Self-learning single-phase phase locking method of power electronic converter and related device

Abstract

The invention discloses a power electronic converter self-learning single-phase phase locking method and a related device, after a power grid voltage signal is converted into a square wave signal, the power grid voltage signal is judged to be a normal signal based on a standard period range and a standard period error, if yes, the power grid voltage signal is added into a historical signal database, and if not, the power grid voltage signal is obtained; based on a historical signal database, a criterion of updating a normal signal is automatically learned, a stable synchronous signal source is fitted, and a stable input signal is provided for the phase-locked loop, so that the output of the phase-locked loop is more stable, large fluctuation caused by abnormal or fault interference of a power grid is avoided, and when the voltage of the power grid abnormally fluctuates or has a fault, the output of the phase-locked loop is ensured to be stable. The phase-locked loop signal is not abnormal or fluctuated, the grid-connected stability of the control system is enhanced, and the technical problem that phase-locked fluctuation of an existing phase-locked loop is likely to be caused by power grid abnormity or fault interference, and consequently instability and overcurrent trawling of a power electronic converter are caused is solved.

Description

Technical Field

[0001] The invention relates to the technical field of phase locking, and in particular to a self-learning single-phase phase locking method for a power electronic converter and a related device. Background Art

[0002] The existing power system is a system composed of generator groups, power grids, power electronic converters and other components. The phase-locked loop is mainly used for synchronization, frequency synthesis and phase detection. The basic components of the phase-locked loop include a phase detector, a low-pass filter and a voltage-controlled oscillator. The phase detector is used to compare the phase difference between the input signal and the reference signal and generate a signal proportional to the phase difference. The low-pass filter is used to filter out the high-frequency components in the output signal of the phase detector to obtain a smooth error signal. The voltage-controlled oscillator is used to adjust the oscillation frequency according to the output signal of the low-pass filter so that it gradually locks to the frequency and phase of the input signal.

[0003] During the operation of the power system, abnormalities or faults may occur, resulting in unstable information, causing phase-locked loop phase fluctuations, or even instability or oscillation, which in turn causes the power electronic converter to become unstable and overcurrent. Therefore, how to improve the stability of the phase-locked loop and avoid phase-locked loop fluctuations caused by abnormal or fault interference in the power grid is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the invention

[0004] The present invention provides a self-learning single-phase phase-locking method and related device for a power electronic converter, which is used to solve the technical problem that the existing phase-locked loop is prone to phase-locked fluctuations due to abnormal power grid or fault interference, resulting in instability and overcurrent of the power electronic converter.

[0005] In view of this, a first aspect of the present invention provides a self-learning single-phase phase-locking method for a power electronic converter, comprising the following steps:

[0006] S1, converting the grid voltage signal into a square wave signal;

[0007] S2. According to the square wave signal, based on the standard cycle range and the standard cycle error, determine whether the grid voltage signal is abnormal. If so, save the grid voltage signal to the abnormal signal database; if not, save the grid voltage signal to the historical signal database, and execute steps S3 and S4;

[0008] S3, intercepting signal data of a first preset length from all signal samples in the historical signal database, determining a positive and negative error range of the signal data according to the intercepted data, and updating the standard periodic error according to the positive and negative error range;

[0009] S4, extracting signal data of a second preset length from the historical signal database at the most recent time, calculating a linear weighted period value and performing phase synchronization, obtaining a grid voltage signal after phase synchronization, and executing step S5;

[0010] S5, determining whether to use the grid voltage signal as the output signal or the grid voltage signal after phase synchronization as the output signal according to the positive and negative error range, and executing step S6;

[0011] S6. Perform phase correction on the output signal and input the output signal into a phase-locked loop.

[0012] Optionally, step S2 specifically includes:

[0013] For a square wave signal with a discontinuous period and a square wave signal with the first period of a continuous period, if both fall within the standard period range and the standard period error, it is judged that the grid voltage signal in the corresponding period is normal; otherwise, it is judged that the grid voltage signal in the corresponding period is abnormal;

[0014] For a non-first periodic square wave signal with continuous cycles, if the previous periodic signal data of the current periodic signal data is a normal signal, then if the continuous periodic variation of the current periodic signal data compared with the previous periodic signal data is less than the preset time length, then the current periodic signal data is judged to be a normal signal; if the continuous periodic variation is not less than the preset time length, then the current periodic signal data is judged to be an abnormal signal; if the previous periodic signal data is an abnormal signal, then if the continuous periodic variation of the current periodic signal data compared with the previous periodic signal data is less than the preset time length + dT, then the current periodic signal data is judged to be a normal signal; if the continuous periodic variation is not less than the preset time length + dT, then the current periodic signal data is judged to be an abnormal signal, wherein dT is the standard periodic error.

[0015] Optionally, step S3 specifically includes:

[0016] Based on all signal samples in the historical signal database, first sliding window data of a first preset length is intercepted, and a first linear weighted period value of each signal sample is calculated according to the first sliding window data. The positive and negative error range is determined according to the average error of the first linear weighted period values ​​of all signal samples, and the standard period error is updated according to the positive and negative error range.

[0017] Optionally, step S4 specifically includes:

[0018] Signal data of a second preset length is intercepted from the historical signal database at the most recent time to obtain second sliding window data, a second linear weighted period value of the second sliding window data is calculated, and the second sliding window data is phase synchronized to obtain the grid voltage signal after phase synchronization, and step S5 is executed.

[0019] Optionally, step S5 specifically includes:

[0020] Perform edge identification on the grid voltage signal and the grid voltage signal after phase synchronization. If the edge error between the grid voltage signal and the grid voltage signal after phase synchronization is within the positive and negative error range, use the grid voltage signal as the output signal and execute step S6. Otherwise, use the grid voltage signal after phase synchronization as the output signal and execute step S6.

[0021] Optionally, the calculation formulas of the first linear weighted period value and the second linear weighted period value are:

[0022]

[0023] in, is the linear weighted period value, is the phase value of n moments arranged in reverse time order, for One-to-one corresponding period weight values, The value decreases linearly between [0,1].

[0024] A second aspect of the present invention provides a self-learning single-phase phase-locking device for a power electronic converter, comprising the following modules:

[0025] A signal conversion module, used for converting a grid voltage signal into a square wave signal;

[0026] A judgment module, used for judging whether the grid voltage signal is abnormal according to the square wave signal, based on the standard cycle range and the standard cycle error, and if so, saving the grid voltage signal to the abnormal signal database; if not, saving the grid voltage signal to the historical signal database, and jumping to the criterion self-learning update module and the cycle weighting module;

[0027] The criterion self-learning update module is used to intercept signal data of a first preset length from all signal samples in the historical signal database, determine the positive and negative error range of the signal data according to the intercepted data, and update the standard periodic error according to the positive and negative error range;

[0028] The cycle weighting module is used to extract the signal data of the second preset length from the historical signal database at the most recent time, calculate the linear weighted cycle value and perform phase synchronization, obtain the grid voltage signal after phase synchronization, and jump to the signal output preprocessing module;

[0029] The signal output preprocessing module is used to determine whether to use the grid voltage signal as the output signal or the grid voltage signal after phase synchronization as the output signal according to the positive and negative error range, and jump to the output module;

[0030] The output module is used to perform phase correction on the output signal and input the output signal into a phase-locked loop.

[0031] Optionally, the judging module is specifically used for:

[0032] For a square wave signal with a discontinuous period and a square wave signal with the first period of a continuous period, if both fall within the standard period range and the standard period error, it is judged that the grid voltage signal in the corresponding period is normal; otherwise, it is judged that the grid voltage signal in the corresponding period is abnormal;

[0033] For a non-first periodic square wave signal with continuous cycles, if the previous periodic signal data of the current periodic signal data is a normal signal, then if the continuous periodic variation of the current periodic signal data compared with the previous periodic signal data is less than the preset time length, then the current periodic signal data is judged to be a normal signal; if the continuous periodic variation is not less than the preset time length, then the current periodic signal data is judged to be an abnormal signal; if the previous periodic signal data is an abnormal signal, then if the continuous periodic variation of the current periodic signal data compared with the previous periodic signal data is less than the preset time length + dT, then the current periodic signal data is judged to be a normal signal; if the continuous periodic variation is not less than the preset time length + dT, then the current periodic signal data is judged to be an abnormal signal, wherein dT is the standard periodic error.

[0034] Optionally, the criterion self-learning update module is specifically used for:

[0035] Based on all signal samples in the historical signal database, first sliding window data of a first preset length is intercepted, and a first linear weighted period value of each signal sample is calculated according to the first sliding window data. The positive and negative error range is determined according to the average error of the first linear weighted period values ​​of all signal samples, and the standard period error is updated according to the positive and negative error range.

[0036] Optionally, the period weighting module is specifically used for:

[0037] Signal data of a second preset length is intercepted from the historical signal database at the most recent time to obtain second sliding window data, a second linear weighted period value of the second sliding window data is calculated, and the second sliding window data is phase synchronized to obtain the grid voltage signal after phase synchronization, and jump to the signal output preprocessing module.

[0038] Optionally, the signal output preprocessing module is specifically used for:

[0039] Edge identification is performed on the grid voltage signal and the grid voltage signal after phase synchronization. If the edge error between the grid voltage signal and the grid voltage signal after phase synchronization is within the positive and negative error range, the grid voltage signal is used as the output signal and jumps to the output module. Otherwise, the grid voltage signal after phase synchronization is used as the output signal and jumps to the output module.

[0040] Optionally, the calculation formulas of the first linear weighted period value and the second linear weighted period value are:

[0041]

[0042] in, is the linear weighted period value, is the phase value of n moments arranged in reverse time order, for One-to-one corresponding period weight values, The value decreases linearly between [0,1].

[0043] Optionally, the first preset length is 30s.

[0044] Optionally, the second preset length is 5s.

[0045] A third aspect of the present invention provides a self-learning single-phase phase-locking device for a power electronic converter, the device comprising a processor and a memory:

[0046] The memory is used to store program codes and transmit the program codes to the processor;

[0047] The processor is used to execute the self-learning single-phase phase-locking method for a power electronic converter according to any one of the first aspects according to the instructions in the program code.

[0048] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the self-learning single-phase phase-locking method for a power electronic converter as described in any one of the first aspects.

[0049] It can be seen from the above technical solutions that the self-learning single-phase phase-locking method for power electronic converter provided by the present invention has the following advantages:

[0050] The self-learning single-phase phase-locked method for a power electronic converter provided by the present invention converts a power grid voltage signal into a square wave signal, and judges whether the power grid voltage signal is a normal signal based on a standard cycle range and a standard cycle error. If so, it is added to a historical signal database, and the judgment criteria of the normal signal are automatically learned and updated based on the historical signal database, and a stable synchronization signal source is fitted to provide a stable input signal to a phase-locked loop, so that the output of the phase-locked loop is more stable and will not be greatly fluctuated by abnormal power grid or fault interference, and ensure that when the power grid voltage fluctuates abnormally or fails, the phase-locked loop signal is free of abnormalities and fluctuations, thereby enhancing the stability of the control system connected to the grid, and solving the technical problem that the existing phase-locked loop is prone to phase-locked fluctuations due to abnormal power grid or fault interference, resulting in instability and overcurrent dragging of the power electronic converter. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 A schematic diagram of a flow chart of a self-learning single-phase phase-locking method for a power electronic converter provided in the present invention;

[0053] Figure 2 A logic diagram of a self-learning single-phase phase-locking method for a power electronic converter provided in the present invention;

[0054] Figure 3 The present invention is a schematic structural diagram of a self-learning single-phase phase-locking device for a power electronic converter. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] For easier understanding, see Figure 1 and Figure 2The present invention provides an embodiment of a self-learning single-phase phase-locking method for a power electronic converter, comprising:

[0057] Step S1, converting the grid voltage signal into a square wave signal.

[0058] It should be noted that, in the present invention, the grid voltage signal is first processed, the grid voltage signal is filtered (a 500Hz low-pass filter can be used to filter out high-frequency signals and DC signals) and compared with hysteresis to generate a square wave signal with a duty cycle of 50%. This step will cause a signal delay Tdelay.

[0059] Step S2: According to the square wave signal, based on the standard cycle range and the standard cycle error, determine whether the grid voltage signal is abnormal. If so, save the grid voltage signal to the abnormal signal database; if not, save the grid voltage signal to the historical signal database and execute steps S3 and S4.

[0060] It should be noted that the stability of the phase-locked loop comes from the signal source and phase-locked loop technology. When applied to the power system, under normal circumstances, the source signal of the power generator system is relatively stable. For example, the power generator system frequency is 48~51.5Hz in the normal range, and the frequency change rate does not exceed 0.2Hz / s. 48~51.5Hz corresponds to the standard cycle range Tavg, and dT is the standard cycle error. For square wave signals with discontinuous periods and the first period square wave signal with continuous periods, if the period value is within the standard period range and the standard period error (i.e. [Tavg-dT, Tavg+dT]), the signal within the period is considered normal. If it exceeds the standard period range and the standard period error (i.e. [Tavg-dT, Tavg+dT]), the signal within the period is considered abnormal. For a non-first periodic square wave signal with a continuous period, if the previous periodic signal data of the current periodic signal data is a normal signal, then the current periodic signal data is compared with the previous periodic signal data, if the continuous periodic change is less than the preset time length, then the current periodic signal data is judged to be a normal signal, if the continuous periodic change is not less than the preset time length, then the current periodic signal data is judged to be an abnormal signal, if the previous periodic signal data is an abnormal signal, then the current periodic signal data is compared with the previous periodic signal data, if the continuous periodic change is less than the preset time length + dT, then the current periodic signal data is judged to be a normal signal, if the continuous periodic change is not less than the preset time length + dT, then the current periodic signal data is judged to be an abnormal signal. In the present invention, the preset time length is 1.6µs.

[0061] If the grid voltage signal is judged to be an abnormal signal, the grid voltage signal is saved in the abnormal signal database for use as a basis for abnormal signal analysis. If the grid voltage signal is judged to be a normal signal, the grid voltage signal is saved in the historical signal database. Among them, a high-frequency pulse with a frequency of fz can be used to sample the signal to record the value of each cycle and the frequency value of the calculated signal, and save them. To ensure the calculation accuracy, fz>10Mhz is required, and the sampling error is approximately calculated = 1 / fz / 20ms. Then execute steps S3 and S4.

[0062] Step S3, intercepting signal data of a first preset length from all signal samples in the historical signal database, determining the positive and negative error range of the signal data according to the intercepted data, and updating the standard periodic error according to the positive and negative error range.

[0063] It should be noted that all signal samples in the historical signal database are extracted, the first sliding window data of the first preset length is intercepted, and the first linear weighted period value of each signal sample is calculated. The first preset length is 30s, that is, the signal samples of the 30s learning time sliding window are intercepted from all signal samples in the historical signal database. Exemplarily, there are about 1500 signal period data samples within 30s. When calculating the first linear weighted period value of the first sliding window data, the closer the period value is to the current moment, the greater the weight ratio is, and the farther the historical period value is from the current moment, the smaller the weight ratio is. The calculation formula for the first linear weighted period value is:

[0064]

[0065] in, is the linear weighted period value, is the phase value of n moments arranged in reverse time order, for One-to-one corresponding period weight values, The value decreases linearly between [0,1].

[0066] Calculate the first linear weighted period value error average of all signal samples, determine the positive and negative error range as [-2×dT0, +2×dT0], dT0 is the error average, and update the standard period error dT according to the positive and negative error range.

[0067] Step S4, extracting signal data of a second preset length from the historical signal database at the most recent time, calculating a linear weighted period value and performing phase synchronization, obtaining a grid voltage signal after phase synchronization, and executing step S5.

[0068] It should be noted that a preset number of signal samples of a second preset length are intercepted from the historical signal database at the most recent time to obtain the second sliding window data. The second preset length is 5s. Then the second linear weighted period value of the second sliding window data is calculated. The calculation formula of the second linear weighted period value is the same as the calculation formula of the first linear weighted period value. The grid voltage signal is phase synchronized based on the second linear weighted period value, so that the grid voltage signal after phase synchronization can be obtained.

[0069] Step S5, determine whether to use the grid voltage signal as the output signal or the grid voltage signal after phase synchronization as the output signal according to the positive and negative error range, and execute step S6.

[0070] It should be noted that the grid voltage signal in step S1 and the grid voltage signal after phase synchronization in step S4 are synthesized, and then edge identification is performed. If the edge error between the grid voltage signal and the grid voltage signal after phase synchronization is within ±dT, the grid voltage signal in step S1 is used as the output signal, and step S6 is executed; if the edge error between the grid voltage signal and the grid voltage signal after phase synchronization is not within ±dT, the grid voltage signal after phase synchronization obtained in step S4 is used as the output signal, and step S6 is executed. The execution of step S5 will cause a signal delay of dT.

[0071] Step S6: perform phase correction on the output signal and input the output signal into a phase-locked loop.

[0072] It should be noted that the output signal output in step S5 is phase-corrected, that is, the signal delay Tdelay caused in step S1 and the signal delay dT in step S5 are corrected. Then the output signal is input into a phase-locked loop as a signal source. The phase-locked loop can be a synchronous reference frame phase-locked loop (SRF-PLL), an enhanced phase-locked loop (EPLL), a phase-locked loop based on Clarke transform, or a second order generalized integrator phase-locked loop (SOGI-PLL).

[0073] The self-learning single-phase phase-locked method for a power electronic converter provided by the present invention converts a power grid voltage signal into a square wave signal, and judges whether the power grid voltage signal is a normal signal based on a standard cycle range and a standard cycle error. If so, it is added to a historical signal database, and the judgment criteria of the normal signal are automatically learned and updated based on the historical signal database, and a stable synchronization signal source is fitted to provide a stable input signal to a phase-locked loop, so that the output of the phase-locked loop is more stable and will not be greatly fluctuated by abnormal power grid or fault interference, and ensure that when the power grid voltage fluctuates abnormally or fails, the phase-locked loop signal is free of abnormalities and fluctuations, thereby enhancing the stability of the control system connected to the grid, and solving the technical problem that the existing phase-locked loop is prone to phase-locked fluctuations due to abnormal power grid or fault interference, resulting in instability and overcurrent dragging of the power electronic converter.

[0074] For easier understanding, see Figure 3 The present invention provides an embodiment of a self-learning single-phase phase-locking device for a power electronic converter, comprising the following modules:

[0075] A signal conversion module, used for converting a grid voltage signal into a square wave signal;

[0076] A judgment module is used to judge whether the grid voltage signal is abnormal based on the square wave signal, the standard cycle range and the standard cycle error. If so, the grid voltage signal is saved in the abnormal signal database; if not, the grid voltage signal is saved in the historical signal database and jumps to the judgment criterion self-learning update module and the cycle weighting module;

[0077] A criterion self-learning update module is used to intercept signal data of a first preset length from all signal samples in a historical signal database, determine a positive and negative error range of the signal data according to the intercepted data, and update a standard periodic error according to the positive and negative error range;

[0078] A cycle weighting module is used to extract signal data of a second preset length from a historical signal database at the most recent time, calculate a linear weighted cycle value and perform phase synchronization, obtain a grid voltage signal after phase synchronization, and jump to a signal output preprocessing module;

[0079] A signal output preprocessing module is used to determine whether to use the grid voltage signal as the output signal or the grid voltage signal after phase synchronization as the output signal according to the positive and negative error range, and jump to the output module;

[0080] The output module is used to perform phase correction on the output signal and input the output signal into a phase-locked loop.

[0081] In one embodiment, the determination module is specifically used for:

[0082] For the square wave signal with discontinuous period and the first period square wave signal with continuous period, if both fall within the standard period range and the standard period error, the grid voltage signal in the corresponding period is judged to be normal; otherwise, the grid voltage signal in the corresponding period is judged to be abnormal;

[0083] For a non-first periodic square wave signal with continuous cycles, if the signal data of the previous cycle of the current cycle signal data is a normal signal, then the current cycle signal data is compared with the previous cycle signal data, if the continuous cycle change is less than the preset time length, then the current cycle signal data is judged to be a normal signal; if the continuous cycle change is not less than the preset time length, then the current cycle signal data is judged to be an abnormal signal; if the previous cycle signal data is an abnormal signal, then the current cycle signal data is compared with the previous cycle signal data, if the continuous cycle change is less than the preset time length + dT, then the current cycle signal data is judged to be a normal signal; if the continuous cycle change is not less than the preset time length + dT, then the current cycle signal data is judged to be an abnormal signal, where dT is the standard cycle error.

[0084] In one embodiment, the criterion self-learning update module is specifically used for:

[0085] Based on all signal samples in the historical signal database, the first sliding window data of the first preset length is intercepted, and the first linear weighted period value of each signal sample is calculated according to the first sliding window data. The positive and negative error range is determined according to the average error of the first linear weighted period value of all signal samples, and the standard period error is updated according to the positive and negative error range.

[0086] In one embodiment, the period weighting module is specifically used for:

[0087] Signal data of a second preset length is intercepted from the historical signal database at the most recent time to obtain second sliding window data, a second linear weighted period value of the second sliding window data is calculated, and the second sliding window data is phase synchronized to obtain the grid voltage signal after phase synchronization, and jump to the signal output preprocessing module.

[0088] In one embodiment, the signal output preprocessing module is specifically used for:

[0089] The grid voltage signal and the grid voltage signal after phase synchronization are edge identified. If the edge error between the grid voltage signal and the grid voltage signal after phase synchronization is within the positive and negative error range, the grid voltage signal is used as the output signal and jumps to the output module. Otherwise, the grid voltage signal after phase synchronization is used as the output signal and jumps to the output module.

[0090] In one embodiment, the calculation formula of the first linear weighted period value and the second linear weighted period value is:

[0091]

[0092] in, is the linear weighted period value, is the phase value of n moments arranged in reverse time order, for One-to-one corresponding period weight values, The value decreases linearly between [0,1].

[0093] In one embodiment, the first preset length is 30 seconds.

[0094] In one embodiment, the second preset length is 5 seconds.

[0095] The present invention provides an embodiment of a self-learning single-phase phase-locking device for a power electronic converter, the device comprising a processor and a memory:

[0096] The memory is used to store the program code and transmit the program code to the processor;

[0097] The processor is used to execute any one of the power electronic converter self-learning single-phase phase-locking methods provided in the present invention according to the instructions in the program code.

[0098] The present invention also provides an embodiment of a computer-readable storage medium, which is used to store program code, and the program code is used to execute any implementation of any power electronic converter self-learning single-phase phase-locking method provided in the present invention.

[0099] An embodiment of the present application also provides a computer program product including instructions, which, when executed on a computer, enables the computer to execute any one of the implementation methods of a self-learning single-phase phase lock of a power electronic converter described in the aforementioned embodiments.

[0100] The power electronic converter self-learning single-phase phase-locked method device, equipment, computer-readable storage medium and computer program product including instructions provided in the present invention are all used to execute the power electronic converter self-learning single-phase phase-locked method provided in the present invention. The principle and technical effects achieved are the same as those of the power electronic converter self-learning single-phase phase-locked method provided in the present invention, and will not be repeated here.

[0101] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0102] The terms "first", "second", etc. in the present invention are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0103] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A self-learning single-phase phase-locking method for a power electronic converter, characterized in that: The following steps are involved: S1, converting the grid voltage signal into a square wave signal; S2. According to the square wave signal, based on the standard cycle range and the standard cycle error, determine whether the grid voltage signal is abnormal. If so, save the grid voltage signal to the abnormal signal database; if not, save the grid voltage signal to the historical signal database, and execute steps S3 and S4; S3, intercepting signal data of a first preset length from all signal samples in the historical signal database, determining a positive and negative error range of the signal data according to the intercepted data, and updating the standard periodic error according to the positive and negative error range; S4, extracting signal data of a second preset length from the historical signal database at the most recent time, calculating a linear weighted period value and performing phase synchronization, obtaining a grid voltage signal after phase synchronization, and executing step S5; S5, determining whether to use the grid voltage signal as the output signal or the grid voltage signal after phase synchronization as the output signal according to the positive and negative error range, and executing step S6; S6. Perform phase correction on the output signal and input the output signal into a phase-locked loop.

2. The self-learning single-phase phase-locking method for power electronic converter according to claim 1, characterized in that: Step S2 specifically includes: For a square wave signal with a discontinuous period and a square wave signal with the first period of a continuous period, if both fall within the standard period range and the standard period error, it is judged that the grid voltage signal in the corresponding period is normal; otherwise, it is judged that the grid voltage signal in the corresponding period is abnormal; For a non-first periodic square wave signal with continuous cycles, if the previous periodic signal data of the current periodic signal data is a normal signal, then if the continuous periodic variation of the current periodic signal data compared with the previous periodic signal data is less than the preset time length, then the current periodic signal data is judged to be a normal signal; if the continuous periodic variation is not less than the preset time length, then the current periodic signal data is judged to be an abnormal signal; if the previous periodic signal data is an abnormal signal, then if the continuous periodic variation of the current periodic signal data compared with the previous periodic signal data is less than the preset time length + dT, then the current periodic signal data is judged to be a normal signal; if the continuous periodic variation is not less than the preset time length + dT, then the current periodic signal data is judged to be an abnormal signal, wherein dT is the standard periodic error.

3. The self-learning single-phase phase-locking method for power electronic converter according to claim 1, characterized in that: Step S3 specifically includes: Based on all signal samples in the historical signal database, first sliding window data of a first preset length is intercepted, and a first linear weighted period value of each signal sample is calculated according to the first sliding window data. The positive and negative error range is determined according to the average error of the first linear weighted period values ​​of all signal samples, and the standard period error is updated according to the positive and negative error range.

4. The self-learning single-phase phase-locking method for power electronic converter according to claim 3, characterized in that: Step S4 specifically includes: Signal data of a second preset length is intercepted from the historical signal database at the most recent time to obtain second sliding window data, a second linear weighted period value of the second sliding window data is calculated, and the second sliding window data is phase synchronized to obtain the grid voltage signal after phase synchronization, and step S5 is executed.

5. The self-learning single-phase phase-locking method for power electronic converter according to claim 1, characterized in that: Step S5 specifically includes: Perform edge identification on the grid voltage signal and the grid voltage signal after phase synchronization. If the edge error between the grid voltage signal and the grid voltage signal after phase synchronization is within the positive and negative error range, use the grid voltage signal as the output signal and execute step S6. Otherwise, use the grid voltage signal after phase synchronization as the output signal and execute step S6.

6. The self-learning single-phase phase-locking method for power electronic converter according to claim 4, characterized in that: The calculation formulas of the first linear weighted period value and the second linear weighted period value are: in, is the linear weighted period value, is the phase value of n moments arranged in reverse time order, for One-to-one corresponding period weight values, The value decreases linearly between [0,1].

7. A self-learning single-phase phase-locking device for a power electronic converter, characterized in that: Includes the following modules: A signal conversion module, used for converting a grid voltage signal into a square wave signal; A judgment module, used for judging whether the grid voltage signal is abnormal according to the square wave signal, based on the standard cycle range and the standard cycle error, and if so, saving the grid voltage signal to the abnormal signal database; if not, saving the grid voltage signal to the historical signal database, and jumping to the criterion self-learning update module and the cycle weighting module; The criterion self-learning update module is used to intercept signal data of a first preset length from all signal samples in the historical signal database, determine the positive and negative error range of the signal data according to the intercepted data, and update the standard periodic error according to the positive and negative error range; The cycle weighting module is used to extract the signal data of the second preset length from the historical signal database at the most recent time, calculate the linear weighted cycle value and perform phase synchronization, obtain the grid voltage signal after phase synchronization, and jump to the signal output preprocessing module; The signal output preprocessing module is used to determine whether to use the grid voltage signal as the output signal or the grid voltage signal after phase synchronization as the output signal according to the positive and negative error range, and jump to the output module; The output module is used to perform phase correction on the output signal and input the output signal into a phase-locked loop.

8. The self-learning single-phase phase-locking device for power electronic converter according to claim 7, characterized in that: The calculation formulas of the first linear weighted period value and the second linear weighted period value are: in, is the linear weighted period value, is the phase value of n moments arranged in reverse time order, for One-to-one corresponding period weight values, The value decreases linearly between [0,1].

9. A self-learning single-phase phase-locking device for power electronic converter, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the self-learning single-phase phase-locking method for a power electronic converter according to any one of claims 1 to 7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program codes, and the program codes are used to execute the self-learning single-phase phase-locking method for a power electronic converter according to any one of claims 1 to 7.

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