Improved HHT algorithm-based cable fault location method and system for distribution network

Abstract

An improved HHT algorithm-based cable fault location method and system for a distribution network. The method comprises: as both the voltage and the current of a traveling wave generated by a fault point and reaching a measurement end will sharply change and thus the wave front of the traveling wave will exhibit in a time-frequency graph a high-frequency leap, a leap point being the arrival time of the wave front, separately performing EEMD on line mode components of the double-end traveling wave to extract respective IMF components to be subjected to Hilbert transform, thereby obtaining the instantaneous frequency thereof, the first frequency leap point in the time-frequency graph thereof corresponding to the arrival time of the wave front of a respective fault traveling wave, and then performing fault location by means of a double-end fault location algorithm. The method and system overcome the shortcoming that single moduli of traditional transform matrices fail to reflect all fault types, and solve the problem that traditional EMD algorithms are prone to mode mixing and thus lead to bad accuracy of calibrating wave front signals of travelling waves and not high fault location precision.

Description

Distribution network cable fault location method and system based on improved HHT algorithm Technical Field

[0001] The present invention belongs to the technical field of power grid fault detection, and in particular relates to a distribution network cable fault location method and system based on an improved HHT algorithm. Background Art

[0002] With the widespread commissioning of power cables in distribution networks, the resulting cable fault location problem urgently needs to be addressed. Traditional cable fault location methods primarily rely on offline methods, which, due to complex external equipment, limited applicability, and low ranging accuracy, cannot meet the needs of smart grid development. Online fault location using traveling waves has rapidly become a research hotspot, owing to its simple principle and strong practicality. The key to traveling wave location is the accurate calibration of the fault traveling wave head. Many researchers have used wavelet analysis to calibrate the traveling wave head, but wavelet analysis is not adaptive, and its results are affected by the type of wavelet basis and the decomposition scale, resulting in low ranging accuracy. The Hilbert-Huang transform (HHT) is a recently developed method for analyzing non-stationary signals. It consists of empirical mode decomposition (EMD) and the Hilbert transform. Using EMD, a complex signal is adaptively decomposed into a finite number of high- and low-frequency intrinsic mode functions (IMFs) with significant instantaneous frequencies and modulated amplitudes or frequencies. The frequency components contained in each IMF component are both dependent on the sampling frequency and vary with the signal itself. Fault traveling wave signals are typically non-stationary signals, making HHT suitable for their analysis. However, modal aliasing occurs when the HHT algorithm performs modal decomposition. Modal aliasing refers to the inclusion of different-scale components within the same IMF component. This directly results in the aliased IMF lacking sufficient physical meaning, leading to confusion in the subsequent time-frequency distribution. To suppress modal aliasing, Huang et al. proposed ensemble empirical mode decomposition (EEMD), a noise-assisted data analysis method that effectively restores the signal's essence. Technical issues

[0003] Through the above analysis, the problems and defects of the existing technology are as follows: the traditional HHT algorithm will produce modal aliasing problems when applied to the calibration of the fault traveling wave head signal, resulting in the mutual mixing of different intrinsic mode functions when the fault traveling wave signal is decomposed into intrinsic mode functions, which makes the time-frequency distribution confused when it is subsequently subjected to Hilbert transform, directly affecting the accurate calibration of the fault traveling wave signal head, resulting in low fault point positioning accuracy, large errors or even ranging failure, affecting actual on-site application. Technical Solutions

[0004] In response to the problems existing in the prior art, the present invention provides an improved HHT cable fault traveling wave ranging method based on EEMD and Hilbert transform, and builds a simulation model through PSCAD to verify the feasibility and ranging accuracy of the algorithm, which can meet the actual needs of the project.

[0005] The present invention is implemented as follows: a distribution network cable fault location method based on an improved HHT algorithm, comprising:

[0006] When the traveling wave generated by the fault point reaches the measurement end, both the voltage and current of the traveling wave undergo sharp changes. The traveling wave head will appear as a high-frequency mutation in the time-frequency diagram. The sampling time corresponding to the mutation point is the arrival time of the head. Therefore, EEMD decomposition is performed on the two-terminal traveling wave line mode components, and the respective IMF1 components are extracted and Hilbert transformed to obtain their instantaneous frequencies. The sampling time corresponding to the first frequency mutation point on the time-frequency diagram corresponds to the arrival time of the respective fault traveling wave head. Fault location is then determined using the two-terminal distance measurement algorithm.

[0007] Furthermore, the dual-end ranging algorithm is as follows:

[0008]

[0009] in 、 are the time it takes for the fault traveling wave to reach the two measuring ends of the cable, is the traveling wave line mode velocity, is the total length of the cable, This is the calculated cable fault distance.

[0010] Furthermore, the specific steps of the distribution network cable fault location method based on the improved HHT algorithm are as follows:

[0011] S1, uses a new phase mode transformation matrix for decoupling;

[0012] S2, Empirical Mode Decomposition (EMD): transform a signal Decomposed into the sum of several natural modal components and residuals:

[0013] ;

[0014] S3, ensemble empirical mode decomposition (EEMD): Gaussian white noise is superimposed on the original signal, multiple EMD decompositions are performed, and the mean of the IMF components is taken as the final result;

[0015] S4, Hilbert transform: The sudden change point of the instantaneous frequency of the traveling wave signal is used to calibrate the arrival time of the wave head. Hilbert transform is used to obtain the instantaneous frequency of the non-stationary signal.

[0016] Furthermore, in S1, the decoupling process is as follows:

[0017]

[0018]

[0019] In the formula 、 、 are the three-phase currents of the line respectively; 、 、 are the decoupled 0-mode current, α-mode current, and β-mode current components, respectively. α-mode and β-mode are both called line-mode components. Further, the velocity equations of traveling waves at different modes can be derived:

[0020]

[0021] in 、 and 、 They are the zero mode and line mode parameters of the cable line respectively.

[0022] Furthermore, S3 specifically includes:

[0023] (1) Give the signal to be analyzed Add white noise;

[0024] (2) Decompose the signal after adding noise to obtain each IMF;

[0025] (3) Repeat steps (1) and (2), but add different white noise each time;

[0026] (4) Take the mean of each IMF component obtained from multiple decompositions as the final result.

[0027] Furthermore, S4 specifically includes:

[0028] set up is a time series, is its Hilbert transform, namely:

[0029]

[0030] Its inverse transform is:

[0031]

[0032] Get the parsed signal:

[0033]

[0034] Where: is the instantaneous amplitude,

[0035] is the phase, , the instantaneous frequency can be defined as:

[0036] After performing EEMD on the collected fault traveling wave signal, a series of natural modal components containing only one vibration mode are obtained. Then, Hilbert transform is performed on them respectively to obtain their instantaneous frequencies, so that the fault traveling wave head can be accurately calibrated through the mutation point of the instantaneous frequency.

[0037] Another object of the present invention is to provide a distribution network cable fault location system based on an improved HHT algorithm using the distribution network cable fault location method based on the improved HHT algorithm, comprising:

[0038] A decoupling module, used for performing decoupling by adopting a new phase mode transformation matrix;

[0039] Empirical Mode Decomposition module: used to transform a signal Decomposed into the sum of several natural modal components and residuals:

[0040] ;

[0041] Ensemble empirical mode decomposition module: used to superimpose Gaussian white noise on the original signal, perform multiple EMD decompositions, and take the mean of the IMF components as the final result;

[0042] Hilbert transform module: used to use the mutation point of the instantaneous frequency of the traveling wave signal to calibrate the arrival time of the wave head. Hilbert transform is used to obtain the instantaneous frequency of the non-stationary signal.

[0043] Another object of the present invention is to provide a computer device, the computer device including a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, the processor executes the steps of the distribution network cable fault location method based on the improved HHT algorithm.

[0044] Another object of the present invention is to provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the distribution network cable fault location method based on the improved HHT algorithm.

[0045] Another object of the present invention is to provide an information data processing terminal, which is used to implement the distribution network cable fault location system based on the improved HHT algorithm. Beneficial effects

[0046] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0047] First, by improving the phase-mode transformation matrix of the fault traveling wave line mode components, this invention overcomes the drawback of the traditional transformation matrix, where a single modulus cannot reflect all fault types. Furthermore, by improving the traditional EMD decomposition algorithm through a noise-assisted analysis method, this method overcomes the problem of modal aliasing, which often occurs in traditional EMD algorithms and leads to poor calibration accuracy of the fault traveling wave head signal, resulting in low ranging accuracy. Based on this, a distribution network cable fault ranging method based on an improved HHT algorithm is proposed, which achieves accurate calibration of the fault traveling wave signal head, achieving higher fault ranging accuracy and more accurate positioning than the traditional HHT algorithm.

[0048] Secondly, the present invention proposes a two-end traveling wave online fault ranging method for distribution network cables based on an improved HHT algorithm. It mainly solves the problem that the traditional HHT algorithm is prone to modal aliasing, resulting in low fault ranging accuracy and large errors in the ranging algorithm, and realizes the accurate calibration of the fault traveling wave signal head and the accurate positioning of the fault point.

[0049] Third, the expected benefits and commercial value of the technical solution after transformation are as follows: This invention proposes a two-terminal fault location method and system for distribution network cable lines based on an improved HHT algorithm. This invention can be used to directly develop related equipment, or to upgrade and modify existing fault location equipment using the technical solution of the invention, greatly improving the accuracy of on-site fault location, thereby ensuring the power supply reliability of the system. This technical solution is easy to transform, has a wide range of application scenarios, and is in high demand, with broad application potential and enormous commercial value.

[0050] Fourth, the distribution network cable fault location method based on the improved HHT algorithm provided by the present invention has indeed brought significant technological progress. The following are some key technical advantages:

[0051] 1. Accuracy: By using EMD and EEMD, the method is able to analyze signals more precisely, thereby improving the accuracy of fault location. This is crucial for quickly and effectively solving distribution network problems.

[0052] 2. Adaptability: Because this method can decouple the signal and decompose it into multiple IMFs, it can adapt to a variety of signal characteristics and distribution network conditions. This makes the method applicable in a variety of different environments and applications.

[0053] 3. Robustness: By superimposing Gaussian white noise on the original signal and performing multiple EMD decompositions, this method can effectively resist noise interference, thereby improving the robustness of fault detection.

[0054] 4. Real-time performance: By using the Hilbert transform, this method can obtain the instantaneous frequency of the signal in real time, and thus detect and locate faults in the distribution network in real time, which is very important for preventing the expansion of faults and reducing power outage time.

[0055] Therefore, this distribution network cable fault location method based on the improved HHT algorithm provides an efficient, accurate and robust solution for fault detection and processing in distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0057] FIG1 is a flow chart of a method for measuring cable faults in a distribution network based on an improved HHT algorithm according to an embodiment of the present invention;

[0058] FIG2 is a structural diagram of a distribution network cable fault location system based on an improved HHT algorithm provided by an embodiment of the present invention;

[0059] FIG3 is a flowchart of EMD decomposition according to an embodiment of the present invention;

[0060] FIG4 is a test signal provided by an embodiment of the present invention and its composition diagram;

[0061] FIG5 is a test signal provided by an embodiment of the present invention EMD result graph;

[0062] FIG. 6 is a test signal provided by an embodiment of the present invention EEMD result diagram;

[0063] 7 is a schematic diagram of a double-ended traveling wave line mode component provided by an embodiment of the present invention;

[0064] FIG8 is a diagram showing the EEMD results of the traveling wave line mode components at the head and end provided by an embodiment of the present invention; wherein (a) is the head end, and (b) is the end end;

[0065] FIG9 is a schematic diagram of the calibration of the moment when the wave head reaches both ends provided by an embodiment of the present invention; wherein, (a) the instantaneous frequency mutation point of IMF1 at the head end, and (b) the instantaneous frequency mutation point of IMF1 at the end end. Modes for Carrying Out the Invention

[0066] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0067] In view of the problems existing in the prior art, the present invention provides a distribution network cable fault location method and system based on an improved HHT algorithm. The present invention is described in detail below with reference to the accompanying drawings.

[0068] The present invention provides a distribution network cable fault location method based on an improved HHT (Hilbert-Huang Transform) algorithm, and two embodiments are provided below.

[0069] Example 1: Urban distribution network cable fault location based on improved HHT algorithm

[0070] 1. S1: First, we select a phase-mode transformation matrix suitable for the urban distribution network for decoupling. The matrix should take into account the characteristics of the urban distribution network, such as the number of transformers, line length and material, etc.

[0071] 2. S2: We then use the EMD method to decompose the decoupled signal into multiple intrinsic mode functions (IMFs) and a residual. Each IMF should have obvious physical meaning, such as representing current or voltage fluctuations in a specific frequency range.

[0072] 3. S3: Next, we use the EEMD method to superimpose Gaussian white noise on the original signal, perform multiple EMD decompositions, and then take the mean of the IMF components as the final result. This can effectively suppress noise and improve the accuracy of fault location.

[0073] 4. S4: Finally, we use the Hilbert transform to obtain the instantaneous frequency of each IMF, and then use the mutation points of these instantaneous frequencies to calibrate the arrival time of the wave head, thereby determining the location of the fault.

[0074] Example 2: Industrial distribution network cable fault location based on improved HHT algorithm

[0075] 1. S1: First, we select a phase-mode transformation matrix suitable for the industrial distribution network for decoupling. The matrix should take into account the characteristics of the industrial distribution network, such as the number of motors and the load conditions of the lines.

[0076] 2. S2: Then, we use the EMD method to decompose the decoupled signal into multiple IMFs and a residue. Each IMF should have obvious physical meaning, such as representing current or voltage fluctuations in a specific frequency range.

[0077] 3. S3: Next, we use the EEMD method to superimpose Gaussian white noise on the original signal, perform multiple EMD decompositions, and then take the mean of the IMF components as the final result. This can better handle complex noise in industrial environments and improve fault location accuracy.

[0078] 4. S4: Finally, we use the Hilbert transform to obtain the instantaneous frequency of each IMF, and then use the mutation points of these instantaneous frequencies to calibrate the arrival time of the wave head, thereby determining the location of the fault.

[0079] The above two embodiments are intended to illustrate how to implement this fault location method in different environments. The specific implementation needs to be adjusted according to the actual situation of the distribution network.

[0080] As shown in FIG1 , the method for measuring cable fault location in a distribution network based on the improved HHT algorithm provided by an embodiment of the present invention comprises the following specific steps:

[0081] S1, uses a new phase mode transformation matrix for decoupling;

[0082] S2, Empirical Mode Decomposition (EMD): transform a signal Decomposed into the sum of several natural modal components and residuals:

[0083] ;

[0084] S3, ensemble empirical mode decomposition (EEMD): Gaussian white noise is superimposed on the original signal, multiple EMD decompositions are performed, and the mean of the IMF components is taken as the final result;

[0085] S4, Hilbert transform: The sudden change point of the instantaneous frequency of the traveling wave signal is used to calibrate the arrival time of the wave head. Hilbert transform is used to obtain the instantaneous frequency of the non-stationary signal.

[0086] As shown in FIG2 , the distribution network cable fault location system based on the improved HHT algorithm provided by an embodiment of the present invention includes:

[0087] A decoupling module, used for performing decoupling by adopting a new phase mode transformation matrix;

[0088] Empirical Mode Decomposition module: used to transform a signal Decomposed into the sum of several natural modal components and residuals:

[0089] ;

[0090] Ensemble empirical mode decomposition module: used to superimpose Gaussian white noise on the original signal, perform multiple EMD decompositions, and take the mean of the IMF components as the final result;

[0091] Hilbert transform module: used to use the mutation point of the instantaneous frequency of the traveling wave signal to calibrate the arrival time of the wave head. Hilbert transform is used to obtain the instantaneous frequency of the non-stationary signal.

[0092] Improvement 1: Fault traveling wave phase mode transformation matrix

[0093] Complex electromagnetic coupling exists between the three phases of a cable, causing distortion of the fault traveling wave's head during propagation and reducing ranging accuracy. Therefore, decoupling of the extracted fault traveling wave signal is necessary. Currently, decoupling is primarily achieved using phase-mode transformation techniques, including symmetrical component transformation, Clarke transform, Karenbauer transform, and Wedpohl transform. However, the moduli calculated from these time-domain phase-mode transformation matrices often suffer from the limitation that a single modulus cannot reflect all fault types.

[0094] To overcome the above shortcomings, the present invention adopts a new phase mode transformation matrix for decoupling.

[0095] The decoupling process is as follows:

[0096] (1)

[0097] (2)

[0098] In the formula 、 、 are the three-phase currents of the line respectively; 、 、 are the decoupled 0-mode current, α-mode current, and β-mode current components, respectively. The α-mode and β-mode components are both called line-mode components. Further, the velocity equations of traveling waves at different moduli can be derived:

[0099] (3)

[0100] in 、 and 、 They are the zero mode and line mode parameters of the cable line respectively.

[0101] Improvement 2: Improved HHT algorithm

[0102] (1) Empirical Mode Decomposition (EMD)

[0103] Empirical mode decomposition is to transform a signal Decomposed into the sum of several natural modal components and residuals:

[0104] (4)

[0105] The specific decomposition steps are shown in Figure 3:

[0106] A simulation signal is used to verify the existence of modal aliasing in EMD, as shown in Figure 4. By sinusoidal signal and intermittent signals The signal Perform EMD decomposition, and the results are shown in Figure 5.

[0107] It can be seen from Figure 3 that there is obvious modal aliasing in the IMF1 component after EMD decomposition.

[0108] (2) Ensemble Empirical Mode Decomposition (EEMD)

[0109] The essence of the EEMD algorithm is to superimpose Gaussian white noise on the original signal, perform multiple EMD decompositions, and take the mean of the IMF components as the final result. This algorithm uses the statistical properties of Gaussian white noise to make the noisy signal continuous at different frequency scales, effectively solving the problem of modal aliasing.

[0110] The specific algorithm is as follows:

[0111] 1) Give the signal to be analyzed Add white noise;

[0112] 2) Decompose the noisy signal to obtain the individual IMFs;

[0113] 3) Repeat steps (1) and (2), but add different white noise each time;

[0114] 4) Take the mean of each IMF component obtained from multiple decompositions as the final result.

[0115] For test signal The EEMD decomposition is performed, and the result is shown in FIG6 .

[0116] From Figure 6 we can get the signal After EEMD decomposition, the modal aliasing phenomenon is well suppressed, the modes are clearly distinguished, and the effect is significantly better than traditional EMD.

[0117] (3) Hilbert transform

[0118] When performing traveling wave fault location, the sudden change point of the traveling wave signal's instantaneous frequency can be used to calibrate the wave head arrival time, while the Hilbert transform can be used to obtain the instantaneous frequency of non-stationary signals.

[0119] set up is a time series, is its Hilbert transform, namely:

[0120] (5)

[0121] Its inverse transform is:

[0122] (6)

[0123] Get the parsed signal:

[0124] (7)

[0125] Where: is the instantaneous amplitude,

[0126] is the phase, The instantaneous frequency can be defined as:

[0127] (8)

[0128] After performing EEMD on the collected fault traveling wave signal, a series of natural modal components containing only one vibration mode are obtained. Then, Hilbert transform is performed on them respectively to obtain their instantaneous frequencies, so that the fault traveling wave head can be accurately calibrated through the mutation point of the instantaneous frequency.

[0129] Specific implementation of traveling wave ranging algorithm

[0130] When the traveling wave generated by the fault point reaches the measurement end, both the voltage and current undergo sharp changes. The traveling wave head will appear as a high-frequency mutation in the time-frequency diagram, with the mutation point corresponding to the arrival time of the head. Therefore, EEMD decomposition is performed on the line mode components of the two-terminal traveling waves. The respective IMF1 components are extracted and Hilbert transformed to obtain their instantaneous frequencies. The first frequency mutation point on the time-frequency diagram corresponds to the arrival time of the respective fault traveling wave head.

[0131] Then the fault location is determined by the dual-terminal distance measurement algorithm. The specific algorithm is as follows:

[0132] (9)

[0133] in 、 are the time it takes for the fault traveling wave to reach the two measuring ends of the cable, is the traveling wave line mode velocity, is the total length of the cable, This is the calculated cable fault distance.

[0134] Ranging simulation example:

[0135] The cable length is set to 12 km. Single-phase ground faults are generated at 1 km, 3 km, 6 km, 7 km, 9 km, 10 km, and 11 km from the headend. Simulation calculations are performed with different transition resistances. The simulation duration is 0.05 seconds, the fault occurs at 0.02 seconds, and the sampling frequency is 1 MHz. The traveling wave line mode velocity in this model is set to 198.26 m / μs. The following example analyzes a fault point 3 km from the headend with a transition resistance of 0.1 Ω.

[0136] (1) The two ends of the cable are marked as A and B respectively. The three-phase current traveling wave signal sampled at both ends is decoupled using a new phase-mode transformation matrix to obtain its line-mode component. The line-mode component of the current traveling wave from t = 0.0198s to 0.0204s is extracted for analysis. The line-mode component of the two-end traveling wave is shown in Figure 7.

[0137] (2) The extracted double-ended line mode components are decomposed by EEMD respectively. The decomposition results are shown in Figure 8.

[0138] (3) Perform Hilbert transform on the IMF 1 component of the fault traveling wave signal after decomposition at the head and tail ends, obtain their time-frequency diagrams, and calibrate the arrival time of the wave head by the mutation point of the instantaneous frequency. The wave head calibration at the head and tail ends is shown in Figure 9.

[0139] From Figure 9, we can see that the moment when the wave head reaches the head end is calibrated as the 216th sampling point, and the moment when the wave head reaches the tail end is calibrated as the 246th sampling point. According to formula (9), it can be calculated that the distance between the fault point and the head end is 3026.1m, with a relative error of 0.87%.

[0140] In order to further verify the effectiveness and superiority of the algorithm proposed in the present invention, a large number of simulations were carried out using the algorithm of the present invention and the traditional algorithm for different transition resistances and different fault locations. The comparative analysis results are shown in Table 1.

[0141] Table 1 Fault location results

[0142]

[0143] Table 1 shows that the proposed ranging algorithm is basically unaffected by the fault resistance and can effectively measure distances at different fault distances. When the fault point is close to the endpoint, the ranging accuracy decreases slightly, but the maximum relative error does not exceed 5%. Compared with the traditional HHT-based ranging algorithm, the proposed algorithm can more accurately calibrate the fault traveling wave head and has higher ranging accuracy, meeting the actual engineering needs.

[0144] An application embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of a distribution network cable fault location method based on an improved HHT algorithm.

[0145] An application embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of a distribution network cable fault location method based on an improved HHT algorithm.

[0146] An application embodiment of the present invention provides an information data processing terminal, which is used to implement a distribution network cable fault location system based on an improved HHT algorithm.

[0147] The embodiments of the present invention have achieved some positive results during the development or use process, and indeed have great advantages compared with the existing technology. The following content describes them in conjunction with data, charts, etc. of the experimental process.

[0148] Table 1 Fault location results

[0149]

[0150] From the analysis of the ranging results in Table 1, it can be seen that the improved HHT ranging algorithm proposed in the present invention has higher accuracy than the traditional ranging algorithm.

[0151] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware portion can be implemented using dedicated logic; the software portion can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art will appreciate that the above-mentioned devices and methods can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0152] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A distribution network cable fault location method based on an improved HHT algorithm, characterized in that: include: When the traveling wave generated by the fault point reaches the measuring end, both the traveling wave voltage and current will change sharply, and the traveling wave head will appear as a high-frequency mutation in the time-frequency diagram, and the mutation point is the arrival time of the wave head. Therefore, the EEMD decomposition of the double-ended traveling wave line mode components is performed respectively, and the respective IMF1 components are extracted and Hilbert transform is performed to obtain their instantaneous frequencies. The first frequency mutation point on the time-frequency diagram corresponds to the arrival time of the respective fault traveling wave heads, and then the fault distance is measured by the double-ended ranging algorithm.

2. The distribution network cable fault location method based on the improved HHT algorithm according to claim 1, characterized in that: The specific algorithm of the dual-end ranging algorithm is as follows: in 、 are the time taken for the fault traveling wave to reach the two measuring ends of the cable, is the traveling wave line mode velocity, is the total length of the cable, This is the calculated cable fault distance.

3. The distribution network cable fault location method based on the improved HHT algorithm according to claim 1, characterized in that: The specific steps of the distribution network cable fault location method based on the improved HHT algorithm are as follows: S1, using a new phase mode transformation matrix for decoupling; S2, Empirical Mode Decomposition: Transform a signal Decomposed into the sum of several natural modal components and residuals: ； S3, ensemble empirical mode decomposition: Gaussian white noise is superimposed on the original signal, multiple EMD decompositions are performed, and the mean of the IMF components is taken as the final result; S4, Hilbert transform: The mutation point of the instantaneous frequency of the traveling wave signal is used to calibrate the arrival time of the wave head, and Hilbert transform is used to obtain the instantaneous frequency of the non-stationary signal.

4. The distribution network cable fault location method based on the improved HHT algorithm as claimed in claim 3, characterized in that: In S1, the decoupling process is as follows: In the formula 、 、 They are the three-phase currents of the line respectively; 、 、 They are the decoupled 0-mode current, α-mode current and β-mode current components respectively; the α-mode and β-mode are both called line-mode components, and the wave velocity equation of the traveling wave under different modes can be further obtained: in 、 and 、 They are the zero mode and line mode parameters of the cable line respectively.

5. The distribution network cable fault location method based on the improved HHT algorithm as claimed in claim 3, characterized in that: S3 specifically includes: (1) Give the signal to be analyzed Add white noise; (2) Decompose the noisy signal to obtain each IMF; (3) Repeat steps (1) and (2), but add different white noise each time; (4) Take the mean of each IMF component obtained from multiple decompositions as the final result.

6. The distribution network cable fault location method based on the improved HHT algorithm as claimed in claim 3, characterized in that: S4 specifically includes: set up is a time series, is its Hilbert transform, that is: Its inverse transformation is: Get the parsed signal: Where: is the instantaneous amplitude, is the phase, , the instantaneous frequency can be defined as: After performing EEMD on the collected fault traveling wave signal, a series of inherent modal components containing only one vibration mode are obtained. Then, the instantaneous frequency can be obtained by performing Hilbert transform on them respectively, so that the fault traveling wave head can be accurately calibrated through the mutation point of the instantaneous frequency.

7. A distribution network cable fault location system based on an improved HHT algorithm using the distribution network cable fault location method based on the improved HHT algorithm as claimed in any one of claims 1 to 6, characterized in that: include: Decoupling module: used to use a new phase transformation matrix for decoupling; Empirical mode decomposition module: used to transform a signal Decomposed into the sum of several natural modal components and residuals: ； Ensemble empirical mode decomposition module: used to superimpose Gaussian white noise on the original signal, perform multiple EMD decompositions, and take the mean of the IMF components as the final result; Hilbert transform module: It is used to calibrate the arrival time of the wave head by using the mutation point of the instantaneous frequency of the traveling wave signal. Hilbert transform is used to obtain the instantaneous frequency of the non-stationary signal.

8. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the distribution network cable fault location method based on the improved HHT algorithm as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to perform the steps of the distribution network cable fault location method based on the improved HHT algorithm as described in any one of claims 1 to 6.

10. An information data processing terminal, used for implementing the distribution network cable fault location measurement system based on the improved HHT algorithm as claimed in claim 7.

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