Dynamic frequency tracking method based on variable twiddle factor and electric energy metering equipment

Abstract

The invention provides a dynamic frequency tracking method based on a variable twiddle factor and electric energy metering equipment, and belongs to the technical field of signal analysis, and the method comprises the steps: obtaining the voltage and current of a power grid; processing the power grid signal based on a target transfer function so as to perform dynamic frequency tracking on the power grid signal; wherein the determination process of the target transfer function comprises the following steps: extracting the fundamental frequency of the power grid signal; based on the SDFT, extracting a fundamental wave frequency corresponding to the specified subharmonic to adjust a twiddle factor of a transfer function in the SDFT to obtain an adjusted twiddle factor; and constructing a target transfer function based on the adjusted twiddle factor. According to the dynamic frequency tracking method based on the variable twiddle factor and the electric energy metering equipment, the power grid frequency can be accurately tracked, and the analysis precision of fundamental wave and harmonic wave amplitudes is improved.

Description

Technical Field

[0001] This application belongs to the field of signal analysis technology, and more specifically, relates to a dynamic frequency tracking method and an energy metering device based on a variable rotation factor. Background Technology

[0002] With the accelerated transformation of the global energy structure, new energy sources, such as wind and solar power, as well as various power electronic equipment, are being connected to the power grid on a large scale. The nonlinear characteristics of power electronic energy metering equipment make it a major source of harmonics, causing severe distortion of the voltage and current waveforms of the power grid. In addition, the power imbalance between power generation and load can easily cause the system frequency to fluctuate near the power frequency. These factors together pose a serious challenge to the accurate extraction and analysis of fundamental and harmonic components.

[0003] The Sliding Discrete Fourier Transform (SDFT) algorithm updates the spectrum through a sliding window mechanism and, based on the property of its transfer function to set zeros and poles at specific frequencies, can effectively extract the amplitude and phase of a specified harmonic. However, when the actual power grid frequency fluctuates, the zero positions of the SDFT mismatch with the actual harmonic frequencies, and non-integer period truncation will cause spectral leakage, severely affecting the analysis accuracy and leading to measurement errors.

[0004] To meet the needs of new power systems for frequency adaptive analysis, there is an urgent need for a method that can accurately extract fundamental and harmonic components even under dynamic changes in grid frequency. Summary of the Invention

[0005] The purpose of this application is to provide a dynamic frequency tracking method and an energy metering device based on a variable rotation factor, which can accurately extract the fundamental and harmonic components even under dynamic changes in the power grid frequency, reduce measurement errors, and improve the analysis accuracy of the fundamental and harmonic components.

[0006] A first aspect of this application provides a dynamic frequency tracking method based on a variable rotation factor, comprising:

[0007] Acquire voltage and current signals from the power grid;

[0008] The power grid signal is processed based on the target transfer function to perform dynamic frequency tracking of the power grid signal;

[0009] The process of determining the target transfer function includes:

[0010] Extract the fundamental frequency of the power grid signal;

[0011] The rotation factor of the transfer function in the SDFT is adjusted based on the fundamental frequency corresponding to the specified subharmonic extracted by the SDFT to obtain the adjusted rotation factor.

[0012] Construct the target transfer function based on the adjusted twitch factor.

[0013] A second aspect of this application provides a dynamic frequency tracking device based on a variable rotation factor, comprising:

[0014] The signal acquisition module is used to acquire the power grid signals of the power grid;

[0015] The frequency tracking module is used to process the power grid signal based on the target transfer function in order to perform dynamic frequency tracking of the power grid signal;

[0016] The process of determining the target transfer function includes:

[0017] Extract the fundamental frequency of the power grid signal;

[0018] The rotation factor of the transfer function in the SDFT is adjusted based on the fundamental frequency corresponding to the specified subharmonic extracted by the SDFT to obtain the adjusted rotation factor.

[0019] Construct the target transfer function based on the adjusted twitch factor.

[0020] A third aspect of this application provides an energy metering device, 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 dynamic frequency tracking method based on a variable rotation factor described above.

[0021] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the dynamic frequency tracking method based on a variable rotation factor described above.

[0022] The beneficial effects of the dynamic frequency tracking method and power metering equipment based on a variable rotation factor provided in this application are as follows: The rotation factor of the transfer function in the SDFT is adjusted by using the fundamental frequency of the power grid signal and the signal sampling frequency. The adjusted rotation factor can be dynamically adjusted with changes in the fundamental frequency, becoming a variable rotation factor. The target transfer function constructed based on the variable rotation factor performs dynamic frequency tracking of the power grid signal. This allows for accurate extraction of the fundamental and harmonic components even under dynamic changes in the power grid frequency, reducing measurement errors and improving the analysis accuracy of the fundamental and harmonic components. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, 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 a dynamic frequency tracking method based on a variable rotation factor provided in an embodiment of this application;

[0025] Figure 2 A structural block diagram of a dynamic frequency tracking device based on a variable rotation factor provided in an embodiment of this application;

[0026] Figure 3 A flowchart illustrating the processing of a VR-SDFT method provided in an embodiment of this application;

[0027] Figure 4 This is a schematic block diagram of an energy metering device provided in an embodiment of this application. Detailed Implementation

[0028] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, power metering devices, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0030] With the large-scale grid connection of renewable energy sources such as wind and solar power, and the widespread application of various power electronic equipment aimed at achieving efficient control and energy conversion, the dynamic fluctuations in the amplitude and frequency of grid signals severely affect the extraction and analysis of fundamental and harmonic components by traditional SDFT algorithms. To address these issues, this application proposes a Variable Rotator SDFT algorithm (VR-SDFT) by improving upon traditional algorithms. This accelerates the tracking speed of the fundamental frequency in complex grid environments and improves the steady-state accuracy of fundamental frequency tracking. It effectively addresses the problems of spectral leakage and harmonic analysis distortion caused by frequency offset, thereby enhancing the accuracy and real-time performance of power quality analysis. The technical solution proposed in this application is described in detail below.

[0031] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a dynamic frequency tracking method based on a variable rotation factor according to an embodiment of this application. The method may include steps S101 and S102.

[0032] S101: Obtain the power grid signal from the power grid.

[0033] The execution entity of this application embodiment can be an energy metering device, a field-programmable gate array, an embedded microprocessor, or an industrial control computer used in power grid monitoring and analysis scenarios.

[0034] This application embodiment can acquire the power grid signal in real time using a voltage acquisition device or a current acquisition device, and then analyze and track the frequency of the power grid signal. The power grid signal can include periodic voltage signals and periodic current signals.

[0035] In the embodiments of this application, the acquired power grid signal generally contains fundamental frequency, harmonics and noise. Frequency tracking of the acquired power grid signal is required to optimize the power grid signal and improve power grid stability.

[0036] S102 processes the power grid signal based on the target transfer function to perform dynamic frequency tracking of the power grid signal.

[0037] The process of determining the target transfer function includes: extracting the fundamental frequency of the power grid signal; adjusting the rotation factor of the transfer function in the SDFT based on the fundamental frequency corresponding to the specified harmonic extracted by the SDFT to obtain the adjusted rotation factor; and constructing the target transfer function based on the adjusted rotation factor.

[0038] The embodiments of this application can perform filtering preprocessing on the collected power grid signal containing fundamental frequency, harmonics and noise through a predetermined target transfer function, and then perform frequency tracking on the filtered and preprocessed power grid signal, thereby realizing dynamic frequency tracking of the power grid signal.

[0039] In the traditional SDFT, the prerequisite for accurately filtering out frequency components other than the target harmonics is that the power grid frequency remains stable and the sampling frequency is an integer multiple of the fundamental frequency. The twitch factor of the transfer function is generally a fixed twitch factor, and its twitch factor expression (1) is:

[0040]

[0041] However, in actual power grids, frequency fluctuations can cause harmonic frequencies to deviate from the zero point of the SDFT transfer function, leading to spectral leakage and causing harmonic analysis errors. Therefore, this embodiment of the application adjusts the rotation factor of the transfer function in the SDFT by extracting the fundamental frequency corresponding to the specified harmonic based on the power grid signal using SDFT, thus obtaining the adjusted rotation factor, which is also known as the variable rotation factor.

[0042] Specifically, the initial values ​​of the power grid signal are first obtained. Discrete Fourier analysis is then performed on the signal to extract the amplitude and phase of a specified harmonic. The phase at that moment is then differentiated to obtain the harmonic frequency and fundamental frequency fb1 at that moment. By comparing the variance or average of the difference between fb1 and the initial frequency value with a set threshold, it is determined whether the power grid frequency has been successfully tracked. If tracking is successful, the fundamental frequency at that moment is substituted into a rotation factor for iteration to obtain a variable rotation factor. Finally, the transfer function in the SDFT can be reconstructed based on the variable rotation factor to obtain the target transfer function of the VR-SDFT.

[0043] In some embodiments of this application, the H of the objective function of VR-SDFT k The expression for (z) can be expressed as equation (2):

[0044]

[0045] W with variable twitch factor f The expression can be Equation (3):

[0046]

[0047] Among them, f b1 f is the current power grid frequency estimate. s Where W is the sampling frequency. f The rotation factor is used in the iteration, which incorporates the latest estimate of the current grid frequency.

[0048] This application embodiment adjusts the rotation factor of the transfer function in the SDFT by extracting the fundamental frequency corresponding to a specified harmonic based on the grid signal using SDFT, thus obtaining the adjusted rotation factor, also known as the variable rotation factor. The target transfer function constructed based on the variable rotation factor performs dynamic frequency tracking of the grid signal. This allows for accurate extraction of the fundamental and harmonic components even under dynamically changing grid frequencies, reducing measurement errors and improving the analysis accuracy of the fundamental and harmonics. Furthermore, the VR-SDFT provided in this application embodiment avoids errors caused by spectral leakage during harmonic analysis.

[0049] This application embodiment considers that the traditional SDFT, due to its high filter order, requires storing the entire window of data, resulting in stringent memory resource requirements. Therefore, the traditional SDFT faces two major challenges in engineering applications: high memory demands and slow response speed. To address these issues, this application embodiment optimizes the SDFT transfer function.

[0050] Considering that the actual sampling frequency far exceeds the Nyquist frequency, the filter order and the number of zeros become excessive. This application's embodiment effectively reduces the filter order by reasonably deleting some of the zeros. This directly reduces data processing volume and storage requirements, while significantly improving the algorithm's tracking response speed under severe system fluctuations.

[0051] The traditional SDFT transfer function can be expressed as equation (4):

[0052]

[0053] Among them, H k (SDFT) represents the transfer function of the SDFT, N represents the transform length, k represents the frequency index in the frequency domain, and W N denoted by , z represents the time-domain sampling index.

[0054] The zero point can be expressed as equation (5):

[0055]

[0056] Zp corresponds to the notch filter for each harmonic in SDFT, and l represents the harmonic order. For example, l = 0 means filtering out the DC component, l = 1 means filtering out the fundamental component, and so on.

[0057] This application embodiment aims to reduce memory requirements, lower system latency, and improve response speed during drastic changes by adjusting the number of zero points. A detailed description follows.

[0058] In the embodiments of this application, before constructing the target transfer function based on the adjusted rotation factor, the method further includes: reducing the number of zeros of the transfer function in the SDFT by a preset number to obtain the adjusted transfer function;

[0059] In this embodiment, after reducing the preset number of zeros, a total of 2m zeros are retained (two zeros for each harmonic component on the positive and negative half-axis), 2m < 2l, where m is the number of other harmonic types remaining besides the specified harmonic extracted by SDFT, and m is adjusted according to the harmonic content contained in the specific application conditions. The adjusted transfer function can be expressed as equation (6):

[0060]

[0061] Where 2l is the total number of zeros in the SDFT, and m is the number of other harmonic types besides the specified harmonic extracted by the SDFT.

[0062] In order to reduce memory requirements, lower system latency, and improve response speed under drastic changes, this application embodiment reduces the filter order by deleting some zeros of the transfer function in the SDFT, thereby reducing the amount of data stored in the window and alleviating the dependence on embedded hardware memory. This is beneficial for achieving high-performance harmonic analysis on a low-cost, low-resource platform.

[0063] In embodiments of this application, the target transfer function is constructed based on the corresponding adjusted rotation factor, including:

[0064] The target transfer function is constructed based on the adjusted twitch factor and the adjusted transfer function.

[0065] Specifically, the adjusted twitch factor is the variable twitch factor, expressed as equation (3), and the adjusted transfer function is expressed as equation (6). The constructed target transfer function can be equation (7):

[0066]

[0067] Among them, H k (z) represents the target transfer function, W f denoted by , where N represents the number of sampling points, z represents the time-domain sampling index, and lm is the m-th zero.

[0068] In this embodiment, the fixed rotation factor is replaced with a variable rotation factor that is dynamically adjusted with the fundamental frequency, so that the algorithm can still be aligned with the actual harmonic frequency when the frequency fluctuates, effectively suppressing spectral leakage and improving analysis accuracy.

[0069] In practical applications, the frequency of the power grid changes dynamically in real time. The zeros of the VR-SDFT transfer function may not correspond perfectly with the harmonic frequencies, which may lead to spectral leakage. The output response may still contain other harmonic components, affecting the measurement.

[0070] To address the aforementioned issues and achieve adaptive tracking of the fundamental frequency, this application's embodiments construct a closed-loop frequency tracking mechanism to dynamically adjust the variable rotation factor, reducing frequency estimation errors and enabling high-precision tracking and analysis of the fundamental and harmonic components. A detailed explanation follows.

[0071] In embodiments of this application, after obtaining the target transfer function, the method may further include:

[0072] The power grid signal is processed to obtain the current period phase of the corresponding harmonic of the power grid signal;

[0073] Perform differential operation on the current period phase to obtain the estimated fundamental frequency of the current period of the power grid signal;

[0074] Calculate the variance between the estimated fundamental frequency of the current period and the fundamental frequency of the power grid, or the average value of the actual frequency within a preset time period;

[0075] The fundamental frequency of the next cycle is determined based on the relationship between the variance or average value of the difference and the preset threshold.

[0076] In this embodiment, after constructing the target transfer function, the power grid signal can be processed to obtain the current period phase of the corresponding harmonics. Subsequently, a differential operation is performed to obtain the estimated fundamental frequency of the power grid signal for the current period. Finally, the fundamental frequency for the next period is updated based on the variance or average of the difference between the estimated fundamental frequency and the actual fundamental frequency of the power grid, thereby achieving real-time tracking of the fundamental frequency of the power grid and avoiding measurement errors.

[0077] Optionally, in embodiments of this application, determining the fundamental frequency of the next cycle based on the relationship between the variance or average value of the difference and a preset threshold includes:

[0078] If the variance or average of the difference is less than or equal to a preset threshold, the estimated fundamental frequency of the current period will be used as the fundamental frequency of the next period.

[0079] If the variance or average of the difference is greater than a preset threshold, the fundamental frequency of the current period will not be used as the fundamental frequency of the next period.

[0080] If the variance or average of the difference is less than or equal to a preset threshold, it indicates that the error between the current period's fundamental frequency estimate and the grid's fundamental frequency is within an acceptable range. At this time, the current period's fundamental frequency estimate can be used as the fundamental frequency for the next week, so as to dynamically adjust the rotation factor of VR-SDFT and ensure dynamic frequency tracking.

[0081] If the variance or average of the difference is greater than the preset threshold, it indicates that the error between the current period's fundamental frequency estimate and the grid fundamental frequency is not within an acceptable range. This may be due to overshoot caused by SDFT, which makes the current period's fundamental frequency estimate too large or too small. In this case, it is not necessary to use the current period's fundamental frequency estimate as the fundamental frequency of the next period; the fundamental frequency should remain unchanged.

[0082] The dynamic frequency tracking method based on a variable rotation factor provided in this application can be labeled as the VR-SDFT method. Figure 3 A flowchart of the VR-SDFT method provided in an embodiment of this application is shown below. Figure 3 As shown, the example execution process is as follows:

[0083] A1: Acquire current and voltage signals through the signal acquisition module and perform AD conversion.

[0084] A2: VR-SDFT adjusts the variable rotation factor parameters based on the fundamental frequency estimate to track the spectral response of each harmonic.

[0085] A3: Frequency tracking can be based on the frequency response of a certain harmonic and then perform differential operations to initially obtain the angular frequency, which is the fundamental frequency estimate.

[0086] A4: After the fundamental frequency passes the threshold judgment and is stable without overshoot, a stable fundamental frequency is output.

[0087] A5: Low-pass filtering is applied to the stable fundamental frequency to obtain the fundamental frequency of the power grid signal.

[0088] A6: Iterate the fundamental frequency of the power grid signal against the rotatable factor of the VR-SDFT to track the spectral response of each harmonic. The estimation error Δf of the fundamental frequency can be reduced through iteration.

[0089] In embodiments of this application, processing the power grid signal to obtain the current periodic phase of the corresponding harmonic of the power grid signal may include:

[0090] The power grid signal is processed based on the target transfer function to obtain the harmonic signal of the power grid signal of a specified order;

[0091] Phase extraction is performed on the harmonic signal to obtain the current periodic phase of the corresponding harmonic in the power grid signal.

[0092] For example, the power grid signal can be defined as Equation (8):

[0093]

[0094] Where ak is the amplitude of the kth signal, and fb is the initial fundamental frequency of the signal. The initial phase of the kth harmonic.

[0095] After inputting the power grid signal into VR-SDFT processing, the frequency response of the kth harmonic of the power grid signal can be obtained, denoted as Equation (9):

[0096]

[0097] Among them, Y k (e jω ) represents the frequency response of the kth harmonic after VR-SDFT.

[0098] Subsequently, an arctangent operation can be performed on the real and imaginary parts to extract the current period phase of the corresponding harmonic of the power grid signal, denoted as Equation (10):

[0099] φ k (n)=arctan(I k (n),Q k (n)) (10)

[0100] Where, φ k (n) represents the phase of the kth harmonic.

[0101] Finally, by utilizing the linear phase characteristics, the phase of the current period can be differentially calculated to estimate the kth harmonic frequency, and then the estimated fundamental frequency of the current period can be derived, denoted as Equation (11):

[0102]

[0103] Where, φ k (n) and φ k (n-1) represents the current time and the kth harmonic phase of the previous time, respectively.

[0104] This application embodiment considers that the current period fundamental frequency estimation is susceptible to harmonic interference and prone to overshoot during drastic signal changes. To prevent VR-SDFT divergence, this application embodiment introduces a threshold judgment module to determine f. b Amplitude limiting is performed. By judging whether the variance of the tracked frequency at each moment and the average value of the actual frequency within a preset time period are within the threshold range, it is determined whether to transmit the estimated frequency. After VR-SDFT low-pass filtering, the fundamental frequency of the next cycle is obtained. If it is determined that transmission is necessary, the value is fed back to the rotation factor update stage to form a closed-loop iteration, continuously reducing the frequency estimation error, thereby achieving high-precision tracking and analysis of the fundamental and harmonic components.

[0105] This application addresses the shortcomings of existing SDFT algorithms in adapting to frequency fluctuation scenarios in novel power systems, offering a fast-response, high-accuracy method for dynamic tracking of the fundamental and harmonic frequencies. This improves the accuracy of harmonic analysis under frequency fluctuation conditions. With the widespread integration of new energy sources and power electronic equipment, power grid signals exhibit strong volatility and nonlinearity, and system frequencies are prone to dynamic changes, leading to significant errors in traditional SDFT algorithms due to spectral leakage. This invention significantly improves the accuracy of fundamental and harmonic analysis by real-time tracking of the system's fundamental frequency, adaptively adjusting the rotation factor, and actually tracking each harmonic frequency.

[0106] This application provides a method to improve the accuracy and robustness of harmonic analysis by introducing a fundamental frequency tracking mechanism into the SDFT structure, dynamically adjusting the rotation factor, and determining the frequency tracking threshold. This method addresses the dynamic fluctuations in the power grid frequency.

[0107] The beneficial effects of the embodiments of this application are as follows:

[0108] (1) Improve the accuracy of harmonic analysis under frequency fluctuation conditions. By constructing a frequency tracking closed loop and correcting the rotation factor in real time, the algorithm can still accurately lock the harmonic frequencies when the system frequency fluctuates, effectively suppressing spectral leakage and thus greatly improving the accuracy of harmonic measurement.

[0109] (2) Reduce memory footprint and device cost. By removing some zeros in the transfer function, the filter order is reduced, significantly reducing data storage requirements. This is suitable for resource-constrained embedded platforms and helps reduce hardware costs.

[0110] (3) Improve response speed and overshoot resistance under dynamic operating conditions. When power grid fluctuations intensify, the output delay of traditional algorithm harmonic estimation is significant, and it is prone to overshoot during frequency abrupt changes, affecting tracking stability. In this application embodiment, the filter order is reduced by deleting some zeros of the transfer function, effectively shortening the response time. On this basis, a frequency deviation threshold judgment mechanism is introduced. In each frequency tracking iteration, the frequency value is used to update the rotation factor by judging whether the frequency variance and the interval mean are within the threshold range, thereby suppressing overshoot and ensuring that the system maintains stable convergence while responding quickly.

[0111] The method provided in this application can be applied to the extraction and analysis of fundamental and harmonic quantities when new energy is connected to the grid. By tracking the fundamental frequency of the power grid and updating the rotation factor in real time, it solves the problem of slow response speed and reduced accuracy of fundamental and harmonic quantity analysis when the system frequency fluctuates due to the access of new energy.

[0112] Corresponding to the dynamic frequency tracking method based on variable rotation factor in the above embodiment, Figure 2 This is a structural block diagram of a VR-SDFT dynamic frequency tracking device based on a variable rotation factor, provided as an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 2 The VR-SDFT dynamic frequency tracking device 20 based on a variable rotation factor includes a signal acquisition module 21 and a frequency tracking module 22.

[0113] Among them, the signal acquisition module 21 is used to acquire the power grid signal of the power grid;

[0114] The frequency tracking module 22 is used to process the power grid signal based on the target transfer function in order to perform dynamic frequency tracking of the power grid signal;

[0115] The process of determining the target transfer function includes:

[0116] Extract the fundamental frequency of the power grid signal;

[0117] The rotation factor of the transfer function in the SDFT is adjusted based on the fundamental frequency corresponding to the specified subharmonic extracted by the SDFT to obtain the adjusted rotation factor.

[0118] Construct the target transfer function based on the adjusted twitch factor.

[0119] In one embodiment of this application, the device 20 further includes:

[0120] The frequency extraction module is used to process the power grid signal after obtaining the target transfer function to obtain the current period phase of the corresponding harmonic of the power grid signal; perform differential operation on the current period phase to obtain the estimated fundamental frequency of the power grid signal in the current period; calculate the variance of the difference between the estimated fundamental frequency of the current period and the fundamental frequency of the power grid, as well as the average value of the actual frequency within a preset time period; and determine the fundamental frequency of the next period based on the relationship between the difference or average value and a preset threshold.

[0121] In one embodiment of this application, the frequency extraction module is specifically used to use the estimated fundamental frequency of the current period as the fundamental frequency of the next period if the variance or average of the difference is less than or equal to a preset threshold.

[0122] In one embodiment of this application, the frequency extraction module is specifically used to process the power grid signal based on the target transfer function to obtain the harmonic signal of a specified order of the power grid signal; and to extract the phase of the harmonic signal to obtain the current period phase of the corresponding harmonic of the power grid signal.

[0123] In one embodiment of this application, the adjusted rotation factor W f for:

[0124]

[0125] Among them, f b1 f is the current power grid frequency estimate. s The sampling frequency.

[0126] In one embodiment of this application, the device 20 further includes:

[0127] The function construction module is used to reduce the number of zeros of the transfer function in the SDFT by a preset number before constructing the target transfer function based on the adjusted rotation factor, so as to obtain the adjusted transfer function.

[0128] Accordingly, the target transfer function is constructed based on the adjusted rotation factor.

[0129] In one embodiment of this application, the target transfer function includes:

[0130]

[0131] Among them, Hk (z) represents the target transfer function, W f denoted by , where N represents the number of sampling points, z represents the time-domain sampling index, and lm is the m-th zero.

[0132] See Figure 4 , Figure 4 This is a schematic block diagram of an electricity metering device provided in one embodiment of this application. Figure 4 The electricity metering device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of the modules in the above-described device embodiments, for example... Figure 2 The functions of the signal acquisition module 21 and the frequency tracking module 22 shown are illustrated.

[0133] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0134] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0135] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory.

[0136] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation method described in the dynamic frequency tracking method based on variable rotation factor provided in the embodiments of this application, or they can execute the implementation method of the power metering device described in the embodiments of this application, which will not be repeated here.

[0137] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0138] The computer-readable storage medium can be an internal storage unit of the energy metering device in any of the foregoing embodiments, such as a hard disk or memory of the energy metering device. The computer-readable storage medium can also be an external storage device of the energy metering device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the energy metering device. Furthermore, the computer-readable storage medium can include both internal storage units and external storage devices of the energy metering device. The computer-readable storage medium is used to store computer programs and other programs and data required by the energy metering device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0139] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0140] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electricity metering equipment and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0141] In the several embodiments provided in this application, it should be understood that the disclosed electricity metering devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or units, or it may be an electrical, mechanical, or other form of connection.

[0142] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0143] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0144] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A dynamic frequency tracking method based on a variable rotation factor, characterized in that, include: Acquire the power grid signal; The power grid signal is processed based on the target transfer function to perform dynamic frequency tracking of the power grid signal; The process of determining the target transfer function includes: Extract the fundamental frequency of the power grid signal; The rotation factor of the transfer function in the SDFT is adjusted based on the fundamental frequency corresponding to the specified subharmonic extracted by the SDFT to obtain the adjusted rotation factor. The target transfer function is constructed based on the adjusted rotation factor.

2. The dynamic frequency tracking method based on a variable rotation factor as described in claim 1, characterized in that, After obtaining the target transfer function, the following is also included: The power grid signal is processed to obtain the current period phase of the corresponding harmonic of the power grid signal; Perform a differential operation on the current period phase to obtain the estimated current period fundamental frequency of the power grid signal; Calculate the variance between the estimated fundamental frequency of the current period and the fundamental frequency of the power grid, and the average value of the actual frequency within the preset time period; The fundamental frequency of the next cycle is determined based on the relationship between the variance or average value of the difference and the preset threshold.

3. The dynamic frequency tracking method based on a variable rotation factor as described in claim 2, characterized in that, The step of determining the fundamental frequency of the next cycle based on the relationship between the variance or average of the difference and a preset threshold includes: If the variance or average of the difference is less than or equal to a preset threshold, then the estimated fundamental frequency of the current period is used as the fundamental frequency of the next period.

4. The dynamic frequency tracking method based on a variable rotation factor as described in claim 2, characterized in that, The process of processing the power grid signal to obtain the current period phase of the corresponding harmonic of the power grid signal includes: The power grid signal is processed based on the target transfer function to obtain the harmonic signal of the power grid signal of a specified order; Phase extraction is performed on the harmonic signal to obtain the current periodic phase of the corresponding harmonic of the power grid signal.

5. The dynamic frequency tracking method based on a variable rotation factor as described in claim 1, characterized in that, The adjusted rotation factor W f for: Among them, f b1 f is the current power grid frequency estimate. s The sampling frequency.

6. The dynamic frequency tracking method based on a variable rotation factor as described in claim 1, characterized in that, Before constructing the target transfer function based on the adjusted twitch factor, the following steps are also included: The adjusted transfer function is obtained by reducing the number of zeros in the transfer function in the SDFT by a preset number. Accordingly, constructing the target transfer function based on the adjusted rotation factor includes: The target transfer function is constructed based on the adjusted twitch factor and the adjusted transfer function.

7. The dynamic frequency tracking method based on a variable rotation factor as described in claim 6, characterized in that, The target transfer function includes: Among them, H k (z) represents the target transfer function, W f denoted by , where N represents the number of sampling points, z represents the time-domain sampling index, and lm is the m-th zero.

8. A dynamic frequency tracking device based on a variable rotation factor, characterized in that, include: The signal acquisition module is used to acquire the power grid signal from the power grid. A frequency tracking module is used to process the power grid signal based on the target transfer function in order to perform dynamic frequency tracking of the power grid signal; The process of determining the target transfer function includes: Extract the fundamental frequency of the power grid signal; The rotation factor of the transfer function in the SDFT is adjusted based on the fundamental frequency corresponding to the specified subharmonic extracted by the SDFT to obtain the adjusted rotation factor. The target transfer function is constructed based on the adjusted rotation factor.

9. An electricity metering device, 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 the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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