FTU-based power distribution network fault detection method and system

Abstract

The invention discloses an FTU-based power distribution network fault detection method and system, and relates to the field of power distribution network fault detection, and the method comprises the steps: employing a wavelet transform technology to analyze electrical parameters, recognizing a fault line of a power distribution network, transmitting a traveling wave to an end point of the fault line through a traveling wave transmitter, and positioning a fault position point of the fault line of the power distribution network; extracting influence parameters based on a fault reason matching result, judging a probability coefficient of a power failure condition in the power supply area of the power distribution network in the target time period, and evaluating the power supply reliability of the power distribution network according to the probability coefficient; and based on the power supply reliability, using the fault terminal device to control the switch device to perform circuit breaking operation, isolating the power distribution network fault line, generating a personnel scheduling instruction, and implementing power distribution network fault line emergency repair processing. According to the method, a highly fine recording data analysis and multi-dimensional dynamic feature extraction mechanism is introduced, so that deep reasoning from a fault phenomenon to a fault cause is realized.

Description

Technical Field

[0001] The present invention relates to the field of distribution network fault detection, and in particular to a distribution network fault detection method and system based on FTU. Background Art

[0002] The distribution network is an important component of the power system. It refers to the network system that reliably transmits medium and high voltage electricity output from substations to end users (such as factories, residences, commercial facilities, etc.) through distribution lines, distribution transformers and related control equipment.

[0003] The Feeder Terminal Unit (FTU) is a core intelligent device in the distribution network. It is mainly deployed in key nodes such as medium-voltage switch stations, ring network cabinets, box-type transformers, and branch boxes in the distribution network. It is used to implement functions such as feeder monitoring, data collection, event detection, remote control, and intelligent judgment.

[0004] Distribution network fault detection is a core link in ensuring stable power supply and rapid fault response. Its goal is to accurately identify and locate the fault line, determine the fault type, and assist in fault isolation and power supply restoration in the shortest possible time.

[0005] However, the distribution network fault detection method in the existing technology can only identify the fault line but not its precise coordinates or geographical segment, making this fuzzy positioning method unable to support subsequent rapid repairs and fault segment isolation, and unable to further infer whether there will be a substantial power outage in the power supply area based on the detected fault results, which in turn affects the intelligent scheduling and operation and maintenance strategy formulation of the distribution network power supply area, resulting in delayed resource allocation and low power restoration efficiency. Summary of the Invention

[0006] In order to solve the above problems, the present invention proposes a distribution network fault detection method and system based on FTU to achieve the purpose of improving the accuracy and timeliness of fault handling.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a distribution network fault detection method based on FTU, comprising: S1. Use the fault terminal equipment to collect the electrical parameters of the distribution network, and use wavelet transform technology to analyze the electrical parameters. Based on the analysis results, calculate the transient energy and identify the fault line of the distribution network; S2. Using a traveling wave transmitter to transmit a traveling wave to the end point of the fault line, extracting a fault characteristic wave based on the characteristics of the reflected traveling wave, and locating the fault location of the fault line in the distribution network according to the wave velocity of the fault characteristic wave; S3. Obtaining recorded wave data at the fault location, extracting dynamic feature measurement values ​​of the recorded wave data based on intrinsic mode decomposition technology, and matching the fault cause of the fault line in the distribution network using the dynamic feature measurement values; S4. Extract influencing parameters based on the fault cause matching results, determine the probability coefficient of a power outage occurring in the distribution network power supply area within the target period, and evaluate the power supply reliability of the distribution network based on the probability coefficient; S5. Based on the reliability of power supply, the fault terminal equipment is used to control the switch equipment to perform circuit breaking operations, isolate the fault line of the distribution network, and generate personnel dispatch instructions to implement emergency repairs on the fault line of the distribution network.

[0008] Preferably, using a traveling wave transmitter to transmit a traveling wave to an end point of the fault line, extracting a fault characteristic wave based on the characteristics of the reflected traveling wave, and locating the fault location point of the fault line of the distribution network according to the wave velocity of the fault characteristic wave includes: S21. Select one end of the fault line as the detection end, and use the detection end as the reference position to transmit a traveling wave to the other end of the fault line using a traveling wave transmitter. The traveling wave propagates along the fault line. S22. During the traveling wave propagation process, when the received traveling wave encounters a fault point on the fault line, the traveling wave reflected to the detection end is used as a fault characteristic wave, and an energy propagation map is constructed based on the fault characteristic wave to obtain a reflected energy trajectory of the traveling wave; S23. Based on the reflected energy trajectory of the traveling wave and the actual length of the fault line, a traveling wave propagation inversion model is constructed to determine the round-trip time and wave speed of the fault characteristic wave from the detection end to the fault point, and locate the position of the fault point of the distribution network fault line.

[0009] Preferably, obtaining the recorded data of the fault location point, extracting the dynamic characteristic measurement value of the recorded data based on the intrinsic mode decomposition technology, and matching the fault cause of the distribution network fault line using the dynamic characteristic measurement value includes: S31, obtaining recorded signal data of the fault location point, and selecting a straight line with the same characteristic attributes as the axial busbar on the surface of the fault location point as a component calculation path to obtain the fault modal component change rate; S32. Decompose and optimize the center frequency of the recorded signal data using a modal decomposition technique based on the rate of change of the fault modal component, and generate a modal component signal corresponding to the fault location point according to the decomposition and optimization result; S33, analyzing the mean square value, standard deviation, sample entropy, permutation entropy, and singular value entropy of the modal component signal as morphological dynamic feature metrics, establishing a hybrid domain feature group, and extracting fault features of the fault location point; S34. Introduce the fault features into the fault cause feature word bag matching network, compare the similarity between the fault features and the fault cause feature word bag, and match the fault cause of the distribution network fault line according to the similarity result.

[0010] Preferably, obtaining recorded signal data of the fault location point, and selecting a straight line having the same characteristic attributes as the axial busbar on the surface of the fault location point as a component calculation path, and obtaining the fault modal component change rate includes: S311, collecting recorded signal data of the fault location of the fault line, and analyzing the strain modal components corresponding to any time point at the fault location and the previous time point, and defining the signal change rate of the fault location; S312. Based on the signal change rate at the fault location, a straight line having the same characteristic attributes as the axial busbar is selected as a component calculation path, and equally spaced displacement modal components are extracted to determine the change rate of the displacement modal components. S313. Based on the signal change rate at the fault location, a straight line parallel to the axial busbar is selected as the identification calculation path, and the axial displacement at any time point is calculated to obtain the identification value, which is combined with the displacement modal component change rate to generate the fault modal component change rate.

[0011] Preferably, analyzing the mean square value, standard deviation, sample entropy, permutation entropy and singular value entropy of the modal component signal as morphological dynamic feature measurement values, establishing a hybrid domain feature group, and extracting the fault features of the fault location point includes: S331, respectively calculating the mean square value, standard deviation, sample entropy, permutation entropy, and singular value entropy of the modal component signal based on the set of equations, and determining the optimal form of the modal component signal based on the calculation results; S332. Perform feature measurement on the modal component signals according to the optimal form to form a hybrid domain feature group of the fault location point of the fault line, and obtain a component fault feature classification matrix based on the hybrid domain feature group; S333, dividing the component fault feature classification matrix into an eigenmode matrix according to the complex wavelet decomposition characteristics, obtaining the cluster center vector of the modal component signal based on the eigenmode matrix, and constructing a characteristic component matrix; S334. Perform optimal feature selection processing on the modal component signal based on the selection constraint condition and the characteristic component matrix, determine the feature extraction relationship of the modal component signal, and obtain the fault feature of the fault location point.

[0012] Preferably, extracting the influencing parameters based on the fault cause matching result, determining the probability coefficient of a power outage occurring in the power supply area of ​​the distribution network within the target period, and evaluating the power supply reliability of the distribution network according to the probability coefficient includes: S41. Extracting the influencing parameters of the corresponding fault causes based on the fault cause matching results, decomposing the influencing parameters into quantized feature vectors, generating topological connectivity motion equations, and constructing a connectivity weakness analysis model; S42. Iteratively solve the connectivity weakness analysis model using the chord-intercept method to generate a discrete state transfer equation, and obtain a topological connectivity state transfer equation within a target duration based on the discrete state transfer equation; S43. Based on the topological connectivity state transition equation, the cumulative sum of Gaussian white noise in the short time interval is separated as a probability analysis model of the topological connectivity weakness, and the power outage link path is generated to calculate the power outage probability coefficient; S44. Based on the power outage probability coefficient, a reliability change surface diagram is constructed to show the evolution trend of the power outage probability coefficient in the future period, and the power supply reliability of the distribution network is evaluated based on the evolution trend.

[0013] Preferably, based on the topological connectivity state transition equation, the cumulative sum of Gaussian white noise in a short time interval is separated as a probability analysis model of the topological connectivity weakness, and the power outage probability coefficient of the power outage link path calculation is generated, which includes: S431. Extracting a transient electrical waveform sequence within a short time window based on a topological connectivity state transition equation, and analyzing the transient electrical waveform sequence using wavelet packet decomposition technology to construct a signal energy leakage map; S432, stripping off Gaussian white noise energy fragments below the kurtosis threshold on the signal energy leakage graph, and using the Gaussian white noise energy fragments as reference base signals to construct an abnormal energy accumulation sum sequence; S433. Based on the abnormal energy accumulation and sequence, the effective connectivity between the distribution network fault line and its adjacent distribution network lines in any time period is analyzed, and a probabilistic analysis model describing the topological connectivity state is constructed; S434. Using the probability analysis model, starting from the operating conditions of the fault point of the distribution network fault line, simulate the power outage link path of the chain fault, and construct a power outage probability coefficient model to calculate the power outage probability coefficient.

[0014] Preferably, the probability analysis model is used to simulate the power outage link path of the chain fault based on the fault point operation of the distribution network fault line, and the power outage probability coefficient model is constructed to calculate the power outage probability coefficient, including: S4341. Use the probability analysis model to determine the effective connectivity between the fault line and the remaining distribution network lines, and define the corresponding path to enter the weakly degraded state when the effective connectivity is lower than the set threshold. S4342. Generate a dynamic connectivity matrix based on the connectivity weak decay state, and introduce a random walk algorithm based on the fault point operation of the distribution network fault line to simulate the power loss link path of the chain fault; S4343. Mark the nodes where the load is lost according to the power failure link path, and construct a power failure probability coefficient model to perform superposition modeling on the power failure probability to form a quantitative prediction of the power outage probability in the distribution network area.

[0015] Preferably, the expression of the power failure probability coefficient model is: ; Where, L t Indicates t The power failure probability coefficient at time a Indicates the fault location point of the fault line in the distribution network, e represents the risk weight of the power-loss link path, c represents the estimated value of the recession intensity parameter, b represents the estimated value of the scale parameter, A ( t ) indicates the time t Covariates that affect the probability of power outage, f represents the estimated value of the regression parameter, h represents the effective connectivity, v Indicates the node location where the load is lost.

[0016] In a second aspect, the present invention further provides a distribution network fault detection system based on FTU, the system comprising: The distribution network fault analysis module is used to analyze electrical parameters using wavelet transform technology to identify the fault line of the distribution network, and use a traveling wave transmitter to transmit traveling waves to the end points of the fault line to locate the fault point of the distribution network fault line; The power supply reliability assessment module is used to extract influencing parameters based on the fault cause matching results, determine the probability coefficient of power outage in the distribution network power supply area within the target period, and evaluate the power supply reliability of the distribution network based on the probability coefficient; The distribution network operation control module is used to control the switch equipment to perform circuit breaking operations based on power supply reliability, isolate the faulty lines in the distribution network, and generate personnel dispatch instructions to implement emergency repairs on the faulty lines in the distribution network.

[0017] The beneficial effects of the present invention are: 1. The present invention uses FTU to collect electrical parameters and combines it with wavelet transform to extract transient energy characteristics, which can achieve high-resolution anomaly identification and preliminary fault line location, improve the sensitivity to sudden disturbances, and introduce a traveling wave transmitter to actively inject high-frequency disturbances and track their reflected characteristic waves to locate the fault location point. It also introduces the intrinsic mode decomposition technology to extract dynamic features from the fault point recording data, constructs a power outage probability coefficient model, and shifts the distribution network from fault response to power outage risk prediction. It also introduces quantitative evaluation of power supply reliability, which helps to form a resilient distribution network with active perception and early warning as the priority. Ultimately, the reliability judgment results are reversely driven to drive the on-site FTU to control the circuit breaker switch equipment, automatically isolate the fault section, and simultaneously generate dispatch instructions to guide emergency repairs, thereby improving the accuracy and timeliness of fault handling.

[0018] 2. The present invention achieves in-depth reasoning from fault phenomena to fault causes by introducing highly sophisticated waveform data analysis and multi-dimensional dynamic feature extraction mechanisms. By converting the fault cause matching results into structural influencing parameters and further quantifying them into feature vectors, it effectively solves the information gap problem between qualitative judgment and quantitative modeling. The connectivity weakness analysis model constructed in combination with the topological connectivity motion equation calculates the probability of occurrence of the power outage link path, and realizes the prediction of power outages under the premise of faulty lines in the distribution network, thereby providing the dispatching center with more refined and forward-looking reliability assessment and resource allocation reference. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0020] Figure 1 is a flow chart of a distribution network fault detection method based on FTU according to an embodiment of the present invention; Figure 2 The figure is a principle block diagram of a distribution network fault detection system based on FTU according to an embodiment of the present invention.

[0021] In the picture: 1. Distribution network fault analysis module; 2. Power supply reliability assessment module; 3. Distribution network operation control module. DETAILED DESCRIPTION

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0024] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0026] See also Figure 1 The present invention provides a distribution network fault detection method based on FTU, comprising: S1. Use the fault terminal equipment to collect the electrical parameters of the distribution network, and use wavelet transform technology to analyze the electrical parameters. Based on the analysis results, calculate the transient energy and identify the fault line of the distribution network.

[0027] In one embodiment, in the process of identifying the fault line of the distribution network, the method is implemented by using an FTU device installed at the main feeder or key node position of the distribution network to collect three-phase current and voltage signals in real time. When the distribution network is in a normal state, the signal is stable and the energy distribution is uniform. When a fault occurs, such as a short circuit, grounding or disconnection, a significant electromagnetic transient disturbance will be generated at the fault point. The FTU can sample these sudden change signals at a frequency above 10kHz and record their change curves; the collected original current signal is processed by wavelet transform. The wavelet transform synchronously expands the signal in the time and frequency domains through multi-scale analysis, which not only retains the sudden change, but also records the change curve. The change point information can be used to separate the energy distribution in different frequency bands. The transient total energy index is formed by summing the energy of all sub-bands. When a fault occurs, a sudden increase will occur in a very short time. An empirical reference threshold is set, and its average value and standard deviation are calculated in the normal operation data. Once the transient total energy index is detected to be greater than the empirical reference threshold, it can be determined that the line where the sampling point is located has a fault. To enhance the accuracy of identification, the transient total energy indicators collected by each FTU node can also be spatially compared. That is, if the FTU on line A shows a strong sudden change in the transient total energy index, while other branches have smaller fluctuations, the confidence that line A is the faulty line is further strengthened.

[0028] Assume that a 10kV distribution network is used as an example. FTUs are deployed on feeders F1, F2, and F3 respectively. The FTU sampling frequency is 20kHz. Assume that a single-phase grounding fault occurs at 13:46:12 on X month X day. The FTU on line F2 collects a strong sudden change waveform in the current of phase A. After five-layer wavelet decomposition, the sub-band energies are D1=8.24, D2=10.32, D3=12.88, D4=6.15, and D5=4.91, respectively. The total transient energy index is 42.5, while the empirical reference threshold under the reference operating state is 18.3. At this time, E tot significantly exceeds the threshold, so it is determined that the F2 line is faulty; and the F1 and F3 collected E tot They are 15.6 and 17.1 respectively, which do not exceed Eref, further confirming that F2 is the fault line.

[0029] S2. Use a traveling wave transmitter to transmit traveling waves to the end point of the fault line, extract the fault characteristic wave based on the characteristics of the reflected traveling wave, and locate the fault location point of the distribution network fault line according to the wave velocity of the fault characteristic wave.

[0030] In one embodiment, using a traveling wave transmitter to transmit a traveling wave to an end point of a fault line, extracting a fault characteristic wave based on characteristics of the reflected traveling wave, and locating a fault location of a fault line in a distribution network according to the wave velocity of the fault characteristic wave includes: S21. Select one end of the fault line as the detection end, and use the detection end as the reference position to transmit a traveling wave to the other end of the fault line using a traveling wave transmitter. The traveling wave propagates along the fault line. S22. During the traveling wave propagation process, when the received traveling wave encounters a fault point on the fault line, the traveling wave reflected to the detection end is used as a fault characteristic wave, and an energy propagation map is constructed based on the fault characteristic wave to obtain a reflected energy trajectory of the traveling wave; S23. Based on the reflected energy trajectory of the traveling wave and the actual length of the fault line, a traveling wave propagation inversion model is constructed to determine the round-trip time and wave speed of the fault characteristic wave from the detection end to the fault point, and locate the position of the fault point of the distribution network fault line.

[0031] It should be explained that when locating the fault location, one end of the line where the fault occurs is selected as the detection end. Usually, the side end close to the feeder protection device or the FTU is selected to ensure stable communication and control. A high-frequency traveling wave signal of a specific frequency is actively transmitted to the other end of the line through the traveling wave transmitter installed at this end. For example, a high-frequency narrow pulse wave of 1MHz is selected, and the signal is propagated along the distribution line in the form of an electromagnetic wave. When the traveling wave signal propagates to the location where the fault occurs in the line (such as a short circuit, grounding or other resistance mutation), a strong reflection is generated due to the impedance discontinuity, and the reflected wave will return to the detection end along the original path. The high-speed recording device records the energy distribution and time axis changes of the return wave. By extracting the characteristic peak value in the signal waveform and filtering out the non-fault reflection signal, and constructing an energy propagation spectrum, the characteristic wave reflection trajectory actually caused by the fault point is identified. Using the known fault line length L and the traveling wave propagation speed n (Usually 0.95 times the speed of light is taken, which is about 2.85×10 8 m / s), combined with the time difference Δ between the initial traveling wave sent by the detection end and the reflected wave received t , using the traveling wave propagation inversion model to calculate the one-way distance from the fault point to the detection end g =( n ×Δ t ) / 2, thereby accurately locating the fault location.

[0032] At the same time, taking a 10kV distribution line as an example, the total line length L =3.6km, and the traveling wave propagation speed is set ton =2.85×10 8 m / s, at the time of failure T 0=12:31:42.120 sends out the excitation signal, T 1=12:31:42.145 received an obvious high-energy reflected wave, then the reflection time difference Δ t = 25 milliseconds = 25 × 10 -3 Seconds, substitute into the formula g =(2.85×10 8 m / s×25×10 -3 )÷2=3.5625km, that is, the fault point is 3.5625km away from the detection end. Since the total length of the line is 3.6km, combined with the direction judgment, it can be inferred that the fault occurred only about 37.5 meters away from the other end. Therefore, it does not rely on multi-point synchronization or topology information, and only one-end detection is required to complete precise positioning. It is suitable for use in scenarios where the middle or end sections of the distribution network have complex structures and frequently changing topologies. It significantly improves fault processing speed, reduces manual troubleshooting costs, and provides accurate reference location information for automated isolation and emergency repair scheduling.

[0033] S3. Obtain the recorded data of the fault location point, extract the dynamic feature measurement value of the recorded data based on the intrinsic mode decomposition technology, and use the dynamic feature measurement value to match the fault cause of the fault line of the distribution network.

[0034] In one embodiment, recording data of a fault location is obtained, and dynamic feature metric values ​​of the recording data are extracted based on the intrinsic mode decomposition technology. The dynamic feature metric values ​​are used to match the fault cause of the fault line in the distribution network, including: S31, obtaining recorded signal data of the fault location point, and selecting a straight line with the same characteristic attributes as the axial busbar on the surface of the fault location point as a component calculation path to obtain the fault modal component change rate; S32. Decompose and optimize the center frequency of the recorded signal data using a modal decomposition technique based on the rate of change of the fault modal component, and generate a modal component signal corresponding to the fault location point according to the decomposition and optimization result; S33, analyzing the mean square value, standard deviation, sample entropy, permutation entropy, and singular value entropy of the modal component signal as morphological dynamic feature metrics, establishing a hybrid domain feature group, and extracting fault features of the fault location point; S34. Introduce the fault features into the fault cause feature word bag matching network, compare the similarity between the fault features and the fault cause feature word bag, and match the fault cause of the distribution network fault line according to the similarity result.

[0035] The recorded signal data of the fault location is obtained, and a straight line with the same characteristic attributes as the axial busbar is selected on the surface of the fault location as the component calculation path. The rate of change of the fault modal component is obtained, which includes: S311, collecting recorded signal data of the fault location of the fault line, and analyzing the strain modal components corresponding to any time point at the fault location and the previous time point, and defining the signal change rate of the fault location; S312. Based on the signal change rate at the fault location, a straight line having the same characteristic attributes as the axial busbar is selected as a component calculation path, and equally spaced displacement modal components are extracted to determine the change rate of the displacement modal components. S313. Based on the signal change rate at the fault location, a straight line parallel to the axial busbar is selected as the identification calculation path, and the axial displacement at any time point is calculated to obtain the identification value, which is combined with the displacement modal component change rate to generate the fault modal component change rate.

[0036] It should be explained that through the fusion of multi-dimensional signal feature extraction and modal change rate, an accurate quantitative description of the dynamic behavior of the fault location can be achieved. The three-phase voltage or current recording signal of the fault location is collected through a high-speed sampling device (such as an electronic transformer or a recorder). The sampling frequency is usually not less than 10kHz to ensure the capture of high-frequency transient components. For the collected discrete signal sequence, the difference between any time point and the previous moment is calculated, and the strain modal component is constructed based on the differential sequence, that is, the energy density distribution of the signal change per unit time. The signal change rate is defined to reflect the sudden change intensity of the signal at the fault point; on the physical surface of the fault location (such as the cable insulation layer or the conductor cross section), a straight line is selected as the component meter along the direction with the same conductive properties and mechanical strength distribution as the axial busbar. Calculate the path, which must meet the same material parameters and electromagnetic field boundary conditions as the busbar. Place virtual sensor nodes along this path at equal intervals (such as one sampling point every 5 mm). Extract the displacement modal component of each node through finite element simulation or measured data (reflecting the mechanical deformation or electromagnetic field distortion caused by the fault disturbance), calculate the displacement change gradient between adjacent nodes, and then obtain the displacement modal component change rate of the entire path. This indicator is used to quantify the degree of local distortion of the physical field caused by the fault. At the same time, select another straight line on the surface of the fault point in a direction parallel to the busbar as an identifier to calculate the path. This path is used to capture the axial propagation characteristics caused by the fault, calculate the fluctuation of the axial displacement at each time point (such as measuring the magnetic field offset through a Hall sensor or inferring the deformation through infrared temperature measurement), and calculate its standard deviation. σ As the identification quantity, the signal change rate is finally R signal , displacement modal change rate R displacement and axial marking amount δGenerate comprehensive fault modal component change rate through weighted fusion R total = α · R signal + β · R displacement +γ· σ · δ , where the weight coefficient α 、 β 、 γ Adaptive adjustment based on fault type (e.g. short circuit fault focus α , mechanical fracture focus β ), through the fusion of electrical-mechanical multi-physical field features, it overcomes the limitations of single signal analysis and significantly improves the sensitivity and diagnostic robustness to complex faults.

[0037] Specifically, the mean square value, standard deviation, sample entropy, permutation entropy, and singular value entropy of the modal component signal are analyzed as morphological dynamic feature metrics, and a hybrid domain feature group is established to extract the fault features of the fault location point, including: S331, respectively calculating the mean square value, standard deviation, sample entropy, permutation entropy, and singular value entropy of the modal component signal based on the set of equations, and determining the optimal form of the modal component signal based on the calculation results; S332. Perform feature measurement on the modal component signals according to the optimal form to form a hybrid domain feature group of the fault location point of the fault line, and obtain a component fault feature classification matrix based on the hybrid domain feature group; S333, dividing the component fault feature classification matrix into an eigenmode matrix according to the complex wavelet decomposition characteristics, obtaining the cluster center vector of the modal component signal based on the eigenmode matrix, and constructing a characteristic component matrix; S334. Perform optimal feature selection processing on the modal component signal based on the selection constraint condition and the characteristic component matrix, determine the feature extraction relationship of the modal component signal, and obtain the fault feature of the fault location point.

[0038] It should be explained that the process of matching the fault cause of the distribution network fault line is based on intrinsic mode decomposition (EMD) as the core, combined with multi-scale feature extraction and modal feature clustering of the recorded signal to realize intelligent judgment from "fault phenomenon" to "fault cause". Specifically, the high sampling rate voltage or current signal of the distribution network fault line location point in a continuous period is collected, and the strain modal component corresponding to any time point and the previous moment is analyzed to construct a signal change rate sequence. Then, a straight line is selected as the calculation path on the surface of the point along the direction with the same electrical and structural properties as the axial busbar, and the displacement modal component is extracted at equal intervals and its change rate is calculated. Further, another straight line is selected on the surface in a direction parallel to the busbar as the identification calculation path, the axial displacement is extracted and an identification quantity is generated, which is combined with the aforementioned displacement modal change rate to form a composite fault modal component change rate, and the sensitive frequency band of the modal component is judged based on the change rate. The original recorded signal is subjected to intrinsic mode decomposition and disassembled into Several intrinsic mode functions (IMFs) with local frequency characteristics are selected, and the center frequency of each IMF is adaptively optimized and screened to eliminate low-energy or redundant components, thereby constructing a set of modal components that best represent the dynamic behavior of the fault. The mean square value, standard deviation, sample entropy, permutation entropy, and singular value entropy of the selected modal components are calculated. These metrics reflect the energy intensity, volatility, complexity, time series structure, and singular structure of the signal, respectively. After fusion, they form a hybrid domain feature group. The modal response morphology corresponding to each type of fault is further extracted based on complex wavelet decomposition. The distribution pattern of different types of faults in the feature space is established through the component fault feature classification matrix. The proposed feature vectors are introduced into the fault cause feature bag-of-words matching network. The network is pre-set with feature word vectors of various typical faults (such as single-phase grounding, metallic wire break, insulation breakdown, etc.). The matching degree of the current feature group is compared with the historical bag-of-words library using similarity functions such as cosine similarity or Mahalanobis distance, thereby achieving intelligent matching of fault causes.

[0039] Taking a ground fault on a 10kV line as an example, the A-phase current recording signal is collected at the fault point with a sampling frequency of 20kHz and a total duration of 200ms. The signal is time-series differentiated to construct a discrete signal sequence. It is found that t = =68ms to 70ms, a significant jump occurs, with a change rate of 0.42A / ms; a straight line A is selected along the busbar direction on the insulation surface of the fault point, and 6 points are arranged at equal intervals. The calculated displacement modal change rate reaches the maximum at the third point, which is 2.1mm / ms; at the same time, a straight line B is selected along the direction parallel to the busbar to calculate the axial displacement mark value. t=68.7ms, the value is 0.38mm / ms, and the two are combined to generate the modal component change rate sequence; the signal is decomposed into 7 IMF components by EMD, and IMF3~IMF5 are retained as effective modal components, among which the center frequencies are 410Hz, 210Hz and 110Hz respectively. The statistical characteristics of these three components are calculated respectively, and the specific results are shown in Table 1 below: Table 1: Statistical characteristics calculation results Modal Mean square value Standard deviation Sample entropy Permutation Entropy Singular value entropy IMF3 0.84 0.29 0.16 0.19 0.31 IMF4 0.66 0.22 0.13 0.15 0.28 IMF5 0.52 0.17 0.11 0.12 0.25 The above features are fused into a feature vector of length 15 and a mixed domain feature group is constructed. The most representative feature triples (standard deviation, permutation entropy, and singular value entropy) are screened based on waveform trends and prior rules. The calculated similarity is 0.97, and the final matching cause of this fault is "single-phase grounding + leakage contact caused by insulation aging." Then, the dynamic behavior characteristics extracted from the recorded data are used to establish cognitive associations of fault knowledge, which helps to form a closed-loop perception-interpretation-attribution mechanism and realize intelligent fault recognition in distribution networks.

[0040] S4. Extract influencing parameters based on the fault cause matching results, determine the probability coefficient of power outage in the distribution network power supply area within the target period, and evaluate the power supply reliability of the distribution network based on the probability coefficient.

[0041] In one embodiment, extracting impact parameters based on the fault cause matching results, determining the probability coefficient of a power outage occurring in the power supply area of ​​the distribution network within the target period, and evaluating the power supply reliability of the distribution network based on the probability coefficient include: S41. Extracting the influencing parameters of the corresponding fault causes based on the fault cause matching results, decomposing the influencing parameters into quantized feature vectors, generating topological connectivity motion equations, and constructing a connectivity weakness analysis model; S42. Iteratively solve the connectivity weakness analysis model using the chord-intercept method to generate a discrete state transfer equation, and obtain a topological connectivity state transfer equation within a target duration based on the discrete state transfer equation; S43. Based on the topological connectivity state transition equation, the cumulative sum of Gaussian white noise in the short time interval is separated as a probability analysis model of the topological connectivity weakness, and the power outage link path is generated to calculate the power outage probability coefficient; S44. Based on the power outage probability coefficient, a reliability change surface diagram is constructed to show the evolution trend of the power outage probability coefficient in the future period, and the power supply reliability of the distribution network is evaluated based on the evolution trend.

[0042] Specifically, in the process of obtaining the topological connectivity state transition equation within the target duration, the fault causes identified in the previous stage are converted into structured influencing factors, and these influencing factors are mapped into quantitative characteristic parameters to model the connectivity weakness model. Specifically, based on the matching results (for example, the fault cause is insulation degradation + metallic grounding), key influencing parameters are extracted, including but not limited to: fault duration, fault current peak, line load rate, node connectivity level (such as trunk, branch, terminal), historical fault statistical weight, etc. For example, the larger the fault current, the longer the duration, and the lower the node level, the greater the impact on the connectivity of the overall network topology. The parameters are constructed as quantitative characteristic vectors, and the topological connectivity motion equation is introduced. Its mathematical form can be: B ( i )= C ( t )⋅ R -G⋅F ,in C ( t ) represents the time-dependent grid structure function, G represents the influence weight factor matrix, F represents the eigenvector. This equation is used to describe the degree of electrical connectivity weakening between different nodes caused by fault disturbances, and ultimately forms an initial connectivity weakness analysis model.

[0043] The connectivity weakness analysis model is numerically iteratively solved using the "Secant Method" rather than the Newton method. This method does not rely on derivative functions and is suitable for problems with complex topological state functions or local discontinuities. The specific approach is to construct a linear approximation at each time step using the weakness function values ​​of the previous two steps and solve it to obtain the connectivity state evolution data at different times, thereby forming a set of discrete state transfer equations, that is, the trajectory of the connectivity capacity between topological nodes evolving over time during the target period.

[0044] It can then capture the dynamic evolution of the distribution network from "normal connectivity state" to "weak connectivity state" or even "power loss state", which is conducive to the early identification of "potential regional island" risks or "chain power outage risks". It is suitable for the evaluation of multiple nodes, weak links, and support capacity degradation in complex distribution network environments. In addition, the use of the chord intercept method avoids the difficulty of deriving high-dimensional complex functions, making the model more universal and feasible in practical engineering.

[0045] Specifically, based on the topological connectivity state transition equation, the cumulative sum of Gaussian white noise in a short time interval is separated as a probability analysis model of topological connectivity weakness, and the power outage probability coefficient of the power outage link path is generated and calculated, including: S431. Extracting a transient electrical waveform sequence within a short time window based on a topological connectivity state transition equation, and analyzing the transient electrical waveform sequence using wavelet packet decomposition technology to construct a signal energy leakage map; S432, stripping off Gaussian white noise energy fragments below the kurtosis threshold on the signal energy leakage graph, and using the Gaussian white noise energy fragments as reference base signals to construct an abnormal energy accumulation sum sequence; S433. Based on the abnormal energy accumulation and sequence, the effective connectivity between the distribution network fault line and its adjacent distribution network lines in any time period is analyzed, and a probabilistic analysis model describing the topological connectivity state is constructed; S434. Using the probability analysis model, starting from the operating conditions of the fault point of the distribution network fault line, simulate the power outage link path of the chain fault, and construct a power outage probability coefficient model to calculate the power outage probability coefficient.

[0046] Among them, the probability analysis model is used to simulate the power outage link path of the chain fault based on the fault point operation of the distribution network fault line, and the power outage probability coefficient model is constructed to calculate the power outage probability coefficient, including: S4341. Use the probability analysis model to determine the effective connectivity between the fault line and the remaining distribution network lines, and define the corresponding path to enter the weakly degraded state when the effective connectivity is lower than the set threshold. S4342. Generate a dynamic connectivity matrix based on the connectivity weak decay state, and introduce a random walk algorithm based on the fault point operation of the distribution network fault line to simulate the power loss link path of the chain fault; S4343. Mark the nodes where the load is lost according to the power failure link path, and construct a power failure probability coefficient model to perform superposition modeling on the power failure probability to form a quantitative prediction of the power outage probability in the distribution network area.

[0047] Among them, the expression of the power failure probability coefficient model is: ; Where, L t Indicates t The power failure probability coefficient at time a Indicates the fault location point of the fault line in the distribution network, e represents the risk weight of the power-loss link path, c represents the estimated value of the recession intensity parameter, b represents the estimated value of the scale parameter, A ( t ) indicates the time t Covariates that affect the probability of power outage, f represents the estimated value of the regression parameter, h represents the effective connectivity, vIndicates the node location where the load is lost.

[0048] It should be explained that based on the fault type obtained by fault identification in the previous stage, such as "single-phase grounding + insulation degradation", the corresponding influencing parameters are extracted, such as fault duration, phase-to-phase short-circuit current amplitude, line insulation level, historical fault frequency, topological position weight of the node, etc., and these multi-dimensional parameters are mapped into quantitative feature vectors. Then, a topological connectivity motion equation is constructed to reflect the current flow response capability between the fault point and other nodes, and a model is built based on connectivity weakness to measure the degradation trend of electrical connectivity under fault conditions. The chord intercept method is used to perform nonlinear iterative solution on the topological weakness analysis model, and the connectivity attenuation of each node under the fault state is calculated segmentally by segment approximation to construct a topological connectivity equation. The state transition equation is discretized to obtain the dynamic evolution trajectory of the distribution network connectivity status within the target time period (such as within 1 hour after the fault occurs). At the same time, perturbation modeling is performed on this state transition trajectory. The transient voltage and current waveforms collected within the short-term window are decomposed into multiple energy sub-bands using wavelet packets. A signal energy leakage map is constructed to identify possible transmission obstacles of electric energy in the topology. Subsequently, Gaussian white noise segments below the set kurtosis threshold (such as 3.2) are stripped off and used as the base reference signal. The abnormally high energy parts are accumulated as "connectivity degradation features". Based on these accumulations and the connection edge weights, the electrical connectivity between the fault line and the adjacent lines is evaluated, an effective connectivity matrix is ​​generated, and a probability analysis model is constructed.

[0049] A probabilistic analysis model is used to construct a chain power outage path starting from the fault point. The random walk algorithm is used to simulate fault conduction. When the connectivity is lower than the set threshold (such as 0.65), the corresponding path is marked as entering a weakly connected degraded state. The potential power outage links are further determined through the dynamic connectivity matrix. Then, the set of nodes that experience load loss within a certain period of time is counted. The regional power outage probability coefficient is calculated based on the superposition of the probability of each link and the node load weight. Based on this probability coefficient, a three-dimensional reliability change surface diagram is constructed to show the reliability evolution trend under different times, node locations and power grid conditions, providing decision support for early warning and resource allocation of the dispatching center.

[0050] At the same time, assuming that a certain area's 10kV distribution network is used as an example, the fault cause of line L2 is currently identified as "metallic single-phase grounding + aging short circuit". The influencing parameters are extracted as follows: short-circuit current amplitude I s =480A, duration T s =0.18s, the historical average monthly failure frequency of node K3 is 0.7 times / month, L2 belongs to the third-level branch in the topology, and the topological transmission coefficient o =0.62, forming the initial eigenvector V=[480,0.18,0.7,3,0.62], then construct the connectivity weakness function p ( t )= ow -kt ,in k =0.03, w Represents a constant, and the chord intercept method is used to t =0 to t = 60 minutes, and piecewise approximation is performed to obtain the state transition equation. Wavelet packet decomposition reveals significant energy leakage in the 10–20 ms frequency band (200–400 Hz) at the L2 node, with a peak of 6.3 kJ, compared to a Gaussian white noise reference floor of only 1.4 kJ and a kurtosis of 3.4. An abnormal energy accumulation sequence is constructed, revealing that the connectivity of nodes K3, K4, and K6 has dropped to 0.54, 0.47, and 0.43, respectively, indicating a weakly degraded state. Subsequently, a link path L2→K3→K6 is constructed. Based on the propagation probabilities and node loads of each path, p1=0.63, p2=0.57, w1=1.2 MW, and w2=0.8 MW, respectively, a power outage probability coefficient of 1.364 is calculated. Finally, a reliability change surface plot indicates that the power outage risk in the L2 region will continue to exceed the set safety line (0.8) within the next two hours, automatically triggering a dispatch strategy warning and recommending the start of the L4 backup branch power supply.

[0051] S5. Based on the reliability of power supply, the fault terminal equipment is used to control the switch equipment to perform circuit breaking operations, isolate the fault line of the distribution network, and generate personnel dispatch instructions to implement emergency repairs on the fault line of the distribution network.

[0052] In one embodiment, based on power supply reliability, the fault terminal device is used to control the switch device to perform a circuit breaking operation, isolate the fault line of the distribution network, and generate a personnel dispatch instruction. The implementation of the distribution network fault line emergency repair process includes: based on the power supply reliability analysis, when it is determined that the current fault has a high risk of power outage or its chain reaction may cause a large-scale power outage, the circuit breaking command initiated by the fault terminal device (such as the feeder terminal FTU, the switch control unit SCU or the substation remote terminal RTU) is immediately triggered. The command is sent to the target circuit breaker, switch knife switch or sectionalizer through the distribution network automation system (DMS) to achieve electrical isolation of the fault line, cut off its connection path with the backbone network or other branches, and prevent the fault from further spreading. After the isolation is completed, the spatial coordinates of the fault section and the unique identification number of the equipment involved are automatically matched based on the GIS geographic information system and the asset file, and combined with the emergency repair resource scheduling model, a targeted emergency repair instruction is generated.

[0053] See also Figure 2 The present invention also provides a distribution network fault detection system based on FTU, the system comprising: The distribution network fault analysis module 1 is used to analyze electrical parameters using wavelet transform technology to identify the fault line of the distribution network, and use a traveling wave transmitter to transmit traveling waves to the end points of the fault line to locate the fault point of the distribution network fault line; Power supply reliability assessment module 2 is used to extract influencing parameters based on the fault cause matching results, determine the probability coefficient of power outage in the distribution network power supply area within the target period, and evaluate the power supply reliability of the distribution network based on the probability coefficient; The distribution network operation control module 3 is used to control the switch equipment to perform circuit breaking operations based on power supply reliability, isolate the faulty line of the distribution network, and generate personnel dispatch instructions to implement emergency repairs on the faulty line of the distribution network.

[0054] Among them, the distribution network fault analysis module 1, the power supply reliability assessment module 2 and the distribution network operation control module 3 are connected in sequence.

[0055] Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0056] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A distribution network fault detection method based on FTU, characterized in that: include: S1. Use the fault terminal equipment to collect the electrical parameters of the distribution network, and use wavelet transform technology to analyze the electrical parameters. Based on the analysis results, calculate the transient energy and identify the fault line of the distribution network; S2. Using a traveling wave transmitter to transmit a traveling wave to the end point of the fault line, extracting a fault characteristic wave based on the characteristics of the reflected traveling wave, and locating the fault location of the fault line in the distribution network according to the wave velocity of the fault characteristic wave; S3. Obtaining recorded wave data at the fault location, extracting dynamic feature measurement values ​​of the recorded wave data based on intrinsic mode decomposition technology, and matching the fault cause of the fault line in the distribution network using the dynamic feature measurement values; S4. Extract influencing parameters based on the fault cause matching results, determine the probability coefficient of a power outage occurring in the distribution network power supply area within the target period, and evaluate the power supply reliability of the distribution network based on the probability coefficient; S5. Based on the reliability of power supply, the fault terminal equipment is used to control the switch equipment to perform circuit breaking operations, isolate the fault line of the distribution network, and generate personnel dispatch instructions to implement emergency repairs on the fault line of the distribution network.

2. A distribution network fault detection method based on FTU according to claim 1, characterized in that: The method of transmitting a traveling wave to an end point of a fault line by a traveling wave transmitter, extracting a fault characteristic wave based on characteristics of the reflected traveling wave, and locating a fault location of a fault line of a distribution network according to the wave velocity of the fault characteristic wave comprises: S21. Select one end of the fault line as the detection end, and use the detection end as the reference position to transmit a traveling wave to the other end of the fault line using a traveling wave transmitter. The traveling wave propagates along the fault line. S22. During the traveling wave propagation process, when the received traveling wave encounters a fault point on the fault line, the traveling wave reflected to the detection end is used as a fault characteristic wave, and an energy propagation map is constructed based on the fault characteristic wave to obtain a reflected energy trajectory of the traveling wave; S23. Based on the reflected energy trajectory of the traveling wave and the actual length of the fault line, a traveling wave propagation inversion model is constructed to determine the round-trip time and wave speed of the fault characteristic wave from the detection end to the fault point, and locate the position of the fault point of the distribution network fault line.

3. A distribution network fault detection method based on FTU according to claim 1, characterized in that: The method of obtaining the recorded data of the fault location, extracting the dynamic characteristic measurement value of the recorded data based on the intrinsic mode decomposition technology, and matching the fault cause of the fault line of the distribution network using the dynamic characteristic measurement value includes: S31, obtaining recorded signal data of the fault location point, and selecting a straight line with the same characteristic attributes as the axial busbar on the surface of the fault location point as a component calculation path to obtain the fault modal component change rate; S32. Decompose and optimize the center frequency of the recorded signal data using a modal decomposition technique based on the rate of change of the fault modal component, and generate a modal component signal corresponding to the fault location point according to the decomposition and optimization result; S33, analyzing the mean square value, standard deviation, sample entropy, permutation entropy, and singular value entropy of the modal component signal as morphological dynamic feature metrics, establishing a hybrid domain feature group, and extracting fault features of the fault location point; S34. Introduce the fault features into the fault cause feature word bag matching network, compare the similarity between the fault features and the fault cause feature word bag, and match the fault cause of the distribution network fault line according to the similarity result.

4. A distribution network fault detection method based on FTU according to claim 3, characterized in that: The step of obtaining the recorded signal data of the fault location point, selecting a straight line having the same characteristic attributes as the axial busbar on the surface of the fault location point as a component calculation path, and obtaining the fault modal component change rate includes: S311, collecting recorded signal data of the fault location of the fault line, and analyzing the strain modal components corresponding to any time point at the fault location and the previous time point, and defining the signal change rate of the fault location; S312. Based on the signal change rate at the fault location, a straight line having the same characteristic attributes as the axial busbar is selected as a component calculation path, and equally spaced displacement modal components are extracted to determine the change rate of the displacement modal components. S313. Based on the signal change rate at the fault location, a straight line parallel to the axial busbar is selected as the identification calculation path, and the axial displacement at any time point is calculated to obtain the identification value, which is combined with the displacement modal component change rate to generate the fault modal component change rate.

5. A distribution network fault detection method based on FTU according to claim 4, characterized in that: The mean square value, standard deviation, sample entropy, permutation entropy and singular value entropy of the analyzed modal component signal are used as morphological dynamic feature measurement values ​​to establish a hybrid domain feature group, and the fault features of the fault location point are extracted, including: S331, respectively calculating the mean square value, standard deviation, sample entropy, permutation entropy, and singular value entropy of the modal component signal based on the set of equations, and determining the optimal form of the modal component signal based on the calculation results; S332. Perform feature measurement on the modal component signals according to the optimal form to form a hybrid domain feature group of the fault location point of the fault line, and obtain a component fault feature classification matrix based on the hybrid domain feature group; S333, dividing the component fault feature classification matrix into an eigenmode matrix according to the complex wavelet decomposition characteristics, obtaining the cluster center vector of the modal component signal based on the eigenmode matrix, and constructing a characteristic component matrix; S334. Perform optimal feature selection processing on the modal component signal based on the selection constraint condition and the characteristic component matrix, determine the feature extraction relationship of the modal component signal, and obtain the fault feature of the fault location point.

6. A distribution network fault detection method based on FTU according to claim 1, characterized in that: The extracting of influencing parameters based on the fault cause matching results, determining the probability coefficient of a power outage occurring in the power supply area of ​​the distribution network within the target period, and evaluating the power supply reliability of the distribution network according to the probability coefficient include: S41. Extracting the influencing parameters of the corresponding fault causes based on the fault cause matching results, decomposing the influencing parameters into quantized feature vectors, generating topological connectivity motion equations, and constructing a connectivity weakness analysis model; S42. Iteratively solve the connectivity weakness analysis model using the chord-intercept method to generate a discrete state transfer equation, and obtain a topological connectivity state transfer equation within a target duration based on the discrete state transfer equation; S43. Based on the topological connectivity state transition equation, the cumulative sum of Gaussian white noise in the short time interval is separated as a probability analysis model of the topological connectivity weakness, and the power outage link path is generated to calculate the power outage probability coefficient; S44. Based on the power outage probability coefficient, a reliability change surface diagram is constructed to show the evolution trend of the power outage probability coefficient in the future period, and the power supply reliability of the distribution network is evaluated based on the evolution trend.

7. A distribution network fault detection method based on FTU according to claim 6, characterized in that: The method of separating the cumulative sum of Gaussian white noise in a short time interval as a probability analysis model of topological connectivity weakness based on the topological connectivity state transition equation and generating a power outage probability coefficient for the power outage link path includes: S431. Extracting a transient electrical waveform sequence within a short time window based on a topological connectivity state transition equation, and analyzing the transient electrical waveform sequence using wavelet packet decomposition technology to construct a signal energy leakage map; S432, stripping off Gaussian white noise energy fragments below the kurtosis threshold on the signal energy leakage graph, and using the Gaussian white noise energy fragments as reference base signals to construct an abnormal energy accumulation sum sequence; S433. Based on the abnormal energy accumulation and sequence, the effective connectivity between the distribution network fault line and its adjacent distribution network lines in any time period is analyzed, and a probabilistic analysis model describing the topological connectivity state is constructed; S434. Using the probability analysis model, starting from the operating conditions of the fault point of the distribution network fault line, simulate the power outage link path of the chain fault, and construct a power outage probability coefficient model to calculate the power outage probability coefficient.

8. A distribution network fault detection method based on FTU according to claim 7, characterized in that: The method of using the probability analysis model to simulate the power outage link path of the chain fault based on the fault point operation of the distribution network fault line and constructing the power outage probability coefficient model to calculate the power outage probability coefficient includes: S4341. Use the probability analysis model to determine the effective connectivity between the fault line and the remaining distribution network lines, and define the corresponding path to enter the weakly degraded state when the effective connectivity is lower than the set threshold. S4342. Generate a dynamic connectivity matrix based on the connectivity weak decay state, and introduce a random walk algorithm based on the fault point operation of the distribution network fault line to simulate the power loss link path of the chain fault; S4343. Mark the nodes where the load is lost according to the power failure link path, and construct a power failure probability coefficient model to perform superposition modeling on the power failure probability to form a quantitative prediction of the power outage probability in the distribution network area.

9. A distribution network fault detection method based on FTU according to claim 8, characterized in that: The expression of the power failure probability coefficient model is: ； Where, L t Indicates t The power failure probability coefficient at time a Indicates the fault location point of the fault line in the distribution network, e represents the risk weight of the power-loss link path, c represents the estimated value of the recession intensity parameter, b represents the estimated value of the scale parameter, A ( t ) indicates the time t Covariates that affect the probability of power outage, f represents the estimated values ​​of the regression parameters, h represents the effective connectivity, v Indicates the node location where the load is lost.

10. A distribution network fault detection system based on FTU, used to implement the distribution network fault detection method based on FTU according to any one of claims 1 to 9, characterized in that: The system includes: The distribution network fault analysis module is used to analyze electrical parameters using wavelet transform technology to identify the fault line of the distribution network, and use a traveling wave transmitter to transmit traveling waves to the end points of the fault line to locate the fault point of the distribution network fault line; The power supply reliability assessment module is used to extract influencing parameters based on the fault cause matching results, determine the probability coefficient of power outage in the distribution network power supply area within the target period, and evaluate the power supply reliability of the distribution network based on the probability coefficient; The distribution network operation control module is used to control the switch equipment to perform circuit breaking operations based on power supply reliability, isolate the faulty lines in the distribution network, and generate personnel dispatch instructions to implement emergency repairs on the faulty lines in the distribution network.

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