A voltage calibration method, system, and medium for frequency converters used in smart grids

Abstract

This application relates to the field of voltage calibration technology, specifically to a voltage calibration method, system, and medium for frequency converters used in smart grids. The method includes: acquiring the output voltage signal from the frequency converter control port; decomposing the entire output voltage signal into multiple Inductively Coupled Function (IMF) components; obtaining a Gaussian kernel for each IMF component based on the frequency characteristics of each sampling point and the average voltage amplitude of all sampling points, thereby eliminating harmonic components in each IMF component, and reconstructing all the obtained IMF components into an output voltage signal; dividing the reconstructed output voltage signal into multiple data segments; obtaining the step size of each data segment based on the instantaneous frequency fluctuation characteristics of each sampling point in each data segment, thereby eliminating harmonic interference in each data segment, and thus calibrating the output voltage signal displayed by the frequency converter. This application improves the accuracy of voltage calibration by more accurately eliminating harmonic interference in the output voltage signal.

Description

Technical Field

[0001] This application relates to the field of voltage calibration technology, specifically to a voltage calibration method, system, and medium for a frequency converter used in smart grids. Background Technology

[0002] Inverters are crucial power control devices in power grids. They use pulse width modulation (PWM) technology to adjust the magnitude and frequency of grid voltage, as well as the rectification and inversion of power, ensuring a stable and accurate output voltage. Otherwise, the normal operation of other electrical equipment will be affected. Among these parameters, the inverter's bus voltage and control port output voltage are critical, directly influencing the calculations of the inverter's control process. Therefore, the accuracy of these parameters is essential for the normal operation of the inverter.

[0003] The PWM modulation technology used by frequency converters during operation frequently switches switching elements, generating harmonic interference and a large amount of chaotic transient switching interference. This leads to mutual interference between the frequency converter and other equipment in the power grid, causing distortion of the measured electrical variable signal data and affecting data reliability. Adaptive Notch Filtering (ANF) can dynamically track the frequency of interference signals and adaptively adjust the parameters in the notch filter to remove interference signals, exhibiting good performance in suppressing harmonic interference. However, when the load on equipment in the power grid fluctuates significantly, the resulting interference frequency changes too rapidly. Traditional adaptive notch filters may lose frequency tracking lock when the frequency drift is too large, causing a decrease in the notch filtering effect of the adaptive notch filter. This can lead to incomplete removal of interference signals or even the elimination of useful signals, thus affecting the accuracy of the electrical variable signal data. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a voltage calibration method, system, and medium for frequency converters used in smart grids. The specific technical solution adopted is as follows:

[0005] In a first aspect, embodiments of this application provide a voltage calibration method for a frequency converter used in a smart grid, the method comprising the following steps:

[0006] Obtain the output voltage signal from the inverter control port;

[0007] The entire output voltage signal is decomposed into multiple IMF components. Based on the number of sampling points in each IMF component whose instantaneous frequency is not an integer multiple of the fundamental frequency, the non-harmonic weight of each IMF component is obtained. Combined with the average voltage amplitude of all sampling points in each IMF component, the non-harmonic significance of each IMF component is obtained. Then, the Gaussian kernel of each IMF component is obtained, and the non-harmonic components in each IMF component are eliminated. All the obtained IMF components are then reconstructed into the output voltage signal.

[0008] The reconstructed output voltage signal is divided into multiple data segments. Based on the difference between the instantaneous frequencies of each sampling point and its adjacent sampling points in each data segment, the harmonic drift significance of each data segment is obtained, and then the step size of each data segment is obtained. In this way, the adaptive notch filter algorithm is used to eliminate harmonic interference in each data segment, and the output voltage signal displayed by the frequency converter is calibrated based on the output voltage signal after eliminating harmonic interference.

[0009] Preferably, the method for obtaining the non-harmonic weights of each IMF component is as follows: if the remainder of dividing the instantaneous frequency of any sampling point in each IMF component by its fundamental frequency is not equal to 0, then the corresponding sampling point is recorded as a non-harmonic sampling point; the ratio of the total number of non-harmonic sampling points in each IMF component to the total number of all sampling points is recorded as the non-harmonic weight of each IMF component.

[0010] Preferably, the nonharmonic significance of each IMF component refers to the product of the nonharmonic weight and the waveform bias of each IMF component; wherein, the waveform bias of each IMF component refers to the ratio between the absolute value of the mean voltage amplitude of all sampling points in each IMF component and the mean of the absolute values ​​of the voltage amplitude of all sampling points.

[0011] Preferably, the formula for calculating the Gaussian kernel of each IMF component is: In the formula, For the first Gaussian kernel for each IMF component; For the first Preset initial Gaussian kernel for each IMF component; For the first Nonharmonic significance of each IMF component.

[0012] Preferably, the specific process of eliminating the non-harmonic components in each IMF component is as follows: each IMF component and its corresponding Gaussian kernel size are used as inputs to the Gaussian filtering algorithm, and the outputs are each IMF component after eliminating the non-harmonic components.

[0013] Preferably, the harmonic drift significance of each data segment refers to the mean of the sum of the absolute differences between each non-endpoint sampling point and its two adjacent sampling points in each data segment.

[0014] Preferably, the formula for calculating the step size of each data segment is: In the formula, Let n be the step size of the nth data segment; This is a function to find the maximum value. The harmonic drift significance of the nth data segment; For the Sigmoid function; The largest eigenvalue of the autocorrelation matrix of the nth data segment; For preset parameter tuning coefficients, and .

[0015] Preferably, the specific process of calibrating the output voltage signal displayed by the frequency converter based on the output voltage signal after eliminating harmonic interference is as follows: adjust the corresponding calibration parameters of the frequency converter so that the difference between the control port output voltage displayed by the frequency converter and the control port output voltage after eliminating harmonic interference is less than a preset voltage threshold.

[0016] Secondly, embodiments of this application provide a voltage calibration medium for a frequency converter used in a smart grid, the voltage calibration medium comprising: a data acquisition module, a voltage denoising module, and a voltage calibration module.

[0017] The data acquisition module is used to acquire the output voltage signal of the inverter control port;

[0018] The voltage denoising module is used to adaptively remove non-harmonic components from the output voltage signal based on its frequency and amplitude characteristics; and to adaptively obtain the step size of each data segment based on its frequency fluctuation characteristics, thereby eliminating harmonic components from the output voltage signal.

[0019] The voltage calibration module is used to calibrate the output voltage signal of the inverter control port.

[0020] Thirdly, embodiments of this application also provide a voltage calibration system for a frequency converter used in a smart grid. The system includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the voltage calibration method for a frequency converter used in a smart grid as described above.

[0021] As can be seen from the above embodiments, the voltage calibration method, system, and medium for a smart grid frequency converter provided in this application have at least the following beneficial effects:

[0022] This application addresses the problem that traditional adaptive notch filtering algorithms struggle to accurately track harmonic frequencies when the output voltage of inverters in smart grids experiences harmonic frequency drift due to load fluctuations, resulting in poor notch filtering performance. By performing variational mode decomposition (VMD) on the output voltage signal to obtain several IMF components, and analyzing the non-harmonic interference characteristics in each IMF component, non-harmonic interference in each IMF component is eliminated, improving the accuracy of subsequent harmonic frequency drift characteristic analysis in the output voltage signal. Furthermore, the IMF components with non-harmonic interference removed are reconstructed into the output voltage signal. Harmonic drift significance is constructed based on the harmonic characteristics of the output voltage signal in different time periods to characterize the harmonic frequency drift in each data segment. An adaptive variable step-size adaptive notch filtering algorithm is then used to notch each data segment, improving the removal effect of the adaptive notch filtering algorithm on harmonic interference. Finally, the notched electrical variable signal is used for inverter voltage calibration, improving the accuracy of voltage calibration. Attached Figure Description

[0023] To more clearly illustrate the technical solutions and advantages in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating the steps of a voltage calibration method for a frequency converter used in a smart grid, as provided in one embodiment of this application;

[0025] Figure 2 This is a schematic diagram of the structure of a voltage calibration medium for a frequency converter used in a smart grid, provided as an embodiment of this application. Detailed Implementation

[0026] To further illustrate the technical means and effects adopted by this application to achieve the intended inventive purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a voltage calibration method, system, and medium for a smart grid frequency converter proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0027] Unless otherwise specified and limited, terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items. All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0028] The following description, in conjunction with the accompanying drawings, details the specific scheme of the voltage calibration method, system, and medium for a frequency converter used in a smart grid provided in this application.

[0029] Please see Figure 1 The diagram illustrates a step-by-step flowchart of a voltage calibration method for a frequency converter used in a smart grid, according to an embodiment of this application. The method includes the following steps:

[0030] Step 1: Obtain the output voltage signal of the inverter control port.

[0031] The output voltage signal of the control port in the frequency converter is obtained through a voltage transformer. The sampling frequency for acquiring the output voltage signal data is FkHz, and the sampling time is set to e minutes. The specific sampling frequency and sampling time can be set by the implementer according to the implementation scenario. In order to ensure the validity of the sampled signal data, the sampling frequency setting should satisfy the Nyquist sampling theorem. In this embodiment, F is set to 10 and e is set to 1.

[0032] Step 2: Decompose the entire output voltage signal into multiple IMF components; based on the number of sampling points in each IMF component whose instantaneous frequency is not an integer multiple of the fundamental frequency, obtain the non-harmonic weight of each IMF component, and combine it with the average voltage amplitude of all sampling points in each IMF component to obtain the non-harmonic significance of each IMF component, and then obtain the Gaussian kernel of each IMF component, thereby eliminating the non-harmonic components in each IMF component, and reconstructing all the obtained IMF components into the output voltage signal.

[0033] Considering the large scale of the power equipment systems controlled in smart grids, when the load of a large number of power equipment in the grid fluctuates frequently (such as drastic changes in motor torque, frequent switching of operating modes, and the need for frequency converters to change output voltage in real time to respond to load changes), the frequency changes of harmonic interference and switching transient interference generated by the rectifier circuit and inverter circuit inside the frequency converter under high-frequency operation are complex. This makes it difficult for the adaptive notch filter algorithm to accurately lock the frequency changes of the interference signal when tracking the interference frequency and updating the notch filter weight. As a result, the adaptive notch filter algorithm cannot accurately adjust the center frequency, affecting its notch filter effect.

[0034] To address the aforementioned issues, this application aims to analyze the harmonic and non-harmonic interference characteristics in the output voltage signal, adaptively obtain the step size of the adaptive notch filter algorithm during the notch filter process, improve the notch filter effect of the adaptive notch filter algorithm, and thus improve the calibration effect of the output voltage signal.

[0035] Specifically, load changes in power equipment under a smart grid introduce interference signals into the output voltage signal data. Furthermore, harmonic interference differs from random noise such as transient switching interference. Harmonic frequency variations are typically integer multiples of the fundamental frequency (e.g., when the power grid frequency is 50Hz, the third harmonic frequency is 150Hz), and harmonics also superimpose periodic sine waves onto the fundamental waveform, causing waveform distortion. The noise generated by transient switching interference is random, disrupting this periodic variation and causing abnormal peaks in local areas, which interferes with the accuracy of subsequent harmonic frequency drift analysis. Therefore, these random noise interferences must be removed before performing harmonic frequency drift analysis.

[0036] Based on the above analysis, variational mode decomposition (VMD) is used to decompose the entire output voltage signal data to obtain... One IMF component, The size can be set by the implementer according to the implementation scenario. In this embodiment, The value is set to 5. The electrical variable signal data is decomposed into multiple IMF components at different frequency bands. Some of the IMF components are frequency-drifted harmonic signals. Therefore, the characteristics of transient switching interference in the bus voltage signal can be analyzed at the level of each IMF component.

[0037] Furthermore, since the frequencies of harmonics are multiples of the fundamental frequency, the harmonics of different frequencies in each IMF component exhibit harmonic characteristics relative to the fundamental frequency; while the frequency of noise caused by transient switching interference is random and independent of the fundamental frequency. Therefore, this characteristic can be used to calculate the content of non-harmonic or non-fundamental components in each IMF component.

[0038] Specifically, taking the k-th IMF component as an example, the following analysis is performed. The k-th IMF component is used as the input to the Fast Fourier Transform (FFT) to obtain the instantaneous frequency of each sampling point in this IMF component. The FFT is an existing technology, and its specific process will not be elaborated further. The instantaneous frequency at the h-th sampling point in the k-th IMF component is denoted as... The fundamental frequency is denoted as Its size is automatically set according to the power frequency of the power equipment in the implementation scenario. In this embodiment... It is 50. If This indicates that the signal at the h-th sampling point is a harmonic or non-fundamental component. The h-th sampling point is denoted as the harmonic sampling point, where % represents the modulo operation. express and The remainder after division.

[0039] In a preferred embodiment, the non-harmonic weight of each IMF component is obtained based on the number of non-harmonic sampling points in each IMF component, which is used to characterize the content of non-harmonic components in each IMF component.

[0040] In this embodiment, the nonharmonic weight of the k-th IMF component is denoted as... Its specific expression is: In the formula, The nonharmonic weight of the k-th IMF component; This represents the total number of non-harmonic sampling points in the k-th IMF component; The total number of sampling points in the k-th IMF component.

[0041] The larger the value, the more non-harmonic or non-fundamental components are contained in the k-th IMF component; conversely, the smaller the value, the more sampling points with instantaneous frequencies that are integer multiples of the fundamental frequency are in the IMF component, and the more harmonic or fundamental components are in the IMF component.

[0042] On the other hand, since the waveform of a single IMF component also satisfies the periodic sinusoidal waveform characteristics of the fundamental or harmonic, transient switching interference will disrupt this periodic change. Specifically, it manifests as an abnormal voltage amplitude on the waveform, generating a certain voltage amplitude bias in the positive or negative direction. In this case, the symmetry of the sinusoidal waveform about the time axis will be disrupted.

[0043] Therefore, based on the average voltage amplitude of all sampling points in each IMF component, the waveform offset of each IMF component is obtained, which is used to characterize the degree to which the waveform of each IMF component is affected by transient switching interference.

[0044] In this embodiment, the waveform offset of the k-th IMF component is denoted as... Its specific expression is: In the formula, Let be the waveform offset of the k-th IMF component. This represents the average voltage amplitude of all sampling points in the k-th IMF component. This represents the mean of the absolute values ​​of the voltage amplitudes at all sampling points in the k-th IMF component. If the IMF component is a fundamental or harmonic signal, then the closer the mean amplitude of the entire signal segment is to 0, the better it satisfies the symmetrical characteristics of a sinusoidal waveform. The closer it is to 0, the better; conversely, when there are a large number of transient switching interferences in the IMF component, this interference is not symmetrical and will only show abnormal amplitude interference at a certain point in the voltage waveform. This will cause the average amplitude of the waveform to be biased in the positive or negative direction. The larger the value, the greater the transient switching interference in that IMF component.

[0045] In a preferred embodiment, the nonharmonic significance of each IMF component is obtained based on the nonharmonic weight and waveform offset of each IMF component, which is used to characterize the significance of nonharmonic interference noise in each IMF component.

[0046] In this embodiment, the nonharmonic significance of the k-th IMF component is denoted as . Its specific expression is: In the formula, The nonharmonic significance of the k-th IMF component; The nonharmonic weight of the k-th IMF component; The waveform offset of the k-th IMF component.

[0047] The larger the value, the more pronounced the non-harmonic interference noise in the k-th IMF component; conversely, the smaller the value, the less pronounced the non-harmonic interference noise. The signals in the signal are mostly harmonics or fundamental components, with less transient switching interference.

[0048] Repeat the above steps to calculate the nonharmonic significance of all IMF components.

[0049] Gaussian filtering algorithms are primarily used to remove random noise from signals. Therefore, this application employs a Gaussian filtering algorithm to remove non-harmonic interference from each IMF component. The size of the Gaussian kernel in the Gaussian filtering algorithm determines the degree of smoothing. A larger Gaussian kernel results in a more pronounced smoothing effect during denoising; conversely, a smaller Gaussian kernel retains more details but produces a less noticeable smoothing effect. Therefore, different Gaussian kernel sizes can be set according to the non-harmonic significance of each IMF component to eliminate non-harmonic noise interference. When the non-harmonic significance of each IMF component is greater, the noise is more pronounced, requiring a greater degree of denoising and a larger Gaussian kernel to make the denoised signal smoother; conversely, a smaller Gaussian kernel should be set to retain more detailed features.

[0050] Therefore, according to the first The nonharmonic significance of the IMF component is calculated. The Gaussian kernel for each IMF component is expressed as follows: In the formula, For the first Gaussian kernel for each IMF component; For the first The preset initial Gaussian kernel of the i-th IMF component is obtained by applying the first... The standard deviation of the voltage signal amplitude in each IMF component is obtained by calculating the standard deviation. For the first Nonharmonic significance of each IMF component.

[0051] The Gaussian kernel size of all IMF components is obtained using the above calculation method. Then, each IMF component and its corresponding Gaussian kernel size are used as input to the Gaussian filtering algorithm, outputting each IMF component after eliminating harmonic components. The Gaussian filtering algorithm is a well-known technique, and its specific process will not be elaborated further.

[0052] All IMF components after eliminating harmonic components are reconstructed to obtain the output voltage signal data after eliminating harmonic interference. Component reconstruction of variational mode decomposition is a well-known technique, and its specific process will not be elaborated further.

[0053] Step 3: Divide the reconstructed output voltage signal into multiple data segments; based on the difference between the instantaneous frequencies of each sampling point and its adjacent sampling points in each data segment, obtain the harmonic drift significance of each data segment, and then obtain the step size of each data segment. In this way, use the adaptive notch filter algorithm to eliminate harmonic interference in each data segment, and calibrate the output voltage signal displayed by the frequency converter based on the output voltage signal after eliminating harmonic interference.

[0054] Furthermore, the signal characteristics of the output voltage signal after eliminating non-harmonic interference are analyzed, and a harmonic drift significance is constructed to characterize the changes in harmonic frequencies in the output voltage signal data.

[0055] Specifically, the loads of power equipment connected to the smart grid are complex and variable. When the load of the equipment changes frequently, a large number of harmonics are generated during the high-speed switching of the switching devices inside the frequency converter. As the switching frequency changes, the frequency drift of the generated harmonics is severe. This makes it difficult for traditional adaptive notch filtering algorithms to accurately track the changes in the center frequency of the harmonics to update the parameters of the notch filter, and thus cannot effectively eliminate harmonic interference.

[0056] As mentioned above, complex changes in the power grid cause variations in harmonics within the frequency converter. The frequencies of these harmonics (such as the third, fifth, and seventh harmonics) are multiples of the power frequency. Compared to the normal power frequency voltage waveform, the fluctuation frequency of these harmonics is faster, and their superposition on the fundamental waveform causes distortion. When the harmonic frequency is stable, the adaptive notch filter algorithm can adaptively update parameters based on the characteristics of the current waveform to counteract harmonic interference. However, when the harmonic frequency drifts significantly within a short period, the interference to the fundamental waveform varies, leading to drastic changes in the voltage waveform acquired over a continuous period. The adaptive notch filter algorithm struggles to identify complex harmonic modes in the voltage waveform and effectively eliminates harmonic interference.

[0057] Based on the above analysis, the output voltage signal data after eliminating non-harmonic interference is divided into multiple data segments according to a preset duration, wherein the value range of the preset duration is [missing information]. The unit is milliseconds (ms), and the specific value can be set by the implementer according to the implementation scenario. In this embodiment, the preset duration is set to 10ms. The instantaneous frequency of each sampling point in each data segment is obtained by fast Fourier transform.

[0058] As a preferred embodiment, the harmonic drift significance of each data segment is obtained based on the difference between the instantaneous frequencies of each sampling point and its adjacent sampling points in each data segment, which is used to characterize the severity of harmonic frequency drift in each data segment.

[0059] In this embodiment, the harmonic drift significance of the nth data segment is denoted as... Its specific expression is: In the formula, The harmonic drift significance of the nth data segment; For the first The total number of sampling points within each data segment; For the first In the data segment, the first The frequency of each sampling point; , The first The first data segment , No. The frequency of each sampling point.

[0060] Indicates the first The larger the frequency difference between each non-endpoint sampling point and its adjacent sampling points in a data segment, the more severe the harmonic frequency drift in that data segment, and the more frequent the harmonic frequency fluctuations in a short period of time; conversely, it indicates that the voltage signal in that data segment is more stable, is not affected by harmonic interference, or the harmonic frequency is stable and the degree of change is small.

[0061] Repeat the above calculation steps to calculate the harmonic drift significance of all data segments in the output voltage signal data after eliminating non-harmonic interference.

[0062] In the adaptive notch filter algorithm, the parameters of the adaptive notch filter are updated using a preset step size and gradient descent method. This estimates the harmonic interference signal and cancels out the harmonic interference in the original signal, thus suppressing harmonic interference. The preset step size is usually fixed. However, if the harmonic frequency drift is severe, a fixed step size is insufficient to meet the requirements of low steady-state error in predicting harmonic interference and fast convergence. Therefore, it is necessary to adaptively set the step size according to the significance of harmonic drift in each data segment for notch filtering.

[0063] Therefore, the step size for each data segment is calculated using the following expression: In the formula, Let n be the step size of the nth data segment; This is a function to find the maximum value of the input data. The harmonic drift significance of the nth data segment; The Sigmoid function is used to... Perform normalization; The largest eigenvalue of the autocorrelation matrix of the nth data segment; For preset parameter tuning coefficients, and Its size can be set by the implementer according to the implementation scenario. In this embodiment, The size is set to 0.004.

[0064] The closer it is to 0, the more significant it is. The less severe the harmonic frequency drift in each data segment, the smaller the standard step size. For the Notch filtering is applied to signals in each data segment; conversely, notch filtering is applied to signals in other data segments. The larger the value, the higher the value. The larger the harmonic frequency drift in a data segment, the larger the step size needs to be to improve the algorithm's ability to quickly track the harmonic center frequency drift.

[0065] Furthermore, the voltage signal within the nth data segment is used as the input to the adaptive notch filter algorithm, and the step size of the calculated nth data segment is used as the step size to output the nth data segment with harmonic interference eliminated. The adaptive notch filter algorithm is a well-known technique, and its specific process will not be elaborated further.

[0066] Repeat the above steps to perform adaptive notch filtering on all data segments in the entire output voltage signal to eliminate harmonic interference and obtain the output voltage signal after harmonic interference elimination.

[0067] Furthermore, the output voltage of the inverter's control port is calibrated. Specifically, the inverter adjusts the corresponding calibration parameters based on the control port output voltage signal after harmonic interference is eliminated, so that the difference between the control port output voltage displayed by the inverter and the control port output voltage after harmonic interference is eliminated is less than a preset voltage threshold, thereby achieving voltage calibration of the inverter.

[0068] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of a voltage calibration medium for a smart grid frequency converter provided in an embodiment of this application. In this embodiment, the terminal includes units used to execute the steps in the embodiment corresponding to the voltage calibration method for a smart grid frequency converter. See also... Figure 2 The voltage calibration medium includes: a data acquisition module, a voltage noise reduction module, and a voltage calibration module.

[0069] The data acquisition module is used to acquire the output voltage signal of the inverter control port;

[0070] The voltage denoising module is used to adaptively remove non-harmonic components from the output voltage signal based on its frequency and amplitude characteristics; and to adaptively obtain the step size of each data segment based on its frequency fluctuation characteristics, thereby eliminating harmonic components from the output voltage signal.

[0071] The voltage calibration module is used to calibrate the output voltage signal of the inverter control port.

[0072] Based on the same inventive concept as the above method, this application embodiment also provides a voltage calibration system for a frequency converter for a smart grid, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the voltage calibration method for a frequency converter for a smart grid described above.

[0073] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0074] It should be noted that, unless otherwise specified and limited, terms such as “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such article or device. Without further limitations, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.

[0075] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not invented in this application.

[0076] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A voltage calibration method for a frequency converter used in smart grids, characterized in that, The method includes the following steps: Obtain the output voltage signal from the inverter control port; The entire output voltage signal is decomposed into multiple IMF components. Based on the number of sampling points in each IMF component whose instantaneous frequency is not an integer multiple of the fundamental frequency, the non-harmonic weight of each IMF component is obtained. Combined with the average voltage amplitude of all sampling points in each IMF component, the non-harmonic significance of each IMF component is obtained. Then, the Gaussian kernel of each IMF component is obtained, and the non-harmonic components in each IMF component are eliminated. All the obtained IMF components are then reconstructed into the output voltage signal. The reconstructed output voltage signal is divided into multiple data segments. Based on the difference between the instantaneous frequencies of each sampling point and its adjacent sampling points in each data segment, the harmonic drift significance of each data segment is obtained, and then the step size of each data segment is obtained. In this way, the adaptive notch filter algorithm is used to eliminate harmonic interference in each data segment, and the output voltage signal displayed by the frequency converter is calibrated based on the output voltage signal after eliminating harmonic interference. The method for obtaining the non-harmonic weights of each IMF component is as follows: if the remainder of dividing the instantaneous frequency of any sampling point in each IMF component by its fundamental frequency is not equal to 0, then the corresponding sampling point is recorded as a non-harmonic sampling point; the ratio of the total number of non-harmonic sampling points in each IMF component to the total number of all sampling points is recorded as the non-harmonic weight of each IMF component. The nonharmonic significance of each IMF component refers to the product of the nonharmonic weight and the waveform offset of each IMF component; wherein, the waveform offset of each IMF component refers to the ratio between the absolute value of the mean voltage amplitude of all sampling points in each IMF component and the mean of the absolute values ​​of the voltage amplitude of all sampling points. The formula for calculating the Gaussian kernel of each IMF component is as follows: In the formula, For the first Gaussian kernel for each IMF component; For the first Preset initial Gaussian kernel for each IMF component; For the first Nonharmonic significance of each IMF component; The harmonic drift significance of each data segment refers to the mean of the sum of the absolute differences between each non-endpoint sampling point and its two adjacent sampling points in each data segment. The formula for calculating the step size of each data segment is: In the formula, Let n be the step size of the nth data segment; This is a function to find the maximum value. The harmonic drift significance of the nth data segment; For the Sigmoid function; The largest eigenvalue of the autocorrelation matrix of the nth data segment; For preset parameter tuning coefficients, and .

2. The voltage calibration method for a frequency converter used in a smart grid as described in claim 1, characterized in that, The specific process for eliminating the non-harmonic components in each IMF component is as follows: each IMF component and its corresponding Gaussian kernel size are used as inputs to the Gaussian filtering algorithm, and the outputs are each IMF component after eliminating the non-harmonic components.

3. The voltage calibration method for a frequency converter used in a smart grid as described in claim 1, characterized in that, The specific process of calibrating the output voltage signal displayed by the frequency converter based on the output voltage signal after eliminating harmonic interference is as follows: adjust the corresponding calibration parameters of the frequency converter so that the difference between the control port output voltage displayed by the frequency converter and the control port output voltage after eliminating harmonic interference is less than a preset voltage threshold.

4. A voltage calibration medium for a frequency converter used in smart grids, characterized in that, A voltage calibration method for a frequency converter used in a smart grid, as described in any one of claims 1-3, wherein the voltage calibration medium comprises: The data acquisition module is used to acquire the output voltage signal of the inverter control port; The voltage denoising module is used to adaptively remove non-harmonic components from the output voltage signal based on its frequency and amplitude characteristics; and to adaptively obtain the step size of each data segment based on its frequency fluctuation characteristics, thereby eliminating harmonic components from the output voltage signal. The voltage calibration module is used to calibrate the output voltage signal of the inverter control port.

5. A voltage calibration system for a frequency converter used in a smart grid, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements a voltage calibration method for a frequency converter for a smart grid as described in any one of claims 1-3.

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