A power conductor fault location system based on the Internet of Things

Abstract

The present invention relates to the technical field of fault location, and specifically to a power conductor fault location system based on the Internet of Things, comprising a data collection module, a fault analysis module and a data visualization module; first, the data collection module acquires conductor data collected by a sensor on the conductor in real time; secondly, the fault analysis module is used to process the conductor data to obtain fault data; a digital twin model of the power conductor is constructed according to the conductor data and the fault data; fault characteristics and environmental parameters are analyzed through the digital twin model of the power conductor to obtain output results; the output results are analyzed through a topology detection algorithm and signal sensitivity adjustment to obtain optimized output results; a formula for the spatiotemporal propagation process of a fault signal is obtained through signal propagation simulation and spatiotemporal analysis according to the output results; the accuracy of the output results is evaluated according to the formula for the spatiotemporal propagation process of the fault signal; finally, a data visualization module generates a visualization chart and an analysis report.

Description

Technical Field

[0001] The present invention relates to the technical field of fault location, and in particular to a power conductor fault location system based on the Internet of Things. Background Art

[0002] In the power system, power conductors are an important part of transmitting electric energy, and their safety is directly related to the stable operation of the power grid. Faults in power conductors not only affect the reliability of power supply, but may also cause more serious power accidents. Therefore, fault detection and location of power conductors has always been one of the core issues in power system maintenance. However, traditional methods have certain limitations, especially when the power grid topology is complex, environmental factors are changeable, and attenuation or noise may occur in signal transmission. The accuracy of fault location is low and cannot fully reflect the actual situation of the fault.

[0003] With the development of the Internet of Things technology, by combining sensors with the Internet of Things technology, real-time collection of power conductor operation data can not only improve the real-time and accuracy of the data, but also obtain comprehensive information on the state of the power grid, helping to more accurately detect and locate faults. However, although the Internet of Things technology has brought new opportunities for power conductor fault detection, how to effectively integrate multi-source data and achieve high-precision fault location in complex environments is still a difficult point in the development of technology.

[0004] In addition, most existing fault analysis methods are based on traditional static models and lack dynamic adaptation to grid topology changes, environmental factors, and multi-dimensional data of power conductors. These factors will have a significant impact on the propagation of fault signals, directly affecting the accuracy of fault location and the assessment of the scope of fault impact.

[0005] Therefore, a power conductor fault location system based on the Internet of Things is proposed. Summary of the invention

[0006] The object of the present invention is to provide a power conductor fault location system based on the Internet of Things, comprising a data collection module, a fault analysis module and a data visualization module; first, the data collection module acquires the conductor data collected by the sensor on the conductor in real time; secondly, the fault analysis module is used to process the conductor data to obtain fault data; a power conductor digital twin model is constructed based on the conductor data and the fault data; the fault characteristics and environmental parameters are analyzed through the power conductor digital twin model to obtain an output result; the output result is analyzed through a topology detection algorithm and signal sensitivity adjustment to obtain an optimized output result; based on the output result, a formula for the spatiotemporal propagation process of the fault signal is obtained through signal propagation simulation and spatiotemporal analysis; the accuracy of the output result is evaluated based on the formula for the spatiotemporal propagation process of the fault signal; finally, the data visualization module generates a visualization chart and an analysis report.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A power conductor fault location system based on the Internet of Things, comprising a data collection module, a fault analysis module and a data visualization module;

[0009] The data collection module acquires the conductor data collected by the sensor on the conductor in real time, wherein the conductor data is the data acquired by the power conductor in the running state, including the traveling wave signal, the power frequency current signal, the voltage signal and the environmental parameters;

[0010] The fault analysis module is used to process the conductor data to obtain fault data, wherein the fault data includes a reconstructed fault waveform and fault characteristics; construct a digital twin model of a power conductor based on the conductor data and the fault data; analyze the fault characteristics and environmental parameters through the digital twin model of the power conductor to obtain an output result, wherein the output result includes fault location data, risk prediction results and simulation waveform characteristics; analyze the output result through a topology detection algorithm and signal sensitivity adjustment to obtain an optimized output result; obtain a formula for the spatiotemporal propagation process of a fault signal through signal propagation simulation and spatiotemporal analysis based on the output result; and evaluate the accuracy of fault location, risk prediction and impact range based on the formula for the spatiotemporal propagation process of the fault signal;

[0011] The data visualization module generates visualization charts and analysis reports based on the temporal and spatial propagation process formula of the fault signal.

[0012] Preferably, the data processing includes noise filtering, signal reconstruction and feature extraction;

[0013] The noise filtering first obtains the fault signal in the conductor data through wavelet transformation, decomposes the fault signal, obtains signals of different frequencies, filters the high-frequency noise, enhances the low-amplitude signal, and obtains standard fault data;

[0014] The signal reconstruction reconstructs the fault signal waveform by using a distributed data fusion algorithm to obtain a reconstructed fault waveform;

[0015] The feature extraction obtains the fault feature by extracting the feature of the reconstructed fault waveform.

[0016] Preferably, the specific process of obtaining the reconstructed fault waveform through the distributed data fusion algorithm is:

[0017] The first fault signal is obtained by calibrating the consistency of the fault signal data time through a high-precision clock synchronization technology.

[0018] Iteratively optimize and estimate the first fault signal by Kalman filtering, and extract low-quality data by least square method to obtain a second fault signal;

[0019] The redundant data collected from the sensor are jointly reconstructed through a data fusion algorithm, and the missing values ​​of the abnormal nodes in the second fault signal are supplemented to obtain a reconstructed fault waveform.

[0020] Preferably, the fault characteristics include traveling wave characteristics, current characteristics, voltage characteristics, frequency domain characteristics and time-space joint characteristics;

[0021] The traveling wave characteristics include the initial wave head arrival time, the traveling wave amplitude and polarity and the zero crossing point;

[0022] The current characteristics include current mutation point, current amplitude and current transient response;

[0023] The voltage characteristics include voltage drop points, voltage phase characteristics and voltage transient fluctuations.

[0024] Preferably, the process of constructing the digital twin model of the power conductor includes physical modeling, data mapping and environmental coupling;

[0025] The physical modeling establishes a mathematical model of electromagnetic propagation of the conductor based on the wire parameters and physical characteristics of the conductor;

[0026] The conductor data is used as initial conditions and mapped into an electromagnetic propagation mathematical model to obtain a first electromagnetic propagation mathematical model of the conductor; the physical properties include conductor resistance, inductance and capacitance;

[0027] The fault data is used as a boundary condition and mapped to a first electromagnetic propagation mathematical model to obtain a second electromagnetic propagation mathematical model of the conductor;

[0028] The environmental parameters collected in real time from the conductor data are input into the second electromagnetic propagation mathematical model to obtain the digital twin model of the power conductor, and the digital twin model of the power conductor is updated in real time based on the actual conductor data obtained.

[0029] Preferably, the power conductor digital twin model includes a data input layer, a simulation and analysis layer, and a feedback and optimization layer;

[0030] The data input layer obtains input data by normalizing the fault characteristics and environmental parameters and inputs the input data into the power conductor digital twin model;

[0031] The simulation and analysis layer performs space-time propagation simulation on the input data, and obtains fault location data, risk prediction results and simulation waveform characteristics through traveling wave equation and time difference location method;

[0032] The feedback and optimization layer dynamically adjusts the data in the digital twin model of the power conductor based on the actual data obtained from the power grid topology structure, and optimizes the fault location data, risk prediction results and simulation waveform characteristics.

[0033] Preferably, the fault location data is calculated by calculating the fault location based on the time difference of the fault signal propagating from the fault point to multiple monitoring points; the specific calculation formula of the fault location is:

[0034] ;

[0035] in, is the distance from the fault point to the monitoring point, is the distance between two monitoring points, is the theoretical propagation time of the traveling wave signal, is the time difference of the traveling wave signal arriving at two monitoring points;

[0036] The risk prediction results are obtained by analyzing the changes in fault waveforms and the dynamic changes in the environment and grid structure; the propagation attenuation characteristics of the fault signal are determined by dynamically monitoring the changes in grid topology, load fluctuations and environmental factors.

[0037] Preferably, the specific formula of the time-space propagation process formula of the fault signal is:

[0038] ;

[0039] in, For the wire position and time The voltage under is the inductance of the wire, Based on temperature The environmental correction factor, For the wire position and time The current under is the resistance of the wire, Based on humidity The environmental correction factor, is the topology correction term.

[0040] Preferably, the topology correction term is obtained by comprehensively obtaining correction functions of load changes, grid reconstruction, new energy access and line faults in the grid topology structure constructed by the power conductors; the specific calculation formula of the topology correction term is:

[0041] ;

[0042] in, is the correction function affecting the weight of load change, is the influence coefficient of load change on signal propagation speed, is the power difference of grid load change, is the correction function influencing weights of power grid reconstruction, is the influence coefficient of power grid reconstruction on signal propagation speed, is the change in line impedance after grid reconstruction, is the correction function affecting the weight of new energy access, is the impact coefficient of new energy access on the propagation path, The voltage change caused by the access of new energy sources, is the influence weight of the correction function of line fault, is the influence coefficient of line fault on propagation speed, The current change caused by the fault.

[0043] Preferably, the visual diagrams include a fault location diagram, a fault signal propagation path diagram, a risk prediction diagram, and an environment and topology correction diagram;

[0044] The analysis report includes a fault location analysis report, a fault location analysis report and an impact range analysis report.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. The present invention introduces multimodal data fusion technology, and reconstructs the fault signal by combining the traveling wave signal, current signal, voltage signal and environmental parameters, and then obtains the fault waveform using the distributed data fusion algorithm. The method first realizes noise filtering through wavelet transform to enhance the characteristics of low-amplitude signals, and then optimizes the signal using Kalman filtering and least squares method. Finally, redundant data and abnormal nodes are supplemented through data fusion to obtain the reconstructed fault waveform, which not only improves the accuracy of fault location, but also can effectively deal with the signal attenuation and noise interference of power conductors.

[0047] 2. The present invention analyzes and optimizes power conductor faults based on a digital twin model. A digital twin model of power conductors is constructed through physical modeling, data mapping, and environmental coupling. The model dynamically adjusts electrical characteristics, such as resistance, inductance, and other physical parameters, based on real-time conductor data and changes in environmental parameters. Through space-time propagation simulation, the fault signal is simulated with high precision, and the simulation results are further optimized using factors such as grid topology correction and environmental correction, effectively evaluating the fault location, predicting fault expansion, and optimizing the operating status of the power conductor, thereby improving the stability and reliability of the power conductor.

[0048] 3. The present invention combines the formula of the space-time propagation process of the fault signal, and accurately models the propagation path, speed and attenuation characteristics of the fault signal through real-time simulation and topological analysis. This formula introduces environmental correction coefficients and topological correction terms on the basis of the traditional traveling wave propagation model, and corrects the propagation errors caused by factors such as grid load fluctuations and environmental changes by dynamically updating the signal propagation model. It not only improves the accuracy of fault location, but also realizes accurate risk prediction and impact range assessment through the analysis of fault waveforms and propagation processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of the structure of a power conductor fault location system based on the Internet of Things provided by the present invention;

[0050] Figure 2 A schematic diagram of the structure of the digital twin model of power conductors provided by the present invention;

[0051] Figure 3 A schematic diagram of the sensor installation position provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0053] Embodiment 1

[0054] See also Figure 1 ,The present invention provides a power conductor fault location system based on the Internet of Things, including a data collection module, a fault analysis module and a data visualization module;

[0055] The data collection module acquires the conductor data collected by the conductor sensor in real time. The conductor data is the data acquired by the power conductor in the running state, including traveling wave signals, power frequency current signals, voltage signals and environmental parameters; wherein the traveling wave signal passes through the traveling wave current sensor to monitor the current traveling wave signal caused by the fault in real time and acquire the traveling wave arrival time information; the power frequency current signal passes through the power frequency current sensor to measure the current amplitude, phase and power frequency transient response; the voltage signal passes through the voltage sensor to measure the voltage amplitude, phase and transient voltage waveform when the fault occurs, and analyzes the fault type in combination with the current data; the environmental parameters include external environmental data such as temperature, humidity, and electromagnetic interference intensity.

[0056] The fault analysis module is used to process the conductor data to obtain fault data, wherein the fault data includes a reconstructed fault waveform and fault characteristics;

[0057] Further, the data processing includes noise filtering, signal reconstruction and feature extraction;

[0058] The noise filtering first obtains the fault signal in the conductor data through wavelet transformation, decomposes the fault signal, obtains signals of different frequencies, filters the high-frequency noise, enhances the low-amplitude signal, and obtains standard fault data;

[0059] The signal reconstruction reconstructs the fault signal waveform by using a distributed data fusion algorithm to obtain a reconstructed fault waveform;

[0060] The feature extraction obtains the fault feature by extracting the feature of the reconstructed fault waveform.

[0061] In this embodiment, the quality of the fault signal and the positioning accuracy are improved by using wavelet transform for noise filtering, distributed data fusion algorithm for signal reconstruction and feature extraction method; wavelet transform can effectively remove high-frequency noise and enhance low-amplitude signals, thereby ensuring the accuracy of the data; signal reconstruction technology restores the complete fault waveform by fusing multi-source data, thereby enhancing the accuracy of fault detection; by extracting the characteristics of the reconstructed waveform, the fault type is accurately identified, providing the power system with more reliable fault analysis and early warning capabilities.

[0062] Furthermore, the specific process of obtaining the reconstructed fault waveform through the distributed data fusion algorithm is as follows:

[0063] The first fault signal is obtained by calibrating the consistency of the fault signal data time through a high-precision clock synchronization technology.

[0064] Iteratively optimize and estimate the first fault signal by Kalman filtering, and extract low-quality data by least square method to obtain a second fault signal;

[0065] The redundant data collected from the sensor are jointly reconstructed through a data fusion algorithm, and the missing values ​​of the abnormal nodes in the second fault signal are supplemented to obtain a reconstructed fault waveform.

[0066] In this embodiment, the quality and accuracy of the fault signal are effectively improved through high-precision clock synchronization technology, Kalman filtering and least squares method. Clock synchronization technology ensures the time consistency of data collected by different sensors, Kalman filtering iteratively optimizes the signal to reduce noise interference, and the least squares method further eliminates low-quality data to ensure the accuracy of the data. For specific data, refer to Table 1. Redundant data are jointly reconstructed through data fusion algorithm, and missing values ​​are supplemented, and finally a high-quality fault waveform after reconstruction is obtained. The reconstruction accuracy of the fault waveform is significantly improved, which helps to locate the fault more accurately and analyze it.

[0067] Table 1 Fault signal reconstruction data table

[0068] Sensor location Traveling wave signal amplitude (mA) Current mutation point (A) Voltage drop point (V) Ambient temperature (℃) Reconstructed signal quality (%) Fault type A 150 25 230 36 95 Short Circuit B 155 28 225 34 92 Grounding C 145 22 210 33 94 Short Circuit D 160 30 235 38 97 High resistance grounding E 148 24 220 35 93 Short Circuit

[0069] The present invention reconstructs the fault signal through a distributed data fusion algorithm, uses wavelet transform to filter out noise, enhances low-amplitude signals, extracts key information of the signal, optimizes the signal through Kalman filtering, and finally extracts features from the reconstructed fault waveform. By extracting traveling wave features, current features, voltage features, and environmental features, accurate fault location and signal analysis are provided for power conductors.

[0070] Furthermore, the fault characteristics include traveling wave characteristics, current characteristics, voltage characteristics, frequency domain characteristics and time-space joint characteristics;

[0071] The traveling wave characteristics include the initial wave head arrival time, the traveling wave amplitude and polarity and the zero crossing point;

[0072] The current characteristics include current mutation point, current amplitude and current transient response;

[0073] The voltage characteristics include voltage drop points, voltage phase characteristics and voltage transient fluctuations.

[0074] In this embodiment, the digital twin model of power conductors constructed based on features can dynamically analyze the changes in fault signals and environmental factors, and optimize fault location data, risk prediction results and simulation waveform characteristics in real time. This significantly improves the accuracy and reliability of fault detection, enhances the power system's ability to identify and respond to complex faults, and effectively ensures the safe and stable operation of the power grid.

[0075] A digital twin model of a power conductor is constructed based on the conductor data and the fault data; the fault characteristics and environmental parameters are analyzed by the digital twin model of the power conductor to obtain an output result, which includes fault location data, risk prediction results and simulation waveform characteristics.

[0076] Furthermore, the process of constructing the digital twin model of the power conductor includes physical modeling, data mapping and environmental coupling;

[0077] The physical modeling establishes a mathematical model of electromagnetic propagation of the conductor based on the wire parameters and physical characteristics of the conductor;

[0078] The conductor data is used as initial conditions and mapped into an electromagnetic propagation mathematical model to obtain a first electromagnetic propagation mathematical model of the conductor; the physical properties include conductor resistance, inductance and capacitance;

[0079] The fault data is used as a boundary condition and mapped to a first electromagnetic propagation mathematical model to obtain a second electromagnetic propagation mathematical model of the conductor;

[0080] The environmental parameters collected in real time from the conductor data are input into the second electromagnetic propagation mathematical model to obtain the digital twin model of the power conductor, and the digital twin model of the power conductor is updated in real time based on the actual conductor data obtained.

[0081] In this embodiment, by constructing a digital twin model based on power conductors, combined with physical modeling, data mapping and environmental coupling, the accuracy of fault location and risk prediction is improved. Through modeling of physical properties such as resistance, inductance and capacitance of power conductors, the conductor data and fault data are mapped to the electromagnetic propagation mathematical model to obtain a dynamically updated digital twin model, which realizes accurate simulation and analysis of fault signals. By optimizing the output results through topology detection and signal sensitivity adjustment, an efficient and real-time fault signal propagation process formula is provided, which helps to improve the response speed and safety of power conductor faults.

[0082] The output result is analyzed through a topology detection algorithm and signal sensitivity adjustment to obtain an optimized output result; based on the output result, a formula for the space-time propagation process of the fault signal is obtained through signal propagation simulation and space-time analysis.

[0083] Furthermore, the power conductor digital twin model includes a data input layer, a simulation and analysis layer, and a feedback and optimization layer, see Figure 2 ;

[0084] The data input layer obtains input data by normalizing the fault characteristics and environmental parameters and inputs the input data into the power conductor digital twin model;

[0085] The simulation and analysis layer performs space-time propagation simulation on the input data, and obtains fault location data, risk prediction results and simulation waveform characteristics through traveling wave equation and time difference location method;

[0086] The feedback and optimization layer dynamically adjusts the data in the digital twin model of the power conductor based on the actual data obtained from the power grid topology structure, and optimizes the fault location data, risk prediction results and simulation waveform characteristics.

[0087] In this embodiment, the multi-level architecture of the power conductor digital twin model, combined with the topology detection algorithm and signal sensitivity adjustment, optimizes the accuracy of fault location, risk prediction and simulation waveform characteristics. This structure significantly improves the response speed and accuracy of the power system when a fault occurs, effectively ensuring the safe operation of the power grid.

[0088] Furthermore, the fault location data is calculated based on the time difference of the fault signal propagating from the fault point to multiple monitoring points to calculate the fault position; the specific calculation formula of the fault position is:

[0089] ;

[0090] in, is the distance from the fault point to the monitoring point, is the distance between two monitoring points, is the theoretical propagation time of the traveling wave signal, is the time difference of the traveling wave signal arriving at two monitoring points;

[0091] The risk prediction results are obtained by analyzing the changes in fault waveforms and the dynamic changes in the environment and grid structure; the propagation attenuation characteristics of the fault signal are determined by dynamically monitoring the changes in grid topology, load fluctuations and environmental factors.

[0092] In this embodiment, the fault location calculation formula based on the time difference is used to accurately determine the fault location, thereby improving the accuracy of fault location. By analyzing the changes in fault waveforms and the grid topology, load fluctuations and environmental factors, the system can dynamically adjust and predict the propagation characteristics of the fault signal, thereby accurately assessing the risk of the fault and its impact range, and improving the effectiveness of grid fault warning and emergency response.

[0093] Evaluate the accuracy of fault location, risk prediction and impact range based on the temporal and spatial propagation process formula of the fault signal;

[0094] Furthermore, the specific formula of the time-space propagation process formula of the fault signal is:

[0095] ;

[0096] in, For the wire position and time The voltage under is the inductance of the wire, Based on temperature The environmental correction factor, For the wire position and time The current under is the resistance of the wire, Based on humidity The environmental correction factor, is the topology correction term.

[0097] In this embodiment, the formula for the spatiotemporal propagation process of the fault signal combines key electrical parameters such as voltage, current, wire inductance, resistance, etc., and introduces environmental correction coefficients such as temperature and humidity and grid topology correction items, so that the propagation process of the fault signal is closer to the actual grid state. This analysis method that comprehensively considers electrical characteristics and environmental factors can dynamically adjust fault location and risk assessment, and enhances the real-time monitoring and emergency response capabilities of grid faults.

[0098] Furthermore, the topology correction term is obtained by comprehensively obtaining correction functions of load changes, grid reconstruction, new energy access and line faults in the grid topology structure constructed by the power conductors; the specific calculation formula of the topology correction term is:

[0099] ;

[0100] in, is the correction function affecting the weight of load change, is the influence coefficient of load change on signal propagation speed, is the power difference of grid load change, is the correction function influencing weights of power grid reconstruction, is the influence coefficient of power grid reconstruction on signal propagation speed, is the change in line impedance after grid reconstruction, is the correction function affecting the weight of new energy access, is the impact coefficient of new energy access on the propagation path, The voltage change caused by the access of new energy sources, is the influence weight of the correction function of line fault, is the influence coefficient of line fault on propagation speed, The current change caused by the fault.

[0101] In this embodiment, the topology correction term improves the accuracy and stability of signal propagation in the power system by comprehensively considering load changes, power grid reconstruction, new energy access and line failures in the power grid topology structure constructed by power conductors, thereby effectively optimizing the operating efficiency of the power system and improving the system's adaptability to external changes.

[0102] In this embodiment, in dynamic environments such as grid topology changes, load fluctuations, and new energy access, the model can perform topology correction based on real-time collected data, optimize fault location and risk prediction results, and thus provide accurate fault analysis and response. Please refer to Table 2 for details.

[0103] Table 2 Power grid topology correction and optimization analysis data table

[0104] Grid topology status Grid load (MW) Proportion of new energy access (%) Fault signal propagation speed (km / s) Risk prediction error (%) Normal operation 500 20 0.8 5 Large load fluctuation 520 18 0.75 7 Line reconstruction 510 22 0.85 4 High access to new energy 530 40 0.78 6 Failure occurs 510 25 0.76 8

[0105] The data visualization module generates visualization charts and analysis reports based on the temporal and spatial propagation process formula of the fault signal.

[0106] Furthermore, the visualization diagram includes a fault location diagram, a fault signal propagation path diagram, a risk prediction diagram, and an environment and topology correction diagram; wherein the fault location diagram uses a three-dimensional coordinate diagram to mark the nodes and lines of the power grid, displaying the location of the fault point in the power grid and its distance relative to each monitoring terminal, helping operation and maintenance personnel to quickly identify the specific location where the fault occurs; the fault signal propagation path diagram displays the propagation path of the fault signal, including information such as propagation speed and signal attenuation, to help analyze the fault extension and impact range; the risk prediction diagram provides an analysis of the impact of fault signal propagation on the power grid, assesses the risk of the fault to other parts, and helps operation and maintenance personnel determine whether further intervention is needed; the environment and topology correction diagram displays the impact of environmental factors (such as temperature, humidity) and power grid topology changes on fault signal propagation, helping to optimize monitoring and analysis strategies.

[0107] The analysis report includes a fault location analysis report, a fault location analysis report and an impact range analysis report; wherein the fault location analysis report includes fault point location (detailed listing of the coordinate information of the fault location and its relative position relationship with other monitoring points), propagation path analysis (displaying the signal's propagation path, propagation speed and time delay) and positioning error analysis (compared with the actual fault location to evaluate the accuracy of fault location).

[0108] The risk prediction analysis report includes risk assessment (analyzing the potential risks of the power grid based on the propagation path and impact range of the fault signal, and predicting whether the fault may extend to other areas) and warning level (providing early warning information to operation and maintenance personnel to help formulate emergency response plans).

[0109] The impact scope analysis report includes the fault impact area (detailed description of the impact of the fault on the power grid, indicating which areas are affected by the fault wave) and extensibility analysis (assessing whether the fault is likely to extend to other lines or areas, and providing corresponding response recommendations).

[0110] In this embodiment, through the combination of visual charts and analysis reports, multi-dimensional information such as fault location, signal propagation path, risk prediction and environmental correction is provided to help users fully understand the occurrence of faults and their impact range, thereby improving fault diagnosis efficiency and emergency response capabilities.

[0111] The present invention can accurately locate faults and predict potential risks by real-time collection of conductor data and combining fault analysis, digital twin model and topology detection algorithm. The system improves the recognition accuracy of fault signals through multi-layer data processing such as noise filtering, signal reconstruction and feature extraction. The distributed data fusion algorithm is adopted to optimize the reconstruction process of fault waveforms, and extract multi-dimensional fault features to help accurately determine the type and location of faults. The construction and real-time update of the digital twin model realize the accurate simulation of the state of power conductors, can dynamically adjust the fault location and risk prediction results, and improve the adaptive ability of the system. The system also deeply analyzes the propagation process of fault signals through the time-space propagation formula and optimizes the accuracy of fault location. At the same time, the topology correction term combines factors such as grid load changes, reconstruction, new energy access and line faults to provide a scientific basis for the correction of signal propagation speed and path. Through visual charts and analysis reports, users can clearly understand the fault location, propagation path, risk prediction and impact range, which greatly improves the fault diagnosis efficiency and the operational safety of the power system.

[0112] Embodiment 2

[0113] A power conductor fault location system based on the Internet of Things, comprising a data collection module, a fault analysis module and a data visualization module;

[0114] The data collection module acquires the conductor data collected by the sensor on the conductor in real time, wherein the conductor data is the data acquired by the power conductor in the running state, including the traveling wave signal, the power frequency current signal, the voltage signal and the environmental parameters;

[0115] The sensor is configured with a set of terminals at the beginning and end of the main line of the distribution line, and one terminal is installed every 5 to 8 kilometers; the number of terminals is increased every 5 to 8 km according to the length of the branch line of the distribution line; branches longer than 5 km are long branches and are configured at the beginning and end; branches longer than 3 km are short branches and are configured at the beginning, see Figure 3 ; The circular mark indicates the location of the sensor.

[0116] The fault analysis module is used to process the conductor data to obtain fault data, wherein the fault data includes a reconstructed fault waveform and fault characteristics; construct a digital twin model of a power conductor based on the conductor data and the fault data; analyze the fault characteristics and environmental parameters through the digital twin model of the power conductor to obtain an output result, wherein the output result includes fault location data, risk prediction results and simulation waveform characteristics; analyze the output result through a topology detection algorithm and signal sensitivity adjustment to obtain an optimized output result; obtain a formula for the spatiotemporal propagation process of a fault signal through signal propagation simulation and spatiotemporal analysis based on the output result; and evaluate the accuracy of fault location, risk prediction and impact range based on the formula for the spatiotemporal propagation process of the fault signal;

[0117] The data visualization module generates visualization charts and analysis reports based on the temporal and spatial propagation process formula of the fault signal.

[0118] Furthermore, the fault characteristics include traveling wave characteristics, current characteristics, voltage characteristics, frequency domain characteristics and time-space joint characteristics;

[0119] The traveling wave characteristics include the initial wave head arrival time, the traveling wave amplitude and polarity and the zero crossing point;

[0120] The current characteristics include current mutation point, current amplitude and current transient response;

[0121] The voltage characteristics include voltage drop points, voltage phase characteristics and voltage transient fluctuations.

[0122] Furthermore, the process of constructing the digital twin model of the power conductor includes physical modeling, data mapping and environmental coupling;

[0123] The physical modeling establishes a mathematical model of electromagnetic propagation of the conductor based on the wire parameters and physical characteristics of the conductor;

[0124] The conductor data is used as initial conditions and mapped into an electromagnetic propagation mathematical model to obtain a first electromagnetic propagation mathematical model of the conductor; the physical properties include conductor resistance, inductance and capacitance;

[0125] The fault data is used as a boundary condition and mapped to a first electromagnetic propagation mathematical model to obtain a second electromagnetic propagation mathematical model of the conductor;

[0126] The environmental parameters collected in real time from the conductor data are input into the second electromagnetic propagation mathematical model to obtain the digital twin model of the power conductor. The digital twin model of the power conductor is then updated in real time based on the actual conductor data. The data can be found in Table 3.

[0127] Table 3 shows the dynamic adjustment of voltage, current, resistance, inductance, and capacitance obtained through physical modeling and data mapping in the power conductor digital twin model, as well as the fault location accuracy under different optimization states. The digital twin model can adjust physical parameters in real time according to environmental changes and grid topology, thereby dynamically optimizing fault location and risk assessment.

[0128] Table 3 Dynamic optimization results of digital twin model

[0129] Model Status Voltage (V) Current (A) Resistance (Ω) Inductance (H) Capacitance (F) Fault location accuracy after optimization (m) Initial state 220 12 0.15 1.2 0.0001 50 Optimization status 240 16 0.11 0.8 0.0002 38

[0130] Furthermore, the power conductor digital twin model includes a data input layer, a simulation and analysis layer, and a feedback and optimization layer;

[0131] The data input layer obtains input data by normalizing the fault characteristics and environmental parameters and inputs the input data into the power conductor digital twin model;

[0132] The simulation and analysis layer performs space-time propagation simulation on the input data, and obtains fault location data, risk prediction results and simulation waveform characteristics through traveling wave equation and time difference location method;

[0133] The feedback and optimization layer dynamically adjusts the data in the digital twin model of the power conductor based on the actual data obtained from the power grid topology structure, and optimizes the fault location data, risk prediction results and simulation waveform characteristics.

[0134] Furthermore, the fault location data is calculated based on the time difference of the fault signal propagating from the fault point to multiple monitoring points to calculate the fault position; the specific calculation formula of the fault position is:

[0135] ;

[0136] in, is the distance from the fault point to the monitoring point, is the distance between two monitoring points, is the theoretical propagation time of the traveling wave signal, is the time difference of the traveling wave signal arriving at two monitoring points;

[0137] The risk prediction results are obtained by analyzing the changes in fault waveforms and the dynamic changes in the environment and grid structure; the propagation attenuation characteristics of the fault signal are determined by dynamically monitoring the changes in grid topology, load fluctuations and environmental factors.

[0138] Furthermore, the specific formula of the time-space propagation process formula of the fault signal is:

[0139] ;

[0140] in, For the wire position and time The voltage under is the inductance of the wire, Based on temperature The environmental correction factor, For the wire position and time The current under is the resistance of the wire, Based on humidity The environmental correction factor, is the topology correction term.

[0141] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A power conductor fault location system based on the Internet of Things, characterized in that: Including data collection module, fault analysis module and data visualization module; The data collection module acquires the conductor data collected by the sensor on the conductor in real time, wherein the conductor data is the data acquired by the power conductor in the running state, including the traveling wave signal, the power frequency current signal, the voltage signal and the environmental parameters; The fault analysis module is used to process the conductor data to obtain fault data, and the fault data includes a reconstructed fault waveform and fault features; the data processing includes noise filtering, signal reconstruction and feature extraction; the noise filtering first obtains the fault signal in the conductor data through wavelet transformation, and decomposes the fault signal to obtain signals of different frequencies, filters high-frequency noise, enhances low-amplitude signals, and obtains standard fault data; the signal reconstruction reconstructs the fault signal waveform through a distributed data fusion algorithm to obtain a reconstructed fault waveform; the feature extraction obtains the fault feature by extracting the features of the reconstructed fault waveform; Constructing a power conductor digital twin model based on the conductor data and the fault data; analyzing the fault characteristics and environmental parameters through the power conductor digital twin model to obtain output results, wherein the output results include fault location data, risk prediction results, and simulation waveform characteristics; The output result is analyzed through a topology detection algorithm and signal sensitivity adjustment to obtain an optimized output result; a time-space propagation process formula of a fault signal is obtained through signal propagation simulation and time-space analysis based on the output result; the accuracy of fault location, risk prediction and impact range is evaluated based on the time-space propagation process formula of the fault signal; The data visualization module generates visualization charts and analysis reports based on the temporal and spatial propagation process formula of the fault signal.

2. The power conductor fault location system based on the Internet of Things according to claim 1 is characterized in that: The specific process of obtaining the reconstructed fault waveform through the distributed data fusion algorithm is as follows: The first fault signal is obtained by calibrating the consistency of the fault signal data time through a high-precision clock synchronization technology. Iteratively optimize and estimate the first fault signal by Kalman filtering, and extract low-quality data by least square method to obtain a second fault signal; The redundant data collected from the sensor are jointly reconstructed through a data fusion algorithm, and the missing values ​​of the abnormal nodes in the second fault signal are supplemented to obtain a reconstructed fault waveform.

3. The power conductor fault location system based on the Internet of Things according to claim 1 is characterized in that: The fault characteristics include traveling wave characteristics, current characteristics, voltage characteristics, frequency domain characteristics and time-space joint characteristics; The traveling wave characteristics include the initial wave head arrival time, the traveling wave amplitude and polarity and the zero crossing point; The current characteristics include current mutation point, current amplitude and current transient response; The voltage characteristics include voltage drop points, voltage phase characteristics and voltage transient fluctuations.

4. The power conductor fault location system based on the Internet of Things according to claim 1, characterized in that: The process of constructing the digital twin model of the power conductor includes physical modeling, data mapping and environmental coupling; The physical modeling establishes a mathematical model of electromagnetic propagation of the conductor based on the wire parameters and physical characteristics of the conductor; The conductor data is used as initial conditions and mapped into an electromagnetic propagation mathematical model to obtain a first electromagnetic propagation mathematical model of the conductor; the physical properties include conductor resistance, inductance and capacitance; The fault data is used as a boundary condition and mapped to a first electromagnetic propagation mathematical model to obtain a second electromagnetic propagation mathematical model of the conductor; The environmental parameters collected in real time from the conductor data are input into the second electromagnetic propagation mathematical model to obtain the digital twin model of the power conductor, and the digital twin model of the power conductor is updated in real time based on the actual conductor data obtained.

5. The power conductor fault location system based on the Internet of Things according to claim 1, characterized in that: The power conductor digital twin model includes a data input layer, a simulation and analysis layer, and a feedback and optimization layer; The data input layer obtains input data by normalizing the fault characteristics and environmental parameters and inputs the input data into the power conductor digital twin model; The simulation and analysis layer performs space-time propagation simulation on the input data, and obtains fault location data, risk prediction results and simulation waveform characteristics through traveling wave equation and time difference location method; The feedback and optimization layer dynamically adjusts the data in the digital twin model of the power conductor based on the actual data obtained from the power grid topology structure, and optimizes the fault location data, risk prediction results and simulation waveform characteristics.

6. The power conductor fault location system based on the Internet of Things according to claim 5, characterized in that: The fault location data is calculated based on the time difference of the fault signal propagating from the fault point to multiple monitoring points. The specific calculation formula of the fault location is: ； in, is the distance from the fault point to the monitoring point, is the distance between two monitoring points, is the theoretical propagation time of the traveling wave signal, is the time difference of the traveling wave signal arriving at two monitoring points; The risk prediction results are obtained by analyzing the changes in fault waveforms and the dynamic changes in the environment and grid structure; the propagation attenuation characteristics of the fault signal are determined by dynamically monitoring the changes in grid topology, load fluctuations and environmental factors.

7. The power conductor fault location system based on the Internet of Things according to claim 1, characterized in that: The specific formula of the time-space propagation process formula of the fault signal is: ； in, For the wire position and time The voltage under is the inductance of the wire, Based on temperature The environmental correction factor, For the wire position and time The current under is the resistance of the wire, Based on humidity The environmental correction factor, is the topology correction term.

8. The power conductor fault location system based on the Internet of Things according to claim 7, characterized in that: The topology correction term is obtained by comprehensively obtaining the correction function of load change, grid reconstruction, new energy access and line fault in the grid topology structure constructed by the power conductors; the specific calculation formula of the topology correction term is: ； in, is the correction function affecting the weight of load change, is the influence coefficient of load change on signal propagation speed, is the power difference of grid load change, is the correction function influencing weights of power grid reconstruction, is the influence coefficient of power grid reconstruction on signal propagation speed, is the change in line impedance after grid reconstruction, is the correction function affecting the weight of new energy access, is the impact coefficient of new energy access on the propagation path, The voltage change caused by the access of new energy sources, is the influence weight of the correction function of line fault, is the influence coefficient of line fault on propagation speed, The current change caused by the fault.

9. The power conductor fault location system based on the Internet of Things according to claim 1, characterized in that: The visual diagrams include a fault location diagram, a fault signal propagation path diagram, a risk prediction diagram, and an environment and topology correction diagram; The analysis report includes a fault location analysis report, a fault location analysis report and an impact range analysis report.

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