Series side converter control method based on dynamic sampling detection

Abstract

The invention relates to the technical field of power system signal processing, in particular to a series-side converter control method based on dynamic sampling detection, and the method comprises the steps: obtaining a three-phase voltage signal of a line where a series-side converter is located; performing modal decomposition on each phase of voltage signal, obtaining a first characteristic value of each modal component based on frequency characteristics, and obtaining a first filter component set and a second filter component set; obtaining a first filter based on the first filter component set; performing wavelet decomposition and feature extraction on each modal component in the second filter component set to obtain a second feature value, and re-assigning the amplitude of a wavelet coefficient; reconstructing all scales after each modal component is subjected to reassignment to obtain a second filter; and controlling the series side converter according to the three-phase voltage signals processed by the two filters. According to the invention, noise interference components in the three-phase voltage are filtered out, and the control effect of the series side converter is improved.

Description

Technical Field

[0001] This application relates to the technical field of power system signal processing, and particularly to a control method for a series-side converter based on dynamic sampling detection. Background Art

[0002] The installation scale of distributed new energy power generation systems in the distribution network has been expanding rapidly. However, with the high proportion of photovoltaic power connected to the distribution network, problems such as randomness and intermittency will have many adverse effects on the power quality of the distribution network after grid connection, such as power quality problems like frequent start and stop of intermittent distributed power sources, voltage fluctuations in the distribution network caused by changes in power output, and low power factor. Among them, intermittent power sources usually need to use power electronic converters to output power that meets the frequency and voltage requirements of user loads.

[0003] By connecting the series-side converter in series with a transformer to the low-voltage side of the transformer and using the three-phase voltage signals collected by the sampling circuit to control the operation of the series-side converter, a compensation voltage can be provided in the circuit, enabling refined voltage regulation and voltage compensation to prevent voltage over-limit. Among them, the accurate acquisition of signals is an important link in the control of the series-side converter. Accurate three-phase voltage signals can help the series-side converter accurately calculate the required voltage compensation amount, thereby quickly and effectively controlling the voltage of the distribution network within a reasonable range.

[0004] However, there are more or less certain drawbacks in the existing sampling circuits, such as poor anti-interference ability and improper processing of sampled voltages, resulting in low reliability and accuracy of the three-phase voltage signals collected by the existing sampling circuits. Furthermore, it affects the control accuracy of the series-side converter in the distribution network. Therefore, a filtering circuit is usually used to filter the sampled voltage signals to improve the signal acquisition accuracy of the three-phase voltage. For example, in the published patent No. CN118944668A, a three-phase voltage, current sampling and analog-to-digital conversion circuit and method are disclosed. However, this method can only filter out high-frequency interference signals in the power grid and cannot effectively filter out other noise interference components existing in the three-phase voltage. Summary of the Invention

[0005] In view of the above, it is necessary to provide a control method for a series-side converter based on dynamic sampling detection to solve the above problems.

[0006] An embodiment of this application provides a control method for a series-side converter based on dynamic sampling detection, and the method includes:

[0007] S1: Obtain three-phase voltage signals of the line where the series-side converter is located;

[0008] S2: Perform modal decomposition on each phase voltage signal, obtain the corresponding components of each modal component of each phase voltage signal in the voltage signals of other phases based on frequency characteristics, and obtain the first eigenvalue of each modal component based on the similarity degree of the spectral characteristics between each modal component and its corresponding component; perform threshold segmentation on all modal components of all phase voltage signals based on the first eigenvalue to obtain the first filter component set and the second filter component set of each phase voltage signal; obtain the first FIR filter of each phase voltage signal based on the spectral range of each modal component in the first filter component set of each phase voltage signal.

[0009] S3: Perform wavelet decomposition of a preset scale on each modal component in the second filter component set of each phase voltage signal and all its corresponding components to obtain all wavelet coefficients of each scale; obtain the second eigenvalue according to the local distribution correlation of the wavelet coefficients within the same scale in different phase voltage signals and in combination with the local distribution correlation of the wavelet coefficients of different scales in each phase voltage signal, and determine whether to reassign the amplitude of the wavelet coefficients of each modal component at each scale; reconstruct all scales after reassigning each modal component, and obtain the second FIR filter of each phase voltage signal based on the spectral range of each reconstructed modal component in the second filter component set.

[0010] S4: Control the series-side converter according to the three-phase voltage signals processed by the two FIR filters.

[0011] Preferably, the corresponding component of each modal component of each phase voltage signal is obtained by selecting the modal component with the smallest absolute value of the difference in center frequency from the modal components in the voltage signals of other phases.

[0012] Preferably, the obtaining of the first eigenvalue of each modal component is specifically as follows:

[0013] Calculate the waveform similarity and spectral similarity between each modal component and each of its corresponding components, and denote the mean value of the waveform similarities between each modal component and all its corresponding components as the first mean value;

[0014] Denote the mean value of the spectral similarities between each modal component and all its corresponding components as the second mean value;

[0015] Take the result of the positive fusion of the first mean value and the second mean value corresponding to each modal component as the first eigenvalue of each modal component.

[0016] Preferably, the obtaining of the first filter component set and the second filter component set of each phase voltage signal is specifically as follows:

[0017] Take the first eigenvalue of all modal components of all phase voltage signals as the input of the maximum inter-class variance algorithm, and output the first threshold value.

[0018] Denote the set composed of all modal components with the first eigenvalue greater than the first threshold value in each phase voltage signal as the first filter component set of each phase voltage signal; denote the set composed of the remaining modal components in each phase voltage signal as the second filter component set of each phase voltage signal.

[0019] Preferably, the specific process of obtaining the first FIR filter for each phase voltage signal is as follows:

[0020] For each modal component in the first filter component set of each phase voltage signal, take the maximum frequency and the minimum frequency of the frequencies with amplitudes greater than 0 in each spectrum as the upper and lower limits of the frequency distribution range respectively.

[0021] Take the union of the frequency distribution ranges of all modal components as the passband frequency range of the FIR filter, and combine with the Parks-McClellan algorithm to obtain the first FIR filter for each phase voltage signal.

[0022] Preferably, the specific process of obtaining the second eigenvalue is as follows:

[0023] Compare the local distribution similarity of each wavelet coefficient of each modal component and its corresponding component at the same scale to obtain the first characteristic factor.

[0024] Compare the local distribution similarity of each wavelet coefficient of each modal component at different scales to obtain the second characteristic factor.

[0025] Take the product of the amplitude of each wavelet coefficient, the first characteristic factor, and the second characteristic factor as the second eigenvalue.

[0026] Preferably, the specific process of obtaining the first characteristic factor is as follows:

[0027] For each wavelet coefficient in each scale, denote the sequence composed of the amplitudes of the preset number of wavelet coefficients with the sampling time closest to each wavelet coefficient as the first sequence of each wavelet coefficient.

[0028] Take the mean value of the correlations between each modal component and the first sequences of each wavelet coefficient of all its corresponding components in each scale as the first characteristic factor.

[0029] Preferably, the second characteristic factor is specifically the mean value of the correlation degrees between each modal component at each scale and the first sequences of each wavelet coefficient in all other scales.

[0030] Preferably, the process of determining whether to reassign the amplitude of the wavelet coefficients in each scale of each modal component is as follows:

[0031] Perform threshold segmentation on the second eigenvalue of all wavelet coefficients in each scale of each modal component to obtain a second threshold; assign the amplitude of all wavelet coefficients with a second eigenvalue less than the second threshold in each scale to 0.

[0032] Preferably, the passband frequency range of the second FIR filter is the union of the spectral distribution ranges of the reconstructed modal components of each modal component in the second filter component set.

[0033] The present application has at least the following beneficial effects:

[0034] 1. In the present application, based on the similarity degree of the spectral characteristics between each modal component and its reference component, the first eigenvalue of each modal component is obtained, and the first FIR filter is constructed based on the first eigenvalue, which can avoid the useful information in the signal components in a certain frequency band without noise in the three-phase voltage signal from being over-filtered.

[0035] 2. In the present application, according to the local distribution correlation of the wavelet coefficients in the same scale of different phase voltage signals, combined with the local distribution correlation of the wavelet coefficients in different scales of each phase voltage signal, the second eigenvalue is obtained, and the second FIR filter is constructed based on the second eigenvalue. Compared with directly using a filter within a preset frequency range to filter the collected three-phase voltage signal, it can make full use of the correlation between wavelet coefficients to improve the filtering effect of the filter on the noise in the three-phase voltage signal and the retention degree of the useful information in the signal.

[0036] 3. In the present application, the filtering process of the three-phase voltage signal is realized by using the constructed FIR filter, and the series-side converter is controlled based on the filtered three-phase voltage. Compared with the existing filtering circuit for filtering the sampled voltage signal, it can effectively filter the noise components in the three-phase voltage signal and retain the useful information in the signal, thereby improving the control accuracy of the series-side converter in the distribution network, realizing the refined voltage regulation and voltage compensation of the distribution network, and preventing voltage over-limit. Description of the Drawings

[0037] Figure 1 It is a flowchart of the control method for the series-side converter based on dynamic sampling detection provided by the present application;

[0038] Figure 2 It is a flowchart for obtaining the second eigenvalue provided by the present application. Detailed Embodiments

[0039] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example", etc. are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary", "or", "for example" is intended to present related concepts in a specific manner.

[0040] Unless otherwise defined, 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 this application belongs. The terms used in the description of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0041] In addition, it should be noted that the terms "first" and "second" in this application and its drawings are used to distinguish similar objects and are not used to describe a specific order or sequence. For the methods disclosed in the embodiments of this application or the methods shown in the flowcharts, including one or more steps for implementing the methods, without departing from the scope of protection of this application, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0042] Unless otherwise defined, 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 this application belongs.

[0043] The present application proposes a control method for a series-side converter based on dynamic sampling detection, which is applied to the technical field of power system signal processing. Referring to the attached Figure 1 , the method includes the following steps:

[0044] S1: Obtain the three-phase voltage signals of the line where the series-side converter is located.

[0045] The present application uses a pulse width modulation (PWM) signal sampling and detection circuit to collect the three-phase voltage signals of the line where the series-side converter is located, and dynamically adjusts the sampling rate of the three-phase voltage signals according to the change of the input signal of the PWM signal sampling and detection circuit to ensure the sampling accuracy and effectiveness of the three-phase voltage signals, and realizes the dynamic sampling detection of the three-phase voltage signals of the line where the series-side converter is located; uses an amplification circuit to perform signal amplification processing on the collected three-phase voltage signals to improve the signal-to-noise ratio; uses an analog-to-digital conversion circuit to process the amplified three-phase voltage signals and converts the analog voltage signals into digital signals for subsequent filtering processing. Among them, the three-phase voltage signals include the A-phase voltage signal, the B-phase voltage signal, and the C-phase voltage signal; the PWM signal sampling and detection circuit, the amplification circuit, and the analog-to-digital conversion circuit are all well-known technologies, and the specific process will not be elaborated.

[0046] S2: Perform modal decomposition on each phase voltage signal, preset the corresponding components of each modal component of each phase voltage signal in the other phase voltage signals, and obtain the first eigenvalue of each modal component based on the similarity degree of the spectral characteristics between each modal component and its corresponding component; perform threshold segmentation on all modal components of all phase voltage signals based on the first eigenvalue to obtain the first filter component set and the second filter component set of each phase voltage signal; obtain the first FIR filter of each phase voltage signal based on the spectral range of each modal component in the first filter component set of each phase voltage signal.

[0047] In the actual control process of the series-side converter in the distribution network, due to factors such as the installation location of equipment, external electromagnetic interference, and the influence of mutual inductance between parallel lines, the collected three-phase voltage signals usually contain noise interference, and the presence of noise will affect the calculation result of the voltage compensation amount of the series-side converter. Therefore, it is necessary to filter the collected three-phase voltage signals.

[0048] Since the useful information in the three-phase voltage signals in the distribution network is usually concentrated in certain specific frequency bands, while the noise is usually distributed in a relatively wide frequency range due to its diversity and complexity, directly using a single filter with a preset frequency range to filter the three-phase voltage signals collected by the series-side converter is likely to cause the useful information in the signal components in a certain frequency band without noise in the three-phase voltage signals to be over-filtered. Therefore, the following processing is carried out.

[0049] Specifically, since the original design intention of the three-phase alternating current system used in the distribution network is to achieve symmetric operation, that is, the amplitudes of the three-phase voltages are equal and the phase angles differ by 120 degrees, this symmetry can ensure the stable operation of the power system and the effective transmission of electric energy, making the three-phase voltage signals in the distribution network should have similar signal distribution characteristics under normal circumstances, that is, the waveforms and frequency changes of each phase voltage should be basically the same. When the signal components of a certain phase voltage signal in a certain frequency band have a large signal distribution difference from the signal components in the similar frequency band of the remaining phase voltage signals, then this phase voltage signal is more likely to be interfered by noise in the similar frequency band, and when using a filter for signal filtering, this frequency should be set outside the pass frequency band.

[0050] Based on the above analysis, the A-phase voltage signal, B-phase voltage signal, and C-phase voltage signal are respectively used as the inputs of the Variational Mode Decomposition (VMD) algorithm, and the K modal components of each phase voltage signal and the central frequency of each modal component are output respectively, for subsequent analysis of the signal distribution differences between the signal components in different phase voltage signals within a similar frequency band. In this embodiment, the number of modes K in the VMD algorithm is set to 10, and the implementer can set it according to the actual situation, and this application does not limit it; it should be noted that the VMD algorithm is a well-known technology, and the specific process will not be elaborated.

[0051] Taking the i-th modal component A(i) in the A-phase voltage signal of the three-phase voltage signal as an example in this embodiment, the Fourier transform is used to extract the spectrum of the modal component A(i). The extraction of the signal spectrum is a well-known technology, and the specific process will not be elaborated.

[0052] The modal components with the smallest absolute value of the difference in the central frequency from the modal component A(i) are respectively selected from the modal components of the B-phase and C-phase voltage signals as the control components of the modal component A(i); calculate the waveform similarity and spectrum similarity between the modal component A(i) and each control component, and denote the mean value of the waveform similarities between the modal component A(i) and all its control components as the first mean value; denote the mean value of the spectrum similarities between the modal component A(i) and all its control components as the second mean value. The result of the positive fusion of the first mean value and the second mean value corresponding to the modal component A(i) is used as the first eigenvalue of the modal component A(i), which is used to characterize the similarity degree of the signal distribution between the signal components in the B-phase and C-phase voltage signals within a similar frequency band to the modal component A(i). In this embodiment, the waveform similarity is obtained through the Pearson correlation coefficient; the spectrum similarity is obtained through the calculation method of cosine similarity; the positive fusion of multiple variables adopts the calculation method of multiplication; it should be noted that both the Pearson correlation coefficient and the cosine similarity are existing well-known technologies, and this application will not elaborate on them.

[0053] Take the first eigenvalue of all modal components of all phase voltage signals as the input of the maximum between-class variance algorithm, and output the first threshold. Taking the A-phase voltage signal as an example, denote the set composed of all modal components in the A-phase voltage signal whose first eigenvalue is greater than the first threshold as the first filter component set D1(A) of the A-phase voltage signal, which is used to represent the set composed of signal components in the frequency band of the A-phase voltage signal that is not affected by noise interference, so as to facilitate the subsequent design of the first filter of the A-phase voltage signal. Denote the set composed of the remaining modal components in the A-phase voltage signal as the second filter component set D2(A) of the A-phase voltage signal, which is used to represent the set composed of signal components in the frequency band of the A-phase voltage signal that may be affected by noise interference, so as to facilitate the subsequent design of the second filter of the A-phase voltage signal. Among them, the maximum between-class variance algorithm is a well-known technology, and the specific process will not be elaborated here.

[0054] Furthermore, extract the spectrum of each modal component in the first filter component set D1(A) of the A-phase voltage signal, and take the maximum frequency and the minimum frequency of the frequencies with amplitudes greater than 0 in each spectrum as the upper and lower limits of the frequency distribution range respectively; denote the union of the frequency distribution ranges of all modal components as the first union, which is used to represent the frequency range corresponding to the signal components in the A-phase voltage signal that are not affected by noise interference, and take the first union as the passband frequency range of the FIR filter, and use the Parks-McClellan algorithm to realize the design of the optimal FIR filter, and obtain the first FIR filter Q1(A) of the A-phase voltage signal. In this embodiment, the filter order of the FIR filter is taken as 60. The extraction of the signal spectrum and the Parks-McClellan algorithm are both well-known technologies, and the specific process will not be elaborated in this application.

[0055] S3: Perform wavelet decomposition of each modal component in the second filter component set of each phase voltage signal and all its corresponding components at a preset scale to obtain all wavelet coefficients at each scale; according to the local distribution correlation of the wavelet coefficients within the same scale in different phase voltage signals, combined with the local distribution correlation of the wavelet coefficients at different scales in each phase voltage signal, obtain the second eigenvalue, and judge whether to reassign the amplitudes of the wavelet coefficients at each scale of each modal component; reconstruct all scales after reassigning each modal component, and based on the spectrum range of each reconstructed modal component in the second filter component set, obtain the second FIR filter of each phase voltage signal.

[0056] When a signal passes through a filter, the filter can only pass signals within a preset frequency range. However, traditional filtering methods usually set the upper and lower limits of the frequency range based on empirical values and process the entire signal. This method does not take into account that in three-phase voltage signals, the noise components of different phase voltage signals may exist in different frequency bands. Therefore, to improve the filtering effect of the filter on the noise in three-phase voltage signals and the retention degree of useful information in the signals, the following processing is carried out.

[0057] Specifically, in a distribution network, the frequency ranges of three-phase voltage signals are similar. Therefore, for the modal components of these signals within similar frequency bands, after wavelet transform, the wavelet coefficients obtained usually exhibit similar distribution characteristics. Noise is different. Due to its randomness and complexity, the wavelet coefficients corresponding to noise often do not show such similar distribution characteristics. Secondly, after the signal is wavelet decomposed, the amplitude of the wavelet coefficients of the signal is usually larger than that of the wavelet coefficients of the noise. And the different propagation characteristics of the wavelet transform coefficients of the signal and the noise at different scales indicate that the wavelet transform of the signal has strong correlation between scales, while the wavelet transform of the noise has no obvious correlation between scales.

[0058] Based on the above analysis, taking the A-phase voltage signal as an example and taking the j-th modal component a(j) in the second filter component set D2(A) as an example, the corresponding components of the modal component a(j) in the B-phase and C-phase voltage signals are respectively denoted as the modal component b(j) and the modal component c(j). The wavelet decomposition is respectively performed on the modal components a(j), b(j), and c(j) to obtain the wavelet coefficients of all scales of the modal components a(j), b(j), and c(j). In this embodiment, the Daubechies 8 wavelet basis function is used for wavelet decomposition; the number of wavelet decomposition layers M = 4, and the implementer can adjust it according to the actual situation, and this application does not limit it; it should be noted that wavelet decomposition is a well-known technology, and the specific process will not be elaborated.

[0059] Taking the n-th wavelet coefficient a(j,m,n) in the m-th scale a(j,m) of the modal component a(j) as an example, the sequence obtained by arranging the amplitudes of the h wavelet coefficients closest to the sampling time of the wavelet coefficient a(j,m,n) in the m-th scale a(j,m) in ascending order according to the sampling time is denoted as the first sequence L(j,m,n) of the wavelet coefficient a(j,m,n) to characterize the local wavelet coefficient distribution of the wavelet coefficient a(j,m,n). In this embodiment, h is a preset quantity, with a value of 10, and the implementer can adjust it according to the actual situation.

[0060] The mean of the Pearson correlation coefficients between the first sequence L(j, m, n) and the first sequence of the nth wavelet coefficient in the mth scale of the modal components b(j) and c(j) is denoted as the first characteristic factor of the wavelet coefficient a(j, m, n), which is used to characterize the similarity of the local distribution between the wavelet coefficient a(j, m, n) and the nth wavelet coefficient of the same scale in the modal components of other phase voltage signals.

[0061] The mean of the Pearson correlation coefficients between the first sequence L(j, m, n) and the first sequence of the nth wavelet coefficient in each scale of the modal component a(j) is denoted as the second characteristic factor of the wavelet coefficient a(j, m, n), which is used to characterize the correlation degree between the local wavelet coefficient distribution characteristics of the wavelet coefficient a(j, m, n) and the remaining scales of the modal component a(j).

[0062] The product of the amplitude, the first characteristic factor, and the second characteristic factor of the wavelet coefficient a(j, m, n) is denoted as the second eigenvalue of the wavelet coefficient a(j, m, n), which is used to characterize the possibility that the wavelet coefficient a(j, m, n) corresponds to the wavelet coefficient of the useful information in the A-phase voltage signal in the mth scale of the modal component a(j). Among them, the flowchart for obtaining the second eigenvalue is as Figure 2 shown.

[0063] Taking the mth scale a(j, m) as an example, the second eigenvalues of all wavelet coefficients in the mth scale a(j, m) are used as the input of the maximum inter-class variance algorithm, and the second threshold is output. The amplitudes of all wavelet coefficients in the mth scale a(j, m) whose second eigenvalues are less than the second threshold are assigned 0, and the mth scale after reassigning the modal component a(j) is obtained to retain all wavelet coefficients corresponding to the useful information in the A-phase voltage signal in the mth scale of the modal component a(j).

[0064] The wavelet inverse transform is used to reconstruct all scales after reassigning the modal component a(j), and the reconstructed modal component of the jth modal component in the second filter component set D2(A) is obtained to retain the signal components corresponding to the useful information in the A-phase voltage signal in the modal component a(j).

[0065] Extract the spectra of the reconstructed modal components of each modal component in the second filter component set D2(A). Take the maximum frequency and the minimum frequency of the frequencies with amplitudes greater than 0 in each spectrum as the upper and lower limits of the frequency distribution range respectively. Denote the union of the frequency distribution ranges of all modal components as the second union, which is used to characterize the frequency range corresponding to the signal components in the second filter component set D2(A) that are not affected by noise. Use the second union as the passband frequency range of the FIR filter, and use the Parks-McClellan algorithm to implement the design of the optimal FIR filter to obtain the second FIR filter Q2(A) of the phase A voltage signal. In this embodiment, the filter order of the FIR filter is taken as 60. The extraction of the signal spectrum and the Parks-McClellan algorithm are well-known technologies, and the specific process will not be elaborated in this application.

[0066] S4: Control the series-side converter according to the three-phase voltage signals processed by the two FIR filters.

[0067] Use the first FIR filter and the second FIR filter of each phase voltage signal in the three-phase voltage signals to filter each phase voltage signal respectively. Take the filtered three-phase voltage signals as the input of the series-side converter, use the dq transformation to generate the corresponding two-phase voltage values, and obtain the corresponding active current i q and reactive current i d , at this time, use the PI control algorithm to obtain the corresponding command signal, and generate the corresponding pulse drive signal through the dq / αβ transformation to control the action of the series-side converter. Among them, the acquisition of the voltage reference value is as follows: the three-phase voltage signals input in the series-side converter obtain the estimated value θ of the grid voltage angle through the phase-locked loop, and then use the three-phase voltage signals and the grid voltage components u α and u β calculated to obtain the grid-side voltage compensation value, and multiply it by the transformer turns ratio N T to obtain the voltage reference value of the series converter.

[0068] In the series-side converter, this embodiment mainly uses the SPWM pulse width modulation method for control, mainly using the power frequency sine wave and the high-frequency triangular wave for control. Among them, the 50Hz power frequency sine wave is used as the modulation wave, and the 6.25kHz triangular wave is used as the carrier wave. Every 16 sampling points form a carrier wave, and then obtain the square wave array, and use this square wave as the signal to drive the converter to act. When the intersection point of the carrier wave and the modulation wave is the rising edge, the lower arm of the converter conducts and the lower arm closes. When the intersection point is the falling edge, the lower arm conducts and the upper arm turns off.

[0069] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the block may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0070] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A series side converter control method based on dynamic sampling detection, characterized in that: The method comprises the following steps: S1: Obtain the three-phase voltage signal of the line where the series-side converter is located; S2: Perform modal decomposition on each phase voltage signal, obtain the reference component of each modal component of each phase voltage signal in other phase voltage signals based on the frequency characteristics, and obtain the first eigenvalue of each modal component based on the similarity between the spectral characteristics of each modal component and its reference component; perform threshold segmentation on all modal components of all phase voltage signals based on the first eigenvalue to obtain the first filter component set and the second filter component set of each phase voltage signal; obtain the first FIR filter of each phase voltage signal based on the spectral range of each modal component in the first filter component set of each phase voltage signal; S3: Perform wavelet decomposition of a preset scale on each modal component and all its control components in the second filter component set of each phase voltage signal to obtain all wavelet coefficients of each scale; obtain a second eigenvalue based on the local distribution correlation of wavelet coefficients in the same scale in different phase voltage signals and the local distribution correlation of wavelet coefficients in different scales in each phase voltage signal, and determine whether to reassign the amplitude of the wavelet coefficient in each scale of each modal component; reconstruct all scales after the reassignment of each modal component, and obtain a second FIR filter for each phase voltage signal based on the frequency spectrum range of each reconstructed modal component in the second filter component set; S4: Control the series side converter according to the three-phase voltage signal processed by the two FIR filters.

2. The series side converter control method based on dynamic sampling detection according to claim 1, characterized in that: The comparison component of each modal component of each phase voltage signal is obtained by selecting the modal component with the smallest absolute value of difference from the center frequency of each modal component from the modal components in the voltage signals of other phases.

3. The series side converter control method based on dynamic sampling detection according to claim 1, characterized in that: The first eigenvalue of each modal component is obtained as follows: Calculate the waveform similarity and spectrum similarity between each modal component and each of its control components, and record the mean of the waveform similarities between each modal component and all of its control components as the first mean; The mean of the spectral similarities between each modal component and all its control components is recorded as the second mean; The result of forward fusion of the first mean and the second mean corresponding to each modal component is taken as the first eigenvalue of each modal component.

4. The series side converter control method based on dynamic sampling detection according to claim 1, characterized in that: The first filter component set and the second filter component set of each phase voltage signal are obtained as follows: The first eigenvalues ​​of all modal components of all phase voltage signals are used as inputs of a maximum inter-class variance algorithm, and a first threshold value is outputted; The set consisting of all modal components whose first eigenvalue in each phase voltage signal is greater than the first threshold is recorded as the first filter component set of each phase voltage signal; the set consisting of the remaining modal components in each phase voltage signal is recorded as the second filter component set of each phase voltage signal.

5. The series side converter control method based on dynamic sampling detection according to claim 1, characterized in that: The first FIR filter for obtaining each phase voltage signal is specifically: For each modal component in the first filter component set of each phase voltage signal, the maximum frequency and the minimum frequency of the frequencies with amplitudes greater than 0 in each spectrum are respectively used as the upper limit and the lower limit of the frequency distribution range; The union of the frequency distribution ranges of all modal components is used as the passband frequency range of the FIR filter, and the first FIR filter of each phase voltage signal is obtained by combining the Parks-McClellan algorithm.

6. The series side converter control method based on dynamic sampling detection according to claim 1, characterized in that: The specific process of obtaining the second eigenvalue is: Compare the local distribution similarity of each wavelet coefficient of each modal component and its control component at the same scale to obtain the first characteristic factor; Compare the local distribution similarity of each wavelet coefficient of each modal component at different scales to obtain the second characteristic factor; The product of the amplitude of each wavelet coefficient, the first eigenfactor and the second eigenfactor is taken as the second eigenvalue.

7. The series side converter control method based on dynamic sampling detection according to claim 6, characterized in that: The first characteristic factor is obtained as follows: For each wavelet coefficient in each scale, a sequence consisting of the amplitudes of a preset number of wavelet coefficients closest to the sampling time of each wavelet coefficient is recorded as the first sequence of each wavelet coefficient; The mean of the correlation between each modal component and the first sequence of each wavelet coefficient in each scale of all its control components is taken as the first eigenfactor.

8. The series side converter control method based on dynamic sampling detection according to claim 7, characterized in that: The second characteristic factor is specifically the mean value of the correlation degree between each modal component at each scale and the first sequence of each wavelet coefficient in all other scales.

9. The series side converter control method based on dynamic sampling detection according to claim 1, characterized in that: The process of determining whether to re-assign the amplitude of the wavelet coefficient in each scale of each modal component is as follows: Performing threshold segmentation on the second eigenvalues ​​of all wavelet coefficients of each modal component in each scale to obtain a second threshold; The amplitudes of all wavelet coefficients whose second eigenvalues ​​in each scale are less than the second threshold are assigned to 0.

10. The series side converter control method based on dynamic sampling detection according to claim 5, characterized in that: The passband frequency range of the second FIR filter is the union of the frequency spectrum distribution ranges of the modal components reconstructed from each modal component in the second filter component set.

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