A method for measuring power frequency voltage frequency

Abstract

The present invention relates to the field of measurement technology, and in particular to a method for measuring power frequency voltage frequency. The method comprises: collecting a power frequency voltage signal, discretizing it, and converting it into a discretized power frequency voltage signal; using a multi-dimensional time-frequency joint adaptive filtering algorithm to adaptively filter the discretized power frequency voltage signal to obtain a power frequency voltage signal processed by the filter; performing phase synchronization processing on the power frequency voltage signal processed by the filter to obtain a filtered synchronized signal; processing the filtered synchronized signal using a zero crossing point detection algorithm based on wavelet transform and fuzzy logic algorithm, while introducing an error correction mechanism to calculate the final corrected period, thereby obtaining a frequency value. The method solves the technical problem of low accuracy and instability in power frequency voltage measurement in a complex power environment.

Description

Technical Field

[0001] The present invention relates to the field of measurement technology, and in particular to a method for measuring the frequency of an industrial frequency voltage. Background Art

[0002] Currently, frequency measurement of power-frequency voltage signals is an integral part of power system monitoring, particularly in areas such as power transmission, distribution, and smart grids. Accurate frequency measurement is crucial for ensuring stable grid operation. Traditional frequency measurement methods primarily rely on digital frequency counters or analog circuits based on zero-crossing point detection. While these methods can effectively measure frequency under certain conditions, they often suffer from complex conditions such as noise, electromagnetic interference, and clock errors, which can lead to inaccurate or unstable measurement results. Furthermore, traditional frequency measurement methods often suffer from long delays, making them incapable of meeting the requirements of real-time monitoring and feedback.

[0003] Most existing frequency measurement techniques rely on time-domain analysis, such as detecting the zero-crossing points of a voltage signal to calculate the period and, in turn, the frequency. However, this approach can lead to increased measurement errors in the presence of high-frequency noise or interference signals. This is particularly true in power systems, where voltage signals not only contain power frequency components but may also be affected by high-frequency signals such as harmonics and sudden voltage fluctuations. Although some improved methods have proposed using filters to remove noise or frequency-domain analysis techniques (such as fast Fourier transforms) to process signals, these methods also have certain limitations, such as spectral leakage and difficulty in adapting filter parameters to dynamically changing signal characteristics.

[0004] Meanwhile, the above-mentioned existing technologies also have the technical problems of low accuracy and instability in measuring the power frequency voltage frequency in a complex power environment. Summary of the Invention

[0005] The present invention provides a method for measuring the frequency of a power frequency voltage, so as to solve the technical problems of low accuracy and instability in measuring the frequency of a power frequency voltage in a complex power environment.

[0006] A method for measuring power frequency voltage frequency of the present invention specifically includes the following technical solutions:

[0007] A method for measuring power frequency voltage frequency, comprising the following steps:

[0008] S1. Collecting a power frequency voltage signal, discretizing it, and converting it into a discretized power frequency voltage signal; adaptively filtering the discretized power frequency voltage signal using a multi-dimensional time-frequency joint adaptive filtering algorithm to obtain a filtered power frequency voltage signal; and performing phase synchronization processing on the filtered power frequency voltage signal to obtain a filtered synchronized signal.

[0009] S2. The signal after filtering and synchronization is processed using a zero-crossing point detection algorithm based on wavelet transform and fuzzy logic algorithm. At the same time, an error correction mechanism is introduced to calculate the final corrected period and thus obtain the frequency value.

[0010] Preferably, the S1 specifically includes:

[0011] In the multi-dimensional time-frequency joint adaptive filtering algorithm, the discretized power frequency voltage signal is windowed to obtain a windowed discrete signal; and the windowed discrete signal is converted from the time domain to the frequency domain to obtain a frequency domain signal.

[0012] Preferably, the S1 specifically includes:

[0013] In the multi-dimensional time-frequency joint adaptive filtering algorithm, a frequency domain filter is designed to process the frequency domain signal. When the filter is initially designed, the parameters of the frequency domain filter are initialized based on the spectral characteristics of the discretized power frequency voltage signal, and the error between the discretized power frequency voltage signal and the output signal after filtering is minimized by adjusting the parameters of the frequency domain filter.

[0014] Preferably, the S1 specifically includes:

[0015] In the multidimensional time-frequency joint adaptive filtering algorithm, regularization terms and dynamic compensation terms are introduced, the objective function is designed, and the parameters of the frequency domain filter are optimized by minimizing the objective function. At the same time, the parameters of the frequency domain filter are updated. After gradient updating, the updated parameters of the frequency domain filter and the updated frequency domain filter are obtained.

[0016] Preferably, the S1 specifically includes:

[0017] In the multi-dimensional time-frequency joint adaptive filtering algorithm, the updated frequency domain filter is used to process the frequency domain signal to obtain a filtered frequency domain signal, and the filtered frequency domain signal is converted back to the time domain to obtain the final filtered signal, that is, the power frequency voltage signal after filter processing; and the power frequency voltage signal after filter processing is phase synchronized to obtain a filtered synchronized signal.

[0018] Preferably, the S2 specifically includes:

[0019] In the implementation process of the zero crossing point detection algorithm based on wavelet transform and fuzzy logic algorithm, the signal after filtering synchronization is decomposed by wavelet transform to obtain the wavelet transformed signal, the low-frequency part after wavelet transform is obtained, and the zero crossing point detection is performed on the low-frequency part after wavelet transform through fuzzy logic reasoning.

[0020] Preferably, the S2 specifically includes:

[0021] In the implementation process of the zero-crossing point detection algorithm based on wavelet transform and fuzzy logic algorithm, the input of fuzzy logic reasoning includes three parameters: the slope of the low-frequency part after wavelet transform, the signal amplitude and the rate of change. Based on these three parameters, it is inferred whether the signal is close to the zero-crossing point; in the fuzzy logic reasoning process, the slope, signal amplitude and rate of change of the low-frequency part after wavelet transform are mapped to the corresponding membership value; based on the membership value, inference is made according to the fuzzy logic reasoning rules, and an output representing the probability of zero-crossing point occurrence is obtained.

[0022] Preferably, the S2 specifically includes:

[0023] In the implementation process of the zero-crossing point detection algorithm based on wavelet transform and fuzzy logic algorithm, the output of the zero-crossing point occurrence probability is defuzzified and the final zero-crossing point moment is calculated.

[0024] Preferably, the S2 specifically includes:

[0025] Based on the zero crossing point moment, an error correction mechanism is introduced to correct the period error between the zero crossing points to obtain the preliminary corrected period between the zero crossing points and the corrected error. The preliminary corrected period between the zero crossing points is combined with the corrected error to obtain the corrected period, and then the frequency value is obtained.

[0026] The beneficial effects of the technical solution of the present invention are:

[0027] 1. The multi-dimensional time-frequency joint adaptive filtering algorithm can effectively remove the noise in the discretized power frequency voltage signal while maintaining high accuracy of frequency measurement. It not only takes into account the advantages of traditional frequency domain filtering, but also uses the combination of time domain and frequency domain to achieve comprehensive filtering of the discretized power frequency voltage signal.

[0028] 2. Multi-scale analysis of the signal after filtering and synchronization is performed through wavelet transform. The signal after wavelet transform can be decomposed into low-frequency and high-frequency parts. The low-frequency part retains the main periodic information of the power frequency voltage signal, while the high-frequency part mainly contains noise components. Combined with fuzzy logic reasoning, it can effectively deal with the uncertainty in the signal and accurately detect the zero crossing point.

[0029] 3. By adopting the differential equation feedback mechanism, the error between the measured period and the predicted period can be calculated to dynamically adjust the period measurement result. By integrating the error, the period deviation caused by clock error, sampling error or signal fluctuation can be eliminated. Finally, the corrected period obtained after error correction can significantly improve the accuracy of frequency measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of a method for measuring power frequency voltage frequency according to the present invention. DETAILED DESCRIPTION

[0031] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0032] Unless defined otherwise, 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 invention belongs.

[0033] The specific scheme of the method for measuring the frequency of an industrial frequency voltage provided by the present invention is described in detail below with reference to the accompanying drawings.

[0034] Refer to the attached Figure 1 , which shows a flow chart of a method for measuring power frequency voltage frequency provided by an embodiment of the present invention, the method comprising the following steps:

[0035] S1. Collecting a power frequency voltage signal, discretizing it, and converting it into a discretized power frequency voltage signal; adaptively filtering the discretized power frequency voltage signal using a multi-dimensional time-frequency joint adaptive filtering algorithm to obtain a filtered power frequency voltage signal; and performing phase synchronization processing on the filtered power frequency voltage signal to obtain a filtered synchronized signal.

[0036] The power frequency voltage signal is acquired through data acquisition equipment such as sensors and voltage probes. After the power frequency voltage signal is acquired, the power frequency voltage signal is converted into a digital-to-analog converter to obtain a discrete power frequency voltage signal. ;

[0037] After the power frequency voltage signal is discretized, a multi-dimensional time-frequency joint adaptive filtering algorithm is used to perform adaptive filtering on the discretized power frequency voltage signal. The multi-dimensional time-frequency joint adaptive filtering algorithm can effectively eliminate the noise in the discretized power frequency voltage signal while meeting the requirements of high-precision frequency measurement, thereby obtaining a power frequency voltage signal after filter processing. The specific implementation process is as follows:

[0038] After the power frequency voltage signal is discretized, windowing is used to ensure that the discretized power frequency voltage signal performs better in frequency domain analysis. The windowing operation is performed by a window function The discretized power frequency voltage signal is weighted to reduce spectrum leakage. According to the specific scenario, such as Hamming window, rectangular window, Gaussian window, etc., after the windowing operation, the windowed discrete signal is obtained. :

[0039] ,

[0040] in, It is a windowed discrete signal, that is, a weighted discrete signal, which ensures that the discretized power frequency voltage signal has no spectrum leakage in the subsequent frequency domain transformation and maintains the time-frequency characteristics of the discretized power frequency voltage signal.

[0041] Furthermore, in order to perform frequency domain analysis on the discretized power frequency voltage signal, the windowed discrete signal is transformed into Convert from time domain to frequency domain to obtain the frequency domain representation of the signal , or the frequency domain signal, contains the energy distribution of the discretized power-frequency voltage signal at each frequency, as well as the frequency components and amplitude information of the discretized power-frequency voltage signal. In the frequency domain, the spectral characteristics of the discretized power-frequency voltage signal can be clearly seen, including the main frequency and high-frequency noise components of the discretized power-frequency voltage signal.

[0042] Further, design a frequency domain filter The frequency domain filter is determined according to the specific application scenario, such as low-pass filter, weighted frequency response filter, etc. Its function is to process the frequency domain signal, remove noise, and retain the main characteristics of the discretized power frequency voltage signal. It will be adjusted according to the current signal characteristics in the subsequent adaptive process. When the filter is initially designed, the parameters of the frequency domain filter are It can be initialized according to the spectrum characteristics of the discretized power frequency voltage signal, but it will be continuously optimized by the adaptive algorithm in the subsequent steps. The goal of the adaptive algorithm is to adjust the parameters of the frequency domain filter , in order to minimize the error between the discretized power frequency voltage signal and the output signal after filtering. To achieve this goal, based on the classic adaptive filtering algorithm, especially the LMS (least mean square error) and RLS (recursive least squares) algorithms, an extension is made, and the objective function is designed by introducing additional regularization terms and dynamic compensation terms. , and optimize the parameters of the frequency domain filter by minimizing the objective function The objective function It contains three terms: signal error, frequency domain filter regularization term, and signal change rate compensation term. The specific form of the objective function is:

[0043] ,

[0044] in, It is the original input power frequency voltage signal, that is, the discretized power frequency voltage signal, which reflects the amplitude of the power frequency voltage at each time point and represents the sampled original signal data; It is the output signal after filtering. After the frequency domain filter adjusts the parameters according to the adaptive algorithm, the output signal after filtering is generated. The goal is to be as close as possible to the discretized power frequency voltage signal. , remove noise and interference simultaneously; It is a regularization factor used to adjust the smoothness of the frequency domain filter in the frequency domain. Its function is to limit the amplitude of the frequency domain filter and prevent the parameters of the frequency domain filter from being over-adjusted, resulting in overfitting or noise amplification. It is determined according to expert experience and the reference value range is ; It is the weight factor of the rate of change compensation term, which is used to control the influence of the rate of change (or instantaneous frequency change) of the discretized power frequency voltage signal on the objective function. Through the weight factor of the rate of change compensation term, the frequency domain filter can perform additional compensation for the rapid changes (such as mutations or noise) of the discretized power frequency voltage signal. It is determined according to specific needs, and the reference value range is ; is the discretized power frequency voltage signal The rate of change represents the rate of change of the discretized power frequency voltage signal at each discrete time sampling point (the approximate derivative of the discretized power frequency voltage signal). It is used to capture the instantaneous changes of the discretized power frequency voltage signal, especially for high-frequency noise or rapidly changing parts. The rate of change is approximately calculated by taking a difference on the discretized power frequency voltage signal. is a discrete time index, indicating the temporal position of the discretized power frequency voltage signal; is the total number of discrete time sampling points; is the error term, which represents the difference between the discretized power frequency voltage signal and the output signal after filtering. Optimizing this term can minimize the distortion of the output signal after filtering. It is the regularization term of the frequency domain filter, which is used to prevent excessive adjustment of the frequency domain filter parameters and maintain the stability of the frequency domain filter; It is a second-order compensation term that takes into account the rate of change of the discretized power-frequency voltage signal. It is used to compensate for the dynamic changes in the discretized power-frequency voltage signal and reduce the error caused by rapid changes.

[0045] In order to optimize the above objective function, the parameters of the frequency domain filter are adjusted using the gradient descent method. Update, and after gradient update, get the updated frequency domain filter parameters And the updated frequency domain filter Next, use the updated frequency domain filter to filter the frequency domain signal Specifically, the frequency domain signal With the updated frequency domain filter Multiply to get the filtered frequency domain signal , which can effectively remove the noise in the frequency domain signal and retain the main frequency components. Finally, the filtered frequency domain signal is transformed into Convert back to the time domain to get the final filtered signal , which is the power-frequency voltage signal after filtering. This processing effectively eliminates interference while retaining the power-frequency characteristics of the discretized power-frequency voltage signal. This filtered power-frequency voltage signal can not only be used for frequency measurement, but also provides accurate data for subsequent signal analysis and monitoring.

[0046] In order to avoid clock error and signal delay, which may lead to deviation of the collected power frequency voltage signal, the phase synchronization technology is introduced after the adaptive filtering process. The phase synchronization technology uses the existing phase-locked loop technology to achieve phase synchronization of the signal and obtain the signal after filtering and synchronization. .

[0047] S2. The signal after filtering and synchronization is processed using a zero-crossing point detection algorithm based on wavelet transform and fuzzy logic algorithm. At the same time, an error correction mechanism is introduced to calculate the final corrected period and thus obtain the frequency value.

[0048] The signal after filtering and synchronization is processed using a zero-crossing detection algorithm based on wavelet transform and fuzzy logic algorithm. The zero-crossing detection algorithm based on wavelet transform and fuzzy logic algorithm detects zero-crossing points by combining wavelet transform and fuzzy logic reasoning, and further uses an error correction mechanism for dynamic adjustment and precise correction, thereby achieving high-precision frequency measurement. The specific implementation process is as follows:

[0049] After filtering and synchronization, the signal , decomposed by wavelet transform, the purpose of wavelet transform is to decompose the signal after filtering synchronization into multi-scale low-frequency parts and high-frequency parts. Through wavelet transform, the main features of the signal after filtering synchronization can be extracted and some high-frequency noise can be removed. Specifically, the signal after filtering synchronization The discrete wavelet transform (DWT) will be performed through the wavelet basis function selected according to the specific application scenario. After the wavelet transform, the wavelet transformed signal is obtained, including the low-frequency part after the wavelet transform. and the high-frequency part after wavelet transformation The low-frequency part contains the main periodic information of the signal after filtering and synchronization; the high-frequency part contains the noise component of the signal after filtering and synchronization.

[0050] Furthermore, fuzzy logic reasoning is used to detect zero crossing points in the low-frequency portion after wavelet transformation. This fuzzy logic reasoning addresses the uncertainty in the low-frequency portion after wavelet transformation, helping to understand and address the ambiguity and lack of clear boundaries in the low-frequency portion after wavelet transformation. The input to fuzzy logic reasoning includes three parameters: the slope of the low-frequency portion after wavelet transformation, the signal amplitude, and the rate of change.

[0051] The slope of the low-frequency part after wavelet transform It is an important indicator to measure the trend of signal change. When the signal rises or falls sharply, the slope will show a larger value. The signal amplitude of the low-frequency part after wavelet transform It measures the strength of the signal, while the rate of change of the low-frequency part after wavelet transformation is It reflects the intensity of the signal change. Through these three parameters, we can infer whether the signal is close to the zero crossing point. The fuzzification process converts these signal features into fuzzy sets. For example, the slope of the low-frequency part after wavelet transformation is It can be fuzzy-coded into several states: "rapid rise", "slow rise", "stable", "slow fall" and "rapid fall"; the amplitude of the low-frequency part after wavelet transform It is blurred into values such as “strong” and “medium”; and the rate of change of the low-frequency part after wavelet transformation is Blurred into "sharp change" or "smooth change".

[0052] Fuzzy logic inference rules, combined with the aforementioned signal characteristics, are used to determine whether the signal is near a zero crossing. For example, a fuzzy logic inference rule can be set as follows: "If the signal's slope is 'rapidly rising' and its amplitude is 'strong', then the signal has a high probability of passing through a zero crossing." This fuzzy logic inference rule is used to calculate the output of the fuzzy logic inference, and the output obtained from the fuzzy logic inference rule indicates the probability of a zero crossing.

[0053] In the fuzzy logic reasoning process, the existing fuzzy membership function such as Gaussian membership function is used. The slope, signal amplitude, and rate of change of the low-frequency part after wavelet transformation are mapped to corresponding membership values. Based on the membership values, fuzzy logic reasoning rules are used to make inferences and produce an output representing the probability of a zero-crossing point.

[0054] Furthermore, in order to obtain the specific zero-crossing point moment, the output of the zero-crossing point probability is defuzzified using the existing maximum membership method (or weighted average method). The defuzzification process uses the maximum membership method (or weighted average method) to calculate the final zero-crossing point moment. ;

[0055] Once the zero crossing moment is detected, an error correction mechanism is introduced to correct the periodic error between zero crossing points through a differential equation feedback mechanism; first, the period between any two zero crossing points is calculated ,in, is the index of the zero crossing point, recorded as the measurement period, and used Represents; then calculate the average value of the period and standard deviation In order to reduce the impact of individual cycle calculation errors, the cycle between zero crossing points is initially corrected to obtain the initially corrected cycle between zero crossing points. :

[0056] ,

[0057] Furthermore, based on the differential equation feedback mechanism, the correction error is obtained :

[0058] ,

[0059] in, Indicates time The correction error at the moment describes the degree of deviation of the prediction period and reflects the calibration status; It is the attenuation coefficient of feedback correction, which is used to control the influence of error on the correction period. It depends on the sensitivity and stability of error. The reference value range is ; It's a historical moment The correction error at the time represents the measurement error at the previous moment. ; Is the gain coefficient of feedback correction, determined according to specific needs, the reference value range is ; It's a historic moment Measurement period It's a historic moment The prediction period of is obtained using the existing error correction model; It is a virtual time variable in the integration process, representing an integration dimension of all historical moments in the error correction process.

[0060] Finally, the final revised cycle is obtained: , and further obtain the frequency value : .

[0061] In summary, a method for measuring power frequency voltage frequency is completed.

[0062] The order in which the embodiments of the invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0063] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0064] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for measuring power frequency voltage frequency, characterized in that: The following steps are involved: S1. Collecting a power frequency voltage signal, discretizing it, and converting it into a discretized power frequency voltage signal; adaptively filtering the discretized power frequency voltage signal using a multi-dimensional time-frequency joint adaptive filtering algorithm to obtain a filtered power frequency voltage signal; and performing phase synchronization processing on the filtered power frequency voltage signal to obtain a filtered synchronized signal. S2. Process the filtered and synchronized signal using a zero-crossing detection algorithm based on wavelet transform and fuzzy logic, while introducing an error correction mechanism to calculate the final corrected period and thus derive the frequency value; In the implementation of the zero-crossing point detection algorithm based on wavelet transform and fuzzy logic algorithm, the signal after filtering synchronization is decomposed through wavelet transform to obtain the wavelet transformed signal, and the low-frequency part after wavelet transform is obtained. The zero-crossing point detection of the low-frequency part after wavelet transform is performed through fuzzy logic reasoning. The input of fuzzy logic reasoning includes three parameters: the slope of the low-frequency part after wavelet transform, the signal amplitude and the rate of change. Based on these three parameters, it is inferred whether the signal is close to the zero-crossing point. In the fuzzy logic reasoning process, the slope, signal amplitude and rate of change of the low-frequency part after wavelet transformation are mapped to the corresponding membership value; Based on the membership values, inference is made according to the fuzzy logic reasoning rules and an output representing the probability of a zero-crossing point occurring is obtained.

2. The method for measuring the power frequency voltage according to claim 1, characterized in that: Said S1 specifically includes: In the multi-dimensional time-frequency joint adaptive filtering algorithm, the discretized power frequency voltage signal is windowed to obtain a windowed discrete signal; and the windowed discrete signal is converted from the time domain to the frequency domain to obtain a frequency domain signal.

3. The method for measuring the power frequency voltage according to claim 2, wherein: Said S1 specifically includes: In the multi-dimensional time-frequency joint adaptive filtering algorithm, a frequency domain filter is designed to process the frequency domain signal. When the filter is initially designed, the parameters of the frequency domain filter are initialized based on the spectral characteristics of the discretized power frequency voltage signal, and the error between the discretized power frequency voltage signal and the output signal after filtering is minimized by adjusting the parameters of the frequency domain filter.

4. The method for measuring the power frequency voltage according to claim 3, characterized in that: Said S1 specifically includes: In the multidimensional time-frequency joint adaptive filtering algorithm, regularization terms and dynamic compensation terms are introduced, the objective function is designed, and the parameters of the frequency domain filter are optimized by minimizing the objective function. At the same time, the parameters of the frequency domain filter are updated. After gradient updating, the updated parameters of the frequency domain filter and the updated frequency domain filter are obtained.

5. The method for measuring the power frequency voltage according to claim 4, characterized in that: Said S1 specifically includes: In the multi-dimensional time-frequency joint adaptive filtering algorithm, the updated frequency domain filter is used to process the frequency domain signal to obtain a filtered frequency domain signal, and the filtered frequency domain signal is converted back to the time domain to obtain the final filtered signal, that is, the power frequency voltage signal after filter processing; and the power frequency voltage signal after filter processing is phase synchronized to obtain a filtered synchronized signal.

6. The method for measuring the power frequency voltage according to claim 1, characterized in that: Said S2 specifically includes: In the implementation process of the zero-crossing point detection algorithm based on wavelet transform and fuzzy logic algorithm, the output of the zero-crossing point occurrence probability is defuzzified and the final zero-crossing point moment is calculated.

7. The method for measuring the power frequency voltage according to claim 6, characterized in that: Said S2 specifically includes: Based on the zero crossing point moment, an error correction mechanism is introduced to correct the period error between the zero crossing points to obtain the preliminary corrected period between the zero crossing points and the corrected error. The preliminary corrected period between the zero crossing points is combined with the corrected error to obtain the corrected period, and then the frequency value is obtained.

Images