A Fault Location Method for Overhead Lines Based on Big Data

Abstract

The present invention discloses a method for fault location of overhead lines based on big data. The present invention relates to the technical field of fault detection. Overall, the invention utilizes big data real-time acquisition and intelligent data processing technologies. Through multi-level risk judgment and data fusion algorithms, key parameters such as the tension and inclination angle of strain poles and overhead lines are dynamically monitored, enabling the detection of equipment abnormalities at an early stage, accurately distinguishing minor deviations from serious risks, thereby achieving preventive maintenance and fault warning. At the same time, the method realizes dynamic monitoring of the stress state of strain poles by real-time collecting the tension data and inclination angles on both sides of the strain poles, and adopting technologies such as difference calculation, continuous monitoring, and hierarchical risk judgment. Through comprehensive detection of the tension parameters of overhead lines and data trend analysis, the accuracy of fault determination for strain poles is further enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of fault detection, and particularly to a method for fault location of overhead lines based on big data. Background Technique

[0002] At present, as an important part of power transmission, the safe operation of overhead lines is directly related to the stability of the entire power grid. The strain pole in the overhead line is a key load-bearing component in the overhead line, mainly used to ensure the force balance and stability of the pole and overhead conductors during the transmission process. By installing guy wires on both sides, it disperses and evenly distributes the external forces (such as force changes caused by strong winds, heavy rains, temperature differences, etc.), preventing uneven local stress from causing the pole to be pulled out or offset, thereby ensuring the overall structural stability of the overhead line. At the same time, the strain pole also plays an anchoring and guiding role in the power grid, ensuring the stable connection of each node during long-distance power transmission.

[0003] Traditional overhead line monitoring methods mainly rely on regular inspections and single-sensor data collection, and often it is difficult to reflect the stress state and inclination of key components such as strain poles in real time. Especially in harsh environments, such as under strong winds, heavy rains, high temperatures or low temperatures, the strain pole may experience abnormal conditions due to uneven stress, equipment aging or environmental interference. However, the existing technology has certain limitations in detecting faults of strain poles, and it is difficult to accurately identify the early signs of faults, resulting in potential risks being ignored, thus triggering large-scale fault accidents and seriously affecting the safe operation of the power grid;

[0004] In addition, with the rapid development of big data, the Internet of Things and intelligent algorithms, the requirements of the power grid monitoring system for real-time performance, accuracy and intelligent level are increasing day by day. Traditional methods not only have a low data collection frequency and insufficient monitoring accuracy, but also lack effective means for fault location of strain poles in overhead lines, and are unable to timely judge and give early warnings about the abnormalities generated by strain poles in harsh environments. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the present invention provides a method for fault location of overhead lines based on big data, which solves the problems in the background technique.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for fault location of overhead lines based on big data, including:

[0007] Step 1: Real-time collect the tension data on both sides of the strain pole, and determine the bilateral tension risk level by analyzing the tension data on both sides. The bilateral tension risk level includes the first-level bilateral tension risk, the second-level bilateral tension risk, and the third-level bilateral tension risk;

[0008] Step 2: Conduct real-time detection of the inclination angle of the strain pole. Based on the inclination angle of the strain pole, determine whether the strain pole is identified as an inclined strain pole.

[0009] Step 3: If there are strain poles determined to have a three-level bilateral tension risk in Step 1 and strain poles determined to have an inclination angle risk in Step 2, then mark the strain poles to generate strain pole fault information.

[0010] Step 4: For the strain poles determined to have a first-level bilateral tension risk and a second-level bilateral tension risk in Step 1, conduct detection of overhead line faults to determine whether strain pole fault information is generated.

[0011] Step 5: Receive the generated strain pole fault signal, determine the specific location of the strain pole based on the strain pole fault signal, and notify the staff for maintenance.

[0012] As a further solution of the present invention: In Step 1, the specific method for determining the bilateral tension risk level is as follows:

[0013] AS1: Real-time obtain the tension data on both sides of the strain pole and mark them as , , where represents the current moment, and then calculate the absolute value of the difference between the two ;

[0014] AS2: Conduct real-time judgment on each calculated , and compare with the preset threshold Ys:

[0015] If , it indicates that the tension data on both sides are not very different, and subsequent monitoring and judgment are carried out;

[0016] If , it indicates that the tension data on both sides are quite different, generate an abnormal signal, and carry out subsequent monitoring and judgment;

[0017] AS3: When an abnormal signal is generated, conduct subsequent monitoring to determine whether the difference in the tension data on both sides is continuous times exceeding the preset threshold , if it is continuous times exceeding the preset threshold , then judge that the strain pole has a force risk. In this case, determine the strain pole to have a first-level bilateral tension risk; in other cases, do not perform any processing; where is a preset value;

[0018] AS4: When the strain pole is determined to have a first-level bilateral tension risk, judge whether each tension data on both sides of the strain pole exceeds the tension limit value :

[0019] If or , in this case, the tension pole is determined to have a secondary bilateral tension risk; among them, the tension limit value represents the maximum tension that the guy wires on both sides of the tension pole can withstand;

[0020] AS5: Then set a preset duration . When the tension pole is determined to have a primary bilateral tension risk or a secondary bilateral tension risk, if within the preset duration time it does not return to the reasonable value range, then the tension pole is determined to have a tertiary bilateral tension risk.

[0021] As a further solution of the present invention: In step AS5, the judgment that it does not return to the reasonable value range within the preset duration time is as follows:

[0022] When the tension pole is determined to have a primary bilateral tension risk, the absolute value of the difference between the tension data on both sides of the tension pole continuously exceeds the preset threshold ;

[0023] When the tension pole is determined to have a secondary bilateral tension risk, the tension data on both sides of the tension pole continuously exceeds the tension limit value .

[0024] As a further solution of the present invention: In the second step, the specific method for performing real-time tilt angle detection on the tension pole and determining whether the tension pole is a tilted tension pole according to the tilt angle of the tension pole is as follows:

[0025] BS1: Set a safe tilt angle threshold ;

[0026] BS2: Obtain the real-time tilt angle of the tension pole through an inertial measurement unit, a high-precision inclinometer, and a data fusion algorithm ;

[0027] BS3: Compare the real-time tilt angle with the safe tilt angle threshold :

[0028] If , it means that the real-time tilt angle exceeds the safe tilt angle threshold , then the tension pole is determined to be a tilted tension pole;

[0029] If , it means that the real-time tilt angle does not exceed the safe tilt angle threshold , then the tension pole is determined to be a normal tension pole.

[0030] As a further solution of the present invention: in step four, for the strain poles determined as first-level bilateral tension risks and second-level bilateral tension risks in step one, the specific method for detecting overhead line faults and judging whether strain pole fault information is generated is as follows;

[0031] CS1: Real-time collect the tension parameters of each overhead line on the strain pole and record them in real time;

[0032] CS2: According to the recorded results, determine the relevant parameter points in the two-dimensional coordinate system, connect the relevant parameter points to form a tension parameter change curve. The abscissa of the change curve in the two-dimensional coordinate system is the time line, and the ordinate is the specific tension parameter value; then establish a tension limit horizontal line in the change curve ;

[0033] CS3: Obtain the number of parameter points where the change curve exceeds the limit horizontal line within the preset time and mark it as , and at the same time determine the total number of parameter points within the preset time , that is, the total number of times of real-time collecting the tension parameters of the overhead lines on the strain pole within the preset time , and mark it as , calculate the ratio of to and mark it as : ; ;

[0034] Among them, the preset time means the time within the past starting from the current moment;

[0035] CS4: Then calculate the average value of all the parameter points within the preset time , compare with the preset value , and determine the weight coefficient or generated according to the comparison result, and determine the tension risk assessment value ;

[0036] CS5: Judge whether to generate strain pole fault information according to the tension risk assessment value :

[0037] If , then mark the strain pole and generate strain pole fault information;

[0038] If , do not perform any processing;

[0039] Among them, is a preset value.

[0040] As a further solution of the present invention: in the step CS4, the specific method for determining the tension risk assessment value is:

[0041] If , generate a weight coefficient , obtain , calculate The result of to obtain the tension risk assessment value of the overhead line ;

[0042] If , generate a weight coefficient ; Obtain , calculate The result of to obtain the tension risk assessment value of the overhead line ;

[0043] Among them, the weight coefficients , are preset values.

[0044] As a further solution of the present invention: in the fifth step, the content of determining the specific position of the strain pole according to the strain pole fault signal includes:

[0045] When generating the strain pole fault signal, attach the number or mark of the strain pole, and judge the specific position where the strain pole is located through the number or mark in the generated strain pole fault signal.

[0046] The present invention provides a method for locating overhead line faults based on big data. Compared with the prior art, it has the following beneficial effects:

[0047] Overall, the invention utilizes big data real-time acquisition and intelligent data processing technology, through multi-level risk judgment and data fusion algorithms, dynamically monitors key parameters such as the tension and inclination angle of the strain pole and the overhead line, can detect equipment abnormalities in the initial stage, accurately distinguish minor deviations from serious risks, so as to achieve preventive maintenance and fault warning. At the same time, this method realizes dynamic monitoring of the stress state of the strain pole by real-time collecting the tension data and inclination angle on both sides of the strain pole, and adopting technologies such as difference calculation, continuous monitoring and hierarchical risk judgment. It can quickly identify the uneven tension and abnormal inclination caused by environmental interference, strong wind or equipment aging, accurately distinguish minor deviations from serious risks, lay a solid data foundation for judging abnormal faults of the strain pole, and effectively reduce the risk of misjudgment.

[0048] In addition, through the comprehensive detection of the tension parameters of the overhead line and the analysis of the data trend, the accuracy of the determination of the failure of the strain pole is further enhanced. By combining the real-time detection results with key indicators such as the preset safety threshold and the tension limit value, the failure information is generated in a timely manner, providing an intuitive and quantitative risk assessment and failure warning, thereby improving the failure location efficiency and the maintenance response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The present invention will be further described below in conjunction with the accompanying drawings.

[0050] Figure 1 It is a flowchart of the steps of a method for locating faults in an overhead line based on big data according to the present invention;

[0051] Figure 2 It is a schematic diagram of the generation of strain pole fault information of a method for locating faults in an overhead line based on big data according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0053] Embodiment 1

[0054] Please refer to Figure 1 - Figure 2 , the present invention provides a method for locating faults in an overhead line based on big data, including;

[0055] Step 1: Real-time collect the tension data on both sides of the strain pole. By analyzing the tension data on both sides, determine the bilateral tension risk level, and the bilateral tension risk level includes the first-level bilateral tension risk, the second-level bilateral tension risk, and the third-level bilateral tension risk;

[0056] It should be noted that the stable installation of the strain pole is equipped with guy wires on both sides, fixing the pole from two directions to make its force balanced and not generate uplift or deviation. The tension data collection on both sides of the strain pole is obtained by a tension sensor, and the tension data on both sides of the strain pole is obtained in real time by installing tension sensors on both sides of the strain pole;

[0057] The specific method for determining the bilateral tension risk level is:

[0058] AS1: Real-time obtain the tension data on both sides of the strain pole and mark them as , , where represents the current moment. By using the formula Calculate the absolute value of the difference between the two ;

[0059] AS2: For each calculation Make real-time judgments and Compare with the preset threshold Ys:

[0060] like , it indicates that the tension data on both sides are not much different, and subsequent monitoring and judgment are carried out;

[0061] like , it indicates that the tension data on both sides differ greatly, generating an abnormal signal and conducting subsequent monitoring and judgment;

[0062] Among them, the preset threshold The specific value is determined by professional staff;

[0063] AS3: When an abnormal signal is generated, follow-up monitoring is performed to determine whether the difference in the tension data on both sides is continuous times exceeding the preset threshold , if continuous times exceeding the preset threshold , then the tension rod is judged to have stress risk, in which case the tension rod is determined to have a first-level double-sided tension risk; in other cases, no action is taken; It is a preset value, which will be determined by professional staff;

[0064] It should be noted that when collecting the tension data on both sides of the tension rod in real time, it is generally set to collect data at intervals, perform difference calculation on the tension data on both sides collected each time, and compare the calculated result with the preset threshold value. Compare and if it exceeds the preset threshold This indicates that the difference between the tension data on both sides is too large. In this case, it may be caused by the influence of the environment, such as strong winds. Then continue to judge the difference between the subsequent tension data on both sides and the preset threshold. If the value exceeds the preset threshold continuously or continuously, When , it indicates that there are some uncertain factors in the tension rod that cause uneven tension on both sides;

[0065] AS4: When the tension rod is determined to be at the first level of double-sided tension risk, determine the tension data on both sides of the tension rod each time Whether the tension limit is exceeded :

[0066] like or In this case, the tension rod is determined to be at the secondary double-sided tension risk; among which, the tension limit value It can be expressed as the maximum tension that the guy wires on both sides of the strain pole can withstand, and the specific value of the tension limit value is specifically determined by professional staff;

[0067] AS5: Then set the preset duration , when the strain pole is determined to be at risk of first-level bilateral tension or second-level bilateral tension, if within the preset duration time, it does not return to the reasonable value range, then the strain pole is determined to be at risk of third-level bilateral tension;

[0068] During the preset duration time, the judgment that it does not return to the reasonable value range is:

[0069] When the strain pole is determined to be at risk of first-level bilateral tension, the absolute value of the difference between the tension data on both sides of the strain pole continuously exceeds the preset threshold ;

[0070] When the strain pole is determined to be at risk of second-level bilateral tension, the tension data on both sides of the strain pole continuously exceeds the tension limit value ;

[0071] This step continuously collects the tension data on both sides of the strain pole in real time and continuously compares the data difference with the preset threshold, so as to timely judge the uneven stress phenomenon caused by factors such as strong wind, environmental interference or equipment aging; using the risk assessment method of continuous monitoring and classification (first level, second level, third level), it can not only detect the abnormal state in the first time, but also effectively distinguish the slight deviation and serious stress risk, provide a scientific basis for the subsequent maintenance measures, significantly reduce the safety hazards caused by uneven equipment stress, and enhance the early warning ability of the entire monitoring system;

[0072] Step two: Detect the real-time tilt angle of the strain pole, and judge whether the strain pole is determined to be a tilted strain pole according to the tilt angle of the strain pole;

[0073] The specific method of detecting the real-time tilt angle of the strain pole and judging whether the strain pole is determined to be a tilted strain pole according to the tilt angle of the strain pole is as follows:

[0074] BS1: Set the safety tilt angle threshold ;

[0075] BS2: Obtain the real-time tilt angle of the strain pole through the inertial measurement unit (IMU), high-precision inclinometer and data fusion algorithm ;

[0076] It should be noted that obtaining the real-time tilt angle of the tension rod through an inertial measurement unit (IMU), a high-precision inclinometer, and a data fusion algorithm is an existing technology. The specific steps are as follows:

[0077] Step 1: Sensor selection and installation;

[0078] Model selection: Install high-precision inertial measurement units (IMUs) at key locations on the tension rods. This sensor integrates accelerometers, gyroscopes, and magnetometers to measure three-dimensional tilt angles and dynamic motion data in real time.

[0079] Installation: Fix the IMU on the tension pole, ensuring that the sensor is installed in a representative position (e.g., both the top and the middle of the pole can be installed). Pre-calibrate the sensor to ensure that the initial state is zero bias.

[0080] Step 2: Baseline data collection and environmental adaptability adjustment;

[0081] Collecting reference data: Under normal and stable conditions, collect tilt data over a period of time to establish the initial reference state of the tension rod and form a zero tilt reference;

[0082] Environmental compensation: Synchronously collect environmental parameters such as temperature and humidity, and automatically compensate for errors caused by temperature changes or other external interference;

[0083] Step 3: Real-time data collection and transmission;

[0084] Data acquisition: The sensor collects the tilt angle and dynamic change data of the tension rod at a high frequency (for example, several times per second);

[0085] Data transmission: Using wireless communication modules, real-time data is sent to the data processing center or local edge computing equipment to ensure minimal delay and stable transmission;

[0086] Step 4: Data preprocessing and filtering;

[0087] Noise filtering: Use digital filtering algorithms (such as Kalman filtering or low-pass filtering) to remove random noise and short-term jitter in sensor data to obtain a smooth and accurate tilt angle signal;

[0088] Abnormal data elimination: Set up a reasonable abnormal data detection mechanism to eliminate abnormal values caused by instantaneous interference or sensor errors to prevent misjudgment;

[0089] Step 5: Data fusion and dynamic tilt angle calculation;

[0090] Fusion algorithm: Combining the data from various sensors within the IMU, a data fusion algorithm (such as the extended Kalman filter) is used to calculate the real-time tilt angle of the tension rod in three directions and extract the main tilt direction;

[0091] Dynamic trend analysis: Conduct trend analysis on continuously collected data to judge the cumulative change and change rate of the tilt angle, and timely detect small long-term offset trends;

[0092] BS3: Compare the real-time tilt angle with the safety tilt angle threshold :

[0093] If , it means that the real-time tilt angle exceeds the safety tilt angle threshold , then determine the strain pole as a tilted strain pole;

[0094] If , it means that the real-time tilt angle does not exceed the safety tilt angle threshold , then determine the strain pole as a normal strain pole;

[0095] This step uses a high-precision inertial measurement unit (IMU), a high-precision inclinometer, and a data fusion algorithm to achieve real-time tilt angle measurement of the strain pole in three directions, and judges the small tilt changes by setting a safety tilt angle threshold; through preprocessing, filtering, and environmental compensation of the sensor data, it enables accurate identification of whether the strain pole has abnormal tilt, thus giving early warnings of possible structural risks, ensuring the stable state of the equipment during long-term operation, and reducing the risk of fracture or offset caused by tilt;

[0096] Step 3: If there are strain poles determined as having a three-level bilateral tension risk in Step 1 and strain poles determined as having a tilt angle risk in Step 2, then mark the strain poles and generate strain pole fault information;

[0097] In the first two steps, when it is detected that the strain pole has a three-level bilateral tension risk or an obvious tilt risk, this step will automatically mark the relevant strain poles and generate fault information; this automatic marking and fault information generation mechanism enables rapid narrowing of the fault troubleshooting scope and timely transmission of potential hazards to maintenance personnel, thus improving the emergency response speed and repair efficiency;

[0098] Step 4: For the strain poles determined as having a first-level bilateral tension risk and a second-level bilateral tension risk in Step 1, conduct detection of overhead line faults to judge whether to generate strain pole fault information;

[0099] The specific method for conducting detection of overhead line faults on the strain poles determined as having a first-level bilateral tension risk and a second-level bilateral tension risk in Step 1 to judge whether to generate strain pole fault information is;

[0100] CS1: Real-time collect the tension parameters of each overhead line on the strain pole and record them in real time;

[0101] It should be noted that the tensile force parameters of each overhead line on the strain pole are obtained by installing tensile sensors at appropriate positions on each overhead line;

[0102] CS2: According to the recorded results, determine the relevant parameter points in the two-dimensional coordinate system, connect the relevant parameter points to form a tensile force parameter change curve. The abscissa of the change curve in the two-dimensional coordinate system is the time line, and the ordinate is the specific tensile force parameter value; then establish a tensile force limit horizontal line in the change curve ;

[0103] CS3: Obtain the number of parameter points where the change curve exceeds the limit horizontal line within the preset time , mark it as , and at the same time determine the total number of parameter points within the preset time , that is, the total number of times of real-time collection of the tensile force parameters of the overhead line on the strain pole within the preset time , and mark it as , calculate and ratio and mark it as : ;

[0104] wherein, the preset time represents the time within the past from the current moment;

[0105] CS4: Then calculate the average value of all the parameter points within the preset time , compare with the preset value , and generate a weight coefficient or according to the comparison result, and determine the tensile force risk assessment value :

[0106] If , generate a weight coefficient , obtain , calculate result, and obtain the tensile force risk assessment value of the overhead line ;

[0107] If , generate a weight coefficient ; obtain , calculate result, and obtain the tensile force risk assessment value of the overhead line ;

[0108] Among them, the weight coefficients , are determined in advance by the staff;

[0109] CS5: According to the tension risk assessment value judge whether to generate strain pole fault information:

[0110] If at this time, then mark the strain pole and generate strain pole fault information;

[0111] If at this time, do not perform any processing;

[0112] Among them, is a preset value, which is specifically determined by professional staff;

[0113] For the strain poles determined to be of first-level and second-level tension risks in Step 1, by arranging tension sensors on each overhead line, the line tension parameters are collected in real time, and a parameter change curve is constructed in a two-dimensional coordinate system. Based on this, the tension limit level is determined; by counting the number of points exceeding the limit level within a preset time, calculating the parameter ratio, and applying the weight coefficient for comprehensive evaluation, this method can accurately judge whether there are continuous abnormal conditions in the overhead line, thereby providing a quantitative basis for fault warning, effectively reducing the risk of a larger-scale fault caused by the spread of local anomalies, and improving the overall safety prevention and control ability;

[0114] Step 5: Receive the generated strain pole fault signal, determine the specific position of the strain pole according to the strain pole fault signal, and notify the staff to perform maintenance.

[0115] Embodiment 2

[0116] In the specific implementation process of this embodiment, on the basis of Embodiment 1, and the difference from Embodiment 1 is that this embodiment further describes Step 5: When generating the strain pole fault signal, the number or mark of the strain pole is attached. In the prior art, the position of a specific strain pole can be judged through the number or mark in the generated strain pole fault signal. Generally, the staff in this field will determine the positions of each different strain pole in advance, and the strain pole with a fault point can be quickly determined according to the generated strain pole fault signal;

[0117] Embodiment 3

[0118] In the specific implementation process of this embodiment, it includes all the implementation processes of the above two groups of embodiments.

[0119] Some of the data in the above formulas are numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well known to those skilled in the art.

[0120] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for fault location of overhead lines based on big data, characterized in that, Including: Step 1: Real-time collect the tension data on both sides of the strain pole. By analyzing the tension data on both sides, determine the bilateral tension risk level, which includes the first-level bilateral tension risk, the second-level bilateral tension risk, and the third-level bilateral tension risk. Step 2: Conduct real-time detection of the tilt angle of the strain pole. Based on the tilt angle of the strain pole, determine whether the strain pole is identified as a tilted strain pole. Step 3: If there is a strain pole determined as a third-level bilateral tension risk in Step 1 and a strain pole determined as a tilt angle risk strain pole in Step 2, then mark the strain pole and generate strain pole fault information. Step 4: For the strain poles determined as the first-level bilateral tension risk and the second-level bilateral tension risk in Step 1, conduct detection of overhead line faults to determine whether strain pole fault information is generated. The specific method for determining whether strain pole fault information is generated is as follows: CS1: Real-time collect the tension parameters of each overhead line on the strain pole and record them in real time. CS2: According to the recorded results, determine the relevant parameter points in the two-dimensional coordinate system, connect the relevant parameter points to form a tensile parameter change curve. The abscissa of the change curve in the two-dimensional coordinate system is the time line, and the ordinate is the specific tensile parameter value; then establish a tensile limit horizontal line in the change curve ; CS3: Get the change curve at a preset time Exceeding the limit level The number of parameter points is marked as , and determine the preset time The total number of parameter points in the preset time The total number of times the tension parameters of the overhead line on the tension rod are collected in real time and marked as ,calculate and The ratio is marked as : ; Among them, the preset time means within the past time starting from the current moment; CS4: Then calculate the average value of all parameter points within the preset time ; Compare with the preset value , and generate a weight coefficient according to the comparison result or , and determine the pulling force risk assessment value ; CS5: According to the tensile risk assessment value Judge whether to generate strain pole fault information: If is true, mark the strain pole and generate strain pole fault information; If No action is taken wherein, is a preset value; Step 5: Receive the generated strain pole fault signal, determine the specific location of the strain pole according to the strain pole fault signal, and notify the staff for maintenance.

2. The method for fault location of overhead lines based on big data according to claim 1, wherein In Step 1, the specific method for determining the bilateral tension risk level is: AS1: Obtain the tension data on both sides of the strain pole in real time and mark them respectively as , , where represents the current moment, and then calculate the absolute value of the difference between the two ; AS2: For each calculation perform real-time judgment, and compare it with the preset threshold Ys: If , it indicates that the tensile force data on both sides are not very different, and subsequent monitoring and judgment are carried out; If , it indicates that the tensile force data on both sides differ significantly, generating an abnormal signal and performing subsequent monitoring and judgment; AS3: When an abnormal signal is generated, subsequent monitoring is carried out to determine whether the difference in the tension data on both sides is continuous times exceeding the preset threshold , if continuous times exceeding the preset threshold , it is determined that there is a risk of force on the strain pole. At this time, the strain pole is determined as a first-level bilateral tension risk; in other cases, no treatment is carried out; among them, is the preset value; AS4: When the tension pole is determined to have a first-level bilateral tension risk, judge the tension data on both sides of the tension pole each time whether it exceeds the tension limit value : If or , at this time, the tension pole is determined to have a secondary bilateral tension risk; among them, the tension limit value represents the maximum tension that the guy wires on both sides of the tension pole can withstand; AS5: Then set the preset duration , when the strain pole is determined to be at a first-level bilateral tension risk or a second-level bilateral tension risk, if it does not return to the reasonable value range within the preset duration time, then the strain pole is determined to be at a third-level bilateral tension risk.

3. The method for fault location of overhead lines based on big data according to claim 2, characterized in that In step AS5, the determination that the value has not returned to the reasonable value range within the preset duration time is as follows: When the tension pole is determined to have a first-level bilateral tension risk, the absolute value of the difference between the tension data on both sides of the tension pole continuously exceeds the preset threshold ; When the tension pole is determined to have a secondary bilateral tension risk, the tension data on both sides of the tension pole continuously exceeds the tension limit value .

4. A method for fault location of overhead lines based on big data according to claim 1, characterized in that In Step 2, the specific method for conducting real-time detection of the tilt angle of the strain pole and determining whether the strain pole is identified as a tilted strain pole based on the tilt angle of the strain pole is: BS1: Set the safety tilt angle threshold ; BS2: Obtain the real-time tilt angle of the strain pole through an inertial measurement unit, a high-precision inclinometer, and a data fusion algorithm ; BS3: Compare the real-time tilt angle with the safe tilt angle threshold as follows: If indicates that the real-time tilt angle exceeds the safe tilt angle threshold , then the strain pole is determined as a tilted strain pole; If , it means that the real-time tilt angle does not exceed the safety tilt angle threshold , then the strain pole is determined as a normal strain pole.

5. A method for fault location of overhead lines based on big data according to claim 1, characterized in that, In Step CS4, the specific method for determining the tension risk assessment value is: If , generate a weight coefficient , obtain , calculate 's result to obtain the tensile risk assessment value of the overhead line ; If , generate a weight coefficient ; Obtain , calculate 's result to obtain the tension risk assessment value of the overhead line ; Among them, the weight coefficients , are preset values.

6. The method for fault location of overhead lines based on big data according to claim 1, wherein In Step 5, the content of determining the specific location of the strain pole according to the strain pole fault signal includes: When generating the strain pole fault signal, attach the number or mark of the strain pole, and judge the specific location where the strain pole is located through the number or mark in the generated strain pole fault signal.

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