A power distribution network fault rapid positioning method, device, equipment and medium

Abstract

The present application belongs to the technical field of power system automation, and particularly relates to a power distribution network fault rapid positioning method, device, equipment and medium. The method comprises the following steps: obtaining power distribution network topology, distributed power supply access and historical fault data, constructing a power supply model for power flow analysis, and training a fault feature recognition model. Real-time node parameters are collected, fault types are identified and features are extracted, and real-time fault data are obtained by comparing system states. The fault distance is calculated by using the traveling wave distance measurement, combined with the impedance method for continuous monitoring, and the preliminary positioning is obtained by cross-validation. Finally, the positioning is corrected based on the distributed power supply model and the adaptive protection algorithm, and high-precision fault positioning is achieved.

Description

Technical Field

[0001] This invention belongs to the field of power system automation technology, specifically relating to a method, device, equipment, and medium for rapid fault location in power distribution networks. Background Technology

[0002] With the increasing demand for electricity in modern society, the complexity of power systems is also growing. As a crucial component of the power system, the reliability and stability of the distribution network directly impact the power quality for users and the operational efficiency of power companies. However, due to its wide coverage, complex line distribution, and dynamic changes in power load, the distribution network is susceptible to faults caused by various factors. Rapid fault location is a technique and method used to quickly pinpoint the location of faults in distribution networks or other power systems. Its main objective is to accurately locate the fault point by analyzing various signals and data in the power system, enabling rapid repair and restoration of normal operation. In distribution networks, faults may be caused by line breaks, equipment failures, short circuits, overloads, and other reasons.

[0003] Traditional fault handling in distribution networks relies on manual inspections, which is inefficient. As distribution networks continue to expand and lines become more complex, coupled with the large-scale integration of distributed power sources, fault location becomes increasingly difficult. Simultaneously, the requirements for the continuity and reliability of power supply in distribution networks are constantly rising, placing higher demands on fault response time and accuracy. Summary of the Invention

[0004] The purpose of this invention is to provide a method, device, equipment and medium for rapid fault location in power distribution networks, so as to solve the problem of low efficiency in manual inspection and investigation in the prior art.

[0005] In a first aspect, the present invention provides a method for rapid fault location in a power distribution network, comprising the following steps: Acquire topology data, distributed generation access data, and historical fault data of the distribution network; construct a distributed generation model based on the topology data and distributed generation access data, and perform power flow analysis to obtain current distribution data under normal operating conditions; train a fault feature recognition model based on historical fault data to obtain a fault feature library. The voltage, current, and phase parameters of the distribution network nodes are collected in real time to obtain multi-dimensional real-time parameter data; the fault section is determined based on the real-time parameter data, and the fault type is identified and features are extracted based on the fault feature library. The system state before and after the fault is compared and analyzed to obtain real-time fault data. Based on real-time fault data, the distance from the fault point to the measurement point is calculated using the traveling wave ranging principle to obtain fault distance data; the impedance change of the line section with fault distance data is continuously monitored to obtain impedance method fault location data. The fault location is cross-validated based on the fault distance data and the impedance method fault location data to obtain preliminary fault location data. The preliminary fault location data is then corrected and the accuracy is improved based on the adaptive protection algorithm according to the distributed power source model to obtain the fault location result data.

[0006] In a second aspect, the present invention provides a rapid fault location device for a power distribution network, comprising: The first data analysis module is used to acquire the topology data, distributed generation access data, and historical fault data of the distribution network; construct a distributed generation model based on the topology data and distributed generation access data, and perform power flow analysis to obtain current distribution data under normal operating conditions; train a fault feature recognition model based on historical fault data to obtain a fault feature library. The second data analysis module is used to collect voltage, current and phase parameters of distribution network nodes in real time to obtain multi-dimensional real-time parameter data; determine the fault section based on the real-time parameter data, identify the fault type and extract features based on the fault feature library, and conduct comparative analysis of the system state before and after the fault to obtain real-time fault data. The third data analysis module is used to calculate the distance from the fault point to the measurement point based on the traveling wave ranging principle according to real-time fault data, and obtain fault distance data; and to continuously monitor the impedance change of the line section with fault distance data to obtain impedance method fault location data. The fault location module is used to perform cross-verification of fault location based on fault distance data and impedance method fault location data to obtain preliminary fault location data; and to perform result correction and accuracy positioning based on the distributed power source model on the preliminary fault location data using an adaptive protection algorithm to obtain the fault location result data.

[0007] In a third aspect, the present invention provides an electronic device including a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the rapid fault location method for power distribution networks as described above.

[0008] In a fourth aspect, the present invention provides a computer-readable storage medium storing at least one instruction that, when executed by a processor, implements the above-described method for rapid fault location in a power distribution network.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a rapid fault location method for distribution networks. By acquiring the topology and distributed generation access data of the distribution network, constructing a model, and performing power flow analysis, it can accurately grasp the current distribution of the system under normal conditions, providing benchmark data for fault occurrence. Using historical fault data to train a fault feature recognition model and establish a fault feature library, it can quickly identify and classify different types of faults, providing intelligent support for fault diagnosis. The acquisition of multi-dimensional real-time parameter data provides a rich information source for determining fault sections and identifying fault types, enhancing the reliability of fault diagnosis. Distance calculation based on the traveling wave ranging principle can quickly estimate the fault location, providing preliminary data for fault location. The application of an online impedance measurement device can monitor impedance changes in the fault area in real time, providing continuously updated data for fault location and improving location accuracy. Cross-validation of fault distance data and impedance-based fault location data enhances the accuracy and reliability of fault location. Using a distributed generation model and adaptive protection algorithm to correct the preliminary fault location data further improves the accuracy of fault location, providing precise location information for fault handling. The distinction between temporary and persistent faults allows for different response strategies, improving the targeting and efficiency of fault handling. The recording and updating of the fault feature database enables the system to continuously learn and adapt to new fault modes, improving its self-adaptability. Emergency response based on fault location results for persistent faults allows for rapid fault isolation and repair, reducing power outage time and ensuring the continuity and reliability of power supply. Overall, this rapid fault location method for distribution networks, by combining real-time monitoring, data analysis, intelligent algorithms, and emergency response, significantly improves the speed and accuracy of fault detection, location, and handling, enhancing the stability of the distribution network and the reliability of power supply. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the steps of a method for rapid fault location in a power distribution network according to an embodiment of the present invention. Figure 2 for Figure 1 A detailed flowchart of step S1; Figure 3 for Figure 1 A detailed flowchart of step S2.

[0011] Figure 4 This is a structural block diagram of a power distribution network fault rapid location device according to an embodiment of the present invention; Figure 5 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0012] Example 1 Please see Figures 1 to 3The present invention provides a method for rapid fault location in a power distribution network, comprising the following steps: Step S1: Obtain the topology data, distributed generation access data, and historical fault data of the distribution network; construct a distributed generation model based on the topology data and distributed generation access data, and perform power flow analysis to obtain current distribution data under normal operating conditions; train a fault feature recognition model based on historical fault data to obtain a fault feature library. In this embodiment of the invention, the collection of topology data, distributed power source access data, and historical fault data can be carried out through the power grid management system and database; using power grid modeling software and power flow analysis tools, a model is constructed based on the collected data and power flow calculation is performed to obtain current distribution data under normal operating conditions; a fault feature recognition model is trained using machine learning algorithms and historical fault data, which can use support vector machines (SVM), neural networks, or other classification algorithms, and the training results are stored in the fault feature database.

[0013] Step S2: Install fault indicators, transient waveform recording devices and synchronous phasor measurement units at the nodes of the distribution network, and collect voltage, current and phase parameters in real time to obtain multi-dimensional real-time parameter data; determine the fault section based on the real-time parameter data, identify the fault type and extract features based on the fault feature library, and conduct a comparative analysis of the system state before and after the fault to obtain real-time fault data. In this embodiment of the invention, fault indicators, transient waveform recorders, and synchronous phasor measurement units (PMUs) are installed at key nodes of the distribution network; these devices are configured to collect voltage, current, and phase parameters in real time, and the data acquisition frequency should be set according to system requirements; using real-time parameter data and a fault feature database, fault sections are determined and fault types are identified through data analysis software.

[0014] Step S3: Calculate the distance from the fault point to the measurement point based on the traveling wave ranging principle according to the real-time fault data to obtain fault distance data; continuously monitor the impedance change of the line section with the fault distance data through an online impedance measurement device to obtain impedance method fault location data. This invention utilizes the traveling wave ranging principle to calculate the distance from the fault point to the measurement point by analyzing the waveform data recorded by the transient waveform recording device, thereby obtaining fault distance data. The deployment and configuration of the online impedance measurement device are used to monitor the impedance changes in the fault section, which can be achieved through an automated monitoring system.

[0015] Step S4: Cross-validate the fault location based on the fault distance data and the impedance method fault location data to obtain preliminary fault location data; perform result correction and accuracy positioning based on the adaptive protection algorithm on the preliminary fault location data according to the distributed power source model to obtain the fault location result data. In this embodiment of the invention, fault distance data and impedance method fault location data are input into the fault location software for cross-validation to improve the accuracy of fault location; the preliminary fault location data are corrected using a distributed power source model and adaptive protection algorithm.

[0016] Preferably, the method further includes step S5: distinguishing between temporary and persistent faults based on real-time fault data, recording and updating the fault feature database based on the fault characteristics of temporary faults, and conducting emergency response based on fault location result data for persistent faults, thereby achieving fault repair.

[0017] This invention distinguishes between temporary and persistent faults using real-time fault data analysis software. The distinction can be made by setting parameter thresholds and conditions based on actual conditions. For temporary faults, the fault feature database is updated, which can be achieved through a database management system. For persistent faults, based on the fault location results, an emergency response is triggered through the distribution network automation system, such as automatic reclosing and isolating the faulty section, thus enabling automated control systems involving the power grid control center.

[0018] This invention acquires the topology and distributed generation access data of the distribution network, constructs a model, and performs power flow analysis to accurately grasp the current distribution of the system under normal conditions, providing benchmark data for fault occurrence. By training a fault feature recognition model using historical fault data and establishing a fault feature library, different types of faults can be quickly identified and classified, providing intelligent support for fault diagnosis. Installing fault indicators, transient waveform recording devices, and synchronous phasor measurement units at distribution network nodes enables real-time monitoring of the power grid's operating status, improving the response speed and accuracy of fault detection. The acquisition of multi-dimensional real-time parameter data provides a rich information source for determining fault sections and identifying fault types, enhancing the reliability of fault diagnosis. Distance calculation based on the traveling wave ranging principle can quickly estimate the fault location, providing preliminary data for fault localization. The application of online impedance measurement devices can monitor impedance changes in the fault area in real time, providing continuously updated data for fault localization and improving localization accuracy. Cross-validation of fault distance data and impedance-based fault localization data enhances the accuracy and reliability of fault localization. By utilizing a distributed power source model and adaptive protection algorithms to refine preliminary fault location data, the accuracy of fault location is further improved, providing precise location information for fault handling. Differentiating between temporary and persistent faults allows for the adoption of different response strategies, enhancing the targetedness and efficiency of fault handling. Recording and updating the fault feature database enables the system to continuously learn and adapt to new fault modes, improving its adaptive capabilities. Emergency response based on fault location results for persistent faults allows for rapid fault isolation and repair, reducing power outage time and ensuring the continuity and reliability of power supply. Overall, this rapid fault location method for distribution networks, by combining real-time monitoring, data analysis, intelligent algorithms, and emergency response, significantly improves the speed and accuracy of fault detection, location, and handling, enhancing the stability of the distribution network and the reliability of power supply.

[0019] As an embodiment of the present invention, reference Figure 2 As shown, step S1 includes the following steps: Step S11: Obtain the topology data of the distribution network, including line connection relationships, transformer locations, and switch status; In this embodiment of the invention, detailed information on lines, transformers, and switches is collected through existing SCADA, GIS, and Enterprise Resource Planning (ERP) systems; the collected data is organized using Excel, database software (such as SQL Server or Oracle), or specialized power grid planning software (such as PowerWorld or Digsilent); and a distribution network node and connection model is created in the planning software, with parameters of all relevant equipment input.

[0020] Step S12: Collect information on the type, capacity, and location of distributed power sources connected to the distribution network to obtain distributed power source access data; In this embodiment of the invention, a field survey of distributed power sources within the distribution network is conducted to collect information on their type, capacity, and location. The collected data is then input into a database and associated with a power grid model to ensure data consistency and accuracy.

[0021] Step S13: Establish a corresponding mathematical model based on the power type in the distributed power access data, and integrate the corresponding mathematical model into the topology data to obtain a distributed power model containing distributed power sources. Based on the characteristics of distributed power sources, this invention develops mathematical models using specialized software (such as MATLAB / Simulink); the developed mathematical models are then imported into the power grid model and integrated using APIs or directly in planning software.

[0022] Step S14: Perform steady-state power flow calculation based on the distributed power source model, and conduct multi-scenario power flow analysis based on the generation characteristics of the distributed power source to obtain current distribution data under normal operating conditions. This invention uses power system analysis software to perform steady-state power flow calculations and analyze the current and power distribution of the power grid under normal operating conditions. Based on the possible output changes of distributed power sources, different operating scenarios are set up and power flow calculations are repeated to evaluate the power grid performance under different conditions.

[0023] Step S15: Obtain historical fault data, including fault type, location, and time information; This invention utilizes data mining tools (such as the Pandas library in Python) to analyze historical fault data and extract key information such as fault type, location, and time; the analysis results are then updated in the fault database to ensure the real-time nature and availability of the information.

[0024] Step S16: Extract fault current characteristics and voltage change characteristics from historical fault data, and use signal processing technology to analyze fault waveforms, extract time-domain and frequency-domain characteristics, thereby obtaining a fault feature database.

[0025] This invention uses signal processing software (such as MATLAB's Signal Processing Toolbox) to perform time-domain and frequency-domain analysis on fault waveforms; it applies algorithms such as Fast Fourier Transform (FFT) to extract features of the fault waveforms, such as frequency components and amplitude changes; and it stores the extracted features in a fault feature database to provide data support for the training and evaluation of fault diagnosis models.

[0026] This invention acquires topology data of the distribution network, which is fundamental to understanding the physical layout of the power grid, including line connections, transformer locations, and switch states. This is crucial for analyzing the grid's operational status and diagnosing faults. Accurate topology data enables rapid identification of affected areas and equipment when a fault occurs, thereby improving fault response speed. Collecting information on the type, capacity, and location of distributed generation sources provides grid operators with a comprehensive view of distributed energy resources. This data is essential for understanding the grid's generation capacity and distribution characteristics, enabling optimization of grid operation and management. Establishing mathematical models of distributed generation sources and integrating them into the grid topology allows for the simulation of the impact of distributed generation sources on grid operation. This integrated model enables more accurate grid analysis and prediction, especially when distributed generation sources have a significant impact on grid stability and reliability. Steady-state power flow calculations based on the distributed generation source model can predict the current and power distribution of the grid under normal operating conditions. Multi-scenario power flow analysis can assess grid performance under different generation conditions, providing decision support for grid planning and operation. Acquiring historical fault data provides a valuable source of information for fault analysis and feature extraction. This data enables understanding the patterns and trends of fault occurrence, providing a basis for preventing and reducing future faults. In-depth analysis of historical fault data, extracting fault current and voltage surge characteristics, allows for the establishment of a fault feature database. Utilizing signal processing techniques for time-domain and frequency-domain analysis of fault waveforms can identify specific fault patterns, improving the accuracy of fault detection and location. The establishment of the fault feature database provides a foundation for developing advanced fault diagnosis algorithms and tools, enabling rapid fault identification and response. Overall, these steps, by comprehensively considering the physical structure of the distribution network, the characteristics of distributed generation sources, and historical fault data, provide grid operators with powerful fault diagnosis and prevention tools. This not only improves the reliability and stability of the power grid but also provides a scientific basis for the optimized operation and planning of the grid.

[0027] Preferably, step S141 includes the following steps: Step S141: Initialize the power flow calculation parameters according to the distributed power source model to obtain the initial state data. The power flow calculation parameters include the convergence condition, the upper limit of the number of iterations, the node voltage, and the phase angle. This invention first determines all parameters for power flow calculation, including convergence conditions (such as error tolerance), upper limit of iterations (to prevent infinite loops), and initial estimates of node voltages and phase angles. These parameters are then set in power system analysis software, such as PowerWorld, MATLAB, or Python's PyPower library. The topology of the distribution network, line parameters, load demand, and model parameters of distributed generation sources are then input.

[0028] Step S142: Calculate the steady-state power flow based on the initial state data using the Newton-Raphson method, and analyze the impact on the power flow based on the distributed generation model to obtain the basic steady-state power flow data. This invention selects the Newton-Raphson method as the algorithm for power flow calculation due to its wide application and accuracy in power system analysis. The algorithm is executed in software to calculate the steady-state power flow of the power grid based on the initial state data, analyze the impact of distributed generation on power flow, and consider the impact of its output fluctuations and location on the stability of the power grid and power flow.

[0029] Step S143: Establish mathematical models of reactive power compensation equipment and voltage regulating transformers; integrate the equipment mathematical models into the calculation process of basic steady-state power flow data, and recalculate the steady-state power flow data and power flow calculation model; This invention develops mathematical models for reactive power compensation equipment and voltage regulating transformers, which should accurately reflect the dynamic behavior of the equipment in the power grid; integrates these equipment models into existing power flow calculation models; performs new power flow calculations, taking into account the newly integrated equipment models, to obtain updated steady-state power flow data.

[0030] Step S144: Design multiple operating scenarios based on the output characteristics of each power source in the distributed power model, and perform power flow calculation based on the power flow calculation model for each scenario to obtain power flow calculation result data for multiple scenarios. This invention designs different operating scenarios based on the possible output range of distributed power sources and the operating conditions of the power grid; it uses an updated power flow calculation model to calculate each scenario and analyzes the power grid performance under different conditions; it collects the power flow calculation results for each scenario to provide data support for further analysis of the robustness and adaptability of the power grid.

[0031] Step S145: Extract the current characteristics of each line segment based on the steady-state power flow data and the power flow calculation results of multiple scenarios, and perform statistical analysis to obtain the current distribution data under normal operating conditions.

[0032] This invention extracts key current features, such as maximum current and current imbalance, from steady-state power flow data and multi-scenario results; uses statistical methods to analyze the extracted current features to identify power grid operation modes and potential problems; and generates current distribution data under normal operating conditions, which can be used for power grid planning, load forecasting, and fault analysis.

[0033] This invention provides a clear starting point for the power grid model by initializing power flow calculation parameters, ensuring the accuracy and consistency of the calculations. Acquiring initial state data lays the foundation for subsequent power flow calculations, where parameters such as convergence conditions and upper limits for iterations are crucial for controlling the calculation process and ensuring the reliability of the results. Utilizing the Newton-Raphson method for steady-state power flow calculations is an efficient and widely used numerical method that can accurately simulate power flow in a power system under normal operating conditions. Analyzing the impact of distributed generation on power flow allows us to understand how these sources alter the power distribution and voltage levels of the grid, which is significant for grid planning and operation management. Establishing mathematical models of reactive power compensation devices and voltage regulating transformers allows for a more accurate simulation of their effects on grid voltage and reactive power regulation. Integrating these mathematical models into power flow calculations improves the accuracy of the results and better reflects the performance of the actual power grid under different operating conditions. Designing multiple operating scenarios and performing power flow calculations enables the evaluation of the grid's performance under different conditions, including changes in distributed generation output. The multi-scenario power flow calculation results provide grid operators with a comprehensive view of grid performance, enabling risk assessment and the development of response strategies. Extracting current characteristics from steady-state power flow data and multi-scenario power flow calculations allows for the identification of key parameters and patterns of the power grid under normal operating conditions. Statistical analysis of these current characteristics reveals the load distribution and potential bottlenecks of the power grid, providing data support for grid optimization and upgrading. Overall, these steps, through meticulous power flow calculations and analysis, provide a deep understanding of the power grid's operating status, improving grid stability and reliability, optimizing grid operating efficiency, and providing a scientific basis for grid planning and fault prevention.

[0034] As an embodiment of the present invention, reference Figure 3 As shown, step S2 includes the following steps: Step S21: Determine the location of key nodes based on the network topology, install fault indicators, transient waveform recording devices and synchronous phasor measurement units, configure equipment parameters, set sampling frequency and trigger conditions, and thus obtain equipment layout scheme data. This invention uses a power grid GIS system to identify key nodes in the power grid, such as substations, main feeders, and load centers; selects suitable fault indicators, transient waveform recorders, and PMUs based on technical parameters and historical fault data; has a professional engineering team responsible for the on-site installation of the equipment to ensure correct wiring and fixation; and sets the sampling frequency (e.g., 10 kHz) and trigger conditions (e.g., current mutation rate) through the equipment's configuration interface.

[0035] Step S22: Collect current amplitude and direction information from the fault indicator in the equipment layout plan data to obtain fault current vector data; In this embodiment of the invention, a fault indicator is configured to collect current amplitude and direction information, which is accomplished through the device's local user interface or a remote configuration tool; the collected current information is converted into fault current vector data, which can be achieved through built-in data processing algorithms or external analysis software.

[0036] Step S23: High-speed sampling of voltage and current waveforms is performed using the transient waveform recording device in the equipment layout plan data to obtain transient waveform data; In this embodiment of the invention, the sampling rate (e.g., 100 kHz) and storage depth of the transient waveform recording device are set to ensure that the transient process of a fault can be captured; when a fault occurs, the voltage and current waveforms before and after the fault are automatically captured by the device’s triggering logic.

[0037] Step S24: Collect synchronous phasor data through the synchronous phasor measurement unit in the equipment layout scheme data; In this embodiment of the invention, a PMU is deployed to synchronously measure the voltage and current phasors of the power grid with high precision. The PMU has an IEEE C37.118 compliant synchronization mechanism and uses the Global Positioning System (GPS) or similar technology to ensure that the PMU data is strictly synchronized with the sampling of other devices.

[0038] Step S25: Integrate the synchronization phasor data, transient waveform data, and fault current vector data into multi-dimensional real-time parameter data; This invention uses an existing data integration platform, such as a timestamp-based data fusion system, to integrate data from different devices and create a multi-dimensional dataset that includes timestamps, node locations, current vectors, transient waveforms, and phasor data.

[0039] Step S26: Determine the fault section based on real-time parameter data, identify the fault type and extract features based on the fault feature library, and conduct a comparative analysis of the system status before and after the fault to obtain real-time fault data.

[0040] This invention applies fault diagnosis algorithms, such as pattern recognition technology based on artificial intelligence, to process real-time parameter data; realizes an automated feature extraction process to identify fault type features from real-time data, such as sudden changes in current and voltage caused by the fault; and uses historical data and real-time data to compare and analyze the changes in system state before and after the fault, and determine the scope and severity of the fault's impact.

[0041] This invention identifies key nodes and installs corresponding monitoring equipment, ensuring real-time monitoring of critical areas of the distribution network and providing the infrastructure for fault detection and location. Equipment parameter configuration, including sampling frequency and trigger condition settings, ensures the accuracy and timeliness of data acquisition, providing high-quality raw data for subsequent data analysis and processing. The current amplitude and direction information collected by the fault indicator provides a direct indicator of the circuit state at the time of fault occurrence, enabling rapid fault identification and location; the obtained fault current vector data provides a quantitative basis for analyzing the nature of the fault (such as short circuit, overload, etc.). The voltage and current waveform data collected by the transient waveform recording device can capture the transient process at the moment of fault occurrence, which is crucial for understanding the dynamic characteristics of fault occurrence; the transient waveform data provides detailed information for analyzing the fault's initiation conditions and development process. The data collected by the synchronization phasor measurement unit provides accurate time stamps and phase information, enabling analysis of the power grid's phase relationship and synchronization state; synchronization phasor data is crucial for analyzing and understanding the impact of faults on the stability of the entire power grid system. Integrating different types of data into multi-dimensional real-time parameter data provides a comprehensive view of power grid operation, enabling a more complete understanding of the power grid status. This data fusion provides a rich information source for subsequent fault analysis and diagnosis, enhancing the accuracy and reliability of fault detection. Fault segment identification based on real-time parameter data allows for rapid location of fault areas, providing guidance for fault isolation and repair. Fault type identification and feature extraction enable fault classification and corresponding countermeasures. Comparative analysis of system states before and after a fault identifies the scope and severity of the fault's impact, providing a basis for power grid recovery and optimization. Overall, these steps, through the comprehensive application of multiple monitoring technologies and data analysis methods, achieve rapid detection, accurate location, and effective analysis of distribution network faults, improving the operational reliability and fault handling efficiency of the power grid.

[0042] Preferably, step S26 includes the following steps: Step S261: Compare the deviation parameters with the real-time parameter data and the steady-state power flow data, and evaluate the operating status indicators based on the deviation parameters to obtain system status evaluation data; This invention acquires real-time parameter data, including current and voltage, from a power grid monitoring system and compares it with steady-state power flow calculation results; calculates the deviation between the real-time data and the steady-state data, including voltage deviation and current deviation; and uses analysis software to evaluate the current state of the power grid and determine whether there are any anomalies based on the deviation parameters and predefined operating standards.

[0043] Step S262: Based on a preset threshold, the system status assessment data is used to trigger the fault location process and the initial timestamp of the fault is recorded to obtain the fault trigger signal data; This invention sets thresholds for different operating status indicators, which are based on historical power grid operating data and engineering experience; develops fault judgment logic, which triggers the fault location process when the indicator exceeds the threshold; and records an accurate timestamp when a fault occurs, which is automatically completed by a system synchronized to a unified clock source (such as GPS).

[0044] Step S263: Based on the fault current vector data and topology data, the fault section is initially determined to obtain preliminary fault section data; Fast Fourier Transform is performed on the transient waveform data, and time-domain and frequency-domain features are extracted to obtain transient feature data. This invention uses power grid topology and fault current vector data to preliminarily determine the sections where faults may occur through analysis software; it applies Fast Fourier Transform (FFT) to transient waveform data to convert it from the time domain to the frequency domain; and it extracts features from the time and frequency domain data, such as waveform amplitude, frequency components, and phase angle.

[0045] Step S264: Calculate the phasor difference before and after the fault based on the synchronization phasor data to obtain phasor change data; This invention utilizes the synchronous phasor data before and after the fault recorded by the PMU to calculate the difference and obtain the voltage and current changes caused by the fault; it analyzes the phasor change data to identify fault characteristics, such as phasor abrupt changes and frequency shifts.

[0046] Step S265: Perform feature matching on transient feature data and phasor change data according to the fault feature library to obtain preliminary fault type data; In this embodiment of the invention, the extracted transient feature data and phasor change data are matched with the fault feature database, and the fault type is identified using pattern recognition technology. Based on the matching results, the preliminary fault type is obtained, such as single-phase grounding, two-phase short circuit, etc.

[0047] Step S266: Perform structured data integration on the preliminary fault section data, transient characteristic data, phasor change data, and preliminary fault type data to obtain real-time fault data.

[0048] This invention integrates preliminary fault segment data, transient characteristic data, phasor change data, and preliminary fault type data in a structured manner; and uses a database management system to form a real-time fault dataset, which facilitates further analysis and processing.

[0049] The deviation comparison between real-time parameter data and steady-state power flow data in this invention can reveal abnormal situations in power grid operation, providing quantitative indicators for real-time monitoring of power grid status. The operational status indicator assessment provides power grid operators with a comprehensive view of system performance, enabling timely detection of potential problems and implementation of preventative measures. The triggering judgment of preset thresholds provides an automated initiation mechanism for the fault location process, ensuring timely fault response. Recording the initial fault timestamp provides an accurate time reference for fault analysis and subsequent processing, enabling the tracking and assessment of fault impacts. The combined use of fault current vector data and topology data provides accurate network status information for the initial determination of fault sections, quickly narrowing the fault search range. The Fast Fourier Transform of transient waveform data and its time-domain and frequency-domain feature extraction provide rich information for a deeper understanding of fault characteristics, improving the accuracy of fault identification. The calculation of phasor differences before and after the fault provides dynamic response information of the power grid at the moment of the fault, enabling the identification of the fault's impact on power grid stability. Phasor change data provides key indicators for the dynamic behavior analysis of the power grid, enabling the assessment of fault severity and possible propagation paths. The feature matching process of the fault feature database provides intelligent identification and classification of transient feature data and phasor change data, improving the accuracy of fault type judgment. The acquisition of preliminary fault type data provides crucial information for fault handling and grid restoration, enabling the development of targeted response strategies. Structured data integration transforms different types of data into a unified format, facilitating subsequent processing and analysis and improving data processing efficiency. The generation of real-time fault data provides grid operators with comprehensive fault information, enabling rapid response and measures to reduce the impact of faults on grid operation. Overall, these steps, through real-time monitoring, data analysis, and intelligent processing, achieve rapid detection, accurate location, and effective classification of distribution network faults, improving grid reliability and fault handling efficiency. Simultaneously, these steps also provide valuable data support for the long-term operation, maintenance, and optimization of the grid.

[0050] Preferably, step S3 includes the following steps: Step S31: Filter and denoise the waveform data in the real-time fault data to obtain real-time traveling wave data; In this embodiment of the invention, the raw waveform data obtained from the transient waveform recording device is first preprocessed, including removing the DC component and trend line; using a bandpass filter to remove high-frequency noise and low-frequency interference from the waveform data, while retaining the frequency components related to the fault; and applying wavelet denoising or other signal denoising techniques to reduce random noise in the data and improve signal quality.

[0051] Step S32: Use wavelet transform technology to identify the traveling wave head in the real-time traveling wave data and determine the arrival time of the traveling wave, thereby obtaining the traveling wave head arrival time data; In this embodiment of the invention, wavelet transform is applied to the filtered traveling wave data to identify the traveling wave head in the time-frequency domain. By observing the wavelet-transformed signal, the characteristics of the traveling wave head, such as the abrupt change points of the wavelet coefficients, are identified. The arrival time of the traveling wave head at each measurement point is determined, corresponding to the local maximum value in the wavelet transform result.

[0052] Step S33: Calculate the time difference of arrival of the traveling wave between different measurement points based on the arrival time data of the traveling wave head, thereby obtaining the traveling wave time difference data; In this embodiment of the invention, the time of the fault occurrence is used as a reference point to calculate the time difference of the arrival of the traveling wave head at different measurement points, ensuring that the time data of all measurement points are synchronized to the same clock source in order to accurately calculate the time difference.

[0053] Step S34: Calculate the traveling wave propagation speed based on the pre-acquired line parameters from the fault point to the measurement point and the medium characteristics, and calculate the distance from the fault point to the measurement point based on the traveling wave time difference data and the traveling wave speed, thereby obtaining preliminary fault distance data for multiple measurement points; According to the embodiments of the present invention, the propagation speed of traveling waves in the line is calculated based on the electrical parameters and dielectric characteristics of the line; the travel wave time difference and travel wave velocity are used to calculate the preliminary distance from the fault point to each measurement point.

[0054] Step S35: Based on the preliminary fault distance data from multiple measurement points, perform weighted average data fusion to improve positioning accuracy, thereby obtaining fault distance data; The embodiments of the present invention employ weighted averaging or other data fusion techniques to combine preliminary fault distance data from multiple measurement points to improve the accuracy of fault location; weights are assigned according to the importance and reliability of the measurement points to ensure that the accuracy of each measurement point is considered in the data fusion process.

[0055] Step S36: Continuously monitor the impedance changes of the line section with fault distance data using an online impedance measurement device to obtain fault location data using the impedance method.

[0056] In this embodiment of the invention, an online impedance measurement device is installed in a critical line section to monitor the impedance changes of the line in real time, continuously collect impedance data of the fault section, and analyze the impedance change trend after the fault occurs. By combining the impedance measurement results with the fault distance data, the fault location is further verified and refined.

[0057] The filtering and denoising processing of waveform data in this invention improves signal quality, eliminates noise and interference that may affect traveling wave feature identification, and ensures the accuracy of subsequent analysis. Real-time traveling wave data acquisition provides crucial raw information for traveling wave fault location, forming the basis for fault detection and localization. The application of wavelet transform technology improves the accuracy of traveling wave head feature identification; wavelet transform has excellent analytical capabilities for local signal characteristics in the time-frequency domain. Acquiring traveling wave head arrival time data provides necessary time information for calculating traveling wave propagation characteristics and fault location. The calculation of traveling wave time difference data utilizes the time difference in wave propagation between the fault point and the measurement point, a key step in traveling wave fault location; this data provides the basis for subsequent fault distance calculation and is an important parameter for achieving accurate fault location. The calculation of traveling wave propagation speed considers line parameters and medium characteristics, which directly affect the propagation speed of the traveling wave; fault distance calculation based on time difference and traveling wave speed can provide a preliminary distance estimate from each measurement point to the fault point, offering multiple possible locations for fault location. Weighted average fusion of preliminary fault distance data from multiple measurement points improves the accuracy and reliability of fault location. Data fusion technology effectively reduces the impact of errors at single measurement points on fault location results, enhancing accuracy. The application of online impedance measurement devices enables continuous monitoring of impedance changes in fault sections; impedance change is a crucial indicator of fault presence. Impedance-based fault location data provides an independent method for fault location, and combining it with other methods further improves accuracy and reliability. Overall, these steps, through the integrated application of signal processing technology, traveling wave theory, data fusion technology, and impedance measurement technology, achieve precise fault location in the distribution network. This not only improves the speed and accuracy of fault detection and location but also enhances the self-healing capability and power supply reliability of the power grid.

[0058] Preferably, step S36 includes the following steps: Step S361: Determine the line section that needs impedance measurement based on the fault distance data, and activate the online impedance measurement device of the corresponding section according to the distributed power source model to obtain the impedance measurement section data. This invention uses fault distance data obtained from traveling wave location to analyze and determine the line sections that require impedance measurement; based on the location and type of distributed power sources in the model, it determines the areas that may affect impedance measurement; and activates the online impedance measurement device for the corresponding section through an automated system or remote control command.

[0059] Step S362: The impedance of the specified line section is measured by an online impedance measuring device based on the impedance measurement section data, and the impedance amplitude and phase angle information are collected to obtain the original impedance measurement data. This invention uses an online impedance measurement device to perform real-time impedance measurement on a specified line section, collecting impedance amplitude and phase angle information. This data is crucial for understanding the behavior of the power grid under fault conditions. The collected raw impedance measurement data is recorded for subsequent analysis and processing.

[0060] Step S363: Filter and smooth the raw impedance measurement data, and perform time series trend analysis to identify impedance abrupt change points or abnormal change intervals, thereby obtaining impedance change trend data. In this embodiment of the invention, the raw impedance measurement data is filtered and smoothed to remove noise and outliers; time series analysis methods, such as moving average or exponential smoothing, are used to identify the trend of impedance data changes; and abrupt changes or abnormal change intervals in the impedance data are identified as signs of fault occurrence.

[0061] Step S364: Calculate the theoretical fault characteristic impedance based on the preliminary fault type data and compare it with the original impedance measurement data to obtain fault characteristic impedance comparison data. According to the fault type and power grid model, the fault characteristic impedance is calculated using circuit theory. The theoretically calculated fault characteristic impedance is compared and analyzed with the actual measured impedance data. The comparison results are analyzed to determine the consistency and difference between the fault characteristics and the measured data.

[0062] Step S365: Based on the impedance change trend data and the fault characteristic impedance ratio data, perform fault location estimation based on the impedance method to obtain impedance method fault location data.

[0063] This invention uses impedance change trend data and fault characteristic impedance comparison data to estimate the location of the fault point using the impedance method; calculates the distance from the fault point to the measurement point based on the impedance data; and obtains fault location data through algorithm processing. This data can be directly used for fault isolation and repair work.

[0064] This invention determines specific line sections for impedance measurement based on fault distance data, improving the targeting and efficiency of measurements and ensuring effective resource utilization. Activating online impedance measurement devices in the corresponding sections enables real-time monitoring of key areas, providing necessary equipment support for fault location. By measuring the impedance of designated line sections using online impedance measurement devices, the electrical characteristics of the line can be directly obtained, providing raw data for fault analysis. Acquiring impedance amplitude and phase angle information provides comprehensive electrical parameters for understanding the behavior of the power grid under fault conditions. Filtering and smoothing the raw impedance measurement data removes noise and outliers, improving data quality. Time series trend analysis can identify abrupt impedance changes or abnormal change intervals during fault occurrence; these characteristics are key clues for fault location. Theoretical fault characteristic impedance calculation provides a predicted impedance change model, which can explain the actual impact of the fault on the power grid impedance. Comparison with the raw impedance measurement data verifies the consistency between fault characteristics and actual measurements, providing a basis for determining the fault type and location. Impedance-based fault location estimation utilizes impedance variation trends and fault characteristic impedance comparison data to provide a fault location method independent of traveling wave localization. The acquisition of impedance-based fault location data provides additional verification for fault diagnosis, increasing the reliability and accuracy of fault location results. Overall, these steps, through precise measurement and analysis of the power grid's impedance characteristics, combined with theoretical models and actual measurement data, improve the accuracy and reliability of fault location. This method not only enables rapid fault identification and isolation but also provides crucial data support for power grid maintenance and optimization.

[0065] Preferably, step S4 includes the following steps: Step S41: Normalize the fault distance data and impedance method fault location data, and assign weights to the traveling wave method and impedance method based on historical fault data, thereby obtaining the location method weight data. This invention uses mathematical methods (such as min-max normalization or Z-score normalization) to process fault distance data and impedance method fault location data to eliminate the influence of different dimensions and magnitudes; based on historical fault data, it analyzes the performance of traveling wave method and impedance method under different fault conditions, and uses statistical methods (such as regression analysis) to determine the weight of each method.

[0066] Step S42: Based on the location method weight data, cross-validate and fuse the fault location data and the impedance method fault location data to obtain preliminary fault location data; In this embodiment of the invention, normalized fault distance data and impedance method fault location data are cross-validated to check the consistency of the data; data fusion technology (such as Kalman filter, weighted average or decision-level fusion) is applied to combine the data from the two methods and fuse them according to the weights determined in step S41 to obtain preliminary fault location data.

[0067] Step S43: Based on the distributed power source model, assess the impact of distributed power sources near the fault point on the preliminary fault location data, and calculate the contribution of distributed power sources to the fault current to obtain the distributed power source influence factor. This invention embodiment uses a distributed power source model to evaluate the impact of distributed power sources near the fault point on the fault current, considering power source type, capacity, and connection point; the contribution of distributed power sources to the fault current is calculated using simulation software.

[0068] Step S44: Initialize the adaptive protection algorithm parameters according to the distributed power source influence factor, and perform backtracking calculation of the theoretical fault current at the fault point to obtain the theoretical fault current data; In this embodiment of the invention, the parameters of the adaptive protection algorithm are adjusted according to the distributed power source influence factor, such as the fault current threshold and time delay; the adjusted parameters are then used to calculate the theoretical fault current at the fault point.

[0069] Step S45: Dynamically adjust the operating threshold of the protection device based on the theoretical fault current data to generate a new protection setting value; The embodiments of the present invention dynamically adjust the action threshold of the protection device based on theoretical fault current data to ensure that the protection device can operate accurately under actual fault conditions; generate new protection setting values ​​and ensure that these values ​​are applicable to the current power grid conditions and fault characteristics.

[0070] Step S46: Based on the preliminary fault location data and protection setting values, correct the fault location results and improve the accuracy of the fault location to obtain the fault location result data.

[0071] In this embodiment of the invention, preliminary fault location data and new protection settings are used to correct the fault location, thereby improving the location accuracy. A precision location algorithm, such as iterative least squares or other optimization methods, is applied, combined with actual measurement and theoretical calculation results, to obtain the final fault location data.

[0072] The normalization processing of fault distance data and impedance-based fault location data in this invention ensures comparability between different data sources, providing a unified standard for subsequent data processing and fusion. Weight allocation based on historical fault data allows the location method to self-optimize based on past experience, improving its adaptability and accuracy. Cross-validation and fusion of fault locations enhance the reliability of fault location; integrating data from multiple location methods reduces potential errors associated with a single method. Acquiring preliminary fault location data provides foundational information for further precise location and fault handling. Assessing the impact of distributed generation near the fault point helps understand the contribution of distributed generation to the fault current, which is crucial for analyzing fault characteristics and grid stability. Calculating the distributed generation impact factor provides a more comprehensive perspective for grid fault analysis, enabling more accurate simulation and prediction of grid behavior under fault conditions. Initialization of adaptive protection algorithm parameters and backtracking calculation of theoretical fault current provide a basis for dynamic adjustment of protection devices based on actual fault conditions. Theoretical fault current data can predict and assess the impact of faults on the grid, providing theoretical support for grid protection and recovery. Dynamically adjusting the operating thresholds of protection devices enables the protection system to respond flexibly based on real-time fault conditions, improving the grid's adaptability to faults. The generation of new protection setting values ​​optimizes the performance of protection devices, ensuring reliable operation under various fault conditions. Fault location result correction and precision positioning further improve the accuracy of fault location, ensuring targeted and effective fault handling. Acquiring fault location data provides grid operators with precise fault location information, enabling rapid response and repair, reducing power outage time, and improving grid reliability. Overall, these steps, through the comprehensive application of data normalization, weight allocation, cross-validation, impact assessment, adaptive protection algorithms, and dynamic adjustment technologies, achieve rapid and accurate fault location in the distribution network and optimize the grid's protection response. This not only improves the grid's operational efficiency and reliability but also provides strong technical support for the intelligent management and maintenance of the grid.

[0073] Preferably, step S43 includes the following steps: Step S431: Identify all distributed power sources within the electrical distance range of the fault point based on the distributed power source model and preliminary fault location data, thereby obtaining a list of relevant distributed power sources; This invention integrates GIS data of the power distribution network and a distributed power management system to determine the geographical location and access point of distributed power sources; uses power system analysis software to construct an electrical model of the power grid, including line impedance, transformer parameters, etc.; and develops automated scripts to automatically identify distributed power sources within the electrical distance range based on fault location data and generate a list.

[0074] Step S432: Obtain the operating status of each distributed power source when the fault occurs based on the relevant distributed power source list, thereby obtaining distributed power source operating status data; This invention ensures access to a real-time monitoring system to obtain the operating status of the distributed power source, such as power output and grid connection status; if necessary, a data interface is developed to extract operating status data from the control system of the distributed power source; real-time synchronization of status data is achieved to ensure that the latest operating status can be obtained quickly in the event of a fault.

[0075] Step S433: Based on the fault type information in the preliminary fault location data and the distributed power source model, conduct an impact mechanism analysis on various types of distributed power sources for different fault types, thereby obtaining power source impact relationship data; This invention investigates the mechanism of the impact of different fault types on distributed power sources, involving in-depth literature review and simulation experiments; develops an analytical model that combines fault types with the characteristics of distributed power sources to predict possible impacts; and establishes a data recording system to store power source impact relationship data under different fault types.

[0076] Step S434: Simulate the response behavior of each distributed power source under fault conditions based on the power source influence relationship data and the distributed power source operation status data, thereby obtaining power source fault response data; The embodiments of this invention refine the simulation model to ensure that it can simulate the detailed response of distributed power sources under various fault conditions; run the simulation model to simulate the response behavior of each distributed power source under fault conditions; analyze the simulation results and extract key response data, such as response time, voltage and frequency changes.

[0077] Step S435: Calculate the equivalent impedance from the fault point to each distributed power source based on the topology data and line parameters of the distribution network, thereby obtaining the equivalent impedance data of the fault point-distributed power source; calculate the fault current contribution of each distributed power source to the fault point based on the power source fault response data and the equivalent impedance data, thereby obtaining the power source fault current contribution data. This invention uses power grid analysis tools to analyze the topology of the distribution network and determine the electrical path between the fault point and the distributed power source; applies circuit analysis tools to calculate the equivalent impedance from the fault point to each distributed power source; and combines the equivalent impedance and power source response data to use a calculation model to evaluate the contribution of each distributed power source to the fault current.

[0078] Step S436: Based on the pre-acquired system main power data and power fault current contribution data, perform the calculation of the total fault current of the main power and all distributed power sources to obtain the total system fault current data. This invention integrates detailed data of the main power supply, including its capacity, parameters, and control strategy; calculates the total system fault current including the main power supply and all distributed power supplies; and integrates fault current data from different power supplies to calculate the total fault current.

[0079] Step S437: Calculate the proportion of each distributed power source's fault current contribution to the total fault current based on the power source fault current contribution data and the system's total fault current data, thereby obtaining the distributed power source influence factor.

[0080] The invention provides a tool for calculating the ratio of the fault current contribution of distributed generation sources to the total fault current of the system; analyzing the calculation results, determining the influence factors of each distributed generation source, generating detailed data reports, and providing decision support for grid operators.

[0081] This invention identifies all distributed power sources (DPS) near the fault point using a distributed power source model and preliminary fault location data, ensuring comprehensive consideration of all DPS that may affect fault characteristics. The resulting list of relevant DPS provides foundational data for further analysis of the impact of DPS on the fault. Obtaining the operating status of each DPS at the time of the fault provides detailed information for understanding the actual situation of the power grid during the fault; DPS operating status data can assess the contribution and impact of each DPS on the power grid during the fault. Analyzing the impact mechanism of different fault types on various DPS allows for a deeper understanding of the propagation and impact of faults in the power grid; power source impact relationship data provides a theoretical basis for assessing and quantifying the contribution of DPS to the fault current. Simulating the response behavior of each DPS under fault conditions can predict the dynamic performance of the power source under actual fault conditions; power source fault response data provides important information for understanding the impact of DPS on fault development. Calculating the equivalent impedance from the fault point to each DPS allows for assessing the degree of obstruction of the fault current by the DPS; power source fault current contribution data provides quantitative data for analyzing the specific contribution of DPS to the fault current. The calculation of total fault current, including that from the main power source and all distributed generation sources, provides a comprehensive view of system-level fault current; the total system fault current data enables the assessment of the impact of faults on the entire power grid. Calculating the proportion of fault current contribution from distributed generation sources to the total fault current provides a quantitative indicator for determining the relative importance of distributed generation sources in the impact of faults; obtaining the distributed generation impact factor allows the role of distributed generation sources to be considered in fault analysis and location, improving the accuracy and effectiveness of fault handling. Overall, these steps, by comprehensively considering the operating status, response behavior, and contribution of distributed generation sources to fault current, achieve a comprehensive assessment of the impact of faults. This not only improves the accuracy of fault location but also enhances the power grid's response capability and adaptability to faults, providing crucial support for the stable operation and optimized management of the power grid.

[0082] Preferably, step S5 includes the following steps: Step S51: Analyze the duration and variation characteristics of fault current and voltage based on real-time fault data and fault location results to obtain preliminary fault persistence judgment data. This invention utilizes various monitoring sensors in a power distribution network automation system to collect real-time data such as current and voltage on the lines. The collected real-time data is analyzed, including characteristics such as the peak value and duration of current and voltage at the time of a fault, and the trends and fluctuations of current and voltage before and after the fault. Combined with fault location results, such as the location and type of the fault, the fault characteristics are further analyzed. Based on the above analysis, a preliminary judgment is made as to whether the fault is temporary or persistent.

[0083] Step S52: Check the log records of the pre-acquired protection devices and power distribution automation system to see if there is automatic reclosing or other self-healing operation, thereby obtaining self-healing operation detection data; This invention obtains operation logs from protection devices and power distribution automation systems, analyzes the logs to detect the occurrence of automatic reclosing or other self-healing operations, and records all detected self-healing operation events, including time, type, and result.

[0084] Step S53: Based on the self-healing operation detection data, the real-time fault data that is initially determined to be a temporary fault based on the fault persistence judgment data is subjected to feature extraction based on waveform, duration and amplitude, so as to obtain temporary fault feature data. In this embodiment of the invention, signal processing techniques are used to extract features such as waveform, duration, and amplitude from data determined to be temporary faults; tools such as Python or MATLAB are applied for data analysis and feature extraction; and the extracted feature data is stored in a database for subsequent analysis and model training.

[0085] Step S54: Update the fault feature database using temporary fault feature data to obtain a fault feature update database; In this embodiment of the invention, newly extracted temporary fault feature data is added to the existing fault feature database. Database management tools are used to integrate the new and old data to ensure the consistency and integrity of the information. Sample tests are used to verify whether the updated database can accurately identify temporary fault features.

[0086] Step S55: Based on the self-healing operation detection data, perform an emergency response based on the fault location results data for real-time fault data that is determined to be a persistent fault by the preliminary fault persistence judgment data, thereby achieving fault repair.

[0087] This invention provides an emergency response process for persistent faults, including measures such as fault isolation and load transfer; based on the fault location results, it executes operations in the emergency response plan, such as using an automated system to remotely isolate the fault area; and it records details of all emergency response operations, including operation time, operation type, and operation result.

[0088] This invention analyzes the duration and variation characteristics of fault current and voltage to determine the nature of the fault, distinguishing between temporary and persistent faults. Preliminary fault persistence assessment data provides a basis for subsequent fault handling, enabling the selection of appropriate response strategies. Monitoring the logs of protection devices and distribution automation systems confirms whether automatic reclosing or other self-healing operations have occurred; self-healing operation detection data provides direct evidence for assessing the grid's self-healing capabilities and understanding the grid's response to faults. Feature extraction from real-time fault data of temporary faults allows for the collection of detailed fault information, such as waveform, duration, and amplitude; acquiring temporary fault feature data provides a deeper understanding of fault characteristics, offering data support for fault analysis and grid optimization. Updating the fault feature database using temporary fault feature data ensures the database contains the latest fault information, improving the accuracy and adaptability of fault diagnosis; this updated database provides valuable knowledge resources for the long-term operation and maintenance of the power grid. Emergency response to real-time fault data of persistent faults allows for rapid repair measures based on fault location results; achieving fault repair reduces power outage time, improves grid reliability, and enhances power supply quality for users. Overall, these steps form a closed-loop fault handling process by analyzing fault data in real time, detecting self-healing operations, extracting fault characteristics, updating the fault characteristic database, and conducting emergency responses. This not only improves the power grid's response speed and handling capabilities to faults but also enhances its self-healing and reliability, providing crucial support for the stable operation and continuous optimization of the power grid.

[0089] Example 2 like Figure 4 As shown, based on the same inventive concept as the above embodiments, the present invention also provides a rapid fault location device for power distribution networks, comprising: The first data analysis module is used to acquire the topology data, distributed generation access data, and historical fault data of the distribution network; construct a distributed generation model based on the topology data and distributed generation access data, and perform power flow analysis to obtain current distribution data under normal operating conditions; train a fault feature recognition model based on historical fault data to obtain a fault feature library. The second data analysis module is used to collect voltage, current and phase parameters of distribution network nodes in real time to obtain multi-dimensional real-time parameter data; determine the fault section based on the real-time parameter data, identify the fault type and extract features based on the fault feature library, and conduct comparative analysis of the system state before and after the fault to obtain real-time fault data. The third data analysis module is used to calculate the distance from the fault point to the measurement point based on the traveling wave ranging principle according to real-time fault data, and obtain fault distance data; and to continuously monitor the impedance change of the line section with fault distance data to obtain impedance method fault location data. The fault location module is used to perform cross-verification of fault location based on fault distance data and impedance method fault location data to obtain preliminary fault location data; and to perform result correction and accuracy positioning based on the distributed power source model on the preliminary fault location data using an adaptive protection algorithm to obtain the fault location result data.

[0090] Example 3 like Figure 5 As shown, the present invention also provides an electronic device 100 for implementing the fast fault location method for a distribution network according to the above embodiments. The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104. The memory 101 can be used to store the computer program 103, and the processor 102 implements the steps of the fast fault location method for a distribution network according to Embodiment 1 by running or executing the computer program stored in the memory 101 and calling data stored in the memory 101.

[0091] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback, image playback, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. Furthermore, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. At least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 can be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, and it connects various parts of the electronic device 100 through various interfaces and lines.

[0092] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for rapid fault location in a power distribution network, and the processor 102 can execute multiple instructions to achieve the following: Acquire topology data, distributed generation access data, and historical fault data of the distribution network; construct a distributed generation model based on the topology data and distributed generation access data, and perform power flow analysis to obtain current distribution data under normal operating conditions; train a fault feature recognition model based on historical fault data to obtain a fault feature library. The voltage, current, and phase parameters of the distribution network nodes are collected in real time to obtain multi-dimensional real-time parameter data; the fault section is determined based on the real-time parameter data, and the fault type is identified and features are extracted based on the fault feature library. The system state before and after the fault is compared and analyzed to obtain real-time fault data. Based on real-time fault data, the distance from the fault point to the measurement point is calculated using the traveling wave ranging principle to obtain fault distance data; the impedance change of the line section with fault distance data is continuously monitored to obtain impedance method fault location data. The fault location is cross-validated based on the fault distance data and the impedance method fault location data to obtain preliminary fault location data. The preliminary fault location data is then corrected and the accuracy is improved based on the adaptive protection algorithm according to the distributed power source model to obtain the fault location result data.

[0093] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM). Those skilled in the art should understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

Claims

1. A method for rapid fault location in a distribution network, characterized in that, Includes the following steps: Acquire topology data, distributed generation access data, and historical fault data of the distribution network; construct a distributed generation model based on the topology data and distributed generation access data, and perform power flow analysis to obtain current distribution data under normal operating conditions; A fault feature recognition model is trained based on historical fault data to obtain a fault feature database; Real-time acquisition of voltage, current, and phase parameters at distribution network nodes yields multi-dimensional real-time parameter data; The fault section is determined based on real-time parameter data, and the fault type is identified and features are extracted based on the fault feature library. The system state before and after the fault is compared and analyzed to obtain real-time fault data. Based on real-time fault data, the distance from the fault point to the measurement point is calculated using the traveling wave ranging principle to obtain fault distance data; Continuous monitoring of impedance changes in line sections with fault distance data yields impedance-based fault location data. The fault location is cross-validated based on the fault distance data and the impedance method fault location data to obtain preliminary fault location data. The preliminary fault location data is then corrected and the accuracy is improved based on the adaptive protection algorithm according to the distributed power source model to obtain the fault location result data.

2. The method for rapid fault location in a distribution network according to claim 1, characterized in that, Acquire topology data, distributed generation access data, and historical fault data of the distribution network; construct a distributed generation model based on the topology data and distributed generation access data, and perform power flow analysis to obtain current distribution data under normal operating conditions; A fault feature recognition model is trained based on historical fault data to obtain a fault feature library, including: Obtain the topology data of the power distribution network, including line connections, transformer locations, and switch status; Collect information on the type, capacity, and location of distributed power sources connected to the distribution network to obtain distributed power source access data; A corresponding mathematical model is established based on the power type in the distributed power access data, and the corresponding mathematical model is integrated and embedded into the topology data to obtain a distributed power model containing distributed power sources. Steady-state power flow calculations are performed based on the distributed power source model, and multi-scenario power flow analysis is conducted based on the generation characteristics of the distributed power source, thereby obtaining current distribution data under normal operating conditions. Acquire historical fault data, including fault type, location, and time information; Fault current characteristics and voltage change characteristics are extracted from historical fault data, and fault waveforms are analyzed using signal processing techniques to extract time-domain and frequency-domain characteristics, thereby obtaining a fault feature database.

3. The method for rapid fault location in a distribution network according to claim 2, characterized in that, Steady-state power flow calculations are performed based on a distributed generation model, and multi-scenario power flow analysis is conducted according to the generation characteristics of distributed generation sources, thereby obtaining current distribution data under normal operating conditions, including: The power flow calculation parameters are initialized according to the distributed power source model to obtain the initial state data. The power flow calculation parameters include the convergence condition, the upper limit of the number of iterations, the node voltage, and the phase angle. The Newton-Raphson method is used to calculate the steady-state power flow based on the initial state data, and the impact on the power flow is analyzed based on the distributed generation model, so as to obtain the basic steady-state power flow data. Establish mathematical models for reactive power compensation equipment and voltage regulating transformers; integrate the equipment mathematical models into the calculation process of basic steady-state power flow data, and recalculate the steady-state power flow data and power flow calculation model; Based on the output characteristics of each power source in the distributed power model, multiple operating scenarios are designed, and power flow calculations are performed on each scenario based on the power flow calculation model to obtain power flow calculation results data for multiple scenarios. Based on steady-state power flow data and power flow calculation results from multiple scenarios, current characteristics of each line segment are extracted and statistically analyzed to obtain current distribution data under normal operating conditions.

4. The method for rapid fault location in a distribution network according to claim 1, characterized in that, Real-time acquisition of voltage, current, and phase parameters at distribution network nodes yields multi-dimensional real-time parameter data; The fault section is determined based on real-time parameter data, and the fault type is identified and features are extracted based on the fault feature library. A comparative analysis of the system state before and after the fault is performed to obtain real-time fault data, including: Based on the network topology, the locations of key nodes are determined, fault indicators, transient waveform recording devices, and synchronous phasor measurement units are installed, and equipment parameters are configured, sampling frequencies and trigger conditions are set, thereby obtaining equipment layout scheme data. The fault current vector data is obtained by collecting current amplitude and direction information from the fault indicator in the equipment layout plan data. The transient waveforms are sampled at high speed by a transient waveform recording device in the equipment layout plan data, thereby obtaining transient waveform data; Synchronous phasor data is collected through the synchronous phasor measurement unit in the equipment layout scheme data; Synchronous phasor data, transient waveform data, and fault current vector data are integrated into multi-dimensional real-time parameter data; The fault section is determined based on real-time parameter data, and the fault type is identified and features are extracted based on the fault feature library. The system status before and after the fault is compared and analyzed to obtain real-time fault data.

5. The method for rapid fault location in a distribution network according to claim 4, characterized in that, The fault section is determined based on real-time parameter data, and the fault type is identified and features are extracted based on the fault feature library. A comparative analysis of the system state before and after the fault is performed to obtain real-time fault data, including: The deviation parameters are compared with the real-time parameter data and the steady-state power flow data, and the operating status indicators are evaluated based on the deviation parameters to obtain system status evaluation data. The system status assessment data is used to trigger the fault location process based on a preset threshold, and the initial timestamp of the fault is recorded to obtain the fault trigger signal data. The fault section is initially determined based on the fault current vector data and topology data, thus obtaining preliminary fault section data; the transient waveform data is subjected to fast Fourier transform, and time-domain and frequency-domain features are extracted, thus obtaining transient feature data; The phasor difference before and after the fault is calculated based on the synchronous phasor data, thereby obtaining the phasor change data; Based on the fault feature library, feature matching is performed on transient feature data and phasor change data to obtain preliminary fault type data; Structured data integration is performed on the initial fault section data, transient characteristic data, phasor change data, and initial fault type data to obtain real-time fault data.

6. The method for rapid fault location in a distribution network according to claim 1, characterized in that, Based on real-time fault data, the distance from the fault point to the measurement point is calculated using the traveling wave ranging principle to obtain fault distance data; Continuous impedance change monitoring is performed on the line sections with fault distance data to obtain impedance-based fault location data, including: The waveform data in the real-time fault data is filtered and denoised to obtain the real-time traveling wave data; The wavelet transform technique is used to identify the wave head in real-time traveling wave data and determine the wave arrival time, thereby obtaining the wave head arrival time data. The arrival time difference of the traveling wave between different measurement points is calculated based on the arrival time data of the traveling wave head, thus obtaining the traveling wave time difference data; Based on the pre-acquired line parameters and medium characteristics from the fault point to the measurement point, the traveling wave propagation speed is calculated, and the distance from the fault point to the measurement point is calculated based on the traveling wave time difference data and the traveling wave speed, thereby obtaining preliminary fault distance data for multiple measurement points. The fault distance data is obtained by performing weighted average data fusion based on the preliminary fault distance data from multiple measurement points to improve positioning accuracy; By continuously monitoring the impedance changes of the line section with fault distance data using an online impedance measurement device, fault location data can be obtained using the impedance method.

7. The method for rapid fault location in a distribution network according to claim 1, characterized in that, Based on the fault distance data and the impedance method fault location data, cross-validation of the fault location is performed to obtain preliminary fault location data. Based on the distributed power source model, the preliminary fault location data is corrected and the accuracy is improved using an adaptive protection algorithm to obtain fault location result data, including: The fault distance data and impedance method fault location data are normalized, and the traveling wave method and impedance method are weighted based on historical fault data to obtain the location method weight data. Based on the weighted data of the location method, the fault distance data and the impedance method fault location data are cross-validated and fused to obtain preliminary fault location data. Based on the distributed power source model, the impact of distributed power sources near the fault point is assessed using the preliminary fault location data, and the contribution of distributed power sources to the fault current is calculated, thereby obtaining the distributed power source impact factor. The parameters of the adaptive protection algorithm are initialized based on the distributed power source influence factor, and the theoretical fault current of the fault point is calculated back to obtain the theoretical fault current data. The operating threshold of the protection device is dynamically adjusted based on theoretical fault current data, thereby generating new protection setting values. Based on the preliminary fault location data and protection setting values, the fault location results are corrected and the accuracy is determined to obtain the fault location result data.

8. A rapid fault location device for power distribution networks, characterized in that, include: The first data analysis module is used to acquire topology data of the distribution network, distributed power source access data, and historical fault data. A distributed power model is constructed based on topology data and distributed power source access data, and power flow analysis is performed to obtain current distribution data under normal operating conditions. A fault feature recognition model is trained based on historical fault data to obtain a fault feature database; The second data analysis module is used to collect voltage, current and phase parameters of distribution network nodes in real time to obtain multi-dimensional real-time parameter data. The fault section is determined based on real-time parameter data, and the fault type is identified and features are extracted based on the fault feature library. The system state before and after the fault is compared and analyzed to obtain real-time fault data. The third data analysis module is used to calculate the distance from the fault point to the measurement point based on the traveling wave ranging principle according to real-time fault data, and obtain fault distance data; and to continuously monitor the impedance change of the line section with fault distance data to obtain impedance method fault location data. The fault location module is used to perform cross-verification of fault location based on fault distance data and impedance method fault location data to obtain preliminary fault location data; and to perform result correction and accuracy positioning based on the distributed power source model on the preliminary fault location data using an adaptive protection algorithm to obtain the fault location result data.

9. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the method for rapid fault location in a distribution network as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the method for rapid fault location in a power distribution network as described in any one of claims 1 to 7.

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