Distribution line fault positioning method and device and computer equipment

Abstract

The invention discloses a distribution line fault positioning method and device and computer equipment. The method comprises the following steps: acquiring original signals acquired by each acquisition node in a distribution line to be tested at the same sampling moment according to a GPS time synchronization signal; performing quantum Fourier transform on the original signal to obtain a time-frequency characteristic of the original signal, and screening a fault signal from the original signal according to the time-frequency characteristic; determining energy characteristics of to-be-detected grids in the to-be-detected distribution line in a preset sampling time period according to the fault signal, and screening out a fault grid from the to-be-detected grids according to the energy characteristics; and determining the fault type of the to-be-detected distribution line according to the energy characteristics of the fault grid, and determining the fault position of the to-be-detected distribution line according to the target acquisition node corresponding to the fault grid. The wide-area synchronous measurement technology is adopted, the type of the fault in the distribution line is accurately judged, and the position of the fault is accurately positioned.

Description

Technical Field

[0001] The present application relates to the technical field of power distribution, and particularly to a method, device and computer equipment for fault location of power distribution lines. Background Art

[0002] With the continuous expansion of the scale of the distribution network and the rapid growth of power load demand, the operating environment of the distribution network has become increasingly complex. Especially in mountainous areas, power distribution lines often face characteristics such as complex terrain, multi-branch structures, and long-distance transmission. These factors have significantly increased the probability of faults and also made it more difficult to locate faults.

[0003] In related technologies, it usually relies on centralized measurement equipment or analyzes based on line models. However, due to the complex topology of power distribution lines, especially in mountainous distribution networks, the multi-branch characteristics are prominent and the signal transmission is severely affected by terrain, the signal characteristics are weak and easily masked by background noise, making it difficult to be accurately identified by conventional detection means. In addition, due to the wide distribution of lines in mountainous distribution networks, there are problems such as pseudo-fault points and rapid attenuation of fault signals, resulting in low fault location accuracy and high false alarm rates, posing a great threat to power supply reliability. Summary of the Invention

[0004] In view of this, the present application provides a method, device and computer equipment for fault location of power distribution lines, which uses wide-area synchronous measurement technology to obtain high-precision current signals and voltage signals of power distribution lines, and accurately determines the type of faults in the power distribution lines and accurately locates the fault positions based on the high-precision current signals and voltage signals.

[0005] According to one aspect of the present application, a method for fault location of power distribution lines is provided, including:

[0006] Obtain the original signals collected by each acquisition node in the power distribution line to be measured at the same sampling moment according to the GPS time synchronization signal, where the original signals include voltage signals and current signals;

[0007] Perform quantum Fourier transform on the original signals to obtain the time-frequency characteristics of the original signals, and screen out fault signals from the original signals according to the time-frequency characteristics;

[0008] Determine the energy characteristics of the grid to be measured in the power distribution line to be measured within a preset sampling time period according to the fault signals, and screen out fault grids from the grids to be measured according to the energy characteristics;

[0009] Determine the type of faults in the power distribution line to be measured according to the energy characteristics of the fault grids, and determine the fault position of the power distribution line to be measured according to the target acquisition node corresponding to the fault grids.

[0010] According to another aspect of the present application, a power distribution line fault location device is provided, including:

[0011] An acquisition module, configured to acquire original signals collected by each acquisition node in the power distribution line to be measured at the same sampling moment according to the GPS time synchronization signal, where the original signals include voltage signals and current signals;

[0012] A screening module, configured to perform quantum Fourier transform on the original signals to obtain the time-frequency characteristics of the original signals, and screen out fault signals from the original signals according to the time-frequency characteristics; and,

[0013] Determine the energy characteristics of the grid to be measured in the power distribution line to be measured within a preset sampling time period according to the fault signals, and screen out the fault grids from the grids to be measured according to the energy characteristics;

[0014] A determination module, configured to determine the fault type of the power distribution line to be measured according to the energy characteristics of the fault grid, and determine the fault location of the power distribution line to be measured according to the target acquisition node corresponding to the fault grid.

[0015] According to still another aspect of the present application, a readable storage medium is provided, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the above-mentioned power distribution line fault location method are implemented.

[0016] According to yet another aspect of the present application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the program, the steps of the above-mentioned power distribution line fault location method are implemented.

[0017] By means of the above technical scheme, the present application provides a method, device and computer equipment for locating faults in a distribution line, and sets distributed acquisition nodes in the distribution line to be tested. Through the cooperation of the acquisition nodes and the high-precision GPS, wide-area synchronous measurement of the distribution line to be tested is realized, and the high-precision original signal of the distribution line to be tested is obtained, thereby improving the quality and reliability of the data. Then, the original signal is quantum Fourier transformed, and then the original signals obtained from different acquisition nodes at the same sampling time are classified according to the time-frequency characteristics of the original signal obtained by the quantum Fourier transform, and the fault signal at each sampling time is accurately identified, providing a more representative basis for identifying different fault types of the distribution line. Furthermore, the present application evenly divides the distribution line to be tested into multiple grids to be tested, so that the subsequent fault location is more targeted and accurate. At the same time, the energy characteristics of each grid to be tested are calculated according to the fault signal within the preset sampling time period, and the faulty grids are screened out according to the energy characteristics, narrowing the fault location area, and then according to the target acquisition nodes corresponding to the faulty grids, the fault position of the distribution line to be tested is further accurately located, and the fault type of the distribution line is determined according to the energy characteristics of the faulty grids, thereby achieving accurate positioning of the distribution line fault position and accurate determination of the fault type, effectively improving the efficiency and accuracy of distribution line fault detection and diagnosis, and providing strong guarantee for the stable operation of the power system.

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 A schematic diagram of a flow chart of a method for locating a power distribution line fault provided in an embodiment of the present application is shown;

[0021] Figure 2 A structural block diagram of a distribution line fault locating device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0022] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0023] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present application, and should not be construed as limiting the present application.

[0024] Those skilled in the art of the present technology can understand that unless specifically stated, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present application means the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when we say that an element is "connected" or "joined" to another element, it can be directly connected or joined to other elements, or there may also be intermediate elements. In addition, the "connection" or "joining" used herein may include wireless connection or wireless coupling. The phrase "and / or" used herein includes all or any unit and all combinations of one or more related listed items.

[0025] Now, exemplary embodiments according to the present application will be described in more detail with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many different forms and should not be construed as being limited only to the embodiments set forth herein. It should be understood that these embodiments are provided so that the disclosure of the present application is thorough and complete, and the concepts of these exemplary embodiments are fully conveyed to those of ordinary skill in the art.

[0026] In this embodiment, a method for fault location of a distribution line is provided, as Figure 1 shown. The method includes:

[0027] Step 101, obtaining the original signals collected by each acquisition node in the distribution line to be measured at the same sampling moment according to the GPS time synchronization signal.

[0028] Among them, the original signals include voltage signals and current signals.

[0029] In this embodiment, acquisition nodes are set at key positions where electrical quantities such as current and voltage in the distribution line to be measured change, as well as places where faults are likely to occur or fault signals are likely to change.

[0030] For example, T-shaped or Y-shaped acquisition nodes are set at the intersection of the main line and the branch line.

[0031] Here, the main line in the distribution line is responsible for introducing electrical energy from the power source, while the branch line distributes the electrical energy to each electrical device. The two together constitute a complex distribution network to ensure the stability and safety of power supply. The T-type acquisition node allows current to shunt on the main line and supply power to different loads through the branch line. This method is applicable to occasions where power needs to be supplied to multiple loads, and can improve the flexibility and reliability of the power system. The Y-type acquisition node connects three or more electrical devices to a common point, which can provide better current distribution and voltage stability, and is applicable to large-scale power systems or occasions where high-efficiency energy transmission is required.

[0032] Furthermore, high-precision current transformers and high-precision GPS receivers are set at the acquisition node to perform wide-area synchronous measurement on the distribution line, realizing the holographic perception of complex distribution lines. Especially for mountainous distribution lines with complex terrain, long lines, and multiple branches, it can obtain distribution line-related data more comprehensively and accurately.

[0033] Specifically, this embodiment relies on the high-precision Global Positioning System (GPS) to broadcast GPS time synchronization signals to the acquisition nodes. The acquisition nodes receive the GPS time synchronization signals through GPS receivers, so that the acquisition nodes align their local clocks with the GPS time synchronization signals, thereby realizing the acquisition of the data of the distribution line to be measured by each acquisition node at the same sampling moment, effectively expanding the monitoring range of the distribution line, and ensuring that the data collected by multiple acquisition nodes within a wide area are synchronous and complete in terms of time and content, so as to realize the wide-area synchronous measurement of the data of the distribution line to be measured, for subsequent synchronous analysis of the data of the distribution line to be measured, improving the data quality and reliability. Furthermore, based on the high-precision data of the distribution line to be measured obtained by wide-area synchronous measurement, rapid fault detection and positioning of the distribution line are realized, improving the anti-interference ability and control ability of the distribution line.

[0034] Here, the data of the distribution line to be measured includes: time dimension data and electrical operation data. Among them, the time dimension data may include: timestamp information such as data sampling moment, traveling wave transmission time, etc., and the electrical operation data may include: dynamic characteristic data such as current, voltage, power, harmonic components, traveling wave waveforms, etc. In this embodiment, current, voltage, and data sampling moment are mainly used for fault detection and positioning of the distribution line to be measured.

[0035] Exemplarily, amorphous or nanocrystalline materials are used as the magnetic core materials of high-precision current transformers to reduce hysteresis loss and improve the measurement accuracy of high-precision current transformers. And a redundant clock based on an oven-controlled crystal oscillator (OCXO) is set at each acquisition node to provide a short-term stable time reference for the local clock of the acquisition node when the GPS time synchronization signal is lost.

[0036] In one embodiment, the distribution line fault location method further includes: predicting a prior state estimate of the local clock of the acquisition node at the next sampling moment based on the posterior state estimate of the local clock of the acquisition node at the current sampling moment, where the posterior state estimate of the local clock of the acquisition node at the current sampling moment is determined according to the initial time synchronization deviation between the GPS time synchronization signal acquired by the acquisition node and the local clock of the acquisition node; predicting a prior covariance matrix of the prior state estimate of the local clock of the acquisition node at the next sampling moment according to the posterior covariance matrix of the posterior state estimate of the local clock of the acquisition node at the current sampling moment and a preset state transition matrix, where the posterior covariance matrix of the posterior state estimate of the local clock of the acquisition node at the current sampling moment is determined according to the initial covariance matrix of the local clock of the acquisition node; determining the Kalman gain of the local clock of the acquisition node at the next sampling moment according to the prior covariance matrix of the prior state estimate of the local clock of the acquisition node at the next sampling moment and a preset observation matrix; determining the posterior state estimate of the local clock of the acquisition node at the next sampling moment according to the prior state estimate of the local clock of the acquisition node at the next sampling moment, the Kalman gain, and the observed value; determining the posterior covariance matrix of the posterior state estimate of the local clock of the acquisition node at the next sampling moment according to the Kalman gain of the local clock of the acquisition node at the next sampling moment, the prior covariance matrix of the prior state estimate, and a preset observation matrix; determining the target time synchronization deviation between the local clock of the acquisition node at the next sampling moment and the GPS time synchronization signal according to the posterior covariance matrix of the posterior state estimate of the local clock of the acquisition node at the next sampling moment; if the target time synchronization deviation does not meet the preset error range, correcting the local clock of the acquisition node, taking the next sampling moment as the current sampling moment, and recalculating the posterior covariance matrix of the posterior state estimate of the local clock of the acquisition node at the next sampling moment until the target time synchronization deviation meets the preset error range.

[0037] In this embodiment, a dynamic correction algorithm based on Kalman filtering is adopted to correct the time synchronization error between the local clock of the acquisition node and the GPS time synchronization signal in real time, ensuring that the time synchronization error of the distributed acquisition nodes is less than 10 ns, so that the acquisition nodes at different positions use the same accurate clock to acquire time-related data, and ensuring that the distributed acquisition nodes maintain high-precision synchronization in time.

[0038] Exemplarily, the time synchronization error between the local clock of acquisition node i at sampling moment t and the GPS time synchronization signal it receives is δ i (t), δ i (t) = δ i (0) + v i (t), δ i(0) is the initial time synchronization deviation between the local clock of the acquisition node i and the GPS time synchronization signal, v i is the deviation rate of the local clock of the acquisition node i. At this time, the true state vector of the local clock of the acquisition node i at the sampling moment t is expressed as:

[0039] Furthermore, the state equation in the Kalman filter algorithm is obtained according to the state vector. The state equation is expressed as: X i (t + 1) = FX i (t) + W i (t). Among them, X i (t + 1) is the true state vector of the local clock of the acquisition node i at the sampling moment t + 1, and the preset state transition matrix W i (t) is the process noise, which is Gaussian noise with a mean of zero and a covariance matrix of Q. W i (t) corresponds to the noise of each component in X i (t), and Q is the modeling error, which is used to predict the uncertainty of the state. By reasonably setting the value of W i (t), the state estimation can more accurately reflect the noise characteristics in the actual system.

[0040] Here, the preset state transition matrix connects the states of two adjacent sampling moments of the local clock of the acquisition node. The state equation assumes that the true state of the local clock of the acquisition node at the moment t + 1 evolves from the true state at the moment t.

[0041] Furthermore, the observation equation in the Kalman filter algorithm is expressed as: Z i (t) = HX i (t) + v t . Among them, the preset observation matrix H = [1 0], which maps the true state space into the observation space, indicating that only the time synchronization error δ i (t) is observed. Z i (t) is the observed value of the true state vector X i (t), and v t is the observation noise, which satisfies Gaussian noise with a mean of zero and a covariance matrix of R. The setting of R is related to the characteristics of the GPS receiver.

[0042] Furthermore, according to the state equation and the observation equation, five basic equations for prediction (formulas (1)-(2)) and correction (formulas (3)-(5)) in the Kalman filter algorithm are determined, expressed as

[0043]

[0044] P i(r + 1|t) = FP i (t|t)F T +Q(2)

[0045] K i (t + 1) = P i (t + 1|r)H T (HP i (t + 1|r)H T +R) -1 (3);

[0046]

[0047] P i (t + 1|r + 1) = (I - K i (t + 1)H)P j (t + 1|r)(5)

[0048] where is the posterior state estimate of the local clock of acquisition node i at sampling time t; is the prior state estimate of the local clock of acquisition node i at sampling time t + 1; P i (t|t) is the posterior covariance matrix of the posterior state estimate of the local clock of acquisition node i at sampling time t; P i (t + 1|t) is the prior covariance matrix of the prior state estimate of the local clock of acquisition node i at sampling time t + 1; K i (t + 1) is the Kalman gain of the local clock of acquisition node i at sampling time t + 1; Z i (t + 1) is the observation value of the local clock of acquisition node i at sampling time t + 1, which is determined according to the prior state estimate and the observation equation of the local clock of acquisition node i at sampling time t + 1; is the posterior state estimate of the local clock of acquisition node i at sampling time t + 1, I is the identity matrix, P i (t + 1|t + 1) is the posterior covariance matrix of the posterior state estimate of the local clock of acquisition node i at sampling time t + 1.

[0049] Here, Equation (1) is used to predict the prior state estimate of the local clock of the acquisition node at the next sampling moment based on the posterior state estimate of the local clock of the acquisition node at the current sampling moment. Equation (2) is used to predict the prior covariance matrix of the prior state estimate of the local clock of the acquisition node at the next sampling moment based on the posterior covariance matrix of the posterior state estimate of the local clock of the acquisition node at the current sampling moment. Equation (3) is used to obtain the Kalman gain of the local clock of the acquisition node at the next sampling moment based on the prior covariance matrix of the prior state estimate of the local clock of the acquisition node at the next sampling moment, where Q and R represent the confidence in the prior value and the observed value respectively, and can affect the weights of the prior value and the observed value by influencing the value of the Kalman gain. The larger R is, the less the observed value is trusted. Equation (4) is used to correct the prior state estimate of the local clock of the acquisition node at the next sampling moment to obtain the posterior state estimate of the local clock of the acquisition node at the next sampling moment. Equation (5) is used to correct the prior covariance matrix of the prior state estimate of the local clock of the acquisition node at the next sampling moment to obtain the posterior covariance matrix of the posterior state estimate of the local clock of the acquisition node at the next sampling moment.

[0050] It should be noted that the prior refers to the judgment of uncertain parameters before observing the data, that is, the cognitive achievement from previous practices. The posterior refers to the update of the prior uncertain parameters after observing some data.

[0051] It can be understood that according to Equations (1)-(5), it can be obtained that the Kalman filter algorithm is a recursive estimation, that is, as long as the estimated value of the current state and the observed value of the next state are known, the estimated value of the next state can be calculated. Therefore, there is no need to record the historical information of observations or estimates. In the prediction stage, Equations (1)-(2) use the estimate of the current state to make an estimate of the next state. In the correction stage, Equations (3)-(5) use the observed value of the next state to optimize the predicted value obtained in the prediction stage to obtain a more accurate new estimated value.

[0052] It is worth mentioning that since the Kalman filter algorithm is a recursive estimation, the initial posterior state estimate of the local clock of the acquisition node is given in this embodiment. The initial posterior state estimate should be determined according to the initial true state of the local clock of the acquisition node. The initial true state is determined according to the state equation. Since the process noise in the state equation and the deviation rate in the state vector cannot be accurately obtained, the initial posterior state estimate is obtained based on the estimate of the initial true state, that is, the initial posterior state estimate is determined only according to the initial time synchronization deviation in the initial true state. Similarly, the initial covariance matrix of the local clock of the acquisition node is given in this embodiment.

[0053] In this embodiment, the target time synchronization deviation between the local clock of the acquisition node and the GPS time synchronization signal at the next sampling moment is determined according to the posterior covariance matrix of the posterior state estimation of the local clock of the acquisition node at the next sampling moment. If the target time synchronization deviation does not meet the preset error range, the target time synchronization deviation of the local clock of the acquisition node is continuously corrected using formulas (1)-(5). After multiple recursive iterations, the target time synchronization deviation of the acquisition node tends to be stable and will be controlled within the preset error range.

[0054] Exemplarily, the target time synchronization deviation is determined according to the square root of the posterior covariance matrix of the posterior state estimation of the local clock of the acquisition node at the next sampling moment. That the target time synchronization deviation meets the preset error range is expressed as:

[0055] In one embodiment, the power distribution line fault location method further includes: The GPS time synchronization signal includes a first frequency band signal and a second frequency band signal. According to the observed frequencies of the first frequency band signal and the second frequency band signal obtained by the acquisition node, and the carrier phase observation values of the observed frequencies, the ionospheric delay amount of the GPS time synchronization signal is determined; The GPS time synchronization signal is corrected according to the ionospheric delay amount to update the GPS time synchronization signal.

[0056] In this embodiment, a dual-frequency GPS time synchronization signal is adopted. The GPS receiver of the acquisition node simultaneously receives the GPS time synchronization signal including the L1 and L2 frequency bands broadcast by the Global Positioning System (GPS), that is, the first frequency band signal and the second frequency band signal. By utilizing the difference in the ionospheric delay of the first frequency band signal and the second frequency band signal, the influence of the ionosphere on the propagation delay of the GPS time synchronization signal is eliminated, and the anti-interference ability is improved, so that the acquisition node can obtain a more accurate GPS time synchronization signal, and further unify the time standards of each acquisition node.

[0057] Specifically, based on the observed values of the first frequency band signal and the second frequency band signal, the ionospheric delay amount is calculated by using the phase difference of two different frequencies of the observed GPS time synchronization signal, and it is subtracted from the GPS time synchronization signal received by the acquisition node, thereby obtaining a more accurate GPS time synchronization signal.

[0058] Exemplarily, the ionospheric delay amount l is expressed as: where f1 and f2 are the observed frequencies of the first frequency band signal and the second frequency band signal respectively, and p1 and p2 are the carrier phase observation values of the first frequency band signal and the second frequency band signal respectively.

[0059] Step 102, perform a quantum Fourier transform on the original signal to obtain the time-frequency characteristics of the original signal, and screen out the fault signal from the original signal according to the time-frequency characteristics.

[0060] In this embodiment, the raw signals obtained from different acquisition nodes at the same sampling moment are normalized, and the raw signals are transformed from the time domain to the time-frequency domain through the quantum Fourier transform, so as to obtain the time-frequency characteristics of the raw signals. Furthermore, through the time-frequency characteristics, the signal changes caused by faults can be clearly captured, so that the fault signals corresponding to the sampling moment can be accurately screened out from the raw signals corresponding to the same sampling moment, providing effective data for subsequent fault location and type judgment.

[0061] Further, as a refinement and extension of the specific implementation manner of the above embodiment, in order to completely illustrate the specific implementation process of this embodiment, the quantum Fourier transform is performed on the raw signal to obtain the time-frequency characteristics of the raw signal, which specifically includes: normalizing the amplitude of the raw signal, and mapping the normalized raw signal to a quantum state; performing the quantum Fourier transform on the quantum state to determine the frequency components of the raw signal; calculating the energy values of the frequency components, and determining the minimum energy value and the maximum energy value among the energy values of the frequency components; and determining the time-frequency characteristics of the raw signal according to the minimum energy value and the maximum energy value.

[0062] In this embodiment, for any sampling moment, the amplitudes of different current and voltage signals (raw signals) obtained from different acquisition nodes at this sampling moment are normalized. After normalization, all signals are within the same scale range, eliminating the interference of dimensions to highlight the relative changes and characteristics of the signals. For some weak signal changes, they may not be obvious when the original amplitude is large, but after normalization, these relative changes will be more significant, facilitating detection and analysis. Normalization can unify the signal amplitudes in different ranges to a standard range (such as between 0 and 1), avoiding the influence of amplitude size differences on subsequent processing, making signal processing more fair and effective.

[0063] Further, the normalized raw signal is mapped to a quantum state, so as to analyze the raw signal by using the characteristics of quantum computing.

[0064] Exemplarily, the quantum state of any raw signal is constructed according to the probability amplitude. In this embodiment, the number of bits of the quantum state is set to 5. In this embodiment, the probability amplitudes of the 32 basis states |00000>, |00001>,..., |11111> corresponding to the 5-bit quantum state are distributed based on the Gaussian function.

[0065] First, calculate the central position μ = α×(2 5-1) = α × 31, where α is the original signal after normalization, and α ∈ [0, 1]. Then, according to the original signal after normalization, the standard deviation σ is set. Among them, a smaller σ will make the distribution of the probability amplitude more concentrated, and a larger σ will make the distribution of the probability amplitude more dispersed. For each ground state |x> (x ranges from 0 to 31), its probability amplitude α x is expressed as: Here, the probability amplitude α x will have a larger value near the central position μ, and the value will be smaller the farther away from μ, thus forming a Gaussian distribution centered on μ.

[0066] Thus, according to the calculated probability amplitude α x the quantum state |ψ> of the original signal is constructed, which is expressed as: n = 5.

[0067] Next, perform a quantum Fourier transform on the original signal converted into a quantum state, convert the original signal from the time domain to the frequency domain, and decompose the original signal into frequency components.

[0068] Exemplarily, the process of the quantum Fourier transform is expressed as: n = 5. Among them, is the original signal after the quantum Fourier transform, |y> is the frequency component of the original signal, and the coefficient b y represents the weight of each frequency domain component in the frequency domain, that is, the intensity information of the original signal on each frequency component, and j is the imaginary unit.

[0069] Furthermore, calculate the energy value of each frequency component of the original signal, use the energy value to represent the time-frequency information and spectral characteristics of each frequency component, and determine the minimum energy value and the maximum energy value in the energy values of the frequency components to clarify the range boundary of the entire energy distribution of the frequency components.

[0070] Exemplarily, in the original signal after the quantum Fourier transform, the energy value E y of each frequency domain component |y> is calculated through E y = |b| y 2 where |b y | is the modulus of the negative number b y .

[0071] Thus, according to the minimum energy value and E min the maximum energy value E max , combined with the energy distribution information of the original signal in the frequency domain and certain time domain information, calculate the time-frequency feature ΔE of the original signal to more comprehensively reflect the characteristics of the signal and provide more information about signal changes.

[0072] Exemplarily, ΔE = abs(E max - E min ) / E max .

[0073] In one embodiment, the distribution line fault location method further includes: determining an original signal with a time-frequency feature greater than a preset value as a fault signal. If the time-frequency feature of the original signal is greater than the preset value, it indicates that the maximum difference in energy in the original signal is relatively large, and the energy distribution of the signal is unstable, which is often caused by abnormal changes in the original signal due to a fault. Therefore, it can be determined that the original signal is a fault signal. Similarly, if the time-frequency feature of the original signal is less than or equal to the preset value, the energy distribution of the original signal is relatively stable, and it is determined as a normal signal, thereby achieving effective signal classification. Here, the preset value is specifically set according to the signal energy change in the actual scenario. For example, the preset value is set to 0.8.

[0074] In one embodiment, the distribution line fault location method further includes: decomposing a fault signal into mode functions with finite bandwidths based on a variational framework; calculating the sample entropy value of the mode functions; removing the bandwidth corresponding to the mode function with a sample entropy value greater than a preset threshold from the fault signal to update the fault signal.

[0075] In this embodiment, based on the variational framework, the fault signals collected from different acquisition nodes at the same time are decomposed into mode functions with finite bandwidths, and the sample entropy values of the mode functions are calculated for noise discrimination, realizing decoupling and noise reduction of the fault signals, reducing the positioning error for subsequent distribution line fault location, improving the accuracy of fault location, and reducing the positioning error caused by noise.

[0076] Exemplarily, based on the variational framework, the fault signals collected from different acquisition nodes at the same time are decomposed into multiple mode functions (Intrinsic Mode Function, IMF) with different frequency bandwidths, and each mode function represents a component of different frequency components in the fault signal.

[0077] Furthermore, for each decomposed mode function, its sample entropy value is calculated. For noise, due to its high randomness and disorder, it shows a relatively scattered distance distribution between vectors in the phase space, resulting in a relatively high calculated sample entropy value, that is, a relatively high complexity. For the true signal part, it usually has a certain regularity and structure, the distance distribution between vectors is relatively concentrated, and the sample entropy value is relatively low, and the complexity is also relatively low. Therefore, the mode function with a sample entropy value greater than the preset threshold, that is, the mode function with low complexity, is determined as noise and removed from the fault signal. By using the sample entropy value to distinguish the signal and noise parts in the fault signal, noise interference is removed, thereby realizing decoupling and noise reduction of the fault signal and retaining meaningful signal components and frequency domain information.

[0078] Step 103, determining energy characteristics of the grids to be tested in the distribution line to be tested within a preset sampling time period according to the fault signal, and screening out faulty grids from the grids to be tested according to the energy characteristics.

[0079] In this embodiment, the entire distribution line to be tested is evenly divided into grids of the same size according to the physical length, branch position, and signal attenuation of the distribution line to be tested, so as to achieve more precise and targeted monitoring of the distribution line. Furthermore, the energy characteristics of each grid to be tested are calculated according to the fault signals at each target sampling time within the preset sampling time period, so as to integrate the scattered fault signals from the time and space dimensions and obtain the representative characteristics of each grid area to be tested. Therefore, when a fault occurs, the fault location range is narrowed from the entire complex distribution line to be tested to the relevant area of ​​the specific fault grid according to the energy characteristics, which provides a basic framework for the subsequent rapid location of the fault, reduces the scope of fault troubleshooting, and improves the efficiency of fault location.

[0080] It should be noted that the size of the grid to be tested is specifically set according to the different characteristics of the distribution lines to be tested in the actual application scenario, so as to avoid ignoring local faults due to the grid area being too large, and avoid excessive calculation and data redundancy due to the grid area being too small.

[0081] Further, as a refinement and extension of the specific implementation methods of the above-mentioned embodiments, in order to fully illustrate the specific implementation process of this embodiment, the energy characteristics of the grid to be tested in the distribution line to be tested within a preset sampling time period are determined according to the fault signal, specifically including: determining the first signal contribution of the first candidate node to the grid to be tested at the target sampling moment according to the distance between the first candidate node and the grid to be tested; sorting the first candidate nodes according to the first signal contribution, determining the first node order, and taking the fault signal collected by the first candidate node at the first position in the first node order at the target sampling moment as the target signal of the grid to be tested at the target sampling moment; determining the energy characteristics of the grid to be tested according to the target signals of the grid to be tested at each target sampling moment within the preset sampling time period.

[0082] The target sampling time is any sampling time within a preset sampling time period, and the first candidate node is a collection node that collects the fault signal at the target sampling time.

[0083] In this embodiment, the target signals of each grid to be measured at each target sampling moment within a preset sampling time period are determined, and the target signals are used to represent the fault condition of the grid to be measured at the target sampling moment, so as to more accurately describe the state of the grid to be measured at the target sampling moment. Therefore, according to the most representative target signals at each target sampling moment, the energy characteristics of the grid to be measured within the preset sampling time period are calculated, so as to quantify the fault condition in the area near the grid to be measured through the energy characteristics, and improve the accuracy of fault detection and location.

[0084] Specifically, for any sampling moment (target sampling moment) of any grid to be measured within the preset sampling time period, according to the distance between the acquisition node (first candidate node) that acquires the fault signal at the target sampling moment and the grid to be measured, the first signal contribution degree of the first candidate node to the grid to be measured is quantified.

[0085] Exemplarily, the first signal contribution degree w of the first candidate node to the grid to be measured is expressed as: where d is the distance between the first candidate node and the grid to be measured.

[0086] It can be understood that the greater the first signal contribution degree of the first candidate node to the grid to be measured, the closer the fault corresponding to the fault signal acquired by the first candidate node is to the area of the grid to be measured.

[0087] Therefore, the first candidate nodes are sorted according to the first signal contribution degree to determine the first node order, and the fault signal acquired by the first candidate node ranked first in the first node order at the target sampling moment is used as the target signal of the grid to be measured at the target sampling moment, so as to quantify the possibility of a fault occurring in the area near the grid to be measured at the target sampling moment.

[0088] Furthermore, according to the target signals of each grid to be measured at each target sampling moment within the preset sampling time period, the energy characteristics of the grid to be measured are determined, so as to comprehensively evaluate the signal condition received by the grid to be measured from the faulty area within the preset sampling time period, intuitively reflect the energy accumulation condition of the fault signals received by the grid to be measured from the faulty area, and thus quantify the possibility of a fault occurring in the area near the grid to be measured.

[0089] Exemplarily, the energy characteristic E of the grid to be measured o o is expressed as: O is the number of grids to be measured, and S o (t) is the target signal of the grid to be measured o at any sampling moment t within the preset sampling time period t1 - t2.

[0090] In one embodiment, the distribution line fault location method further includes: determining the energy peak value of the energy characteristic of each grid to be measured, and determining the grid with the largest energy peak value as the fault grid.

[0091] In this embodiment, the energy feature is used to characterize the fault possibility near the grid to be measured. Under normal circumstances, the energy of the fault signal received by the grid to be measured from the vicinity is relatively low. When the energy feature exceeds a certain threshold, it is determined that a fault is likely to have occurred in the area near the grid to be measured, thereby effectively screening out the fault grid, narrowing the scope of fault investigation, and providing strong support for accurately locating the fault location.

[0092] Step 104: According to the energy feature of the fault grid, determine the fault type of the power distribution line to be measured, and according to the target acquisition node corresponding to the fault grid, determine the fault location of the power distribution line to be measured.

[0093] In this embodiment, the energy feature of the fault grid is used to accurately detect the fault type of the power distribution line to be measured, and according to the target acquisition node corresponding to the fault grid, the fault location of the power distribution line to be measured is accurately located, improving the reliability of power distribution line fault location.

[0094] Furthermore, as a refinement and extension of the specific implementation manner of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, determining the fault type of the power distribution line to be measured according to the energy feature of the fault grid specifically includes: determining the energy difference between the fault grid and the preset fault type according to the preset signal feature energy response of the preset fault type and the energy feature of the fault grid; sorting the preset fault types according to the energy difference to determine the fault order, and determining the preset fault type at the top of the fault order as the fault type of the power distribution line to be measured.

[0095] In this embodiment, the signal feature energy response of each fault type is determined in advance to reflect the energy characteristics of the fault signals with different characteristics generated by different fault types in the power distribution line. Thus, by calculating the energy difference between the preset signal feature energy response of the preset fault type and the energy feature of the fault grid, the actual energy feature of the fault grid is quantitatively compared with the energy features of various known preset fault types. The smaller the energy difference, the closer the fault condition of the fault grid is to the preset fault type corresponding to this energy difference. Therefore, the fault type corresponding to the smallest energy difference argmin o' |E o -E fault,o' | is determined as the fault type of the power distribution line to be measured. Among them, E fault,o' is the preset signal feature energy response of the preset fault type o'. Thus, the fault types of complex and difficult-to-identify power distribution network such as lightning strikes and high-resistance arc lights occurring in the power distribution line to be measured are accurately determined, reducing false fault points, reducing the false alarm rate, and improving the reliability of fault detection.

[0096] Further, as a refinement and extension of the specific implementation manner of the above embodiment, to fully illustrate the specific implementation process of this embodiment, determining the fault location of the power distribution line to be measured according to the target acquisition node corresponding to the fault grid includes: determining the second signal contribution degree of the second candidate node to the fault grid within a preset sampling time period according to the distance between the second candidate node and the fault grid, where the second candidate node is an acquisition node that has acquired a fault signal within the preset sampling time period; sorting the second candidate nodes according to the second signal contribution degree to obtain a second node order, and determining the second candidate nodes before the preset order in the second node order as the target acquisition nodes corresponding to the fault grid; determining the distance between the fault location and the target acquisition node according to the sampling time when the target acquisition node acquired the first fault signal within the preset sampling time period, the distance between the target acquisition nodes, and the propagation speed of the fault signal, and positioning the fault location in the power distribution line to be measured according to the distance between the fault location and the target acquisition node.

[0097] In this embodiment, after determining the area where the fault occurs (the area near the fault grid) in the complex and wide-area power distribution line to be measured through step 103, the target acquisition node of the fault grid is used to accurately locate the fault location in the power distribution line to be measured by using the double-ended traveling wave method, improving the accuracy of fault location and ensuring the reliability of fault location.

[0098] Specifically, after step 103 narrows down the fault area to the area near the fault grid, according to the distance between all the acquisition nodes (second candidate nodes) that have acquired the fault signal within the preset sampling time period and the fault grid, calculate the second signal contribution degree of the second candidate node to the fault grid within the preset sampling time period, and quantify the possibility of a fault occurring near the second candidate node through the second signal contribution degree, so as to determine the target acquisition node corresponding to the fault grid, and then gradually narrow down the search range of the fault location to a more likely area through the target acquisition node.

[0099] Exemplarily, similar to calculating the first signal contribution degree, calculate the second signal contribution degree of the second candidate node to the fault grid within the preset sampling time period. The greater the second signal contribution degree of the second candidate node, the closer the second candidate node is to the area where the fault occurs, and it is most likely that a fault occurs near the second candidate node. Therefore, sort the second candidate nodes according to the second signal contribution degree to obtain a second node order, and determine the first and second (before the preset order) second candidate nodes in the second node order as the target acquisition nodes corresponding to the fault grid.

[0100] Here, when calculating the distance between the second candidate node and the faulty grid, the faulty grid is abstracted as the central point inside it, and the distance between points is calculated. If the second candidate node is located inside the faulty grid, the second signal contribution degree of the second candidate node is set to 1.

[0101] Further, according to the sampling moment when the target acquisition node collects the first fault signal within the preset sampling time period, the distance between the two target acquisition nodes, and the propagation speed of the fault signal, the double-ended traveling wave method is used to calculate the distances between the fault location and the two target acquisition nodes respectively.

[0102] Exemplarily, where L is the distance between the two target acquisition nodes, L1 and L2 are the distances between the fault location and the two target acquisition nodes respectively, t'1 and t'2 are the sampling moments when the two target acquisition nodes collect the first fault signal within the preset sampling time period, and v is the propagation speed of the fault signal. After determining the distances from the fault location to the two target acquisition nodes, the specific location of the fault location on the to-be-tested power distribution line can be located.

[0103] It should be noted that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0104] Further, as Figure 2 shown, as a specific implementation of the above power distribution line fault location method, an embodiment of the present application provides a power distribution line fault location device 200, and the power distribution line fault location device 200 includes: an acquisition module 201, a screening module 202, and a determination block 203.

[0105] Among them, the acquisition module 201 is used to acquire the original signals collected by each acquisition node in the to-be-tested power distribution line at the same sampling moment according to the GPS time synchronization signal, and the original signals include voltage signals and current signals;

[0106] The screening module 202 is used to perform quantum Fourier transform on the original signals to obtain the time-frequency characteristics of the original signals, and screen out the fault signals from the original signals according to the time-frequency characteristics; and,

[0107] Determine the energy characteristics of the to-be-tested grids in the to-be-tested power distribution line within the preset sampling time period according to the fault signals, and screen out the faulty grids from the to-be-tested grids according to the energy characteristics;

[0108] The determination module 203 is used to determine the fault type of the to-be-tested power distribution line according to the energy characteristics of the faulty grids, and determine the fault location of the to-be-tested power distribution line according to the target acquisition nodes corresponding to the faulty grids.

[0109] In one embodiment, the screening module 202 is specifically configured to decompose a fault signal into modal functions with finite bandwidth based on a variational framework; calculate the sample entropy values of the modal functions; remove the bandwidths corresponding to the modal functions with sample entropy values greater than a preset threshold from the fault signal to update the fault signal.

[0110] In one embodiment, the distribution line fault location device 200 further includes:

[0111] A noise reduction module, configured to normalize the amplitude of an original signal and map the normalized original signal to a quantum state; perform a quantum Fourier transform on the quantum state to determine the frequency components of the original signal; calculate the energy values of the frequency components, and determine the minimum energy value and the maximum energy value among the energy values of the frequency components; determine the time-frequency characteristics of the original signal according to the minimum energy value and the maximum energy value.

[0112] In one embodiment, the screening module 202 is specifically configured to determine, according to the distance between a first candidate node and a grid to be measured, a first signal contribution degree of the first candidate node to the grid to be measured at a target sampling moment, where the target sampling moment is any sampling moment within a preset sampling time period, and the first candidate node is a collection node that has collected a fault signal at the target sampling moment; sort the first candidate nodes according to the first signal contribution degree to determine a first node order, and use the fault signal collected by the first candidate node ranked first in the first node order at the target sampling moment as the target signal of the grid to be measured at the target sampling moment; determine the energy characteristics of the grid to be measured according to the target signals of the grid to be measured at each target sampling moment within the preset sampling time period.

[0113] In one embodiment, the determination module 203 is specifically configured to determine, according to the distance between a second candidate node and a fault grid, a second signal contribution degree of the second candidate node to the fault grid within a preset sampling time period, where the second candidate node is a collection node that has collected a fault signal within the preset sampling time period; sort the second candidate nodes according to the second signal contribution degree to obtain a second node order, and determine the target collection nodes corresponding to the fault grid as the second candidate nodes before a preset order in the second node order; determine the distance between the fault location and the target collection nodes according to the sampling moment when the target collection nodes first collected a fault signal within the preset sampling time period, the distance between the target collection nodes, and the propagation speed of the fault signal, and locate the fault location in the distribution line to be measured according to the distance between the fault location and the target collection nodes.

[0114] In one embodiment, the determination module 203 is specifically configured to determine the energy difference between the fault grid and the preset fault type according to the preset signal feature energy response of the preset fault type and the energy feature of the fault grid; sort the preset fault types according to the energy difference to determine the fault order, and determine the preset fault type at the top of the fault order as the fault type of the power distribution line to be measured.

[0115] In one embodiment, the power distribution line fault location device 200 further includes:

[0116] The correction module is configured to predict the prior state estimate of the local clock of the acquisition node at the next sampling moment based on the posterior state estimate of the local clock of the acquisition node at the current sampling moment, where the posterior state estimate of the local clock of the acquisition node at the current sampling moment is determined according to the initial time synchronization deviation between the GPS time synchronization signal acquired by the acquisition node and the local clock of the acquisition node; predict the prior covariance matrix of the prior state estimate of the local clock of the acquisition node at the next sampling moment according to the posterior covariance matrix of the posterior state estimate of the local clock of the acquisition node at the current sampling moment and the preset state transition matrix, where the posterior covariance matrix of the posterior state estimate of the local clock of the acquisition node at the current sampling moment is determined according to the initial covariance matrix of the local clock of the acquisition node; determine the Kalman gain of the local clock of the acquisition node at the next sampling moment according to the prior covariance matrix of the prior state estimate of the local clock of the acquisition node at the next sampling moment and the preset observation matrix; determine the posterior state estimate of the local clock of the acquisition node at the next sampling moment according to the prior state estimate of the local clock of the acquisition node at the next sampling moment, the Kalman gain, and the observed value; determine the posterior covariance matrix of the posterior state estimate of the local clock of the acquisition node at the next sampling moment according to the Kalman gain of the local clock of the acquisition node at the next sampling moment, the prior covariance matrix of the prior state estimate, and the preset observation matrix; determine the target time synchronization deviation between the local clock of the acquisition node at the next sampling moment and the GPS time synchronization signal according to the posterior covariance matrix of the posterior state estimate of the local clock of the acquisition node at the next sampling moment; if the target time synchronization deviation does not meet the preset error range, correct the local clock of the acquisition node, and use the next sampling moment as the current sampling moment, and recalculate the posterior covariance matrix of the posterior state estimate of the local clock of the acquisition node at the next sampling moment until the target time synchronization deviation meets the preset error range.

[0117] In one embodiment, the correction module is specifically configured to determine the ionospheric delay of the GPS time synchronization signal according to the observed frequencies of the first frequency band signal and the second frequency band signal acquired by the acquisition node and the carrier phase observation value of the observed frequency; correct the GPS time synchronization signal according to the ionospheric delay to update the GPS time synchronization signal.

[0118] Specific limitations on the distribution line fault location device can be referred to the limitations on the distribution line fault location method in the above text, which will not be elaborated here. Each module in the above distribution line fault location device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor of the computer device in the form of hardware or be independent of it, or be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0119] Based on the above as Figure 1 shown method, correspondingly, an embodiment of the present application also provides a readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the distribution line fault location method as Figure 1 shown above.

[0120] Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, and the software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various implementation scenarios of the present application.

[0121] Based on the above as Figure 1 shown method, and Figure 2 shown virtual device embodiment, in order to achieve the above purpose, an embodiment of the present application also provides a computer device, specifically a personal computer, a server, a network device, etc. The computer device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to implement the distribution line fault location method as Figure 1 shown above.

[0122] Optionally, the computer device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, sensors, an audio circuit, a WI-FI module, etc. The user interface may include a display screen (Display), an input unit such as a keyboard (Keyboard), etc. Optionally, the user interface may further include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a Bluetooth interface, a WI-FI interface), etc.

[0123] Those skilled in the art can understand that the structure of a computer device provided in this embodiment does not constitute a limitation on the computer device, and it may include more or fewer components, or combine certain components, or have different component arrangements.

[0124] The storage medium may further include an operating system and a network communication module. The operating system is a program for managing and storing the hardware and software resources of a computer device, and supports the operation of information processing programs and other software and / or programs. The network communication module is used to implement communication between components within the storage medium, as well as communication with other hardware and software in the entity device.

[0125] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or the embodiments of the present application can be implemented through hardware.

[0126] Those skilled in the art can understand that the drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the drawings are not necessarily essential for implementing the present application. Those skilled in the art can understand that the modules in the devices in the implementation scenarios can be distributed in the devices in the implementation scenarios according to the description of the implementation scenarios, or can be correspondingly changed and located in one or more devices different from the present implementation scenario. The modules in the above implementation scenarios can be combined into one module, or can be further split into multiple sub-modules.

[0127] The above serial numbers of the present application are only for description and do not represent the advantages or disadvantages of the implementation scenarios. The above-disclosed are only several specific implementation scenarios of the present application. However, the present application is not limited thereto, and any changes that can be thought of by those skilled in the art should fall within the protection scope of the present application.

Claims

1. A method for locating a distribution line fault, characterized in that: The method comprises: Acquire the original signals collected by each collection node in the distribution line to be tested at the same sampling time according to the GPS time synchronization signal, wherein the original signals include voltage signals and current signals; Performing a quantum Fourier transform on the original signal to obtain a time-frequency feature of the original signal, and screening out a fault signal from the original signal according to the time-frequency feature; Determine the energy characteristics of the grid to be tested in the distribution line to be tested within a preset sampling time period according to the fault signal, and select the faulty grid from the grid to be tested according to the energy characteristics; The fault type of the distribution line to be tested is determined according to the energy characteristics of the fault grid, and the fault location of the distribution line to be tested is determined according to the target acquisition node corresponding to the fault grid.

2. The method for locating a distribution line fault according to claim 1, characterized in that: The performing a quantum Fourier transform on the original signal to obtain the time-frequency characteristics of the original signal includes: Normalizing the amplitude of the original signal, and mapping the normalized original signal into a quantum state; Performing a quantum Fourier transform on the quantum state to determine the frequency component of the original signal; Calculating energy values ​​of the frequency components, and determining a minimum energy value and a maximum energy value among the energy values ​​of the frequency components; The time-frequency characteristics of the original signal are determined according to the minimum energy value and the maximum energy value.

3. The method for locating a distribution line fault according to claim 1, characterized in that: The method further comprises: Based on a variational framework, the fault signal is decomposed into modal functions with finite bandwidth; Calculating a sample entropy value of the modal function; The bandwidth corresponding to the modal function whose sample entropy value is greater than a preset threshold is removed from the fault signal to update the fault signal.

4. The method for locating a distribution line fault according to claim 1, characterized in that: The step of determining the energy characteristics of the grid to be tested in the power distribution line to be tested within a preset sampling time period according to the fault signal includes: Determine, according to the distance between the first candidate node and the grid to be measured, a first signal contribution of the first candidate node to the grid to be measured at a target sampling time, wherein the target sampling time is any sampling time within the preset sampling time period, and the first candidate node is the acquisition node that acquires the fault signal at the target sampling time; Sort the first candidate nodes according to the first signal contribution, determine a first node order, and use the fault signal collected by the first candidate node at the first position in the first node order at the target sampling time as the target signal of the grid to be tested at the target sampling time; The energy characteristics of the grid to be measured are determined according to the target signal of the grid to be measured at each target sampling moment within the preset sampling time period.

5. The method for locating a distribution line fault according to claim 1, characterized in that: The determining the fault location of the distribution line to be tested according to the target acquisition node corresponding to the fault grid includes: Determine, according to the distance between the second candidate node and the fault grid, a second signal contribution of the second candidate node to the fault grid within the preset sampling time period, wherein the second candidate node is the collection node that collects the fault signal within the preset sampling time period; Sort the second candidate nodes according to the second signal contribution to obtain a second node sequence, and determine the second candidate nodes located before a preset position in the second node sequence as the target acquisition nodes corresponding to the fault grid; The distance between the fault location and the target acquisition node is determined based on the sampling time at which the target acquisition node collects the first fault signal within the preset sampling time period, the distance between the target acquisition nodes, and the propagation speed of the fault signal, and the fault location is located in the distribution line to be tested based on the distance between the fault location and the target acquisition node.

6. The method for locating a distribution line fault according to claim 1, characterized in that: The determining the fault type of the distribution line to be tested according to the energy characteristics of the fault grid includes: Determining an energy difference between the fault grid and the preset fault type according to a preset signal characteristic energy response of the preset fault type and an energy characteristic of the fault grid; The preset fault types are sorted according to the energy difference to determine a fault sequence, and the preset fault type at the first position in the fault sequence is determined as the fault type of the power distribution line to be tested.

7. The method for locating a distribution line fault according to claim 1, characterized in that: The method further comprises: Based on the a posteriori state estimate of the local clock of the collection node at the current sampling time, predicting the a priori state estimate of the local clock of the collection node at the next sampling time, wherein the a posteriori state estimate of the local clock of the collection node at the current sampling time is determined according to the initial time synchronization deviation between the GPS time synchronization signal acquired by the collection node and the local clock of the collection node; Predicting the a priori covariance matrix of the a priori state estimate of the local clock of the acquisition node at the next sampling moment based on the a posteriori covariance matrix of the a posteriori state estimate of the local clock of the acquisition node at the current sampling moment and the preset state transfer matrix, wherein the a posteriori covariance matrix of the a posteriori state estimate of the local clock of the acquisition node at the current sampling moment is determined based on the initial covariance matrix of the local clock of the acquisition node; Determine the Kalman gain of the local clock of the acquisition node at the next sampling moment according to the a priori covariance matrix of the a priori state estimation of the local clock of the acquisition node at the next sampling moment and the preset observation matrix; Determine the a posteriori state estimate of the local clock of the acquisition node at the next sampling moment according to the a priori state estimate, Kalman gain and observation value of the local clock of the acquisition node at the next sampling moment; Determine the posterior covariance matrix of the posterior state estimation of the local clock of the acquisition node at the next sampling moment according to the Kalman gain of the local clock of the acquisition node at the next sampling moment, the prior covariance matrix of the prior state estimation and the preset observation matrix; Determine the target time synchronization deviation between the local clock of the acquisition node and the GPS time synchronization signal at the next sampling moment according to the a posteriori covariance matrix of the a posteriori state estimate of the local clock of the acquisition node at the next sampling moment; If the target time synchronization deviation does not meet the preset error range, the local clock of the acquisition node is corrected, and the next sampling moment is used as the current sampling moment, and the posterior covariance matrix of the posterior state estimate of the local clock of the acquisition node at the next sampling moment is recalculated until the target time synchronization deviation meets the preset error range.

8. The method for locating a distribution line fault according to claim 7, characterized in that: The GPS time synchronization signal includes a first frequency band signal and a second frequency band signal, and the method further includes: Determine the ionospheric delay of the GPS time synchronization signal according to the observation frequencies of the first frequency band signal and the second frequency band signal acquired by the acquisition node and the carrier phase observation values ​​of the observation frequencies; The GPS time synchronization signal is corrected according to the ionospheric delay amount to update the GPS time synchronization signal.

9. A distribution line fault location device, characterized in that: The device comprises: An acquisition module is used to acquire the original signals collected by each acquisition node in the distribution line to be tested at the same sampling time according to the GPS time synchronization signal, wherein the original signals include voltage signals and current signals; a screening module, configured to perform a quantum Fourier transform on the original signal to obtain a time-frequency feature of the original signal, and screen out a fault signal from the original signal according to the time-frequency feature; and Determine the energy characteristics of the grid to be tested in the distribution line to be tested within a preset sampling time period according to the fault signal, and select the faulty grid from the grid to be tested according to the energy characteristics; The determination module is used to determine the fault type of the distribution line to be tested according to the energy characteristics of the fault grid, and determine the fault location of the distribution line to be tested according to the target acquisition node corresponding to the fault grid.

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the program, the distribution line fault locating method according to any one of claims 1 to 8 is implemented.

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