System oscillation signal amplitude-frequency modulation feature extraction method based on particle swarm optimization

Abstract

The invention belongs to the field of power system control, and particularly discloses a system oscillation signal amplitude-frequency modulation feature extraction method based on a particle swarm optimization algorithm, and the method comprises the steps: segmenting an oscillation waveform into a plurality of intervals according to a time sequence; traversing each interval according to a time sequence, solving an objective function through a particle swarm algorithm for the traversed intervals, and adding 1 to the value of m under the condition that the fitting degree corresponding to the m + 1-time modulation function is superior to the fitting degree corresponding to the m-time modulation function; wherein the target function is used for representing the fitting degree between the modulation waveform of the modulation function and the actual waveform of the corresponding interval, the amplitude-frequency modulation characteristic parameters in the modulation function serve as particles of the particle swarm algorithm, and the modulation function is an m-order modulation function or an m + 1-order modulation function. According to the invention, the extracted signal features can accurately reflect the evolution law of the AC electrical quantity in the dynamic process of the power system.

Description

Technical Field

[0001] The present application belongs to the field of power system control, and more specifically, to a method for extracting amplitude-frequency modulation features of system oscillation signals based on a particle swarm algorithm. Background Art

[0002] In the dynamic process of power electronic power system, after the power electronic equipment senses the unbalanced power at the port, it usually changes the output AC voltage and current of the port by adjusting the potential amplitude / frequency of its output, so the amplitude / frequency of the alternating signal changes instantaneously in the dynamic process of the system. Among the existing oscillation signal analysis measures, Fourier transform believes that the distorted AC signal is formed by the superposition of harmonics, so the AC signal is expanded and the amplitude and frequency of each harmonic are used as the characteristics of the AC signal; while the Prony algorithm believes that the oscillation signal is a linear weighted combination of exponential functions, so the amplitude, frequency and damping of the exponential function are used as the characteristics of the signal; short-time Fourier transform and wavelet transform can calculate the amplitude and frequency of signals in different time windows to obtain more accurate parameters, but in essence, it is still believed that the signal in each time window is formed by the superposition of each harmonic; the above studies have not recognized the characteristics of the amplitude-frequency time-varying evolution process of the AC signal in the dynamic process of the system. However, due to the inability to determine the signal expression in the actual process, the correspondence between the narrowband signal component obtained by Hilbert-Huang transform based on empirical mode decomposition and the actual signal oscillation component is questionable, and the physical meaning of the signal oscillation characteristics finally obtained is not clear enough. Therefore, the existing oscillation characteristic analysis is still very limited in helping to understand the system oscillation process. The signal characteristics extracted by the traditional power system signal analysis method cannot accurately reflect the evolution law of AC electrical quantities (three-phase oscillation signals) in the dynamic process of the power system. Summary of the invention

[0003] In view of the defects of the prior art, the purpose of this application is to realize that the extracted signal characteristics cannot accurately reflect the evolution law of AC electrical quantities in the dynamic process of the power system.

[0004] To achieve the above objectives, in a first aspect, the present application provides a method for extracting amplitude-frequency modulation features of a system oscillation signal based on a particle swarm algorithm, comprising: Divide the oscillation waveform into multiple intervals according to the time sequence; Traverse each interval in time sequence, solve the objective function for the traversed intervals through the particle swarm algorithm, and The corresponding fit is better than In the case of the corresponding degree of fit, The value of is increased by 1; Among them, the objective function is used to characterize the degree of fit between the modulation waveform of the modulation function and the actual waveform of the corresponding interval. The amplitude-frequency modulation characteristic parameters in the modulation function are used as particles of the particle swarm algorithm. The modulation function is or , express The sub-modulation function, express The sub-modulation function, The initial value of is 1.

[0005] In one possible implementation, the objective function is determined based on three types of fit degree; The first type of fitting degree is the fitting degree between the instantaneous value fitting waveform of the modulation function and the instantaneous value actual waveform; The second type of fitting degree is the fitting degree between the amplitude fitting waveform of the modulation function and the actual amplitude waveform; The third type of fitting degree is the fitting degree between the frequency fitting waveform of the modulation function and the actual frequency waveform.

[0006] In one possible implementation, the objective function is determined by the following formula: ; in, represents the objective function, Indicates the degree of waveform fitting. The first value on the instantaneous value fitting waveform The size of the sampling points, The instantaneous value of the actual waveform The size of the sampling points, The first The size of the sampling points, The actual waveform of the amplitude The size of the sampling points, The frequency fitting waveform The size of the sampling points, The actual waveform of the frequency The size of the sampling points, is the number of sampling points on the waveform.

[0007] In a possible implementation, the method further includes: By sampling the signal in each interval, the instantaneous value actual waveform, amplitude actual waveform and frequency actual waveform corresponding to each interval are determined.

[0008] In one possible implementation, for Submodulation Function The The amplitude modulation function and The amplitude modulation function, The amplitude of the second amplitude modulation function is smaller than that of the The maximum amplitude change in the sub-amplitude modulation function, .

[0009] In a possible implementation, the constraint range of the amplitude-frequency modulation characteristic parameters other than the amplitude in each amplitude modulation function is determined by taking the corresponding fitting initial value as the center point of the constraint range; The initial fitting value is obtained by solving the objective function by using the amplitude and frequency of the oscillation waveform as the initial constraint range of the amplitude-frequency modulation characteristic parameters through the particle swarm algorithm.

[0010] In a second aspect, the present application provides a system oscillation signal amplitude-frequency modulation feature extraction device based on a particle swarm algorithm, comprising: An interval segmentation module is used to segment the oscillation waveform into multiple intervals according to the time sequence; The interval traversal module is used to traverse each interval in time sequence, and solve the objective function through the particle swarm algorithm for the traversed intervals. The corresponding fit is better than In the case of the corresponding degree of fit, The value of is increased by 1; Among them, the objective function is used to characterize the degree of fit between the modulation waveform of the modulation function and the actual waveform of the corresponding interval. The amplitude-frequency modulation characteristic parameters in the modulation function are used as particles of the particle swarm algorithm. The modulation function is or , express The sub-modulation function, express The sub-modulation function, The initial value of is 1.

[0011] In a third aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the first aspect or any possible implementation of the first aspect.

[0012] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0013] In a fifth aspect, the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0014] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art: For the traversed interval, the particle swarm algorithm is used to solve the objective function, which can be compared Submodulation Function The corresponding degree of fit is Submodulation Function The corresponding degree of fit is The corresponding fit is better than The corresponding fitting degree indicates that the next amplitude-frequency modulation evolution occurs in the following interval waveform, and then The value of is increased by 1 to ensure the effect of fitting the next interval. Therefore, by traversing each interval in time sequence, the primary amplitude-frequency modulation feature can be extracted first, and as the oscillation further develops, the secondary amplitude-frequency modulation feature can be extracted, and so on, until the amplitude-frequency modulation feature of the entire oscillation signal is obtained. The extracted signal features can accurately reflect the evolution law of the AC electrical quantity (three-phase oscillation signal) in the dynamic process of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is one of the flow charts of the method for extracting amplitude-frequency modulation features of system oscillation signals based on the particle swarm algorithm provided in the embodiment of the present application; Figure 2 This is the second flow chart of the method for extracting amplitude-frequency modulation features of system oscillation signals based on particle swarm algorithm provided in the embodiment of the present application; Figure 3 is a schematic diagram of the formation and evolution process of the oscillation signal provided in the embodiment of the present application; Figure 4 It is a flow chart of continuous amplitude-frequency modulation signal demodulation based on particle swarm algorithm provided in an embodiment of the present application; Figure 5 is a grid-connected structure diagram of a converter provided in an embodiment of the present application; Figure 6 is a schematic diagram of an oscillation waveform of an instantaneous value of an internal potential provided in an embodiment of the present application; Figure 7 It is a schematic diagram of the fitting waveform of the instantaneous value of the internal potential in the first amplitude-frequency modulation fitting result of the internal potential oscillation waveform provided in the embodiment of the present application; Figure 8 It is a schematic diagram of the internal potential amplitude fitting waveform in the first amplitude-frequency modulation fitting result of the internal potential oscillation waveform provided in the embodiment of the present application; Fig. 9 It is a schematic diagram of the internal potential frequency fitting waveform in the first amplitude-frequency modulation fitting result of the internal potential oscillation waveform provided in the embodiment of the present application; Fig.10It is a schematic diagram of the fitting waveform of the instantaneous value of the internal potential in the secondary amplitude-frequency modulation fitting result of the internal potential oscillation waveform provided in the embodiment of the present application; Fig.11 It is a schematic diagram of the internal potential amplitude fitting waveform in the secondary amplitude-frequency modulation fitting result of the internal potential oscillation waveform provided in the embodiment of the present application; Fig.12 It is a schematic diagram of the internal potential frequency fitting waveform in the secondary amplitude-frequency modulation fitting result of the internal potential oscillation waveform provided in the embodiment of the present application; Fig.13 It is a structural schematic diagram of a system oscillation signal amplitude-frequency modulation feature extraction device based on a particle swarm algorithm provided in an embodiment of the present application; Fig.14 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0017] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0018] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more than two. For example, multiple processing units refer to two or more processing units, etc.; multiple elements refer to two or more elements, etc.

[0019] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0020] Figure 1 FIG. 1 is one of the flow charts of the method for extracting amplitude-frequency modulation features of system oscillation signals based on particle swarm algorithm provided in the embodiment of the present application, such as Figure 1 As shown, the method includes the following steps S101 and S102.

[0021] Step S101, dividing the oscillation waveform into multiple intervals according to the time sequence; Step S102, traverse each interval in time sequence, solve the objective function for the traversed intervals by particle swarm algorithm, and The corresponding fit is better than In the case of the corresponding degree of fit, The value of is increased by 1; Among them, the objective function is used to characterize the degree of fit between the modulation waveform of the modulation function and the actual waveform of the corresponding interval. The amplitude-frequency modulation characteristic parameters in the modulation function are used as particles of the particle swarm algorithm. The modulation function is or , express The sub-modulation function, express The sub-modulation function, The initial value of is 1.

[0022] The expressions of each modulation function can be predetermined, and the modulation function corresponding to each interval and the amplitude-frequency modulation characteristic parameters in the modulation function are quantities to be solved.

[0023] Submodulation Function Can be composed of multiple sub-functions. Including but not limited to the following sub-functions: 1st amplitude modulation function , the second amplitude modulation function ,…,No. Sub-amplitude modulation function , No. Sub-amplitude modulation function , the first frequency modulation function , the second frequency modulation function ,…,No. Sub-Frequency Modulation Function , No. Sub-Frequency Modulation Function , .in, Used to adjust the amplitude of the oscillation signal waveform Modulate For amplitude modulation function The amplitude in is modulated, and so on. For amplitude modulation function The amplitude is modulated. Used to adjust the frequency of the oscillation signal waveform Modulate For frequency modulation function The frequency in is modulated, and so on. For frequency modulation function The frequency in is modulated.

[0024] It is understandable that for the traversed interval, the particle swarm algorithm is used to solve the objective function, which can be compared Submodulation Function The corresponding degree of fit is Submodulation Function The corresponding degree of fit is The corresponding fit is better than The corresponding fitting degree indicates that the next amplitude-frequency modulation evolution occurs in the following interval waveform, and then The value of is increased by 1 to ensure the effect of fitting the next interval. Therefore, by traversing each interval in time sequence, the primary amplitude-frequency modulation feature can be extracted first, and as the oscillation further develops, the secondary amplitude-frequency modulation feature can be extracted, and so on, until the amplitude-frequency modulation feature of the entire oscillation signal is obtained. The extracted signal features can accurately reflect the evolution law of the AC electrical quantity (three-phase oscillation signal) in the dynamic process of the power system.

[0025] The following is a description of the method for extracting amplitude-frequency modulation features of system oscillation signals based on a particle swarm algorithm provided in the present application through several examples.

[0026] Figure 2 FIG. 2 is a flow chart of a method for extracting amplitude-frequency modulation features of a system oscillation signal based on a particle swarm algorithm according to an embodiment of the present application. Figure 2 As shown, the method includes the following steps S1-S5.

[0027] Step S1: Collect the three-phase AC instantaneous value of the power system oscillation signal after the disturbance as the waveform to be demodulated, which is used as the input of the algorithm.

[0028] Step S2: Determine the steady-state amplitude frequency , the amplitude in each amplitude modulation function (the amplitude is the amplitude modulation characteristic parameter in the amplitude modulation function) and the initial constraint range of other amplitude-frequency modulation characteristic parameters, the three-phase AC instantaneous value waveform of the oscillation signal is divided into intervals, and sample and demodulate each interval in turn.

[0029] Step S3: Set the number of population iterations , population size para, learning factors c1 and c2, and inertia weight w.

[0030] Step S4: Use the amplitude-frequency modulation characteristic parameter (amplitude-frequency modulation characteristic parameter) as a particle and initialize the population, and use the m-th amplitude-frequency modulation function and the m+1-th amplitude-frequency modulation function to calculate the fitting degree of each particle in the current interval. As the objective function, compare the amplitude-frequency modulation function that better fits the current interval waveform, update the particle speed and position, and the particle population passes through Iterate until the amplitude-frequency modulation characteristic parameters and modulation times with the best fitting degree in the current waveform interval are obtained.

[0031] Step S5: Determine whether the modulation times in the current interval are consistent with the optimal modulation times in the previous interval. If the m+1 amplitude-frequency modulation function has a better fitting effect, it means that the waveform in the following interval has undergone the next amplitude-frequency modulation evolution. Then the waveform in the next interval is fitted using the m+1 amplitude-frequency modulation function and the m+2 amplitude-frequency modulation function. Repeat S4 and fit the waveform in each interval in turn. Finally, the amplitude-frequency modulation characteristics of the entire waveform are obtained, and the corresponding amplitude-frequency modulation times and fitting degree are output.

[0032] The constraints in step S2 are as follows: (1) The constraint range of the amplitude in each amplitude modulation function (the amplitude is the amplitude modulation characteristic parameter in the amplitude modulation function); The physical meaning of the amplitude modulation characteristic parameter of the amplitude in a modulation process and the amplitude modulation characteristic parameter of the amplitude in the subsequent modulation process is the disturbance of the amplitude of the last modulation waveform at the equilibrium point, which is constrained by the change of the amplitude of the last modulation waveform, that is: (1) In the formula, the amplitude of the oscillation signal waveform Modulation, corresponding to the amplitude modulation function The amplitude in (the first amplitude modulation function) (this amplitude is the amplitude modulation characteristic parameter in the amplitude modulation function) is ; For the amplitude modulation function The amplitude in is modulated, corresponding to the amplitude modulation function The amplitude in (the second amplitude modulation function) is ; For the amplitude modulation function The amplitude in is modulated, corresponding to the amplitude modulation function The amplitude in (the third amplitude modulation function) is ; Similarly, for the amplitude modulation function (No. The amplitude in the sub-amplitude modulation function is modulated, corresponding to the amplitude modulation function (No. The amplitude of the sub-amplitude modulation function is . for The maximum change range, for The maximum change range, for The maximum change range of for The maximum change in .

[0033] (2) Initial constraint ranges of other amplitude-frequency modulation characteristic parameters; Since the value range information of other amplitude-frequency modulation characteristic parameters except (1) cannot be directly seen from the instantaneous value waveform of the oscillation signal, the amplitude / frequency of the oscillation waveform is first used as the range constraint of the subsequent modulation characteristic parameters. Then, the particle swarm algorithm is used to demodulate the oscillation waveform to obtain the initial fitting values ​​of each amplitude-frequency modulation characteristic parameter. The initial fitting value is then used as the center point of the constraint range to further constrain the amplitude-frequency modulation characteristics.

[0034] (2) In the formula, is the characteristic parameter of amplitude-frequency modulation except the parameter in (1) (amplitude in each amplitude modulation function), is the proposed initial value obtained by the particle swarm algorithm.

[0035] The objective function in step S4 is constructed as follows: The quality of the fitting effect mainly depends on the degree of approximation between the modulation waveform and the actual oscillation signal waveform after the disturbance occurs. The closer the modulation waveform is to the actual oscillation signal waveform, the smaller the deviation of the points on the modulation waveform from the actual waveform at the same time. Therefore, this paper uses the degree of deviation of the corresponding points on the two waveforms as the objective function to describe the degree of waveform fitting. In addition, the instantaneous value waveform of the oscillation signal has two-dimensional constraints of amplitude and frequency. It is difficult to accurately describe all the parameters of the oscillation signal based only on the instantaneous value waveform of the oscillation signal. This paper introduces the amplitude waveform and frequency waveform of the oscillation signal as the target together with the instantaneous value waveform of the oscillation signal, which can effectively reduce the situation of local optimal solution.

[0036] (3) In the formula, Indicates the degree of waveform fitting. The first value on the instantaneous value fitting waveform The size of the sampling points, The instantaneous value of the actual waveform The size of the sampling points, The amplitude of the oscillation signal is fitted on the waveform The size of the sampling points, The amplitude of the oscillation signal is the actual waveform of The size of the sampling points, The frequency fitting waveform of the oscillation signal is The size of the sampling points, The actual waveform of the oscillation signal frequency The size of the sampling points, is the number of sampling points on the waveform.

[0037] It can be understood that the present application aims to propose a method for extracting the amplitude and frequency changes of oscillation signals in the dynamic process of power systems using a particle swarm algorithm. This method can accurately extract the new amplitude and frequency parameters generated by the continuous evolution of the amplitude and frequency of oscillation signals over time. The results extracted by the algorithm reflect the evolution law of each alternating electrical quantity in the dynamic process of the power system, providing an effective control method for the system as a reference.

[0038] For example, the three-phase signal of the power system in steady state , , The formation process expression is as follows: (4) In the formula, is the potential rotation vector in the device port, The instantaneous amplitude of the internal potential; is the instantaneous angular velocity of the internal potential; is the initial phase of the internal potential.

[0039] When a disturbance occurs, the evolution process of the three-phase oscillation signal is expressed as follows: (5) In the formula, , is the amplitude and frequency of the waveform in steady state, , , , , , , , is the characteristic parameter of the amplitude-frequency modulation of the oscillation signal. Its specific meaning is as follows: Modulation, corresponding to the modulation function The amplitude, frequency, index and phase in are , , , ; For the oscillation signal waveform frequency Modulation, corresponding to the modulation function The amplitude, frequency, index and phase in are , , , .

[0040] , , , , , , , , , , , , , , , is the characteristic parameter of the secondary amplitude-frequency modulation of the oscillation signal, and its specific meaning is as follows: The exponent in is modulated, corresponding to the modulation function The amplitude, frequency, index and phase in are , , , ; For the modulation function The frequency in is modulated, corresponding to the modulation function The amplitude, frequency, index and phase in are , , , ; For the modulation function The exponent in is modulated, corresponding to the modulation function The amplitude, frequency, index and phase in are , , , ; For the modulation function The frequency in is modulated, corresponding to the modulation function The amplitude, frequency, index and phase in are , , , .

[0041] As time goes by, the oscillation signal evolves according to the modulation law shown in formula (5). The modulation process is as follows: Figure 3 As shown, the present application first extracts the primary amplitude-frequency modulation feature through the algorithm, and then extracts the secondary amplitude-frequency modulation feature as the oscillation further develops, and repeats this process until the amplitude-frequency modulation feature of the entire oscillation signal is obtained. Taking the potential oscillation waveform in the grid-connected inverter system as an example, the method of the present application is used to extract its secondary amplitude-frequency modulation feature. The specific implementation process is as follows Figure 4 shown.

[0042] Step S201: Use Matlab / Simulink to build a converter grid-connected system such as Figure 5 The system parameters are shown in Table 1-1, Table 1-2 and Table 1-3.

[0043] Table 1-1 Converter grid-connected system parameters

[0044] Table 1-2 Circuit parameter table

[0045] Table 1-3 Control parameters

[0046] The state after the last system operation stabilized (4s) is used as the initial value of the current system operation, and the phase-locked frequency is set to deviate from its steady-state value operating point by 0.5rad / s. The waveform after the internal potential instantaneous value oscillates and diverges is recorded as follows: Figure 6 shown.

[0047] Step S202: taking the 0-1.2s instantaneous value oscillation waveform of the internal potential as the input of the method of the present application.

[0048] Step S203: determine the steady-state amplitude frequency, the amplitude variation constraint of each modulation amplitude, and the initial constraint range of each amplitude-frequency modulation feature, and divide the three-phase AC instantaneous value waveform of the oscillation signal into 12 intervals.

[0049] Step S204: Set the number of population iterations S=50, the population size para=200, the learning factors c1=2 and c2=2, and the inertia weight w=0.8.

[0050] Step S205: The amplitude-frequency modulation characteristic parameters are used as particles and the population is initialized. The steady-state amplitude-frequency function of formula (6) and the first-order amplitude-frequency modulation function of formula (7) are used to calculate the fitting degree of each particle in the first waveform interval as the target function. The amplitude-frequency modulation function that better fits the waveform of the current interval is compared, and the particle speed and position are updated. After 50 iterations, the particle population obtains the amplitude-frequency modulation characteristic parameters and modulation times with the best fitting degree in the first waveform interval.

[0051] (6) (7) Step S206: Determine whether the modulation times in the current interval are consistent with the reference modulation times. It can be seen that the fitting effect of the modulation function is better, which means that the waveform in the following interval has undergone the next amplitude-frequency modulation evolution.

[0052] Step S207: The new amplitude-frequency modulation characteristic parameters are used as particles and the population is initialized. The first amplitude-frequency modulation function of formula (7) and the second amplitude-frequency modulation function of formula (8) are used to calculate the fitting degree of each particle in the second waveform interval as the target function, and the amplitude-frequency modulation function that better fits the waveform of the current interval is compared. The particle speed and position are updated. After 50 iterations, the particle population obtains the amplitude-frequency modulation characteristic parameters and modulation times with the best fitting degree in the second waveform interval.

[0053] (8) Step S208: Repeat step S207 to fit the waveform of each interval in turn, and finally obtain the amplitude-frequency modulation characteristics of the entire waveform, and output the corresponding amplitude-frequency modulation times and fitting degree. The fitting result of the internal potential oscillation waveform is as follows: Figure 7-9 The fitting results of the first amplitude-frequency modulation of the instantaneous value of the internal potential are shown in Table 2. The fitting results of the second amplitude-frequency modulation of the internal potential oscillation waveform are shown in Figure 10-12 The fitting results of the quadratic amplitude-frequency modulation of the instantaneous value of the internal potential are shown in Table 3.

[0054] Table 2 The fitting results of the instantaneous value of internal potential by amplitude-frequency modulation

[0055] Table 3. Secondary amplitude-frequency modulation fitting results of the instantaneous value of internal potential

[0056] Depend on Figure 8 and Fig. 9 It can be seen that after 0.9s, the simulation waveform gradually deviates from the primary amplitude-frequency modulation waveform, and the deviation gradually increases with time. However, the internal potential amplitude waveform and the internal potential frequency waveform have a good fitting effect within 0-0.9s, indicating that the waveform has undergone secondary amplitude-frequency modulation evolution after 0.9s. Fig.11 The internal potential amplitude waveform, after the secondary amplitude-frequency modulation function fitting was introduced at 0.9s, is Figure 8 , the deviation between the simulation waveform and the fitting waveform is greatly reduced within 0.9s to 1.2s, see Fig.12 The internal potential frequency waveform, after the secondary amplitude-frequency modulation function fitting is introduced at 0.9s, is Fig. 9 , the deviation between the simulation and fitting waveforms is reduced within 0.9s to 1.2s. The output corresponding amplitude-frequency modulation times and fitting degree are shown in Table 4.

[0057] Table 4 Comparison of waveform fitting degree under different modulation rules

[0058] It can be seen from Table 4 that the internal potential waveform oscillates from 0s, and the degree of fit between the steady-state waveform and the oscillation waveform becomes lower and lower with time, reflecting that the internal potential oscillation waveform has undergone an amplitude-frequency modulation evolution. Therefore, after 0s, the first amplitude-frequency modulation function fitting is introduced, and the degree of fitting is significantly improved, but after 0.9s, the degree of fit between the first amplitude-frequency modulation waveform and the oscillation waveform becomes lower and lower with time, reflecting that the internal potential oscillation waveform has undergone a second amplitude-frequency modulation evolution. Therefore, after 0.9s, the second amplitude-frequency modulation function fitting is introduced, and the degree of fit between the second amplitude-frequency modulation waveform and the oscillation waveform is significantly improved, proving that the amplitude-frequency modulation feature extraction effect of the oscillation signal of this application is very good.

[0059] The following is a description of the system oscillation signal amplitude-frequency modulation feature extraction device based on the particle swarm algorithm provided in the present application. The system oscillation signal amplitude-frequency modulation feature extraction device based on the particle swarm algorithm described below and the system oscillation signal amplitude-frequency modulation feature extraction method based on the particle swarm algorithm described above can be referenced to each other.

[0060] Fig.13 is a schematic diagram of the structure of a system oscillation signal amplitude-frequency modulation feature extraction device based on a particle swarm algorithm provided in an embodiment of the present application, such as Fig.13 The device comprises: an interval segmentation module 10 and an interval traversal module 20. Wherein: An interval segmentation module 10, for segmenting the oscillation waveform into a plurality of intervals according to a time sequence; The interval traversal module 20 is used to traverse each interval in time sequence, solve the objective function for the traversed interval by using the particle swarm algorithm, and The corresponding fit is better than In the case of the corresponding degree of fit, The value of is increased by 1; Among them, the objective function is used to characterize the degree of fit between the modulation waveform of the modulation function and the actual waveform of the corresponding interval. The amplitude-frequency modulation characteristic parameters in the modulation function are used as particles of the particle swarm algorithm. The modulation function is or , express The sub-modulation function, express The sub-modulation function, The initial value of is 1.

[0061] It can be understood that the detailed functional implementation of each of the above-mentioned units / modules can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.

[0062] It should be understood that the above-mentioned device is used to execute the method in the above-mentioned embodiment. The implementation principle and technical effect of the corresponding program module in the device are similar to those described in the above-mentioned method. The working process of the device can refer to the corresponding process in the above-mentioned method, which will not be repeated here.

[0063] Based on the method in the above embodiment, an embodiment of the present application provides an electronic device, Fig.14 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Fig.14 As shown, the electronic device may include: a processor (Processor) 810, a communication interface (Communications Interface) 820, a memory (Memory) 830 and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the method in the above embodiment.

[0064] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0065] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0066] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0067] It is understandable that the processor in the embodiment of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0068] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0069] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0070] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0071] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements 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 extracting amplitude-frequency modulation features of system oscillation signals based on particle swarm algorithm, characterized in that: include: Divide the oscillation waveform into multiple intervals according to the time sequence; Traverse each interval in time sequence, solve the objective function for the traversed intervals through the particle swarm algorithm, and The corresponding fit is better than In the case of the corresponding degree of fit, The value of is increased by 1; Among them, the objective function is used to characterize the degree of fit between the modulation waveform of the modulation function and the actual waveform of the corresponding interval. The amplitude-frequency modulation characteristic parameters in the modulation function are used as particles of the particle swarm algorithm. The modulation function is or , express The sub-modulation function, express The sub-modulation function, The initial value of is 1.

2. According to the particle swarm algorithm-based system oscillation signal amplitude-frequency modulation feature extraction method of claim 1, it is characterized in that: The objective function is determined based on three types of fit degree; The first type of fitting degree is the fitting degree between the instantaneous value fitting waveform of the modulation function and the instantaneous value actual waveform; The second type of fitting degree is the fitting degree between the amplitude fitting waveform of the modulation function and the actual amplitude waveform; The third type of fitting degree is the fitting degree between the frequency fitting waveform of the modulation function and the actual frequency waveform.

3. The method for extracting amplitude-frequency modulation features of system oscillation signals based on particle swarm algorithm according to claim 2, characterized in that: The objective function is determined by the following formula: ； in, represents the objective function, Indicates the degree of waveform fitting. The first value on the instantaneous value fitting waveform The size of the sampling points, The instantaneous value of the actual waveform The size of the sampling points, The first The size of the sampling points, The actual waveform of the amplitude The size of the sampling points, The frequency fitting waveform The size of the sampling points, The actual waveform of the frequency The size of the sampling points, is the number of sampling points on the waveform.

4. The method for extracting amplitude-frequency modulation features of system oscillation signals based on particle swarm algorithm according to claim 2, characterized in that: Also includes: By sampling the signal in each interval, the instantaneous value actual waveform, amplitude actual waveform and frequency actual waveform corresponding to each interval are determined.

5. The method for extracting amplitude-frequency modulation features of system oscillation signals based on particle swarm algorithm according to any one of claims 1 to 4, characterized in that: for Submodulation Function The The amplitude modulation function and The amplitude modulation function, The amplitude of the second amplitude modulation function is smaller than that of the The maximum amplitude change in the sub-amplitude modulation function, .

6. The method for extracting amplitude-frequency modulation features of system oscillation signals based on particle swarm algorithm according to claim 5, characterized in that: The constraint range of the amplitude-frequency modulation characteristic parameters other than the amplitude in each amplitude modulation function is determined by taking the corresponding fitting initial value as the center point of the constraint range; The initial fitting value is obtained by solving the objective function by using the amplitude and frequency of the oscillation waveform as the initial constraint range of the amplitude-frequency modulation characteristic parameters through the particle swarm algorithm.

7. A device for extracting amplitude-frequency modulation features of system oscillation signals based on particle swarm algorithm, characterized in that: include: An interval segmentation module is used to segment the oscillation waveform into multiple intervals according to the time sequence; The interval traversal module is used to traverse each interval in time sequence, and solve the objective function through the particle swarm algorithm for the traversed intervals. The corresponding fit is better than In the case of the corresponding degree of fit, The value of is increased by 1; Among them, the objective function is used to characterize the degree of fit between the modulation waveform of the modulation function and the actual waveform of the corresponding interval. The amplitude-frequency modulation characteristic parameters in the modulation function are used as particles of the particle swarm algorithm. The modulation function is or , express The sub-modulation function, express The sub-modulation function, The initial value of is 1.

8. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program runs on a processor, the processor is caused to execute the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that When the computer program product runs on a processor, the processor is caused to execute the method according to any one of claims 1 to 6.

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