An online prediction method for line insulation faults

Abstract

The present invention discloses an online prediction method for line insulation fault, which relates to the field of data processing technology, including: installing a first traveling wave sensor and a second traveling wave sensor at both ends of the line, and obtaining a comparison time period according to the line length; obtaining a first transient value collected by the first traveling wave sensor at a first collection time, and obtaining a fault prediction index based on a stability model and the first transient value; judging whether the fault prediction index exceeds a preset threshold, if exceeded, obtaining a collection threshold according to the first transient value, and obtaining collection data of the second traveling wave sensor within the comparison time period, wherein the collection data includes a plurality of second transient values; obtaining a second collection time corresponding to a second transient value exceeding the collection threshold according to the collection data; obtaining a fault prediction position according to the first collection time, the second collection time and the line length. The present invention has the advantages of accurate prediction, position determination and real-time and fast speed.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to an online prediction method for line insulation faults. Background Art

[0002] In the power system, the insulation performance of transmission lines, as a key component of power transmission, is directly related to the safe and stable operation of the power grid. However, due to factors such as erosion of the natural environment, aging of equipment, and human damage, the insulation layer of the transmission line may be gradually damaged, resulting in a decrease in insulation performance, which in turn causes insulation failure. Insulation failure will not only cause power outages and affect the normal power consumption of users, but may also cause safety accidents such as fires and electric shocks, posing a serious threat to the safety of people's lives and property.

[0003] The existing line insulation fault detection method adopts regular inspection and offline testing. Although this method can detect and handle insulation faults to a certain extent, it has problems such as long detection cycle and poor real-time performance. Especially under the complex and changeable natural environment and the growing electricity demand, the traditional detection method can no longer meet the requirements of modern power systems for safe, reliable and efficient operation.

[0004] Therefore, developing an online prediction method that can collect and analyze electrical signals on the line in real time, accurately predict and detect the occurrence of insulation faults, and quickly locate the fault position is of great significance to improving the operational safety and reliability of the power system. Summary of the invention

[0005] In view of the defects in the prior art, the present invention provides an online prediction method for line insulation faults.

[0006] An online prediction method for line insulation faults comprises: installing a first traveling wave sensor and a second traveling wave sensor at both ends of the line respectively, and obtaining a comparison time period according to the line length; obtaining a first transient value collected by the first traveling wave sensor at a first collection moment, and obtaining a fault prediction index based on a stability model and the first transient value; judging whether the fault prediction index exceeds a preset threshold, and if so, obtaining a collection threshold according to the first transient value, and obtaining collection data of the second traveling wave sensor within the comparison time period, wherein the collection data comprises a plurality of second transient values; obtaining a second collection moment corresponding to a second transient value exceeding the collection threshold according to the collection data; and obtaining a fault prediction position according to the first collection moment, the second collection moment and the line length.

[0007] Optionally, the comparison time period is obtained according to the line length and is expressed as: ;in, To compare time periods, is the adjustment coefficient, is the line length, is the traveling wave propagation speed, is the first delay term.

[0008] Optionally, acquiring the first transient value collected by the first traveling wave sensor at the first collection moment includes: acquiring a first voltage value and a first current value collected by the first traveling wave sensor at the first collection moment; and acquiring the first transient value according to the first voltage value and the first current value.

[0009] Optionally, obtaining a fault prediction index based on a stability model and a first transient value includes: setting a time window, wherein the end of the time window is a first acquisition moment; obtaining voltage data and current data collected by a first traveling wave sensor within the time window, and obtaining a plurality of comparative transient values ​​based on the voltage data and the current data; obtaining a transient value change curve based on the plurality of comparative transient values ​​and the first transient value; and obtaining a fault prediction index based on the stability model and the transient value change curve.

[0010] Optionally, the stability model in the fault prediction index obtained based on the stability model and the transient value change curve is expressed as: ;in, is the failure prediction index, is the transient value change curve, is the first collection moment, is the time window.

[0011] Optionally, acquiring the collection threshold according to the first transient value includes: acquiring a reduction coefficient according to the line length; and acquiring the collection threshold according to the reduction coefficient and the first transient value.

[0012] Optionally, the acquisition threshold is obtained according to the reduction coefficient and the first transient value and is expressed as: ;in, is the acquisition threshold, is the reduction factor.

[0013] Optionally, acquiring the predicted fault position according to the first acquisition moment, the second acquisition moment and the line length includes: acquiring a time difference according to the first acquisition moment and the second acquisition moment; and acquiring a distance between the fault in the line and the first traveling wave sensor according to the time difference.

[0014] Optionally, the time difference obtained according to the first acquisition moment and the second acquisition moment is expressed as: ;in, is the time difference, The second collection time.

[0015] Optionally, the distance between the fault in the line and the first traveling wave sensor is obtained according to the time difference and is expressed as: ; is the distance between the fault in the line and the first traveling wave sensor, is the second delay term.

[0016] The beneficial effects of the present invention are embodied in:

[0017] In the online prediction method of the insulation fault of the entire line, by installing the first traveling wave sensor and the second traveling wave sensor at both ends of the line respectively, the voltage and current traveling wave signals in the power system can be captured in real time. These signals contain rich fault information and provide basic data for subsequent fault prediction; further, the fault prediction index is calculated using the stability model and the first transient value collected by the first traveling wave sensor, so that the stability state of the current power system can be evaluated in real time. Once the fault prediction index exceeds the preset threshold, the further data collection and analysis process is immediately triggered; further, by setting the collection threshold and obtaining the collected data of the second traveling wave sensor in the comparison time period, the scheme can screen out transient signals that may be related to the fault and accurately record the second collection time when these signals are captured; finally, combined with the first collection time, the second collection time and the known line length, the signal propagation speed and time difference calculation principle are used to accurately predict the fault location, thereby realizing the real-time, accurate and rapid positioning of the line insulation fault; in summary, the whole method effectively shortens the fault detection cycle, improves the real-time and reliability of detection, and greatly reduces the risk of power outages and safety accidents caused by insulation faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0019] Figure 1 It is a schematic diagram of the steps of the online prediction method of line insulation fault of the present invention;

[0020] Figure 2 It is a schematic diagram of a part of the steps of S2 in the online prediction method of line insulation fault of the present invention;

[0021] Figure 3 It is another part of the step schematic diagram of S2 in the online prediction method of line insulation fault of the present invention;

[0022] Figure 4 It is a schematic diagram of some steps of S3 in the online prediction method of line insulation fault of the present invention;

[0023] Figure 5 It is a schematic diagram of some steps of S5 in the online prediction method of line insulation fault of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0025] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In addition, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0027] like Figure 1 As shown, an online prediction method for line insulation fault is provided, comprising:

[0028] S1. Install a first traveling wave sensor and a second traveling wave sensor at both ends of the line, and obtain a comparison time period according to the line length;

[0029] S2. acquiring a first transient value collected by a first traveling wave sensor at a first collection moment, and acquiring a fault prediction index based on a stability model and the first transient value;

[0030] S3, judging whether the fault prediction index exceeds a preset threshold value, and if so, obtaining a collection threshold value according to the first transient value, and obtaining the collection data of the second traveling wave sensor in the comparison time period, wherein the collection data includes a plurality of second transient values;

[0031] S4, obtaining a second collection time corresponding to a second transient value exceeding the collection threshold according to the collected data;

[0032] S5. Obtain the predicted fault location according to the first collection time, the second collection time and the line length.

[0033] In this embodiment, it should be noted that, in S1, first, a first traveling wave sensor and a second traveling wave sensor are installed at both ends of the line respectively; a traveling wave sensor is a sensor that can sensitively capture voltage or current traveling wave signals generated by faults or operations in the power system. These traveling wave signals propagate along the transmission line at a speed close to the speed of light and carry rich fault information. The main function of the traveling wave sensor is to convert these traveling wave signals into measurable electrical signals for subsequent signal processing and analysis; for the transmission line, since both the current and voltage signals contain important fault information, the first traveling wave sensor and the second traveling wave sensor both include a voltage traveling wave sensing module and a current traveling wave sensing module. Furthermore, the first traveling wave sensor and the second traveling wave sensor can be arbitrarily installed at the starting end (or called the transmitting end) and the end (or called the receiving end) of the line. Such a layout can ensure that the traveling wave signal on the line is completely captured during the transmission process, and facilitates the subsequent analysis of the time difference of signal propagation. During installation, the sensor should fit tightly to the circuit to ensure the integrity and accuracy of signal transmission. At the same time, the sensor should also have sufficient mechanical strength and protection level to withstand harsh conditions in the natural environment, such as high temperature, low temperature, humidity, corrosion, etc.

[0034] At the same time, in S1, it is also necessary to obtain a comparison time period according to the line length. The comparison time period refers to the maximum time range from when the first traveling wave sensor (the traveling wave sensor corresponding to the fault prediction index that first discovers a fault that exceeds a preset threshold, and this embodiment assumes that the first traveling wave sensor first discovers it) captures the signal to when the second traveling wave sensor may capture the same or related signal. Among them, the line length is a key factor affecting the signal propagation time. In the power system, the propagation speed of the traveling wave signal will be affected by factors such as the line material and structure, which is approximately equal to 0.7 times the speed of light. When determining the comparison time period, it is necessary to comprehensively consider the line length and signal propagation speed, calculate the time required for the signal to be transmitted from the transmitting end to the receiving end and use it as part of the comparison time period; at the same time, in long-distance transmission lines, the traveling wave signal may encounter impedance mismatching points (such as line joints, transformers, etc.) during the transmission process, so the time to reach the receiving end will also be delayed accordingly. When determining the comparison time period, the influence of signal delay and attenuation should be fully considered.

[0035] Assume that we have a 100-kilometer transmission line with the first and second traveling wave sensors installed at both ends of the line. Based on the influence of the line material on the signal propagation speed, the propagation speed of the traveling wave signal on the line is about 70% of the speed of light (about 210,000 km / s). The time required for the signal to be transmitted from the transmitter to the receiver is: 100 km ÷ (210,000 km / s) ≈ 0.000476 seconds (or about 476 microseconds). Taking into account the influence of signal reflection and attenuation, we can set the comparison time period to twice the signal propagation time (about 740 microseconds) to ensure that all possible signals can be captured.

[0036] In S2, the first transient value collected by the first traveling wave sensor at the first collection moment is obtained, and the fault prediction index is calculated based on the stability model and the first transient value. Specifically, in the power system, when an insulation fault occurs in the transmission line, a sudden change in voltage and current will occur. These sudden change signals are called transient signals. The first transient value is the specific value of these transient signals captured by the first traveling wave sensor at the first collection moment. After obtaining the first transient value, we need to use the stability model to calculate the fault prediction index; the stability model is a mathematical model used to describe the behavioral characteristics of the first transient value, which can calculate the degree of sudden change of the first transient value by mathematical methods, that is, the stability index of the entire line power system; the fault prediction index is a numerical index calculated based on the stability model and the first transient value, which is used to evaluate the possibility that the current power system is in a state where a fault has occurred or is about to occur. The larger the fault prediction index, the greater the possibility that the current power system is in a state where a fault has occurred or is about to occur.

[0037] In S3, it is determined whether the fault prediction index exceeds the preset threshold, and based on this judgment result, it is determined whether it is necessary to further obtain the collected data of the second traveling wave sensor. Specifically, the fault prediction index is a numerical indicator used to evaluate the possibility that the current power system is in a state of having failed or about to fail. At the beginning of S3, this fault prediction index needs to be compared with a preset threshold; the preset threshold is a numerical value set according to the actual situation of the power system and the fault detection requirements, and the preset threshold represents the boundary for determining the state of having failed or about to fail; further, the preset threshold is obtained through expert experience, historical data analysis, experimental verification or simulation, and the setting of the preset threshold should take into account factors such as the stability and safety of the power system and the accuracy and real-time performance of fault detection. If the fault prediction index exceeds the preset threshold, then we believe that the power system may be in a fault state or is about to fail. In this case, we need to further obtain the collected data of the second traveling wave sensor during the comparison time period. The collected data includes multiple second transient values ​​in order to complete the entire prediction process more accurately; at the same time, it is also necessary to set an acquisition threshold. The acquisition threshold is a value set based on the first transient value and the actual situation of the power system. It is used to filter out transient signals collected by the second traveling wave sensor that may be related to the fault. The setting of the acquisition threshold should ensure that we will not miss any fault-related signals, and at the same time will not introduce too much noise and interference.

[0038] Assume that we have a transmission line with a length of 100 kilometers, with a first traveling wave sensor and a second traveling wave sensor installed at both ends of the line. In step S2, we have calculated the fault prediction index to be 0.8 (assuming that this value indicates that the system has a higher risk of failure). We compare the fault prediction index 0.8 with a preset threshold (such as 0.7). Since 0.8 is greater than 0.7, we believe that the system may be in a fault state or is about to fail. According to the first transient value and the actual situation of the power system, we set a collection threshold (such as a transient value) and obtain the collection data including multiple second transient values ​​collected by the second traveling wave sensor within the comparison time period (such as 740 microseconds).

[0039] In S4, the collected data obtained from the second traveling wave sensor is processed and analyzed to find out the second transient values ​​that exceed the previously set collection threshold, and record their corresponding second collection moments. This step is crucial for the subsequent determination of the fault location because it provides the exact time information for the fault signal to propagate to the other end of the line. Specifically, at the beginning of step S4, it is necessary to check these second transient values ​​one by one, compare them with the collection threshold, and find out the second transient values ​​that exceed the collection threshold. These second transient values ​​may represent the moment when the fault signal is captured by the second traveling wave sensor during the propagation process. For each second transient value that exceeds the collection threshold, we need to record its corresponding second collection moment.

[0040] Suppose we have a 100-kilometer transmission line with a first traveling wave sensor and a second traveling wave sensor installed at both ends of the line. In the previous step, we have calculated the fault prediction index to be 0.8 (indicating that the system has a high risk of failure) and set a collection threshold (such as a transient value). The collected data within the comparison time period (such as 740 microseconds) is obtained from the second traveling wave sensor, and these data contain multiple second transient values. We check these second transient values ​​one by one and compare them with the collection threshold. Suppose we find a second transient value that exceeds the collection threshold we set. For this second transient value that exceeds the collection threshold, we record its corresponding second collection time, such as 0.0004 seconds (or 400 microseconds).

[0041] In S5, first, the first acquisition moment and the second acquisition moment are obtained from the previous steps. These two moments represent the specific time when the fault signal is captured by the traveling wave sensors at both ends of the line. Secondly, the line length of the transmission line is known. The specific calculation method is to multiply the time difference between the first acquisition moment and the second acquisition moment by the propagation speed of the signal on the line (taking into account the influence of the line material and structure on the signal propagation speed), and then subtract the line length from the previous result, and then divide the result obtained here by two as the distance between the fault and the first traveling wave sensor. In this way, we can get a rough prediction of the fault location, thereby guiding the subsequent fault detection and repair work.

[0042] In summary, in the online prediction method of the insulation fault of the entire line, by installing the first traveling wave sensor and the second traveling wave sensor at both ends of the line respectively, the voltage and current traveling wave signals in the power system can be captured in real time. These signals contain rich fault information and provide basic data for subsequent fault prediction; further, the fault prediction index is calculated using the stability model and the first transient value collected by the first traveling wave sensor, so that the stability state of the current power system can be evaluated in real time. Once the fault prediction index exceeds the preset threshold, the further data collection and analysis process is immediately triggered; further, by setting the collection threshold and obtaining the collected data of the second traveling wave sensor in the comparison time period, the scheme can screen out transient signals that may be related to the fault and accurately record the second collection time when these signals are captured; finally, combined with the first collection time, the second collection time and the known line length, the signal propagation speed and time difference calculation principle are used to accurately predict the fault location, thereby realizing the real-time, accurate and rapid positioning of the line insulation fault; in summary, the whole method effectively shortens the fault detection cycle, improves the real-time and reliability of detection, and greatly reduces the risk of power outages and safety accidents caused by insulation faults.

[0043] In one embodiment, obtaining the comparison time period according to the line length in S1 is expressed as: ;in, To compare time periods, is the adjustment coefficient, is the line length, is the traveling wave propagation speed, Delayed item.

[0044] In this embodiment, it should be noted that: For comparison purposes, the time period is defined as the maximum time range from when the first traveling wave sensor captures a signal to when the second traveling wave sensor may capture the same or related signal. It is an adjustment coefficient, which is used to adjust the length of the comparison time period to adapt to different line conditions, environmental factors or detection accuracy requirements. The introduction of the adjustment coefficient improves the flexibility and adaptability of the expression. The line length, i.e. the physical length of the transmission line, is a key factor affecting the signal propagation time. The propagation speed of traveling waves. In power systems, the propagation speed of traveling wave signals is affected by factors such as line materials and structures, and is usually close to but lower than the speed of light. is a delay term, which is used to consider the delay caused by impedance mismatch points (such as line joints, transformers, etc.) that the signal may encounter during transmission, as well as the influence of signal reflection and attenuation. In summary, by considering the line length, traveling wave propagation speed and delay term, this expression can calculate the comparison time period more accurately and quickly, thereby ensuring that all possible signals can be captured within the comparison time period and improving the accuracy of fault detection.

[0045] like Figure 2 As shown, in one embodiment, obtaining the first transient value collected by the first traveling wave sensor at the first collection moment in S2 includes:

[0046] S21. Acquire a first voltage value and a first current value collected by a first traveling wave sensor at a first collection moment;

[0047] S22. Obtain a first transient value according to the first voltage value and the first current value.

[0048] In this embodiment, it should be noted that in S21, the first voltage value and the first current value collected by the first traveling wave sensor are obtained at the first collection moment. Among them, when the voltage on the transmission line changes suddenly (such as a voltage drop or increase caused by an insulation fault), the first traveling wave sensor can quickly capture this change and convert it into a measurable electrical signal. The captured voltage value is the first voltage value, which represents the voltage state on the transmission line at a certain moment when the fault has occurred or is about to occur. Similar to the voltage value, when the current on the transmission line changes suddenly (such as a current surge caused by a short circuit or overload), the first traveling wave sensor can also capture this change. The captured current value is the first current value, which reflects the current state on the transmission line at a certain moment when the fault has occurred or is about to occur. At the first collection moment, these two values ​​are recorded simultaneously, providing key data for the subsequent calculation of the fault prediction index.

[0049] In S22, the system aims to integrate the first voltage value and the first current value obtained in step S21 to form a comprehensive indicator that can reflect the current state of the transmission line - the first transient value. The transient value is usually obtained quickly by normalizing the voltage and current values ​​or directly weighting them. The first transient value will be used in the subsequent fault prediction index calculation, providing a key basis for the online prediction of insulation faults.

[0050] like Figure 3 As shown, in one embodiment, obtaining the fault prediction index based on the stability model and the first transient value in S2 includes:

[0051] S23, setting a time window, wherein the end of the time window is the first collection moment;

[0052] S24, acquiring voltage data and current data collected by the first traveling wave sensor within the time window, and acquiring a plurality of comparative transient values ​​according to the voltage data and the current data;

[0053] S25, obtaining a transient value variation curve according to the multiple compared transient values ​​and the first transient value;

[0054] S26. Obtain a fault prediction index based on the stability model and the transient value change curve.

[0055] In this embodiment, it should be noted that in S23, a time range, i.e., a time window, is set for subsequent data analysis. The end of this time window is set to the first acquisition moment, that is, the moment when the first traveling wave sensor captures the first transient value. The time window is set to ensure that there is enough reliable data to calculate the fault prediction index corresponding to the first transient value collected at the first acquisition moment. However, the time window should not be too long to avoid introducing too much irrelevant data. In practical applications, the length of the time window may need to be comprehensively considered based on factors such as the actual situation of the power system, the requirements of fault detection, and the sampling frequency of the sensor.

[0056] In S24, the voltage data and the current data collected by the first traveling wave sensor in the time window are obtained, and a plurality of comparative transient values ​​are obtained according to the voltage data and the current data, wherein the comparative transient values ​​represent all adjacent data before the first collection moment.

[0057] In S25, a transient value change curve is obtained based on multiple comparison transient values ​​and the first transient value. The goal of the system is to draw a transient value change curve based on multiple comparison transient values ​​and the first transient value. This curve reflects the change of transient values ​​of the power system over time before and after the fault occurs. When drawing the transient value change curve, time is used as the horizontal coordinate and the transient value is used as the vertical coordinate. Through this curve, the transient value change trend before the first acquisition moment can be intuitively observed. The drawing of the transient value change curve is of great significance for the subsequent analysis of fault characteristics, judgment of fault type, and positioning of fault location.

[0058] In S26, a fault prediction index is calculated based on the stability model and the transient value change curve. This fault prediction index is a numerical indicator used to evaluate the possibility that the current power system is in a state where a fault has occurred or is about to occur. Through the calculation of S26, the system can obtain a fault prediction index. The larger the index, the greater the possibility that the current power system is in a state where a fault has occurred or is about to occur. This index will be used in the subsequent fault judgment and processing process to guide the safe operation and maintenance of the power system.

[0059] In one embodiment, the stability model in obtaining the fault prediction index based on the stability model and the transient value change curve in S26 is expressed as:

[0060] ;in,

[0061] is the failure prediction index, is the transient value change curve, is the first collection moment, is the time window.

[0062] In this embodiment, it should be noted that: It is the fault prediction index, which reflects the degree of change of the transient value within the time window. It is the transient value change curve, which shows the change of transient value over time. is the first acquisition moment, which is the current moment we are concerned about. is the length of the time window, which determines how long in the past we consider data to calculate the fault prediction index.

[0063] In the whole expression, , we can calculate the cumulative value of the square of the difference between the transient value and the current transient value within the time window. This cumulative value reflects the overall change degree of the transient value during this period of time. At the same time, the integral can fully consider the influence of all data points in the time window, thereby obtaining a more accurate evaluation result. Further, the square term is used , no matter whether the transient value increases or decreases, its change will be correctly reflected in the fault prediction index. At the same time, the square term also amplifies the larger change value, so that those changes that have a greater impact on system stability are more reflected in the fault prediction index. Furthermore, by setting the time window , we can limit the data range used in calculating the fault prediction index, and exclude those data points that are far away from the current moment and have little impact on the current state of the system, so as to more accurately evaluate the current state of the system. At the same time, the length of the time window can also be adjusted according to actual needs to meet the fault prediction needs in different situations. By dividing the time window by , we normalized the integral results. The advantage of this is that no matter how the length of the time window changes, the fault prediction index They can all be kept in a relatively stable range, which makes it easier for us to compare the fault prediction results in different time windows and to correlate the fault prediction index with other indicators.

[0064] like Figure 4As shown, in one embodiment, obtaining the acquisition threshold according to the first transient value in S3 includes:

[0065] S31, obtaining a reduction coefficient according to the line length;

[0066] S32: Obtain a collection threshold according to the reduction coefficient and the first transient value.

[0067] In this embodiment, it should be noted that in S31, the line length will affect the traveling wave transmission loss. The increase in line length may lead to an increase in power loss, an increase in voltage drop, and a decrease in the stability of the power system. Therefore, when setting the acquisition threshold, the line length factor needs to be considered. The reduction factor is a parameter used to adjust or correct the acquisition threshold, which may vary depending on the line length. Specifically, the longer the line, the larger the reduction factor may be required to reflect the impact of the line length on the transmission and distribution of power. This reduction factor may be obtained through historical data, expert experience, or simulation experiments to ensure that the acquisition threshold can accurately reflect the actual situation of the power system under different line lengths.

[0068] In S31, after obtaining the reduction coefficient and the first transient value, they can be combined to calculate the acquisition threshold; the acquisition threshold is used to filter out transient signals that may be related to the fault and are collected by the second traveling wave sensor; the setting of the acquisition threshold should ensure that we will not miss any fault-related signals, and at the same time will not introduce too much noise and interference.

[0069] In one embodiment, obtaining the acquisition threshold according to the reduction coefficient and the first transient value in S32 is expressed as:

[0070] ;in,

[0071] is the acquisition threshold, is the reduction factor.

[0072] In this embodiment, it should be noted that by introducing the reduction coefficient , which can take into account the effect of line length on the acquisition threshold. The longer the line, the larger the reduction factor may be needed to reflect the effect of line length on power transmission and distribution. This setting ensures that the acquisition threshold can accurately reflect the actual situation of the power system under different line lengths.

[0073] By comprehensively considering the first transient value and the reduction factor , this expression can calculate a more accurate acquisition threshold, which can be used to filter out the transient signals collected by the second traveling wave sensor that may be related to the fault, thereby improving the accuracy and reliability of fault detection.

[0074] like Figure 5 As shown, in one embodiment, obtaining the predicted fault location according to the first acquisition time, the second acquisition time and the line length in S5 includes:

[0075] S51, obtaining a time difference according to the first collection time and the second collection time;

[0076] S52. Obtain the distance between the fault in the line and the first traveling wave sensor according to the time difference.

[0077] In this embodiment, it should be noted that, in S51, the first acquisition moment and the second acquisition moment are first obtained, and these two moments respectively represent the specific time when the fault signal is captured by the traveling wave sensors at both ends of the line (i.e., the first traveling wave sensor and the second traveling wave sensor). Specifically, the first acquisition moment is the moment when the fault signal is first captured by the first traveling wave sensor (usually installed at the starting end or the sending end of the line), and the determination of this moment is based on the first transient value collected by the first traveling wave sensor, which reflects the voltage or current mutation on the line when the fault occurs or is about to occur, and the second acquisition moment is the moment when the fault signal is subsequently captured by the second traveling wave sensor (installed at the end or the receiving end of the line).

[0078] Finally, the time difference between them is calculated by simple subtraction operation. This time difference represents the time required for the fault signal to propagate from the location where the line fault occurs to the starting end and the end end respectively.

[0079] In S52, the distance between the fault in the line and the first traveling wave sensor is obtained based on the time difference. We use the time difference calculated in S51, as well as the known line length and traveling wave propagation speed, to calculate the distance between the fault and the first traveling wave sensor. It should be noted that since the signal may encounter impedance mismatch points (such as line joints, transformers, etc.) during transmission, causing the signal propagation speed to change or the signal to be reflected, the impact of these factors on the results needs to be considered in the actual calculation to obtain a rough prediction of the fault location.

[0080] In one embodiment, the time difference obtained according to the first acquisition time and the second acquisition time in S51 is expressed as:

[0081] ;in,

[0082] is the time difference, The second collection time.

[0083] In this embodiment, it should be noted that it has been described in detail in other embodiments and will not be repeated here.

[0084] In one embodiment, the distance between the fault in the line and the first traveling wave sensor is obtained according to the time difference in S52 and is expressed as:

[0085] ;in,

[0086] is the distance between the fault in the line and the first traveling wave sensor, is the second delay term.

[0087] In this embodiment, it should be noted that: is the distance between the fault in the line and the first traveling wave sensor. is the line length, i.e. the physical length of the transmission line. is the time difference, calculated by S51. It is the speed of traveling wave propagation, which is affected by factors such as line material and structure. It is close to the speed of light but lower than the speed of light. It is the second delay term, which is used to take into account the additional delay caused by the impedance mismatch points (such as line connectors, transformers, etc.) that the signal may encounter during transmission.

[0088] This expression is used to calculate the distance between the fault location and the first traveling wave sensor. In the power system, the time difference of the fault signal propagating from the fault point to the sensors at both ends of the line is proportional to the distance from the fault location to the sensors at both ends. However, since the signal may encounter impedance mismatch points during transmission, causing the signal propagation speed to change or the signal to be reflected, it is necessary to introduce a second delay term α to correct the time difference. By considering the line length, time difference, traveling wave propagation speed and the second delay term, the fault location can be calculated more accurately.

[0089] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0090] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0091] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. An online prediction method for line insulation fault, characterized in that: include: A first traveling wave sensor and a second traveling wave sensor are respectively installed at both ends of the line, and a comparison time period is obtained according to the length of the line; Acquire a first transient value acquired by a first traveling wave sensor at a first acquisition moment, and acquire a fault prediction index based on a stability model and the first transient value; Acquiring a fault prediction index based on a stability model and a first transient value includes: setting a time window, wherein the end of the time window is a first acquisition moment; acquiring voltage data and current data collected by a first traveling wave sensor within the time window, and acquiring a plurality of comparative transient values ​​according to the voltage data and the current data; acquiring a transient value variation curve according to the plurality of comparative transient values ​​and the first transient value; acquiring a fault prediction index based on a stability model and a transient value variation curve; wherein the stability model in acquiring a fault prediction index based on a stability model and a transient value variation curve is expressed as: ;in, is the failure prediction index, is the transient value change curve, is the first collection moment, is the time window; Determine whether the fault prediction index exceeds a preset threshold value, and if so, obtain a collection threshold value according to the first transient value, and obtain the collection data of the second traveling wave sensor within the comparison time period, wherein the collection data includes a plurality of second transient values; Acquire a second acquisition time corresponding to a second transient value exceeding an acquisition threshold value according to the acquired data; The predicted fault location is obtained according to the first collection time, the second collection time and the line length.

2. The online prediction method for line insulation fault according to claim 1, characterized in that: The comparison time period obtained according to the line length is expressed as: ;in, To compare time periods, is the adjustment coefficient, is the line length, is the traveling wave propagation speed, is the first delay term.

3. The online prediction method for line insulation fault according to claim 2, characterized in that: The step of acquiring the first transient value collected by the first traveling wave sensor at the first collection moment comprises: Acquire a first voltage value and a first current value collected by the first traveling wave sensor at a first collection moment; A first transient value is acquired according to the first voltage value and the first current value.

4. The online prediction method for line insulation fault according to claim 3, characterized in that: The acquiring the acquisition threshold according to the first transient value comprises: Obtain reduction factor based on line length; A collection threshold is obtained according to the reduction coefficient and the first transient value.

5. The online prediction method for line insulation fault according to claim 4, characterized in that: The acquisition threshold value obtained according to the reduction coefficient and the first transient value is expressed as: ;in, is the acquisition threshold, is the reduction factor.

6. The online prediction method for line insulation fault according to claim 5, characterized in that: The obtaining of the predicted fault location according to the first acquisition time, the second acquisition time and the line length comprises: Acquire a time difference according to the first collection moment and the second collection moment; The distance between the fault in the line and the first traveling wave sensor is obtained according to the time difference.

7. The online prediction method for line insulation fault according to claim 6, characterized in that: The time difference obtained according to the first acquisition moment and the second acquisition moment is expressed as: ;in, is the time difference, The second collection time.

8. The online prediction method for line insulation fault according to claim 7, characterized in that: The distance between the fault in the line and the first traveling wave sensor obtained according to the time difference is expressed as: ;in, is the distance between the fault in the line and the first traveling wave sensor, is the second delay term.

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