Self-adaptive random pulse noise position identification method based on broadband signal

Abstract

The invention discloses an adaptive random pulse noise position identification method based on a broadband signal. The method comprises the following steps: sampling to obtain a data sequence; determining a fluctuation threshold; the number of iterations and a random pulse noise position interval mark number are set in an initialized mode; obtaining an adjacent angle coefficient change rate between the data points, judging whether the adjacent angle coefficient change rate is smaller than a fluctuation threshold value or not, and if yes, judging the number of iterations; if not, labeling the random pulse noise position interval by + 1, then judging whether the number of iterations is greater than or equal to n-2, if so, adding 1 to the number of iterations, and returning to continuously execute the operation of obtaining the change rate of the adjacent angle coefficients between the data points; and if not, obtaining a random pulse noise position interval. According to the method, the signal fluctuation threshold can be obtained through self-adaptive calculation, the position of random pulse noise is detected, the noise interval is determined, and the accuracy of subsequent signal processing is enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system impulse noise identification, and particularly relates to an adaptive random impulse noise position identification method based on broadband signals. Background Art

[0002] In a "dual-high" power system with a high proportion of renewable energy and a high proportion of power electronic devices, the electromagnetic oscillation problem generated by power electronic devices cannot be ignored. Its oscillation frequency range is wider than that of traditional oscillations, ranging from several Hz to several kHz. At present, it is urgent to study and explore broadband signal measurement technologies for "dual-high" power systems based on broadband signal data. This measurement technology can provide accurate frequency band analysis data for each new energy station in real time and can be used as input data for subsequent oscillation monitoring, path tracing, fault analysis, etc.

[0003] In the actually obtained broadband signals, the measurement data often contains random impulse noise, and the amplitude of some random impulse noise is much larger than the signal envelope. Random impulse noise has uncertainty, with a low incidence rate but a higher amplitude than that of fundamental waves, harmonics, and interharmonics. The switching of high-power electrical equipment, sudden changes in large load currents, the switching of power electronic devices, relay actions, etc. will all introduce local random impulse noise on the basis of normal signals, and its existence will greatly affect the accuracy of data processing. Therefore, it is necessary to filter the broadband signal measurement data before analyzing the broadband signal measurement data to eliminate random impulse noise. Therefore, determining the position of random impulse noise is a crucial link in filtering.

[0004] Random impulse noise is difficult to distinguish from signals and other types of noise in the frequency domain, and its position can only be determined in the time domain. Existing robust linear filtering methods contain a random impulse noise detection link, but its threshold value is determined by calculating the median of the absolute value of the filtering residue, lacking stability, and its calculation is complex, not suitable for online fast position detection. In addition, the high precision and short time requirements of broadband signal measurement require that the detection effect of random impulse noise has good stability in the shortest possible time to obtain accurate data for subsequent research on broadband oscillations. Therefore, there is an urgent need to propose a high-precision position recognition algorithm for random impulse noise.

[0005] Therefore, it can be seen that it is very necessary to design an adaptive random impulse noise position identification method based on broadband signals. Summary of the Invention

[0006] In order to solve the above problems, an adaptive random impulse noise position identification method based on broadband signals is proposed, and the following technical solutions are adopted:

[0007] An adaptive random pulse noise position recognition method based on broadband signals, characterized by comprising: Sampling to obtain a data sequence; After obtaining the data sequence, determining a fluctuation threshold; According to the determined fluctuation threshold, initializing the number of iterations and the label of the random pulse noise position interval; After initialization, calculating the angular coefficient between data points, and then obtaining the change rate of adjacent angular coefficients between data points; Judging whether the obtained change rate of adjacent angular coefficients is less than the fluctuation threshold. If the change rate of adjacent angular coefficients is less than the fluctuation threshold, then judge the number of iterations; If the change rate of adjacent angular coefficients is greater than the fluctuation threshold, then increment the label of the random pulse noise position interval by 1, and then judge the number of iterations; Judging whether the number of iterations is greater than or equal to n - 2. If the number of iterations is less than n - 2, then increment the number of iterations by 1, and return to continue to execute the operation of obtaining the change rate of adjacent angular coefficients between data points; If the number of iterations is greater than or equal to n - 2, then obtain the random pulse noise position interval.

[0008] Further, determining the fluctuation threshold includes: arbitrarily selecting 5 different starting calculation data points, and calculating 5 change rates of adjacent angular coefficients β a , β b , β c , β d , β e , taking the median of the 5 change rates of adjacent angular coefficients, and defining it as the change rate of adjacent angular coefficients β base without random pulse noise, and then obtaining the fluctuation threshold λ.

[0009] Further, calculating the angular coefficient between data points, and then obtaining the change rate of adjacent angular coefficients between data points, includes: Calculating the angular coefficient k1 between the i-th and the (i + 1)-th data points, and the angular coefficient k2 between the (i + 1)-th and the (i + 2)-th data points, and then calculating the change rate of adjacent angular coefficients β i among the i-th, (i + 1)-th, and (i + 2)-th data points.

[0010] Further, the fluctuation threshold λ has the following expression: λ = k wave ×β base (1 - 1) Where k wave is the fluctuation coefficient.

[0011] Further, the change rate of adjacent angular coefficients β i has the following expression: Among them, k1 is the angular coefficient between the i-th and the (i + 1)-th data points, and k2 is the angular coefficient between the (i + 1)-th and the (i + 2)-th data points. The expressions of k1 and k2 are as follows: Among them, x i , x i+1 , x i+2 are the i-th, the (i + 1)-th, and the (i + 2)-th data points respectively, where i = 1, 2, 3, …, n - 2, and ΔT = 1.

[0012] Furthermore, if the change rate of adjacent angular coefficients is greater than the fluctuation threshold, then label the position interval of the random pulse noise as +1, including: defining the (i + 1)-th data point at this time as the starting data point of the random pulse noise, denoted as N start.m , the iteration number i + 1, returning to execute the step of calculating the angular coefficient between data points, and then obtaining the change rate of adjacent angular coefficients between data points until the calculated change rate of adjacent angular coefficients is less than the fluctuation threshold. Define the i-th data point at this time as the ending data point of the random pulse noise, denoted as N end.m , and label the position interval of the random pulse noise as m + 1.

[0013] Furthermore, the position interval of the random pulse noise is [N start.m , N end.m , where m = 1, 2, ….

[0014] An adaptive random pulse noise position recognition device based on a broadband signal, comprising: A sampling module for acquiring a data sequence; A threshold module for determining the fluctuation threshold; An initialization module for initializing the iteration number and the label of the random pulse noise position interval; A calculation module for obtaining the change rate of adjacent angular coefficients between data points; A calculation and judgment module for judging whether the obtained change rate of adjacent angular coefficients is less than the fluctuation threshold. If the change rate of adjacent angular coefficients is less than the fluctuation threshold, then judge the iteration number; if the change rate of adjacent angular coefficients is greater than the fluctuation threshold, then label the random pulse noise position interval as +1 and then judge the iteration number; An iteration judgment module for judging whether the iteration number is greater than or equal to n - 2. If the iteration number is less than n - 2, then increase the iteration number by 1 and return to execute the operation of obtaining the change rate of adjacent angular coefficients between data points; if the iteration number is greater than or equal to n - 2, then obtain the random pulse noise position interval.

[0015] Further, an initialization module is used to initialize and set the number of iterations and the label of the random pulse noise position interval, where the number of iterations is set to i = 1, and the label of the random pulse noise position interval m = 1.

[0016] A computer device includes a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, it implements the steps of the method for adaptively identifying the position of random pulse noise based on broadband signals according to any one of claims 1-7.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0018] Using the measured broadband signal data, according to the change rate of adjacent corner coefficients without random pulse noise, the fluctuation threshold is adaptively calculated and determined. By judging the relationship between the change rate of adjacent corner coefficients and the fluctuation threshold at each data point, the adaptive identification of the random pulse noise position is realized. Since the fluctuation threshold is adaptively determined by randomly selecting some existing data and remains constant once determined, the random pulse noise position can be quickly identified, and the random pulse noise position can be effectively and accurately determined whether the sampled data changes rapidly or slowly. The present invention can adaptively calculate the signal fluctuation threshold, detect the position of random pulse noise, determine the noise interval, and enhance the accuracy of subsequent signal processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings forming a part of this invention are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation of the present invention.

[0020] Figure 1 is a flowchart of the present invention;

[0021] Figure 2 is the simulation experiment result of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0023] It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0024] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they specify the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0025] Embodiment 1

[0026] As Figure 1 shown, the process steps are as follows:

[0027] 1) Sample at a sampling frequency f s to obtain an n-point sampling data sequence X = {x k , k = 1, …, n}, and proceed to step 2);

[0028] 2) Arbitrarily select 5 different starting calculation data points, calculate 5 adjacent angular coefficient change rates β a , β b , β c , β d , β e , take the median of the 5 adjacent angular coefficient change rates, define it as the adjacent angular coefficient change rate β base without random pulse noise, and further calculate the fluctuation threshold λ, and proceed to step 3);

[0029] 3) Set the number of iterations as i = 1, initialize the random pulse noise position interval label m = 1, and proceed to step 4);

[0030] 4) Calculate the angular coefficient k1 between the i-th and the (i + 1)-th data points, the angular coefficient k2 between the (i + 1)-th and the (i + 2)-th data points, and further calculate the adjacent angular coefficient change rate β i between the i-th, (i + 1)-th, and (i + 2)-th data points, and proceed to step 5);

[0031] 5) Make a judgment based on the relationship between the calculated β i and the fluctuation threshold λ: If the calculated β i is less than the fluctuation threshold λ, increment the number of iterations i by 1, and return to step 4); If the calculated β i is greater than the fluctuation threshold λ, define the (i + 1)-th data point at this time as the starting data point of the random pulse noise, denoted as N start.m , increment the number of iterations i by 1, and return to step 4) to continue the iteration until the calculated β i is less than the fluctuation threshold λ, and define the i-th data point at this time as the ending data point of the random pulse noise, denoted as Nend.m , label the position interval of the random pulse noise as m + 1;

[0032] 6) When the number of iterations i = n - 2, the iteration ends, and all the position intervals of the random pulse noise [N start.m , N end.m are obtained, where m = 1, 2, ….

[0033] Example 2

[0034] As shown in the Figure 2 simulation experiment results, the input signal is

[0035]

[0036] N B (t) is the background noise with a signal-to-noise ratio of 50 dB, and N I (t) is the random pulse noise, the sampling frequency is f s = 10 kHz, and the number of sampling points N = 10000. It can be seen that Figure 2 this simulation is disturbed by random pulse noise near the 1000th data point, where the signal rises rapidly. The algorithm adaptively determines the fluctuation threshold λ as 10.65, and then determines that the starting point of the pulse is the 999th data point, and the ending point is the 1004th data point. Obviously, the simulation results are consistent with the positions of the pulse noise in the signal. The method for adaptively identifying the position of random pulse noise in the broadband signal of the present invention can effectively identify the position of the random pulse noise and its starting point.

[0037] Example 3

[0038] The fluctuation threshold λ has the following expression: λ = k wave ×β base (1 - 1) where k wave is the fluctuation coefficient, and its value is 1.2.

[0039] Example 4

[0040] If the calculated β i is less than the fluctuation threshold λ, it is regarded as normal signal fluctuation.

[0041] Example 5

[0042] If the calculated β i is greater than the fluctuation threshold λ, it is regarded as the occurrence of random pulse noise.

[0043] Example 6

[0044] Continue to iterate until the calculated βi Less than the fluctuation threshold λ, indicating the end of the random pulse noise.

[0045] Embodiment 7

[0046] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which when executed, implements an adaptive random pulse noise position recognition method based on a broadband signal as described in Embodiments 1-6.

[0047] Embodiment 8

[0048] Based on the same inventive concept, an embodiment of the present invention further provides a computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, it implements an adaptive random pulse noise position recognition method based on a broadband signal as described in Embodiments 1-6.

[0049] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0050] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0051] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the specified functions in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0052] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the functions specified in one process or a plurality of processes and / or blocks Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps in a block or a plurality of blocks.

[0053] The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0054] Although the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, they are not limitations on the protection scope of the present invention. Those skilled in the art should understand that, based on the technical solutions of the present invention, various modifications or deformations that can be made by those skilled in the art without creative efforts are still within the protection scope of the present invention.

Claims

1. An adaptive random pulse noise position recognition method based on broadband signals, characterized in that Including: Sampling to obtain a data sequence; After obtaining the data sequence, determining a fluctuation threshold; According to the determined fluctuation threshold, initializing the number of iterations and the label of the random pulse noise position interval; After the initialization setting, calculating the angular coefficient between data points, and further obtaining the change rate of adjacent angular coefficients between data points; Judging whether the obtained change rate of adjacent angular coefficients is less than the fluctuation threshold. If the change rate of adjacent angular coefficients is less than the fluctuation threshold, then judge the number of iterations; if the change rate of adjacent angular coefficients is greater than the fluctuation threshold, then increment the label of the random pulse noise position interval by 1, and then judge the number of iterations; Judging whether the number of iterations is greater than or equal to n - 2. If the number of iterations is less than n - 2, then increment the number of iterations by 1, and return to execute the operation of obtaining the change rate of adjacent angular coefficients between data points; if the number of iterations is greater than or equal to n - 2, then obtain the random pulse noise position interval.

2. The adaptive random pulse noise position recognition method based on broadband signals according to claim 1, characterized in that The determining of the fluctuation threshold includes: Randomly select 5 different starting calculation data points and calculate the change rates β of 5 adjacent angular coefficients a , β b , β c , β d , β e , take the median of the change rates of 5 adjacent angular coefficients and define it as the change rate β of adjacent angular coefficients without random pulse noise base , and then calculate the fluctuation threshold λ.

3. The adaptive random pulse noise position recognition method based on broadband signals according to claim 1, characterized in that The calculating of the angular coefficient between data points and further obtaining the change rate of adjacent angular coefficients between data points includes: Calculate the angular coefficient k1 between the i-th and the (i + 1)-th data points, and the angular coefficient k2 between the (i + 1)-th and the (i + 2)-th data points, and then calculate the adjacent angular coefficient change rate β among the i-th, (i + 1)-th, and (i + 2)-th data points i .

4. The adaptive random pulse noise position recognition method based on broadband signals according to claim 1, characterized in that The fluctuation threshold λ has the following expression: λ = k wave × β base (1 - 1) where k wave is the fluctuation coefficient.

5. The adaptive random pulse noise position recognition method based on broadband signals according to claim 3, characterized in that The adjacent angle coefficient change rate β i , and the expression is as follows: Wherein, k1 is the angular coefficient between the i-th and the (i + 1)-th data points, k2 is the angular coefficient between the (i + 1)-th and the (i + 2)-th data points, and the expressions of k1 and k2 are as follows: where x i , x i+1 , x i+2 are the i-th, (i + 1)-th, and (i + 2)-th data points respectively, i = 1, 2, 3, …, n - 2, and ΔT = 1.

6. The adaptive random pulse noise position recognition method based on broadband signals according to claim 1, characterized in that If the change rate of the adjacent angle coefficient is greater than the fluctuation threshold, label the position interval of the random pulse noise as +1, including: defining the (i + 1)-th data point at this time as the starting data point of the random pulse noise, denoted as N start.m , the iteration number is i + 1, return to execute the step of calculating the angle coefficient between data points, and then obtain the change rate of the adjacent angle coefficient between data points until the calculated change rate of the adjacent angle coefficient is less than the fluctuation threshold. Define the i-th data point at this time as the ending data point of the random pulse noise, denoted as N end.m , and label the position interval of the random pulse noise as m + 1.

7. The adaptive random pulse noise position recognition method based on broadband signals according to claim 1, characterized in that The random pulse noise position interval is [N start.m , N end.m , where m = 1, 2, ….

8. An adaptive random pulse noise position recognition device based on broadband signals, characterized in that Including: A sampling module for obtaining a data sequence; A threshold module for determining a fluctuation threshold; An initialization module for initializing the number of iterations and the label of the random pulse noise position interval; A calculation module for obtaining the change rate of adjacent angular coefficients between data points; A calculation and judgment module for judging whether the obtained change rate of adjacent angular coefficients is less than the fluctuation threshold. If the change rate of adjacent angular coefficients is less than the fluctuation threshold, then judge the number of iterations; if the change rate of adjacent angular coefficients is greater than the fluctuation threshold, then increment the label of the random pulse noise position interval by 1, and then judge the number of iterations; An iteration judgment module for judging whether the number of iterations is greater than or equal to n - 2. If the number of iterations is less than n - 2, then increment the number of iterations by 1, and return to execute the operation of obtaining the change rate of adjacent angular coefficients between data points; if the number of iterations is greater than or equal to n - 2, then obtain the random pulse noise position interval.

9. An adaptive random pulse noise position recognition device based on a broadband signal according to claim 8, characterized in that, The initialization module is used to initialize the number of iterations and the label of the random pulse noise position interval, wherein the number of iterations is set as i = 1, and the label of the random pulse noise position interval m = 1.

10. A computer device, characterized in that, Including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, the steps of an adaptive random pulse noise position recognition method based on a broadband signal according to any one of claims 1 - 7 are implemented.

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