Cable early fault detection method based on segmented Teager-Kaiser energy operator

Abstract

The invention belongs to the technical field of relay protection of power system distribution network, and provides a cable early fault detection method based on segmented Teager-Kaiser energy operator, comprising the following steps: S1, sheath phase current signal decomposition; S2, signal reconstruction; S3, segmented energy operator derivation; S4, early fault detection; when an early fault occurs in an ungrounded system, the fault characteristics are weak and difficult to be specifically analyzed due to the influence of complex topological structure of the distribution network and external environmental factors; the invention is based on a simulation model of a three-phase single-core cable of a branchless 10kV system, and by analyzing an equivalent circuit of the early fault, the line characteristics are obtained, and the source of the early fault sheath phase current is determined, thereby laying a foundation for detecting early faults based on the segmented Teager-Kaiser energy operator. At the same time, the detection method proposed by the invention has the advantage of not being interfered by strong noise, and effectively improves the reliability of early fault detection.

Description

Technical Field

[0001] The invention belongs to the technical field of relay protection of power system distribution network, in particular to a cable early fault detection method based on a segmented Teager-Kaiser energy operator. Background Art

[0002] Cables are important equipment in the power transmission and distribution lines of the power system. After installation, they are affected by soil, moisture and mechanical stress, which can easily cause early cable failures. The discharge duration of early cable failures is short, the fault characteristics are weak, and conventional overcurrent protection cannot be triggered. Fault finding is difficult. If not detected and handled in time, the fault may gradually worsen and develop into a permanent fault within a certain period of time. In addition, the existing early fault detection methods are difficult to ensure sensitivity and reliability at the same time.

[0003] To this end, those skilled in the art have proposed a cable early fault detection method based on a segmented Teager-Kaiser energy operator to solve the problem raised in the background art. Summary of the invention

[0004] The purpose of the present invention is to provide a cable early fault detection method based on a segmented Teager-Kaiser energy operator, which solves the problem in the prior art that the fault characteristics of the cable early fault are weak, resulting in difficulty in detection.

[0005] The technical solution adopted by the present invention is a cable early fault detection method based on a segmented Teager-Kaiser energy operator, which is specifically implemented according to the following steps:

[0006] Step 1: Decomposition of sheath phase current signal;

[0007] Step 2: Reconstruct the signal;

[0008] Step 3, deriving the piecewise energy operator;

[0009] Step 4: Early fault detection.

[0010] Preferably, step 1 is implemented specifically according to the following steps:

[0011] The cable sheath phase current is decomposed using the optimal variational mode decomposition (OVMD). The required decomposition mode number K is determined by the center frequency method. The center frequencies of the two modes are ω i When they are similar, the corresponding mode number K is the optimal decomposition number; after decomposition, K intrinsic mode functions IMF (Intrinsic Mode Functions) are obtained.

[0012] Preferably, in step 2, the impulse factor and cosine similarity of each intrinsic mode function IMF component are calculated respectively, the IMF with the largest impulse factor is defined as the characteristic mode, and the IMF with the largest cosine similarity is defined as the most similar mode; the characteristic mode and the most similar mode are added and reconstructed to obtain a reconstructed signal, which is specifically implemented according to the following steps:

[0013] Step 2.1, calculate the peak value and the average value of the absolute value of each IMF component respectively, and use the ratio of the peak value to the average value of the absolute value to calculate the impulse factor of each IMF. Define the IMF with the largest impulse factor as the characteristic mode, and the impulse factor The calculation is as follows:

[0014]

[0015] In the formula, x p is the peak value; x arv is the average of the absolute values; x i ( n ) is the i The current value of each sampling point in the IMF component, i =1,2,…,K; n is the number of sampling points;

[0016] Step 2.2, calculate the cosine similarity of each IMF and the original signal respectively, define the IMF with the largest cosine similarity as the most similar mode, and calculate the cosine similarity as follows:

[0017]

[0018] In the formula, cos( θ ) is the cosine similarity, x i ( n ) is the i IMF components, y ( n ) is the original signal, x ij ( n ) is the i IMF component j The current value of the sampling point, y j ( n ) is the first j The current value of the sampling point, j =1,2,…,n;

[0019] Step 2.3: After obtaining the impulse factor and cosine similarity of each IMF, take the characteristic mode and the most similar mode for addition and reconstruction to obtain the reconstructed signal :

[0020]

[0021] In the formula, IMF p is the characteristic mode, IMF q is the most similar mode.

[0022] Preferably, in step 3, the Teager-Kaiser energy operator kteo of the reconstructed signal is calculated, and the Teager-Kaiser energy operator is calculated as follows:

[0023]

[0024] In the formula, z ( n ) is the reconstructed signal at the sampling point n The value at z ( n +1) is the value of the reconstructed signal at sampling point n+1; z ( n -1) is the reconstructed signal at the sampling point n The value at -1; To reconstruct the signal at the sampling point n Teager-Kaiser energy at

[0025] Energy operator waveform in time t Equal m part, m ≥2, recorded as E 1[ t 0， t 1] , E 2[ t 1， t 2] , …, E m[ t m-1， t m] ; Calculate the first-order derivative of the energy operator waveform in each time period and obtain m The segment derivative waveform is denoted as F 1[ t 0， t 1] , F2[ t 1， t 2] , …, F m[ t m-1， t m] ; Calculate the maximum value of the energy operator derivative of each segment, denoted as max F 1, max F 2, …, max F m .

[0026] Preferably, after obtaining the maximum value of each energy operator derivative in step 4, the maximum value of each energy operator derivative is compared with the maximum value of the energy operator derivative of the previous segment, and recorded as k 1, k 2, ..., k m , using the sum of all ratios S Construct the detection criteria of early faults and implement early fault detection. The specific implementation is as follows:

[0027] Step 4.1: The ratio of the maximum value of the derivative of each energy operator to the maximum value of the derivative of the previous energy operator is expressed as k 1, k 2.… k r …, k m , calculated as follows:

[0028]

[0029] In the formula, r =1,2,…,m;

[0030] Step 4.2: The sum of all ratios S is calculated as follows:

[0031]

[0032] Step 4.3: The early fault detection criteria are as follows: S < δ , it is judged that the cable has no early fault; if S > δ , it is judged that the cable has an early fault, among which δ The threshold to set.

[0033] The cable early fault detection device based on the segmented Teager-Kaiser energy operator is applicable to the above-mentioned cable early fault detection method based on the segmented Teager-Kaiser energy operator, comprising:

[0034] Signal acquisition module: The cable sheath phase current signal is obtained through the signal acquisition module;

[0035] Signal decomposition module: Use the signal decomposition module to perform OVMD decomposition on the collected signal to obtain IMF components;

[0036] Signal reconstruction module: The signal reconstruction module selects the characteristic mode and the most similar mode and performs additive reconstruction;

[0037] Energy operator calculation module: The energy operator calculation module performs kteo calculation on the reconstructed signal;

[0038] Segmented Derivative Module: The segmented derivative module divides the energy operator waveform into segments and calculates the derivative waveform of each segment and its maximum value;

[0039] Fault detection module: The fault detection criterion calculation module makes fault judgment based on the maximum value of the derivative waveform and the preset threshold;

[0040] Result display module: The result display module displays real-time data and fault detection results.

[0041] A processor is configured to execute the above-mentioned cable early fault detection method based on segmented Teager-Kaiser energy operator.

[0042] A machine-readable storage medium stores instructions, which, when executed by a processor, enable the processor to be configured to execute the above-mentioned cable early fault detection method based on segmented Teager-Kaiser energy operator.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. Line characteristic analysis: When an early fault occurs in an ungrounded system, the fault characteristics are weak and difficult to analyze specifically due to the complex topological structure of the distribution network and external environmental factors. The present invention is based on a simulation model of a three-phase single-core cable in a branchless 10kV system. By analyzing the equivalent circuit of the early fault, the line characteristics are obtained and the source of the early fault sheath phase current is determined, which lays the foundation for detecting early faults based on the segmented Teager-Kaiser energy operator.

[0045] 2. Signal processing: In view of the problem that the fault duration is short and the fault characteristics are weak when an early fault occurs, it is proposed to use OVMD to decompose the electrical phase current of the sheath phase, determine the number of decompositions K by the center frequency method, calculate the pulse factor and cosine similarity of each mode, the characteristic mode with the largest pulse factor reflects rich fault information, and the mode with the largest cosine similarity is most similar to the fault signal. The characteristic mode and the most similar mode are added and reconstructed to obtain a new signal, and the Teager-Kaiser energy operator (tkeo) of the reconstructed signal is calculated, and it is divided into m segments and the derivative and the maximum value of the derivative size are respectively calculated. The maximum value of the derivative size of each energy operator segment is compared with the maximum value of the derivative size of the previous energy operator segment, and the sum of all ratios S is used to construct the detection criterion of the early fault, so as to realize accurate detection of the early fault; the detection method proposed in the present invention has the advantage of not being interfered by strong noise, and effectively improves the reliability of early fault detection;

[0046] 3. Fault detection criteria: By calculating tkeo for the reconstructed signal containing rich fault information, dividing tkeo into m segments, finding the derivative, taking the maximum value, and calculating the ratio of the maximum value of the derivative of the energy operator in each segment to the maximum value of the derivative of the energy operator in the previous segment, the sum S of all ratios is used to construct the early fault detection criterion, thereby improving the effectiveness and reliability of fault detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of the cable early fault detection method based on the segmented Teager-Kaiser energy operator of the present invention;

[0048] Figure 2 It is a schematic diagram of a simulation model of a three-phase single-core cable of a 10kV system without branches according to an embodiment of the present invention;

[0049] Figure 3 It is a schematic diagram of simulation results of sheath phase current in a normal state of an embodiment of the present invention;

[0050] Figure 4 This is a schematic diagram of simulation results of the sheath phase current when a metal sheath grounding fault occurs 255m away from the busbar and the fault resistance is 1000Ω according to an embodiment of the present invention;

[0051] Figure 5 It is a schematic diagram of characteristic modes and most similar modes after OVMD decomposes the sheath phase current when the metal sheath is grounded in an embodiment of the present invention;

[0052] Figure 6 It is a schematic diagram of a reconstructed signal waveform after adding and reconstructing the characteristic mode and the most similar mode when the metal sheath is grounded in an embodiment of the present invention;

[0053] Figure 7It is a schematic diagram of a segmented Teager-Kaiser energy operator waveform of a reconstructed signal when a metal sheath is grounded in an embodiment of the present invention;

[0054] Figure 8 It is a schematic diagram of the waveform of the segmented Teager-Kaiser energy operator derivative of the reconstructed signal when the metal sheath is grounded in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0056] The present invention provides a cable early fault detection method based on a segmented Teager-Kaiser energy operator, such as Figure 1 As shown, the following steps are included:

[0057] Step 1: Decomposition of sheath phase current signal; Step 1 is specifically implemented according to the following steps:

[0058] The cable sheath phase current is decomposed using the optimal variational mode decomposition (OVMD). The required decomposition mode number K is determined by the center frequency method. The center frequencies of the two modes are ω i When they are similar, the corresponding mode number K is the optimal decomposition number; after decomposition, K intrinsic mode functions IMF are obtained;

[0059] Step 2, reconstruct the signal; in step 2, respectively calculate the impulse factor and cosine similarity of each intrinsic mode function IMF component, define the IMF with the largest impulse factor as the characteristic mode, and define the IMF with the largest cosine similarity as the most similar mode; add the characteristic mode and the most similar mode to reconstruct and obtain the reconstructed signal, which is specifically implemented in the following steps:

[0060] Step 2.1, calculate the peak value and the average value of the absolute value of each IMF component respectively, and use the ratio of the peak value to the average value of the absolute value to calculate the impulse factor of each IMF. Define the IMF with the largest impulse factor as the characteristic mode, and the impulse factor The calculation is as follows:

[0061]

[0062] In the formula, x p is the peak value; x arv is the average of the absolute values; x i ( n ) is the i The current value of each sampling point in the IMF component,i =1,2,…,K; n is the number of sampling points;

[0063] Step 2.2, calculate the cosine similarity of each IMF and the original signal respectively, define the IMF with the largest cosine similarity as the most similar mode, and calculate the cosine similarity as follows:

[0064]

[0065] In the formula, cos( θ ) is the cosine similarity, x i ( n ) is the i IMF components, y ( n ) is the original signal, x ij ( n ) is the i IMF component j The current value of the sampling point, y j ( n ) is the first j The current value of the sampling point, j =1,2,…,n;

[0066] Step 2.3: After obtaining the impulse factor and cosine similarity of each IMF, take the characteristic mode and the most similar mode for addition and reconstruction to obtain the reconstructed signal :

[0067]

[0068] In the formula, IMF p is the characteristic mode, IMF q is the most similar mode;

[0069] Step 3, derive the piecewise energy operator; in step 3, calculate the Teager-Kaiser energy operator kteo of the reconstructed signal, and the Teager-Kaiser energy operator is calculated as follows:

[0070]

[0071] In the formula, z ( n ) is the reconstructed signal at the sampling point n The value at z ( n +1) is the value of the reconstructed signal at sampling point n+1; z ( n -1) is the reconstructed signal at the sampling pointn The value at -1; To reconstruct the signal at the sampling point n Teager-Kaiser energy at

[0072] Energy operator waveform in time t Equal m part, m ≥2, recorded as E 1[ t 0， t 1] , E 2[ t 1， t 2] , …, E m[ t m-1， t m] ; Calculate the first-order derivative of the energy operator waveform in each time period and obtain m The segment derivative waveform is denoted as F 1[ t 0， t 1] , F 2[ t 1， t 2] , …, F m[ t m-1， t m] ; Calculate the maximum value of the energy operator derivative of each segment, denoted as max F 1, max F 2, …, max F m ;

[0073] Step 4, early fault detection: After obtaining the maximum value of each energy operator derivative in step 4, the maximum value of each energy operator derivative is compared with the maximum value of the previous energy operator derivative, which is recorded as k 1, k 2, ..., k m , using the sum of all ratios S Construct the detection criteria of early faults and implement early fault detection. The specific implementation is as follows:

[0074] Step 4.1: The ratio of the maximum value of the derivative of each energy operator to the maximum value of the derivative of the previous energy operator is expressed as k 1, k 2.… k r …, k m , calculated as follows:

[0075]

[0076] In the formula, r =1,2,…,m;

[0077] Step 4.2: The sum of all ratios S is calculated as follows:

[0078]

[0079] Step 4.3: The early fault detection criteria are as follows: S < δ , it is judged that the cable has no early fault; if S > δ , it is judged that the cable has an early fault, among which δ The threshold to set.

[0080] Working principle of the invention

[0081] 1. Cable sheath phase current

[0082] When an early fault occurs in a cable in a distribution network, the fault duration is short, the characteristics are weak, and the core current fluctuation is not obvious. Therefore, the sheath phase current can be used to detect the early fault; the sheath phase current is composed of the core induced current and the core leakage current;

[0083] When an alternating current flows through the cable core, an alternating magnetic field is generated around the alternating current, forming a magnetic flux that intersects with the cable metal sheath, thereby generating an induced electromotive force on the metal sheath; taking phase A as an example, the induced current of the phase A sheath can be expressed as:

[0084]

[0085] In the formula, M AA It is the mutual inductance between the A-phase sheath and the A-phase core; M BA It is the mutual inductance between the A phase sheath and the B phase core; M CA It is the mutual inductance between the A phase sheath and the C phase core; I A is the A phase core current; I B is the core current of phase B;I C is the C phase core current; Z AS is the A phase sheath impedance; ω is the angular frequency;

[0086] Analyze the leakage current size and source in the sheath current. Taking phase A as an example, the sheath leakage current of phase A can be expressed as:

[0087]

[0088]

[0089] In the formula, Z SL is the equivalent impedance of the left side of the metal sheath, Z SR is the equivalent impedance on the right side of the metal sheath, R g is the ground resistance; I l It is the current that flows from the core through the main insulation and leaks to the metal sheath. I ll is the current flowing to the left end of the sheath; I lr is the current flowing to the right end of the sheath.

[0090] 2. Optimal Variational Mode Decomposition (OVMD)

[0091] Variational mode decomposition (VMD) is an emerging multi-resolution adaptive non-recursive signal decomposition technology. It can extract multiple modal components from the original signal f(t) at the same time, and match the center frequency for each component by iteratively seeking the optimal solution. Its main steps are as follows:

[0092] 1) Assume that the original signal is decomposed into K modal components, i =1,2,…,K, then the i-th modal component u i ( t )for:

[0093]

[0094] Where: A i ( t ) is the amplitude function; Φ i ( t ) is the phase function; u i is the modal component after decomposition;

[0095] 2) Perform Hilbert transform, spectrum modulation and gradient two-norm operation on each modal component in turn to estimate the bandwidth of each modal component, and use this to construct a constrained variational model:

[0096]

[0097] Where: K is the number of modes to be decomposed (a positive integer), { u i}、{ ω i} respectively correspond to the decomposition i modal components and center frequencies, δ ( t ) is the Dirac function, * is the convolution operator;

[0098] 3) Make full use of the quadratic penalty factor α and the Lagrangian operator λ ( t ), the above constrained variational problem is converted into an unconstrained variational problem, namely:

[0099]

[0100] 4) Use the alternating direction multiplier method to iteratively solve the unconstrained variational problem. u i , ω i and λ ( t )The iterative process is:

[0101]

[0102] Where: For the remaining amount Obtained through Wiener filtering; q is the number of iterations; is the center of gravity of the power spectrum of the current modal function; τ is the update step size of the Lagrangian operator; The inverse Fourier transform gives ;

[0103] For a given solution accuracy, the iteration stops when equation (23) satisfies:

[0104]

[0105] Where: ε is the set threshold;

[0106] 5) Analyze the center frequency of the decomposed mode under different K values ω iOnce similar frequencies appear, the K at this time is determined as the optimal K for decomposition; then, the optimal Lagrange multiplier update step size is selected by minimizing the residual error. τ :

[0107]

[0108] Where n represents the number of signal samples; when the residual REL reaches the minimum, the optimal τ value is determined.

[0109] 3. Pulse Factor Theory

[0110] The crest factor is a dimensionless parameter used to quantify the ratio of the most extreme transient value in a signal to its average level. In VMD decomposition, each independent mode function (IMF) contains different frequency components caused by the fault. Among them, the IMF with the largest crest factor contains the richest fault information and can extract fault features more effectively. The crest factor is defined as follows:

[0111]

[0112] In the formula, x p is the peak value; x arv is the average of the absolute values; x i ( n ) is the i The current value of each sampling point in the IMF component, i =1,2,…,K; n is the number of sampling points;

[0113] 4. Cosine Similarity

[0114] Cosine similarity is a similarity metric used to measure the angle between two vectors; it measures the similarity (angular similarity) between two vectors, and its value is usually between 0 and 1, and in certain cases between -1 and 1, and is defined as follows:

[0115]

[0116] In the formula, cos( θ ) is the cosine similarity, x i ( n ) is the i IMF components, y ( n ) is the original signal, x ij ( n ) is the i IMF componentj The current value of the sampling point, y j ( n ) is the current value of the jth sampling point in the original signal, j =1,2,…, n ;

[0117] The values ​​of cosine similarity have the following meanings: 1 means that the two vectors are exactly the same; 0 means that the two vectors are orthogonal, that is, there is no similarity; -1 means that the two vectors are completely opposite.

[0118] 5. Teager-Kaiser energy operator

[0119] The Teager-Kaiser energy operator (kteo) is a nonlinear local differential operator that does not involve integral transformation. z kteo (n) is defined as:

[0120]

[0121] In the formula, z ( n ) is the value of the reconstructed signal at sampling point n; z ( n +1) is the reconstructed signal at the sampling point n The value at +1; z ( n -1) is the value of the reconstructed signal at sampling point n-1; To reconstruct the signal at the sampling point n The Teager-Kaiser energy at .

[0122] From the above, it can be seen that when an early fault occurs in an ungrounded system, the fault characteristics are weak and difficult to analyze specifically due to the complex topological structure of the distribution network and external environmental factors. The present invention is based on a simulation model of a three-phase single-core cable in a branchless 10kV system. By analyzing the equivalent circuit of the early fault, the line characteristics are obtained, and the source of the sheath phase current of the early fault is determined, laying a foundation for detecting early faults based on the segmented Teager-Kaiser energy operator. At the same time, in view of the problem that the fault duration is short and the fault characteristics are weak when the early fault occurs, it is proposed to use OVMD to decompose the sheath phase current, determine the number of decompositions K by the center frequency method, calculate the pulse factor and cosine similarity of each mode, the characteristic mode with the largest pulse factor reflects the richest fault information, the mode with the largest cosine similarity is the most similar to the fault signal, and the characteristic modes and the most similar modes are reconstructed by adding them. A new signal is obtained by similar mode, and the Teager-Kaiser energy operator (tkeo) of the reconstructed signal is calculated, which is divided into m segments and the derivatives and the maximum values ​​of the derivatives are calculated respectively. The maximum value of the derivative of each segment of the energy operator is compared with the maximum value of the derivative of the previous segment of the energy operator, and the sum S of all ratios is used to construct the detection criterion of early faults, so as to achieve accurate detection of early faults. The detection method proposed in the present invention has the advantage of not being disturbed by strong noise, and effectively improves the reliability of early fault detection. Moreover, by calculating tkeo for the reconstructed signal containing rich fault information, dividing tkeo into m segments, calculating the derivative, taking the maximum value, calculating the ratio of the maximum value of the derivative of each segment of the energy operator to the maximum value of the derivative of the previous segment of the energy operator, and using the sum S of all ratios to construct the early fault detection criterion, the effectiveness and reliability of fault detection are improved.

[0123] The above technical content is further described below in conjunction with embodiments:

[0124] Example 1

[0125] The present invention is based on the segmented Teager-Kaiser energy operator cable early fault detection method, the flow chart is as follows Figure 1 As shown, the specific implementation steps are as follows:

[0126] Step 1, decomposing the sheath phase current signal;

[0127] Step 1 is implemented according to the following steps:

[0128] The cable sheath phase current is decomposed using the optimal variational mode decomposition (OVMD). The required decomposition mode number K is determined by the center frequency method. When the center frequencies ωi of the first and second modes are similar, the corresponding mode number K is the optimal decomposition number. After decomposition, K intrinsic mode functions (IMFs) are obtained.

[0129] Step 2: Reconstruct the signal;

[0130] In step 2, the impulse factor and cosine similarity of each intrinsic mode function IMF component are calculated respectively, the IMF with the largest impulse factor is defined as the characteristic mode, and the IMF with the largest cosine similarity is defined as the most similar mode; the characteristic mode and the most similar mode are added and reconstructed to obtain the reconstructed signal, which is specifically implemented in the following steps:

[0131] Step 2.1, calculate the peak value and the average value of the absolute value of each IMF component respectively, and use the ratio of the peak value to the average value of the absolute value to calculate the impulse factor of each IMF. Define the IMF with the largest impulse factor as the characteristic mode, and the impulse factor The calculation is as follows:

[0132]

[0133] In the formula, x p is the peak value; x arv is the average of the absolute values; x i ( n ) is the i The current value of each sampling point in the IMF component, i =1,2,…,K; n is the number of sampling points;

[0134] Step 2.2, calculate the cosine similarity of each IMF and the original signal respectively, define the IMF with the largest cosine similarity as the most similar mode, and calculate the cosine similarity as follows:

[0135]

[0136] In the formula, cos( θ ) is the cosine similarity, x i ( n ) is the i IMF components, y ( n ) is the original signal, xij ( n ) is the i IMF component j The current value of the sampling point, y j ( n ) is the first j The current value of the sampling point, j =1,2,…,n;

[0137] Step 2.3: After obtaining the impulse factor and cosine similarity of each IMF, take the characteristic mode and the most similar mode for addition and reconstruction to obtain the reconstructed signal :

[0138]

[0139] In the formula, IMF p is the characteristic mode, IMF q is the most similar mode;

[0140] Step 3, deriving the piecewise energy operator;

[0141] Step 4: Early fault detection;

[0142] After obtaining the maximum value of each energy operator derivative in step 4, the maximum value of each energy operator derivative is compared with the maximum value of the previous energy operator derivative, which is recorded as k 1, k 2, ..., k m , using the sum of all ratios S Construct the detection criteria of early faults and implement early fault detection. The specific implementation is as follows:

[0143] Step 4.1: The ratio of the maximum value of the derivative of each energy operator to the maximum value of the derivative of the previous energy operator is expressed as k 1, k 2.… k r …, k m , calculated as follows:

[0144]

[0145] In the formula, r =1,2,…,m;

[0146] Step 4.2: Sum of all ratios S The calculation is as follows:

[0147]

[0148] Step 4.3: The early fault detection criteria are as follows: S < δ , it is judged that the cable has no early fault; if S > δ , it is judged that the cable has an early fault, among which δ The threshold to set.

[0149] Example 2

[0150] The present invention is based on the segmented Teager-Kaiser energy operator cable early fault detection method, the flow chart is as follows Figure 1 As shown, the specific implementation steps are as follows:

[0151] Step 1, decomposing the sheath phase current signal;

[0152] Step 1 is implemented according to the following steps:

[0153] The cable sheath phase current is decomposed using the optimal variational mode decomposition (OVMD). The required decomposition mode number K is determined by the center frequency method. When the center frequencies ωi of the first and second modes are similar, the corresponding mode number K is the optimal decomposition number. After decomposition, K intrinsic mode functions (IMFs) are obtained.

[0154] Step 2: Reconstruct the signal;

[0155] In step 2, the impulse factor and cosine similarity of each intrinsic mode function IMF component are calculated respectively, the IMF with the largest impulse factor is defined as the characteristic mode, and the IMF with the largest cosine similarity is defined as the most similar mode; the characteristic mode and the most similar mode are added and reconstructed to obtain the reconstructed signal, which is specifically implemented in the following steps:

[0156] Step 2.1, calculate the peak value and the average value of the absolute value of each IMF component respectively, and use the ratio of the peak value to the average value of the absolute value to calculate the impulse factor of each IMF. Define the IMF with the largest impulse factor as the characteristic mode, and the impulse factor The calculation is as follows:

[0157]

[0158] In the formula, x p is the peak value; x arv is the average of the absolute values; x i ( n ) is thei The current value of each sampling point in the IMF component, i =1,2,…,K; n is the number of sampling points;

[0159] Step 2.2, calculate the cosine similarity of each IMF and the original signal respectively, define the IMF with the largest cosine similarity as the most similar mode, and calculate the cosine similarity as follows:

[0160]

[0161] In the formula, cos( θ ) is the cosine similarity, x i ( n ) is the i IMF components, y ( n ) is the original signal, x ij ( n ) is the i IMF component j The current value of the sampling point, y j ( n ) is the first j The current value of the sampling point, j =1,2,…,n;

[0162] Step 2.3: After obtaining the impulse factor and cosine similarity of each IMF, take the characteristic mode and the most similar mode for addition and reconstruction to obtain the reconstructed signal :

[0163]

[0164] In the formula, IMF p is the characteristic mode, IMF q is the most similar mode;

[0165] Step 3, deriving the piecewise energy operator;

[0166] In step 3, the Teager-Kaiser energy operator kteo of the reconstructed signal is calculated. The Teager-Kaiser energy operator is calculated as follows:

[0167]

[0168] In the formula, z ( n ) is the reconstructed signal at the sampling point n The value at z ( n+1) is the reconstructed signal at the sampling point n The value at +1; z ( n -1) is the reconstructed signal at the sampling point n The value at -1; To reconstruct the signal at the sampling point n Teager-Kaiser energy at

[0169] Energy operator waveform in time t Equal m part, m ≥2, recorded as E 1[ t 0， t 1] , E 2[ t 1， t 2] , …, E m[ t m-1， t m] ; Calculate the first-order derivative of the energy operator waveform in each time period to obtain m segments of derivative waveforms, recorded as F 1[ t 0， t 1] , F 2[ t 1， t 2] , …, F m[ t m-1， t m] ; Calculate the maximum value of the energy operator derivative of each segment, denoted as max F 1, max F 2, …, max F m ;

[0170] Step 4: Early fault detection.

[0171] Application example: Use PSCAD to build a simulation model of a three-phase single-core cable in a 10kV system without branches, such as Figure 2As shown in the figure; the system is a feeder composed of three-phase single-core cables A, B, and C, with a length of 500m. The parameters of the single-core cable are shown in Table 1; the simulation system is used to simulate three types of early faults, including metal sheath grounding fault, high resistance fault between the core and the sheath, and insulation degradation fault at a certain point of the cable after the metal sheath is grounded; more early faults are simulated by changing the working conditions of the three types of faults; the total simulation time is 0.2s, the early fault is set to occur at 0.1006s, and the fault duration is 0.005s.

[0172] Table 1 Parameters of single-core cable

[0173]

[0174] Simulation Analysis

[0175] Cable early failure: Cable early failure simulation is as follows Figure 2 As shown in ; the cable length is 500 meters. When an early fault occurs in the cable in the distribution network, the fault duration is short, the characteristics are weak, the core current fluctuation is not obvious, and the sheath phase current has obvious fluctuations; three typical early faults, namely, metal sheath grounding fault, high resistance fault between the core and the sheath, and insulation drop fault at a certain point of the cable after the metal sheath is grounded, are simulated and analyzed, the phase current of the fault phase sheath head end is extracted, the sheath phase current is decomposed using the optimal VMD, the number of decompositions K=4 is determined by the center frequency method, the pulse factor and cosine similarity of each IMF component are calculated, the characteristic mode and the most similar mode are added and reconstructed to obtain the reconstructed signal, where the characteristic mode and the most similar mode are as follows Figure 5 As shown, the reconstructed signal is Figure 6 As shown; again, calculate the Teager-Kaiser energy operator (kteo) of the reconstructed signal, and divide the energy operator waveform into 6 segments, as shown Figure 7 As shown; the derivative of each energy operator waveform is calculated separately, such as Figure 8 As shown; calculate the maximum value of the derivative of each energy operator; finally, use the maximum value of the derivative of each energy operator to compare with the maximum value of the derivative of the previous energy operator, and calculate the sum of all ratios S ,like S <1000, the system has no early failure; if S >1000, the system has an early fault; no early fault is recorded as 0, and early fault is recorded as 1; taking the C phase fault as an example, the analysis results are as follows:

[0176] 1. Cable metal sheath grounding fault: When a metal sheath grounding fault occurs at a distance of 255m from the busbar and the fault resistance is 1000Ω, the simulation results of the sheath phase current are as follows: Figure 4; Changing the fault location and fault resistance simulates the early fault conditions of different cable metal sheath grounding, and the test results are shown in Table 2;

[0177] Table 2 Detection results of cable metal sheath grounding fault under different working conditions

[0178]

[0179] According to the analysis of the results in Table 2, it is found that when an early fault occurs in the system at a distance of 30m, 255m or 470m from the busbar, and the fault resistance range is 100Ω to 1000Ω, the detection method of the present invention can accurately detect the occurrence of the early fault; this shows that the proposed detection method has high sensitivity and applicability, and can effectively deal with early faults in different locations and different resistance ranges.

[0180] 2. Fault between cable core and sheath: A fault between the cable core and sheath occurs at a distance of 255m from the busbar. The fault resistance is 100Ω and the sheath phase current is simulated. The fault position and fault resistance are changed to simulate different early fault conditions between the core and sheath. The test results are shown in Table 3.

[0181] Table 3 Detection results of cable core and sheath faults under different working conditions

[0182]

[0183] It can be seen from Table 3 that when early faults occur at 30m, 255m and 470m away from the busbar and the fault resistance is 20Ω~100Ω, the detection method of the present invention can accurately detect the occurrence of early faults.

[0184] 3. Fault of insulation drop at a certain point of the cable after the metal sheath is grounded: When a sheath grounding fault occurs 255m away from the busbar and an insulation drop fault occurs at a certain point of the cable, the sheath grounding resistance R1 is 1000Ω, and the resistance between the core and the sheath R2 is 100Ω, the sheath phase current is simulated; the fault position and fault resistance are changed to simulate the early fault situation of insulation drop at a certain point of the cable after different metal sheaths are grounded. The detection results are shown in Table 4;

[0185] Table 4 Detection results of insulation drop fault at a certain point of the cable under different working conditions after the metal sheath is grounded

[0186]

[0187] It can be seen from Table 4 that when early faults occur at 30m, 255m and 470m away from the busbar, the sheath grounding resistance R1 is 100Ω~1000Ω, and the resistance between the core and the sheath R2 is 20Ω~100Ω, the detection method of the present invention can accurately detect the occurrence of early faults.

[0188] The present invention is aimed at early cable faults. The center frequency method is used to determine the number of decompositions of the optimal VMD signal. The pulse factor and cosine similarity of each mode are calculated. The tkeo of the reconstructed signal is calculated by reconstructing the characteristic mode and the most similar mode. m The maximum value of the segment derivative and derivative size is calculated by comparing the maximum value of the derivative size of each segment energy operator with the maximum value of the derivative size of the previous segment energy operator, and calculating the sum of all ratios S ,Compare S The experimental results show that under different fault conditions, the system can accurately identify early faults and show good sensitivity and reliability.

[0189] The cable early fault detection device based on the segmented Teager-Kaiser energy operator comprises a signal acquisition module, a signal decomposition module, a signal reconstruction module, an energy operator calculation module, a segmented derivation module, a fault detection module, and a result display module, wherein the signal acquisition module, the signal decomposition module, the signal reconstruction module, the energy operator calculation module, the segmented derivation module, the fault detection module, and the result display module are electrically connected in sequence:

[0190] Signal acquisition module: The cable sheath phase current signal is obtained through the signal acquisition module;

[0191] Signal decomposition module: Use the signal decomposition module to perform OVMD decomposition on the collected signal to obtain IMF components;

[0192] Signal reconstruction module: The signal reconstruction module selects the characteristic mode and the most similar mode and performs additive reconstruction;

[0193] Energy operator calculation module: The energy operator calculation module performs kteo calculation on the reconstructed signal;

[0194] Segmented Derivative Module: The segmented derivative module divides the energy operator waveform into segments and calculates the derivative waveform of each segment and its maximum value;

[0195] Fault detection module: The fault detection criterion calculation module makes fault judgment based on the maximum value of the derivative waveform and the preset threshold;

[0196] Result display module: The human-computer interaction module displays real-time data and fault detection results.

[0197] In this embodiment, the cable early fault detection device based on the segmented Teager-Kaiser energy operator includes a processor and a memory, wherein the processor includes a kernel, and the kernel retrieves the corresponding program unit from the memory. One or more kernels can be set, and the above-mentioned cable early fault detection method based on the segmented Teager-Kaiser energy operator is implemented by adjusting kernel parameters.

[0198] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0199] An embodiment of the present application provides a processor configured to execute the above-mentioned cable early fault detection method based on the segmented Teager-Kaiser energy operator.

[0200] An embodiment of the present application provides a machine-readable storage medium having instructions stored thereon. When the instructions are executed by a processor, the processor is configured to execute the above-mentioned cable early fault detection method based on the segmented Teager-Kaiser energy operator.

[0201] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0202] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0203] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0204] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0205] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0206] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0207] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0208] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0209] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A cable early fault detection method based on segmented Teager-Kaiser energy operator, characterized by: The following steps are involved: S1, sheath phase current signal decomposition; S2, reconstructed signal; S3, derivation of piecewise energy operator; S4, early fault detection; In the step S2, the impulse factor and cosine similarity of each intrinsic mode function IMF component are calculated respectively, the IMF with the largest impulse factor is defined as the characteristic mode, and the IMF with the largest cosine similarity is defined as the most similar mode; The characteristic mode and the most similar mode are added and reconstructed to obtain a reconstructed signal; In the step S3, the Teager-Kaiser energy operator kteo of the reconstructed signal is calculated; Energy operator waveform in time t Equal m part, m ≥2, recorded as E 1[ t 0， t 1] , E 2[ t 1， t 2] , …, E m[ t m-1， t m] ; Calculate the first-order derivative of the energy operator waveform in each time period and obtain m The segment derivative waveform is denoted as F 1[ t 0， t 1] , F 2[ t 1， t 2] , …, F m[ t m-1， t m] ; Calculate the maximum value of the energy operator derivative of each segment, denoted as max F 1, max F 2, …, max F m ; After obtaining the maximum value of each energy operator derivative in step S4, the maximum value of each energy operator derivative is compared with the maximum value of the energy operator derivative of the previous segment, and recorded as k 1, k 2, ..., k m , the sum S of all ratios is used to construct the early fault detection criterion and realize early fault detection.

2. The cable early fault detection method based on the segmented Teager-Kaiser energy operator as claimed in claim 1, characterized in that: The step S1 is specifically implemented according to the following steps: The cable sheath phase current is decomposed using the optimal variational mode decomposition (OVMD). The required decomposition mode number K is determined by the center frequency method. The center frequencies of the two modes are ω i When they are similar, the corresponding mode number K is the optimal decomposition number; after decomposition, K intrinsic mode functions IMF are obtained.

3. A cable early fault detection device based on segmented Teager-Kaiser energy operator, characterized in that: The method for detecting early faults of cables based on the segmented Teager-Kaiser energy operator according to any one of claims 1 to 2 comprises: Signal acquisition module: The cable sheath phase current signal is obtained through the signal acquisition module; Signal decomposition module: Use the signal decomposition module to perform OVMD decomposition on the collected signal to obtain IMF components; Signal reconstruction module: The signal reconstruction module selects the characteristic mode and the most similar mode and performs additive reconstruction; Energy operator calculation module: The energy operator calculation module performs kteo calculation on the reconstructed signal; Segmented Derivative Module: The segmented derivative module divides the energy operator waveform into segments and calculates the derivative waveform of each segment and its maximum value; Fault detection module: The fault detection criterion calculation module makes fault judgment based on the maximum value of the derivative waveform and the preset threshold; Result display module: The human-computer interaction module displays real-time data and fault detection results.

4. A processor, characterized in that: The method is configured to execute the cable early fault detection method based on the segmented Teager-Kaiser energy operator according to any one of claims 1 to 2.

5. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to execute the cable early fault detection method based on the segmented Teager-Kaiser energy operator according to any one of claims 1 to 2.

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