A self-learning single-phase phase-locked loop method and related device for power electronic converters

Abstract

This invention discloses a self-learning single-phase phase-locked loop (PLL) method and related apparatus for power electronic converters. After converting the grid voltage signal into a square wave signal, the method determines whether the grid voltage signal is normal based on the standard period range and standard period error. If so, it is added to the historical signal database. Based on the historical signal database, the method automatically learns and updates the criteria for normal signals and fits a stable synchronization signal source to provide a stable input signal to the PLL, making the PLL output more stable and preventing large fluctuations caused by grid anomalies or faults. This ensures that the PLL signal is normal and unfluctuating even when the grid voltage fluctuates abnormally or there is a fault, thus enhancing the grid connection stability of the control system. This invention solves the technical problem that existing PLLs are prone to phase-locked loop fluctuations caused by grid anomalies or faults, leading to instability, overcurrent, and grid tethering of the power electronic converter.

Description

Technical Field

[0001] This invention relates to the field of phase-locked loop technology, and in particular to a self-learning single-phase phase-locked loop method and related apparatus for power electronic converters. Background Technology

[0002] Existing power systems consist of generator groups, power grids, and power electronic converters. Phase-locked loops (PLLs) are primarily used for synchronization, frequency synthesis, and phase detection. The basic components of a PLL include a phase detector, a low-pass filter, and a voltage-controlled oscillator (VCO). The phase detector compares the phase difference between the input signal and a reference signal and generates a signal proportional to the phase difference. The low-pass filter removes high-frequency components from the phase detector's output signal, resulting in a smooth error signal. The VCO adjusts its oscillation frequency based on the low-pass filter's output signal, gradually locking it to the frequency and phase of the input signal.

[0003] During the operation of a power system, anomalies or faults may occur, resulting in unstable information that causes phase-locked loop (PLL) fluctuations, or even instability or oscillations, leading to power electronic converter instability, overcurrent, and grid tethering. Therefore, improving the stability of the PLL and avoiding phase-locked loop fluctuations caused by grid anomalies or faults is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] This invention provides a self-learning single-phase phase-locked loop method and related apparatus for power electronic converters, which solves the technical problem that existing phase-locked loops are prone to phase-locked loop fluctuations due to grid anomalies or fault interference, leading to instability, overcurrent, and grid tethering of power electronic converters.

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

[0006] S1. Convert the grid voltage signal into a square wave signal;

[0007] S2. Based on the square wave signal, determine whether the grid voltage signal is abnormal based on the standard period range and standard period error. If it is, save the grid voltage signal to the abnormal signal database. If not, save the grid voltage signal to the historical signal database. Then execute steps S3 and S4.

[0008] S3. Extract 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 based on the extracted data, and update the standard period error based on the positive and negative error range.

[0009] S4. Extract signal data of the second preset length from the historical signal database according to the most recent time, calculate the linear weighted period value and perform phase synchronization to obtain the phase-synchronized grid voltage signal, and then execute step S5.

[0010] S5. Determine whether to use the grid voltage signal or the phase-synchronized grid voltage signal as the output signal based on the positive and negative error range, and then proceed to step S6.

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

[0012] Optionally, step S2 specifically includes:

[0013] For both discontinuous square wave signals 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 within the corresponding period is judged to be normal; otherwise, the grid voltage signal within the corresponding period is judged to be abnormal.

[0014] For a non-first-cycle square wave signal with continuous periodicity, if the previous cycle signal data of the current cycle signal data is a normal signal, then compared with the previous cycle signal data, if the continuous periodic change of the current cycle signal data is less than a preset duration, then the current cycle signal data is determined to be a normal signal; if the continuous periodic change is not less than the preset duration, then the current cycle signal data is determined to be an abnormal signal. If the previous cycle signal data is an abnormal signal, then compared with the previous cycle signal data, if the continuous periodic change of the current cycle signal data is less than a preset duration + dT, then the current cycle signal data is determined to be a normal signal; if the continuous periodic change is not less than the preset duration + dT, then the current cycle signal data is determined to be an abnormal signal, where dT is the standard periodic error.

[0015] Optionally, step S3 specifically includes:

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

[0017] Optionally, step S4 specifically includes:

[0018] The signal data of the second preset length is extracted from the historical signal database according to the most recent time to obtain the second sliding window data. The second linear weighted period value of the second sliding window data is calculated, and the phase of the second sliding window data is synchronized to obtain the phase-synchronized grid voltage signal. Then, step S5 is executed.

[0019] Optionally, step S5 specifically includes:

[0020] Edge discrimination is performed on the grid voltage signal and the phase-synchronized grid voltage signal. If the edge error of the grid voltage signal and the phase-synchronized grid voltage signal is within the positive and negative error range, the grid voltage signal is used as the output signal and step S6 is executed. Otherwise, the phase-synchronized grid voltage signal is used as the output signal and step S6 is executed.

[0021] Optionally, the formulas for calculating the first linearly weighted periodic value and the second linearly weighted periodic value are as follows:

[0022]

[0023] in, The periodic values ​​are linearly weighted. These are the phase values ​​of n moments arranged in reverse chronological order. for One-to-one corresponding periodic weight value, The value decreases linearly between [0,1].

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

[0025] The signal conversion module is used to convert the mains voltage signal into a square wave signal;

[0026] The judgment module is used to determine whether the grid voltage signal is abnormal based on the square wave signal, the standard period range and the standard period error. If it is abnormal, the grid voltage signal is saved to the abnormal signal database. If it is abnormal, the grid voltage signal is saved to the historical signal database, and the process jumps to the criterion self-learning update module and the period weighting module.

[0027] The criterion self-learning update module is used to extract 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 based on the extracted data, and update the standard period error based on the positive and negative error range.

[0028] The period weighting module is used to extract signal data of a second preset length from the historical signal database according to the most recent time, calculate the linear weighting period value, and perform phase synchronization to obtain the phase-synchronized grid voltage signal, and then jump to the signal output preprocessing module.

[0029] The signal output preprocessing module is used to determine, based on the positive and negative error range, whether to use the grid voltage signal or the phase-synchronized grid voltage signal as the output signal, and then switch to the output module.

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

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

[0032] For both discontinuous square wave signals 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 within the corresponding period is judged to be normal; otherwise, the grid voltage signal within the corresponding period is judged to be abnormal.

[0033] For a non-first-cycle square wave signal with continuous periodicity, if the previous cycle signal data of the current cycle signal data is a normal signal, then compared with the previous cycle signal data, if the continuous periodic change of the current cycle signal data is less than a preset duration, then the current cycle signal data is determined to be a normal signal; if the continuous periodic change is not less than the preset duration, then the current cycle signal data is determined to be an abnormal signal. If the previous cycle signal data is an abnormal signal, then compared with the previous cycle signal data, if the continuous periodic change of the current cycle signal data is less than a preset duration + dT, then the current cycle signal data is determined to be a normal signal; if the continuous periodic change is not less than the preset duration + dT, then the current cycle signal data is determined to be an abnormal signal, where 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, a first sliding window of data of a first preset length is extracted. The first linear weighted period value of each signal sample is calculated based on the first sliding window data. The positive and negative error range is determined based on the average error of the first linear weighted period values ​​of all signal samples. The standard period error is updated based on the positive and negative error range.

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

[0037] The signal data of the second preset length is extracted from the historical signal database according to the most recent time to obtain the second sliding window data. The second linear weighted period value of the second sliding window data is calculated, and the phase of the second sliding window data is synchronized to obtain the phase-synchronized grid voltage signal. Then, the signal output preprocessing module is switched to the signal output preprocessing module.

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

[0039] Edge discrimination is performed on the grid voltage signal and the phase-synchronized grid voltage signal. If the edge error of the grid voltage signal and the phase-synchronized grid voltage signal is within the positive and negative error range, the grid voltage signal is used as the output signal and the output module is switched. Otherwise, the phase-synchronized grid voltage signal is used as the output signal and the output module is switched.

[0040] Optionally, the formulas for calculating the first linearly weighted periodic value and the second linearly weighted periodic value are as follows:

[0041]

[0042] in, The periodic values ​​are linearly weighted. These are the phase values ​​of n moments arranged in reverse chronological order. for One-to-one corresponding periodic weight value, The value decreases linearly between [0,1].

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

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

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

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

[0047] The processor is used to execute the self-learning single-phase lock-in method for power electronic converters according to the instructions in the program code, as described in any of the first aspects.

[0048] A fourth aspect of the present invention provides a computer-readable storage medium for storing program code for executing the self-learning single-phase lock-in method for power electronic converters according to any one of the first aspects.

[0049] As can be seen from the above technical solutions, the self-learning single-phase lock-in method for power electronic converters provided by this invention has the following advantages:

[0050] The self-learning single-phase lock-in method for power electronic converters provided by this invention converts the grid voltage signal into a square wave signal. Based on the standard period range and standard period error, it determines whether the grid voltage signal is a normal signal. If so, it adds it to the historical signal database. Based on the historical signal database, it automatically learns and updates the criteria for normal signals and fits a stable synchronization signal source to provide a stable input signal to the phase-locked loop (PLL). This makes the PLL output more stable and prevents large fluctuations caused by grid anomalies or faults. It ensures that the PLL signal is normal and unfluctuating when the grid voltage fluctuates abnormally or there is a fault, thus enhancing the grid connection stability of the control system. This solves the technical problem that existing PLLs are prone to phase-locking fluctuations caused by grid anomalies or faults, leading to instability, overcurrent, and grid tethering of the power electronic converter. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart illustrating a self-learning single-phase lock-in method for a power electronic converter provided in this invention.

[0053] Figure 2 This is a logic diagram of a self-learning single-phase lock-in method for a power electronic converter provided in this invention;

[0054] Figure 3 This is a schematic diagram of the structure of a self-learning single-phase phase-locked device for a power electronic converter provided in this invention. Detailed Implementation

[0055] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] For easier understanding, please refer to Figure 1 and Figure 2This invention provides an embodiment of a self-learning single-phase lock-in method for power electronic converters, comprising:

[0057] Step S1: Convert the grid voltage signal into a square wave signal.

[0058] It should be noted that in this invention, the grid voltage signal is first processed by filtering (using a 500Hz low-pass filter to remove high-frequency signals and DC signals) and comparing with hysteresis to generate a square wave signal with a 50% duty cycle. This step will cause a signal delay Tdelay.

[0059] Step S2: Based on the square wave signal, determine whether the grid voltage signal is abnormal according to the standard period range and standard period error. If so, save the grid voltage signal to the abnormal signal database; otherwise, 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 a phase-locked loop (PLL) comes from the signal source and the PLL technology. When applied to a power system, under normal circumstances, the source signal of the power generator system is relatively stable. For example, within the normal range, the frequency of the power generator system is 48~51.5Hz, and the frequency change rate does not exceed 0.2Hz / s. 48~51.5Hz corresponds to the standard period range Tavg, and dT is the standard period error. For square wave signals with discontinuous periods and square wave signals 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 that 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 that period is considered abnormal. For a non-first-cycle square wave signal with continuous periodicity, if the previous cycle signal data of the current cycle signal data is a normal signal, then compared with the previous cycle signal data, if the continuous periodic change of the current cycle signal data is less than a preset duration, then the current cycle signal data is determined to be a normal signal; if the continuous periodic change is not less than the preset duration, then the current cycle signal data is determined to be an abnormal signal. If the previous cycle signal data is an abnormal signal, then compared with the previous cycle signal data, if the continuous periodic change of the current cycle signal data is less than a preset duration + dT, then the current cycle signal data is determined to be a normal signal; if the continuous periodic change is not less than the preset duration + dT, then the current cycle signal data is determined to be an abnormal signal. In this invention, the preset duration is 1.6µs.

[0061] If the grid voltage signal is determined to be an abnormal signal, it is saved to the abnormal signal database for analysis. If it is determined to be a normal signal, it is saved to the historical signal database. The process involves sampling the signal using a high-frequency pulse (fz) to calculate and save the value of each cycle and frequency. To ensure accuracy, fz > 10 MHz, and the approximate sampling error is calculated as 1 / fz / 20 ms. Then, steps S3 and S4 are executed.

[0062] Step S3: Extract 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 based on the extracted data, and update the standard period error based on the positive and negative error range.

[0063] It should be noted that all signal samples are extracted from the historical signal database, and a first sliding window of data of a first preset length is selected to calculate the first linearly weighted period value for each signal sample. The first preset length is 30 seconds, meaning that a 30-second learning time sliding window of signal samples is selected from all signal samples in the historical signal database. For example, there are approximately 1500 signal period data samples within 30 seconds. When calculating the first linearly weighted period value for the first sliding window data, the period value closer to the current time has a larger weight, and the historical period value farther from the current time has a smaller weight. The formula for calculating the first linearly weighted period value is:

[0064]

[0065] in, The periodic values ​​are linearly weighted. These are the phase values ​​of n moments arranged in reverse chronological order. for One-to-one corresponding periodic weight value, The value decreases linearly between [0,1].

[0066] Calculate the average error of the first linear weighted periodic value of all signal samples, and determine the positive and negative error range as [-2×dT0, +2×dT0], where dT0 is the average error. Update the standard periodic error dT based on the positive and negative error range.

[0067] Step S4: Extract signal data of the second preset length from the historical signal database according to the most recent time, calculate the linear weighted period value, and perform phase synchronization to obtain the phase-synchronized grid voltage signal. Then, proceed to step S5.

[0068] It should be noted that a second sliding window of data is obtained by extracting a preset number of signal samples of a second preset length from the historical signal database, based on the most recent time. The second preset length is 5 seconds. Then, the second linearly weighted period value of the second sliding window data is calculated. The calculation formula for the second linearly weighted period value is the same as that for the first linearly weighted period value. Based on the second linearly weighted period value, the grid voltage signal is phase-synchronized, thereby obtaining the phase-synchronized grid voltage signal.

[0069] Step S5: Determine whether to use the grid voltage signal or the phase-synchronized grid voltage signal as the output signal based on the positive and negative error range, and then proceed to step S6.

[0070] It should be noted that the grid voltage signal from step S1 and the phase-synchronized grid voltage signal from step S4 are synthesized, and then edge discrimination is performed. If the edge error between the grid voltage signal and the phase-synchronized grid voltage signal is within ±dT, the grid voltage signal from step S1 is used as the output signal, and step S6 is executed; if the edge error between the grid voltage signal and the phase-synchronized grid voltage signal is not within ±dT, the phase-synchronized grid voltage signal 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 the phase-locked loop.

[0072] It should be noted that phase correction is performed on the output signal output in step S5, 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 used as a signal source input into the phase-locked loop. The phase-locked loop can be a Synchronous Reference Frame PLL (SRF-PLL), an Enhanced PLL (EPLL), a Clarke transform-based phase-locked loop, or a Second Order Generalized Integrator PLL (SOGI-PLL).

[0073] The self-learning single-phase lock-in method for power electronic converters provided by this invention converts the grid voltage signal into a square wave signal. Based on the standard period range and standard period error, it determines whether the grid voltage signal is a normal signal. If so, it adds it to the historical signal database. Based on the historical signal database, it automatically learns and updates the criteria for normal signals and fits a stable synchronization signal source to provide a stable input signal to the phase-locked loop (PLL). This makes the PLL output more stable and prevents large fluctuations caused by grid anomalies or faults. It ensures that the PLL signal is normal and unfluctuating when the grid voltage fluctuates abnormally or there is a fault, thus enhancing the grid connection stability of the control system. This solves the technical problem that existing PLLs are prone to phase-locking fluctuations caused by grid anomalies or faults, leading to instability, overcurrent, and grid tethering of the power electronic converter.

[0074] For easier understanding, please refer to Figure 3 This invention provides an embodiment of a self-learning single-phase lock-in device for a power electronic converter, comprising the following modules:

[0075] The signal conversion module is used to convert the mains voltage signal into a square wave signal;

[0076] The judgment module is used to determine whether the grid voltage signal is abnormal based on the square wave signal, the standard period range and the standard period error. If it is, the grid voltage signal is saved to the abnormal signal database; if not, the grid voltage signal is saved to the historical signal database, and the system jumps to the criterion self-learning update module and the period weighting module.

[0077] The criterion self-learning update module is used to extract 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 based on the extracted data, and update the standard period error based on the positive and negative error range.

[0078] The periodic weighting module is used to extract signal data of a second preset length from the historical signal database according to the most recent time, calculate the linear weighting period value, and perform phase synchronization to obtain the phase-synchronized grid voltage signal, and then jump to the signal output preprocessing module.

[0079] The signal output preprocessing module is used to determine whether to use the grid voltage signal or the phase-synchronized grid voltage signal as the output signal based on the positive and negative error range, and then 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 the phase-locked loop.

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

[0082] For both discontinuous square wave signals 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 within the corresponding period is judged to be normal; otherwise, the grid voltage signal within the corresponding period is judged to be abnormal.

[0083] For a non-first-cycle square wave signal with continuous periodicity, if the previous cycle signal data of the current cycle signal data is a normal signal, then compared with the previous cycle signal data, if the continuous periodic change of the current cycle signal data is less than a preset duration, then the current cycle signal data is judged to be a normal signal; if the continuous periodic change is not less than the preset duration, then the current cycle signal data is judged to be an abnormal signal. If the previous cycle signal data is an abnormal signal, then compared with the previous cycle signal data, if the continuous periodic change of the current cycle signal data is less than a preset duration + dT, then the current cycle signal data is judged to be a normal signal; if the continuous periodic change is not less than a preset duration + dT, then the current cycle signal data is judged to be an abnormal signal, where dT is the standard periodic 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, a first sliding window of data of a first preset length is extracted. The first linear weighted period value of each signal sample is calculated based on the first sliding window data. The positive and negative error range is determined based on the average error of the first linear weighted period values ​​of all signal samples. The standard period error is updated based on the positive and negative error range.

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

[0087] The signal data of the second preset length is extracted from the historical signal database according to the most recent time to obtain the second sliding window data. The second linear weighted period value of the second sliding window data is calculated, and the phase of the second sliding window data is synchronized to obtain the phase-synchronized grid voltage signal. Then, the signal output preprocessing module is switched to the signal output preprocessing module.

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

[0089] Edge discrimination is performed on the grid voltage signal and the phase-synchronized grid voltage signal. If the edge error of the grid voltage signal and the phase-synchronized grid voltage signal is within the positive and negative error range, the grid voltage signal is used as the output signal and the process jumps to the output module. Otherwise, the phase-synchronized grid voltage signal is used as the output signal and the process jumps to the output module.

[0090] In one embodiment, the formulas for calculating the first linearly weighted period value and the second linearly weighted period value are as follows:

[0091]

[0092] in, The periodic values ​​are linearly weighted. These are the phase values ​​of n moments arranged in reverse chronological order. for One-to-one corresponding periodic weight value, 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] This invention provides an embodiment of a self-learning single-phase lock-in device for a power electronic converter, the device including a processor and a memory:

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

[0097] The processor is used to execute any of the self-learning single-phase lock-in methods for power electronic converters provided in this invention according to the instructions in the program code.

[0098] The present invention also provides an embodiment of a computer-readable storage medium for storing program code for executing any implementation of any of the self-learning single-phase lock-in methods for power electronic converters provided in the present invention.

[0099] This application also provides a computer program product including instructions that, when run on a computer, cause the computer to execute any of the implementation methods of a power electronic converter self-learning single-phase lock-in described in the foregoing embodiments.

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

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

[0102] The terms "first," "second," etc., used in this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0103] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A self-learning single-phase lock-in method for power electronic converters, characterized in that, Includes the following steps: S1. Convert the grid voltage signal into a square wave signal; S2. Based on the square wave signal, determine whether the grid voltage signal is abnormal based on the standard period range and standard period error. If it is, save the grid voltage signal to the abnormal signal database. If not, save the grid voltage signal to the historical signal database. Then execute steps S3 and S4. S3. Extract 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 based on the extracted data, and update the standard period error based on the positive and negative error range. S4. Extract signal data of the second preset length from the historical signal database according to the most recent time, calculate the linear weighted period value and perform phase synchronization to obtain the phase-synchronized grid voltage signal, and then execute step S5. S5. Determine whether to use the grid voltage signal or the phase-synchronized grid voltage signal as the output signal based on the positive and negative error range, and then proceed to step S6. S6. Perform phase correction on the output signal and input the output signal into the phase-locked loop; Step S3 specifically includes: Based on all signal samples in the historical signal database, a first sliding window of data of a first preset length is extracted. The first linear weighted period value of each signal sample is calculated based on the first sliding window data. The positive and negative error range is determined based on the average error of the first linear weighted period values ​​of all signal samples. The standard period error is updated based on the positive and negative error range. Step S4 specifically includes: The signal data of the second preset length is extracted from the historical signal database according to the most recent time to obtain the second sliding window data. The second linear weighted period value of the second sliding window data is calculated, and the phase of the second sliding window data is synchronized to obtain the phase-synchronized grid voltage signal. Then, step S5 is executed.

2. The self-learning single-phase lock-in method for power electronic converters according to claim 1, characterized in that, Step S2 specifically includes: For both discontinuous square wave signals 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 within the corresponding period is judged to be normal; otherwise, the grid voltage signal within the corresponding period is judged to be abnormal. For a non-first-cycle square wave signal with continuous periodicity, if the previous cycle signal data of the current cycle signal data is a normal signal, then compared with the previous cycle signal data, if the continuous periodic change of the current cycle signal data is less than a preset duration, then the current cycle signal data is determined to be a normal signal; if the continuous periodic change is not less than the preset duration, then the current cycle signal data is determined to be an abnormal signal. If the previous cycle signal data is an abnormal signal, then compared with the previous cycle signal data, if the continuous periodic change of the current cycle signal data is less than a preset duration + dT, then the current cycle signal data is determined to be a normal signal; if the continuous periodic change is not less than the preset duration + dT, then the current cycle signal data is determined to be an abnormal signal, where dT is the standard periodic error.

3. The self-learning single-phase lock-in method for power electronic converters according to claim 1, characterized in that, Step S5 specifically includes: Edge discrimination is performed on the grid voltage signal and the phase-synchronized grid voltage signal. If the edge error of the grid voltage signal and the phase-synchronized grid voltage signal is within the positive and negative error range, the grid voltage signal is used as the output signal and step S6 is executed. Otherwise, the phase-synchronized grid voltage signal is used as the output signal and step S6 is executed.

4. The self-learning single-phase lock-in method for power electronic converters according to claim 1, characterized in that, The formulas for calculating the first linearly weighted period value and the second linearly weighted period value are as follows: ； in, The periodic values ​​are linearly weighted. These are the phase values ​​of n moments arranged in reverse chronological order. for One-to-one corresponding periodic weight value, The value decreases linearly between [0,1].

5. A self-learning single-phase phase-locked loop device for a power electronic converter, characterized in that, Includes the following modules: The signal conversion module is used to convert the mains voltage signal into a square wave signal; The judgment module is used to determine whether the grid voltage signal is abnormal based on the square wave signal, the standard period range and the standard period error. If it is abnormal, the grid voltage signal is saved to the abnormal signal database. If it is abnormal, the grid voltage signal is saved to the historical signal database, and the process jumps to the criterion self-learning update module and the period weighting module. The criterion self-learning update module is used to extract 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 based on the extracted data, and update the standard period error based on the positive and negative error range. The period weighting module is used to extract signal data of a second preset length from the historical signal database according to the most recent time, calculate the linear weighting period value, and perform phase synchronization to obtain the phase-synchronized grid voltage signal, and then jump to the signal output preprocessing module. The signal output preprocessing module is used to determine, based on the positive and negative error range, whether to use the grid voltage signal or the phase-synchronized grid voltage signal as the output signal, and then switch to the output module. The output module is used to perform phase correction on the output signal and input the output signal into the phase-locked loop; The criterion self-learning update module is specifically used for: Based on all signal samples in the historical signal database, a first sliding window of data of a first preset length is extracted. The first linear weighted period value of each signal sample is calculated based on the first sliding window data. The positive and negative error range is determined based on the average error of the first linear weighted period values ​​of all signal samples. The standard period error is updated based on the positive and negative error range. The periodic weighting module is specifically used for: The signal data of the second preset length is extracted from the historical signal database according to the most recent time to obtain the second sliding window data. The 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 phase synchronized grid voltage signal. Then, the signal output preprocessing module is switched to the signal output preprocessing module.

6. The power electronic converter self-learning single-phase phase-locked device according to claim 5, characterized in that, The formulas for calculating the first linearly weighted period value and the second linearly weighted period value are as follows: ； in, The periodic values ​​are linearly weighted. These are the phase values ​​of n moments arranged in reverse chronological order. for One-to-one corresponding periodic weight value, The value decreases linearly between [0,1].

7. A self-learning single-phase phase-locked loop device for a power electronic converter, characterized in that, The device includes 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 lock-in method for power electronic converters according to any one of the instructions in the program code.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the power electronic converter self-learning single-phase lock-in method according to any one of claims 1-5.

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