Power distribution network single-phase earth fault line selection method based on waveform similarity

Abstract

The invention provides a power distribution network single-phase earth fault line selection method based on waveform similarity, and the method comprises the steps: determining a fault moment, obtaining a transient zero-sequence current as a fault feature, carrying out the adaptive mean filtering of a collected transient waveform, and obtaining a fault line based on the feature difference of the zero-sequence current of a sound line and a fault line in a time-frequency domain. And calculating a composite correlation coefficient matrix, and based on the composite correlation coefficient matrix, selecting a fault line through a classification algorithm.

Description

Technical Field

[0001] The present invention relates to the technical fields of power electrical equipment, distribution network single-phase grounding fault processing, and in particular to a distribution network single-phase grounding fault line selection method based on waveform similarity. Background Art

[0002] With the rapid development of power grid construction, the scale of distribution networks continues to expand and has many branches, and the network topology is becoming increasingly complex. In harsh environments or due to human errors, distribution networks are prone to failure. As the last link directly facing power users, the distribution network must maintain a safe and stable state during operation, which is directly related to the production efficiency of enterprises and the quality of life of residents. Therefore, in order to ensure the safety and reliability of the distribution network operation, it is necessary to accurately and quickly locate the fault section after the fault occurs. This is the basis and key to timely remove the fault, which is of great significance for restoring power supply and improving the reliability of the distribution network. According to statistics, single-phase grounding faults account for more than 80% of distribution network faults. Accurate and effective handling of single-phase grounding faults can lay a solid foundation for improving the reliability and stability of the distribution network.

[0003] For the neutral point operation mode using resonant grounding, that is, the distribution network with the neutral point grounded through an arc suppression coil. When a single-phase grounding fault occurs in the distribution network, the inductive current generated by the arc suppression coil has the opposite polarity to the capacitive grounding current, which can suppress arc burning and reduce the amplitude of the grounding fault current. However, if the system operates with a fault for a long time, it may break down the insulation at a weak point, thereby expanding the scope of the fault. Therefore, it is very important to find the fault and effectively locate the fault section of the resonant grounding system. Summary of the invention

[0004] In view of the defects and shortcomings of the above-mentioned prior arts, the present invention proposes a new method for selecting single-phase grounding fault lines in distribution networks based on waveform similarity. First, the fault moment is determined and the transient zero-sequence current is obtained as the fault feature. Then, the collected transient waveforms are subjected to adaptive mean filtering, and the characteristic differences between the zero-sequence currents of the sound line and the faulty line in the time-frequency domain are deeply analyzed, and their composite correlation coefficient matrix is ​​calculated. Finally, classification algorithms such as support vector machines, random forests, decision trees, K-means clustering, etc. are used to select the fault line. Experimental results show that the proposed method has good fault line selection capability and adaptability.

[0005] The present invention specifically adopts the following technical solutions:

[0006] A method for selecting a single-phase grounding fault line in a distribution network based on waveform similarity determines the fault moment, obtains the transient zero-sequence current as the fault feature, performs adaptive mean filtering on the collected transient waveform, calculates the composite correlation coefficient matrix based on the characteristic difference between the zero-sequence current of the sound line and the fault line in the time-frequency domain, and selects the fault line through a classification algorithm based on the composite correlation coefficient matrix.

[0007] Furthermore, the specific process of determining the fault time is as follows:

[0008] Taking advantage of the fact that the current polarity of the fault line is opposite to that of the normal line after the fault occurs, the first-order derivative of the collected current signal is obtained;

[0009] Perform mathematical morphology opening and closing operations on the first-order derivative waveform;

[0010] Subtract the closed operation from the open signal to obtain the differential signal, which amplifies the characteristics of the slope mutation point;

[0011] Find the maximum mutation point in the differential signal, take one third of the maximum mutation point value as the start threshold, and take the point that exceeds the threshold and is closest to the maximum mutation point as the fault occurrence time point.

[0012] Furthermore, after the fault moment is determined, the wave recording device is started to obtain the transient zero-sequence current.

[0013] Furthermore, the specific process of performing adaptive mean filtering on the collected transient waveform is as follows: when analyzing the transient zero-sequence current signal, the collected zero-sequence current signal is normalized to the range of [-1,1]; a peak-to-valley algorithm is used to detect the starting point of the fault to locate the moment when the fault occurs, and a total of 1.5 cycles of transient waveforms before and after the fault occurs are recorded; and adaptive mean filtering is applied to the recorded waveform to extract fault characteristics.

[0014] Furthermore, the specific process of calculating the composite correlation coefficient matrix based on the characteristic difference between the zero-sequence current of the healthy line and the faulty line in the time-frequency domain is as follows:

[0015] The time-frequency domain correlation coefficient matrix C is calculated based on the characteristic differences between the zero-sequence current of the healthy line and the faulty line in the time-frequency domain. O1 , C O2 and C O3 :

[0016] Extract time domain features and use rank correlation coefficient algorithm to calculate the correlation coefficient matrix C of transient time domain signals of each line O1 , rank relationship number ρ(x,y):

[0017]

[0018] Where: xi ,y i are the transient zero-sequence current signal points of two different lines respectively; n is the sampling data length;

[0019] The interval slope is used to describe the polarity characteristics of the zero-sequence current; for each sampling point, the slope interval L is defined, L = N T / 10, where N T is the number of sampling points in each cycle; use the Hough transform algorithm to linearly fit the curve in each interval to obtain the slope and obtain a slope sequence with the same number of sampling points; calculate the rank correlation coefficient of the current slope signal after fitting to obtain the correlation symmetric matrix C O2 ;

[0020] Extract frequency domain features; calculate the rank correlation coefficient of frequency domain signals of different lines to obtain the correlation coefficient matrix C O3 ;

[0021] By transforming the time-frequency domain correlation coefficient matrix C O1 , C O2 and C O3 Combined, the composite correlation coefficient matrix is ​​calculated.

[0022] Furthermore, the time-frequency domain correlation coefficient matrix C O1 , C O2 and C O3 Combined, the specific process of calculating the composite correlation coefficient matrix is:

[0023] Calculate the correlation coefficient matrix C for the original data O1 , C O2 , C O3 , classified by classification algorithms such as support vector machine, random forest, decision tree, and K-means clustering, and calculate the accuracy of each correlation coefficient matrix, denoted as K1, K2, and K3;

[0024] The weight is calculated based on the accuracy of each correlation coefficient matrix; the weight formula is as follows:

[0025]

[0026] Combine the correlation coefficient matrices in the time-frequency domain according to the weights to obtain the correlation coefficient matrix C O , as shown below:

[0027]

[0028] Furthermore, the correlation coefficient matrix C O Optimize line selection performance through dynamic updates and adjustments.

[0029] And, a single-phase ground fault line selection device for a distribution network based on waveform similarity, comprising: a fault feature extraction module for determining the fault moment and obtaining a transient zero-sequence current as a fault feature;

[0030] A filtering module, used for performing adaptive mean filtering on the collected transient waveforms;

[0031] A composite correlation coefficient matrix calculation module is used to calculate the composite correlation coefficient matrix based on the characteristic difference between the zero-sequence current of the sound line and the fault line in the time and frequency domain;

[0032] And a classification module (the classification algorithm of the classification module can adopt conventional classification algorithms such as support vector machine, random forest, decision tree, K-means clustering, etc.) is used to select the fault line through the classification algorithm based on the composite correlation coefficient matrix.

[0033] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the steps of the above method are implemented when the processor executes the program.

[0034] A non-transitory computer-readable storage medium stores a computer program, which implements the steps of the method described above when executed by a processor.

[0035] Compared with the prior art, the present invention and its preferred embodiments have at least the following outstanding features and beneficial effects:

[0036] 1. The proposed line selection scheme abandons the reliance on preset thresholds in traditional methods, and also gets rid of the need to train complex neural network models with large amounts of data. This simplification not only reduces the complexity of the algorithm, but also improves its flexibility and ease of use in practical applications. Users can automatically adapt to different distribution network topologies without tedious parameter adjustments, and can demonstrate excellent performance both in actual power grids and simulated environments.

[0037] 2. While maintaining high accuracy, the computational efficiency is significantly improved. Both measured and simulated data can be used to quickly and accurately identify faulty lines in a very short time. This efficiency improvement means that in the detection and maintenance of distribution network faults, quick responses and actions can be taken, greatly reducing the power outage time caused by faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0039] Figure 1 This is a main wiring diagram of a 10kV real-type experimental system according to an embodiment of the present invention;

[0040] Figure 2 It is a zero-sequence current diagram of different grounding points of different lines in an embodiment of the present invention;

[0041] Figure 3 This is a structural diagram of a 10kV simulation distribution network model according to an embodiment of the present invention;

[0042] Figure 4 The present invention is an embodiment of the method to achieve the flow chart. DETAILED DESCRIPTION

[0043] Hereinafter, the specific embodiments of the present application will be described in detail with reference to the accompanying drawings. According to these detailed descriptions, those skilled in the art can clearly understand the present application and can implement the present application. Without violating the principles of the present application, the features in the various embodiments can be combined to obtain new implementations, or certain features in certain embodiments can be replaced to obtain other preferred implementations.

[0044] In order to make the features and advantages of the present invention more clearly understood, the following embodiments are specifically described in detail with reference to the accompanying drawings:

[0045] like Figure 4 As shown, the steps of a method for selecting a single-phase grounding fault line in a distribution network based on waveform similarity provided by an embodiment of the present invention include: first, determining the fault moment, and obtaining a transient zero-sequence current as a fault feature. Next, the collected transient waveforms are subjected to adaptive mean filtering, and the characteristic differences between the zero-sequence currents of the sound line and the faulty line in the time-frequency domain are deeply analyzed, and their composite correlation coefficient matrix is ​​calculated. Finally, classification algorithms such as support vector machines, random forests, decision trees, K-means clustering, etc. are used to select the fault line. Experimental results show that the proposed method has good fault line selection capability and adaptability.

[0046] The main implementation process includes the following:

[0047] 1. The traditional threshold start mechanism of the fault line selection device has some problems. For example, due to measurement errors and other reasons, the actual value may fail to exceed the set threshold, thereby causing insufficient sensitivity, and its anti-interference ability is relatively limited. Therefore, the present invention proposes an algorithm that uses differential signal start-up, does not need to set a threshold, and adaptively finds the mutation point of the fault. The specific steps are as follows:

[0048] 1) First, the characteristic that the current polarity of the fault line and the normal line is opposite after the fault occurs is utilized. Since the positive and negative values ​​of the first-order derivative can more intuitively reflect the polarity of the transient zero-sequence current, the first-order derivative of the collected current signal is obtained.

[0049] 2) Using the contraction of the mathematical morphology opening operation and the expansibility of the closing operation, the mathematical morphology opening and closing operations are performed on the first-order derivative waveform.

[0050] 3) When a line fails, the slope mutation of the signal is more obvious. Subtract the closed operation from the open signal to obtain the differential signal, and amplify the characteristics of the slope mutation point.

[0051] 4) Find the maximum mutation point in the differential signal, then use one third of the maximum mutation point value as the start threshold, and use the point that exceeds the threshold and is closest to the maximum mutation point as the fault occurrence time point.

[0052] 5) The recording device starts.

[0053] 2. When analyzing the transient zero-sequence current signal, the collected zero-sequence current signal is normalized to the range of [-1,1]. The peak-to-valley algorithm is used to detect the starting point of the fault, accurately locate the moment of the fault, and record the transient waveform for a total of 1.5 cycles before and after the fault. An adaptive mean filter is applied to the recorded waveform to filter out interference information in order to extract more accurate fault features.

[0054] 3. Calculate the time-frequency domain correlation coefficient matrix C by using the characteristic differences between the zero-sequence current of the sound line and the faulty line in the time-frequency domain O1 , C O2 , C O3 .

[0055] 3.1. Extract time domain features. Through the measured time domain waveform, it can be found that in the transient zero-sequence current, the signals of the fault line and the normal line show opposite polarity. The rank correlation coefficient algorithm is used to calculate the correlation coefficient matrix C of the transient time domain signals of each line. O1 , rank relationship number ρ(x,y):

[0056]

[0057] Where: x i ,y i They are the transient zero-sequence current signal points of two different lines. n is the sampling data length.

[0058] At the same time, the interval slope is used to describe the polarity characteristics of the zero-sequence current. For each sampling point, a slope interval L is defined, L = N T / 10, where N T is the number of sampling points in each cycle. The Hough transform algorithm is used to linearly fit the curve in each interval to obtain the slope of the point and obtain a slope sequence with the same number of sampling points. Finally, the rank correlation coefficient of the current slope signal after fitting is calculated to obtain the correlation symmetric matrix C O2 .

[0059] 3.2. Extract frequency domain features. Calculate the rank correlation coefficient of frequency domain signals of different lines and obtain the correlation coefficient matrix C O3 .

[0060] 4. Determine the coefficients of the correlation coefficient matrix. After using a single time domain or frequency domain correlation coefficient matrix for classification, the accuracy is often not very high. Therefore, this method combines the time and frequency domain correlation coefficients to ensure that the proposed features can fully characterize the zero-sequence current signal. The specific process is as follows:

[0061] 1) First, calculate the correlation coefficient matrix C for the original data O1 , C O2 , C O3 , and classify them separately, such as using support vector machine, random forest, decision tree, K-means clustering, etc., and calculate the accuracy of each correlation coefficient matrix, recorded as K1, K2, K3.

[0062] 2) Next, the weight is calculated based on the accuracy of each correlation coefficient matrix. The weight formula is as follows:

[0063]

[0064] 3) Finally, the correlation coefficient matrix in the time-frequency domain is combined according to the weights to obtain the final correlation coefficient matrix C O , as shown below:

[0065]

[0066] The third step is the training process. If more raw data is input, the correlation coefficient matrix will also change, which will in turn change the accuracy, thus affecting the weight and changing the final correlation coefficient matrix C. O In other words, this whole process is a dynamic adjustment process. If the original data is more, the correlation coefficient matrix C O There will be better line selection performance.

[0067] The present invention is further described and demonstrated below in conjunction with a specific simulation example.

[0068] In this embodiment, a 10 kV real-type experimental system (such as Figure 1 As shown in Figure 2, the HIF faults passing through different media are simulated in the second section of line 1, the first section of line 2, the third section of line 3, and the second section of line 4. The zero sequence current at the head end of each line is as follows: Figure 2 shown.

[0069] Figure 2(a) and (b) show the effect of different grounding resistances on the zero-sequence current waveform. As the grounding resistance increases, the zero-sequence current waveform decreases. The smaller the zero-sequence current is, the more susceptible it is to noise. Figure 2 (c) is the HIF process through the tree branches, the zero-sequence current waveform is distorted and its amplitude gradually increases. Figure 2 (d) is the masonry HIF process, and its zero-sequence current transient characteristics are more obvious. By calculation, the composite correlation coefficient matrices of these four groups of experiments are C O (1) , C O (2) , C O (3) , C O (4) , as shown below.

[0070]

[0071] Each element in the matrix represents the correlation coefficient of the time-frequency domain waveforms of line i and line j. The closer the correlation coefficient is to 1, the higher the similarity of the waveforms of the two lines. The K-means clustering algorithm is executed on each composite correlation coefficient matrix, and the clustering results are [1,2,2,2], [2,1,2,2], [2,2,1,2], [2,2,2,1]. According to the line selection rules, the corresponding fault lines are line 1, line 2, line 3 and line 4. In other words, under different fault points and different grounding media, the proposed algorithm can select the fault line more accurately.

[0072] In order to verify the adaptability of the proposed method to different topological structures, a 10kV simulation distribution network model was built in PSCAD. Figure 3 The HIF model is connected to different fault points on the 8 lines, and the fault phase angles are set to 0°, 30°, and 60° respectively. The line selection results are shown in Table 1. It can be seen that with the random changes of the fault phase angle and the switched line, the line selection results still maintain a high accuracy.

[0073] Table 1 Simulation system line selection results

[0074]

[0075] 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 in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0080] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0081] It needs to be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium, on which a computer program is stored, and the computer program is executed by a processor to execute the above method. The storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0082] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0083] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, the present disclosure may have various changes and improvements, and these changes and improvements fall within the scope of the present disclosure to be protected.

[0084] The present invention is not limited to the above-mentioned optimal implementation mode. Anyone can derive various other forms of a single-phase grounding fault line selection method for a distribution network based on waveform similarity under the inspiration of the present invention. All equal changes and modifications made within the scope of the patent application of the present invention should fall within the scope of the present invention.

Claims

1. A method for selecting a single-phase ground fault line in a distribution network based on waveform similarity, characterized in that: Determine the fault moment, obtain the transient zero-sequence current as the fault feature, perform adaptive mean filtering on the collected transient waveform, calculate the composite correlation coefficient matrix based on the characteristic difference between the zero-sequence current of the sound line and the faulty line in the time and frequency domain, and select the faulty line through a classification algorithm based on the composite correlation coefficient matrix.

2. A method for selecting a single-phase ground fault line in a distribution network based on waveform similarity according to claim 1, characterized in that: The specific process of determining the fault time is as follows: Taking advantage of the fact that the current polarity of the fault line is opposite to that of the normal line after the fault occurs, the first-order derivative of the collected current signal is obtained; Perform mathematical morphology opening and closing operations on the first-order derivative waveform; Subtract the closed operation from the open signal to obtain the differential signal, which amplifies the characteristics of the slope mutation point; Find the maximum mutation point in the differential signal, take one third of the maximum mutation point value as the start threshold, and take the point that exceeds the threshold and is closest to the maximum mutation point as the fault occurrence time point.

3. A method for selecting a single-phase ground fault line in a distribution network based on waveform similarity according to claim 2, characterized in that: After determining the fault moment, start the recording device to obtain the transient zero-sequence current.

4. The method for selecting a single-phase ground fault line in a distribution network based on waveform similarity according to claim 1, characterized in that: The specific process of performing adaptive mean filtering on the collected transient waveform is as follows: when analyzing the transient zero-sequence current signal, the collected zero-sequence current signal is normalized to the range of [-1,1]; the peak-to-valley algorithm is used to detect the starting point of the fault to locate the moment when the fault occurs, and the transient waveform of 1.5 cycles before and after the fault occurs is recorded; and adaptive mean filtering is applied to the recorded waveform to extract the fault characteristics.

5. The method for selecting a single-phase ground fault line in a distribution network based on waveform similarity according to claim 1, characterized in that: The specific process of calculating the composite correlation coefficient matrix based on the characteristic difference between the zero-sequence current of the sound line and the fault line in the time-frequency domain is as follows: The time-frequency domain correlation coefficient matrix C is calculated by the characteristic difference between the zero-sequence current of the sound line and the fault line in the time-frequency domain. O1 , C O2 and C O3 : Extract time domain features and use the rank correlation coefficient algorithm to calculate the correlation coefficient matrix C of transient time domain signals of each line O1 , rank relationship number ρ(x,y): Where: x i ,y i are the transient zero-sequence current signal points of two different lines respectively; n is the sampling data length; The interval slope is used to describe the polarity characteristics of the zero-sequence current; for each sampling point, the slope interval L is defined, L = N T / 10, where N T is the number of sampling points in each cycle; use the Hough transform algorithm to linearly fit the curve in each interval to obtain the slope and obtain a slope sequence with the same number of sampling points; calculate the rank correlation coefficient of the current slope signal after fitting to obtain the correlation symmetric matrix C O2 ; Extract frequency domain features; Calculate the rank correlation coefficient of frequency domain signals of different lines and obtain the correlation coefficient matrix C O3 ; By transforming the time-frequency domain correlation coefficient matrix C O1 , C O2 and C O3 Combined, the composite correlation coefficient matrix is ​​calculated.

6. A method for selecting a single-phase ground fault line in a distribution network based on waveform similarity according to claim 5, characterized in that: The time-frequency domain correlation coefficient matrix C O1 , C O2 and C O3 Combined, the specific process of calculating the composite correlation coefficient matrix is: Calculate the correlation coefficient matrix C for the original data O1 , C O2 , C O3 , classify them respectively through classification algorithms, and calculate the accuracy of each correlation coefficient matrix, denoted as K1, K2, K3; The weight is calculated based on the accuracy of each correlation coefficient matrix; the weight formula is as follows: Combine the correlation coefficient matrices in the time-frequency domain according to the weights to obtain the correlation coefficient matrix C O , as shown below:

7. A method for selecting a single-phase ground fault line in a distribution network based on waveform similarity according to claim 6, characterized in that: The correlation coefficient matrix C O Optimize line selection performance through dynamic updates and adjustments.

8. A single-phase ground fault line selection device for distribution network based on waveform similarity, characterized in that: include: A fault feature extraction module is used to determine the fault moment and obtain the transient zero-sequence current as the fault feature; A filtering module, used for performing adaptive mean filtering on the collected transient waveforms; A composite correlation coefficient matrix calculation module is used to calculate the composite correlation coefficient matrix based on the characteristic difference between the zero-sequence current of the sound line and the fault line in the time and frequency domain; and a classification module for selecting the faulty line through a classification algorithm based on the composite correlation coefficient matrix.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of a method for selecting a single-phase grounding fault line in a distribution network based on waveform similarity are implemented as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for selecting a single-phase grounding fault line in a distribution network based on waveform similarity are implemented as described in any one of claims 1 to 7.

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