Overhead line fault positioning method based on big data

Abstract

The invention discloses an overhead line fault positioning method based on big data, and relates to the technical field of fault detection, and the method employs the big data real-time collection and intelligent data processing technology as a whole, and achieves the fault positioning of an overhead line through a multi-level risk judgment and data fusion algorithm. Key parameters such as tension and inclination angles of the tension-resistant rod and the overhead line are dynamically monitored, equipment abnormity can be found in the initial stage, slight deviation and serious risks can be accurately distinguished, and therefore preventive maintenance and fault early warning are achieved; meanwhile, the method achieves the dynamic monitoring of the stress state of the tension rod through the real-time collection of the tension data and the inclination angle of the two sides of the tension rod, and the adoption of the technologies of difference calculation, continuous monitoring, grading risk judgment and the like, and achieves the monitoring of the tension state of the tension rod through the comprehensive detection and data trend analysis of the tension parameters of the overhead line. And the judgment accuracy of the fault of the strain rod 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 forces from causing the pole to be uprooted or displaced, 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 strain pole faults, and it is difficult to accurately identify the early signs of faults, resulting in potential risks being ignored, thereby triggering large-scale fault accidents and seriously affecting the safe operation of the power grid; 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 intelligence 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 cannot timely judge and give early warnings about the abnormalities generated by strain poles in harsh environments. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a method for fault location of overhead lines based on big data, which solves the problems in the background technique.

[0005] 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: 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 first-level bilateral tension risk, second-level bilateral tension risk, and third-level bilateral tension risk; Step 2: Conduct real-time inclination angle detection on the strain pole, and judge whether the strain pole is determined to be an inclined strain pole according to the inclination angle of the strain pole; Step 3: If there are tension poles determined as level-three bilateral tension risks in Step 1 and tension poles determined as inclination angle risk tension poles in Step 2, mark the tension poles to generate tension pole fault information; Step 4: Detect overhead line faults for the tension poles determined as level-one bilateral tension risks and level-two bilateral tension risks in Step 1, and determine whether to generate tension pole fault information; Step 5: Receive the generated tension pole fault signal, determine the specific location of the tension pole according to the tension pole fault signal, and notify the staff for maintenance.

[0006] As a further solution of the present invention: In Step 1, the specific method for determining the bilateral tension risk level is as follows: AS1: Real-time obtain the tension data on both sides of the tension pole, and respectively mark them as , , where represents the current moment, and then calculate the absolute value of the difference between the two ; AS2: Make a real-time judgment on the calculated each time, and compare with the preset threshold Ys: If , it indicates that the tension data on both sides are not very different, and subsequent monitoring and judgment are carried out; If , it indicates that the tension data on both sides are quite different, generate an abnormal signal, and carry out subsequent monitoring and judgment; 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 , it is determined that the tension pole has a stress risk. In this case, the tension pole is determined as a level-one bilateral tension risk; in other cases, no treatment is done; where is a preset value; AS4: When the tension pole is determined as a level-one bilateral tension risk, determine whether the tension data on both sides of the tension pole each time exceeds the tension limit value : If or , in this case, the tension pole is determined as a level-two bilateral tension risk; where the tension limit value represents the maximum tension that the guy wires on both sides of the tension pole can withstand; AS5: Then set a preset duration , when the strain pole is determined to be at the first-level bilateral tension risk or the second-level bilateral tension risk, if it does not return to the reasonable value range within the preset time period, the strain pole is determined to be at the third-level bilateral tension risk.

[0007] 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 time period is as follows: When the strain pole is determined to be at the first-level bilateral tension risk, the absolute value of the difference between the tension data on both sides of the strain pole continuously exceeds the preset threshold ; When the strain pole is determined to be at the second-level bilateral tension risk, the tension data on both sides of the strain pole continuously exceeds the tension limit value .

[0008] As a further solution of the present invention: in the second step, the specific method for performing real-time tilt angle detection on the strain pole and determining whether the strain pole is a tilted strain pole according to the tilt angle of the strain pole is as follows: 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 safety tilt angle threshold : If , it means that the real-time tilt angle exceeds the safety tilt angle threshold , then the strain pole is determined to be 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 to be a normal strain pole.

[0009] As a further solution of the present invention: in the fourth step, the specific method for detecting the overhead line fault of the strain poles determined to be at the first-level bilateral tension risk and the second-level bilateral tension risk in the first step and judging whether a 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 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 ; 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 line on the strain pole within the preset time , and mark it as , calculate the ratio of to and mark it as : ; ; Among them, the preset time means the time in the past 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 the weight coefficient or according to the comparison result, and determine the tension risk assessment value ; CS5: Judge whether to generate the strain pole fault information according to the tension risk assessment value : If , then mark the strain pole and generate the strain pole fault information; If , do nothing; Among them, is the preset value.

[0010] As a further solution of the present invention: In the step CS4, the specific method for determining the tension risk assessment value is: If , generate the weight coefficient , obtain , calculate the result of to obtain the tension risk assessment value of the overhead line; If , generate the weight coefficient ; Obtain , calculate the result of to obtain the tension risk assessment value of the overhead line; Among them, the weight coefficients , are preset values.

[0011] 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: When generating the strain pole fault signal, the number or mark of the strain pole is attached, and the specific position where the strain pole is located is judged through the number or mark in the generated strain pole fault signal.

[0012] The present invention provides a method for fault location of overhead lines based on big data. Compared with the prior art, it has the following beneficial effects: As a whole, the invention utilizes big data real-time acquisition and intelligent data processing technology. Through multi-level risk judgment and data fusion algorithms, it dynamically monitors key parameters such as the tension and inclination angle of the strain pole and the overhead line, can detect equipment anomalies 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 the 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 phenomena caused by environmental interference, strong wind or equipment aging, accurately distinguish minor deviations from serious risks, lay a solid data foundation for judging the abnormal faults of the strain pole, and effectively reduce the risk of misjudgment.

[0013] In addition, through the comprehensive detection of the tension parameters of the overhead line and the analysis of the data trend, the accuracy of judging the faults of the strain pole is further enhanced. By combining the real-time detection results with key indicators such as preset safety thresholds and tension limit values, fault information is generated in a timely manner, providing intuitive and quantitative risk assessment and fault warning, thereby improving the fault location efficiency and maintenance response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be further described below with reference to the accompanying drawings.

[0015] Figure 1 is the step flow chart of a method for fault location of overhead lines based on big data according to the present invention; Figure 2 is the schematic diagram of generating the fault information of the strain pole of a method for fault location of overhead lines based on big data according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to 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. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0017] Embodiment 1 Please refer to Figure 1 - Figure 2 , the present invention provides a method for fault location of overhead lines based on big data, 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. 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; It should be noted that the stable installation of the strain pole is equipped with guy wires on both sides, which fix the pole from two directions to make its force balanced and not generate uplift or offset. The acquisition of the tension data 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; The specific method for determining the bilateral tension risk level is: 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 ; AS2: Make a real-time judgment on each calculated , and compare with the preset threshold Ys: If , it indicates that the tension data on both sides is not much different, and subsequent monitoring and judgment are carried out; If , it indicates that the tension data on both sides is relatively large, generate an abnormal signal, and carry out subsequent monitoring and judgment; Among them, the specific value of the preset threshold is determined by professional staff; AS3: When an abnormal signal is generated, conduct subsequent monitoring to judge whether the difference in the tension data on both sides is continuous times exceeds the preset threshold . If it continuously times exceeds the preset threshold , then judge that the strain pole has a force risk. In this case, determine the strain pole as the first-level bilateral tension risk; in other cases, do not do anything; among them, is a preset value, which is specifically determined by professional staff; It should be noted that when real-time collecting the tension data on both sides of the strain pole, it is generally set to collect data at regular intervals. Calculate the difference of the bilateral tension data collected each time, and compare the calculated result with the preset threshold Compare. If it exceeds the preset threshold It indicates that the tension data on both sides differ significantly. In this case, the consequences may be caused by environmental influences. For example, in the case of strong winds, then continuously judge the difference between the subsequent bilateral tension data and the preset threshold If it continuously or persistently exceeds the preset threshold It indicates that there are some uncertain factors in the strain pole that cause uneven tension on both sides; AS4: When the strain pole is determined to be at the first-level bilateral tension risk, judge whether the tension data on both sides of the strain pole each time exceeds the tension limit value : If or , in this case, the strain pole is determined to be at the second-level bilateral tension risk; among them, the tension limit value 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; AS5: Then set a preset duration . When the strain pole is determined to be at the first-level bilateral tension risk or the 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 the third-level bilateral tension risk; The judgment that it does not return to the reasonable value range within the preset duration is as follows: When the strain pole is determined to be at the first-level bilateral tension risk, the absolute value of the difference between the tension data on both sides of the strain pole continuously exceeds the preset threshold ; When the strain pole is determined to be at the second-level bilateral tension risk, the tension data on both sides of the strain pole continuously exceed the tension limit value ; This step continuously compares the tension data collected in real time on both sides of the strain pole with the preset threshold by using the data difference, so as to timely judge the uneven stress phenomenon caused by factors such as strong winds, environmental interference or equipment aging; by using the continuous monitoring and hierarchical (first-level, second-level, third-level) risk assessment method, it can not only detect abnormal states in the first time, but also effectively distinguish minor deviations and serious stress risks, provide a scientific basis for subsequent maintenance measures, significantly reduce the safety hazards caused by uneven equipment stress, and enhance the early warning ability of the entire monitoring system; Step two: Detect the real-time inclination angle of the strain pole, and judge whether the strain pole is determined to be an inclined strain pole according to the inclination angle of the strain pole; The specific method for performing real-time tilt angle detection on the strain pole and determining whether the strain pole is a tilted strain pole according to the tilt angle of the strain pole is as follows: BS1: Set the safety tilt angle threshold ; BS2: Obtain the real-time tilt angle of the strain pole through an inertial measurement unit (IMU), a high-precision inclinometer, and a data fusion algorithm ; It should be noted that obtaining the real-time tilt angle of the strain pole 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: Step 1: Sensor selection and installation; Selection: Install a high-precision inertial measurement unit (IMU) at the key parts of the strain pole. This sensor integrates an accelerometer, a gyroscope, and a magnetometer, and can measure three-dimensional tilt angles and dynamic motion data in real time; Installation: Fix the IMU on the strain pole to ensure that the installation position of the sensor is representative (for example, both the pole top and the middle section of the pole can be arranged), and at the same time, pre-calibrate the sensor to ensure that the initial state is zero bias; Step 2: Benchmark state data acquisition and environmental adaptability adjustment; Collect benchmark data: Under normal and stable conditions, collect tilt data for a period of time to establish the initial reference state of the strain pole and form a zero-tilt benchmark; Environmental compensation: Synchronously collect environmental parameters such as temperature and humidity, and automatically compensate for errors caused by temperature changes or other external interferences; Step 3: Real-time data acquisition and transmission; Data acquisition: The sensor collects the tilt angle and dynamic change data of the strain pole at a high frequency (for example, sampling several times per second); Data transmission: Use a wireless communication module to send the real-time data to the data processing center or local edge computing device to ensure the minimum delay and stable transmission; Step 4: Data preprocessing and filtering; Noise filtering: Adopt a digital filtering algorithm (such as Kalman filtering or low-pass filtering) to remove random noise and short-term jitter in the sensor data and obtain a smooth and accurate tilt angle signal; Abnormal data rejection: Set a reasonable abnormal data detection mechanism to reject abnormal values caused by instantaneous interference or sensor errors to prevent misjudgment; Step 5: Data fusion and dynamic tilt angle calculation; Fusion algorithm: Combine the data of each sensor inside the IMU, and through a data fusion algorithm (such as extended Kalman filtering), calculate the real-time tilt angles of the strain pole in three directions and extract the main tilt direction; 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 tiny long-term offset trends; BS3: Compare the real-time tilt angle with the safety tilt angle threshold as follows: If , it means that the real-time tilt angle exceeds the safety tilt angle threshold , then the strain pole is determined as an inclined 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; 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 tiny tilt changes by setting the safety tilt angle threshold; through preprocessing, filtering, and environmental compensation of the sensor data, it enables accurate identification of whether there is abnormal tilt of the strain pole, 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; 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; 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 maintenance efficiency; 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 strain pole fault information is generated; 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 and judging 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; It should be noted that the tension parameters of each overhead line on the strain pole are obtained by installing tension sensors at appropriate positions on each overhead line; CS2: Determine the relevant parameter points in the two-dimensional coordinate system according to the recorded results, 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: Obtain the number of parameter points on the change curve that exceed the limit horizontal line within the preset time and mark it as . 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 tensile parameters of the overhead line on the strain pole within the preset time , and mark it as . Calculate the ratio of to and mark it as : ; ; Among them, the preset time means the time within the past starting from the current moment; 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 risk assessment value : If , generate a weight coefficient , obtain , calculate the result of to get the tensile risk assessment value of the overhead line; If , generate a weight coefficient ; obtain , calculate the result of to get the tensile risk assessment value of the overhead line; Among them, the weight coefficients , are determined in advance by the staff; CS5: Judge whether to generate strain pole fault information according to the tensile risk assessment value : If , then mark the strain pole and generate strain pole fault information; If When it is, no processing is performed; Among them, is a preset value, which is specifically determined by professional staff; For the strain poles determined to have 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 is a continuous abnormal situation 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; 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.

[0018] Embodiment 2 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 explains Step 5: When generating the strain pole fault signal, attach the number or mark of the strain pole. In the prior art, the specific position of the 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 the fault point can be quickly determined according to the generated strain pole fault signal; Embodiment 3 In the specific implementation process of this embodiment, it includes all the implementation processes of the above two groups of embodiments.

[0019] Some of the data in the above formula 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.

[0020] The above embodiments are only used to illustrate the technical method of the present invention and not 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 locating overhead line faults based on big data, characterized in that: include: Step 1: collect the tension data on both sides of the tension rod in real time, and determine the risk level of the tension on both sides by analyzing the tension data on both sides. The risk level of the tension on both sides includes level 1, level 2 and level 3. Step 2: Perform real-time tilt angle detection on the tension rod, and determine whether the tension rod is a tilted tension rod according to the tilt angle of the tension rod; Step 3: If there is a tension rod determined as a third-level double-side tension risk in step 1 and a tension rod determined as a tilt angle risk in step 2, the tension rod is marked and tension rod fault information is generated; Step 4: For the tension poles determined as the first-level double-side tension risk and the second-level double-side tension risk in step 1, an overhead line fault detection is performed to determine whether tension pole fault information is generated; Step 5: Receive the generated tension rod fault signal, determine the specific position of the tension rod according to the tension rod fault signal, and notify the staff to perform maintenance.

2. The method for locating overhead line faults based on big data according to claim 1, characterized in that: In step 1, the specific method of determining the risk level of bilateral tension is: AS1: Real-time acquisition of the tension data on both sides of the tension rod, and marked them as , ,in, Represents the current moment, and then calculates the absolute value of the difference between the two ; AS2: For each calculation Make real-time judgments Compare with the preset threshold Ys: like , it indicates that the tension data on both sides are not much different, and subsequent monitoring and judgment are carried out; like , it indicates that the tension data on both sides differ greatly, generating an abnormal signal and conducting subsequent monitoring and judgment; 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 exceeds the preset threshold , then the tension rod is judged to have stress risk, in which case the tension rod is determined to be a first-level double-sided tension risk; in other cases, no treatment is performed; among them, is the default value; 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 : 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 is expressed as the maximum tension that the tension wires on both sides of the tension rod can withstand; AS5: Then set the preset duration , when the tension rod is determined to be at the first level of double-sided tension risk or the second level of double-sided tension risk, if within the preset time If the value fails to recover to a reasonable range within a certain period of time, the tension rod will be determined to be at level 3 double-sided tension risk.

3. The method for locating overhead line faults based on big data according to claim 2, characterized in that: In step AS5, the If the value does not return to a reasonable range within the time, the judgment is: When the tension rod is determined to be at the first level of double-sided tension risk, the absolute value of the difference between the tension data on both sides of the tension rod is continuously greater than the preset threshold. ; When the tension rod is determined to be at the secondary double-sided tension risk, the tension data on both sides of the tension rod Continuously exceeding the tension limit .

4. The method for locating overhead line faults based on big data according to claim 1, characterized in that: In the step 2, the specific method of performing real-time tilt angle detection on the tension rod and judging whether the tension rod is a tilted tension rod according to the tilt angle of the tension rod is: BS1: Setting safe tilt angle threshold ; BS2: The real-time tilt angle of the tension rod is obtained through an inertial measurement unit, a high-precision inclinometer and a data fusion algorithm. ; BS3: Set real-time tilt angle and safe tilt angle threshold For comparison: like , indicating that the real-time tilt angle exceeds the safe tilt angle threshold , then the tension rod is determined as an inclined tension rod; like , indicating that the real-time tilt angle does not exceed the safe tilt angle threshold , the tension rod is determined as a normal tension rod.

5. The method for locating overhead line faults based on big data according to claim 1, characterized in that: In the step 4, the specific method of detecting the overhead line fault for the tension rod determined as the first-level double-side tension risk and the second-level double-side tension risk in the step 1 and determining whether to generate the tension rod fault information is as follows; CS1: collects the tension parameters of each overhead line on the tension rod in real time and records 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, and form a tension parameter change curve. The horizontal axis of the change curve in the two-dimensional coordinate system is the time line, and the vertical axis is the specific tension parameter value; then establish a tension 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 pole are collected in real time and marked as ,calculate and The ratio is marked as : ; The preset time Indicates that from the current moment, the past within the time; CS4: Then calculate the preset time The average value of all parameter points within ,Will With the default value Compare and generate weight coefficients based on the comparison results or , determine the tensile risk assessment value ; CS5: Risk assessment based on tension Determine whether to generate tension rod fault information: like When the tension rod is faulty, the tension rod is marked and the tension rod fault information is generated; like No processing is done when in, is the default value.

6. The method for locating overhead line faults based on big data according to claim 5, characterized in that: In step CS4, the specific method of determining the tensile risk assessment value is: like , generating weight coefficients , get ,calculate The result is the tensile risk assessment value of the overhead line. ; like , generating weight coefficients ; Get ,calculate The result is the tensile risk assessment value of the overhead line. ; Among them, the weight coefficient , is the default value.

7. The method for locating overhead line faults based on big data according to claim 5, characterized in that: In the step 5, the content of determining the specific position of the tension rod according to the tension rod fault signal includes: When a tension rod fault signal is generated, the number or mark of the tension rod is attached, and the specific position of the tension rod is determined by the number or mark in the generated tension rod fault signal.

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