Power transmission line hazard discharge identification method, and system

Abstract

The present invention relates to the field of power systems and automation. Disclosed in the present invention are a power transmission line hazard discharge identification method, and a system. The method comprises: acquiring power transmission line hazard discharge measured data, acquiring and processing power transmission line hazard high-frequency discharge waveform data, and acquiring and processing power transmission line hazard discharge event data; establishing and initializing a hazard identification requirements table, and updating all elements in the table in chronological order; and for each collected new waveform segment, looking up the table to determine a maintenance requirement level corresponding to a newly collected discharge waveform, and performing line hazard state comprehensive determination and early warning. In the present invention, development processes of power transmission line hazards, particularly nonlinearly-developing insulation degradation and suspended foreign objects, are fully taken into account. Compared with existing methods, the present invention is suitable for guiding line maintenance. The identification method provided by the present invention has a simple and clear implementation process, and compared with methods using deep learning and the like, has lower data volume requirements, lower hardware computing power requirements and a wider application range.

Description

A method and system for identifying hidden discharge hazards in power transmission lines Technical Field

[0001] This invention relates to the field of power systems and automation, and in particular to a method and system for identifying potential discharge hazards in transmission lines. Background Technology

[0002] Power transmission and distribution lines are prone to damage due to factors such as insulator deterioration, contamination, and floating foreign objects. This can lead to weakened insulation between high-voltage conductors and ground wires or towers, resulting in potential discharges. If not cleared in time, these discharges can develop into faults, eventually causing the line to trip and shut down, resulting in significant economic losses and safety risks.

[0003] To accurately and promptly identify and eliminate discharges on power lines, it is necessary to first identify the presence and type of potential discharges. Currently, most transmission and distribution lines at various voltage levels are equipped with distributed traveling wave fault location devices. These devices collect high-frequency traveling waves generated and propagating from the discharge point at both ends of the line, calculate their characteristics, and compare them to determine whether they exhibit abnormal features. This allows for the identification of the presence and type of discharge, and the notification of line maintenance personnel to carry out repair work.

[0004] Current technologies for identifying and classifying hazardous discharges primarily extract high-frequency discharge waveform segments, calculate specified features such as amplitude, phase, and power frequency-related periodicity, and utilize pattern classification algorithms such as decision trees, support vector machines, and neural networks to output a label indicating the presence or type of the hazard. However, in reality, the causes of transmission line hazards are diverse, and their development is not linearly progressive until insulation breakdown and tripping. For example, small floating objects may trigger early discharges, but after being burned and charred during the discharge process, they may detach, terminating the discharge. Therefore, existing technologies that extract single or several waveform segments and calculate features do not consider the abnormal development and accumulation of line hazards, and cannot comprehensively judge the hazard status of the line from the entire process or predict future development trends, thus easily leading to false alarms and missed alarms. Summary of the Invention

[0005] Given the aforementioned existing problems, the causes of potential hazards in transmission lines are diverse, and their development is not linearly progressive until insulation breakdown and tripping. For example, small floating objects may trigger early discharges, but after being burned and charred during the discharge process, they may detach, terminating the discharge process. Therefore, the current method of extracting single or several waveform segments and calculating characteristics does not take into account the abnormal development and accumulation of line hazards, and cannot comprehensively judge the hazard status of the line from the entire process, predict future development trends, thus easily leading to false alarms and missed alarms.

[0006] To address the aforementioned technical problems, a method for identifying potential discharge hazards in transmission lines is proposed, including:

[0007] Acquire measured discharge data of potential hazards in transmission lines; acquire and process high-frequency discharge waveform data of potential hazards in transmission lines; acquire and process discharge event data of potential hazards in transmission lines; establish and initialize a hazard identification requirement table, and update all elements in the table in chronological order; for each newly acquired waveform segment, look up the table to determine the degree of operation and maintenance requirement corresponding to the newly acquired discharge waveform and make a comprehensive judgment and early warning on the hazard status of the line.

[0008] As a preferred embodiment of the method for identifying hidden discharge hazards in transmission lines according to the present invention, the method for obtaining measured data on hidden discharge hazards in transmission lines includes high-frequency discharge waveform data of hidden hazards and discharge event data of hidden hazards.

[0009] The high-frequency discharge waveform data of the potential hazard includes cutting the high-frequency discharge waveform data into N short waveforms every 10ms, i.e., half a power frequency cycle, and calculating F key features Z1…Z for each short waveform. i …Z F Where Z1 is the maximum amplitude value in the short waveform, Z F It is the power frequency phase where the maximum amplitude is located, for each feature Z i Extract the maximum value Z from the current feature set of all short waveforms. i,max and minimum value Z i,min For the amplitude characteristic Z1, the maximum and minimum amplitudes among all short waveforms are Z1 and Z2, respectively. 1,max and Z 1,min Where (1) to (n) are the short waveform numbers: Z i,max =max(Z) i (1),Z i (2),…,Z i (n)) Z i,min =min(Z) i (1),Z i (2),…,Z i (n))

[0010] Based on each feature Z i Find the minimum and maximum values, establish a discretization interval, and divide the range from the minimum to the maximum value into Y equal parts. i Intervals: [Z] i,min Z i,min +Z id ), [Z i,min +Z id Z i,min +2×Z id ), ..., [Z i,min +(Y i -1)×Zid Z i,max Z id =(Z i,max -Z i,min ) / Y i

[0011] Among them, Z id This represents the interval width.

[0012] Let N be the sum of the number of all intervals for all features, and N = Y1 + Y2 + ... + Y... i +…+Y F Establish a set of interval numbers S = [1, 2, ..., N], with a total of N elements, where each element corresponds to a discretized interval.

[0013] The key features of each short waveform, and the i-th feature Z of the n-th short waveform. i (n), converted to the S value corresponding to the interval, and based on the interval S values ​​generated from all F features of all n waveforms, a new feature interval matrix C is formed:

[0014] Each column of matrix C represents the feature interval corresponding to all features of a waveform, and each row represents the feature interval of all waveforms under any feature type. The order of each column is arranged according to the acquisition time of the waveforms. The waveforms represented by the left column have an acquisition time earlier than the waveforms represented by the right column.

[0015] As a preferred embodiment of the method for identifying hidden discharge hazards in transmission lines according to the present invention, the hidden discharge event data includes acquiring a set of hidden discharge event data for transmission lines, W = [W1, W2, ..., W...]. m ], where 1 to m are the data numbers, the time range covered by the data includes the time range covered by the discharge waveform data, and the known hidden events within the time corresponding to the waveform are: W1 is the discharge caused by the accumulation of dirt on the insulator; W2 is the discharge caused by the burning of hanging objects and the discharge caused by excessive vegetation; W3 is the fault trip caused by the deterioration and failure of the insulator; W4 is the power supply restored by the patrol personnel clearing the fault.

[0016] The severity level sequence data of the hidden danger includes setting a quantification value E for each known hidden danger event according to the actual line operation and maintenance needs, with a range of 0-4, and setting the level range to 0, 1, 2, 3: E=0 indicates that there is no hidden danger discharge phenomenon.

[0017] E=1 indicates that slight discharge is being monitored.

[0018] E=2 indicates a moderate level of discharge, which will cause flashover tripping. Local inspection should be performed during line inspection.

[0019] E=3 indicates a very serious situation, which has caused a fault trip and requires immediate proactive maintenance.

[0020] W1 indicates that the accumulated dirt on the insulator has begun to cause discharge, which is an early-stage hidden danger and is of a minor degree. E1 = 1.

[0021] W2 indicates that the discharge is more intense due to the burning of the hanging objects and the excessive vegetation causing discharge, which leads to a fault under long-term conditions. E2 = 2.

[0022] W3 indicates a fault trip caused by insulator deterioration and failure. Since the circuit has already tripped, it must be repaired immediately. E3 = 3.

[0023] W4 indicates that the patrol personnel cleared the fault and restored power, meaning the potential hazard was eliminated and no incident occurred; therefore, E4 = 0.

[0024] The resulting hazard level sequence is E = [1, 2, 3, 0].

[0025] In a preferred embodiment of the method for identifying hidden discharge hazards in transmission lines according to the present invention, the acquisition and processing of hidden discharge event data in transmission lines includes evaluating the activity of the hazard event based on the discharge interval time according to the system identification model.

[0026] Where n represents the number of discharge events occurring within the assessment period, E is the severity level of the potential hazard, and λ j t is the decay rate of the j-th discharge event, t1 and t2 are the start and end times of the evaluation period, respectively. j Let t' be the time of the j-th discharge event, Φ represent the normalized difference in discharge interval time, and Δ(t') represent the discharge interval time at time t'. σ represents the average value of the discharge interval time. Δ This represents the standard deviation of the discharge interval time.

[0027] When the Φ value is stable between 0.45 and 0.55, it indicates that the frequency and severity of discharge events are in line with the average level, and monitoring should be maintained. When the Φ value is greater than 0.55, it indicates that the discharge interval is longer than the average time, and a flashing green light indicates that the activity of potential hazards has decreased.

[0028] When the Φ value is less than 0.45, it indicates that the discharge interval is shorter than the average time, and an A-level alarm is issued to indicate that the activity of the current potential hazard event has increased.

[0029] When the value of F(t) decreases, the discharge events occur but the frequency decreases, indicating that the long-term accumulation of current hidden dangers will cause transmission line faults. A Level B alarm is issued to identify the hidden danger and notify staff to carry out maintenance and inspection work.

[0030] When the value of F(t) increases, the frequent occurrence of discharge events indicates that the current hidden danger event will cause a transmission line fault in the short term. A level D alarm is issued, and the current hidden danger event needs to be identified and maintenance work is carried out immediately.

[0031] As a preferred embodiment of the method for identifying hidden discharge hazards in transmission lines according to the present invention, the rule for establishing the hidden discharge hazard identification requirement table is as follows: it consists of a rows and b columns, where a is the number of feature intervals obtained from the hidden discharge waveform data, b is the number of discharge severity levels obtained from the hidden discharge event data, and the initial value of all elements is the level value E corresponding to the column they belong to. This indicates that when a certain feature of the waveform belongs to the feature interval represented by a, the current discharge severity level is b, representing the maintenance requirement. The column represented by the maximum value of the current row represents the maintenance action most needed for the current feature interval. When F>1, and the same waveform has multiple features, the maximum values ​​of each row are compared together, and the maximum value is taken as the maintenance requirement of the current waveform.

[0032] Based on the feature interval matrix C, obtain the earliest short waveform u and the waveform v of the next adjacent time. Combined with the hazard identification requirement table, determine the feature interval value S of the i-th feature of the u-th waveform. i (u) represents the row, while the Eu value corresponding to the waveform represents the column. In the table, the Sth column... i The element in row (u) and column Eu represents the maintenance requirement level corresponding to the discharge level Eu for the i-th feature of the waveform. Let the table element be T(S). i (u),Eu).

[0033] For each feature i, update table element T'(S) i (u),Eu): T'(S) i (u),Eu)=0.9×T(S i (u),Eu)+0.8×(Ev-T(S i (u),Eu))

[0034] Here, 0.9 represents the guiding role of historically similar hazard discharge characteristics in marking the severity of newly added discharge hazards, and 0.8 represents the predictive significance of future hazard discharges within the same discharge waveform in marking the current severity.

[0035] Following the order of acquisition time, the table elements are updated sequentially for each waveform and the next waveform at the adjacent time, until all waveforms have been processed and the table content is filled.

[0036] As a preferred embodiment of the method for identifying hidden discharge hazards in transmission lines according to the present invention, the comprehensive judgment and early warning of the hidden hazard status of the line includes calculating the current hazard value Hd:

[0037] A higher Hd value indicates a more serious hidden danger, while a lower Hd value indicates a less serious hidden danger. Here, B represents the discharge amplitude, and d... m The waveform shape is represented by M, the number of waveform parameters is represented by α and β, which are parameters adjusted according to the actual situation to control the intensity of exponential decay and information filtering. Norm represents normalization of the summation result, and Filter is used to filter the waveform shape parameters.

[0038] To predict that long-term accumulation of faults will cause transmission line faults, a comprehensive assessment of the transmission line's condition is conducted.

[0039] A larger P value indicates a more severe hidden danger condition of the transmission line. Here, P is the comprehensive judgment value of the hidden danger condition of the transmission line at time t, t3 is the initial time of the prediction period, t4 is the end time of the prediction period, and λ... Hd Hd is the decay coefficient representing the rate at which a hidden danger decays over time, K is the number of characteristic values ​​of the hidden danger, and Hd is the decay coefficient representing the rate at which a hidden danger decays over time. k Let σ be the characteristic value of the i-th hidden danger. k Let μ be the standard deviation of the i-th hazard characteristic value. k Let be the mean of the i-th hidden danger characteristic value.

[0040] As a preferred embodiment of the method for identifying hidden discharge hazards in transmission lines according to the present invention, the comprehensive state judgment includes: when the system acquires a new high-frequency discharge waveform, obtaining a new hidden discharge waveform y, extracting F features, and converting them into F feature interval values ​​S1(y), S2(y), ..., S F (y), according to the rules of the hazard identification requirement table, look up the maximum value in the row containing the characteristic interval and compare it to find the maximum value. The E value of the discharge severity level corresponding to the column containing the maximum value is the severity level represented by the new hazard discharge waveform. This is then combined with the overall condition of the transmission line for judgment:

[0041] When the new quantitative value E is less than 1 and the P value decreases, it indicates that the current level of hidden danger is minor and shows a long-term decreasing trend. Therefore, monitoring should be maintained and regular maintenance should be performed.

[0042] When the new quantification value E is less than 1 and the P value increases, it indicates that the current hidden danger is minor, but it shows a long-term increasing trend. It is believed that no fault will occur within the prediction period, and the fault period will be delayed. A level B alarm is used for local detection and local maintenance.

[0043] When the new quantitative value E is greater than 1 and the value P decreases, it indicates that the current hidden danger is serious, but the long-term trend is decreasing. It is believed that the transmission line will fail within the predicted period. A level D alarm is adopted, and maintenance is carried out immediately and the long-term trend is re-predicted until the hidden danger event shows a decreasing trend.

[0044] When the new quantitative value E is greater than 1 and the value P increases, it indicates that the current hidden danger is serious and has a long-term increasing trend. It is believed that the transmission line will fail before the predicted period, so a level G alarm is adopted, and work is immediately stopped and maintenance measures are taken to intervene.

[0045] Another objective of this invention is to provide a power transmission line hidden discharge identification system. This invention accurately identifies and assesses the hidden discharge status of power transmission lines by real-time monitoring and analysis of hidden discharge data, issues timely warnings, and provides a basis for line maintenance and repair, thereby ensuring the safe and stable operation of power transmission lines.

[0046] As a preferred embodiment of the power transmission line hidden discharge identification system of the present invention, it is characterized by including a data acquisition and processing module, a hidden danger identification requirement table module, and an early warning and status assessment module.

[0047] The data acquisition and processing module is responsible for acquiring measured data of potential discharge hazards in transmission lines, including high-frequency discharge waveform data and discharge event data. It processes the high-frequency discharge waveform data to extract key features and establish a feature interval matrix. It processes the discharge event data to establish a sequence of hazard severity levels.

[0048] The hazard identification requirement table module establishes a hazard identification requirement table based on the feature interval matrix and the hazard severity level sequence, updates all elements in the table in chronological order, acquires the maintenance requirement level corresponding to the newly generated discharge waveform, and performs a comprehensive judgment and early warning of the hazard status of the line.

[0049] The early warning and status assessment module assesses the activity of potential hazards based on the system identification model and discharge interval time, calculates the current hazard value and the comprehensive status judgment value of the transmission line, and issues early warnings of different levels based on the assessment results, and takes corresponding maintenance and repair measures.

[0050] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of a method for identifying potential discharge hazards in power transmission lines.

[0051] A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of a method for identifying potential discharge hazards in power transmission lines.

[0052] The beneficial effects of this invention are threefold. First, it fully considers the development process of potential hazards in transmission lines, especially non-linear insulation degradation and foreign object entrapment, where discharge trends are difficult to predict. The discharge may terminate before tripping or develop too rapidly, leading to situations unpredictable by existing technologies. Therefore, the accuracy and reliability are higher. Second, this invention fully integrates known hazard discharge severity indicators into the parameter matrix. Compared to existing methods using machine learning or deep learning, it offers stronger interpretability and observability during implementation, making it suitable for guiding line operation and maintenance. Third, the identification method proposed in this invention is simple and straightforward to implement. Compared to methods using deep learning, it requires less data and has lower hardware computing power requirements, making it more widely applicable.

[0053] Furthermore, this invention establishes a hazard early warning requirement table. Through a special design of the element update process in the table, it realizes the functional requirement of transforming discharge data into hazard identification, and fully integrates historical and future data, thereby improving the reliability and reference value of the identification. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0055] Figure 1 is a flowchart of a method for identifying hidden discharge hazards in power transmission lines according to an embodiment of the present invention.

[0056] Figure 2 is a system scheme flowchart of a power transmission line hidden discharge identification system provided by an embodiment of the present invention. Detailed Implementation

[0057] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0058] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0059] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive, either alone or selectively, with other embodiments.

[0060] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0061] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0062] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0063] Example 1

[0064] Referring to Figure 1, which illustrates the first embodiment of the present invention, this embodiment provides a method for identifying potential discharge hazards in transmission lines, comprising:

[0065] S1: Obtain measured data of discharge hazards in transmission lines, obtain and process high-frequency discharge waveform data of transmission line hazards, and obtain and process discharge event data of transmission line hazards.

[0066] Furthermore, the measured data of potential discharge hazards in transmission lines includes high-frequency discharge waveform data and discharge event data.

[0067] The high-frequency discharge waveform data of the potential hazard includes cutting the high-frequency discharge waveform data into N short waveforms every 10ms, i.e., half a power frequency cycle, and calculating F key features Z1…Z for each short waveform. i …Z FFor example, F=2, which means calculating two characteristics, where Z1 is the maximum amplitude value in the short waveform and Z2 is the power frequency phase where the maximum amplitude value is located.

[0068] Where Z1 is the maximum amplitude value in the short waveform, Z F It is the power frequency phase where the maximum amplitude is located, for each feature Z i Extract the maximum value Z from the current feature set of all short waveforms. i,max and minimum value Z i,min For the amplitude characteristic Z1, the maximum and minimum amplitudes among all short waveforms are Z1 and Z2, respectively. 1,max and Z 1,min Where (1) to (n) are the short waveform numbers: Z i,max =max(Z) i (1),Z i (2),…,Z i (n)) Z i,min =min(Z) i (1),Z i (2),…,Z i (n))

[0069] For example, for the amplitude characteristic Z1, the maximum and minimum amplitudes among all short waveforms are Z1 and Z2, respectively. 1,max and Z 1,min Z 1,max =max(Z1(1),Z1(2),…,Z1(n)) Z 1,min =min(Z1(1),Z1(2),…,Z1(n))

[0070] Similarly, for phase characteristic Z2, the largest phase among all short waveforms is Z. 2,max The smallest is Z. 2,min .

[0071] Based on each feature Z i Find the minimum and maximum values, establish a discretization interval, and divide the range from the minimum to the maximum value into Y equal parts. i Intervals: [Z] i,min Z i,min +Z id ), [Z i,min +Z id Z i,min +2×Z id ), ..., [Z i,min +(Y i -1)×Z id Z i,max Z id =(Z i,max -Z i,min ) / Y i

[0072] Among them, Z id This represents the interval width.

[0073] For example, for the amplitude feature Z1, three discretization intervals are established, Y1 = 3, which are: [Z 1,min Z 1,min +Z 1d ), [Z 1,min +Z 1d Z 1,min +2×Z 1d ), ..., [Z 1,min +(Y1-1)× Z 1d Z 1,max Z 1d =(Z 1,max -Z 1,min ) / 3

[0074] Similarly, establish the discretization interval for the three phase values ​​(Y2 = 3): [Z 2,min Z 2,min +Z 2d ), [Z 2,min +Z 2d Z 2,min +2×Z 2d ), [Z 2,min +(Y2-1)×Z 2d Z 2,max ]

[0075] Let N be the sum of the number of all intervals for all features, and N = Y1 + Y2 + ... + Y... i +…+Y F For example, in the previous example, N = 3 + 3 = 6, we establish a set of interval numbers S = [1, 2, ..., N], with a total of N elements, where each element corresponds to a discretized interval.

[0076] For example, 1, 2, and 3 represent the three intervals of amplitude feature Z1, and 4, 5, and 6 represent the three intervals of phase Z2. If the amplitude of a waveform is 1, and the three established amplitude intervals are [-1, 0), [0, 1), and [1, 2], then this feature belongs to the third amplitude interval. i(j) =3.

[0077] For example, if the phase characteristic of a waveform is -π / 2, and the three established phase intervals are [-π, -π / 3), [-π / 3, π / 3), and [π / 3, π], then this characteristic belongs to the first phase interval and is also the fourth in set S. i(j) =4.

[0078] The key features of each short waveform, and the i-th feature Z of the n-th short waveform. i (n), converted to the S value corresponding to the interval, and based on the interval S values ​​generated from all F features of all n waveforms, a new feature interval matrix C is formed:

[0079] Each column of matrix C represents the feature interval corresponding to all features of a waveform, and each row represents the feature interval of all waveforms under any feature type. The order of each column is arranged according to the acquisition time of the waveforms. The waveforms represented by the left column have an acquisition time earlier than the waveforms represented by the right column.

[0080] It should be noted that the dataset W = [W1, W2, ..., W...] is used to obtain the data set of potential discharge events in transmission lines. m ], where 1 to m are the data numbers, the time range covered by the data includes the time range covered by the discharge waveform data, and the known hidden events within the time corresponding to the waveform are: W1 is the discharge caused by the accumulation of dirt on the insulator; W2 is the discharge caused by the burning of hanging objects and the discharge caused by excessive vegetation; W3 is the fault trip caused by the deterioration and failure of the insulator; W4 is the power supply restored by the patrol personnel clearing the fault.

[0081] The severity level sequence data of the hidden danger includes setting a quantification value E for each known hidden danger event according to the actual line operation and maintenance needs, with a range of 0-4, and setting the level range to 0, 1, 2, 3: E=0 indicates that there is no hidden danger discharge phenomenon.

[0082] E=1 indicates that slight discharge is being monitored.

[0083] E=2 indicates a moderate level of discharge, which will cause flashover tripping. Local inspection should be performed during line inspection.

[0084] E=3 indicates a very serious situation, which has caused a fault trip and requires immediate proactive maintenance.

[0085] W1 indicates that the accumulated dirt on the insulator has begun to cause discharge, which is an early-stage hidden danger and is of a minor degree. E1 = 1.

[0086] W2 indicates that the discharge is more intense due to the burning of the hanging objects and the excessive vegetation causing discharge, which leads to a fault under long-term conditions. E2 = 2.

[0087] W3 indicates a fault trip caused by insulator deterioration and failure. Since the circuit has already tripped, it must be repaired immediately. E3 = 3.

[0088] W4 indicates that the patrol personnel cleared the fault and restored power, meaning the potential hazard was eliminated and no incident occurred; therefore, E4 = 0.

[0089] The resulting hazard level sequence is E = [1, 2, 3, 0].

[0090] It should also be noted that the activity of potential incidents is assessed by using the discharge interval time:

[0091] Where n represents the number of discharge events occurring within the assessment period, E is the severity level of the potential hazard, and λ j t is the decay rate of the j-th discharge event, t1 and t2 are the start and end times of the evaluation period, respectively. j Let t' be the time of the j-th discharge event, Φ represent the normalized difference in discharge interval time, and Δ(t') represent the discharge interval time at time t'. σ represents the average value of the discharge interval time. Δ This represents the standard deviation of the discharge interval time.

[0092] When the Φ value is stable between 0.45 and 0.55, it indicates that the frequency and severity of discharge events are in line with the average level, and monitoring should be maintained. When the Φ value is greater than 0.55, it indicates that the discharge interval is longer than the average time, and a flashing green light indicates that the activity of potential hazards has decreased.

[0093] When the Φ value is less than 0.45, it indicates that the discharge interval is shorter than the average time, and an A-level alarm is issued to indicate that the activity of the current potential hazard event has increased.

[0094] When the value of F(t) decreases, the discharge events occur but the frequency decreases, indicating that the long-term accumulation of current hidden dangers will cause transmission line faults. A Level B alarm is issued to identify the hidden danger and notify staff to carry out maintenance and inspection work.

[0095] When the value of F(t) increases, the frequent occurrence of discharge events indicates that the current hidden danger event will cause a transmission line fault in the short term. A level D alarm is issued, and the current hidden danger event needs to be identified and maintenance work is carried out immediately.

[0096] S2: Create and initialize the hazard identification requirement table, and update all elements in the table in chronological order.

[0097] Furthermore, the rules for the hazard identification requirement table are as follows: it consists of a rows and b columns, where a is the number of feature intervals obtained from the hazard discharge waveform data, b is the number of discharge severity levels obtained from the hazard discharge event data, and the initial value of all elements is the level value E corresponding to the column. This indicates that when a certain feature of the waveform belongs to the feature interval represented by a, the current discharge severity level is b, representing the maintenance requirement. The maximum value of the current row represents the maintenance action most needed for the current feature interval. When F>1, if the same waveform has multiple features, the maximum values ​​of each row are compared together, and the maximum value is taken as the maintenance requirement of the current waveform.

[0098] For example, following the example above, the table has 6 rows and 4 columns. If the amplitude of the x-th waveform is 1, the characteristic interval value S1(x) = 3. This corresponds to the 3rd row in the table. Assume that the four values ​​in this row are [1, 5, 15, 2]. The third value, 15, is the largest, indicating that the severity level corresponding to an amplitude of 1 is 2 (the third of the severity levels 0, 1, 2, and 3).

[0099] If the phase characteristic interval S2(x) of the waveform is 4, and the four values ​​in the fourth row are [11,9,8,2], with the first value 11 being the largest, it means that the severity level corresponding to this phase characteristic is the first level 0.

[0100] Comparing the maximum values ​​in the rows corresponding to the two characteristic intervals, we can see that 15 > 11. Taking the larger one, the overall discharge severity level corresponding to this waveform is 2.

[0101] Based on the feature interval matrix C, obtain the earliest short waveform u and the waveform v of the next adjacent time. Combined with the hazard identification requirement table, determine the feature interval value S of the i-th feature of the u-th waveform. i (u) represents the row, while the Eu value corresponding to the waveform represents the column. In the table, the Sth column... i The element in row (u) and column Eu represents the maintenance requirement level corresponding to the discharge level Eu for the i-th feature of the waveform. Let the table element be T(S). i (u),Eu).

[0102] For each feature i, update table element T'(S) i (u),Eu): T'(S) i (u),Eu)=0.9×T(S i (u),Eu)+0.8×(Ev-T(S i (u),Eu))

[0103] Here, 0.9 represents the guiding role of historically similar hazard discharge characteristics in marking the severity of newly added discharge hazards, and 0.8 represents the predictive significance of future hazard discharges within the same discharge waveform in marking the current severity.

[0104] Following the order of acquisition time, the table elements are updated sequentially for each waveform and the next waveform at the adjacent time, until all waveforms have been processed and the table content is filled.

[0105] S3: For each newly acquired waveform segment, look up the table to determine the level of maintenance requirements corresponding to the newly acquired discharge waveform and make a comprehensive judgment and early warning on the potential hazards of the line.

[0106] Furthermore, calculate the current hazard value Hd:

[0107] A higher Hd value indicates a more serious hidden danger, while a lower Hd value indicates a less serious hidden danger. Here, B represents the discharge amplitude, and d... m The waveform shape is represented by M, the number of waveform parameters is represented by α and β, which are parameters adjusted according to the actual situation to control the intensity of exponential decay and information filtering. Norm represents normalization of the summation result, and Filter is used to filter the waveform shape parameters.

[0108] To predict that long-term accumulation of faults will cause transmission line faults, a comprehensive assessment of the transmission line's condition is conducted.

[0109] A larger P value indicates a more severe hidden danger condition of the transmission line. Here, P is the comprehensive judgment value of the hidden danger condition of the transmission line at time t, t3 is the initial time of the prediction period, t4 is the end time of the prediction period, and λ... Hd Hd is the decay coefficient representing the rate at which a hidden danger decays over time, K is the number of characteristic values ​​of the hidden danger, and Hd is the decay coefficient representing the rate at which a hidden danger decays over time. k Let σ be the characteristic value of the i-th hidden danger. k Let μ be the standard deviation of the i-th hazard characteristic value. k Let be the mean of the i-th hidden danger characteristic value.

[0110] It should be noted that when the system acquires a new high-frequency discharge waveform, obtains a new potential hazard discharge waveform y, extracts F features, and converts them into F feature interval values ​​S1(y), S2(y), ..., S F (y), according to the rules of the hazard identification requirement table, look up the maximum value in the row containing the characteristic interval and compare it to find the maximum value. The E value of the discharge severity level corresponding to the column containing the maximum value is the severity level represented by the new hazard discharge waveform. This is then combined with the overall condition of the transmission line for judgment:

[0111] When the new quantitative value E is less than 1 and the P value decreases, it indicates that the current level of hidden danger is minor and shows a long-term decreasing trend. Therefore, monitoring should be maintained and regular maintenance should be performed.

[0112] When the new quantification value E is less than 1 and the P value increases, it indicates that the current hidden danger is minor, but it shows a long-term increasing trend. It is believed that no fault will occur within the prediction period, and the fault period will be delayed. A level B alarm is used for local detection and local maintenance.

[0113] When the new quantitative value E is greater than 1 and the value P decreases, it indicates that the current hidden danger is serious, but the long-term trend is decreasing. It is believed that the transmission line will fail within the predicted period. A level D alarm is adopted, and maintenance is carried out immediately and the long-term trend is re-predicted until the hidden danger event shows a decreasing trend.

[0114] When the new quantitative value E is greater than 1 and the value P increases, it indicates that the current hidden danger is serious and has a long-term increasing trend. It is believed that the transmission line will fail before the predicted period, so a level G alarm is adopted, and work is immediately stopped and maintenance measures are taken to intervene.

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0116] Example 2

[0117] The second embodiment of the present invention provides a method for identifying hidden discharge hazards in power transmission lines. To verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0118] We first collected high-frequency discharge waveform data of the transmission line under normal operating conditions and recorded relevant discharge event data. This data was used to establish an initial hazard identification requirements table.

[0119] At 10ms intervals, we trimmed the collected waveform data into multiple short waveforms and calculated F key features for each short waveform. These features included the maximum amplitude and power frequency phase of the short waveform. Based on the collected data, we initialized a hazard identification requirement table and updated all elements in the table in chronological order. For each newly collected waveform segment, we determined its corresponding maintenance requirement level by looking up the table and performed a comprehensive judgment and early warning of the line hazard status.

[0120] During the data acquisition and processing, we recorded the characteristic values ​​of different discharge events, the sequence data of the severity level of hidden dangers, and the evaluation results of the discharge interval time, as shown in Table 1.

[0121] Table 1

[0122] On transmission line C, the combined assessment value of discharge severity and hazard status is high, indicating a relatively serious hazard on the line. By using the method of this invention, we can more accurately identify and warn of these hazards, thereby improving operation and maintenance efficiency and safety.

[0123] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0124] Example 3

[0125] The third embodiment of the present invention differs from the first two embodiments in that:

[0126] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0128] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0129] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0130] Example 4

[0131] Referring to Figure 2, which is the fourth embodiment of the present invention, this embodiment provides a power transmission line hidden discharge identification system, including a data acquisition and processing module, a hidden danger identification requirement table module, and an early warning and status assessment module.

[0132] The data acquisition and processing module is responsible for acquiring measured data of potential discharge hazards in transmission lines, including high-frequency discharge waveform data and discharge event data. It processes the high-frequency discharge waveform data, extracts key features, and establishes a feature interval matrix. It processes the discharge event data to establish a sequence of hazard severity levels.

[0133] The hazard identification requirement table module establishes a hazard identification requirement table based on the feature interval matrix and the hazard severity level sequence, updates all elements in the table in chronological order, acquires the maintenance requirement level corresponding to the newly generated discharge waveform, and performs a comprehensive judgment and early warning of the hazard status of the line.

[0134] The early warning and status assessment module assesses the activity of potential hazards based on the system identification model and discharge interval time, calculates the current hazard value and the comprehensive status judgment value of the transmission line, and issues early warnings of different levels based on the assessment results, and takes corresponding maintenance and repair measures.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for identifying hidden discharge hazards in power transmission lines, characterized in that: include, Acquire measured data of discharge hazards in transmission lines, acquire and process high-frequency discharge waveform data of transmission line hazards, and acquire and process discharge event data of transmission line hazards. Create and initialize a hazard identification requirement table, and update all elements in the table in chronological order; For each newly acquired waveform segment, a table is consulted to determine the level of maintenance requirements corresponding to the newly acquired discharge waveform, and a comprehensive judgment and early warning of potential hazards in the line is made.

2. The method for identifying hidden discharge hazards in transmission lines as described in claim 1, characterized in that: The measured data of potential discharge hazards in transmission lines includes high-frequency discharge waveform data and discharge event data. The high-frequency discharge waveform data of the potential hazard includes cutting the high-frequency discharge waveform data into N short waveforms every 10ms, i.e., half a power frequency cycle, and calculating F key features Z1…Z for each short waveform. i …Z F Where Z1 is the maximum amplitude value in the short waveform, Z F It is the power frequency phase where the maximum amplitude is located, for each feature Z i Extract the maximum value Z from the current feature set of all short waveforms. i,max and minimum value Z i,min For the amplitude characteristic Z1, the maximum and minimum amplitudes among all short waveforms are Z1 and Z2, respectively. 1,max and Z 1,min Where (1) to (n) are the short waveform numbers: WITH i,max =max(Z i (1),With i (2),…,Z i (n)) WITH i,min =min(Z i (1),With i (2),…,Z i (n)) Based on each feature Z i Find the minimum and maximum values, establish a discretization interval, and divide the range from the minimum to the maximum value into Y equal parts. i Each interval: [WITH i,min ,WITH i,min +Z id ),[WITH i,min +Z id ,WITH i,min +2×Z id ),…,[WITH i,min +(Y i -1)×Z id ,WITH i,max ] Z id =(Z i,max -Z i,min ) / Y i Among them, Z id The interval width; Let N be the sum of the number of all intervals for all features, and N = Y1 + Y2 + ... + Y... i +…+Y F Establish a set of interval numbers S = [1, 2, ..., N], with a total of N elements, where each element corresponds to a discretized interval; The key features of each short waveform, and the i-th feature Z of the n-th short waveform. i (n), converted to the S value corresponding to the interval, and based on the interval S values ​​generated from all F features of all n waveforms, a new feature interval matrix C is formed: Each column of matrix C represents the feature interval corresponding to all features of a waveform, and each row represents the feature interval of all waveforms under any feature type. The order of each column is arranged according to the acquisition time of the waveforms. The waveforms represented by the left column have an acquisition time earlier than the waveforms represented by the right column.

3. The method for identifying hidden discharge hazards in transmission lines as described in claim 2, characterized in that: The hazardous discharge event data includes acquiring a set of hazardous discharge event data for transmission lines, W = [W1, W2, ..., W...]. m ], where 1 to m are the data numbers, the time range covered by the data includes the time range covered by the discharge waveform data, and the known hidden events within the time corresponding to the waveform are: W1 is the discharge caused by the accumulation of dirt on the insulator; W2 is the discharge caused by the burning of hanging objects and the discharge caused by excessive vegetation; W3 is the fault trip caused by the deterioration and failure of the insulator; W4 is the power supply restored by the patrol personnel after clearing the fault. The severity level sequence data of the hidden danger includes setting a quantification value E for each known hidden danger event according to the actual line operation and maintenance needs, with a range of 0-4, and setting the level range to 0, 1, 2, 3: E=0 indicates that there is no hidden danger discharge phenomenon; E=1 indicates that monitoring of minor discharge is maintained; E=2 indicates that the discharge level is moderate, which will cause flashover tripping. Local inspection should be carried out during line inspection. E=3 indicates a very serious situation, which has caused a fault trip and requires immediate proactive maintenance. W1 indicates that the accumulation of dirt on the insulator has begun to trigger discharge, which is an early and minor hidden danger, and E1 = 1. W2 indicates that the discharge is more intense due to the burning of the hanging objects and the excessive vegetation causing the discharge, which leads to a fault under long-term conditions. E2 = 2. W3 indicates a fault trip caused by insulator deterioration and failure. It has already tripped and must be repaired immediately. E3 = 3. W4 indicates that the patrol personnel cleared the fault and restored power, meaning the potential hazard was eliminated and no incident occurred; E4 = 0. The resulting hazard level sequence is E = [1, 2, 3, 0].

4. The method for identifying hidden discharge hazards in transmission lines as described in claim 3, characterized in that: The acquisition and processing of transmission line hazard discharge event data includes assessing the activity of hazard events based on the discharge interval time using the system identification model: Where n represents the number of discharge events occurring within the assessment period, E is the severity level of the potential hazard, and λ j t is the decay rate of the j-th discharge event, t1 and t2 are the start and end times of the evaluation period, respectively. j Let t' be the time of the j-th discharge event, Φ represent the normalized difference in discharge interval time, and Δ(t') represent the discharge interval time at time t'. σ represents the average value of the discharge interval time. Δ The standard deviation of the discharge interval time; When the Φ value is stable between 0.45 and 0.55, it indicates that the frequency and severity of discharge events are in line with the average level, and monitoring should be maintained. When the Φ value is greater than 0.55, it indicates that the discharge interval is longer than the average time, and a flashing green light indicates that the activity of potential hazards has decreased. When the Φ value is less than 0.45, it indicates that the discharge interval is shorter than the average time, and an A-level alarm is issued to indicate that the activity of the current potential hazard event is enhanced. When the value of F(t) decreases, the discharge events occur but the frequency decreases, indicating that the long-term accumulation of current hidden dangers will cause transmission line faults. A Level B alarm is issued to identify the hidden danger and notify staff to carry out maintenance and inspection work. When the value of F(t) increases, the frequent occurrence of discharge events indicates that the current hidden danger event will cause a transmission line fault in the short term. A level D alarm is issued, and the current hidden danger event needs to be identified and maintenance work is carried out immediately.

5. The method for identifying hidden discharge hazards in transmission lines as described in claim 4, characterized in that: The rules for establishing the hazard identification requirement table are as follows: it consists of a rows and b columns, where a is the number of feature intervals obtained from the hazard discharge waveform data, b is the number of discharge severity levels obtained from the hazard discharge event data, and the initial value of all elements is the level value E corresponding to the column. This indicates that when a certain feature of the waveform belongs to the feature interval represented by a, the current discharge severity level is b, representing the maintenance requirement. The column represented by the maximum value of the current row represents the maintenance action most needed for the current feature interval. When F>1, if the same waveform has multiple features, the maximum values ​​of each row are compared together, and the maximum value is taken as the maintenance requirement of the current waveform. Based on the feature interval matrix C, obtain the earliest short waveform u and the waveform v of the next adjacent time. Combined with the hazard identification requirement table, determine the feature interval value S of the i-th feature of the u-th waveform. i (u) represents the row, while the Eu value corresponding to the waveform represents the column. In the table, the Sth column... i The element in row (u) and column Eu represents the maintenance requirement level corresponding to the discharge level Eu for the i-th feature of the waveform. Let the table element be T(S). i (u),Eu); For each feature i, update table element T'(S) i (u),Eu): T'(S i (u),Eu)=0.9×T(S i (u),Eu)+0.8×(Ev-T(S i (u),Eu)) Wherein, 0.9 represents the guiding role of the same historically similar hazard discharge characteristics in marking the severity of newly added hazard discharges, and 0.8 represents the predictive significance of future hazard discharges in the same discharge waveform for marking the current severity. Following the order of acquisition time, the table elements are updated sequentially for each waveform and the next waveform at the adjacent time, until all waveforms have been processed and the table content is filled.

6. The method for identifying hidden discharge hazards in transmission lines as described in claim 5, characterized in that: The comprehensive assessment and early warning of potential hazards along the line includes calculating the current hazard value Hd: A higher Hd value indicates a more serious hidden danger, while a lower Hd value indicates a less serious hidden danger. Here, B represents the discharge amplitude, and d... m The waveform shape is represented by M, the number of waveform parameters is represented by α and β, which are parameters that are adjusted according to the actual situation and are used to control the intensity of exponential decay and information filtering. Norm represents normalizing the summation result, and Filter is used to filter the waveform shape parameters. To predict that long-term accumulation of faults will cause transmission line faults, a comprehensive assessment of the transmission line's condition is conducted. A larger P value indicates a more severe hidden danger condition of the transmission line. Here, P is the comprehensive judgment value of the hidden danger condition of the transmission line at time t, t3 is the initial time of the prediction period, t4 is the end time of the prediction period, and λ... Hd Hd is the decay coefficient representing the rate at which a hidden danger decays over time, K is the number of characteristic values ​​of the hidden danger, and Hd is the decay coefficient representing the rate at which a hidden danger decays over time. k Let σ be the characteristic value of the i-th hidden danger. k Let μ be the standard deviation of the i-th hazard characteristic value. k Let be the mean of the i-th hidden danger characteristic value.

7. The method for identifying hidden discharge hazards in transmission lines as described in claim 6, characterized in that: The comprehensive state judgment includes, when the system acquires a new high-frequency discharge waveform, obtaining a new potential hazard discharge waveform y, extracting F features, and converting them into F feature interval values ​​S1(y), S2(y), ..., S F (y), according to the rules of the hazard identification requirement table, look up the maximum value in the row containing the characteristic interval and compare it to find the maximum value. The E value of the discharge severity level corresponding to the column containing the maximum value is the severity level represented by the new hazard discharge waveform. This is then combined with the overall condition of the transmission line for judgment: When the new quantitative value E is less than 1 and the P value decreases, it indicates that the current risk level is low and the risk is decreasing over a long period of time. Therefore, monitoring should be maintained and regular maintenance should be performed. When the new quantification value E is less than 1 and the P value increases, it indicates that the current hidden danger is minor, but it shows a long-term increasing trend. It is believed that no fault will occur within the prediction period, and the fault period will be delayed. A level B alarm is used for local detection and local maintenance. When the new quantitative value E is greater than 1 and the value P decreases, it indicates that the current hidden danger is serious, but the long-term trend is decreasing. It is believed that the transmission line will fail within the prediction period. A level D alarm is adopted, and maintenance is carried out immediately and the long-term trend is re-predicted until the hidden danger event shows a decreasing trend. When the new quantitative value E is greater than 1 and the value P increases, it indicates that the current hidden danger is serious and has a long-term increasing trend. It is believed that the transmission line will fail before the predicted period, so a level G alarm is adopted, and work is immediately stopped and maintenance measures are taken to intervene.

8. A system employing the method for identifying hidden discharge hazards in transmission lines as described in any one of claims 1 to 7, characterized in that: It includes a data acquisition and processing module, a hazard identification requirements table module, and an early warning and status assessment module; The data acquisition and processing module is responsible for acquiring measured data of hidden discharge hazards in transmission lines, including high-frequency discharge waveform data and discharge event data of hidden hazards. It processes the high-frequency discharge waveform data of hidden hazards, extracts key features, and establishes a feature interval matrix. It processes the discharge event data of hidden hazards and establishes a sequence of hazard severity levels. The hidden danger identification requirement table module establishes a hidden danger identification requirement table based on the feature interval matrix and the hidden danger severity level sequence, updates all elements in the table in chronological order, acquires the maintenance requirement level corresponding to the new discharge waveform, and performs a comprehensive judgment and early warning of the hidden danger status of the line. The early warning and status assessment module assesses the activity of potential hazards based on the system identification model and discharge interval time, calculates the current hazard value and the comprehensive status judgment value of the transmission line, and issues early warnings of different levels based on the assessment results, and takes corresponding maintenance and repair measures.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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