Real-time acquisition method and system of high-voltage line parameters based on Internet of Things

Abstract

The present invention relates to the field of data processing, and the present invention relates to a method and system for real-time acquisition of high-voltage line parameters based on the Internet of Things, wherein the method comprises: collecting current data of the high-voltage line and transmitting it to a data center through the Internet of Things, performing EMD decomposition on the collected current data sequence to obtain multiple IMF components, and denoising each IMF component; using the denoised IMF components to reconstruct each current data point in the current data sequence to obtain denoised current data, and calculating the high-voltage line parameters based on the denoised current data. The method of the present invention can greatly improve the accuracy of the acquired high-voltage line parameters.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and more specifically, to a method and system for real-time acquisition of high-voltage line parameters based on the Internet of Things. Background Art

[0002] High-voltage transmission lines are an important part of the power system, and the accuracy of their parameter measurement is directly related to the stable operation of the power grid. By measuring the parameters of high-voltage lines, the electrical characteristics of the lines, such as positive-sequence impedance, zero-sequence impedance, etc., can be obtained. The parameters of high-voltage lines are the basis for power grid analysis and calculation. The method for obtaining high-voltage line parameters in the prior art is to first obtain the current of the high-voltage line, denoise it, and then calculate various parameters of the high-voltage line based on the current of the denoised high-voltage line.

[0003] In order to denoise the current data, the common practice is to use the EMD decomposition method to filter and denoise the current data. EMD decomposition is an adaptive signal processing method that does not require any basis functions or parameters to be set in advance. Therefore, it has significant advantages in processing current data. When using the EMD decomposition method to denoise the current data, it is necessary to first perform EMD decomposition on the current data to obtain multiple IMF components, then denoise each IMF component, and finally reconstruct the denoised IMF components to obtain the denoised current data, which can effectively remove the noise in the current data. However, due to the complex environment around the high-voltage line, it is susceptible to various noise interferences when collecting its current data, which means that there are noises of different frequencies and amplitudes in the current data. Then, when using EMD decomposition, the noise will be decomposed into different IMF components. The reconstruction method of traditional EMD decomposition often simply adds the IMF components, which may cause the loss of current information, resulting in poor denoising effect, and then the low accuracy of the obtained high-voltage line parameters.

[0004] For example, the adaptive denoising method for fiber optic gyroscope measurement data disclosed in the Chinese patent application document with application publication number CN117992732A first obtains the original data and performs empirical mode decomposition on it to obtain multiple intrinsic mode components, then screens the intrinsic mode components, performs wavelet threshold denoising on the screened intrinsic mode components, and finally reconstructs the denoised intrinsic mode components to obtain denoised data; however, this method simply adds the IMF components when reconstructing the denoised intrinsic mode components. When the original data is current data, this may result in a loss of current information, resulting in a poor denoising effect, and further resulting in a low accuracy of the acquired high-voltage line parameters. Summary of the invention

[0005] In order to solve the technical problem of low accuracy of high-voltage line parameters obtained in the prior art, the present invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a method for real-time acquisition of high-voltage line parameters based on the Internet of Things, comprising:

[0007] The current data of the high-voltage line is collected and transmitted to the data center through the Internet of Things. The collected current data sequence is decomposed by EMD to obtain multiple IMF components, and each IMF component is denoised.

[0008] The denoised IMF components are used to reconstruct each current data point in the current data sequence to obtain the denoised current data. When reconstructing a certain current data, the weight calculation expression of each IMF component is:

[0009] ;

[0010] In the formula, represents the weight of the jth IMF component when reconstructing the i-th current data point, Indicates the noise performance of the i-th current data point, represents the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component, T is the noise discrimination threshold; the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component refers to: the similarity between the data change law of the neighborhood range of the i-th current data point and the data change law of the neighborhood range of the data point at the corresponding moment of the j-th IMF component; norm() is the normalization function;

[0011] Calculate the high-voltage line parameters based on the denoised current data.

[0012] The beneficial effect is as follows: when the real-time acquisition method of high-voltage line parameters based on the Internet of Things of the present invention utilizes each IMF component to reconstruct each current data point, the weight corresponding to each IMF component is first calculated based on the similarity between the data point at the corresponding position of the ith current data point and the jth IMF component, and the noise performance degree of the ith current data point. When calculating the weight, the influence of noise and the similarity between the current data point and the IMF component are fully considered. When the ith current data point is likely to be noise, the calculated weight is inversely proportional to the similarity. When the ith current data point is likely to be noise, the calculated weight is proportional to the similarity, thereby greatly improving the reconstruction accuracy of the current data point, and further improving the accuracy of the acquired high-voltage line parameters.

[0013] Preferably, for the i-th current data point, the method for obtaining the noise performance degree thereof includes:

[0014] Starting from the current data point, multiple data points with the same value as the current data point are selected in sequence from near to far, and are sorted according to the corresponding collection time to obtain a reference data point set;

[0015] Obtain the midpoints of two adjacent data points in the reference data point set, and select two midpoints located on both sides of the current data point and closest to the data point as two relevant points of the current data point;

[0016] Calculate the time distances between midpoints adjacent to each other and average them, and calculate the time distances between two related points of the current data point;

[0017] The noise performance degree of the current data point is calculated according to the deviation between the mean value and the time distance between the two related points, and the noise performance degree is negatively correlated with the deviation.

[0018] The beneficial effect is that for the i-th current data point, the regularity of the values ​​of the data points in the neighborhood of the current data point can be used to calculate its noise performance, and the better the regularity, the lower the noise performance. For a group of data points, the consistency of the time distance between adjacent data points can be used to measure the regularity of the group of data points. However, due to the characteristics of the sine wave, when the values ​​of two data points are equal, the time distance between the two data points is not necessarily half a period of the sine wave. The consistency of the time distance between adjacent data points in the reference data point set is not good enough to directly reflect its regularity.

[0019] Considering that the midpoint of every two reference data points in the reference data point set must be the peak of the sine wave under ideal circumstances, and the time distance between every two adjacent peaks of the sine wave is fixed, which is half a period of the sine wave, the consistency of the time distance between the midpoints of two adjacent data points in the reference data point set is used to reflect the regularity of the data points in the reference data point set, so that the calculation of the noise performance degree is more accurate. In addition, when calculating the consistency of the time distance between the midpoints of two adjacent data points in the reference data point set, the time distance between the two midpoints closest to the current data point and the mean of the time distance between the two adjacent midpoints are first calculated, and the deviation between the time distance between the two midpoints closest to the current data point and the mean is used to measure the consistency of the time distance between the midpoints of two adjacent data points in the reference data point set, so that the calculation result of the noise performance degree of the current data point is more accurate.

[0020] Preferably, the calculation expression of the noise performance degree is:

[0021] ;

[0022] In the formula, Indicates the noise performance of the i-th current data point, represents the number of data points in the reference data point set, represents the number of midpoints, N-2 represents the number of time distances corresponding to N-1 midpoints, and Respectively represent the time corresponding to the qth and q+1th midpoints, and They respectively represent the time corresponding to the two midpoints related to the i-th current data point, and exp() is an exponential function with e as the base.

[0023] The beneficial effect is that the noise performance degree of each current data point can be quickly and accurately calculated by using the noise performance degree calculation expression of the present invention.

[0024] Preferably, the value of N is 10.

[0025] Preferably, the method for calculating the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component comprises: establishing a rectangular coordinate system with the value of the j-th IMF component as the abscissa and the value of the current data point as the ordinate;

[0026] A plurality of current data points within the neighborhood range of the current data point are used as a neighborhood data point set of the current data point; points are plotted on the rectangular coordinate system using the values ​​of the data points in the neighborhood data point set as ordinates and the values ​​on the j-th IMF component curve corresponding to the data point collection time as abscissas, thereby obtaining coordinate points in the neighborhood range;

[0027] The similarity is calculated according to the density of the coordinate points, and the similarity is positively correlated with the density.

[0028] Its beneficial effect is that the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component can be expressed by comparing the similarity between the data change in the local range of the current data point and the data change in the corresponding range of the corresponding IMF component. When comparing the similarity between the data change in the local range of the current data point and the data change in the corresponding range of the corresponding IMF component, the present invention first establishes a rectangular coordinate system with the value on the IMF component as the horizontal coordinate and the value on the current data point as the vertical coordinate, and then plots the points to observe the density of the points in the coordinate system. The higher the density of the coordinate points, the more relevant the corresponding IMF component is to the data change in the local range of the current data point, and the higher the similarity. The method of the present invention can accurately calculate the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component.

[0029] Preferably, the calculation expression for the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component is:

[0030] ;

[0031] In the formula, Indicates the similarity between the change law of the neighborhood data of the i-th current data point and the change law of the neighborhood data of the j-th IMF component at the corresponding moment, is the maximum and minimum normalization function, represents the length of the shortest path obtained by using the Dijkstra algorithm for the coordinate points in the neighborhood range, Indicates the number of coordinate points in the neighborhood corresponding to the i-th current data point.

[0032] Its beneficial effect is: when the calculation expression of the present invention calculates the similarity between the current data point and the data point at the corresponding position of the IMF component, the density of the neighborhood range coordinate point is used as the similarity between the current data point and the data point at the corresponding position of the IMF component. When calculating the density of the neighborhood range coordinate point, the Dijkstra algorithm is first used to find the shortest path for the neighborhood range coordinate point, and the ratio of the number of neighborhood range coordinate points to the length of the shortest path is used to measure the density of the neighborhood range coordinate point. The larger the ratio, the larger the density of the coordinate point and the higher the similarity. The calculation expression of the sampling present invention can quickly and accurately calculate the similarity between the current data point and the data point at the corresponding position of the IMF component.

[0033] Preferably, the number of data points in the neighborhood data point set is 25.

[0034] Preferably, the value of T is 0.65.

[0035] Preferably, a wavelet threshold denoising method is used when denoising the IMF component, wherein the wavelet basis function is the Daubechies wavelet, the number of decomposition layers is set to 3, and the threshold uses a soft threshold function.

[0036] The beneficial effect is that by performing wavelet threshold denoising on each IMF component, multi-resolution analysis of the IMF component and frequency domain processing on it, the noise component can be effectively and meticulously removed, thereby making the current signal clearer and more accurate, and further improving the accuracy of the acquired high-voltage line parameters.

[0037] In a second aspect, the present invention provides a high-voltage line parameter real-time acquisition system based on the Internet of Things, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the high-voltage line parameter real-time acquisition method based on the Internet of Things of the present invention is implemented.

[0038] In summary, the beneficial effect of the present invention is that the real-time acquisition method of high-voltage line parameters based on the Internet of Things of the present invention can greatly improve the accuracy of the acquired high-voltage line parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0040] Figure 1 is a flowchart schematically showing a method for real-time acquisition of high-voltage line parameters based on the Internet of Things according to an embodiment of the present invention;

[0041] Figure 2 is a schematic diagram schematically illustrating a method for obtaining two related points of a current data point according to an embodiment of the present invention;

[0042] Figure 3 The figure schematically shows the structure of a high-voltage line parameter real-time acquisition system based on the Internet of Things according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0044] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0045] Embodiment of a method for real-time acquisition of high-voltage line parameters based on the Internet of Things:

[0046] like Figure 1 As shown, the real-time acquisition method of high-voltage line parameters based on the Internet of Things of the present invention includes:

[0047] S101, obtaining IMF components corresponding to the current data sequence, and denoising the IMF components, specifically: collecting current data of the high-voltage line and transmitting it to the data center through the Internet of Things, performing EMD decomposition on the collected current data sequence to obtain multiple IMF components, and denoising each IMF component;

[0048] The current transformer or current sensor can be used to collect the current of the high-voltage line. The collection frequency can be 50 Hz. In other embodiments, other suitable collection frequencies can also be used. The Internet of Things can use communication networks such as WIFI and ZigRee.

[0049] Performing EMD decomposition on the current data sequence will yield multiple IMF components, which can be recorded as In this embodiment, the first seven IMF components are selected from all the obtained IMF components to perform subsequent steps, that is, n takes the empirical value of 7. In other embodiments, the first six IMF components or other appropriate numbers of IMF components may also be selected.

[0050] Through EMD decomposition, the high-voltage line current data can be decomposed into multiple IMF components, which can reflect the characteristics on different time scales. Then, denoising the IMF components can effectively improve the signal-to-noise ratio of the current signal, making the current signal clearer and more accurate.

[0051] In this embodiment, the IMF component may be denoised using a wavelet threshold denoising method, a variational mode decomposition method or other appropriate methods.

[0052] S102, obtaining denoised current data, specifically: reconstructing each current data point in the current data sequence using the denoised IMF components, thereby obtaining denoised current data. When reconstructing a certain current data, the weight calculation expression of each IMF component is:

[0053] ;

[0054] In the formula, represents the weight of the jth IMF component when reconstructing the i-th current data point, Indicates the noise performance of the i-th current data point, represents the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component, T is the noise discrimination threshold; the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component refers to: the similarity between the change law of the neighborhood range data of the i-th current data point and the change law of the neighborhood range data of the data point at the corresponding moment of the j-th IMF component; when When , the point is considered as noise. The higher the noise performance and the lower the similarity, the higher the weight. When the point is considered to be normal data, the lower the noise performance and the higher the similarity, the higher the weight. norm() is a normalization function, which makes the sum of the weights of all IMF components for current data points equal to 1.

[0055] In this embodiment, T takes an empirical value of 0.65, and may take other values ​​in other embodiments.

[0056] When reconstructing a current data point, first calculate the weights of each IMF component corresponding to the current data point, and then perform weighted summation on the values ​​of each IMF component at the time of acquisition of the current data point to obtain the reconstructed value of the current data point. For example: Assume that there are three IMF components, the acquisition time of the current data point is t, the value of the first IMF component at time t is I1, the value of the second IMF component at time t is I2, and the value of the third IMF component at time t is I3. The weight of the first IMF component is 0.3, the weight of the second IMF component is 0.5, and the weight of the third IMF component is 0.2. Then the reconstructed value of the current data point is .

[0057] If the noise performance degree of the i-th current data point is greater than the preset threshold, it means that the current data point is likely to be noise data. At this time, if the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component is higher, it means that the possibility of noise at the corresponding position on the j-th IMF component is greater. In order to avoid excessive error in the i-th current data point after reconstruction, the weight corresponding to the j-th IMF component should be smaller; conversely, if the noise performance degree of the i-th current data point is less than the preset threshold, it means that the current data point is likely to be normal data. At this time, if the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component is higher, it means that the possibility of noise at the corresponding position on the j-th IMF component is smaller. In order to ensure that the i-th current data point after reconstruction is more accurate, a larger weight should be given to the j-th IMF component. When the weight calculation expression of the IMF component of this embodiment is used to calculate the weight of each IMF component, when the noise performance degree of the i-th current data point is greater than the preset threshold, the weight corresponding to the j-th IMF component is inversely proportional to the similarity, and when the noise performance degree of the i-th current data point is less than the preset threshold, the weight corresponding to the j-th IMF component is proportional to the similarity, so that the reconstructed value of the i-th current data point obtained after weighted summation of each IMF component is more accurate.

[0058] S103: Calculate high-voltage line parameters based on the denoised current data.

[0059] In this embodiment, the high-voltage line parameters include positive-sequence impedance and zero-sequence impedance. In other embodiments, the high-voltage line parameters also include capacitive reactance, inductive reactance, insulation resistance to ground, phase-to-phase insulation resistance and other parameters.

[0060] In the prior art, when reconstructing data points using IMF components, the IMF components are simply added together. However, modal aliasing or endpoint effects may occur when reconstructing data points using IMF components, resulting in poor reconstructed signal effects. Because there are various noise data in the current data sequence, that is, the noise data may be decomposed into different IMF components, and different IMF components contribute differently to the data, this will cause deviations in the data obtained by directly adding multiple IMF components. The method for real-time acquisition of high-voltage line parameters based on the Internet of Things of the present invention, when using each IMF component to reconstruct each current data point, first calculates the weight corresponding to each IMF component according to the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component, and the noise performance degree of the i-th current data point. When calculating the weight, the influence of noise and the similarity between the current data point and the IMF component are fully considered. When the i-th current data point is likely to be noise, the calculated weight is inversely proportional to the similarity. When the i-th current data point is likely to be noise, the calculated weight is proportional to the similarity, thereby greatly improving the reconstruction accuracy of the current data point, and then improving the accuracy of the acquired high-voltage line parameters.

[0061] In one embodiment, for the i-th current data point, a method for obtaining the noise performance level thereof includes:

[0062] S201, starting from the current data point, sequentially select multiple data points with the same value as the current data point from near to far, and sort them according to corresponding collection times to obtain a reference data point set;

[0063] In this embodiment, 10 data points with the same value as the current data point are selected to construct a reference data point set. In other embodiments, 12, 15, 20 or other suitable numbers of data points may be selected to construct a reference data point set.

[0064] S202, obtaining midpoints of two adjacent data points in the reference data point set, and selecting two midpoints located on both sides of the current data point and closest to the data point as two relevant points of the current data point;

[0065] like Figure 2 As shown, assuming that the data point is point C, the data points in the reference data point set are point A, point B, point C, point D and point E, the midpoint of point A and point B is point J, the midpoint of point B and point C is point K, the midpoint of point C and point D is point L, and the midpoint of point D and point E is point M. Then the two related points of the data point are point K and point L.

[0066] S203, calculating the time distances between midpoints adjacent to each other and averaging them, and calculating the time distances between two related points of the current data point;

[0067] The time distance between two data points refers to the difference between the collection times corresponding to the two data points.

[0068] S204 , calculating the noise performance degree of the current data point according to the deviation between the mean value and the time distance between the two related points, wherein the noise performance degree is negatively correlated with the deviation.

[0069] Normally, the waveform of the current signal on the high-voltage line is a sine wave, and the change of the current is a regular change of the sine wave. However, due to the complex electromagnetic environment in which the high-voltage line is located, it may be interfered by the electromagnetic field, power supply noise and other electrical equipment from the surrounding environment, resulting in a lot of noise in the collected current data sequence I. These noises will break the original regular changes of the AC power supply. Therefore, for the i-th current data point, the regularity of the values ​​of the data points within the neighborhood of the current data point can be used to calculate its noise performance degree, and the better the regularity, the lower the noise performance degree. For a group of data points, the consistency of the time distance between adjacent data points can be used to measure the regularity of the group of data points. However, due to the characteristics of the sine wave, when the values ​​of two data points are equal, the time distance between the two data points is not necessarily half a period of the sine wave. The consistency of the time distance between adjacent data points in the reference data point set is not good enough to directly reflect its regularity.

[0070] In this embodiment, considering that the midpoint of every two reference data points in the reference data point set must be the peak of the sine wave under ideal circumstances, and the time distance between every two adjacent peaks of the sine wave is fixed, which is half a period of the sine wave, the consistency of the time distance between the midpoints of two adjacent data points in the reference data point set is used to reflect the regularity of the data points in the reference data point set, so that the calculation of the noise performance degree is more accurate. In addition, when calculating the consistency of the time distance between the midpoints of two adjacent data points in the reference data point set, the time distance between the two midpoints closest to the current data point and the mean of the time distance between the two adjacent midpoints are first calculated, and the deviation between the time distance between the two midpoints closest to the current data point and the mean is used to measure the consistency of the time distance between the midpoints of two adjacent data points in the reference data point set, so that the calculation result of the noise performance degree of the current data point is more accurate.

[0071] In one embodiment, the calculation expression of the noise performance degree is:

[0072] ;

[0073] In the formula, Indicates the noise performance of the i-th current data point, represents the number of data points in the reference data point set, represents the number of midpoints, N-2 represents the number of time distances corresponding to N-1 midpoints, and Respectively represent the time corresponding to the qth and q+1th midpoints, and They respectively represent the time corresponding to the two midpoints related to the i-th current data point, and exp() is an exponential function with e as the base. It means , The time distance between Then it represents the mean time distance between every two adjacent midpoints in the midpoint set Z; Represents the time distance between two related points of the i-th current data point.

[0074] By using the noise performance degree calculation expression of this embodiment, the noise performance degree of each current data point can be calculated quickly and accurately.

[0075] In one embodiment, a method for calculating the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component includes:

[0076] S301, establishing a rectangular coordinate system with the value of the jth IMF component as the horizontal axis and the value of the current data point as the vertical axis;

[0077] S302, taking a plurality of current data points within the neighborhood range of the current data point as a neighborhood data point set of the current data point; plotting points on the rectangular coordinate system using the values ​​of the data points in the neighborhood data point set as ordinates and the values ​​of the j-th IMF components corresponding to the data point collection time as abscissas, thereby obtaining coordinate points within the neighborhood range;

[0078] In this embodiment, the number of data points in the neighborhood data point set is 25. If the number of data points in the neighborhood range is less than 25, then the data points are discarded accordingly. In other embodiments, the number may be any other appropriate number.

[0079] S303: Calculate the similarity according to the density of the coordinate points, wherein the similarity is positively correlated with the density.

[0080] The similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component can be expressed by comparing the similarity between the data change in the local range of the current data point and the data change in the corresponding range of the corresponding IMF component. When comparing the similarity between the data change in the local range of the current data point and the data change in the corresponding range of the corresponding IMF component, the present embodiment first establishes a rectangular coordinate system with the value on the IMF component as the horizontal coordinate and the value on the current data point as the vertical coordinate, and then plots the points to observe the density of the points in the coordinate system. The higher the density of the coordinate points, the more relevant the corresponding IMF component is to the data change in the local range of the current data point, and the higher the similarity. The method of the present embodiment can accurately calculate the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component.

[0081] In one embodiment, the calculation expression for the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component is:

[0082] ;

[0083] In the formula, Indicates the similarity between the change law of the neighborhood data of the i-th current data point and the change law of the neighborhood data of the j-th IMF component at the corresponding moment, is the maximum and minimum normalization function, represents the length of the shortest path obtained by using the Dijkstra algorithm for the coordinate points in the neighborhood range, Indicates the number of coordinate points in the neighborhood corresponding to the i-th current data point.

[0084] When the calculation expression of this embodiment calculates the similarity between the current data point and the data point at the corresponding position of the IMF component, the density of the neighborhood range coordinate points is used as the similarity between the current data point and the data point at the corresponding position of the IMF component. When calculating the density of the neighborhood range coordinate points, the Dijkstra algorithm is first used to find the shortest path for the neighborhood range coordinate points, and the ratio of the number of neighborhood range coordinate points to the length of the shortest path is used to measure the density of the neighborhood range coordinate points. The larger the ratio, the greater the density of the coordinate points and the higher the similarity. The calculation expression of the sampling embodiment can quickly and accurately calculate the similarity between the current data point and the data point at the corresponding position of the IMF component.

[0085] It can be seen from the above embodiments that after obtaining the IMF components, the IMF components need to be denoised. In one embodiment, a wavelet threshold denoising method is used to denoise the IMF components, wherein the wavelet basis function is the Daubechies wavelet, the number of decomposition layers is set to 3, and the threshold uses a soft threshold function.

[0086] By performing wavelet threshold denoising on each IMF component, multi-resolution analysis of the IMF component and frequency domain processing on it, the noise component can be effectively and meticulously removed, making the current signal clearer and more accurate, and further improving the accuracy of the acquired high-voltage line parameters.

[0087] Example of real-time acquisition system of high-voltage line parameters based on the Internet of Things:

[0088] The present invention also provides a real-time acquisition system for high-voltage line parameters based on the Internet of Things. Figure 3 As shown, the real-time acquisition system for high-voltage line parameters based on the Internet of Things includes a processor and a memory, and the memory stores computer program instructions. When the computer program instructions are executed by the processor, the real-time acquisition method for high-voltage line parameters based on the Internet of Things in the above embodiment is implemented.

[0089] The real-time acquisition system for high-voltage line parameters based on the Internet of Things also includes other components familiar to those skilled in the art, such as a communication bus and a communication interface, whose settings and functions are known in the art and will not be described in detail here.

[0090] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0091] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0092] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for real-time acquisition of high-voltage line parameters based on the Internet of Things, characterized in that: include: The current data of the high-voltage line is collected and transmitted to the data center through the Internet of Things. The collected current data sequence is decomposed by EMD to obtain multiple IMF components, and each IMF component is denoised. The denoised IMF components are used to reconstruct each current data point in the current data sequence to obtain denoised current data. When the current data is reconstructed, the weight calculation expression of each IMF component is: ； In the formula, represents the weight of the jth IMF component when reconstructing the i-th current data point, Indicates the noise performance of the i-th current data point, represents the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component, T is the noise discrimination threshold; the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component refers to: the similarity between the change law of the neighborhood range data of the i-th current data point and the change law of the neighborhood range data of the data point at the corresponding moment of the j-th IMF component; norm() is the normalization function; for the i-th current data point, the method for obtaining the degree of noise performance includes: Starting from the current data point, multiple data points with the same value as the current data point are selected in sequence from near to far, and are sorted according to the corresponding collection time to obtain a reference data point set; Obtain the midpoints of two adjacent data points in the reference data point set, and select two midpoints located on both sides of the current data point and closest to the data point as two relevant points of the current data point; Calculate the time distances between midpoints adjacent to each other and average them, and calculate the time distances between two related points of the current data point; Calculating the noise performance degree of the current data point according to the deviation between the mean value and the time distance between the two related points, wherein the noise performance degree is negatively correlated with the deviation; Calculate the high-voltage line parameters based on the denoised current data.

2. The method for real-time acquisition of high-voltage line parameters based on the Internet of Things according to claim 1, characterized in that: The calculation expression of the noise performance degree is: ； In the formula, Indicates the noise performance of the i-th current data point, represents the number of data points in the reference data point set, represents the number of midpoints, N-2 represents the number of time distances corresponding to N-1 midpoints, and Respectively represent the time corresponding to the qth and q+1th midpoints, and They respectively represent the time corresponding to the two midpoints related to the i-th current data point, and exp() is an exponential function with e as the base.

3. The method for real-time acquisition of high-voltage line parameters based on the Internet of Things according to claim 2, characterized in that: The value of N is 10.

4. The method for real-time acquisition of high-voltage line parameters based on the Internet of Things according to claim 1, characterized in that: A method for calculating the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component includes: establishing a rectangular coordinate system with the value of the j-th IMF component as the abscissa and the value of the current data point as the ordinate; A plurality of current data points within the neighborhood range of the current data point are used as a neighborhood data point set of the current data point; points are plotted on the rectangular coordinate system using the values ​​of the data points in the neighborhood data point set as ordinates and the values ​​on the j-th IMF component curve corresponding to the data point collection time as abscissas, thereby obtaining coordinate points in the neighborhood range; The similarity is calculated according to the density of the coordinate points, and the similarity is positively correlated with the density.

5. The method for real-time acquisition of high-voltage line parameters based on the Internet of Things according to claim 4, characterized in that: The calculation expression for the similarity between the i-th current data point and the data point at the corresponding position of the j-th IMF component is: ； In the formula, Indicates the similarity between the change law of the neighborhood data of the i-th current data point and the change law of the neighborhood data of the j-th IMF component at the corresponding moment, is the maximum and minimum normalization function, represents the length of the shortest path obtained by using the Dijkstra algorithm for the coordinate points in the neighborhood range, Indicates the number of coordinate points in the neighborhood corresponding to the i-th current data point.

6. The method for real-time acquisition of high-voltage line parameters based on the Internet of Things according to claim 4, characterized in that: The number of data points in the neighborhood data point set is 25.

7. The method for real-time acquisition of high-voltage line parameters based on the Internet of Things according to claim 1, characterized in that: The value of T is 0.

65.

8. The method for real-time acquisition of high-voltage line parameters based on the Internet of Things according to any one of claims 1 to 7, characterized in that: The wavelet threshold denoising method is used to denoise the IMF component, in which the wavelet basis function is Daubechies wavelet, the number of decomposition levels is set to 3, and the threshold uses a soft threshold function.

9. A high-voltage line parameter real-time acquisition system based on the Internet of Things, comprising a processor and a memory, wherein the memory stores computer program instructions, characterized in that: When the computer program instructions are executed by the processor, the real-time acquisition method of high-voltage line parameters based on the Internet of Things as described in any one of claims 1 to 8 is implemented.

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