A method, device and system for online detection of distribution line faults

Abstract

The present application relates to the technical field of distribution line fault detection, and specifically to a method, device, and system for online detection of distribution line faults, the method comprising: obtaining the actual current of each phase line in the distribution line, and using Karen Bell to obtain the line mode current of the distribution line; calculating the peak significance coefficient of each data segment and the first significance value of each data segment, and then obtaining the lightning fault significance value of the line mode current to calculate the line mode current anomaly value of the distribution line; obtaining the actual current anomaly value of the distribution line based on the degree of difference between the actual currents of any two phase lines and the extreme difference of the actual currents of each phase line; obtaining the lightning fault assessment value of the distribution line based on the line mode current anomaly value and the actual current anomaly value of the distribution line, and detecting the distribution line fault. The present application can improve the fault detection accuracy of the distribution line.

Description

Technical Field

[0001] The present application relates to the technical field of distribution line fault detection, and in particular to a method, device and system for online detection of distribution line faults. Background Art

[0002] The distribution network is a vital component of the power system. Real-time monitoring of the distribution network, identifying fault points, and determining fault types are of great significance to the protection of the power system. Among the many types of distribution line faults, short circuits and lightning strikes are particularly common. For example, aging of equipment insulation, one-way grounding shorts caused by foreign objects, and phase-to-phase shorts can easily cause tripping when a distribution line is struck by lightning. The traveling waves generated by distribution line faults contain a wealth of fault information, and transient traveling waves can be used to quickly and accurately identify the fault type. Lightning strikes are particularly damaging to distribution lines and related equipment. However, lightning disturbances, short circuits, and phase-to-phase current imbalances can cause the non-fault phase current to exhibit a power frequency waveform similar to that of the fault. Existing online monitoring technologies can achieve fault detection to a certain extent, but in actual detection, they are easily affected by these factors, leading to misjudgments.

[0003] Patent CN114545154A describes a regional distribution line insulation fault detection system. This system uses current transformers to collect line current signals, and an electronic controller analyzes and compares the collected data to detect line faults. However, this system fails to account for interference from other factors in actual applications, which can result in similar power frequency waveforms to those of faults, making misjudgment more likely. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a method, device and system for online detection of distribution line faults. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for online detection of distribution line faults, comprising the following steps:

[0006] Obtain the actual current of each phase line in the distribution line, and use Karen Bell to obtain the line mode current of the distribution line;

[0007] The line mode current is divided into data segments. The peak significance coefficient of each data segment is obtained based on the discreteness of the duration between peaks and the peak amplitude deviation in each data segment. The first significance value of each data segment is obtained by combining the average level of the amplitude of the line mode current at each frequency in the frequency domain within each data segment. The difference in the first significance value between adjacent data segments and the accumulation of the first significance values ​​of all data segments are analyzed to obtain the lightning fault significance value of the line mode current. Combined with the fitting deviation of the line mode current, the line mode current abnormal value of the distribution line is obtained.

[0008] According to the difference between the actual currents of any two phase lines and the extreme difference of the actual currents of each phase line, the actual current abnormal value of the distribution line is obtained;

[0009] According to the abnormal values ​​of the line mode current and the actual current of the distribution line, the lightning fault assessment value of the distribution line is obtained, and the distribution line fault is detected.

[0010] Preferably, the acquisition of each data segment specifically includes: dividing the line mode current data into a preset number of data segments of equal length.

[0011] Preferably, the peak significance coefficient of each data segment is calculated as follows:

[0012] ,in, is the peak significance coefficient of the i-th data segment, is the mean of the difference between the jth peak and its two adjacent data, B is the standard deviation of the distance between all adjacent peaks, and N is the number of peaks in the i-th data segment. The peaks in each data segment are obtained by the peak extraction algorithm. To avoid constants with denominators equal to 0.

[0013] Preferably, the method for obtaining the first significant value of each data segment is:

[0014] The line mode current in each data segment is converted into the frequency domain, and the calculation formula of the first significant value of each data segment is: , where is the mean of the amplitudes of all frequencies except the fundamental frequency in the frequency domain corresponding to the i-th data segment, is the first significant value of the i-th data segment, is the peak significance coefficient of the i-th data segment.

[0015] Preferably, the lightning fault significance value of the line mode current is calculated as follows:

[0016] , where is the lightning fault significance value of the line mode current, S is the cumulative sum of the first significance values ​​of all data segments, M is the total number of data segments, are the first significant values ​​of the i-th data segment and the i+1-th data segment respectively.

[0017] Preferably, the method for obtaining the abnormal value of the line mode current of the distribution line is:

[0018] The moment corresponding to the first non-zero data in the line mode current is taken as the starting moment of the fluctuation, and the preset time length from the starting moment of the fluctuation is taken as the fluctuation duration. The line mode current within the fluctuation duration is filtered, and then a sine wave fitting is performed to obtain a fitting curve. The product of the determination coefficient of the fitting curve and the lightning fault significance value of the line mode current is taken as the line mode current abnormal value of the distribution line.

[0019] Preferably, the method for obtaining the actual current abnormal value of the distribution line is:

[0020] During the fluctuation period of the line mode current, the range of the actual current data of each phase line is calculated, the SBD distance between the actual current data of any two phase lines is calculated, and the product of the mean of the SBD distances between all any two phase lines and the accumulated results of the ranges corresponding to all phase lines is taken as the actual current anomaly value of the distribution line.

[0021] Preferably, the lightning fault assessment value of the distribution line is an average value of the line mode current abnormal value and the actual current abnormal value of the distribution line.

[0022] In the second aspect, an embodiment of the present application also provides an online detection system for distribution line faults, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned methods for online detection of distribution line faults.

[0023] In a third aspect, an embodiment of the present application further provides a device for online detection of distribution line faults, wherein a computer program is stored in the device, and when the computer program is executed by a processor, any one of the above-mentioned methods for online detection of distribution line faults is implemented.

[0024] As can be seen from the above, the method, device, and system for online detection of distribution line faults provided by this application have at least the following beneficial effects:

[0025] This application deeply analyzes the differences in line-mode current characteristics between lightning faults and other conditions. By capturing the characteristics of sinusoidal power frequency variations, including sharp jitter, peak levels, and their gradual reduction, it calculates line-mode current anomalies. This approach has the advantage of reducing interference from lightning disturbances and short-circuit anomalies. Furthermore, combining the transient impacts and phase-to-phase differences experienced by the actual current, it calculates a lightning fault assessment value, enabling online detection of distribution line faults. This helps improve the accuracy of fault detection and lightning fault identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0027] Figure 1 This is a flowchart of the steps of a method for online detection of distribution line faults provided in this application. DETAILED DESCRIPTION

[0028] To further illustrate the technical means and effectiveness of this application to achieve the intended invention objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a method, device, and system for online detection of distribution line faults proposed in this application. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0029] Unless otherwise specified and limited, terms such as "comprises", "includes" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device comprising a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the article or device comprising the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs.

[0030] The following describes in detail a method, device and system for online detection of power distribution line faults provided by the present application with reference to the accompanying drawings.

[0031] See also Figure 1 , which shows a flow chart of a method for online detection of distribution line faults provided by an embodiment of the present application, including the following steps:

[0032] Step 1: Obtain the actual current of each phase line in the distribution line, and use Karen Bell to obtain the line mode current of the distribution line.

[0033] This embodiment performs online detection and identification of common lightning faults during the operation of distribution lines. When a power line fault occurs, a rapidly propagating electromagnetic wave, namely a traveling wave, is generated in the line. The traveling wave signal contains rich fault information, such as the fault type, fault location, and fault time. These traveling wave signals appear within a short period of time after the fault occurs and are also called transient traveling waves. Therefore, distribution lines often use traveling wave protection methods to achieve rapid detection of lines. When a lightning strike or short circuit fault occurs in a distribution line, the transient traveling wave current detected by the line protection includes not only the current of the normal operation of the transmission line, but also the additional current generated by the lightning current or the faulty power supply.

[0034] Therefore, in order to perform real-time online monitoring, this embodiment first collects the current signal of each phase line in the distribution line. Then, in order to highlight the characteristics of transient traveling wave current, this embodiment uses the Karen Bell transformation technology to obtain the line mode current in the distribution line system, and sets its change matrix as , output Three modulus components, for The components are superimposed to obtain the line mode current, that is, the transient traveling wave current. It should be noted that the specific acquisition process of the line mode current is common knowledge to those skilled in the art and will not be repeated in this embodiment.

[0035] So far, the actual current data and line mode current data of each phase line in the distribution line can be obtained through the above process of this embodiment.

[0036] Step 2: Divide the line mode current into data segments, and obtain the peak significance coefficient of each data segment based on the discreteness of the duration between peaks in each data segment and the peak amplitude deviation. Combined with the average level of the amplitude corresponding to each frequency in the frequency domain of the line mode current in each data segment, obtain the first significance value of each data segment. Analyze the difference in the first significance value between adjacent data segments and the accumulation of the first significance values ​​of all data segments to obtain the lightning fault significance value of the line mode current. Combined with the fitting deviation of the line mode current, obtain the line mode current abnormal value of the distribution line.

[0037] When a distribution line is struck by lightning, it acts like a short-duration current source superimposed at the strike point. If the lightning current amplitude is small, it cannot penetrate the insulators. There are no impedance discontinuities in the line, and the traveling current waves induced by the lightning current are continuously refracted and reflected at both ends of the line. In this case, the distribution line remains undamaged. If the lightning current amplitude is large, the voltage across the insulators exceeds the withstand voltage, causing them to break down and flash over. This causes the conductors to connect to the ground through the insulators and towers, resulting in a ground fault. Since distribution lines are overhead, strong winds can exacerbate the swing of the conductors. If the distance between conductors is small during this swing, a phase-to-phase short circuit can occur. Foreign objects adhering to the distribution lines can also cause short circuits. However, these short circuits have a relatively minor impact on distribution line operation. Lightning strikes can easily cause severe damage to distribution line equipment, making accurate identification of lightning strike faults crucial.

[0038] Under normal conditions, the line-mode current of a distribution line is zero. When struck by lightning, the line-mode current waveform exhibits sharp jitter and numerous spikes. A subsequent ground fault occurs, and due to the presence of the faulty power source, the line-mode current exhibits a sinusoidal waveform with a power-frequency variation. As the line-mode current changes, the jitter in its waveform gradually decreases. When a distribution line short-circuits, the current traveling wave generated at the fault location propagates along the line to both ends, reflecting and refracting at the fault point and both ends, accompanied by the generation of a small amount of high-frequency components. This causes the line-mode circuit waveform in the short-circuit state to also exhibit sinusoidal power-frequency variation characteristics, but without significant spikes and with low jitter. During a lightning disturbance, the line-mode current is primarily affected by the lightning current, resulting in a line-mode current waveform that also exhibits sharp jitter and numerous spikes, but lacks a sinusoidal power-frequency variation. It can be seen from this that the line mode current under short circuit fault and lightning disturbance has some similar change characteristics to lightning fault. Therefore, the lightning fault can be accurately identified based on the specific change characteristics of the line mode current under the above lightning fault.

[0039] First, the rapid jitter changes and peak characteristics of the line mode current are analyzed. Since the line mode current under lightning faults shows the characteristics of significant local peak protrusions and relatively close spacing between adjacent peaks, and there are more high-frequency signals generated by rapid jitter, in addition, the above characteristics have a tendency to gradually slow down. In this embodiment, the line mode current data is divided into 5 data segments of equal length. Taking the i-th data segment as an example, the peak extraction algorithm is used to obtain the peaks of each data segment. In this embodiment, all peaks in the data segment are obtained by the AMPD (Automatic Multiscale-based Peak Detection) algorithm. Since the peak spacing of the line mode current under lightning faults is relatively close and the peak jitter amplitude is relatively large, in this embodiment, the standard deviation of the distances between all adjacent peaks is calculated, and the mean of the difference between each peak and its two adjacent data is obtained. Further, based on this, the formula for obtaining the peak significance coefficient corresponding to the data segment is:

[0040] ,in, is the peak significance coefficient of the i-th data segment, is the mean of the difference between the jth peak and its two adjacent data, B is the standard deviation of the distances between all adjacent peaks, N is the number of peaks in the i-th data segment, To avoid a constant with a denominator of 0, the value range is 0.001 to 0.1, and in this embodiment the value is 0.01. The larger the value is, the more obvious the spike abnormality of the line mode current in the corresponding data segment is, which is a sign of a lightning fault.

[0041] Furthermore, to obtain the high-frequency characteristics of its rapid jitter, this embodiment uses the spectrum of each data segment using fast Fourier transform technology. The frequency corresponding to the highest amplitude in the spectrum is the fundamental frequency of the data. The average of the amplitudes corresponding to all frequencies other than the fundamental frequency is calculated to reflect the degree of rapid and rapid jitter of the line mode current. From this, a significant value with the characteristics of peak significance and rapid jitter is calculated as the first significant value of each data segment. The formula for the first significant value is: , where is the mean of the amplitudes of all frequencies except the fundamental frequency in the frequency domain corresponding to the i-th data segment, is the first significant value of the i-th data segment, which reflects the peak and jitter degree corresponding to the data segment.

[0042] Furthermore, considering the gradual slowdown of the jitter and peak characteristics of the lightning fault in the line mode current, the significant value of the line mode current with the characteristics of sharp jitter and slowdown is calculated as the lightning fault significant value of the line mode current. The formula for the lightning fault significant value is: , where is the lightning fault significance value of the line mode current, S is the cumulative sum of the first significance values ​​of all data segments, M is the total number of data segments, The larger the H is, the more obvious the sharp jitter change and peak characteristics of the line mode current are, and there is a trend of gradual slowdown.

[0043] If an abnormality occurs in the distribution line, the line mode current will fluctuate rapidly, and the transient traveling wave will appear for a short time. The line mode current is always zero under normal conditions. If a fault occurs during the online monitoring process, the line mode current will suddenly increase. The moment corresponding to the first non-zero data in the line mode current is taken as the starting moment of the fluctuation, and the preset time length from the starting moment of the fluctuation is taken as the fluctuation duration. In this embodiment, the fluctuation time within 0.1ms from the starting moment of the fluctuation is taken as the fluctuation duration. In this embodiment, the preset time length is 0.1ms, and the line mode current data within the fluctuation duration is analyzed. First, the sinusoidal power frequency variation characteristics of the line mode current are analyzed. In order to reduce the interference of its large number of peaks on the subsequent fitting, the line mode current is subjected to median filtering to obtain the filtered current data, and the trigonometric function fitting technology is used to perform sinusoidal fitting on the filtered current data to obtain a fitting curve. Since the line mode current under a lightning fault shows an overall power frequency variation trend of a sinusoidal waveform, if the line mode circuit fluctuation is caused by a lightning fault, the obtained fitting curve has a high degree of fit with the filtered current data. Then, the degree of fitting of the fitting curve is obtained. The degree of fitting can be obtained by calculating the determination coefficient, mean square error and mean absolute error. This embodiment uses the calculation of the determination coefficient to evaluate the goodness of fit of the fitting curve. This value reflects the significance of the sinusoidal industrial frequency variation characteristics of the line mode current.

[0044] Since the line mode current under lightning fault has both sinusoidal waveform power frequency variation and sharp jitter variation characteristics, while the above characteristics are not obvious under lightning disturbance and short circuit fault, the formula for calculating the abnormal value of the line mode current of the distribution line is: , where K is the abnormal value of the line mode current of the distribution line, is the determination coefficient of the fitting curve corresponding to the data after line mode current filtering, is the lightning fault significance value of the line mode current. It can be understood that the larger the obtained K is, the more likely the line mode current is to contain lightning fault characteristics.

[0045] Step 3: Obtain the actual current abnormal value of the distribution line based on the difference between the actual currents of any two phase lines and the extreme difference of the actual current of each phase line.

[0046] Furthermore, the distribution line is often transmitted in the form of three-phase alternating current. When a lightning fault occurs, the resulting phase-to-phase current imbalance is more obvious, and a higher transient impact current is shown in the actual current data. The transient impact characteristics of the actual current data collected from each phase line are then analyzed. Within the fluctuation duration of the line mode current, that is, within the fluctuation duration of 0.1ms from the start of the fluctuation in this embodiment, the range of the actual current data corresponding to each phase line is calculated separately, and the cumulative result of the range corresponding to all phase lines is obtained. The range reflects the impact intensity of the distribution line current. Since there is a certain phase difference between the currents of each phase line, the SBD (Shape based Distance) distance between the actual current data of any two phase lines is calculated, and the average of the SBD distances between all any two phase lines is recorded as the phase-to-phase difference value of the distribution line for the convenience of understanding and expression in this embodiment. Furthermore, preferably, in this embodiment, the product of the phase difference value and the accumulated result of the extreme differences corresponding to all lines is used as the actual current abnormality value of the distribution line, which reflects the imbalance of the actual current and the transient impact characteristics under the lightning fault.

[0047] Step 4: Based on the abnormal line mode current value and the abnormal actual current value of the distribution line, the lightning fault assessment value of the distribution line is obtained, and the distribution line fault is detected.

[0048] In this embodiment, the line mode current abnormal value and the actual current abnormal value of the distribution line reflect the current characteristics under the lightning fault from the perspective of different electrical parameters. Preferably, the average value of the line mode current abnormal value and the actual current abnormal value of the distribution line is used as the lightning fault assessment value of the distribution line, which reflects the possibility of a lightning fault in the distribution line.

[0049] This embodiment deeply analyzes the sinusoidal power frequency variation characteristics of the line mode current under lightning faults, the characteristics of sharp jitter, peak degree and gradual reduction, and further combines the transient impact and phase difference of the actual current to calculate the lightning fault assessment value, so as to reflect the possibility of lightning faults in the distribution line.

[0050] Preferably, in this embodiment, for quantitative detection, the tanh function is used to normalize the lightning fault assessment value. In this embodiment, a fault detection interval is set. Specifically, [0, 0.8] indicates that the distribution line is normal, and (0.8, 1] indicates that the line has a lightning fault. In this embodiment, the distribution line lightning fault assessment value can improve the accuracy of fault detection and lightning fault identification.

[0051] Thus far, according to the above process of this embodiment, the insulation fault detection of the flexible cable can be performed, and the location of the insulation fault can be obtained.

[0052] Based on the same inventive concept as the above method, an embodiment of the present application also provides an online detection system for distribution line faults, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned methods for online detection of distribution line faults are implemented.

[0053] At the same time, an embodiment of the present application also provides a distribution line fault online detection device, in which a computer program is stored. When the computer program is executed by a processor, any one of the above-mentioned distribution line fault online detection methods is implemented.

[0054] It should be understood that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0055] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0056] The above content is only an implementation method of the present application and is not intended to limit the scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the scope of protection of the present application.

Claims

1. A method for online detection of distribution line faults, characterized in that: The following steps are involved: Obtain the actual current of each phase line in the distribution line, and use Karen Bell to obtain the line mode current of the distribution line; The line mode current is divided into data segments. The peak significance coefficient of each data segment is obtained based on the discreteness of the duration between peaks and the peak amplitude deviation in each data segment. The first significance value of each data segment is obtained by combining the average level of the amplitude of the line mode current at each frequency in the frequency domain within each data segment. The difference in the first significance value between adjacent data segments and the accumulation of the first significance values ​​of all data segments are analyzed to obtain the lightning fault significance value of the line mode current. Combined with the fitting deviation of the line mode current, the line mode current abnormal value of the distribution line is obtained. According to the difference between the actual currents of any two phase lines and the extreme difference of the actual currents of each phase line, the actual current abnormal value of the distribution line is obtained; According to the abnormal values ​​of the line mode current and the actual current of the distribution line, the lightning fault assessment value of the distribution line is obtained, and the distribution line fault is detected; The calculation method of the peak significance coefficient of each data segment is: ,in, is the peak significance coefficient of the i-th data segment, is the mean of the difference between the jth peak and its two adjacent data, B is the standard deviation of the distance between all adjacent peaks, and N is the number of peaks in the i-th data segment. The peaks in each data segment are obtained by the peak extraction algorithm. To avoid constants with denominators equal to 0; The method for obtaining the first significant value of each data segment is: The line mode current in each data segment is converted into the frequency domain, and the calculation formula of the first significant value of each data segment is: , where is the mean of the amplitudes of all frequencies except the fundamental frequency in the frequency domain corresponding to the i-th data segment, is the first significant value of the i-th data segment, is the peak significance coefficient of the i-th data segment; The calculation method of the lightning fault significance value of the line mode current is: , where is the lightning fault significance value of the line mode current, S is the cumulative sum of the first significance values ​​of all data segments, M is the total number of data segments, are the first significant values ​​of the i-th data segment and the i+1-th data segment respectively.

2. A method for online detection of distribution line faults according to claim 1, characterized in that: The acquisition of each data segment specifically includes: dividing the line mode current data into a preset number of data segments of equal length.

3. The method for online detection of distribution line faults according to claim 1, wherein: The method for obtaining the abnormal value of the line mode current of the distribution line is: The moment corresponding to the first non-zero data in the line mode current is taken as the starting moment of the fluctuation, and the preset time length from the starting moment of the fluctuation is taken as the fluctuation duration. The line mode current within the fluctuation duration is filtered, and then a sine wave fitting is performed to obtain a fitting curve. The product of the determination coefficient of the fitting curve and the lightning fault significance value of the line mode current is taken as the line mode current abnormal value of the distribution line.

4. A method for online detection of distribution line faults according to claim 3, characterized in that: The method for obtaining the actual current abnormal value of the distribution line is: During the fluctuation period of the line mode current, the range of the actual current data of each phase line is calculated, the SBD distance between the actual current data of any two phase lines is calculated, and the product of the mean of the SBD distances between all any two phase lines and the accumulated results of the ranges corresponding to all phase lines is taken as the actual current anomaly value of the distribution line.

5. The method for online detection of distribution line faults according to claim 1, wherein: The lightning fault assessment value of the distribution line is an average value of the abnormal line mode current value and the abnormal actual current value of the distribution line.

6. A distribution line fault online detection system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method for online detection of distribution line faults as described in any one of claims 1 to 5 are implemented.

7. A device for online detection of power distribution line faults, wherein a computer program is stored in the device, characterized in that: When the computer program is executed by a processor, the method for online detection of distribution line faults as claimed in any one of claims 1 to 5 is implemented.

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