A method and system for extracting traveling wave pulse features of power distribution network line faults

Abstract

This invention discloses a method for extracting traveling wave pulse features of distribution network line faults, comprising the following steps: 100: collecting traveling wave current data of the distribution network lines over a period of time to obtain a traveling wave current time series; 200: constructing a traveling wave current vector matrix based on the traveling wave current time series; 300: obtaining the sample covariance matrix based on the traveling wave current vector matrix, and obtaining its eigenvalues ​​based on the sample covariance matrix; 400: sorting the eigenvalues, and taking the time series corresponding to the traveling wave current vector with the largest eigenvalue as a suspected fault pulse signal sequence; 500: generating a three-dimensional phase space trajectory based on the suspected fault pulse signal sequence; 600: obtaining the weight dimension of the three-dimensional phase space trajectory based on the three-dimensional phase space trajectory; 700: comparing the weight dimension with a set threshold, and if it is greater than the threshold, determining that the suspected fault pulse signal sequence contains a fault pulse. This invention achieves rapid and accurate identification of traveling wave pulses of distribution network line faults.

Description

Technical Field

[0001] This invention relates to a signal detection method and system, and more particularly to a method and system for detecting line fault signals. Background Technology

[0002] Currently, medium- and low-voltage distribution networks are mainly composed of overhead lines, which have complex structures, numerous branches, and are prone to faults. Statistics show that power outages caused by distribution network faults account for over 95% of all power outages during operation. To achieve rapid isolation of distribution network faults and restore normal operation, accurate and rapid fault location is crucial.

[0003] When a line fault occurs, a transient signal called a "traveling wave" is emitted, starting from the fault point and propagating along the line network. By using distributed detection devices to detect the arrival time of the traveling wave at different locations, the location of the fault point can be calculated.

[0004] The key to the accuracy of traveling wave location is the accurate identification of traveling wave pulses. Because there are many interference signals on the line, and the amplitude of the fault traveling wave signal is small, the traveling wave pulse is often submerged in noise, which seriously affects the fault location accuracy. Summary of the Invention

[0005] One of the objectives of this invention is to provide a method for extracting the features of traveling wave pulses in power distribution network faults, which can achieve rapid and accurate identification of fault traveling wave pulses.

[0006] In accordance with the above-mentioned objective, this invention proposes a method for extracting the traveling wave pulse characteristics of a power distribution network line fault, comprising the following steps:

[0007] 100: Collect line traveling wave current data of the distribution network over a period of time to obtain the line traveling wave current time series;

[0008] 200: Construct a traveling wave current vector matrix based on the traveling wave current time series of the line;

[0009] 300: Obtain the sample covariance matrix based on the traveling wave current vector matrix, and obtain its eigenvalues ​​based on the sample covariance matrix;

[0010] 400: Sort the eigenvalues ​​and take the time sequence corresponding to the traveling wave current vector of the largest eigenvalue as the suspected fault pulse signal sequence;

[0011] 500: Based on the suspected fault pulse signal sequence, generate its three-dimensional phase space trajectory;

[0012] 600: Based on the three-dimensional phase space trajectory, obtain the weight dimension of the three-dimensional phase space trajectory;

[0013] 700: Compare the weight dimension with a set threshold. If it is greater than the threshold, then determine that the suspected fault pulse signal sequence contains a fault pulse.

[0014] Furthermore, in step 100 of the method for extracting traveling wave pulse features of power distribution network line faults according to the present invention, the traveling wave current data of the power distribution network line over a period of time is collected and normalized to obtain the time series of the traveling wave current.

[0015] Furthermore, in the method for extracting traveling wave pulse features of power distribution network line faults according to the present invention, the weight dimension E is obtained based on the following formula:

[0016]

[0017] Where r represents dividing the three-dimensional phase trajectory diagram into a uniform grid of r... 3 The size of one of the small cubes; T r This represents the percentage of the three-dimensional phase space trajectory that falls within the small cube for different sizes of r; p t This represents the probability that the trajectory in the three-dimensional phase space is occupied in the t-th small cube.

[0018] Furthermore, in step 700 of the method for extracting traveling wave pulse features of power distribution line faults according to the present invention, the threshold is selected from the range of 0.55-0.65.

[0019] Furthermore, the method for extracting traveling wave pulse features of power distribution line faults according to the present invention also includes step 800: when it is determined that the suspected fault pulse signal sequence contains a fault pulse, an alarm signal is output.

[0020] Another objective of this invention is to provide a system for extracting the traveling wave pulse characteristics of faults in power distribution networks, which can achieve rapid and accurate identification of fault traveling wave pulses.

[0021] In accordance with the aforementioned objective, this invention proposes a system for extracting traveling wave pulse features of faults in distribution network lines, comprising:

[0022] The signal acquisition module collects the traveling wave current data of the distribution network over a period of time to obtain the time series of the traveling wave current.

[0023] The matrix construction module constructs a traveling wave current vector matrix based on the time series of the traveling wave current of the line.

[0024] The extraction module obtains the sample covariance matrix based on the traveling wave current vector matrix, and obtains its eigenvalues ​​based on the sample covariance matrix.

[0025] The sorting module sorts the eigenvalues ​​and uses the time sequence corresponding to the traveling wave current vector of the largest eigenvalue as the suspected fault pulse signal sequence.

[0026] The trajectory generation module generates a three-dimensional phase space trajectory based on the suspected fault pulse signal sequence.

[0027] The judgment module obtains the weight dimension of the three-dimensional phase space trajectory based on the three-dimensional phase space trajectory; and compares the weight dimension with a set threshold. If the weight dimension is greater than the threshold, it determines that the suspected fault pulse signal sequence contains a fault pulse.

[0028] Furthermore, in the power distribution network line fault traveling wave pulse feature extraction system of the present invention, the signal acquisition module acquires the line traveling wave current data of the power distribution network over a period of time, and performs normalization processing on it to obtain the line traveling wave current time series.

[0029] Furthermore, in the power distribution network line fault traveling wave pulse feature extraction system of the present invention, the judgment module obtains the weight dimension E based on the following formula:

[0030]

[0031] Where r represents dividing the three-dimensional phase trajectory diagram into a uniform grid of r... 3 The size of one of the small cubes; T r This represents the percentage of the three-dimensional phase space trajectory that falls within the small cube for different sizes of r; p t This represents the probability that the trajectory in the three-dimensional phase space is occupied in the t-th small cube.

[0032] Furthermore, in the power distribution line fault traveling wave pulse feature extraction system of the present invention, the threshold is selected from the range of 0.55-0.65.

[0033] Furthermore, the power distribution line fault traveling wave pulse feature extraction system of the present invention also includes an alarm module. When it is determined that the suspected fault pulse signal sequence contains a fault pulse, the alarm module outputs an alarm signal.

[0034] The method for extracting traveling wave pulse features of faults in distribution network lines described in this invention uses nonlinear time series analysis to extract data windows suspected of containing traveling wave pulses by matrix eigenvalue analysis for fault traveling wave current signals. Then, it uses phase space trajectory analysis to convert the traveling wave pulse current signal into a trajectory curve in three-dimensional phase space, and then extracts the feature values ​​of the phase space trajectory curve, thereby realizing the rapid and accurate identification of traveling wave pulses in faults in distribution network lines.

[0035] The power distribution network line fault traveling wave pulse feature extraction system described in this invention has the same advantages and beneficial effects. Attached Figure Description

[0036] Figure 1 The flowchart illustrates the steps of one embodiment of the method for extracting traveling wave pulse features of power distribution line faults according to the present invention.

[0037] Figure 2 The illustration shows a suspected fault pulse signal obtained in a specific example using the method for extracting traveling wave pulse features of power distribution line faults according to the present invention.

[0038] Figure 3 The image shows the three-dimensional phase space trajectory of a suspected fault pulse signal obtained in a specific example using the method for extracting traveling wave pulse features of power distribution line faults according to the present invention.

[0039] Figure 4 The diagram shows the architecture of the power distribution line fault traveling wave pulse feature extraction system according to one embodiment of the present invention. Detailed Implementation

[0040] The following will further explain and illustrate the method and system for extracting traveling wave pulse features of power distribution line faults according to the present invention, in conjunction with the accompanying drawings and specific embodiments. However, this explanation and illustration do not constitute an undue limitation on the technical solution of the present invention.

[0041] In some implementations, the method for extracting traveling wave pulse features of a power distribution network line fault may include the following steps:

[0042] 100: Collect traveling wave current data of the distribution network lines over a period of time, normalize the data, and obtain a normalized time series of traveling wave currents. For example, a time series of traveling wave currents of length N, i(t) = {i1,i2,i3,...,i...} N}

[0043] 200: Constructing the traveling wave current vector matrix I based on the time series of the traveling wave current of the line:

[0044] For a line traveling wave current time series of length N, i(t) = {i1,i2,i3,...,i...} N}, divide it into length N m The m segments are used to construct a structure of length N. m m-dimensional phasor sequence I:

[0045]

[0046] Where m is the dimension of the traveling wave current vector matrix, which also represents the data filtering window; I is Nm The traveling wave current vector matrix consisting of m-dimensional points.

[0047] 300: Obtain the sample covariance matrix S based on the traveling wave current vector matrix:

[0048]

[0049] In the formula, I T It is the conjugate matrix of I.

[0050] Calculate its eigenvalues ​​based on the sample covariance matrix S:

[0051] In linear algebra, an eigenvalue is a matrix whose product with a vector equals the product of that vector with a constant. For a matrix S and a non-zero vector x, if the following condition is satisfied:

[0052] Sx=λx

[0053] Where λ is a constant, then λ is called the eigenvalue of matrix S, and x is called the eigenvector corresponding to the eigenvalue λ.

[0054] The eigenvalues ​​of a matrix can be solved using the eigenvalue decomposition method described in textbooks.

[0055] 400: Sort the eigenvalues ​​and select the traveling wave current vector I corresponding to the largest eigenvalue. max The corresponding time series As a sequence of suspected fault pulse signals. For example Figure 2 The image shows a sequence of suspected fault pulse signals obtained in a specific instance.

[0056] 500: Based on the suspected fault pulse signal sequence, generate its three-dimensional phase space trajectory, where the coordinates of a point on the trajectory are represented as (x... t y t , z t ):

[0057] x t =i max (t-2τ)

[0058] y t =i max (t-τ)

[0059] z t =i max (t)

[0060] Where t represents time t. τ This represents a sampling period.

[0061] Figure 3 Showing targets Figure 2 The three-dimensional phase space trajectory generated by the suspected fault pulse signal sequence is shown.

[0062] 600: Based on the three-dimensional phase space trajectory, the weight dimension E of the three-dimensional phase space trajectory is obtained using the following formula:

[0063] For a three-dimensional phase space trajectory, the cubic region containing the trajectory coordinates in the phase plane is divided into r... 3 A small cube is used to determine whether a trajectory point occupies or falls within the small cube. In some more specific implementations, a binary matrix D∈r can be used. 3 The result of the judgment is represented by the binary matrix element, which is 0 or 1. 0 indicates that the small cube contains phase trajectory points, and 1 indicates that the small cube is not occupied by phase trajectories.

[0064] In this manner, in some more specific implementations, the specific gravity dimension E can be calculated based on the following formula:

[0065]

[0066] Where r represents dividing the three-dimensional phase trajectory diagram into a uniform grid of r... 3 The size of one of the small cubes; T r This represents the percentage of the three-dimensional phase space trajectory that falls within the small cube for different sizes of r; p t This represents the probability that the trajectory in the three-dimensional phase space is occupied in the t-th small cube.

[0067] 700: Set a threshold. Compare the calculated weight dimension E with the set threshold. If it is greater than the threshold, it is determined that the suspected fault pulse signal sequence contains a fault pulse.

[0068] In some more specific implementations, the threshold may be selected from an empirical value in the range of 0.55-0.65, for example, in a specific instance, the threshold is 0.6.

[0069] In addition, in some embodiments, step 800 can be performed after step 700: when it is determined that the suspected fault pulse signal sequence contains a fault pulse, an alarm signal is output.

[0070] In another embodiment of the present invention, a system for extracting traveling wave pulse features of faults in distribution network lines is also proposed, comprising:

[0071] The signal acquisition module 101 acquires the line traveling wave current data of the distribution network over a period of time to obtain the line traveling wave current time series.

[0072] Matrix construction module 102 constructs a traveling wave current vector matrix based on the traveling wave current time series of the line.

[0073] The extraction module 103 obtains the sample covariance matrix based on the traveling wave current vector matrix, and obtains its eigenvalues ​​based on the sample covariance matrix.

[0074] The sorting module 104 sorts the feature roots and takes the time sequence corresponding to the traveling wave current vector corresponding to the largest feature root as the suspected fault pulse signal sequence.

[0075] The trajectory generation module 105 generates a three-dimensional phase space trajectory based on the suspected fault pulse signal sequence.

[0076] The judgment module 106 obtains the weight dimension of the three-dimensional phase space trajectory based on the three-dimensional phase space trajectory; and compares the weight dimension with a set threshold. If the weight dimension is greater than the threshold, it determines that the suspected fault pulse signal sequence contains a fault pulse.

[0077] In some more specific implementations, the signal acquisition module 101 acquires the line traveling wave current data of the distribution network over a period of time, and performs normalization processing on it to obtain the line traveling wave current time series.

[0078] In some more specific implementations, the determination module 106 obtains the weight dimension E based on the following formula:

[0079]

[0080] Where r represents dividing the three-dimensional phase trajectory diagram into a uniform grid of r... 3 The size of one of the small cubes; T r This represents the percentage of the three-dimensional phase space trajectory that falls within the small cube for different sizes of r; p t This represents the probability that the trajectory in the three-dimensional phase space is occupied in the t-th small cube.

[0081] In some embodiments, the power distribution line fault traveling wave pulse feature extraction system of the present invention may further include an alarm module 107, which outputs an alarm signal when it is determined that the suspected fault pulse signal sequence contains a fault pulse.

[0082] In this invention, the signal acquisition module 101, matrix construction module 102, extraction module 103, sorting module 104, trajectory generation module 105, judgment module 106, and alarm module 107 can be implemented in any suitable manner. For example, they can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320.

[0083] Furthermore, those skilled in the art will recognize that, besides implementing the controller using purely computer-readable program code, the method steps can be logically programmed to enable the modules to perform the same function in the form of logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a module can be considered a hardware component, and the devices included within it for implementing various functions can also be considered structures within that hardware component. Alternatively, the devices for implementing various functions can be considered as both software modules implementing the method and structures within a hardware component.

[0084] It should be noted that the scope of protection of the prior art in this invention is not limited to the embodiments given in this application. All prior art that does not contradict the solution of this invention, including but not limited to prior patent documents, prior publications, prior public uses, etc., can be included in the scope of protection of this invention.

[0085] Furthermore, the combination of the technical features in this case is not limited to the combination methods described in the claims of this case or the combination methods described in the specific embodiments. All technical features described in this case can be freely combined or combined in any way, unless they contradict each other.

[0086] It should also be noted that the embodiments listed above are merely specific embodiments of the present invention. Obviously, the present invention is not limited to the above embodiments, and similar changes or modifications made thereto are those that can be directly derived or easily conceived by those skilled in the art from the content disclosed in the present invention, and should all fall within the protection scope of the present invention.

Claims

1. A method for extracting traveling wave pulse features of faults in distribution network lines, characterized in that, Including the following steps: 100: Collect line traveling wave current data of the distribution network over a period of time to obtain the line traveling wave current time series; 200: Construct a traveling wave current vector matrix based on the traveling wave current time series of the line; 300: Obtain the sample covariance matrix based on the traveling wave current vector matrix, and obtain its eigenvalues ​​based on the sample covariance matrix; 400: Sort the eigenvalues ​​and take the time sequence corresponding to the traveling wave current vector of the largest eigenvalue as the suspected fault pulse signal sequence; 500: Based on the suspected fault pulse signal sequence, generate its three-dimensional phase space trajectory; 600: Based on the three-dimensional phase space trajectory, obtain the weight dimension E of the three-dimensional phase space trajectory; 700: Compare the weight dimension with a set threshold. If it is greater than the threshold, then determine that the suspected fault pulse signal sequence contains a fault pulse.

2. The method for extracting traveling wave pulse features of distribution network line faults as described in claim 1, characterized in that, In step 100, the traveling wave current data of the distribution network over a period of time is collected and normalized to obtain the time series of the traveling wave current.

3. The method for extracting traveling wave pulse features of distribution network line faults as described in claim 1, characterized in that, The specific gravity dimension E is obtained based on the following formula: Where r represents dividing the three-dimensional phase trajectory diagram into a uniform grid of r 3 The size of one of the small cubes; T r This represents the percentage of the three-dimensional phase space trajectory that falls within the small cube for different sizes of r; p t This represents the probability that the trajectory in the three-dimensional phase space is occupied in the t-th small cube.

4. The method for extracting traveling wave pulse features of distribution network line faults as described in claim 1, characterized in that, In step 700, the threshold is selected from the range of 0.55-0.

65.

5. The method for extracting traveling wave pulse features of distribution network line faults as described in claim 1, characterized in that, It also includes step 800: when it is determined that the suspected fault pulse signal sequence contains a fault pulse, an alarm signal is output.

6. A system for extracting traveling wave pulse features of faults in power distribution networks, characterized in that, include: The signal acquisition module collects the traveling wave current data of the distribution network over a period of time to obtain the time series of the traveling wave current. The matrix construction module constructs a traveling wave current vector matrix based on the time series of the traveling wave current of the line. The extraction module obtains the sample covariance matrix based on the traveling wave current vector matrix, and obtains its eigenvalues ​​based on the sample covariance matrix. The sorting module sorts the eigenvalues ​​and uses the time sequence corresponding to the traveling wave current vector of the largest eigenvalue as the suspected fault pulse signal sequence. The trajectory generation module generates a three-dimensional phase space trajectory based on the suspected fault pulse signal sequence. The judgment module, based on the three-dimensional phase space trajectory, obtains the weight dimension of the three-dimensional phase space trajectory; The weight dimension is compared with a set threshold. If it is greater than the threshold, the suspected fault pulse signal sequence is determined to contain a fault pulse.

7. The distribution network line fault traveling wave pulse feature extraction system as described in claim 6, characterized in that, The signal acquisition module collects the traveling wave current data of the distribution network over a period of time, and performs normalization processing on it to obtain the time series of the traveling wave current.

8. The distribution network line fault traveling wave pulse feature extraction system as described in claim 6, characterized in that, The judgment module obtains the weight dimension E based on the following formula: Where r represents dividing the three-dimensional phase trajectory diagram into a uniform grid of r 3 The size of one of the small cubes; T r This represents the percentage of the three-dimensional phase space trajectory that falls within the small cube for different sizes of r; p t This represents the probability that the trajectory in the three-dimensional phase space is occupied in the t-th small cube.

9. The distribution network line fault traveling wave pulse feature extraction system as described in claim 6, characterized in that, The threshold is selected from the range of 0.55-0.

65.

10. The distribution network line fault traveling wave pulse feature extraction system as described in claim 6, characterized in that, It also includes an alarm module, which outputs an alarm signal when it is determined that the suspected fault pulse signal sequence contains a fault pulse.

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