High-precision fault positioning method for power transmission overhead line

Abstract

The invention discloses a high-precision fault positioning method for a power transmission overhead line, and relates to the technical field of circuit fault positioning, and the method comprises the following specific steps: precise sensing and data collection: employing a non-intrusive voltage and current integrated sensing technology, installing a sensor on a power transmission overhead line of 220kV or below, and carrying out the current measurement, the sensor adopts a permalloy magnetic core technology for measurement, for voltage measurement and voltage measurement through a special electric field coupling structure, through comprehensive multi-physics field coupling analysis, complex physical phenomena and interaction in a line when a fault occurs can be more comprehensively considered, so that the positioning process not only depends on single electrical quantity data, but also depends on multi-physics field coupling analysis. Compared with the prior art, the method combines the change characteristics of various physical fields, thereby remarkably improving the fault positioning precision, and meanwhile, a dynamic error compensation mechanism can correct errors caused by the dynamic change of line parameters and environmental factors in real time, thereby further enhancing the positioning reliability.

Description

Technical Field

[0001] The invention relates to the technical field of line fault positioning, and in particular to a high-precision fault positioning method for a power transmission overhead line. Background Art

[0002] As an important part of the power system, overhead transmission lines undertake the key task of electric energy transmission. The stability and safety of their operating status are directly related to the reliable operation of the entire power system. With the continuous development of the power system and the increasing scale of the power grid, the fault location technology of overhead transmission lines is particularly important. Accurately and quickly locating the fault point can not only quickly restore power supply and reduce power outage losses, but also effectively ensure the safety of operation and maintenance personnel and improve the overall operation efficiency of the power system.

[0003] Traditional methods for locating faults in overhead transmission lines mainly rely on manual inspections and simple electrical quantity measurements. These methods have many shortcomings when faced with complex working conditions, dynamic changes in line parameters, and the mutual influence of multiple physical fields. Specifically, manual inspections are not only inefficient, but also difficult to detect hidden faults in the first place. Simple electrical quantity measurements are easily affected by factors such as environmental noise and line aging, resulting in low positioning accuracy and poor stability. In addition, traditional methods lack effective early warning and remote monitoring methods, which means that it often takes a long time for operation and maintenance personnel to arrive at the scene after a fault occurs, further prolonging the fault recovery time and posing a potential threat to the stable operation of the power system.

[0004] In view of the above problems, it is necessary to optimize the existing high-precision fault positioning method of overhead transmission lines, realize self-power supply and stable operation by customizing high-precision sensor elements and building data processing and transmission links, and comprehensively use multi-field fusion analysis, dynamic error compensation, intelligent optimization and adaptive adjustment and other technical means to improve the accuracy and stability of fault positioning. Therefore, it is of great significance to develop a high-precision fault positioning method for overhead transmission lines that can comprehensively realize the above characteristics. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and provide a high-precision fault positioning method for overhead transmission lines. It can achieve accurate positioning and timely warning of overhead transmission line faults by integrating multiple advanced technologies. Specifically, a non-invasive voltage and current integrated sensor is used, combined with Permalloy core technology, to improve the accuracy of current measurement. By constructing a multi-physical field coupling model and a dynamic error compensation model, the complex physical phenomena at the time of faults and the dynamic changes of line parameters are fully considered. At the same time, intelligent early warning and remote monitoring technology are introduced to achieve real-time monitoring and efficient management of line operation status, significantly improving the accuracy and timeliness of overhead transmission line fault positioning, and providing a strong guarantee for the stable operation of the power system.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for high-precision fault location of a power transmission overhead line, the method comprising the following specific steps:

[0007] Precise sensing and data acquisition: Adopting non-invasive voltage and current integrated sensing technology, sensors are installed on overhead transmission lines of 220kV and below. For current measurement, the sensor uses Permalloy core technology for measurement, and for voltage measurement, the voltage is measured through a special electric field coupling structure. The sensor collects voltage and current data in real time, and transmits data through "narrowband + broadband" networking communication technology;

[0008] Self-power supply guarantee and stable operation steps: Energy is obtained from the magnetic field of the transmission line through a magnetic induction power-taking device. The device consists of a magnetic core coil, an energy conversion circuit and a power regulation circuit. When current passes through the line, an induced electromotive force is generated according to the principle of electromagnetic induction. The induced electromotive force converts AC power into DC power through the energy conversion circuit to power subsequent equipment. The power regulation circuit adopts adaptive control technology to monitor the current size of the transmission line in real time and automatically adjust the power-taking parameters according to the current changes;

[0009] Multi-field fusion analysis and error correction: Based on the principles of electromagnetism, thermodynamics and solid mechanics, a physical field coupling model of electromagnetic field, temperature field and stress field is constructed when a transmission overhead line fails. For the electromagnetic field, the electromagnetic field distortion caused by the instantaneous increase of current at the fault point is considered, and the electromagnetic field distribution is described using the Maxwell equations. For the temperature field, the line temperature rise is calculated based on the thermal effect of the current, and the temperature field model is constructed following the heat conduction equation. For the stress field, the thermal expansion of the material and the electromagnetic force caused by temperature changes are considered, and the stress field model is established based on the theory of elastic mechanics. At the same time, the interaction relationship between the physical fields is established. Through experimental data collection and theoretical analysis, the relationship between temperature changes, line aging and environmental factors in different seasons and changes in line resistance, inductance and capacitance parameters is determined, and a dynamic error compensation model is established. The model input is the real-time monitored line environment data and operating time information, and the output is the corresponding line parameter correction value. The fault location algorithm is used to calculate the fault location by combining the multi-physical field coupling analysis results and the corrected line parameters;

[0010] Intelligent optimization and adaptive adjustment steps: Real-time monitoring of the error fluctuation during the positioning calculation process, and judging whether the error fluctuation exceeds the normal range by comparing the calculation results with the preset error threshold. At the same time, combined with the line operation data and meteorological data collected in real time, the analysis algorithm is used to identify the current line working conditions, and the relevant parameters in the positioning calculation are adaptively adjusted according to the error compensation strategies under different working conditions established in advance;

[0011] The integration steps of intelligent early warning and remote control are as follows: real-time collection and in-depth analysis of voltage and current data collected by sensors, multi-physical field monitoring data and fault location results, judging potential risks through fault prediction models, and establishing a remote monitoring platform. The fault location host and the monitoring center are connected through a communication network to receive and store data. It also has visualization functions, and operation and maintenance personnel can log in remotely for interactive operations.

[0012] Furthermore, in the precise sensing and data acquisition step, for current measurement, the sensor uses Permalloy core technology to measure, and the formula Calculate the measured current value, where I measured is the current value measured by the sensor and subsequent processing, I true is the real current value in the overhead transmission line, that is, the actual current size, μ0: vacuum permeability, μ p is the magnetic permeability of the Permalloy core, ΔB noise It is the change in magnetic induction intensity caused by external magnetic field noise interference during current measurement, ΔB signal It is the change in magnetic induction intensity generated by the real current and effectively sensed by the Permalloy core.

[0013] Furthermore, in the self-power supply guarantee and stable operation step, for voltage measurement, the voltage is measured through a special electric field coupling structure, and the calculation formula is: Among them, P out is the output power of the power-taking device, η is the energy conversion efficiency, μ π is the relative magnetic permeability of the core coil, N1 is the number of primary turns of the core coil, S is the cross-sectional area of ​​the core coil, l is the average length of the core coil, I line is the current in the transmission line, N2 is the number of secondary turns of the core coil, C r is the resonant capacitance value, L r is the equivalent inductance of the core coil, and ω is the angular frequency of the transmission line current.

[0014] Furthermore, in the multi-field fusion analysis and error correction step, for the electromagnetic field, the electromagnetic field distortion caused by the instantaneous increase of the current at the fault point is considered, and the electromagnetic field distribution is described using the Maxwell equations, and the calculation formula is: in, is the electric field strength vector, is the magnetic induction intensity vector, is the magnetic field strength vector, is the current density vector in the transmission line, is the electric displacement vector, ρ line is the charge density of the transmission line, ∈0 is the dielectric constant of vacuum, c0 is the speed of light in vacuum, and this formula describes the electromagnetic field distribution by associating electrical and magnetic related parameters.

[0015] Furthermore, in the multi-field fusion analysis and error correction step, for the temperature field, the line temperature rise is calculated according to the thermal effect of the current, and the temperature field model is constructed according to the heat conduction equation, and its calculation formula is: Among them, ρ m is the density of the circuit material, c m is the specific heat capacity of the circuit material, T is the temperature of the circuit, k m is the thermal conductivity of the circuit material, I line is the current in the transmission line, R line is the resistance of the transmission line, S line is the cross-sectional area of ​​the transmission line.

[0016] Furthermore, in the multi-field fusion analysis and error correction step, for the stress field, the thermal expansion of the material caused by temperature change and the electromagnetic force are considered, and a stress field model is established based on the elastic mechanics theory, and the model formula is: Among them, σ ij is the stress tensor, C ijklis the elastic constant tensor, ε kl is the strain tensor, μ0 is the vacuum permeability, μ r is the relative permeability of the line material, is the current density vector in the transmission line, S line is the cross-sectional area of ​​the transmission line, c m is the specific heat capacity of the circuit material.

[0017] Furthermore, in the multi-field fusion analysis and error correction step, the interaction relationship between the physical fields is established, and the relationship between temperature change, line aging and environmental factors in different seasons and changes in line resistance, inductance and capacitance parameters is determined through experimental data collection and theoretical analysis. The formula is expressed as: ΔR t =a1×ΔT+a2×Δt r +a3×ΔS e +a4×ΔI line , ΔL t =b1×ΔT+b2×Δt r +b3×ΔS e +b4×ΔI line , ΔC t =c1×ΔT+c2×Δt r +c3×ΔS e +c4×ΔI line , where ΔR t is the correction value of line resistance after considering temperature change, operating time and environmental factors. a1, a2, a3 and a4 are the influence coefficients of temperature change, operating time, seasonal environmental impact and transmission line current change on the resistance correction value. ΔT is the difference between the real-time monitored line ambient temperature and the initial temperature. Δt r is the difference between the operating time of the transmission line and the initial operating time, ΔS e The difference between the environmental factors caused by seasonal environmental impact and the initial environmental factors, ΔI line is the difference between the transmission line current and the initial current, ΔL t : Correction value of line inductance after considering the above factors, b1, b2, b3, b4: respectively the influence coefficients of temperature change, operating time, seasonal environmental impact, and transmission line current change on the inductance correction value, ΔC t : Correction value of line capacitance after considering the above factors, c1, c2, c3, c4: are the influence coefficients of temperature change, operating time, seasonal environmental impact, and transmission line current change on the capacitance correction value.

[0018] Furthermore, in the multi-field fusion analysis and error correction step, the multi-physical field coupling analysis results and the corrected line parameters are integrated to calculate the fault location through a fault location algorithm, and the formula of the fault location algorithm is: Among them, L f is the distance between the fault location and one end of the line, p is the number of physical field categories, λ k is the weight coefficient of each physical field category, n k is the number of relevant parameters under each physics category, μ ki is the weight coefficient of each parameter under each physical field category, ΔF ki is the variation of each parameter under each physical field category, m is the quantity related to the line parameters, ξ j is the weight coefficient of the line parameter, ΔP j is the change in line parameters after correction, L t is the total length of the transmission line, and ∈ is the correction term.

[0019] Furthermore, in the intelligent optimization and adaptive adjustment step, the error fluctuation in the positioning calculation process is monitored in real time, and by comparing the calculation result with the preset error threshold, it is determined whether the error fluctuation exceeds the normal range. The calculation formula of the error fluctuation is: At the same time, combined with the real-time collected line operation data and meteorological data, the analysis algorithm is used to identify the current line working conditions. The comprehensive indicators of the current line working conditions are: I c =w1×V c +w2×I c +w3×T c +w4×S w +w5×S h +w6×ΔE r , where E r is the error volatility, is the actual calculation error of the i-th dimension, is the preset error threshold of the i-th dimension, I c It is a comprehensive indicator used to intelligently identify the current line working conditions. w1, w2, w3, w4, w5, and w6 are weighted coefficients corresponding to voltage, current, temperature, wind speed, humidity, and error fluctuation rate, respectively. According to the error compensation strategy under different working conditions established in advance, the relevant parameters in the positioning calculation are adaptively adjusted. The optimization adjustment formula is: Among them, P l is the adjusted line inductance parameter, is the original line inductance parameter, k1 is the coefficient related to the inductance parameter adjustment, ΔI c It is the line condition change calculated based on the condition identification result. is the ratio of the error volatility to the preset error threshold, C i is the adjusted current weight coefficient, is the original current weight coefficient, k2 is the coefficient related to the current weight coefficient adjustment, ΔI c It is the line condition change calculated based on the condition identification result. It is the ratio of the error fluctuation rate to the preset error threshold, which is used to measure the degree of error fluctuation and adjust the current weight coefficient accordingly.

[0020] Furthermore, in the step of integrating intelligent early warning and remote control, potential risks are judged by a fault prediction model, and the model formula is: Among them, P f Represents the predicted probability of failure. When the probability reaches the threshold P MAX When , it is judged as a potential failure risk, α and β are weight coefficients related to the square error term and the logarithmic term, respectively, w i is the weight coefficient of the i-th eigenvalue, F i represents the i-th eigenvalue in the real-time monitoring data, F i 0 is the reference value corresponding to the ith eigenvalue under normal operating conditions, and σ is the standard deviation.

[0021] Compared with the prior art, this method for high-precision fault location of overhead transmission lines has the following beneficial effects:

[0022] 1. The present invention can more comprehensively consider the complex physical phenomena and their interactions in the line when a fault occurs through comprehensive multi-physical field coupling analysis, so that the positioning process not only relies on a single electrical quantity data, but combines the changing characteristics of multiple physical fields, thereby significantly improving the accuracy of fault location. At the same time, the dynamic error compensation mechanism can correct the errors caused by dynamic changes in line parameters and environmental factors in real time, further enhancing the reliability of positioning.

[0023] 2. The present invention can adapt to different line conditions and environmental conditions through multi-field fusion analysis and error correction technology. The dynamic error compensation model can learn and adapt to the changing patterns of line parameters with factors such as temperature, humidity, and season, thereby providing accurate line parameter correction values ​​under different conditions. It not only improves the performance of the positioning system in various complex environments, but also enhances its robustness. Moreover, by continuously optimizing model parameters and algorithms, it can continuously adapt to changes in line status, providing strong support for the safe and reliable operation of transmission lines.

[0024] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 A flow chart of a method for high-precision fault location of a power transmission overhead line;

[0027] Figure 2 The present invention is a flow chart of a method for high-precision fault location of a power transmission overhead line. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Embodiment 1

[0030] This embodiment describes in detail a specific application of a high-precision fault positioning method for overhead transmission lines in the field of fault positioning and monitoring of transmission lines in mountainous areas. Through the present invention, high-precision fault positioning, intelligent operation and maintenance management, and timely and effective remote monitoring are achieved, providing a solid guarantee for the stable operation of transmission lines in mountainous areas.

[0031] In the stage of precise sensing and data collection, a non-invasive voltage and current integrated sensor was installed on a 110kV overhead power transmission line in a mountainous area according to the design requirements. Considering the strong magnetic field interference that may exist in the mountainous environment, a Permalloy core with high magnetic permeability was specially selected as the core sensing component for current measurement. The sensor was installed in a position that is convenient for obtaining the line current magnetic field information and can avoid being touched by foreign objects such as branches. After a heavy rain, the humidity around the line increased significantly and there may be local magnetic field fluctuations. The sensor accurately collected the voltage and current data of the line, where the current measurement is performed through the formula Even under complex environmental interference, the measurement accuracy can still be guaranteed to be within 3%. The collected weak signals are amplified by the preamplifier and the low-pass filter is used to filter out the high-frequency noise interference that may be caused by heavy rain. Then the analog-to-digital conversion is performed, and finally the data is transmitted to the fault location host at the foot of the mountain with the help of "narrowband + broadband" networking communication technology. Narrowband communication gives priority to transmitting key fault feature data such as current mutation values, while broadband communication completely transmits a large amount of monitoring data and equipment status information.

[0032] During the self-power supply and stable operation stage, a magnetic induction power supply device was installed on the transmission line. The magnetic core coil was wound with nanocrystalline alloy material with high magnetic permeability and low loss, tightly surrounding the transmission line. The output power is calculated and each component is carefully debugged to ensure that under different current conditions of mountain lines, especially under low current conditions that may occur after heavy rain, a stable output power of more than 10W can be achieved, providing a continuous and stable power supply for the fault location host and other related monitoring equipment. For example, when heavy rain causes a slight drop in line current, the power regulation circuit uses adaptive control technology to dynamically adjust the power supply parameters according to current changes, such as appropriately increasing the coil turns ratio, fine-tuning the resonant capacitor value, etc., to ensure that the output power is not affected and maintain the normal operation of the entire system.

[0033] In the multi-field fusion analysis and error correction stage, when a line fault occurs (assuming it is an insulator flashover fault caused by lightning), the fault location host first calculates the electromagnetic field distribution at the moment of the fault based on the collected voltage and current data and the original parameters of the transmission line, and then accurately calculates the electromagnetic field distribution.

[0034] formula The distribution of physical quantities such as electric field intensity vector and magnetic induction intensity vector is accurately obtained. Then, the formula is accurately constructed through the temperature field model based on the thermal effect of current. The temperature rise of the line was calculated and it was found that the temperature near the fault point rose sharply. At the same time, considering the influence of temperature change on the mechanical properties of the material, the formula was constructed through the stress field model. The stress field changes are calculated to obtain the stress concentration at the fault point. In addition, through long-term data collection and analysis of mountain lines in different seasons and weather conditions, the relationship between factors such as temperature changes, line aging, environmental impacts in different seasons and changes in key parameters such as line resistance, inductance, and capacitance is determined, and a dynamic error compensation model is established. The formula ΔR t =a1×ΔT+a2×Δt r +a3×ΔS e +a4×ΔIline , ΔL t =b1×ΔT+b2×Δt r +b3×ΔS e +b4×ΔI line , ΔC t =c1×ΔT+c2×Δt r +c3×ΔS e +c4×ΔI line Calculate the line parameter correction value and dynamically adjust the line parameters in real time during the calculation process, effectively reducing the positioning error caused by the dynamic changes of line parameters due to the complex environment of mountainous areas.

[0035] In the intelligent optimization and adaptive adjustment stage, the error fluctuation in the positioning calculation process is monitored in real time. The error fluctuation rate is calculated and it is found that the instantaneous error fluctuation rate of the fault caused by lightning exceeds the preset range. Combined with the real-time collected line voltage, current, temperature, wind speed (the wind speed is higher during heavy rain), humidity and other data as well as meteorological data, the error fluctuation rate is calculated by formula I c =w1×V c +w2×I c +w3×T c +w4×S w +w5×S h +w6×ΔE r The comprehensive index of working condition identification was calculated to determine that the error fluctuation was mainly caused by the working condition changes caused by lightning strikes and heavy rain. According to the error compensation strategy under different working conditions established in advance, the formula Adjust the line inductance parameters through the formula The current weight coefficient is adjusted to further improve the stability and reliability of positioning accuracy.

[0036] In the integration stage of intelligent early warning and remote control, the intelligent early warning system based on big data analysis and artificial intelligence algorithms collects and deeply analyzes the voltage and current data collected by sensors, multi-physical field monitoring data, and fault location results in real time, and uses the fault prediction model formula to The predicted probability of a fault is calculated. When the predicted probability reaches the set threshold, it is determined to be a potential fault risk. Since this fault was caused by lightning, the predicted probability is high, and multiple warning methods are triggered in time, such as sending text messages to the operation and maintenance personnel at the operation and maintenance station at the foot of the mountain. At the same time, a system pop-up window pops up on the remote monitoring platform to prompt the fault information, accompanied by an audible and visual alarm device to sound an alarm. The operation and maintenance personnel remotely log in to the monitoring platform through terminal devices such as computers or mobile phones to intuitively view the line operation data, fault location information and early warning notifications. They can reasonably dispatch maintenance personnel based on this information, such as arranging personnel familiar with mountain line repairs to carry corresponding tools and equipment to the fault point as soon as possible. At the same time, the monitoring equipment can be remotely controlled through the remote monitoring platform, such as adjusting the sensor sampling frequency to more accurately obtain line data in the subsequent repair process.

[0037] To sum up, in the application of power transmission lines in mountainous areas, the various links of the present invention work closely together and collaboratively, effectively overcoming the many challenges brought about by the complex geographical environment and changeable climate in mountainous areas, achieving high-precision fault location, intelligent operation and maintenance management, and timely and effective remote monitoring, providing a solid guarantee for the stable operation of power transmission lines in mountainous areas.

[0038] Embodiment 2

[0039] This embodiment describes in detail a specific application of a high-precision fault location method for overhead transmission lines in the field of coastal transmission line fault location and monitoring.

[0040] In the stage of precise sensing and data collection, a non-invasive voltage and current integrated sensor was installed on a 220kV coastal transmission overhead line. Considering the serious salt spray corrosion in coastal areas, the sensor shell was protected by special anti-corrosion materials. At the same time, the internal Permalloy core as a current measurement component was also treated with corresponding anti-corrosion. In daily operation, the sensor accurately collects the voltage and current data of the line. The current measurement is also based on the formula To ensure the measurement accuracy, the collected signals are processed and then transmitted to the fault location host at the coastal monitoring station through the "narrowband + broadband" networking communication technology. Narrowband communication gives priority to transmitting key fault feature data such as voltage fluctuation range, while broadband communication transmits complete monitoring data and equipment status information.

[0041] In the self-power supply and stable operation stage, the magnetic induction power supply equipment installed on the coastal transmission line has a magnetic core coil made of high magnetic permeability and low loss nanocrystalline alloy material, and is specially treated with anti-corrosion to resist salt spray corrosion. According to the formula The output power is calculated and debugged to ensure that under different current conditions of coastal lines, especially under low current conditions that may occur after a typhoon passes, the output power can be stably output at more than 10W, providing a continuous and stable power supply for the fault location host and other related equipment. For example, when a typhoon hits, the line current will fluctuate greatly due to factors such as line swing caused by strong winds. The power regulation circuit uses adaptive control technology to dynamically adjust the power parameters according to current changes, such as changing the coil turns ratio, adjusting the resonant capacitor value, etc., to ensure that the output power is not affected and maintain the normal operation of the entire system.

[0042] In the multi-field fusion analysis and error correction stage, when a line fault occurs (assuming that the fault is caused by poor contact at the line joint due to salt spray corrosion), the fault location host calculates the electromagnetic field distribution at the moment of the fault based on the collected voltage and current data and the original parameters of the transmission line. The distribution of physical quantities such as the electric field intensity vector and the magnetic induction intensity vector is obtained through the precise calculation formula of the electromagnetic field distribution, and the change of the electromagnetic field at the moment of the fault is accurately grasped. Then, based on the thermal effect of the current, the temperature field model is used to accurately construct a formula to calculate the temperature rise of the line. It is found that the temperature near the fault point has increased. At the same time, considering the influence of temperature changes on the mechanical properties of materials, the stress field change is calculated through the stress field model. The stress concentration at the fault point is obtained. Through long-term data collection and analysis of coastal lines in different seasons and weather conditions, the relationship between factors such as temperature changes, line aging, and environmental impacts in different seasons and changes in key parameters such as line resistance, inductance, and capacitance is determined, and a dynamic error compensation model is established. Through the formula ΔR t =a1×ΔT+a2×Δt r +a3×ΔS e +a4×ΔI line , ΔL t =b1×ΔT+b2×Δt r +b3×ΔS e +b4×ΔI line , ΔC t =c1×ΔT+c2×Δt r +c3×ΔS e +c4×ΔI ine Calculate the line parameter correction value and dynamically adjust the line parameters in real time during the calculation process, effectively reducing the positioning error caused by the dynamic changes of line parameters due to the complex coastal environment.

[0043] In the intelligent optimization and adaptive adjustment stage, the error fluctuation in the positioning calculation process is monitored in real time. The error fluctuation rate is calculated and it is found that the instantaneous error fluctuation rate of the fault caused by salt spray corrosion exceeds the limit. Combined with the real-time collected line voltage, current, temperature, wind speed (the wind speed is extremely high during typhoons), humidity and other data as well as meteorological data, the formula I is used to calculate the error fluctuation rate. c =w1×V c +w2×I c +w3×T c +w4×S w +w5×S h +w6×ΔE r The comprehensive index of working condition identification was calculated, and it was determined that the error fluctuation was mainly caused by the change of working conditions caused by salt spray corrosion and typhoon weather. According to the error compensation strategy under different working conditions established in advance, the formula Adjust the line inductance parameters through the formula The current weight coefficient is adjusted to further improve the stability and reliability of positioning accuracy.

[0044] In the integration stage of intelligent early warning and remote control, the intelligent early warning system based on big data analysis and artificial intelligence algorithms collects and deeply analyzes the voltage and current data collected by sensors, multi-physical field monitoring data, and fault location results in real time, and uses the fault prediction model formula to The predicted probability of a fault is calculated. When the predicted probability reaches the set threshold, it is determined to be a potential fault risk. Since this fault is caused by salt spray corrosion, the predicted probability is high. The system promptly triggers multiple warning methods, such as sending text messages to the operation and maintenance personnel at the seaside operation and maintenance station. At the same time, a system pop-up window pops up on the remote monitoring platform to prompt the fault information, accompanied by an audible and visual alarm device to sound an alarm. The operation and maintenance personnel remotely log in to the monitoring platform through terminal devices such as computers or mobile phones to intuitively view the line operation data, fault location information and early warning notifications. They can reasonably dispatch maintenance personnel based on this information, such as arranging personnel with experience in handling salt spray corrosion faults to carry corresponding tools and equipment to the fault point as soon as possible.

[0045] To sum up, in the application of coastal power transmission lines, the present invention has successfully overcome the many challenges brought by the special environment of coastal areas through the coordinated operation of various technical points, achieved high-precision fault location, intelligent operation and maintenance management, and timely and effective remote monitoring, and effectively ensured the stable operation of coastal power transmission lines.

[0046] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for high-precision fault location of a power transmission overhead line, characterized in that: The method comprises the following specific steps: Precise sensing and data acquisition: Adopting non-invasive voltage and current integrated sensing technology, sensors are installed on overhead transmission lines of 220kV and below. For current measurement, the sensor uses Permalloy core technology for measurement, and for voltage measurement, the voltage is measured through a special electric field coupling structure. The sensor collects voltage and current data in real time, and transmits data through "narrowband + broadband" networking communication technology; Self-power supply guarantee and stable operation steps: Energy is obtained from the magnetic field of the transmission line through a magnetic induction power-taking device. The device consists of a magnetic core coil, an energy conversion circuit and a power regulation circuit. When current passes through the line, an induced electromotive force is generated according to the principle of electromagnetic induction. The induced electromotive force converts AC power into DC power through the energy conversion circuit to power subsequent equipment. The power regulation circuit adopts adaptive control technology to monitor the current size of the transmission line in real time and automatically adjust the power-taking parameters according to the current changes; Multi-field fusion analysis and error correction: Based on the principles of electromagnetism, thermodynamics and solid mechanics, a physical field coupling model of electromagnetic field, temperature field and stress field is constructed when a transmission overhead line fails. For the electromagnetic field, the electromagnetic field distortion caused by the instantaneous increase of current at the fault point is considered, and the electromagnetic field distribution is described using the Maxwell equations. For the temperature field, the line temperature rise is calculated based on the thermal effect of the current, and the temperature field model is constructed following the heat conduction equation. For the stress field, the thermal expansion of the material and the electromagnetic force caused by temperature changes are considered, and the stress field model is established based on the theory of elastic mechanics. At the same time, the interaction relationship between the physical fields is established. Through experimental data collection and theoretical analysis, the relationship between temperature changes, line aging and environmental factors in different seasons and changes in line resistance, inductance and capacitance parameters is determined, and a dynamic error compensation model is established. The model input is the real-time monitored line environment data and operating time information, and the output is the corresponding line parameter correction value. The fault location algorithm is used to calculate the fault location by combining the multi-physical field coupling analysis results and the corrected line parameters; Intelligent optimization and adaptive adjustment steps: Real-time monitoring of the error fluctuation during the positioning calculation process, and judging whether the error fluctuation exceeds the normal range by comparing the calculation results with the preset error threshold. At the same time, combined with the line operation data and meteorological data collected in real time, the analysis algorithm is used to identify the current line working conditions, and the relevant parameters in the positioning calculation are adaptively adjusted according to the error compensation strategies under different working conditions established in advance; The integration steps of intelligent early warning and remote control are as follows: real-time collection and in-depth analysis of voltage and current data collected by sensors, multi-physical field monitoring data and fault location results, judging potential risks through fault prediction models, and establishing a remote monitoring platform. The fault location host and the monitoring center are connected through a communication network to receive and store data. It also has visualization functions, and operation and maintenance personnel can log in remotely for interactive operations.

2. A method for high-precision fault location of a power transmission overhead line according to claim 1, characterized in that: In the precise sensing and data acquisition steps, for current measurement, the sensor uses Permalloy core technology to measure, and the formula Calculate the measured current value, where I measured is the current value measured by the sensor and subsequent processing, I true is the real current value in the overhead transmission line, that is, the actual current size, μ0: vacuum permeability, μ p is the magnetic permeability of the Permalloy core, ΔB noise It is the change in magnetic induction intensity caused by external magnetic field noise interference during current measurement, ΔB signal It is the change in magnetic induction intensity generated by the real current and effectively sensed by the Permalloy core.

3. A method for high-precision fault location of a power transmission overhead line according to claim 1, characterized in that: In the self-power supply guarantee and stable operation step, for voltage measurement, the voltage is measured through a special electric field coupling structure, and the calculation formula is: Among them, P out is the output power of the power-taking device, η is the energy conversion efficiency, μ π is the relative magnetic permeability of the core coil, N1 is the number of primary turns of the core coil, S is the cross-sectional area of ​​the core coil, l is the average length of the core coil, I line is the current in the transmission line, N2 is the number of secondary turns of the core coil, C r is the resonant capacitance value, L r is the equivalent inductance of the core coil, and ω is the angular frequency of the transmission line current.

4. A method for high-precision fault location of a power transmission overhead line according to claim 1, characterized in that: In the multi-field fusion analysis and error correction step, for the electromagnetic field, the electromagnetic field distortion caused by the instantaneous increase of the current at the fault point is considered, and the Maxwell equations are used to describe the electromagnetic field distribution, and the calculation formula is: in, is the electric field strength vector, is the magnetic induction intensity vector, is the magnetic field strength vector, is the current density vector in the transmission line, is the electric displacement vector, ρ line is the charge density of the transmission line, ∈0 is the dielectric constant of vacuum, c0 is the speed of light in vacuum, and this formula describes the electromagnetic field distribution by associating electrical and magnetic related parameters.

5. The method for high-precision fault location of a power transmission overhead line according to claim 1, characterized in that: In the multi-field fusion analysis and error correction step, for the temperature field, the line temperature rise is calculated according to the thermal effect of the current, and the temperature field model is constructed according to the heat conduction equation. The calculation formula is: Among them, ρ m is the density of the circuit material, c m is the specific heat capacity of the circuit material, T is the temperature of the circuit, k m is the thermal conductivity of the circuit material, I line is the current in the transmission line, R line is the resistance of the transmission line, S line is the cross-sectional area of ​​the transmission line.

6. A method for high-precision fault location of a power transmission overhead line according to claim 1, characterized in that: In the multi-field fusion analysis and error correction step, for the stress field, the thermal expansion of the material caused by temperature change and the electromagnetic force are considered, and a stress field model is established based on the elastic mechanics theory, and the model formula is: Among them, σ ij is the stress tensor, C ijkl is the elastic constant tensor, ε kl is the strain tensor, μ0 is the vacuum permeability, μ r is the relative permeability of the line material, is the current density vector in the transmission line, S line is the cross-sectional area of ​​the transmission line, c m is the specific heat capacity of the circuit material.

7. A method for high-precision fault location of a power transmission overhead line according to claim 1, characterized in that: In the multi-field fusion analysis and error correction step, the interaction relationship between the physical fields is established. Through experimental data collection and theoretical analysis, the relationship between temperature change, line aging and environmental factors in different seasons and changes in line resistance, inductance and capacitance parameters is determined. The formula is expressed as: ΔR t =a1×ΔT+a2×Δt r +a3×ΔS e +a4×ΔI line , ΔL t =b1×ΔT+b2×Δt r +b3×ΔS e +b4×ΔI line , ΔC t =c1×ΔT+c2×Δt r +c3×ΔS e +c4×ΔI line , where ΔR t is the correction value of line resistance after considering temperature change, operating time and environmental factors. a1, a2, a3 and a4 are the influence coefficients of temperature change, operating time, seasonal environmental impact and transmission line current change on the resistance correction value. ΔT is the difference between the real-time monitored line ambient temperature and the initial temperature. Δt r is the difference between the operating time of the transmission line and the initial operating time, ΔS e The difference between the environmental factors caused by seasonal environmental impact and the initial environmental factors, ΔI line is the difference between the transmission line current and the initial current, ΔL t : Correction value of line inductance after considering the above factors, b1, b2, b3, b4: respectively the influence coefficients of temperature change, operating time, seasonal environmental impact, and transmission line current change on the inductance correction value, ΔC t : Correction value of line capacitance after considering the above factors, c1, c2, c3, c4: are the influence coefficients of temperature change, operating time, seasonal environmental impact, and transmission line current change on the capacitance correction value.

8. The method for high-precision fault location of a power transmission overhead line according to claim 1, characterized in that: In the multi-field fusion analysis and error correction step, the multi-physical field coupling analysis results and the corrected line parameters are integrated to calculate the fault location through a fault location algorithm. The formula of the fault location algorithm is: Among them, L f is the distance between the fault location and one end of the line, p is the number of physical field categories, λ k is the weight coefficient of each physical field category, n k is the number of relevant parameters under each physics category, μ ki is the weight coefficient of each parameter under each physical field category, ΔF ki is the variation of each parameter under each physical field category, m is the quantity related to the line parameters, ξ j is the weight coefficient of the line parameter, ΔP j is the change in line parameters after correction, L t is the total length of the transmission line, and ③ is the correction term.

9. A method for high-precision fault location of a power transmission overhead line according to claim 1, characterized in that: In the intelligent optimization and adaptive adjustment step, the error fluctuation in the positioning calculation process is monitored in real time. By comparing the calculation result with the preset error threshold, it is determined whether the error fluctuation exceeds the normal range. The calculation formula of the error fluctuation is: At the same time, combined with the real-time collected line operation data and meteorological data, the analysis algorithm is used to identify the current line working conditions. The comprehensive indicators of the current line working conditions are: I c =w1×V c +w2×I c +w3×T c +w4×S w +w5×S j +w6×ΔE r , where E r is the error volatility, is the actual calculation error of the i-th dimension, is the preset error threshold of the i-th dimension, I c It is a comprehensive indicator used to intelligently identify the current line working conditions. w1, w2, w3, w4, w5, and w6 are weighted coefficients corresponding to voltage, current, temperature, wind speed, humidity, and error fluctuation rate, respectively. According to the error compensation strategy under different working conditions established in advance, the relevant parameters in the positioning calculation are adaptively adjusted. The optimization adjustment formula is: Among them, P l is the adjusted line inductance parameter, is the original line inductance parameter, k1 is the coefficient related to the inductance parameter adjustment, ΔI c It is the line condition change calculated based on the condition identification result. is the ratio of the error volatility to the preset error threshold, C i is the adjusted current weight coefficient, is the original current weight coefficient, k2 is the coefficient related to the current weight coefficient adjustment, ΔI c It is the line condition change calculated based on the condition identification result. It is the ratio of the error fluctuation rate to the preset error threshold, which is used to measure the degree of error fluctuation and adjust the current weight coefficient accordingly.

10. The method for high-precision fault location of a power transmission overhead line according to claim 1, characterized in that: In the step of integrating intelligent early warning and remote control, potential risks are judged by a fault prediction model, and the model formula is: Among them, P f Represents the predicted probability of failure. When the probability reaches the threshold P MAX When , it is judged as a potential failure risk, α and β are weight coefficients related to the square error term and the logarithmic term, respectively, w i is the weight coefficient of the i-th eigenvalue, F i represents the i-th eigenvalue in the real-time monitoring data, F i 0 is the reference value corresponding to the ith eigenvalue under normal operating conditions, and σ is the standard deviation.

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