A FTU disconnection fault monitoring method and related equipment

Abstract

The present invention relates to a method for monitoring a line break fault of an FTU, comprising the following steps: collecting electrical data of a line of the FTU through a preset electrical mutual inductor to obtain electrical data; wherein the electrical data includes voltage data and current data; performing phase angle calculation on the voltage data and the current data to obtain a phase angle difference; if the phase angle difference is not within a standard range, calling a database to obtain attributes of the line of the FTU; locating the line break based on the attributes of the line, the phase angle and the electrical data through a preset distributed parameter model to obtain a line break area; performing dynamic feature recognition on electrical parameters corresponding to the line break area to obtain dynamic fault features, performing fault type analysis on the line break area based on the dynamic fault features to obtain a fault type monitoring report. The method solves the technical problem that traditional fault monitoring methods rely on manual inspections and simple fault indicators, making it difficult to meet positioning accuracy and determine fault types.

Description

Technical Field

[0001] The present invention relates to the technical field of fault monitoring, and in particular to a method for monitoring a disconnection fault of an FTU and related equipment. Background Art

[0002] As power systems expand in size and complexity, timely and accurate monitoring of line faults is becoming increasingly important. Traditional fault monitoring methods rely on manual inspections and simple fault indicators. These methods have limitations in fault response speed, location accuracy, and fault type determination, making them difficult to meet the high efficiency and reliability requirements of modern power systems. Line faults, in particular, are sudden and have a wide impact. Once they occur, they can cause widespread power outages, seriously impacting the safe and stable operation of power systems. Therefore, it is particularly urgent to develop a method that can automatically, quickly, and accurately monitor and locate line faults. Summary of the Invention

[0003] The main purpose of the present invention is to provide a FTU disconnection fault monitoring method and related equipment, which solves the technical problem that traditional fault monitoring methods rely on manual inspections and simple fault indicators, and are difficult to meet the requirements of positioning accuracy and fault type judgment.

[0004] To achieve the above object, the present invention provides a method for monitoring a line disconnection fault of an FTU, comprising the following steps:

[0005] The electrical data of the FTU circuit is collected through the preset electrical transformer to obtain electrical data; wherein the electrical data includes voltage data and current data;

[0006] Performing phase angle calculation on the voltage data and the current data to obtain a phase angle difference;

[0007] Determine whether the phase angle difference is within a standard range. If the phase angle difference is not within the standard range, call the database to obtain the attributes of the FTU line;

[0008] Using a preset distributed parameter model, based on the attributes of the line, the phase angle and the electrical data, the line is disconnected and a disconnection area is obtained;

[0009] Dynamic feature recognition is performed on the electrical parameters corresponding to the disconnection area to obtain dynamic features of the fault, and a fault type analysis is performed on the disconnection area based on the dynamic features of the fault to obtain a fault type monitoring report.

[0010] Furthermore, electrical data of the FTU circuit is collected through a preset electrical transformer to obtain electrical data, including:

[0011] The electrical signal of the FTU circuit is collected through the preset electrical transformer to obtain the electrical analog signal;

[0012] Converting the electrical analog signal into a digital signal using a preset ADC converter to obtain a digital conversion signal;

[0013] amplifying the digital conversion signal to obtain a digital amplified signal;

[0014] A voltage signal and a current signal in the digitally amplified signal are extracted to obtain an electrical signal, and the electrical signal is used as electrical data.

[0015] Furthermore, performing phase angle calculation on the voltage data and the current data to obtain a phase angle difference includes:

[0016] Using a preset clock system to perform time sequence synchronization on the voltage data and the current data respectively, to obtain corresponding voltage time sequence synchronization data and current time sequence synchronization data;

[0017] Converting the voltage time-series synchronization data and the current time-series synchronization data into complex form to obtain corresponding voltage complex form data and current complex form data; wherein the complex form includes a real part and an imaginary part, the real part represents the instantaneous amplitude of the electrical signal, and the imaginary part reflects the phase information of the electrical signal;

[0018] Performing phase angle calculation on the voltage complex form data to obtain a voltage phase angle;

[0019] Performing phase angle calculation on the current complex form data to obtain the current phase angle;

[0020] A phase angle difference is calculated for the voltage phase angle and the current phase angle point by point to obtain a phase angle difference.

[0021] Furthermore, the line is located based on the line attributes, the phase angle and the electrical data through a preset distributed parameter model to obtain a line break area, including:

[0022] Inputting the line attributes, the phase angle, and the electrical data into a preset distributed parameter model to identify the fault location and obtain a potential fault identification location;

[0023] Extracting a fault signal from the potential fault identification location to obtain a fault characteristic signal; wherein the fault characteristic signal includes an impedance change characteristic signal and a reflection waveform characteristic signal;

[0024] Performing waveform calculation on the impedance change characteristic signal to obtain abnormal waveform parameters; wherein the abnormal waveform parameters include peak distortion coefficient, zero-crossing drift, and increased harmonic content;

[0025] Performing spectrum calculation on the reflected waveform characteristic signal to obtain a spectrum distortion index; wherein the spectrum distortion index includes harmonic phase angle, spectrum flatness, and interharmonic distortion rate;

[0026] By using a preset traveling wave algorithm, the line is located on the basis of the abnormal waveform parameters and the spectrum distortion index to obtain the line break area.

[0027] Furthermore, the line is located based on the abnormal waveform parameters and the spectrum distortion index by using a preset traveling wave algorithm to obtain a disconnection area, including:

[0028] Extracting key parameters of the abnormal waveform parameters to obtain abnormal key parameters; wherein the abnormal key parameters include the fault occurrence time, wavefront steepness, and waveform zero crossing time;

[0029] Performing frequency domain identification on the spectrum distortion index to obtain harmonic frequencies and frequency response mutations; wherein the frequency response mutations include changes in harmonic frequencies and shifts in fundamental frequencies;

[0030] Obtaining a time difference between at least two preset sensors when receiving a fault traveling wave, and obtaining a distance from the fault point to each of the sensors based on the time difference and the speed of the fault traveling wave;

[0031] Within the distance from the fault point to each of the sensors, the abnormal key parameters and the sudden changes in harmonic frequency and frequency response are used to locate the disconnection and obtain the disconnection area.

[0032] Furthermore, dynamic feature recognition is performed on the electrical parameters corresponding to the disconnection area to obtain dynamic features of the fault, including:

[0033] Extracting a fault signal corresponding to the disconnection area to obtain a fault extraction feature signal;

[0034] Performing spectrum content analysis on the fault extraction feature signal using fast Fourier transform to obtain a spectrum feature set; wherein the spectrum feature set includes frequency harmonics, subharmonic components and amplitude changes;

[0035] Performing signal spectrum calculation on the spectrum feature set to obtain an entropy value of the fault feature signal and an energy distribution of the fault feature signal;

[0036] Drawing a frequency drift trajectory of the fault characteristic signal based on the entropy value of the fault characteristic signal and the energy distribution of the fault characteristic signal;

[0037] The fault characteristic signal is captured and identified based on the frequency drift trajectory, and the fault dynamic characteristics of the fault characteristic signal evolving over time are obtained.

[0038] Furthermore, based on the dynamic characteristics of the fault, the fault type of the disconnected area is analyzed to obtain a fault type monitoring report, including:

[0039] constructing a fault dynamic matrix based on the fault dynamic characteristics;

[0040] Performing projection dimensionality reduction on the fault dynamic matrix to obtain a projection matrix;

[0041] Input the projection matrix into a preset fault type analysis algorithm for a decomposition to obtain a classification matrix and a probability matrix mapped by the classification matrix; wherein the elements of the classification matrix represent classification labels, and the elements of the probability matrix represent probability values ​​corresponding to the classification labels;

[0042] Determine whether the probability values ​​corresponding to the elements of the probability matrix are within a preset probability value. If not, delete the classification matrix elements that are not within the preset probability value to obtain a deletion matrix.

[0043] Inputting the deletion matrix into a preset fault type analysis algorithm for secondary decomposition until the probability values ​​corresponding to the elements of the deletion matrix are within the preset probability values;

[0044] If the probability value corresponding to the deleted matrix element is within the preset probability value, the standard classification matrix is ​​obtained;

[0045] Based on the standard classification matrix, a fault type analysis is performed on the disconnection area to obtain a fault type monitoring report.

[0046] The present invention also provides a FTU disconnection fault monitoring device, comprising:

[0047] The acquisition module is used to collect electrical data of the FTU circuit through a preset electrical transformer to obtain electrical data; wherein the electrical data includes voltage data and current data;

[0048] a calculation module, configured to perform phase angle calculation on the voltage data and the current data to obtain a phase angle difference;

[0049] A calling module is used to determine whether the phase angle difference is within a standard range. If the phase angle difference is not within the standard range, the database is called to obtain the attributes of the FTU line;

[0050] a positioning module, configured to locate the line break based on the line attributes, the phase angle, and the electrical data using a preset distributed parameter model to obtain a break area;

[0051] The analysis module is used to perform dynamic feature recognition on the electrical parameters corresponding to the disconnection area to obtain dynamic fault features, perform fault type analysis on the disconnection area based on the dynamic fault features, and obtain a fault type monitoring report.

[0052] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0053] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0054] The present invention provides a method for monitoring a line break fault of an FTU, comprising the following steps: collecting electrical data of the line of the FTU through a preset electrical mutual inductor to obtain electrical data; wherein the electrical data includes voltage data and current data; performing phase angle calculation on the voltage data and the current data to obtain a phase angle difference; judging whether the phase angle difference is within a standard range, and if the phase angle difference is not within the standard range, calling a database to obtain the attributes of the line of the FTU; locating the line break based on the attributes of the line, the phase angle and the electrical data through a preset distributed parameter model to obtain a line break area; performing dynamic feature identification on the electrical parameters corresponding to the line break area to obtain a dynamic fault feature, and performing a fault type analysis on the line break area based on the dynamic fault feature to obtain a fault type monitoring report; the above technical solution solves the technical problem that the traditional fault monitoring method relies on manual inspections and simple fault indicators, which is difficult to meet the positioning accuracy and fault type judgment requirements, and achieves fast and accurate fault monitoring and positioning, which helps to isolate faults in a timely manner, prevent fault spread, and ensure the stable operation of the power system and the continuity of power supply to users. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 1 is a schematic diagram of the steps of a method for monitoring a line break fault of an FTU according to an embodiment of the present invention;

[0056] Figure 2 1 is a structural block diagram of a line break fault monitoring device for an FTU according to an embodiment of the present invention;

[0057] Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0058] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0060] like Figure 1 As shown, Figure 1 1. It is a schematic diagram of the steps of a method for monitoring a line break fault of an FTU according to one embodiment of the present invention;

[0061] In one embodiment of the present invention, a method for monitoring a FTU line break fault is provided, comprising the following steps:

[0062] Step S1, collecting electrical data of the FTU circuit through a preset electrical transformer to obtain electrical data; wherein the electrical data includes voltage data and current data.

[0063] Specifically, in this step, electrical data collection from the FTU's lines is performed using pre-installed electrical transformers, a fundamental step in power system fault monitoring and management. Specifically, this process involves the following key points: Electrical transformers: These are common sensor devices in power systems. They are pre-installed (i.e., "pre-installed") at key nodes on the power lines monitored by the Feeder Terminal Unit (FTU). They sense and convert current and voltage signals, making them suitable for further measurement and analysis. Current transformers primarily detect current flow within the lines, while voltage transformers measure voltage levels. FTU lines: FTUs, or Feeder Terminal Units, are intelligent devices installed on feeders in power distribution networks. They monitor line status and perform protection and control functions. "FTU lines" here refers to the portion of the power transmission network directly monitored by the FTU, including all cables or overhead lines from the substation to the user access point. Electrical data collection: This involves the continuous collection of real-time electrical parameter activity on the power lines using electrical transformers. This data primarily includes voltage and current data. Voltage data reflects the potential difference between the two ends of a power line, while current data indicates the intensity of charge flow through the line. These two data sets are essential for assessing line operating status and identifying abnormal conditions. Obtaining electrical data: Through transformer conversion and signal conditioning, the raw physical quantities (current and voltage) are converted into easily processable electrical or digital signals, which are then recorded by a data acquisition system. This collected electrical data—real-time voltage and current measurements—forms the basis for subsequent fault detection, phase angle calculation, and various analyses. It is crucial for determining whether the line is operating properly and for rapidly responding to faults. In summary, this step, through physical equipment (electrical transformers) and electronic technology, enables real-time monitoring of key electrical parameters of power lines, providing essential data support for fault warning, location, and maintenance of power systems.

[0064] Step S2: performing phase angle calculation on the voltage data and the current data to obtain a phase angle difference.

[0065] Specifically, calculating the phase angle of the voltage and current data to obtain the phase angle difference is a crucial analysis step in power system monitoring. The voltage and current data are real-time measurements collected from the lines monitored by the FTU using electrical transformers in the previous step. Phase angle is a fundamental characteristic of alternating current (AC), representing the instantaneous position of the voltage or current waveform relative to a reference point (typically the zero-phase reference point). In a three-phase system, the phase angle also relates to the relative positions of the phases. Phase angle calculation involves converting the voltage and current waveform data into complex form, typically using signal processing techniques such as fast Fourier transform (FFT) to convert the time-domain signals into the frequency domain, enabling direct reading of the voltage and current phase angles. By comparing the phase relationship of each pair of voltage and current data, the phase angle difference can be determined. Phase angle difference: Under normal power system operation, the load is typically purely resistive. In this case, the current and voltage are in phase, and the phase angle difference is close to zero. However, when a line fault such as a break, short circuit, or ground fault occurs, the line's impedance characteristics change, causing the phase relationship between voltage and current to change. This means the phase angle difference is no longer zero or the standard value. Therefore, by calculating and analyzing these phase angle differences, the operating status of the line and potential fault information can be reflected. In summary, calculating the phase angles of voltage and current data and further deriving the phase angle difference can serve as an effective fault warning and preliminary diagnosis method, as it can sensitively reflect abnormal changes in electrical parameters in the power system, providing important clues for subsequent fault location and resolution.

[0066] Step S3: determine whether the phase angle difference is within a standard range. If the phase angle difference is not within the standard range, call the database to obtain the attributes of the FTU line.

[0067] Specifically, in monitoring power systems, determining whether the phase angle difference is within a standard range is a key step in assessing the normal operation of a line. The phase angle difference refers to the phase difference between voltage and current signals, obtained by analyzing them. Under normal operating conditions, the phase angle difference typically remains within a preset standard range, reflecting the expected state of system design and operation. If the phase angle difference is outside the standard range, this typically indicates a potential line anomaly, such as a line break, short circuit, or sudden load change. These faults or anomalies affect the line's impedance characteristics, leading to a change in the voltage-current phase relationship. At this point, the system automatically triggers the next action—calling the database to retrieve the FTU's line attributes. Calling the database to retrieve the FTU's line attributes means the system uses pre-entered database information to query detailed line information associated with the FTU. These attributes include, but are not limited to, line length, material, cross-sectional area, geographic location, and historical fault records. This information is crucial for subsequent fault location and resolution. By combining the real-time monitored phase angle difference abnormal information with the specific properties of the line, the cause of the fault can be analyzed more accurately, guiding fault investigation and emergency repair work, and ensuring the stable operation and rapid recovery of the power system.

[0068] Step S4: using a preset distributed parameter model, based on the attributes of the line, the phase angle and the electrical data, the line is disconnected and a disconnection area is obtained.

[0069] Specifically, in the field of power system fault diagnosis, locating line breaks using a pre-set distributed parameter model is an advanced analytical method. The operational process for this step can be understood as follows: The distributed parameter model is a mathematical model used to describe and analyze the behavior of continuously distributed systems such as power lines. Compared to lumped parameter models, it considers the spatial distribution of system parameters (such as resistance, inductance, and capacitance) and is more suitable for handling transient phenomena on long-distance transmission lines, such as traveling wave propagation and reflection. The model pre-sets the physical laws and mathematical relationships of the line, enabling simulation and calculation of the line's response under various operating conditions. Based on the line's attributes, "attributes" here refer to information related to the line's physical structure and electrical characteristics, including but not limited to line length, conductor type, cross-sectional area, material, and geographic environment. These attributes directly affect the line's impedance and signal propagation characteristics and are essential input parameters for the model. The phase angle and electrical data are derived from the phase angle difference calculated in the previous step, along with real-time collected voltage and current electrical data. This information directly reflects the fault characteristics. Abnormal changes in the phase angle indicate changes in the system impedance, while the electrical data provides specific values ​​for the energy flow and the relationship between voltage and current. The combination of the two is the key basis for locating line breaks. Locating line breaks: The above information is input into the distributed parameter model, and the model simulates the propagation and reflection process of the traveling wave in the line when a fault occurs. By calculating the time difference (TOA) between the arrival of the traveling wave at different monitoring points or analyzing the characteristics of the traveling wave, combined with the line properties and electrical parameters, the model can reversely trace and locate the area where the fault occurred, that is, determine the line break area. In summary, this step integrates physical models, real-time monitoring data and basic line information, and uses advanced computing technology to achieve accurate positioning of power line break faults, providing a scientific basis for rapid response and emergency repairs.

[0070] Step S5: Dynamically identify the electrical parameters corresponding to the disconnected area to obtain dynamic fault characteristics, and analyze the fault type of the disconnected area based on the dynamic fault characteristics to obtain a fault type monitoring report.

[0071] Specifically, in the power system fault diagnosis process, dynamic feature identification of the electrical parameters corresponding to the disconnected area is a key step in gaining a deeper understanding of the fault's characteristics and progression. Specifically, this process includes: Dynamic feature identification: First, for the located disconnected area, the system intensively collects the area's electrical parameters, such as instantaneous voltage and current changes, as well as the trends of these parameters over time. This data reflects the dynamic changes in the line state after the disconnection, including but not limited to current surges, voltage drops, and frequency fluctuations. Advanced signal processing techniques, such as wavelet analysis and empirical mode decomposition (EMD), are used to extract features reflecting the dynamic behavior of the fault from the continuous electrical parameters, such as changes in specific frequency components, waveform distortion, and oscillation patterns. These are the dynamic fault features. Fault type analysis: Next, based on the extracted dynamic fault features, machine learning algorithms or expert systems are used to conduct an in-depth analysis of the type of disconnection fault. This analysis process involves the construction of feature vectors, pattern recognition, and the application of classification algorithms. For example, by comparing the characteristics with a database of known fault patterns, it is possible to identify whether the fault is a single-phase, two-phase, or multi-phase outage, or whether it is accompanied by other complex conditions such as a ground fault. The algorithm improves the accuracy and speed of identification by learning from the characteristic manifestations of similar faults in history. Fault Type Monitoring Report Generation: Finally, the above analysis results are summarized and compiled into a fault type monitoring report. This report not only clearly identifies the specific type of outage but also includes dynamic feature analysis details supporting this determination, such as characteristic parameter values, waveform charts, and spectrum analysis results. The report may also assess the severity of the fault, the scope of impact, and potential risks, providing a scientific basis for subsequent emergency repair decisions and system restoration. Through this series of analysis processes, power system maintenance personnel can quickly grasp the comprehensive situation of the outage and implement targeted countermeasures to ensure a safe and stable power supply.

[0072] In a specific embodiment, electrical data of the FTU circuit is collected through a preset electrical transformer to obtain electrical data, including:

[0073] The electrical signal of the FTU circuit is collected through the preset electrical transformer to obtain the electrical analog signal;

[0074] Converting the electrical analog signal into a digital signal using a preset ADC converter to obtain a digital conversion signal;

[0075] amplifying the digital conversion signal to obtain a digital amplified signal;

[0076] A voltage signal and a current signal in the digitally amplified signal are extracted to obtain an electrical signal, and the electrical signal is used as electrical data.

[0077] Specifically, real-time electrical status monitoring of distribution network lines is a critical component of automated monitoring and protection mechanisms for power systems. The proposed solution details how to acquire and process electrical data from physical lines through a series of devices and steps. The specific steps are as follows: Electrical Signal Acquisition: First, pre-installed electrical transformers are used to monitor the lines managed by the feeder terminal unit (FTU). Electrical transformers are sensors that safely sense electrical signals from high-voltage or high-current lines and convert them into measurable low-voltage or low-current analog signals without directly interfering with the operation of the main circuit. This process ensures operator safety and effectively captures information about the actual operating status of the lines. Analog Signal to Digital Conversion: Next, the acquired electrical analog signals need to be digitized for computer processing and remote transmission. This step is accomplished using a pre-installed ADC (analog-to-digital converter). The ADC converts continuously varying analog signals into discrete digital signals by sampling and quantizing the analog signals, enabling digital storage and analysis. Digital Signal Amplification: The converted digital signals often require further processing to enhance their readability and analytical accuracy. Digital amplification applies amplification to these signals, increasing their amplitude and ensuring that even subtle signal details can be clearly identified and analyzed. This is crucial for accurately detecting line conditions. Signal Extraction and Electrical Data Formation: Finally, the amplified digital signals contain line voltage and current information. Software or hardware filtering and signal processing techniques are used to extract the voltage and current signals from this composite signal. These signals are key indicators for assessing line health, power flow, and potential faults. The extracted voltage and current information is integrated to form complete electrical data. This data can then be used for a variety of applications, including real-time monitoring, fault diagnosis, and load forecasting, helping power companies efficiently manage their distribution networks. In summary, this solution, through a series of precise operations, effectively transforms physical lines into analyzable data, providing a solid data foundation for intelligent power system management.

[0078] In a specific embodiment, performing phase angle calculation on the voltage data and the current data to obtain a phase angle difference includes:

[0079] Using a preset clock system to perform time sequence synchronization on the voltage data and the current data respectively, to obtain corresponding voltage time sequence synchronization data and current time sequence synchronization data;

[0080] Converting the voltage time-series synchronization data and the current time-series synchronization data into complex form to obtain corresponding voltage complex form data and current complex form data; wherein the complex form includes a real part and an imaginary part, the real part represents the instantaneous amplitude of the electrical signal, and the imaginary part reflects the phase information of the electrical signal;

[0081] Performing phase angle calculation on the voltage complex form data to obtain a voltage phase angle;

[0082] Performing phase angle calculation on the current complex form data to obtain the current phase angle;

[0083] A phase angle difference is calculated for the voltage phase angle and the current phase angle point by point to obtain a phase angle difference.

[0084] Specifically, in power system fault monitoring, accurately calculating the phase angle difference between voltage and current is a key step in identifying system abnormalities. The following is a detailed explanation of the steps provided: Timing Synchronization: To ensure the consistency of voltage and current data on the time axis, a preset clock system is used to precisely synchronize the two. This ensures that, regardless of when the voltage and current signals are collected, their corresponding time points are perfectly aligned, which is the basis for subsequent phase analysis. Timing synchronization yields synchronized voltage and current data, ensuring accurate analysis. Complex Form Conversion: The synchronized time series data is further converted to complex form, a common and powerful technique in signal processing. Complex representation can simultaneously capture both signal amplitude and phase information, with the real part representing the instantaneous amplitude (i.e., signal strength) and the imaginary part reflecting the phase information (i.e., the signal's position on the time axis). This conversion provides a convenient mathematical foundation for subsequent phase calculations, resulting in complex voltage and current data, respectively. Phase Angle Calculation: Next, the phase angle is calculated for the converted complex voltage and current data. The phase angle is the angle between the signal waveform and a reference point (usually the positive zero crossing point). It directly reflects the signal's phase state. Through mathematical operations, the voltage and current phase angles are extracted from the complex number's amplitude angle. These angles are typically measured in degrees or radians. Phase angle difference calculation: Finally, the voltage and current phase angles at each corresponding time point are calculated point by point to determine the phase difference between them. This phase difference is an important indicator for analyzing the operating status of power systems, especially for identifying fault types (such as line breaks and short circuits). Abnormal phase angle differences often indicate the presence of faults or nonlinear loads in the system, providing direct clues for fault diagnosis. In summary, through strict time synchronization, complex number conversion, and analysis, the resulting phase angle differences provide valuable information for assessing the operating status of power lines and identifying fault areas, providing a scientific basis for fault monitoring and maintenance in power systems.

[0085] In a specific embodiment, a line is located based on the attributes of the line, the phase angle, and the electrical data using a preset distributed parameter model to obtain a line break area, including:

[0086] Inputting the line attributes, the phase angle, and the electrical data into a preset distributed parameter model to identify the fault location and obtain a potential fault identification location;

[0087] Extracting a fault signal from the potential fault identification location to obtain a fault characteristic signal; wherein the fault characteristic signal includes an impedance change characteristic signal and a reflection waveform characteristic signal;

[0088] Performing waveform calculation on the impedance change characteristic signal to obtain abnormal waveform parameters; wherein the abnormal waveform parameters include peak distortion coefficient, zero-crossing drift, and increased harmonic content;

[0089] Performing spectrum calculation on the reflected waveform characteristic signal to obtain a spectrum distortion index; wherein the spectrum distortion index includes harmonic phase angle, spectrum flatness, and interharmonic distortion rate;

[0090] By using a preset traveling wave algorithm, the line is located on the basis of the abnormal waveform parameters and the spectrum distortion index to obtain the line break area.

[0091] Specifically, in power system fault diagnosis, the aforementioned line-break fault location solution utilizes a distributed parameter model and advanced signal processing technology. The specific steps are as follows: Model Input and Potential Fault Identification: First, known line attributes (such as line length, material, and cross-sectional area), phase angle information (reflecting the phase relationship between current and voltage), and real-time electrical data (including voltage and current signals) are integrated into a pre-defined distributed parameter model. Based on the laws of physics and line characteristics, the distributed parameter model conducts a comprehensive line analysis through complex mathematical calculations and simulations, preliminarily identifying potential fault locations—i.e., areas suspected of line breaks. Fault Signature Signal Extraction: Fault signal extraction is performed at these potential fault locations, isolating the signal components directly related to the fault. These signals are subdivided into impedance variation signature signals and reflection waveform signature signals. Impedance variation reflects line impedance anomalies, while reflection waveforms are unique signal patterns caused by traveling wave reflections at the breakpoint. Together, these signals constitute the fault signature signal. Abnormal Waveform Parameter Analysis: In-depth waveform calculations are performed on the impedance change characteristic signal to quantify the degree of abnormality and extract abnormal waveform parameters such as peak distortion (the degree of deformation at the top of the waveform), zero-crossing drift (the shift in the waveform zero-crossing position), and increased harmonic content (an increase in non-fundamental components). These parameters reveal the electrical parameter changes caused by the disconnection. Spectral Distortion Index Calculation: Spectral calculations are performed on the reflected waveform characteristic signal to analyze its frequency domain performance, obtaining spectral distortion indicators, including harmonic phase angle (the phase shift of harmonics relative to the fundamental), spectral flatness (the uniformity of spectral energy distribution), and interharmonic distortion rate (the content of components that are not integer multiples of the fundamental frequency). These indicators help identify specific fault characteristics. The Traveling Wave Algorithm Locates the Disconnection Area: Finally, using the preset traveling wave algorithm, combined with the previously obtained abnormal waveform parameters and spectral distortion indicators, the propagation and reflection characteristics of the traveling wave at the fault point are calculated to accurately locate the disconnection area. The algorithm compares the fault characteristic signal with the waveform predicted by the model and uses time difference information to narrow the location range, ultimately determining the exact location of the disconnection. The entire process integrates physical model simulation, signal processing technology and algorithm analysis, achieving efficient and accurate positioning of power line break faults and providing strong technical support for rapid fault repair.

[0092] In a specific embodiment, a preset traveling wave algorithm is used to locate the line break based on the abnormal waveform parameters and the spectrum distortion index to obtain the break area, including:

[0093] Extracting key parameters of the abnormal waveform parameters to obtain abnormal key parameters; wherein the abnormal key parameters include the fault occurrence time, wavefront steepness, and waveform zero crossing time;

[0094] Performing frequency domain identification on the spectrum distortion index to obtain harmonic frequencies and frequency response mutations; wherein the frequency response mutations include changes in harmonic frequencies and shifts in fundamental frequencies;

[0095] Obtaining a time difference between at least two preset sensors when receiving a fault traveling wave, and obtaining a distance from the fault point to each of the sensors based on the time difference and the speed of the fault traveling wave;

[0096] Within the distance from the fault point to each of the sensors, the abnormal key parameters and the sudden changes in harmonic frequency and frequency response are used to locate the disconnection and obtain the disconnection area.

[0097] Specifically, the line break location solution adopts a comprehensive analysis method to accurately locate the line break area in the power line through multiple steps, as follows: Key parameter extraction: First, the most critical indicators are extracted from the abnormal waveform parameters, including the moment of fault occurrence, that is, the time point when the fault first appears; the wavefront steepness, which reflects the sharp change in the slope of the waveform front, which is related to the intensity of energy release when the fault occurs; and the waveform zero-crossing time, which identifies the waveform changes caused by the fault by analyzing the deviation of the waveform zero-crossing point. These key parameters provide a direct basis for fault identification and location. Frequency domain identification and harmonic analysis: Then, the spectrum distortion indicators are deeply analyzed to identify the changes in harmonic frequency, that is, how the frequency components other than the fundamental wave are abnormal due to the fault; at the same time, the sudden changes in the frequency response are observed, including the increase or decrease of the harmonic frequency and the shift of the fundamental frequency. These sudden changes reveal the impact of the fault on the frequency characteristics of the system and further indicate the fault characteristics. Time Difference Calculation and Distance Estimation: Using at least two sensors placed at different locations, the time difference (TOA) between each receiving the fault's traveling wave is measured. Combined with the known propagation speed of the traveling wave (approximately equal to the speed of light), the approximate distance from each sensor to the fault can be estimated by simply multiplying the time difference by the speed formula. Comprehensive Location of the Disconnected Area: Finally, a pre-defined traveling wave algorithm is used to perform a comprehensive analysis based on key parameters extracted from the abnormal waveform (fault onset time, wavefront steepness, waveform zero-crossing time), frequency domain analysis results (harmonic frequency changes and frequency response abrupt changes), and the distance information from each sensor to the fault. This algorithm utilizes a complex mathematical model capable of processing this multi-source information. Through an optimization algorithm or iterative process, one or more most likely disconnected areas are ultimately determined within the distance range from the fault to each sensor. This step improves location accuracy and reliability by integrating time and frequency domain features, providing precise fault location information for rapid response and repair.

[0098] In a specific embodiment, dynamic feature recognition is performed on the electrical parameters corresponding to the disconnection area to obtain dynamic features of the fault, including:

[0099] Extracting a fault signal corresponding to the disconnection area to obtain a fault extraction feature signal;

[0100] Performing spectrum content analysis on the fault extraction feature signal using fast Fourier transform to obtain a spectrum feature set; wherein the spectrum feature set includes frequency harmonics, subharmonic components and amplitude changes;

[0101] Performing signal spectrum calculation on the spectrum feature set to obtain an entropy value of the fault feature signal and an energy distribution of the fault feature signal;

[0102] Drawing a frequency drift trajectory of the fault characteristic signal based on the entropy value of the fault characteristic signal and the energy distribution of the fault characteristic signal;

[0103] The fault characteristic signal is captured and identified based on the frequency drift trajectory, and the fault dynamic characteristics of the fault characteristic signal evolving over time are obtained.

[0104] Specifically, this step involves conducting an in-depth electrical parameter analysis of the located fault area to identify the dynamic characteristics of the fault. Details are as follows: Fault Signal Extraction: First, the signal portion directly related to the fault is extracted from the monitoring data of the fault area. This process is called fault feature extraction. This process aims to separate the fault signal from the complex background noise of the power grid, providing a clean input for subsequent analysis. Spectral Content Analysis: Using the Fast Fourier Transform (FFT), the extracted fault feature signal is converted from the time domain to the frequency domain to obtain a spectral feature set. This transformation reveals the different frequency components contained in the signal, with particular attention paid to harmonics (frequencies that are integer multiples of the fundamental wave), subharmonics (frequencies that are non-integer multiples of the fundamental wave), and their corresponding amplitude variations. This information is crucial for understanding the nature of the fault. Signal Spectrum Calculation: Further, the spectral feature set is analyzed to calculate the entropy and energy distribution of the fault feature signal. Entropy measures the complexity or uncertainty of the signal spectrum, while energy distribution reflects the contribution of different frequency components. Together, they provide an overview of the spectral characteristics of the fault signal. Frequency drift trajectory drawing: Based on the entropy value and energy distribution calculated above, the frequency drift trajectory of the fault characteristic signal is displayed graphically. This trajectory can help intuitively understand the changing trend of the fault over time, that is, the dynamic behavior of the fault signal in the frequency domain, including frequency offset or fluctuation. Fault dynamic feature identification: Finally, by analyzing the frequency drift trajectory, the dynamic characteristics of the fault characteristic signal evolving over time are captured and identified. This means identifying the specific pattern of fault development, the changing laws of frequency characteristics, etc., which is extremely important for diagnosing the fault type, assessing the severity of the fault, and predicting the development trend of the fault. The entire process improves the accuracy and timeliness of fault diagnosis by tracking and analyzing these dynamic characteristics.

[0105] In a specific embodiment, a fault type analysis is performed on the disconnection area based on the dynamic characteristics of the fault to obtain a fault type monitoring report, including:

[0106] constructing a fault dynamic matrix based on the fault dynamic characteristics;

[0107] Performing projection dimensionality reduction on the fault dynamic matrix to obtain a projection matrix;

[0108] Input the projection matrix into a preset fault type analysis algorithm for a decomposition to obtain a classification matrix and a probability matrix mapped by the classification matrix; wherein the elements of the classification matrix represent classification labels, and the elements of the probability matrix represent probability values ​​corresponding to the classification labels;

[0109] Determine whether the probability values ​​corresponding to the elements of the probability matrix are within a preset probability value. If not, delete the classification matrix elements that are not within the preset probability value to obtain a deletion matrix.

[0110] Inputting the deletion matrix into a preset fault type analysis algorithm for secondary decomposition until the probability values ​​corresponding to the elements of the deletion matrix are within the preset probability values;

[0111] If the probability value corresponding to the deleted matrix element is within the preset probability value, the standard classification matrix is ​​obtained;

[0112] Based on the standard classification matrix, a fault type analysis is performed on the disconnection area to obtain a fault type monitoring report.

[0113] Specifically, this step involves in-depth data processing and analysis of the acquired fault dynamic features to accurately identify the fault type in the disconnected area and generate a detailed monitoring report. The specific process is as follows: Constructing a Fault Dynamic Matrix: First, the extracted fault dynamic features are organized into a mathematical representation, the fault dynamic matrix. This matrix integrates all information about the fault's temporal evolution, providing a structured data foundation for subsequent fault type analysis. Projection Dimensionality Reduction: Because the original fault dynamic matrix may contain a large number of characteristic dimensions, projection dimensionality reduction techniques are used to convert it into a more compact projection matrix to improve analysis efficiency and reduce computational complexity. This step aims to retain the most critical information while removing redundant dimensions. Primary Decomposition and Classification Matrix Generation: Next, the resulting projection matrix is ​​input into a pre-defined fault type analysis algorithm for a first decomposition. This process produces two outputs: a classification matrix and a probability matrix. Each element in the classification matrix represents a fault type label, while the corresponding element in the probability matrix represents the confidence or probability of the fault type being identified. Probability screening: The probability matrix is ​​examined. If the probability values ​​of certain categories fall below a preset threshold (i.e., fall outside the preset probability range), the corresponding low-probability elements in the classification matrix are deleted, generating a deletion matrix. This operation helps eliminate ambiguous or low-confidence classification results. Secondary decomposition until conditions are met: The fault type analysis algorithm is repeated, further decomposing the deletion matrix until the probability values ​​of all remaining categories meet or exceed the preset probability standard. This iterative process ensures the accuracy and reliability of the final classification. Obtaining a standard classification matrix: When all remaining category probabilities meet the preset criteria, the resulting matrix is ​​considered a standard classification matrix. This indicates that each category within it has sufficient confidence and can serve as a valid basis for fault type analysis. Fault type analysis and report generation: Based on the standard classification matrix, the fault type of the disconnected area is finally determined, and a fault type monitoring report is compiled. This report summarizes the fault type, probability, and analysis basis, providing a scientific basis for subsequent maintenance decisions.

[0114] The above describes the FTU disconnection fault monitoring method in the embodiment of the present invention. The following describes the FTU disconnection fault monitoring device in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a device for monitoring a line break fault of an FTU includes:

[0115] The acquisition module 21 is used to acquire electrical data of the FTU circuit through a preset electrical transformer to obtain electrical data; wherein the electrical data includes voltage data and current data;

[0116] A calculation module 22 is used to calculate the phase angle of the voltage data and the current data to obtain a phase angle difference;

[0117] A calling module 23 is configured to determine whether the phase angle difference is within a standard range. If the phase angle difference is not within the standard range, the calling module 23 calls a database to obtain the attributes of the FTU line.

[0118] A positioning module 24 is configured to locate the line break based on the line attributes, the phase angle, and the electrical data using a preset distributed parameter model to obtain a break area.

[0119] The analysis module 25 is configured to perform dynamic feature recognition on the electrical parameters corresponding to the disconnection area to obtain dynamic fault features, perform fault type analysis on the disconnection area based on the dynamic fault features, and obtain a fault type monitoring report.

[0120] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0121] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided, wherein the internal structure of the computer device can be as follows: Figure 3 As shown. The computer device includes a processor, memory, display screen, input device, network interface and database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above method is implemented.

[0122] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0123] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the above-described method when executed by a processor. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0124] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media provided herein and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM.

[0125] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0126] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for monitoring a line break fault of an FTU, characterized in that: The following steps are involved: The electrical data of the FTU circuit is collected through the preset electrical transformer to obtain electrical data; wherein the electrical data includes voltage data and current data; Performing phase angle calculation on the voltage data and the current data to obtain a phase angle difference; Determine whether the phase angle difference is within a standard range. If the phase angle difference is not within the standard range, call the database to obtain the attributes of the FTU line; Using a preset distributed parameter model, based on the attributes of the line, the phase angle and the electrical data, the line is disconnected and a disconnection area is obtained; Performing dynamic feature recognition on electrical parameters corresponding to the disconnection area to obtain dynamic fault features, and performing fault type analysis on the disconnection area based on the dynamic fault features to obtain a fault type monitoring report; The line is located by using a preset distributed parameter model based on the line attributes, the phase angle, and the electrical data to obtain a line break area, including: Inputting the line attributes, the phase angle, and the electrical data into a preset distributed parameter model to identify the fault location and obtain a potential fault identification location; Extracting a fault signal from the potential fault identification location to obtain a fault characteristic signal; wherein the fault characteristic signal includes an impedance change characteristic signal and a reflection waveform characteristic signal; Performing waveform calculation on the impedance change characteristic signal to obtain abnormal waveform parameters; wherein the abnormal waveform parameters include peak distortion coefficient, zero-crossing drift, and increased harmonic content; Performing spectrum calculation on the reflected waveform characteristic signal to obtain a spectrum distortion index; wherein the spectrum distortion index includes harmonic phase angle, spectrum flatness, and interharmonic distortion rate; By using a preset traveling wave algorithm, the line is located on the basis of the abnormal waveform parameters and the spectrum distortion index to obtain the line break area.

2. The FTU disconnection fault monitoring method according to claim 1, characterized in that: The electrical data of the FTU circuit is collected through the preset electrical transformer to obtain electrical data, including: The electrical signal of the FTU circuit is collected through the preset electrical transformer to obtain the electrical analog signal; Converting the electrical analog signal into a digital signal using a preset ADC converter to obtain a digital conversion signal; amplifying the digital conversion signal to obtain a digital amplified signal; A voltage signal and a current signal in the digitally amplified signal are extracted to obtain an electrical signal, and the electrical signal is used as electrical data.

3. The FTU disconnection fault monitoring method according to claim 2, characterized in that: Performing phase angle calculation on the voltage data and the current data to obtain a phase angle difference includes: Using a preset clock system to perform time sequence synchronization on the voltage data and the current data respectively, to obtain corresponding voltage time sequence synchronization data and current time sequence synchronization data; Converting the voltage time-series synchronization data and the current time-series synchronization data into complex form to obtain corresponding voltage complex form data and current complex form data; wherein the complex form includes a real part and an imaginary part, the real part represents the instantaneous amplitude of the electrical signal, and the imaginary part reflects the phase information of the electrical signal; Performing phase angle calculation on the voltage complex form data to obtain a voltage phase angle; Performing phase angle calculation on the complex current data to obtain a current phase angle; A phase angle difference is calculated for the voltage phase angle and the current phase angle point by point to obtain a phase angle difference.

4. The FTU disconnection fault monitoring method according to claim 1, characterized in that: Using a preset traveling wave algorithm, the line is disconnected and located based on the abnormal waveform parameters and the spectrum distortion index to obtain a disconnection area, including: Extracting key parameters of the abnormal waveform parameters to obtain abnormal key parameters; wherein the abnormal key parameters include the fault occurrence time, wavefront steepness, and waveform zero crossing time; Performing frequency domain identification on the spectrum distortion index to obtain harmonic frequencies and frequency response mutations; wherein the frequency response mutations include changes in harmonic frequencies and shifts in fundamental frequencies; Obtaining a time difference between at least two preset sensors when receiving a fault traveling wave, and obtaining a distance from the fault point to each of the sensors based on the time difference and the speed of the fault traveling wave; Within the distance from the fault point to each of the sensors, the abnormal key parameters and the sudden changes in harmonic frequency and frequency response are used to locate the disconnection and obtain the disconnection area.

5. The FTU disconnection fault monitoring method according to claim 1, characterized in that: Performing dynamic feature recognition on the electrical parameters corresponding to the disconnection area to obtain dynamic features of the fault, including: Extracting a fault signal corresponding to the disconnection area to obtain a fault extraction feature signal; Performing spectrum content analysis on the fault extraction feature signal using fast Fourier transform to obtain a spectrum feature set; wherein the spectrum feature set includes frequency harmonics, subharmonic components and amplitude changes; Performing signal spectrum calculation on the spectrum feature set to obtain an entropy value of the fault feature signal and an energy distribution of the fault feature signal; Drawing a frequency drift trajectory of the fault characteristic signal based on the entropy value of the fault characteristic signal and the energy distribution of the fault characteristic signal; The fault characteristic signal is captured and identified based on the frequency drift trajectory, and the fault dynamic characteristics of the fault characteristic signal evolving over time are obtained.

6. The FTU disconnection fault monitoring method according to claim 1, characterized in that: Based on the dynamic characteristics of the fault, the fault type of the disconnected area is analyzed to obtain a fault type monitoring report, including: constructing a fault dynamic matrix based on the fault dynamic characteristics; Performing projection dimensionality reduction on the fault dynamic matrix to obtain a projection matrix; Input the projection matrix into a preset fault type analysis algorithm for a decomposition to obtain a classification matrix and a probability matrix mapped by the classification matrix; wherein the elements of the classification matrix represent classification labels, and the elements of the probability matrix represent probability values ​​corresponding to the classification labels; Determine whether the probability values ​​corresponding to the elements of the probability matrix are within a preset probability value. If not, delete the classification matrix elements that are not within the preset probability value to obtain a deletion matrix. Inputting the deletion matrix into a preset fault type analysis algorithm for secondary decomposition until the probability values ​​corresponding to the elements of the deletion matrix are within the preset probability values; If the probability value corresponding to the deleted matrix element is within the preset probability value, the standard classification matrix is ​​obtained; Based on the standard classification matrix, a fault type analysis is performed on the disconnection area to obtain a fault type monitoring report.

7. A FTU disconnection fault monitoring device, characterized in that: include: The acquisition module is used to collect electrical data of the FTU circuit through a preset electrical transformer to obtain electrical data; wherein the electrical data includes voltage data and current data; a calculation module, configured to perform phase angle calculation on the voltage data and the current data to obtain a phase angle difference; A calling module is used to determine whether the phase angle difference is within a standard range. If the phase angle difference is not within the standard range, the database is called to obtain the attributes of the FTU line; a positioning module, configured to locate the line break based on the line attributes, the phase angle, and the electrical data using a preset distributed parameter model to obtain a break area; An analysis module is configured to perform dynamic feature recognition on the electrical parameters corresponding to the disconnection area to obtain dynamic fault features, perform fault type analysis on the disconnection area based on the dynamic fault features, and obtain a fault type monitoring report; The line is located by using a preset distributed parameter model based on the line attributes, the phase angle, and the electrical data to obtain a line break area, including: Inputting the line attributes, the phase angle, and the electrical data into a preset distributed parameter model to identify the fault location and obtain a potential fault identification location; Extracting a fault signal from the potential fault identification location to obtain a fault characteristic signal; wherein the fault characteristic signal includes an impedance change characteristic signal and a reflection waveform characteristic signal; Performing waveform calculation on the impedance change characteristic signal to obtain abnormal waveform parameters; wherein the abnormal waveform parameters include peak distortion coefficient, zero-crossing drift, and increased harmonic content; Performing spectrum calculation on the reflected waveform characteristic signal to obtain a spectrum distortion index; wherein the spectrum distortion index includes harmonic phase angle, spectrum flatness, and interharmonic distortion rate; By using a preset traveling wave algorithm, the line is located on the basis of the abnormal waveform parameters and the spectrum distortion index to obtain the line break area.

8. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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