth component, and remember Then there is

[0137]

[0138] According to the signal amplitude calculated at t1 and t1+ΔT in the previous step We can calculate δ m :

[0139]

[0140] Thus, combined with the angular frequency ω of the mth component m Get the damping ratio ζ m :

[0141]

[0142] according to It can be seen that when m <0, the signal amplitude A m As (t) increases with time, this signal component oscillates.

[0143] b. Implementation steps

[0144] 1) Calculate the damping ratio according to the amplitude and frequency information of each signal component;

[0145] 2) Set the damping ratio threshold ζ lim , lim <0; if the damping ratio of the mth component is ζ m <ζ lim , it means that the signal amplitude at this time is increasing with time, and the signal component is oscillating; otherwise, it means that the amplitude of the signal component remains unchanged or decreases with time, and there is no risk of oscillation.

[0146] Therefore, the present invention uses the normalized minimum mean square root algorithm to perform adaptive filtering and noise reduction on the broadband oscillation sampling signal. The broadband oscillation signal after noise reduction can be analyzed using the TFT algorithm to analyze its oscillation frequency, amplitude and other information, and further calculate the damping ratio of each frequency component, and perform oscillation judgment based on the damping ratio. The oscillation adaptive analysis of the broadband acquisition signal is realized.

[0147] Exemplary Devices

[0148] Figure 5 FIG. 1 is a schematic diagram of a structure of a broadband oscillation signal adaptive analysis device based on Taylor Fourier transform provided by an exemplary embodiment of the present invention. Figure 5 As shown, the device 500 includes:

[0149] The output module 510 is used to perform adaptive noise reduction processing on the broadband oscillation signal of the signal source by using a normalized minimum mean square root algorithm, and output a noise signal;

[0150] A first determination module 520, configured to determine a desired broadband oscillation signal after noise reduction according to the broadband oscillation signal and the noise signal;

[0151] An acquisition module 530 is used to perform signal analysis on the desired broadband oscillation signal to obtain the amplitude and frequency of each component of the desired broadband oscillation signal;

[0152] A calculation module 540, configured to calculate the damping ratio of each signal component of the desired broadband oscillation signal according to the amplitude and frequency of each component of the desired broadband oscillation signal;

[0153] The second determination module 550 is configured to determine the oscillation risk of each signal component of the broadband oscillation signal according to the damping ratio of each signal component.

[0154] Optionally, the output module 510 includes:

[0155] An extraction submodule, used to input the collected broadband oscillation signal into a digital high-pass filter to extract a high-frequency noise signal from the broadband oscillation signal;

[0156] The output submodule is used to input the high-frequency noise signal into the normalized minimum mean square algorithm for weight vector adjustment and output the sampled noise signal.

[0157] Optionally, the weight vector update formula of the normalized least mean square algorithm is:

[0158]

[0159] Where W(n)=[w0(n)...w L-1 (n)] is the weight vector composed of weight parameters, L is the order of the normalized least mean square algorithm, X(n) = [x(n)...x(n-L+1)] is the input vector composed of the broadband oscillation signal x(n), μ∈(0,1] is the normalized weight update step, and a is a constant tending to 0.

[0160] Optionally, the optimization objective of the normalized least mean square algorithm is:

[0161] Min E{|x(n)-y(n)| 2}=E{|S(n)| 2}+Min E{|e0(n)-y(n)| 2}

[0162] Wherein, x(n) is the broadband oscillation signal, y(n) is the noise signal, x(n)=S(n)+e0(n), S(n) is the desired broadband oscillation signal, and e0(n) is the sampled noise signal.

[0163] Optionally, the acquisition module 530 includes:

[0164] Construct a sampling sequence according to the expected broadband oscillation signal;

[0165] According to the sampling sequence, the initial frequency of Taylor Fourier transform is determined;

[0166] According to the initial frequency, construct the Taylor Fourier basis vector matrix;

[0167] According to the sampling sequence and Taylor Fourier basis vector matrix, the derivative vectors of each order of the phasor are solved;

[0168] Iteratively update the initial frequency according to each order derivative vector of the phasor to obtain the update frequency;

[0169] When the maximum number of iterations is reached, the derivative vectors of each order of the final phasor are obtained;

[0170] According to the derivative vectors of each order of the final phasor, the amplitude, phase and frequency of each component of the expected broadband oscillation signal are calculated.

[0171] Alternatively, the formula for the Taylor Fourier basis vector matrix is:

[0172] B=[B1...B m ...B M ]

[0173]

[0174] In the formula, is the conjugate operator; B is the Taylor Fourier basis vector matrix with dimension (2N+1)×(2k+2)M; 2N+1 is the number of sampling points; k is the order of Taylor expansion; M is the total number of signal components; Ts is the sampling period; f m is the frequency of the mth signal component; i is the complex number sign.

[0175] Optionally, the formulas for solving the derivative vectors of each order of the phasor and updating the frequency are:

[0176]

[0177] In the formula, is a column vector consisting of the derivatives of each order of the phasor and its conjugate terms with a dimension of (2k+2)M×1; H is the Hermitian operator; are the amplitude, phase and frequency of the mth component of the broadband oscillation signal respectively; f m0 is the initial value of the frequency of the mth component.

[0178] Optionally, the second determining module 550 includes:

[0179] Calculate the damping ratio of each signal component according to the amplitude and frequency of each signal component of the expected broadband oscillation signal;

[0180] If the damping ratio of the signal component is less than a preset damping ratio threshold, it is determined that the signal component has an oscillation risk; otherwise, it is determined that the signal component has no oscillation risk.

[0181] Optionally, the damping ratio is calculated as:

[0182]

[0183] in,

[0184]

[0185] In the formula, A m is the amplitude of the mth signal component, ζ m is the damping ratio of the mth signal component, is the undamped oscillation frequency of the mth signal component, ω m =2πf m is the angular frequency of the mth component, and remember An intermediate amount.

[0186] Exemplary Electronic Devices

[0187] Figure 6 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. Figure 6 As shown, the electronic device 60 includes one or more processors 61 and a memory 62 .

[0188] The processor 61 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0189] The memory 62 may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 61 may run the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may also include: an input device 63 and an output device 64, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0190] In addition, the input device 63 may also include, for example, a keyboard, a mouse, etc.

[0191] The output device 64 can output various information to the outside, and can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0192] Of course, to simplify, Figure 6 Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0193] Exemplary computer program products and computer-readable storage media

[0194] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above "Exemplary Method" section of this specification.

[0195] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0196] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0197] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0198] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details disclosed above are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0199] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0200] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The word "such as" used here refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0201] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware or any combination of software, hardware, firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present invention are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0202] It should also be noted that in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in the field to make or use the present invention. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but in accordance with the widest range consistent with the principles and novel features disclosed here.

[0203] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for adaptively analyzing broadband oscillation signals based on Taylor Fourier transform, characterized in that: include: The normalized minimum mean square root algorithm is used to perform adaptive noise reduction on the broadband oscillation signal of the signal source and output a noise signal; Determining a desired broadband oscillation signal after noise reduction according to the broadband oscillation signal and the noise signal; Performing signal analysis on the desired broadband oscillation signal to obtain the amplitude and frequency of each component of the desired broadband oscillation signal; Calculating the damping ratio of each signal component of the desired broadband oscillation signal according to the amplitude and frequency of each component of the desired broadband oscillation signal; The oscillation risk of each signal component of the broadband oscillating signal is determined according to the damping ratio of each signal component.

2. The method according to claim 1, characterized in that The normalized minimum mean square root algorithm is used to perform adaptive noise reduction on the broadband oscillation signal of the signal source, and the sampled noise signal is output, including: Inputting the collected broadband oscillation signal into a digital high-pass filter to extract a high-frequency noise signal of the broadband oscillation signal; The high-frequency noise signal is input into the normalized least mean square root algorithm for weight vector adjustment, and the sampled noise signal is output.

3. The method according to claim 2, characterized in that The weight vector update formula of the normalized least mean square algorithm is: Where W(n)=[w0(n)...w L-1 (n)] is a weight vector composed of weight parameters, L is the order of the normalized least mean square algorithm, X(n)=[x(n)...x(n-L+1)] is an input vector composed of a broadband oscillation signal x(n), μ∈(0,1] is the normalized weight update step, and a is a constant tending to 0.

4. The method according to claim 2, characterized in that: The optimization goal of the normalized least mean square algorithm is: Min E{|x(n)-y(n)| 2 }=E{|S(n)| 2 }+Min E{|e0(n)-y(n)| 2 } Wherein, x(n) is the broadband oscillation signal, y(n) is the noise signal, x(n)=S(n)+e0(n), S(n) is the desired broadband oscillation signal, and e0(n) is the sampled noise signal.

5. The method according to claim 1, characterized in that Performing signal analysis on the expected broadband oscillation signal to obtain the amplitude and frequency corresponding to each component of the expected broadband oscillation signal includes: Constructing a sampling sequence according to the expected broadband oscillation signal; Determining the initial frequency of the Taylor Fourier transform according to the sampling sequence; Constructing a Taylor Fourier basis vector matrix according to the initial frequency; According to the sampling sequence and the Taylor Fourier basis vector matrix, solving the derivative vectors of each order of the phasor; Iteratively updating the initial frequency according to each order derivative vector of the phasor to obtain an updated frequency; When the maximum number of iterations is reached, the derivative vectors of each order of the final phasor are obtained; The amplitude, phase and frequency of each component of the desired broadband oscillation signal are calculated according to each order derivative vector of the final phasor.

6. The method according to claim 5, characterized in that The formula of the Taylor Fourier basis vector matrix is: In the formula, is the conjugate operator; B is the Taylor Fourier basis vector matrix with dimension (2N+1)×(2k+2)M; 2N+1 is the number of sampling points; k is the order of Taylor expansion; M is the total number of signal components; Ts is the sampling period; f m is the frequency of the mth signal component; i is the complex number sign.

7. The method according to claim 6, characterized in that The formula for solving the derivative vector of each order of the phasor and the formula for updating the frequency are: In the formula, is a column vector consisting of the derivatives of each order of the phasor and its conjugate terms with a dimension of (2k+2)M×1; H is the Hermitian operator; are the amplitude, phase and frequency of the mth component of the broadband oscillation signal respectively; f m0 is the initial value of the frequency of the mth component.

8. The method according to claim 1, characterized in that Determining the oscillation risk of each signal component of the broadband oscillation signal according to the damping ratio of each signal component comprises: Calculating the damping ratio of each signal component according to the amplitude and frequency of each signal component of the desired broadband oscillation signal; If the damping ratio of the signal component is less than a preset damping ratio threshold, it is determined that the signal component has an oscillation risk; otherwise, it is determined that the signal component has no oscillation risk.

9. The method according to claim 8, characterized in that The calculation formula of the damping ratio is: in, In the formula, A m is the amplitude of the mth signal component, ζ m is the damping ratio of the mth signal component, is the undamped oscillation frequency of the mth signal component, ω m =2πf m is the angular frequency of the mth component, and remember An intermediate amount.

10. A wide-band oscillation signal adaptive analysis device based on Taylor Fourier transform, characterized in that: include: An output module, used for performing adaptive noise reduction processing on the broadband oscillation signal of the signal source by using a normalized minimum mean square root algorithm, and outputting a noise signal; A first determination module, configured to determine a desired broadband oscillation signal after noise reduction according to the broadband oscillation signal and the noise signal; An acquisition module, used for performing signal analysis on the expected broadband oscillation signal to acquire the amplitude and frequency of each component of the expected broadband oscillation signal; A calculation module, used for calculating the damping ratio of each signal component of the expected broadband oscillation signal according to the amplitude and frequency of each component of the expected broadband oscillation signal; The second determination module is used to determine the oscillation risk of each signal component of the broadband oscillation signal according to the damping ratio of each signal component.

11. The device according to claim 10, characterized in that Output modules, including: An extraction submodule, used for inputting the collected broadband oscillation signal into a digital high-pass filter to extract a high-frequency noise signal of the broadband oscillation signal; The output submodule is used to input the high-frequency noise signal into the normalized least mean square algorithm to adjust the weight vector and output the sampled noise signal.

12. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 9.

13. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1 to 9.

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