Power transmission line single-phase earth fault identification method based on empirical mode approximate entropy

Abstract

The invention relates to a power transmission line single-phase earth fault identification method based on empirical mode approximate entropy, and the method comprises the following specific steps: 1), after a fault occurs, sampling the fault phase voltage value of the head end of a line within a certain period of time; 2) performing empirical mode decomposition on the sampled fault phase voltage value at the head end of the line to obtain a series of empirical mode function values IMF from high frequency to low frequency; 3) forming a vector by the IMF values of the components obtained by decomposition in sequence, calculating approximate entropy values of the vector, and summing the approximate entropy values; and 4) when the single-phase earth fault occurs, the difference between the empirical mode approximate entropy values of the phase end voltages of the instantaneous single-phase earth fault and the permanent single-phase earth fault is large, the fault type is judged by comparing the entropy values with 1, and if the entropy values are larger than 1, the fault is the instantaneous fault. According to the invention, fault identification is carried out by solving the empirical mode approximate entropy value of the fault phase end voltage transient signal when the power transmission line has the single-phase earth fault, the sensitivity is high, the anti-noise performance is good, and online rapid identification is realized.

Description

Technical Field

[0001] This application relates to the field of single-phase grounding fault identification in transmission lines, specifically to a method for identifying single-phase grounding faults in transmission lines based on empirical mode approximate entropy. Background Art

[0002] Over 70% of transmission line faults are single-phase grounding faults, and about 80% of these are transient. To restore the line to operation after a transient single-phase fault is cleared, and to improve the reliability of power supply and the stability of the power system through automatic reclosing, effective identification of single-phase fault types is necessary. Existing identification methods suffer from low sensitivity, poor noise immunity, and long computation time. A method with high sensitivity, good noise immunity, and rapid online identification is needed. Summary of the Invention

[0003] The purpose of this application is to provide a method for identifying single-phase grounding faults in transmission lines based on empirical mode approximate entropy, which identifies the fault by obtaining the empirical mode approximate entropy value of the transient voltage signal at the fault phase terminal during a single-phase grounding fault in the transmission line.

[0004] To achieve the above objectives, this application provides the following technical solutions:

[0005] This application provides a method for identifying single-phase grounding faults in transmission lines based on empirical mode approximate entropy, including the following specific steps:

[0006] 1) After a fault occurs, sample the voltage value of the faulty phase at the beginning of the line within a certain period of time;

[0007] 2) Perform empirical mode decomposition on the sampled fault phase voltage values ​​at the beginning of the line to obtain a series of empirical mode function (IMF) values ​​from high frequency to low frequency;

[0008] 3) Arrange the IMF values ​​of each component obtained from the decomposition into a vector in order, calculate its approximate entropy value, and sum the approximate entropy values;

[0009] 4) When a single-phase ground fault occurs, the approximate entropy value of the empirical mode of the phase terminal voltage of a transient and a permanent single-phase ground fault is very different. The fault type can be determined by comparing the entropy value with 1. If it is greater than 1, it is a transient fault.

[0010] The empirical mode decomposition (IMF) of the sampled fault phase voltage at the beginning of the line to obtain a series of empirical mode function values ​​(IMFs) from high frequency to low frequency is specifically as follows:

[0011] Determine all the maximum and minimum points of the signal s(t). Fit the upper and lower envelopes of the original data sequence with a cubic spline function based on these extreme points, and take their mean m1. Subtract m1 from the original data sequence s(t) to obtain a new data sequence h1. Usually, h1 is not a stationary data sequence, so repeat the above process n times. When the IMF condition is met, the first IMF component c1 is obtained, which represents the highest frequency component in the signal data.

[0012] Subtracting c1 from s(t) yields a new data sequence with the highest frequency component removed. Repeating the above steps results in a series of c... i And the last indivisible sequence r, where r represents the mean or trend term of s(t), until r is smaller than a predetermined value or r becomes a monotonic function. In this way, the original data sequence s(t) can be represented as the sum of n intrinsic modal components and one residual, i.e.

[0013]

[0014] When determining whether the obtained signal meets the IMF conditions, the following formula is used.

[0015]

[0016] Where: h (k-1) (t) is the remaining signal after k-1 rounds of filtering; h (k) (t) is the remaining signal after k rounds of filtering, s D The value ranges from 0.2 to 0.3.

[0017] The process involves arranging the IMF values ​​of each component obtained from the decomposition into a vector in order, calculating its approximate entropy value, and summing the approximate entropy values.

[0018] The original data for each component empirical mode are {u(i), i = 0, 1, 2, ..., N}, with the mode dimension m and the similarity tolerance value δ (δ > 0) pre-given.

[0019] Arrange the sequence {u(i)} into an m-dimensional vector X(i) in order, that is...

[0020] X(i)=[u(i),u(i+1),...,u(i+m-1)]

[0021] For each value i, calculate the distance between vector X(i) and the other vectors X(j):

[0022]

[0023] Given a similarity tolerance value δ, for each value i, count the number of vectors where d[X(i),X(j)] < δ and the ratio of this number to the total number of vectors N-m+1, denoted as […].

[0024] First Take the logarithm, then calculate its average over all i, denoted as . Right now

[0025]

[0026] Repeat the above process for m+1 to obtain...

[0027] Therefore, theoretically, the approximate entropy of this sequence is:

[0028]

[0029] Compared with existing technologies, the beneficial effects of this invention are: when a single-phase ground fault occurs in a transmission line, the transient voltage signal at the fault phase terminal of a transient fault is more complex than that of a permanent fault. The transient voltage signal at the fault phase terminal, after calculation using empirical mode approximation entropy, can be clearly expressed in the entropy value. It has low sensitivity to noise and is unaffected by factors such as transition resistance, short-circuit location, and the presence of parallel reactors. Attached Figure Description

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 This is a circuit diagram of an embodiment of this application.

[0032] Figure 2 This is a flowchart illustrating a method according to an embodiment of this application. Detailed Implementation

[0033] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. It should be noted that similar reference numerals and letters in the following drawings indicate similar items; therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0034] Reference Figure 1 Assume a ground fault occurs in phase A of the line at 0.1s, and the circuit breakers on both sides of the line trip at 0.15s. Table 1 shows the empirical mode approximate entropy values ​​of the transient voltage signal at the fault phase without parallel reactors at both ends of the line, and Table 2 shows the empirical mode approximate entropy values ​​of the transient voltage signal at the fault phase with parallel reactors at both ends of the line.

[0035] Table 1. Approximate Entropy of Empirical Modes without Parallel Reactors

[0036]

[0037] Table 2. Approximate Entropy of Empirical Modes with Parallel Reactors

[0038]

[0039] like Figure 2 As shown, a method for identifying single-phase grounding faults in transmission lines based on empirical mode approximate entropy includes the following specific steps:

[0040] 1) After a fault occurs, sample the voltage value of the faulty phase at the beginning of the line within a certain period of time;

[0041] 2) Perform empirical mode decomposition on the sampled fault phase voltage values ​​at the beginning of the line to obtain a series of empirical mode function (IMF) values ​​from high frequency to low frequency;

[0042] 3) Arrange the IMF values ​​of each component obtained from the decomposition into a vector in order, calculate its approximate entropy value, and sum the approximate entropy values;

[0043] 4) When a single-phase ground fault occurs, the approximate entropy value of the empirical mode of the phase terminal voltage of a transient and a permanent single-phase ground fault is very different. The fault type can be determined by comparing the entropy value with 1. If it is greater than 1, it is a transient fault.

[0044] The empirical mode decomposition (IMF) of the sampled fault phase voltage at the beginning of the line to obtain a series of empirical mode function values ​​(IMFs) from high frequency to low frequency is specifically as follows:

[0045] Determine all the maximum and minimum points of the signal s(t). Fit the upper and lower envelopes of the original data sequence with a cubic spline function based on these extreme points, and take their mean m1. Subtract m1 from the original data sequence s(t) to obtain a new data sequence h1. Usually, h1 is not a stationary data sequence, so repeat the above process n times. When the IMF condition is met, the first IMF component c1 is obtained, which represents the highest frequency component in the signal data.

[0046] Subtracting c1 from s(t) yields a new data sequence with the highest frequency component removed. Repeating the above steps results in a series of c... i And the last indivisible sequence r, where r represents the mean or trend term of s(t), until r is smaller than a predetermined value or r becomes a monotonic function. In this way, the original data sequence s(t) can be represented as the sum of n intrinsic modal components and one residual, i.e.

[0047]

[0048] When determining whether the obtained signal meets the IMF conditions, the following formula is used.

[0049]

[0050] Where: h (k-1) (t) is the remaining signal after k-1 rounds of filtering; h (k) (t) is the remaining signal after k rounds of filtering.

[0051] The process involves arranging the IMF values ​​of each component obtained from the decomposition into a vector in order, calculating its approximate entropy value, and summing the approximate entropy values.

[0052] The original data for each component empirical mode are {u(i), i = 0, 1, 2, ..., N}, with the mode dimension m and the similarity tolerance value δ (δ > 0) pre-given.

[0053] Arrange the sequence {u(i)} into an m-dimensional vector X(i) in order, that is...

[0054] X(i)=[u(i),u(i+1),...,u(i+m-1)]

[0055] For each value i, calculate the distance between vector X(i) and the other vectors X(j):

[0056]

[0057] Given a similarity tolerance value δ, for each value i, count the number of vectors where d[X(i),X(j)] < δ and the ratio of this number to the total number of vectors N-m+1, denoted as […].

[0058] First Take the logarithm, then calculate its average over all i, denoted as . Right now

[0059]

[0060] Repeat the above process for m+1 to obtain...

[0061] Therefore, theoretically, the approximate entropy of this sequence is:

[0062]

[0063] When a transmission line fault occurs, the empirical mode approximation entropy, which combines empirical mode decomposition (EMD) with approximate entropy, can effectively extract features from the transient fault signal. In the case of a single-phase ground fault, the EMD approximate entropy values ​​differ between transient and permanent faults. Under different fault locations and transition resistances, the EMD approximate entropy values ​​of the transient phase voltage signal for transient faults are all much larger than 1, while the EMD approximate entropy values ​​of the transient phase voltage signal for permanent faults are all smaller than 1. This indicates that, since EMD itself has denoising capabilities and approximate entropy is insensitive to noise, using the EMD approximate entropy value as the basis for identifying transient and permanent faults is effective.

[0064] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for identifying single-phase grounding faults in power transmission lines based on empirical mode approximate entropy, characterized in that: The specific steps include: 1) After the fault occurs, sample the fault phase voltage value at the head end of the line within a certain period of time; 2) Perform empirical mode decomposition on the sampled line head-end fault phase voltage value to obtain a series of empirical mode function values ​​IMF from high frequency to low frequency; 3) The IMF values ​​of each component obtained by decomposition are sequentially organized into vectors, their approximate entropy values ​​are calculated, and the approximate entropy values ​​are summed; 4) When a single-phase grounding fault occurs, the approximate entropy values ​​of the empirical mode of the phase terminal voltage for transient and permanent single-phase grounding faults are very different. The fault type is determined by comparing the entropy value with 1. If it is greater than 1, it is a transient fault.

2. The method for identifying single-phase grounding faults in power transmission lines based on empirical mode approximate entropy according to claim 1 is characterized in that: The empirical mode decomposition is performed on the sampled line head end fault phase voltage value to obtain a series of empirical mode function values ​​IMF from high frequency to low frequency, specifically, Determine all the maximum and minimum points of the signal s(t), and use the cubic spline function to fit the upper and lower envelopes of the original data sequence based on these extreme points. Take the mean m1, and subtract m1 from the original data sequence s(t) to get a new data sequence h1. Usually h1 is not a stationary data sequence, so repeat the above process n times. When the IMF condition is met, the first IMF component c1 is obtained, which represents the highest frequency component in the signal data; s(t) minus c1 to get a new data sequence without the highest frequency component. Repeat the above steps to get a series of c i and the last indecomposable sequence r, where r represents the mean or trend term of s(t), until r is smaller than a predetermined value or r becomes a monotonic function. In this way, the original data sequence s(t) can be represented by the sum of n intrinsic modal components and 1 residual, that is, 3. The method for identifying single-phase grounding fault of a power transmission line based on empirical mode approximate entropy according to claim 2 is characterized in that: When judging whether the obtained signal meets the IMF condition, the following formula is used Where: h (k-1) (t) is the remaining signal after k-1 screenings; h (k) (t) is the remaining signal after k screenings, s D The value of is between 0.2 and 0.

3.

4. The method for identifying single-phase grounding fault of a power transmission line based on empirical mode approximate entropy according to claim 1 is characterized in that: The IMF values ​​of each component obtained by decomposition are sequentially composed into vectors, and their approximate entropy values ​​are calculated. The sum of the approximate entropy values ​​is specifically: The original empirical modal data of each component is {u(i), i = 0, 1, 2, ..., N}, and the mode dimension m and the similarity tolerance value δ (δ> 0) are given in advance. The sequence {u(i)} is sequentially composed into an m-dimensional vector X(i), that is, X(i)=[u(i),u(i+1),...,u(i+m-1)] For each value of i, calculate the distance between vector X(i) and the remaining vectors X(j): According to the given similarity tolerance value δ, for each i value, the number of d[X(i),X(j)] < δ and the ratio of this number to the total number of vectors N-m+1 are counted, denoted as First Take the logarithm and find its average value for all i, and record it as Right now Repeat the above process for m+1, and we get So theoretically the approximate entropy of this sequence is:

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