Amplitude-frequency modulation feature extraction method of system oscillation signal based on particle swarm optimization

Abstract

The present application belongs to the field of power system control and specifically discloses a method for extracting amplitude-frequency modulation features of system oscillation signals based on a particle swarm algorithm. The method comprises: dividing an oscillation waveform into multiple intervals according to a time sequence; traversing each interval according to a time sequence, solving an objective function for each traversed interval using a particle swarm algorithm, and increasing the value of m by 1 when the degree of fit corresponding to the m+1 modulation function is better than the degree of fit corresponding to the m-order modulation function; wherein 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 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. Through the present application, the extracted signal features can accurately reflect the evolution law of AC electrical quantities 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, relates to a method for extracting amplitude-frequency modulation features of system oscillation signals based on a particle swarm algorithm. Background Art

[0002] During the dynamic process of power electronics systems, power electronic devices, upon sensing unbalanced power at their ports, typically adjust the amplitude / frequency of their output potential, thereby altering the output AC voltage and current. Consequently, the amplitude / frequency of the AC signal changes instantaneously during the system's dynamics. Existing oscillation signal analysis methods include the Fourier transform, which assumes that distorted AC signals are formed by the superposition of harmonics and therefore expands the AC signal, using the amplitude and frequency of each harmonic as the AC signal's characteristics. The Prony algorithm, on the other hand, considers the oscillation signal to be a linearly weighted combination of exponential functions and uses the amplitude, frequency, and damping of the exponential function as the signal's characteristics. Short-time Fourier transforms and wavelet transforms can calculate the amplitude and frequency of signals in different time windows, thereby obtaining more precise parameters. However, they still essentially assume that the signal within each time window is formed by the superposition of harmonics. None of these studies recognizes the characteristics of the time-varying amplitude-frequency evolution of AC signals during system dynamics. Due to the inability to determine the actual signal representation, the correspondence between the narrowband signal components obtained by the Hilbert-Huang transform based on empirical mode decomposition and the actual signal's oscillation components is questionable, and the physical meaning of the resulting signal oscillation characteristics is unclear. Therefore, the existing oscillation characteristic analysis is still very limited in helping to understand the system oscillation process. The signal characteristics extracted by traditional power system signal analysis methods 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 existing technology, 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:

[0005] Divide the oscillation waveform into multiple intervals according to the time sequence;

[0006] Traverse each interval in time sequence, solve the objective function by particle swarm algorithm for the traversed interval, and The corresponding fitting degree is better than In the case of the corresponding degree of fit, The value of is increased by 1;

[0007] 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 submodulation function, express The submodulation function, The initial value of is 1.

[0008] In one possible implementation, the objective function is determined based on three types of fit;

[0009] 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;

[0010] The second type of fitting degree is the fitting degree between the amplitude fitting waveform of the modulation function and the actual amplitude waveform;

[0011] The third type of fitting degree is the fitting degree between the frequency fitting waveform of the modulation function and the actual frequency waveform.

[0012] In one possible implementation, the objective function is determined by the following formula:

[0013] ;

[0014] 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 actual waveform of the instantaneous value The size of the sampling points, The first value on the amplitude fitting waveform The size of the sampling points, The actual waveform of the amplitude The size of the sampling points, The first 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.

[0015] In a possible implementation, the method further includes:

[0016] 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.

[0017] In one possible implementation, Submodulation Function The The amplitude modulation function and the 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, .

[0018] 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 initial fitting value as the center point of the constraint range;

[0019] The initial fitting value is obtained by solving the objective function through the particle swarm algorithm, using the amplitude and frequency of the oscillation waveform as the initial constraint range of the amplitude-frequency modulation characteristic parameters.

[0020] In a second aspect, the present application provides a device for extracting amplitude-frequency modulation features of a system oscillation signal based on a particle swarm algorithm, comprising:

[0021] An interval segmentation module is used to segment the oscillation waveform into multiple intervals according to the time sequence;

[0022] 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 interval. The corresponding fitting degree is better than In the case of the corresponding degree of fit, The value of is increased by 1;

[0023] 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 submodulation function, express The submodulation function, The initial value of is 1.

[0024] 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.

[0025] 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.

[0026] In a fifth aspect, the present application provides a computer program product, which, when executed on a processor, enables the processor to execute the method described in the first aspect or any possible implementation of the first aspect.

[0027] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the existing technologies:

[0028] For the traversed interval, the objective function is solved by the particle swarm algorithm, which can be compared Submodulation Function The corresponding degree of fit is Submodulation Function The corresponding degree of fit is The corresponding fitting degree is better than The corresponding fitting degree indicates that the next amplitude-frequency modulation evolution occurs in the waveform of the following interval, and then The value of is increased by 1 to ensure the effectiveness of the fitting in the next interval. Therefore, by traversing each interval in time sequence, we can first extract the primary amplitude-frequency modulation feature. As the oscillation further develops, we then extract the secondary amplitude-frequency modulation feature, 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 of the AC electrical quantity (three-phase oscillation signal) during the dynamic process of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] 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;

[0030] Figure 2 This is the second flow chart of the method for extracting amplitude-frequency modulation features of system oscillation signals based on the particle swarm algorithm provided in an embodiment of the present application;

[0031] Figure 3 is a schematic diagram of the formation and evolution process of the oscillation signal provided in an embodiment of the present application;

[0032] Figure 4 This is a flow chart of continuous amplitude-frequency modulation signal demodulation based on particle swarm optimization provided in an embodiment of the present application;

[0033] Figure 5 This is a diagram of the grid-connected structure of the converter provided in an embodiment of the present application;

[0034] Figure 6 Schematic diagram of the oscillation waveform of the instantaneous value of the internal potential provided in an embodiment of the present application;

[0035] Figure 7 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 an embodiment of the present application;

[0036] Figure 8 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 an embodiment of the present application;

[0037] Figure 9 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 an embodiment of the present application;

[0038] Figure 10 Schematic diagram of the fitting waveform of the instantaneous value of the internal potential in the fitting result of the secondary amplitude-frequency modulation of the internal potential oscillation waveform provided in the embodiment of the present application;

[0039] Figure 11 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;

[0040] Figure 12 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;

[0041] Figure 13 2 is a schematic diagram of the structure of a system oscillation signal amplitude-frequency modulation feature extraction device based on a particle swarm algorithm according to an embodiment of the present application;

[0042] Figure 14 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0044] In the embodiments of this 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 this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0045] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more, for example, multiple processing units means two or more processing units, etc.; multiple elements means two or more elements, etc.

[0046] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0047] 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, such as Figure 1 As shown, the method includes the following steps S101 and S102.

[0048] Step S101, dividing the oscillation waveform into multiple intervals according to the time sequence;

[0049] Step S102: traverse each interval in time sequence, solve the objective function for the traversed interval by particle swarm algorithm, and The corresponding fitting degree is better than In the case of the corresponding degree of fit, The value of is increased by 1;

[0050] 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 submodulation function, express The submodulation function, The initial value of is 1.

[0051] 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 the quantities to be solved.

[0052] 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 , 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 the modulator is modulated.

[0053] It is understandable that for the traversed interval, the objective function can be solved by the particle swarm algorithm. Submodulation Function The corresponding degree of fit is Submodulation Function The corresponding degree of fit is The corresponding fitting degree is better than The corresponding fitting degree indicates that the next amplitude-frequency modulation evolution occurs in the waveform of the following interval, and then The value of is increased by 1 to ensure the effectiveness of the fitting in the next interval. Therefore, by traversing each interval in time sequence, we can first extract the primary amplitude-frequency modulation feature. As the oscillation further develops, we then extract the secondary amplitude-frequency modulation feature, 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 of the AC electrical quantity (three-phase oscillation signal) during the dynamic process of the power system.

[0054] The following uses several examples to illustrate the method for extracting amplitude-frequency modulation features of system oscillation signals based on the particle swarm algorithm provided in this application.

[0055] Figure 2 This is a second flow chart 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 As shown, the method includes the following steps S1-S5.

[0056] 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.

[0057] Step S2: Determine the steady-state amplitude frequency , the amplitude of each amplitude modulation function (the amplitude is the amplitude modulation characteristic parameter of the amplitude modulation function) and the initial constraint range of other amplitude-frequency modulation characteristic parameters, the oscillation signal three-phase AC instantaneous value waveform is divided into intervals, and sample and demodulate each interval in turn.

[0058] Step S3: Set the number of population iterations , population size para, learning factors c1 and c2, inertia weight w.

[0059] Step S4: Use the amplitude-frequency modulation characteristic parameter (amplitude-frequency modulation characteristic parameter) as the particle and initialize the population. 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 target function, compare the amplitude-frequency modulation function that is more suitable for 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 of the current waveform interval are obtained.

[0060] 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 subsequent 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 of 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.

[0061] The constraints in step S2 are as follows:

[0062] (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);

[0063] 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 subsequent modulation processes is the disturbance of the amplitude of the last modulation waveform at the equilibrium point, and both are constrained by the change in the amplitude of the last modulation waveform, that is:

[0064] (1)

[0065] 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 ; Amplitude modulation function The amplitude in is modulated, corresponding to the amplitude modulation function The amplitude in the (second amplitude modulation function) is ; 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 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.

[0066] (2) Initial constraint ranges of other amplitude-frequency modulation characteristic parameters;

[0067] 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 fitting initial values of each amplitude-frequency modulation characteristic parameter. The fitting initial value is then used as the center point of the constraint range to further constrain the amplitude-frequency modulation characteristics.

[0068] (2)

[0069] Where, is the characteristic parameter of amplitude-frequency modulation except the parameters in (1) (amplitude in each amplitude modulation function), is the proposed initial value obtained by the particle swarm algorithm.

[0070] The objective function in step S4 is constructed as follows:

[0071] The quality of the fitting depends primarily on the degree of approximation between the modulated waveform and the actual oscillation signal waveform after the disturbance. The closer the modulated waveform is to the actual oscillation signal waveform, the less the points on the modulated waveform deviate from the actual waveform at the same moment. Therefore, this paper uses the degree of deviation between corresponding points on the two waveforms as the objective function to describe the degree of waveform fit. Furthermore, the instantaneous waveform of the oscillation signal is constrained by two dimensions: amplitude and frequency. Accurately describing all parameters of the oscillation signal based solely on the instantaneous waveform is difficult. This paper introduces the amplitude and frequency waveforms of the oscillation signal as joint targets alongside the instantaneous waveform, effectively reducing the occurrence of local optimal solutions.

[0072] (3)

[0073] Where, Indicates the degree of waveform fitting, The first value on the instantaneous value fitting waveform The size of the sampling points, The actual waveform of the instantaneous value The size of the sampling points, The amplitude of the oscillation signal is fitted on the waveform The size of the sampling points, The actual waveform of the oscillation signal is the amplitude of the oscillation signal. The size of the sampling points, The frequency of the oscillation signal is fitted on the waveform 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.

[0074] It is understood that the purpose of this application is to propose a method for extracting the amplitude and frequency changes of oscillation signals during 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 of various alternating electrical quantities during the dynamic process of the power system, providing a reference for effective control methods for the system.

[0075] For example, the three-phase signal of the power system in steady state 、 、 The formation process expression is as follows:

[0076] (4)

[0077] Where, is the potential rotation vector inside the device port, Instantaneous amplitude of internal potential; is the instantaneous angular velocity of the internal potential; is the initial phase of the internal potential.

[0078] When a disturbance occurs, the evolution process of the three-phase oscillation signal is expressed as follows:

[0079] (5)

[0080] Where, 、 are the amplitude and frequency of the waveform in steady state, 、 、 、 、 、 、 、 It 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 are 、 、 、 ; For the oscillation signal waveform frequency Modulation, corresponding to the modulation function The amplitude, frequency, index and phase are 、 、 、 .

[0081] 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 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 are 、 、 、 ; For the modulation function The frequency in is modulated, corresponding to the modulation function The amplitude, frequency, index and phase are 、 、 、 ; For the modulation function The exponent in is modulated, corresponding to the modulation function The amplitude, frequency, index and phase are 、 、 、 ; For the modulation function The frequency in is modulated, corresponding to the modulation function The amplitude, frequency, index and phase are 、 、 、 .

[0082] As time goes by, the oscillation signal evolves according to the modulation rule 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 so on and so forth 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.

[0083] Step S201: Use Matlab / Simulink to build a converter grid-connected system. Figure 5 The system parameters are shown in Table 1-1, Table 1-2, and Table 1-3.

[0084] Table 1-1 Converter grid-connected system parameters

[0085]

[0086] Table 1-2 Circuit parameter table

[0087]

[0088] Table 1-3 Control Parameters

[0089]

[0090] The state after the system last ran stable (4s) is used as the initial value of the current system operation. The phase-locked frequency is set to deviate from its steady-state value by 0.5rad / s. The waveform after the instantaneous value of the internal potential oscillates and diverges is recorded as follows: Figure 6 shown.

[0091] Step S202: Take the 0-1.2s instantaneous value oscillation waveform of the internal potential as input for the method of the present application.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] (6)

[0096] (7)

[0097] 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, indicating that the waveform in the subsequent interval has undergone the next amplitude-frequency modulation evolution.

[0098] Step S207: The new amplitude-frequency modulation characteristic parameters are used as particles and the population is initialized. The first-order amplitude-frequency modulation function of formula (7) and the second-order 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. 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 second waveform interval.

[0099] (8)

[0100] 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 10-12The fitting results of the quadratic amplitude-frequency modulation of the instantaneous value of the internal potential are shown in Table 3.

[0101] Table 2. Fitting results of the instantaneous value of internal potential using amplitude-frequency modulation

[0102]

[0103] Table 3. Secondary amplitude-frequency modulation fitting results of the instantaneous value of internal potential

[0104]

[0105] Depend on Figure 8 and Figure 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. Figure 11 The internal potential amplitude waveform, after the secondary amplitude-frequency modulation function fitting was introduced at 0.9s, is compared with Figure 8 , the deviation between the simulation waveform and the fitting waveform is greatly reduced within 0.9s~1.2s, see Figure 12 The internal potential frequency waveform, after the secondary amplitude-frequency modulation function fitting was introduced at 0.9s, is compared with Figure 9 The deviation between the simulation and fitting waveforms decreases within 0.9s to 1.2s. The corresponding amplitude-frequency modulation times and fitting degree of the output are shown in Table 4.

[0106] Table 4 Comparison of waveform fitting degree under different modulation rules

[0107]

[0108] As can be seen from Table 4, the internal potential waveform begins to oscillate from 0s onwards, and the degree of fit between the steady-state waveform and the oscillating waveform becomes increasingly lower over time, reflecting that the internal potential oscillation waveform has undergone a single amplitude-frequency modulation evolution. Therefore, after 0s, the first amplitude-frequency modulation function fitting is introduced, and the degree of fit is significantly improved. However, after 0.9s, the degree of fit between the first amplitude-frequency modulation waveform and the oscillating waveform becomes increasingly lower over time, reflecting that the internal potential oscillation waveform has undergone a secondary amplitude-frequency modulation evolution. Therefore, after 0.9s, the secondary amplitude-frequency modulation function fitting is introduced, and the degree of fit between the secondary amplitude-frequency modulation waveform and the oscillating waveform is significantly improved, proving that the amplitude-frequency modulation feature extraction of the oscillation signal in this application is very effective.

[0109] The following describes the system oscillation signal amplitude-frequency modulation feature extraction device based on the particle swarm algorithm provided in this 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.

[0110] Figure 13 : is a structural diagram of a system oscillation signal amplitude-frequency modulation feature extraction device based on a particle swarm algorithm according to an embodiment of the present application. Figure 13 The device includes: an interval segmentation module 10 and an interval traversal module 20.

[0111] The interval segmentation module 10 is used to segment the oscillation waveform into multiple intervals according to the time sequence;

[0112] The interval traversal module 20 is used to traverse each interval in time sequence, solve the objective function for the traversed interval by particle swarm algorithm, and The corresponding fitting degree is better than In the case of the corresponding degree of fit, The value of is increased by 1;

[0113] 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 submodulation function, express The submodulation function, The initial value of is 1.

[0114] It is understandable that the detailed functional implementation of each of the above units / modules can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.

[0115] 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 and will not be repeated here.

[0116] Based on the method in the above embodiment, an embodiment of the present application provides an electronic device, Figure 14 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Figure 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. The processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the method in the above embodiment.

[0117] In addition, the logic instructions in the aforementioned memory 830 can be implemented in the form of a software functional unit and, when sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application.

[0118] 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.

[0119] 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.

[0120] It is understood that the processor in the embodiments of the present application may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), 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.

[0121] 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, which 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 an integral part of the processor. The processor and storage medium can be located in an ASIC.

[0122] The above embodiments can be implemented in whole or in part using software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions. When loaded and executed on a computer, the computer program instructions fully or partially produce the processes or functions described in the embodiments of this application. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted via the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via 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 can be any available medium accessible by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic media (e.g., floppy disk, hard disk, tape), optical media (e.g., DVD), or semiconductor media (e.g., solid-state drive (SSD)).

[0123] It will be understood that the various numerical numbers involved in the embodiments of the present application are merely distinctions for the convenience of description and are not intended to limit the scope of the embodiments of the present application.

[0124] It is easy for those skilled in the art to understand that the above 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 should 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 optimization, 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 by particle swarm algorithm for the traversed interval, and The corresponding fitting degree is better than Under the corresponding fitting degree, it is determined that the next amplitude-frequency modulation evolution occurs in the waveform of the following interval, and the The value of increases by 1, and the next interval uses the value after the increase of 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 submodulation function, express The submodulation function, The initial value of is 1.

2. The method for extracting amplitude-frequency modulation features of system oscillation signals based on particle swarm optimization according to claim 1, characterized in that: The objective function is determined based on three types of fitting 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 optimization 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 actual waveform of the instantaneous value The size of the sampling points, The first value on the amplitude fitting waveform The size of the sampling points, The actual waveform of the amplitude The size of the sampling points, The first 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 optimization 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 optimization according to any one of claims 1 to 4, characterized in that: for Submodulation Function The The amplitude modulation function and the 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 optimization 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 through the particle swarm algorithm, using the amplitude and frequency of the oscillation waveform as the initial constraint range of the amplitude-frequency modulation characteristic parameters.

7. A device for extracting amplitude-frequency modulation features of system oscillation signals based on particle swarm optimization, 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 interval. The corresponding fitting degree is better than Under the corresponding fitting degree, it is determined that the next amplitude-frequency modulation evolution occurs in the waveform of the following interval, and the The value of increases by 1, and the next interval uses the value after the increase of 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 submodulation function, express The submodulation 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 is executed 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 is run on a processor, the processor is enabled to perform the method according to any one of claims 1 to 6.

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