Waveform change detection method and system based on dynamic sum

Abstract

The invention relates to the technical field of power transmission line monitoring, and provides a waveform change detection method and system based on a dynamic sum. The method comprises the following steps: carrying out maximum and minimum value normalization processing on obtained power frequency waveform data or traveling wave waveform data of a measured line, and carrying out feature processing on the processed power frequency waveform data or traveling wave waveform data to obtain power frequency waveform data or traveling wave waveform data with prominent features; calculating the amplitude difference between the current moment and the previous moment based on the power frequency waveform data or the traveling wave waveform data after feature highlighting, and calculating the positive change dynamic sum and the negative change dynamic sum; and performing fault identification and fault distance measurement based on the positive change dynamic sum and the negative change dynamic sum. The method can be applied to power transmission line fault identification and fault distance measurement, and the identification accuracy and the distance measurement precision are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission line monitoring, and in particular to a waveform change detection method and system based on dynamic sum. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] As an important channel for power transmission, the safe and stable operation of transmission lines is of vital importance. When a line fails or an abnormal situation occurs, the power frequency waveform and traveling wave waveform collected by the monitoring equipment will change.

[0004] The existing waveform change detection algorithms mainly include global threshold, wavelet transform, target detection, Fourier transform, etc. Their shortcomings are: traditional waveform detection methods often rely on fixed thresholds or specific frequency analysis, which may lead to reduced detection accuracy in complex environments. Wavelet transform needs to select a suitable wavelet basis according to different situations, and the peak of the wavelet head is not consistent with the position of the original waveform head, and the accuracy is difficult to control. The target detection method requires a large amount of reliable data, and switching different scenes requires retraining the model, which is inefficient. The image format will introduce a large amount of pixel information, while the core features of the waveform may only require a small amount of data to represent, and it also requires a large amount of computing power. The Fourier transform converts the signal from the time domain to the frequency domain, which may cause the loss of time domain information and cannot accurately locate the time point when the change occurs. In addition, it has poor effect on non-stationary signals because the Fourier transform assumes that the signal is stable throughout the time period. Summary of the invention

[0005] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a waveform change detection method and system based on dynamic and technology. The present invention monitors the changes of waveforms in real time through dynamic and technology, can more flexibly adapt to the dynamically changing waveform characteristics, and improve the robustness and accuracy of detection.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A first aspect of the present invention provides a waveform change detection method based on dynamic sum.

[0008] A waveform change detection method based on dynamic sum, comprising:

[0009] Performing maximum and minimum value normalization processing on the acquired power frequency waveform data or traveling wave waveform data of the measured line, and performing feature processing on the processed power frequency waveform data or traveling wave waveform data to obtain power frequency waveform data or traveling wave waveform data with highlighted features;

[0010] Based on the power frequency waveform data or the traveling wave waveform data after the features are highlighted, the amplitude difference between the current moment and the previous moment is calculated, and the positive change dynamic sum and the negative change dynamic sum are calculated;

[0011] Based on the positive change dynamic sum and the negative change dynamic sum, fault identification and fault distance measurement are performed.

[0012] Furthermore, the processed power frequency waveform data or traveling wave waveform data is subjected to feature processing, the method comprising: performing autocorrelation filtering on the processed power frequency waveform data, subtracting the data of the first cycle from the data of each cycle, using a sliding window to traverse the entire waveform, and obtaining the power frequency waveform data with highlighted features;

[0013] The processed traveling wave waveform data is subjected to first-order difference processing, and the amplitude difference between the current value and the previous value is calculated to obtain the traveling wave waveform data after the features are highlighted.

[0014] Furthermore, the calculation of the positive change dynamic sum is expressed by the following formula:

[0015] h + (t) = max(h + (t-1)+e(t)-d-λ t ,0)

[0016] Among them, h + (t) represents the dynamic sum of positive changes, max is the maximum value of the current dynamic sum and 0, h + (t-1) is the positive change dynamic sum of the previous value after waveform optimization, e(t) is the amplitude difference between the current value and the previous value after waveform optimization, d is the drift correction parameter, λ t is the penalty coefficient at time t.

[0017] Furthermore, the negative change dynamic sum is calculated and expressed by the following formula:

[0018] h - (t) = max(h - (t-1)-e(t)-d-λ t ,0)

[0019] Among them, h - (t) represents the dynamic sum of negative changes, max is the maximum value of the current dynamic sum and 0, h - (t-1) is the negative change dynamic sum of the previous value after waveform optimization, e(t) is the amplitude difference between the current value and the previous value after waveform optimization, d is the drift correction parameter, λ t is the penalty coefficient at time t.

[0020] Furthermore, the penalty coefficient λ t , expressed by the following formula:

[0021] λ t =β·|T t -T t-1 ∣+γ

[0022] T t =α·x t +(1-α)·T t-1

[0023] Among them, β is the sensitivity adjustment parameter, γ is the benchmark penalty value, T t is the trend value at time t, α is the smoothing coefficient, x t is the amplitude at time t.

[0024] Furthermore, the method for performing fault identification and fault distance measurement based on the positive change dynamic sum and the negative change dynamic sum includes: a starting point corresponds to an end point to represent a change process, and the positions of the starting point and the end point of each waveform change are recorded, and the number is counted; based on the positive change dynamic sum and the negative change dynamic sum of the power frequency waveform data, if the number of the detected waveform starting points is greater than or equal to a first set value, it is determined that a fault occurs;

[0025] Based on the positive change dynamic sum and negative change dynamic sum of the traveling wave waveform data, if the number of starting points of the detected waveform is greater than or equal to the second set value, the first wave head is found, and the first starting point is the starting point of the traveling wave waveform data. The fault occurrence time is calculated according to the sampling rate and used for fault distance measurement.

[0026] Furthermore, the maximum and minimum values ​​are normalized using the following formula:

[0027]

[0028] Among them, x is the current value of the original data, x max is the maximum value of the original data, x min is the minimum value of the original data.

[0029] A second aspect of the present invention provides a waveform change detection system based on dynamic sum.

[0030] A waveform change detection system based on dynamic sum, comprising:

[0031] A data processing module is configured to: perform maximum and minimum value normalization processing on the acquired power frequency waveform data or traveling wave waveform data of the measured line, perform feature processing on the processed power frequency waveform data or traveling wave waveform data, and obtain the power frequency waveform data or traveling wave waveform data with highlighted features;

[0032] A calculation module is configured to: calculate the amplitude difference between the current moment and the previous moment based on the power frequency waveform data or the traveling wave waveform data after the feature is highlighted, and calculate the positive change dynamic sum and the negative change dynamic sum;

[0033] The waveform detection module is configured to perform fault identification and fault distance measurement based on the positive change dynamic sum and the negative change dynamic sum.

[0034] A third aspect of the present invention provides a computer-readable storage medium.

[0035] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the waveform change detection method based on dynamic sum as described in the first aspect above.

[0036] A fourth aspect of the present invention provides a computer device.

[0037] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the waveform change detection method based on dynamic sum as described in the first aspect above are implemented.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention introduces dynamic and waveform change detection of the transmission line, identifies the fault type of the power frequency waveform, and identifies the starting point of the traveling wave waveform, thereby completing the distance measurement. Through fault distance measurement, the fault point can be accurately and quickly found, the fault can be eliminated, and power supply can be restored, so as to ensure the safe and stable operation of the power system and provide strong guarantee for economic and social development.

[0040] The present invention can be applied to transmission line fault identification and fault distance measurement, improves identification accuracy and distance measurement precision, and can realize real-time monitoring and detection capabilities of data changes. The present invention adjusts parameters according to specific application scenarios, improves adaptability and accuracy, and has a wide range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0042] Figure 1 It is a flow chart of the waveform change detection method based on dynamic sum shown in the present invention. DETAILED DESCRIPTION

[0043] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0044] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0045] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0046] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and systems according to various embodiments of the present disclosure. It should be noted that each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code, and the module, program segment, or a part of a code may include one or more executable instructions for implementing the logical functions specified in each embodiment. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the flowchart and / or block diagram, and the combination of boxes in the flowchart and / or block diagram can be implemented using a dedicated hardware-based system that performs a specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0047] Embodiment 1

[0048] like Figure 1As shown, this embodiment provides a waveform change detection method based on dynamic and. This embodiment uses the method applied to a server as an example for illustration. It can be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communications, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in this application. In this embodiment, the method includes the following steps:

[0049] Step 1: Use a distributed traveling wave monitoring device to obtain voltage or current waveform data of the measured line.

[0050] Step 2: Normalize the acquired waveform data.

[0051] Step 3: Use autocorrelation filtering or first-order difference to perform feature processing on the normalized data.

[0052] Step 4: Calculate the amplitude difference between time t and time t-1 and the dynamic sum of positive and negative changes.

[0053] Step 5: Set a threshold and compare the size of the dynamic sum with the threshold. When the dynamic sum exceeds the set threshold, it is considered that the waveform has changed significantly or is abnormal. The starting and ending points of multiple changes can be recorded to realize waveform change detection based on the dynamic sum. When the signal exceeds the preset threshold, it is marked as the starting point of the change; when the signal returns to the normal range, it is marked as the end point of the change.

[0054] Among them, the dynamic sum of positive changes and negative changes are both dynamic sums, which is a detector.

[0055] In some embodiments, the voltage or current waveform data of the measured line in step one includes: current (voltage) power frequency waveform data and current (voltage) traveling wave waveform data.

[0056] In some embodiments, the specific method of step 2 includes: normalizing the maximum and minimum values ​​of the power frequency waveform data and the traveling wave waveform data, respectively. The specific method formula (1) is:

[0057]

[0058] Among them, x is the current value of the original data; x maxis the maximum value of the original data; x min is the minimum value of the original data.

[0059] In some embodiments, the specific method of step three includes: performing autocorrelation filtering on the normalized power frequency waveform data, that is, subtracting the data of the first cycle from the data of each cycle, using a sliding window to traverse the entire waveform, so as to highlight the changing waveform and smooth the steady-state waveform; performing first-order difference processing on the normalized traveling wave waveform data, that is, calculating the amplitude difference between the current value and the previous value. For a stable waveform, the first-order difference will be close to zero, making it smoother. For a fast-changing waveform, the first-order difference can amplify the change and make the fluctuation more obvious. The specific method formula (2) is:

[0060] X n = x′ n -x′1 (2)

[0061] Among them, x′ n is the data of the nth cycle, and x′1 is the data of the 1st cycle.

[0062] According to the algorithm formula (2), the power frequency waveform data after waveform optimization is calculated.

[0063] According to the algorithm formula (3), the traveling wave waveform data after waveform optimization is calculated.

[0064] X(t)=x′(t)-x′(t-1) (3)

[0065] Where x′(t) is the current amplitude of the normalized data, and x′(t-1) is the previous amplitude of the normalized data.

[0066] In some embodiments, the specific method of step 4 includes: performing first-order difference processing on the power frequency waveform data and the traveling wave waveform data processed in step 3, respectively, and calculating the amplitude difference between the current value and the previous value; then adding or subtracting the calculated amplitude difference from the dynamic sum of the previous value to obtain the dynamic sum of positive change or negative change, respectively. The specific method formula (4) is:

[0067] e(t)=X(t)-X(t-1) (4)

[0068] Among them, X(t) is the current amplitude after waveform optimization; X(t-1) is the previous amplitude after waveform optimization.

[0069] According to the algorithm formula (4), the amplitude difference between the power frequency waveform data and the traveling wave waveform data after waveform optimization is calculated respectively.

[0070] According to algorithm formulas (5) and (6), the dynamic sum of positive changes and the dynamic sum of negative changes are calculated respectively.

[0071] h+ (t) = max(h + (t-1)+e(t)-d-λ t ,0)(5)

[0072] h - (t) = max(h - (t-1)-e(t)-d-λ t ,0)(6)

[0073] Among them, h + (t-1) is the positive change dynamic sum of the previous value after waveform optimization; h - (t-1) is the dynamic sum of negative changes of the previous value after waveform optimization; e(t) is the amplitude difference between the current value and the previous value after waveform optimization; d is the drift correction parameter (if you seek faster detection speed, you can reduce the value of d; if you need to reduce false alarms, you can increase the value of d).

[0074] The trend value is calculated according to formula (7):

[0075] T t =α·x t +(1-α)·T t-1 (7)

[0076] Among them, T t is the trend value at time t, α is the smoothing coefficient, x t is the amplitude at time t, and the penalty coefficient is adjusted according to the trend value. The penalty coefficient is calculated according to formula (8):

[0077] λ t =β·|T t -T t-1 ∣+γ (8)

[0078] Among them, λ t is the penalty coefficient at time t, β is the sensitivity adjustment parameter, γ is the baseline penalty value, and max is the maximum value between the current dynamic sum and 0. When e(t) changes rapidly, h + (t) or h - (t) can be quickly reset to zero and sensitively capture changes. After reset, the changes in the next stage are accumulated again, so for small changes not exceeding d, h + (t) or h - (t) is equal to zero, which has a good filtering effect on some small noises.

[0079] The present invention can adjust the drift correction parameter d, ignore the influence of small amplitude noise, and add a dynamic penalty coefficient λ t , improve the stability and accuracy of change detection, accurately capture major trend changes, and the precise starting point of changes.

[0080] In some embodiments, the specific method of step 5 includes: setting the threshold value as threshold, h + (t) The initial value is 0, h - (t) The initial value is 0, and the change point is t c is t; if h + (t)>threshold or h - (t)>threshold, then the waveform is detected to have changed, and the dynamic sum always starts from zero.

[0081] According to the specific application scenario, a suitable threshold is set. When the dynamic sum of positive changes is greater than the threshold, or the dynamic sum of negative changes is greater than the threshold, it is considered that the waveform has changed significantly or abnormally. A starting point corresponds to an end point to represent a change process, and the starting point and end point positions of each waveform change are recorded and the number is counted. For the power frequency waveform, after the sliding window difference in step three, the changed power frequency waveform will be more prominent. At this time, the dynamic and detection waveform is used. If the number of starting points detected is greater than or equal to 4 (at least one of the dynamic sum of positive changes and the dynamic sum of negative changes meets the condition), it means that a waveform mutation is detected and there are at least two continuous cycles, each cycle is considered to be 2 changes, indicating that a fault occurs. For the traveling wave waveform, after the first-order difference, the dynamic and detection waveform in step four is used. If the number of starting points detected is greater than or equal to 1 (at least one of the dynamic sum of positive changes and the dynamic sum of negative changes meets the condition), it means that a waveform mutation is detected, the first wave head is found, and the first starting point is the starting point of the traveling wave current head. The fault occurrence time can be calculated according to the sampling rate for fault distance measurement.

[0082] Assuming that the first wave head time of the fault point reaching the monitoring points at both ends (end M and end N) is t1 and t2, the distance from the fault point to the monitoring points at both ends is calculated according to formula (9) and formula (10) respectively:

[0083]

[0084] Among them, L is the distance between M and N, and v is the wave velocity.

[0085] The present invention introduces dynamic and waveform change detection of the transmission line, identifies the fault type of the power frequency waveform, and identifies the starting point of the traveling wave waveform, thereby completing the distance measurement. Through fault distance measurement, the fault point can be accurately and quickly found, the fault can be eliminated, and power supply can be restored, so as to ensure the safe and stable operation of the power system and provide strong guarantee for economic and social development.

[0086] Embodiment 2

[0087] This embodiment provides a waveform change detection system based on dynamic sum.

[0088] A waveform change detection system based on dynamic sum, comprising:

[0089] A data processing module is configured to: perform maximum and minimum value normalization processing on the acquired power frequency waveform data or traveling wave waveform data of the measured line, perform feature processing on the processed power frequency waveform data or traveling wave waveform data, and obtain the power frequency waveform data or traveling wave waveform data with highlighted features;

[0090] A calculation module is configured to: calculate the amplitude difference between the current moment and the previous moment based on the power frequency waveform data or the traveling wave waveform data after the feature is highlighted, and calculate the positive change dynamic sum and the negative change dynamic sum;

[0091] The waveform detection module is configured to perform fault identification and fault distance measurement based on the positive change dynamic sum and the negative change dynamic sum.

[0092] In some embodiments, the data processing module is further configured to: perform autocorrelation filtering on the processed power frequency waveform data, subtract the data of the first cycle from the data of each cycle, and use a sliding window to traverse the entire waveform to obtain the power frequency waveform data with highlighted features;

[0093] The processed traveling wave waveform data is subjected to first-order difference processing, and the amplitude difference between the current value and the previous value is calculated to obtain the traveling wave waveform data after the features are highlighted.

[0094] In some embodiments, the calculation of the positive change dynamic sum is expressed by the following formula:

[0095] h + (t) = max(h + (t-1)+e(t)-d-λ t ,0)

[0096] Among them, h + (t) represents the dynamic sum of positive changes, max is the maximum value of the current dynamic sum and 0, h + (t-1) is the positive change dynamic sum of the previous value after waveform optimization, e(t) is the amplitude difference between the current value and the previous value after waveform optimization, d is the drift correction parameter, λ t is the penalty coefficient at time t.

[0097] In some embodiments, the negative change dynamic sum is calculated using the following formula:

[0098] h - (t) = max(h - (t-1)-e(t)-d-λ t ,0)

[0099] Among them, h- (t) represents the dynamic sum of negative changes, max is the maximum value of the current dynamic sum and 0, h - (t-1) is the negative change dynamic sum of the previous value after waveform optimization, e(t) is the amplitude difference between the current value and the previous value after waveform optimization, d is the drift correction parameter, λ t is the penalty coefficient at time t.

[0100] In some embodiments, the penalty coefficient λ t , expressed by the following formula:

[0101] λ t =β·|T t -T t-1 ∣+γ

[0102] T t =α·x t +(1-α)·T t-1

[0103] Among them, β is the sensitivity adjustment parameter, γ is the benchmark penalty value, T t is the trend value at time t, α is the smoothing coefficient, x t is the amplitude at time t.

[0104] In some embodiments, the waveform detection module is configured as follows: a starting point corresponds to an ending point to represent a change process, and the starting point and ending point positions of each waveform change are recorded, and the number is counted; based on the positive change dynamic sum and the negative change dynamic sum of the industrial frequency waveform data, if the number of starting points of the detected waveform is greater than or equal to a first set value, it is determined that a fault has occurred; based on the positive change dynamic sum and the negative change dynamic sum of the traveling wave waveform data, if the number of starting points of the detected waveform is greater than or equal to a second set value, the first wave head is found, and the first starting point is the starting point of the traveling wave waveform data, and the fault occurrence time is calculated according to the sampling rate for fault ranging.

[0105] In some embodiments, the maximum and minimum values ​​are normalized using the following formula:

[0106]

[0107] Among them, x is the current value of the original data, x max is the maximum value of the original data, x min is the minimum value of the original data.

[0108] Embodiment 3

[0109] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the waveform change detection method based on dynamic sum as described in the first embodiment above are implemented.

[0110] Embodiment 4

[0111] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the waveform change detection method based on dynamic sum as described in the first embodiment above are implemented.

[0112] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0113] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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.

[0114] 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.

[0115] 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.

[0116] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0117] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A waveform change detection method based on dynamic sum, characterized in that: include: Performing maximum and minimum value normalization processing on the acquired power frequency waveform data or traveling wave waveform data of the measured line, and performing feature processing on the processed power frequency waveform data or traveling wave waveform data to obtain power frequency waveform data or traveling wave waveform data with highlighted features; Based on the power frequency waveform data or the traveling wave waveform data after the features are highlighted, the amplitude difference between the current moment and the previous moment is calculated, and the positive change dynamic sum and the negative change dynamic sum are calculated; Based on the positive change dynamic sum and the negative change dynamic sum, fault identification and fault distance measurement are performed.

2. The waveform change detection method based on dynamic sum according to claim 1 is characterized in that: The method of performing feature processing on the processed power frequency waveform data or traveling wave waveform data includes: performing autocorrelation filtering on the processed power frequency waveform data, subtracting the data of the first cycle from the data of each cycle, using a sliding window to traverse the entire waveform, and obtaining the power frequency waveform data with highlighted features; The processed traveling wave waveform data is subjected to first-order difference processing, and the amplitude difference between the current value and the previous value is calculated to obtain the traveling wave waveform data after the features are highlighted.

3. The waveform change detection method based on dynamic sum according to claim 1 is characterized in that: The calculation of the positive change dynamic sum is expressed by the following formula: h + (t)=max(h + (t-1)+e(t)-d-λ t ,0) Among them, h + (t) represents the dynamic sum of positive changes, max is the maximum value of the current dynamic sum and 0, h + (t-1) is the positive change dynamic sum of the previous value after waveform optimization, e(t) is the amplitude difference between the current value and the previous value after waveform optimization, d is the drift correction parameter, λ t is the penalty coefficient at time t.

4. The waveform change detection method based on dynamic sum according to claim 1 is characterized in that: The calculation of the dynamic sum of negative changes is expressed by the following formula: h - (t)=max(h - (t-1)-e(t)-d-λ t ,0) Among them, h - (t) represents the dynamic sum of negative changes, max is the maximum value of the current dynamic sum and 0, h - (t-1) is the dynamic sum of the negative changes of the previous value after waveform optimization, e(t) is the amplitude difference between the current value and the previous value after waveform optimization, d is the drift correction parameter, λ t is the penalty coefficient at time t.

5. The waveform change detection method based on dynamic sum according to claim 4 is characterized in that: Penalty coefficient λ t , expressed by the following formula: l t =β·∣T t -T t-1 ∣+γ T t =α·x t +(1-a)·T t-1 Among them, β is the sensitivity adjustment parameter, γ is the benchmark penalty value, T t is the trend value at time t, α is the smoothing coefficient, x t is the amplitude at time t.

6. The waveform change detection method based on dynamic sum according to claim 1, characterized in that: The method for performing fault identification and fault distance measurement based on the positive change dynamic sum and the negative change dynamic sum comprises: a starting point corresponds to an end point to represent a change process, and the positions of the starting point and the end point of each waveform change are recorded and the number is counted; based on the positive change dynamic sum and the negative change dynamic sum of the power frequency waveform data, if the number of the detected starting points of the waveform is greater than or equal to a first set value, it is determined that a fault occurs; Based on the positive change dynamic sum and negative change dynamic sum of the traveling wave waveform data, if the number of starting points of the detected waveform is greater than or equal to the second set value, the first wave head is found, and the first starting point is the starting point of the traveling wave waveform data. The fault occurrence time is calculated according to the sampling rate and used for fault distance measurement.

7. The waveform change detection method based on dynamic sum according to claim 1, characterized in that: The maximum and minimum value normalization process adopts the following formula: Among them, x is the current value of the original data, x max is the maximum value of the original data, x min is the minimum value of the original data.

8. A waveform change detection system based on dynamic sum, characterized in that: include: A data processing module is configured to: perform maximum and minimum value normalization processing on the acquired power frequency waveform data or traveling wave waveform data of the measured line, perform feature processing on the processed power frequency waveform data or traveling wave waveform data, and obtain the power frequency waveform data or traveling wave waveform data with highlighted features; A calculation module is configured to: calculate the amplitude difference between the current moment and the previous moment based on the power frequency waveform data or the traveling wave waveform data after the feature is highlighted, and calculate the positive change dynamic sum and the negative change dynamic sum; The waveform detection module is configured to perform fault identification and fault distance measurement based on the positive change dynamic sum and the negative change dynamic sum.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the waveform change detection method based on dynamic summation as described in any one of claims 1 to 7 are implemented.

10. A computer 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 in the waveform change detection method based on dynamic sum as described in any one of claims 1 to 7 are implemented.

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