A no-inrush switching device and its control method

Abstract

This application relates to the technical field of inrush-free switching control, and specifically relates to an inrush-free switching device and its control method. The method includes: obtaining data of various electrical parameters on a diode circuit; performing frequency-domain analysis on the data of various electrical parameters, and respectively performing clustering processing on the frequency-domain data of various electrical parameters, calculating the frequency complexity and within-cluster chaos degree of each clustering cluster, and further obtaining the noise distribution difference degree of each electrical parameter. Combining the average level of the within-cluster chaos degree of all clustering clusters, the noise distribution complexity of each electrical parameter is obtained; analyzing the principal components of the modal components of each electrical parameter, combining the noise distribution complexity to obtain the steady-state change factor of each electrical parameter, adjusting the step factor of the filter, filtering the data of various electrical parameters on the diode circuit, and controlling the relay according to the filtered electrical parameter data, so as to complete the inrush-free switching control of the capacitor. This application can improve the control accuracy of inrush-free switching.

Description

Technical Field

[0001] This application relates to the technical field of inrush-free switching control, and particularly relates to an inrush-free switching device and its control method. Background Art

[0002] Due to the need for reactive power compensation in the power grid, capacitors need to be connected to the power grid or disconnected from the power grid through relays at appropriate times to reduce energy waste, improve the effective utilization rate of electric energy, and ensure the stable operation of the power grid. Currently, the microcontroller analyzes the half-wave conduction characteristics of the diode through the current and voltage on the diode circuit, and controls the relay to perform inrush-free switching-on and switching-off operations under half-wave conduction, so as to realize the switching of capacitors in the power grid, reduce energy waste, and improve the effective utilization rate of electric energy.

[0003] However, during the inrush-free switching control process, it is necessary to frequently switch the capacitor according to the current and voltage conditions in the diode. However, due to the presence of background noise interference in the actual power grid, these background noises will seriously interfere with the current and voltage on the diode circuit, causing the current and voltage on the diode circuit to deviate from the normal values, affecting the accuracy of subsequent inrush-free switching control, and unable to ensure the safety of the capacitor and the service life of the switching device. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide an inrush-free switching device and its control method, and the specific technical solutions adopted are as follows:

[0005] This application embodiment provides an inrush-free switching control method, including the following steps:

[0006] Obtain the data of each electrical parameter on the diode circuit, and the electrical parameters include current and voltage;

[0007] Perform frequency domain analysis on the data of each electrical parameter, and perform clustering processing on the frequency domain data of each electrical parameter respectively;

[0008] For each electrical parameter, analyze the frequency change and fluctuation degree of the frequency domain data within each clustering cluster to obtain the frequency complexity of each clustering cluster. Combine the amplitude change situation of the frequency domain data within each clustering cluster to obtain the within-cluster chaos degree of each clustering cluster. According to the differences in the average frequency level and amplitude average level of the frequency domain data within different clustering clusters, obtain the noise distribution difference degree of each electrical parameter. Combine the average level of the within-cluster chaos degree of all clustering clusters to obtain the noise distribution complexity of each electrical parameter.

[0009] Perform modal decomposition on the data of each electrical parameter, conduct principal component analysis on the modal components, and combine the noise distribution complexity to obtain the steady-state change factor of each electrical parameter. Adjust the step factor of the filter based on the steady-state change factor and the autocorrelation characteristics of the data of each electrical parameter;

[0010] Filter the data of each electrical parameter on the diode circuit based on the adjusted step factor, and control the relay according to the filtered electrical parameter data, thereby completing the no-inrush switching control of the capacitor.

[0011] Preferably, the method for obtaining the frequency complexity of each clustering cluster is as follows:

[0012] Arrange the frequencies of all frequency-domain data within each clustering cluster in ascending order to form the frequency sequence of each clustering cluster, and take the product of the mean and variance of all elements in the first-order difference sequence of the frequency sequence as the frequency complexity of each clustering cluster.

[0013] Preferably, the method for obtaining the within-cluster chaos degree of each clustering cluster is: calculate the information entropy of the amplitude values of the frequency-domain data within each clustering cluster, and take the product of the information entropy and the frequency complexity as the within-cluster chaos degree of each clustering cluster.

[0014] Preferably, the calculation method for the noise distribution difference degree of each electrical parameter is:

[0015] The calculation formula for the noise distribution difference degree of current is: ;

[0016] In the formula, is the noise distribution difference degree of current, is the number of clustering clusters of all frequency-domain data points in the current time series, and are the frequency means of all frequency-domain data points within the i-th and (i - 1)-th clustering clusters respectively, and are the mean values of the frequency-domain amplitudes of all frequency-domain data points within the i-th and (i - 1)-th clustering clusters respectively.

[0017] Preferably, the construction method for the current time series is: arrange the current within a preset time period before the current moment in chronological order to obtain the current time series.

[0018] Preferably, the method for obtaining the noise distribution complexity of each electrical parameter is: take the product of the mean value of the within-cluster chaos degrees of all clustering clusters corresponding to each electrical parameter and the noise distribution difference degree as the noise distribution complexity of each electrical parameter.

[0019] Preferably, the calculation method for the steady-state change factor of each electrical parameter is:

[0020] For the current data, the calculation formula for the steady-state change factor V of the current is as follows: ; In the formula, is the noise distribution complexity of the current, is the mean value of the variance contribution rates of all principal components in the modal matrix corresponding to the current time series, is a constant to avoid a zero denominator.

[0021] Preferably, the method for obtaining the modal matrix corresponding to the current time series is: performing modal decomposition on the current time series, and using each modal component as a row vector to form the modal matrix corresponding to the current time series.

[0022] Preferably, the calculation method for the adjusted step size factor is:

[0023] ;

[0024] In the formula, is the step size factor of the adjusted LMS adaptive filter, is the ceiling function, is the maximum eigenvalue in the autocorrelation matrix of the current time series and the voltage time series, is the exponential function with the natural constant as the base, and are the steady-state change factors of the current and voltage respectively.

[0025] The embodiment of the present application also provides a no-inrush switching 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, the steps of the no-inrush switching control method described in any one of the above are implemented.

[0026] As can be seen from the above, the no-inrush switching device and its control method provided by the present application have at least the following beneficial effects:

[0027] By performing clustering analysis on the frequency-domain data of the electrical parameters in the diode circuit and using the methods of intra-cluster analysis and inter-cluster analysis, the present application accurately measures the distribution complexity characteristics of the frequency-domain noise, enabling more accurate adjustment of the step size factor of the LMS adaptive filter in the future;

[0028] Considering the distribution complexity characteristics of the frequency-domain noise and the variance contribution rate characteristics of the modal components, the present application accurately measures the steady-state change characteristics of the electrical parameters in the diode circuit and accurately adjusts the step size factor in the LMS adaptive filter through the steady-state change characteristics of the electrical parameters in the diode circuit, eliminating the interference caused by complex noise existing in the actual power grid to the subsequent switching control;

[0029] By accurately adjusting the step size factor in the LMS adaptive filter, this application eliminates the complex background noise existing in the actual power grid, thereby improving the precision of controlling inrush-free switching, and can effectively ensure the safety of capacitors and the service life of switching devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0031] Figure 1 It is a flowchart of the steps of a method for controlling inrush-free switching provided by this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] To further elaborate on the technical means and effects adopted by this application to achieve the intended invention purpose, the following will, in conjunction with the drawings and preferred embodiments, detail the specific implementation manners, structures, features, and effects of an inrush-free switching device and its control method proposed according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0033] Unless otherwise specified and limited, terms such as "including", "comprising", or any other variant thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article, or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the article or device including the said element. Additionally, the term "and / or" used herein includes any and all combinations of one or more of the related listed items. All technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0034] The following will specifically describe the specific solutions of an inrush-free switching device and its control method provided by this application in conjunction with the drawings.

[0035] Please refer to Figure 1 , which shows a flowchart of the steps of a method for controlling inrush-free switching provided by an embodiment of this application, including the following steps:

[0036] Step 1: Obtain the electrical parameter data on the diode circuit, where the electrical parameters include current and voltage.

[0037] In the control method for inrush - free switching, it is first necessary to filter the electrical parameters of the diode circuit to eliminate the interference caused by the complex noise in the actual power grid to the subsequent switching control, thereby improving the accuracy of the subsequent inrush - free switching control. Among them, in this embodiment, the electrical parameters include current and voltage.

[0038] To improve the accuracy of inrush - free switching control, the current and voltage on the diode circuit are collected by a current sensor and a voltage sensor, and the sampling frequency is 1 kHz. In actual application scenarios, the implementer can set the sampling frequency by himself.

[0039] Furthermore, to improve the filtering accuracy of the adaptive filter for the current and voltage on the diode circuit, the current and voltage within one minute before the current moment are sorted in chronological order respectively to obtain the current time series and the voltage time series.

[0040] Step 2: Perform frequency - domain analysis on the data of each electrical parameter, and perform clustering processing on the frequency - domain data of each electrical parameter respectively.

[0041] To improve the accuracy of capacitor inrush - free switching control, the current signal and voltage signal on the diode circuit are filtered by an LMS adaptive filter to eliminate the interference caused by the complex noise in the actual power grid to the subsequent inrush - free switching control.

[0042] However, due to the complexity of the noise interference in the actual power grid, it is necessary to adjust the step - size factor of the LMS adaptive filter according to the complex noise interference characteristics of the current and voltage in the diode circuit, in order to improve the filtering accuracy of the current and voltage on the diode circuit, so as to more accurately control the inrush - free input operation and cut - off operation of the relay under half - wave conduction, and realize the switching of the capacitor to the power grid.

[0043] Analyze the frequency - domain characteristics of the current and voltage on the diode circuit. There are many existing frequency - domain transformation methods. Among them, the Fourier transform includes the discrete Fourier transform or the fast Fourier transform. The current time series is input into the Fourier transform. In this embodiment, the fast Fourier transform is used to obtain all the frequency - domain data in the current time series. The abscissa of the frequency - domain data is frequency, and the ordinate is the frequency - domain amplitude. The Fourier transform is a well - known technology, and the specific process will not be elaborated here.

[0044] Further, in order to accurately analyze the complex distribution characteristics of noise under the influence of noise interference, so as to more accurately adjust the step factor of the LMS adaptive filter, all the frequency-domain data in the current time series are input into a clustering algorithm, which can be DBSCAN clustering or DPC density peak clustering. In this embodiment, DPC density peak clustering is used to obtain each clustering cluster of all the frequency-domain data in the current time series. DPC density peak clustering is a well-known technology and will not be elaborated further.

[0045] By using a clustering algorithm to perform clustering analysis on the frequency-domain data, it is possible to accurately analyze the complexity of the frequency-domain amplitudes corresponding to different frequencies in the current time series, which is used to more accurately adjust the step factor of the LMS adaptive filter in the subsequent process, so as to eliminate the interference caused by the complex noise existing in the actual power grid to the subsequent switching control, thereby improving the accuracy of the inrush-free switching control.

[0046] Step 3: For each electrical parameter, analyze the frequency change and fluctuation degree of the frequency-domain data within each clustering cluster to obtain the frequency complexity of each clustering cluster. Combine the amplitude change situation of the frequency-domain data within each clustering cluster to obtain the intra-cluster chaos degree of each clustering cluster. According to the differences in the average frequency level and amplitude average level of the frequency-domain data within different clustering clusters, obtain the noise distribution difference degree of each electrical parameter. Combine the average level of the intra-cluster chaos degree of all clustering clusters to obtain the noise distribution complexity of each electrical parameter.

[0047] If the current on the diode circuit is more severely interfered by the complex environment of the power grid, the higher the complexity of the frequency-domain data within the same clustering cluster in the clustering result of the frequency-domain data, and the greater the difference in the frequency-domain data between different clustering clusters, the more it can reflect the complex characteristics of the noise distribution under the influence of noise interference.

[0048] Further, arrange the frequencies of all the frequency-domain data within each clustering cluster in ascending order to form the frequency sequence of each clustering cluster, and calculate the first-order difference sequence of the frequency sequence. The first-order difference sequence reflects the continuous difference in the frequencies of all the frequency-domain data points within each clustering cluster. If the continuous frequency difference is larger and more discrete, the more it can reflect the complexity of the continuous frequency difference within the clustering cluster.

[0049] Therefore, take the product of the mean and variance of all the elements in the first-order difference sequence as the frequency complexity of each clustering cluster. The frequency complexity reflects the complexity of the continuous frequency difference within the clustering cluster. If the complexity of the continuous frequency difference within the clustering cluster and the complexity of the frequency-domain amplitude within the clustering cluster are higher, the more it can comprehensively reflect the chaos degree of the frequency-domain data within the clustering cluster.

[0050] Further, calculate the information entropy of the amplitude values of the frequency-domain data within each clustering cluster, and take the product of the information entropy and the frequency complexity as the within-cluster chaos degree of each clustering cluster. The within-cluster chaos degree reflects the complexity of the frequency-domain data within each clustering cluster. If the complexity of the frequency-domain data within a clustering cluster is higher, it indicates that the frequency-domain data is more likely to contain the frequency components and frequency-domain amplitudes corresponding to noise, thus reflecting the complex characteristics of the noise distribution.

[0051] Meanwhile, calculate the frequency mean and amplitude mean of all the frequency-domain data points within each clustering cluster respectively. If the current on the diode circuit is more complexly interfered by external noise, different clustering clusters are more likely to contain the frequencies and frequency-domain amplitudes corresponding to noise, and the differences between different types of noise are relatively large, showing an obvious difference in the noise distribution.

[0052] Through the above analysis, calculate the noise distribution difference degree of the current:

[0053] ;

[0054] In the formula, is the noise distribution difference degree of the current, is the number of clustering clusters of all the frequency-domain data points in the current time series, and are the frequency means of all the frequency-domain data points in the i-th and (i - 1)-th clustering clusters respectively, and are the frequency-domain amplitude means of all the frequency-domain data points in the i-th and (i - 1)-th clustering clusters respectively.

[0055] The noise distribution difference degree reflects the difference degree of the distributions of different types of noise under the influence of noise interference. If the difference degree of the distributions of different types of noise is larger, and the complexity of the frequency-domain data within each clustering cluster is higher, it can more significantly reflect the complex characteristics of the noise distribution. At this time, the step factor of the LMS adaptive filter should be adjusted more to eliminate the interference caused by complex noise in the actual power grid to the subsequent switching control.

[0056] Further, calculate the mean value of the within-cluster chaos degrees of all the clustering clusters corresponding to the frequency-domain data of the current time series, and take the product of the mean value of the within-cluster chaos degree and the noise distribution difference degree as the noise distribution complexity of the current.

[0057] Correspondingly, for the voltage data, use the above method of this embodiment to process the voltage time series to obtain the noise distribution complexity of the voltage.

[0058] The complexity of the noise distribution can reflect the distribution complexity characteristics of the frequency-domain noise in the current time series and voltage time series. By clustering the frequency-domain data in the current time series and voltage time series and using the within-cluster analysis and between-cluster analysis methods, the distribution complexity characteristics of the frequency-domain noise are accurately measured, enabling more accurate adjustment of the step factor of the LMS adaptive filter in the subsequent process, thereby more accurately controlling the inrush-free switching-on operation and switching-off operation of the relay under half-wave conduction, and ultimately realizing the switching of capacitors in the power grid.

[0059] Step 4: Perform modal decomposition on the data of each electrical parameter, conduct principal component analysis on the modal components, and combine the noise distribution complexity to obtain the steady-state change factor of each electrical parameter. Adjust the step factor of the filter based on the steady-state change factor and the autocorrelation characteristics of each electrical parameter data.

[0060] In order to more accurately adjust the step factor of the LMS adaptive filter, comprehensively analyze the steady-state change characteristics of the electrical parameters in the diode circuit according to the noise interference complexity of the current and voltage and in combination with the variance contribution rate characteristics of the time-domain modes of the current and voltage on the diode circuit.

[0061] More specifically, input the current time series into the modal decomposition algorithm. There are many existing modal decomposition algorithms, such as empirical mode decomposition or variational mode decomposition. In this embodiment, empirical mode decomposition is used to obtain each modal component sequence of the current time series. Empirical mode decomposition is a well-known technology and will not be elaborated in this embodiment.

[0062] Due to the influence of power grid noise interference, significant variation characteristics will appear in each modal component, and the more complex the fluctuation changes on the modal component, the more prominent the non-steady-state changes under the influence of noise interference.

[0063] Therefore, take each modal component sequence as each row vector of the modal matrix, input the modal matrix composed of each modal component sequence into the PCA (Principal Components Analysis) algorithm, and use the PCA algorithm to obtain the variance contribution rate of each principal component in the modal matrix. The larger the variance contribution rate, the greater the variation characteristics and fluctuation characteristics under the influence of noise interference within the corresponding modal component sequence. The acquisition process of the PCA algorithm and the variance contribution rate is a well-known technology and will not be elaborated in this embodiment.

[0064] Through the above analysis, calculate the steady-state change factor of the current:

[0065] ;

[0066] In the formula, is the steady-state change factor of the current, is the noise distribution complexity of the current, is the mean of the variance contribution rates of all principal components in the modal matrix corresponding to the current time series, is a constant to avoid a zero denominator, The value range of is (0.001, 0.01), and its value range is a relatively small constant. In this example, takes the value of 0.001.

[0067] The steady-state change factor reflects the steady-state change characteristics within the current time series. If the degree of variation and fluctuation of the current time series in the time domain are higher, it indicates that the steady-state change characteristics under the influence of noise interference are smaller. At the same time, if the noise distribution complexity of the current time series in the frequency domain is higher, at this time, the change of the current within the current time series is difficult to maintain a steady change, indicating that the influence of noise interference is greater. At this time, the step size factor of the LMS adaptive filter should be adjusted larger, so that the LMS adaptive filter can more quickly and accurately eliminate the interference caused by complex noise existing in the actual power grid to the subsequent switching control, thereby improving the accuracy of inrush-free switching control.

[0068] Correspondingly, for the voltage data, the method for obtaining the steady-state change factor of the current in the above embodiment of this example is adopted to process the voltage time series, and the steady-state change factor of the voltage is obtained.

[0069] Furthermore, the step size factor in the LMS adaptive filter is adjusted through the steady-state change factors of the current and voltage:

[0070] ;

[0071] In the formula, is the adjusted step size factor of the LMS adaptive filter, is the ceiling function, is the maximum eigenvalue in the autocorrelation matrix of the current time series and the voltage time series, is the exponential function with the natural constant as the base, and are the steady-state change factors of the current and voltage respectively. Among them, the acquisition of the autocorrelation matrix and the calculation of the maximum eigenvalue are well-known technologies and will not be elaborated in this example.

[0072] When the maximum eigenvalue in the autocorrelation matrix of current and voltage data is smaller, and the steady-state change factor of current and voltage data is smaller, it indicates that the current and voltage in the diode circuit are greatly affected by noise interference in the power grid. At this time, the step factor of the adaptive filter is larger, enabling the current and voltage in the diode circuit to quickly converge to the steady state and improving the filtering effect of the current and voltage in the diode circuit. On the contrary, it indicates that the current and voltage in the diode circuit are less affected by noise interference in the power grid. At this time, the step factor of the adaptive filter is smaller, ensuring the steady-state effect of the adaptive filter's filtering process, thereby improving the filtering effect of the current and voltage in the diode circuit, enabling more rapid and accurate elimination of the interference caused by complex noise existing in the actual power grid to subsequent switching control, and thus improving the accuracy of inrush-free switching control.

[0073] Step 5: Filter the data of each electrical parameter on the diode circuit based on the adjusted step factor, and control the relay according to the filtered electrical parameter data, thereby completing the inrush-free switching control of the capacitor.

[0074] Further, for the switching control of the capacitor, the inrush-free switching device in this embodiment includes a capacitor, a microcontroller, a relay, a current sensor, a voltage sensor, and an LMS adaptive filter. The adjusted step factor of the LMS adaptive filter is used as the step factor during the electrical parameter filtering process of the LMS adaptive filter. The LMS adaptive filter is used to filter the electrical parameters on the diode circuit. It should be noted that the specific filtering process is prior art and will not be elaborated in detail in this embodiment. The electrical parameters include current and voltage. The interference caused by complex noise existing in the actual power grid to subsequent switching control is eliminated, and the filtered current and voltage are transmitted to the microcontroller. The microcontroller outputs a control signal according to the half-wave conduction performance of the diode and controls the input or cut-off of the relay through the control signal. It should be noted that the output of the control signal by the microcontroller and the specific control process of the control signal are well-known technologies to those skilled in the art and will not be described in detail in this embodiment. Therefore, further, the input or cut-off of the relay is controlled through the control signal, enabling the capacitor to be switched into or out of the power grid through the control of the relay, thereby completing the inrush-free switching control of the capacitor.

[0075] Based on the same inventive concept as the above method, an embodiment of the present application also provides an inrush-free switching 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 any one of the above inrush-free switching control methods.

[0076] It can be understood that the above-mentioned sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. Moreover, the above description of specific embodiments of this specification has been made. Additionally, the processes depicted in the drawings do not necessarily require the specific order or consecutive order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0077] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

[0078] The above content is only the implementation manner of the present application and is not used to limit the scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the protection scope of the present application.

Claims

1. A no-inrush switching control method, characterized in that, Including the following steps: Obtain data of each electrical parameter on the diode circuit, where the electrical parameters include current and voltage; Conduct frequency-domain analysis on the data of each electrical parameter, and perform clustering processing on the frequency-domain data of each electrical parameter respectively; For each electrical parameter, analyze the frequency change and fluctuation degree of the frequency-domain data within each clustering cluster to obtain the frequency complexity of each clustering cluster. Combine the amplitude change situation of the frequency-domain data within each clustering cluster to obtain the within-cluster chaos degree of each clustering cluster. According to the differences in the average frequency level and amplitude average level of the frequency-domain data within different clustering clusters, obtain the noise distribution difference degree of each electrical parameter. Combine the average level of the within-cluster chaos degree of all clustering clusters to obtain the noise distribution complexity of each electrical parameter; Perform modal decomposition on the data of each electrical parameter, and conduct principal component analysis on the modal components. Combine the noise distribution complexity to obtain the steady-state change factor of each electrical parameter. Adjust the step factor of the filter based on the steady-state change factor and the autocorrelation characteristics of the data of each electrical parameter; Filter the data of each electrical parameter on the diode circuit based on the adjusted step factor, and control the relay according to the filtered electrical parameter data, thereby completing the no-inrush switching control of the capacitor.

2. The no-inrush switching control method according to claim 1, characterized in that The method for obtaining the frequency complexity of each clustering cluster is: Arrange the frequencies of all frequency-domain data within each clustering cluster in ascending order to form the frequency sequence of each clustering cluster. Take the product of the mean and variance of all elements in the first-order difference sequence of the frequency sequence as the frequency complexity of each clustering cluster.

3. The no-inrush switching control method according to claim 1, characterized in that, The method for obtaining the within-cluster chaos degree of each clustering cluster is: Calculate the information entropy of the amplitudes of the frequency-domain data within each clustering cluster, and take the product of the information entropy and the frequency complexity as the within-cluster chaos degree of each clustering cluster.

4. The no-inrush switching control method according to claim 1, wherein The calculation method for the noise distribution difference degree of each electrical parameter is: The calculation formula for the difference degree of the noise distribution of the current is as follows: ; Wherein, is the difference degree of the noise distribution of the current, is the number of clustering clusters of all frequency domain data points in the current time series, and are the average frequencies of all frequency domain data points in the i-th and (i - 1)-th clustering clusters respectively, and are the average frequency domain amplitudes of all frequency domain data points in the i-th and (i - 1)-th clustering clusters respectively.

5. The no-inrush switching control method according to claim 4, wherein, The method for constructing the current time series is: Arrange the currents within a preset time period before the current moment in chronological order to obtain the current time series.

6. The no-inrush switching control method according to claim 1, wherein The method for obtaining the noise distribution complexity of each electrical parameter is: Take the product of the mean of the within-cluster chaos degrees of all clustering clusters corresponding to each electrical parameter and the noise distribution difference degree as the noise distribution complexity of each electrical parameter.

7. The no-inrush switching control method according to claim 5, wherein The calculation method for the steady-state change factor of each electrical parameter is: For the current data, the calculation formula of the steady-state change factor V of the current is as follows: ; In the formula, is the noise distribution complexity of the current, is the mean value of the variance contribution rates of all principal components in the modal matrix corresponding to the current time series, is a constant to avoid a zero denominator.

8. The no-inrush switching control method according to claim 7, characterized in that, The method for obtaining the modal matrix corresponding to the current time series is: Perform modal decomposition on the current time series, and use each modal component as a row vector to form the modal matrix corresponding to the current time series.

9. The no-inrush switching control method according to claim 5, characterized in that The calculation method for the adjusted step factor is: ； In the formula, is the step size factor of the adjusted LMS adaptive filter, is the ceiling function, is the maximum eigenvalue in the autocorrelation matrix of the current time series and the voltage time series, is the exponential function with the natural constant as the base, and are the steady-state change factors of the current and voltage respectively.

10. A no-inrush switching 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 a no-inrush switching control method as described in any one of claims 1-9.

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