A method and system for locating power transmission line faults based on BeiDou timing and positioning technology

Abstract

This invention relates to the field of transmission line fault location technology, and discloses a method and system for transmission line fault location based on BeiDou timing and positioning technology. The method includes: constructing a state-space model based on monitored transmission line parameters; obtaining a posterior extended state estimate using an unscented Kalman filter algorithm, where the dimension representing the fault point's location on the transmission line is non-zero, and proceeding to the next step; inputting the covariance matrix at the first moment and the transmission line parameters at the first moment into an instantaneous switching model, which outputs an instantaneous covariance matrix; replacing the covariance matrix at the first moment with the instantaneous covariance matrix, continuing to run the unscented Kalman filter algorithm, and then obtaining the posterior extended state estimate at the third moment, decoding to obtain the fault point's location; this invention optimizes the shortcomings of traditional transmission line fault location algorithms by combining artificial intelligence with unscented Kalman filtering.

Description

Technical Field

[0001] This invention relates to the field of power transmission line fault location technology, and more specifically, to a method and system for power transmission line fault location based on BeiDou time and positioning technology. Background Technology

[0002] Traditional transmission line fault location requires current transient traveling waves to identify the fault type. Different fault types have different characteristics of current transient traveling waves. Next, it is necessary to analyze the current transient traveling waves according to the fault type to find the peaks of the primary and secondary echoes, and then calculate the distance by time difference to locate the fault. The steps are relatively cumbersome. Moreover, traveling waves are superimposed and interfered with. Incorrect fault type identification will also affect the accuracy of fault location. Overall, the location accuracy of such methods is basically only a few hundred meters. Summary of the Invention

[0003] This invention provides a method and system for locating transmission line faults based on BeiDou timing and positioning technology, which solves the technical problem of low accuracy in traditional transmission line fault location.

[0004] This invention provides a method for locating power transmission line faults based on BeiDou timing and positioning technology, including:

[0005] Step 101: Construct a state-space model based on the parameters of the monitored transmission line. The state-space model includes state vectors, state equations, measurement equations, and measurement vectors.

[0006] Step 102: Add a dimension representing the location of the fault point on the transmission line to the state vector to obtain an extended state vector, and update the state equation and measurement equation based on the extended state vector.

[0007] Step 103: Obtain the posterior extended state estimate for each monitoring time using the unscented Kalman filter algorithm. If the dimension representing the location of the fault point on the transmission line in the posterior extended state estimate is non-zero, proceed to the next step and define this monitoring time as the first time.

[0008] Step 104: Input the covariance matrix at the first moment and the parameters of the transmission line at the first moment into the instantaneous switching model, and the instantaneous switching model outputs the instantaneous covariance matrix;

[0009] Step 105: Replace the covariance matrix at the first moment with the instantaneous covariance matrix, continue running the unscented Kalman filter algorithm, and then obtain the extended state estimate at the third moment. Decode the dimension of the extended state estimate representing the location of the fault point on the transmission line to obtain the location of the fault point.

[0010] The first moment and the third moment are separated by a second moment. The first moment, the second moment and the third moment are three adjacent moments, and they are ordered from front to back in time.

[0011] Furthermore, the instantaneous switching model includes:

[0012] The system consists of a graph neural module, a first convolutional module, an upsampling layer, a tensor layer, and a fully connected layer. The graph neural module takes as input graph structure data constructed based on the parameters of the transmission line at the first time step and outputs a first feature vector. The first convolutional module convolves the input covariance matrix to obtain a first feature map. The upsampling layer upsamples the first feature map to generate an instantaneous covariance matrix. The instantaneous covariance matrix is ​​input into the tensor layer to obtain a second feature vector. The first feature vector and the second feature vector are concatenated and then input into the fully connected layer. The fully connected layer outputs a classification result representing the fault type.

[0013] Furthermore, the method for constructing graph structure data based on the parameters of the transmission line at the first moment includes:

[0014] The graph structure data includes nodes and edges connecting the nodes. The nodes include a first node representing a line segment and a second node representing the entire transmission line.

[0015] The node characteristics of the first node are obtained by encoding the operating parameters of the corresponding line segment;

[0016] The node characteristics of the second node are obtained by encoding the global operating parameters of the transmission line;

[0017] There are edges between the second node and all first nodes;

[0018] The line segments represent lines where there are edges between the first nodes that are connected.

[0019] The calculation formula for the graph neural module is as follows:

[0020] Message passing:

[0021]

[0022] Where m i←j It is the message that node j transmits to node i, h i and h j These are the current representations of nodes i and j, respectively. It is a message function;

[0023] Message aggregation:

[0024] m i =ρ ← ({m i←j |j∈N(i)})

[0025] Where mi It is the aggregation of all messages received by node i, N(i) is the set of neighboring nodes of node i, and ρ ← It is a message aggregation function;

[0026] Node update:

[0027]

[0028] Where h i 'Is node i based on message m' i The updated representation, It is a node update function;

[0029] Repeat the process K times to obtain a higher-order node representation, which is the second node representation after the iterations. As the first eigenvector.

[0030] Furthermore, the types of faults include: lightning strikes, lightning backflashovers, tree obstructions, wildfires, hanging objects, icing, wind deflection, and construction equipment touching power lines.

[0031] Furthermore, the state-space model of a uniform transmission line of length l is represented as follows:

[0032] Equations of state:

[0033] x k =f(x) k-1 u k-1 )+w k-1

[0034] Measurement equation:

[0035] z k =h(x k u k )+v k

[0036] In the state equation and measurement equation:

[0037] It is the state vector at time k, and the state vector includes multiple dimensions;

[0038] It is the control input vector at time k;

[0039] It is the measurement vector at time k;

[0040] It is a nonlinear state transition function;

[0041] It is a nonlinear measurement function;

[0042] It is process noise, with a mean of 0 and a covariance of Q. k Gaussian distribution;

[0043] It is a measurement noise, with a mean of 0 and a covariance of R. k The Gaussian distribution; the extended state vector is:

[0044]

[0045] Accordingly, the state equation and measurement equation are modified as follows:

[0046]

[0047] z k =h(y k u k )+v k

[0048] Failure distance ratio refers to:

[0049]

[0050] in:

[0051] α is the fault distance ratio, which ranges from [0,1].

[0052] d f Indicates the distance from the fault point to the line terminal;

[0053] l represents the total length of the entire transmission line.

[0054] Furthermore, the state vector includes state variables such as voltage and current on all measured line segments.

[0055] Furthermore, the measurement vector includes voltage and current measurements at both ends of all line segments.

[0056] Furthermore, the steps of the unscented Kalman filter algorithm are as follows:

[0057] initialization:

[0058] Initial mean: y0 = E[y0]

[0059] Initial state covariance: P0 = E[(y0-y0)(y0-y0)] T ]

[0060] For k = 1, 2, ..., perform the following steps:

[0061] a. Calculate 2n+1 sigma points:

[0062]

[0063] in α is the scaling parameter, which determines the distribution of sigma points around y(k-1), and is usually taken as a very small positive value; k is a non-negative second moment parameter, which is usually taken as 3-n.

[0064] Updated in time:

[0065] State prediction:

[0066]

[0067] State prediction covariance:

[0068]

[0069] in and As weight, satisfying

[0070] Commonly used methods for calculating weights are as follows:

[0071]

[0072] Here, β is used to merge higher-order information, and β = 2 is optimal for a Gaussian distribution.

[0073] Measurement Update:

[0074] Measurement and prediction:

[0075]

[0076] Measure the predicted covariance:

[0077]

[0078] Cross covariance:

[0079]

[0080] Kalman gain:

[0081]

[0082] State and covariance updates:

[0083]

[0084] P k This represents the covariance obtained at time k after the update.

[0085] This invention provides a power transmission line fault location system based on BeiDou timing and positioning technology, comprising: a signal detection terminal, a master station system, and a mobile handheld terminal;

[0086] The signal detection terminal is installed on the conductors or towers of the transmission line, with one set installed every 20 to 30 km. It detects the traveling wave signal on the conductor in real time and sends it to the main station system via GPRS.

[0087] Main site system:

[0088] Software system: used to execute the power transmission line fault location method based on BeiDou timing and positioning technology as described in any one of claims 1-8;

[0089] Hardware system: application server, data server, data network communication equipment, and human-computer interaction equipment.

[0090] A computer storage medium, characterized in that it stores computer-readable instructions, which, when read by a computer, enable the execution of the aforementioned power transmission line fault location method based on BeiDou timing and positioning technology.

[0091] The beneficial effects of this invention are as follows:

[0092] This invention employs a lightweight system based on the Kalman filter algorithm to ensure timely system response. Considering that Kalman filtering relies on continuous temporal state transitions, and that transmission line faults are infrequent (meaning that the proportion of abnormal signals caused by faults in a signal sequence over a period of time is extremely small), ideally, when the covariance matrix of the Kalman filter converges to a stable value, the Kalman gain will also tend to stabilize. This means the filter has found an optimal balance point, effectively combining model prediction and measurement information. However, the abnormal signals from faults can disrupt the balance of the Kalman filter after long periods of convergence with fault-free signals, making it difficult to further reduce the error in fault location. This invention optimizes the shortcomings of traditional transmission line fault location algorithms by combining artificial intelligence with unscented Kalman filtering. Attached Figure Description

[0093] Figure 1 This is a flowchart of the transmission line fault location method based on BeiDou time synchronization and positioning technology of the present invention;

[0094] Figure 2 This is an architecture diagram of the instantaneous switching model of the present invention;

[0095] Figure 3 This is an architecture diagram of the power transmission line fault location system based on BeiDou time synchronization and positioning technology of the present invention;

[0096] Figure 4 This is a physical image of the signal detection terminal of the present invention. Detailed Implementation

[0097] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0098] At least one embodiment of the present invention discloses a method for locating transmission line faults based on BeiDou timing and positioning technology, such as... Figure 1 As shown, it includes:

[0099] Step 101: Construct a state-space model based on the parameters of the monitored transmission line. The state-space model includes state vectors, state equations, measurement equations, and measurement vectors.

[0100] Step 102: Add a dimension representing the location of the fault point on the transmission line to the state vector to obtain an extended state vector, and update the state equation and measurement equation based on the extended state vector.

[0101] Step 103: Obtain the posterior extended state estimate for each monitoring time using the unscented Kalman filter algorithm. If the dimension representing the location of the fault point on the transmission line in the posterior extended state estimate is non-zero, proceed to the next step and define this monitoring time as the first time.

[0102] Time is a continuous value and needs to be discretized for analysis. The interval between two adjacent monitoring times is generally the sampling period of the measurement unit.

[0103] Step 104: Input the covariance matrix at the first moment and the parameters of the transmission line at the first moment into the instantaneous switching model, and the instantaneous switching model outputs the instantaneous covariance matrix;

[0104] Step 105: Replace the covariance matrix at the first moment with the instantaneous covariance matrix, continue running the unscented Kalman filter algorithm, and then obtain the extended state estimate at the third moment. Decode the dimension of the extended state estimate representing the location of the fault point on the transmission line to obtain the location of the fault point.

[0105] The first moment and the third moment are separated by a second moment. The first moment, the second moment and the third moment are three adjacent moments, and they are ordered from front to back in time.

[0106] This embodiment provides a lightweight online monitoring technology for transmission lines that uses an unscented Kalman filter algorithm for dynamic monitoring, and combines deep learning technology to improve the accuracy of fault location by applying it to the system optimization during fault transients.

[0107] like Figure 2 As shown, in one embodiment of the present invention, the instantaneous switching model includes:

[0108] The system consists of a graph neural module, a first convolutional module, an upsampling layer, a tensor layer, and a fully connected layer. The graph neural module takes as input graph structure data constructed based on the parameters of the transmission line at the first time step and outputs a first feature vector. The first convolutional module convolves the input covariance matrix to obtain a first feature map. The upsampling layer upsamples the first feature map to generate an instantaneous covariance matrix. The instantaneous covariance matrix is ​​input into the tensor layer to obtain a second feature vector. The first feature vector and the second feature vector are concatenated and then input into the fully connected layer. The fully connected layer outputs a classification result representing the fault type.

[0109] The aforementioned method for constructing graph structure data based on the parameters of the transmission line at the first moment includes:

[0110] The graph structure data includes nodes and edges connecting the nodes. The nodes include a first node representing a line segment and a second node representing the entire transmission line.

[0111] The node characteristics of the first node are obtained by encoding the operating parameters of the corresponding line segment;

[0112] The node characteristics of the second node are obtained by encoding the global operating parameters of the transmission line;

[0113] There are edges between the second node and all first nodes;

[0114] The line segments represent lines where there are edges between the first nodes that are connected.

[0115] The calculation formula for the graph neural module is as follows:

[0116] Message passing:

[0117]

[0118] Where m i←j It is the message that node j transmits to node i, h i and h j These are the current representations of nodes i and j, respectively. It is a message function;

[0119] Message aggregation:

[0120] m i =ρ ← ({m i←j|j∈N(i)})

[0121] Where m i It is the aggregation of all messages received by node i, N(i) is the set of neighboring nodes of node i, and ρ ← It is a message aggregation function;

[0122] Node update:

[0123]

[0124] Where h i 'Is node i based on message m' i The updated representation, It is a node update function;

[0125] Repeat the process K times to obtain a higher-order node representation, which is the second node representation after the iterations. As the first eigenvector.

[0126] As mentioned above, the tensor quantization method for tensor layers involves concatenating the row vectors of a matrix in sequence.

[0127] As mentioned above, upsampling can be achieved through methods such as deconvolution.

[0128] The aforementioned fault types include: lightning strikes, lightning backflashovers, tree obstructions, wildfires, hanging objects, icing, wind deflection, and construction equipment touching power lines.

[0129] During training, the loss function, such as the cross-entropy loss function, is calculated based on the results of fault type prediction.

[0130] Furthermore, the loss from predicting the location of the fault point is used as the local loss function for the upsampling layer and the first convolutional module. This can be used for transfer learning of pre-trained instantaneous switching models.

[0131] As mentioned above, the first convolutional module can use CNN models such as LeNet or VGGNet.

[0132] In one embodiment of the present invention, a system for locating power transmission line faults using the aforementioned method based on BeiDou timing and positioning technology is provided, comprising: a traveling wave signal detection terminal (hereinafter referred to as 'signal detection terminal'), a master station system, and a mobile handheld terminal;

[0133] Signal detection terminal:

[0134] Signal detection terminals are installed on transmission line conductors or towers, with one set installed every 20 to 30 km or more. They detect traveling wave signals on the conductors in real time and transmit them to the main station system via GPRS.

[0135] Main site system:

[0136] - Software systems: fault analysis and location application software, database software, operating system, and other application software;

[0137] -Hardware system: application servers and their peripherals, data servers, data network communication equipment, and human-computer interaction devices, etc.;

[0138] Mobile handheld terminals:

[0139] -3G mobile handheld terminal: a handheld terminal with an intelligent embedded operating system;

[0140] It also includes the necessary BeiDou timing module, which uses BeiDou satellite timing to synchronize the clocks of all signal detection devices.

[0141] In one embodiment of the present invention, the remote communication interface between the signal detection device and the system master station adopts the IEC61850 and IEC104 communication protocols, which can also meet the needs of accessing the current mainstream integrated automation and dispatch automation systems.

[0142] In one embodiment of the present invention, various models of signal detection terminal devices are designed and manufactured to meet the application requirements of AC / DC transmission lines of various voltage levels and different line types.

[0143] These signal detection terminals are installed on transmission line conductors or towers, primarily to detect traveling wave signals generated by the following faults:

[0144] - Lightning fault: Monitor, record, and transmit lightning fault current waveforms in power lines;

[0145] - (Non-lightning fault) Single-phase and phase-to-phase faults: Monitor, record, and transmit transient current traveling wave waveforms in power lines.

[0146] The configuration and selection of signal detection terminals are shown in Table 1.

[0147] Table 1. Signal Detection Terminal Configuration Selection Table

[0148]

[0149] In one embodiment of the present invention,

[0150] By deploying several field monitoring terminals along the transmission line, the line is divided into several sections (each section ≤ 30km in length), and fault data and signals in each section are monitored and recorded separately. This monitoring method, which involves a distributed layout along the line rather than a centralized layout at both ends of the substation, not only discretizes the monitoring of inherent parameters affecting monitoring errors, such as transmission line length and conductor sag, but also effectively reduces the impact of small traveling wave velocity variations and attenuation on accurate fault location due to the shorter monitoring section of each monitoring device, thus significantly improving the accuracy of transmission line fault location.

[0151] Consider a uniform transmission line of length l, whose state-space model can be represented as:

[0152] Equations of state:

[0153] x k =f(x) k-1 u k-1 )+w k-1

[0154] Measurement equation:

[0155] z k =h(x k u k )+v k

[0156] State equations describe the evolution of a system's state over time and are typically modeled using the system's differential or difference equations. Taking a power transmission line as an example, the state vector may include variables such as voltage and current at each node on the line.

[0157] Then the state equation x k =f(x) k-1 u k-1 )+w k-1 Differential or difference equations can then be established using Kirchhoff's laws, circuit theory, etc., and process noise w can be added. k-1 To describe the uncertainty of the system.

[0158] The measurement equation describes how to obtain observations from the system state, depending on the actual measurement equipment and methods. Taking a transmission line as an example, the measured values ​​might be telemetry values ​​such as voltage and current at both ends of the line. Therefore, the measurement equation z... k =h(x k u k )+v k This can be modeled based on measurement principles, such as the working principles of voltage and current sensors, and by incorporating measurement noise v. k This describes the uncertainty of measurement.

[0159] The measurement equations and state equations described in this embodiment can be referenced using existing nonlinear equations for transmission line fault location, or even directly referenced from existing ones.

[0160] Since this invention does not optimize the state equations and measurement equations, and the innovation of this invention lies in optimizing the shortcomings of traditional transmission line fault location algorithms by combining artificial intelligence with unscented Kalman filtering, it is also necessary for this invention not to optimize the state equations and measurement equations derived from existing physical models. Of course, this does not mean that the choice of state equations and measurement equations has no impact on the results. It must be noted that modeling equations that can better express the transmission line system can bring better location results.

[0161] In the state equation and measurement equation:

[0162] It is the state vector at time k, and the state vector includes multiple dimensions;

[0163] In one embodiment of the present invention, it is necessary to take into account the hardware system on which the present invention is based, and to set up a data acquisition unit on each line segment of the transmission line. Therefore, the state vector includes state variables such as voltage and current on all line segments.

[0164] It is the control input vector at time k, including line parameters, load conditions, etc.

[0165] It is the measurement vector at time k, which includes the voltage and current measurements at both ends of all line segments.

[0166] It is a nonlinear state transition function.

[0167] It is a nonlinear measurement function.

[0168] It is process noise, with a mean of 0 and a covariance of Q. k Gaussian distribution;

[0169] It is a measurement noise, with a mean of 0 and a covariance of R. k Gaussian distribution;

[0170] To incorporate the fault location into the state-space model, the fault distance ratio α∈[0,1] is defined as the proportion of the distance from the fault point to the terminal to the total line length. The extended state vector is:

[0171]

[0172] Accordingly, the state equation and measurement equation are modified as follows:

[0173]

[0174] Failure distance ratio refers to:

[0175]

[0176] in:

[0177] α is the fault distance ratio, which ranges from [0,1].

[0178] d f Indicates the distance from the fault point to the line terminal;

[0179] l represents the total length of the entire transmission line;

[0180] Considering concurrent failures, although this situation is relatively rare, the present invention provides an embodiment for this situation, which configures two or more dimensions to represent the location of the failure point, so that concurrent failures can be resolved.

[0181] The main steps of the UKF algorithm are as follows:

[0182] (1) Initialization:

[0183] Initial mean: y0 = E[y0]

[0184] Initial state covariance: P0 = E[(y0-y0)(y0-y0)] T ]

[0185] (2) For k = 1, 2, ..., perform the following steps:

[0186] a. Calculate 2n+1 sigma points:

[0187]

[0188] in α is the scaling parameter, which determines the distribution of sigma points around y(k-1), and is usually taken as a very small positive value; k is a non-negative second moment parameter, which is usually taken as 3-n.

[0189] b. Time update (prediction):

[0190] State prediction:

[0191]

[0192] State prediction covariance:

[0193]

[0194] in and As weight, satisfying

[0195] Commonly used methods for calculating weights are as follows:

[0196]

[0197] Here, β is used to merge higher-order information, and β = 2 is optimal for a Gaussian distribution.

[0198] c. Measurement update (calibration):

[0199] Measurement and prediction:

[0200]

[0201] Measure the predicted covariance:

[0202]

[0203] Cross covariance:

[0204]

[0205] Kalman gain:

[0206]

[0207] State and covariance updates:

[0208]

[0209] P k This represents the covariance obtained at time k after the update;

[0210] Using the UKF algorithm described above, the posterior extended state estimate at each time step k can be obtained. This includes the failure distance ratio α k The estimation results.

[0211] The important fault type identification criteria are as follows:

[0212] (1) Three-phase short circuit fault:

[0213] (2) The amplitudes of the three-phase currents change abruptly and become nearly equal, with a phase difference of nearly 120°.

[0214] (2) Two-phase short circuit fault:

[0215] The amplitudes of the two-phase currents abruptly change and become nearly equal, while being much larger than the third-phase current, and the phase difference between the two-phase currents is close to 180°.

[0216] (3) Single-phase ground fault:

[0217] The amplitude of the faulty phase current is significantly increased and is significantly greater than the amplitudes of the other two phase currents.

[0218] (4) Two-phase ground fault:

[0219] The amplitudes of the two-phase currents abruptly change and become nearly equal, and are significantly greater than the third-phase current. The phase difference between the two-phase currents may deviate from 180°.

[0220] In at least one embodiment of the present invention, a computer storage medium is provided that stores computer-readable instructions, which, when read by a computer, can execute the aforementioned method for locating power transmission line faults based on BeiDou timing and positioning technology.

[0221] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A method for locating transmission line faults based on BeiDou timing and positioning technology, characterized in that, include: Step 101: Construct a state-space model based on the parameters of the monitored transmission line. The state-space model includes state vectors, state equations, measurement equations, and measurement vectors. Step 102: Add a dimension representing the location of the fault point on the transmission line to the state vector to obtain an extended state vector, and update the state equation and measurement equation based on the extended state vector. Step 103: Obtain the posterior extended state estimate for each monitoring time using the unscented Kalman filter algorithm. If the dimension representing the location of the fault point on the transmission line in the posterior extended state estimate is non-zero, proceed to the next step and define this monitoring time as the first time. Step 104: Input the covariance matrix at the first moment and the parameters of the transmission line at the first moment into the instantaneous switching model, and the instantaneous switching model outputs the instantaneous covariance matrix; Instantaneous switching models include: The system consists of a graph neural module, a first convolutional module, an upsampling layer, a tensor layer, and a fully connected layer. The graph neural module takes graph structure data constructed based on the parameters of the transmission line at the first time step as input and outputs a first feature vector. The first convolutional module convolves the input covariance matrix to obtain a first feature map. The upsampling layer upsamples the first feature map to generate an instantaneous covariance matrix. The instantaneous covariance matrix is ​​input into the tensor layer to obtain a second feature vector. The first feature vector and the second feature vector are concatenated and then input into the fully connected layer. The fully connected layer outputs a classification result representing the fault type. Methods for constructing graph structure data based on the parameters of the transmission line at the first moment include: The graph structure data includes nodes and edges connecting the nodes. The nodes include a first node representing a line segment and a second node representing the entire transmission line. The node characteristics of the first node are obtained by encoding the operating parameters of the corresponding line segment; The node characteristics of the second node are obtained by encoding the global operating parameters of the transmission line; There are edges between the second node and all first nodes; The first node of the line segment represents a connection between two nodes, and there are edges between them. The calculation formula for the graph neural module is as follows: Message passing: ； in It is the message that node j sends to node i. and These are the current representations of nodes i and j, respectively. It is a message function; Message aggregation: ； in It is the aggregation of all messages received by node i. It is the set of neighboring nodes of node i. It is a message aggregation function; Node update: ； in Node i is based on the message The updated representation, It is a node update function; Repeat the process K times to obtain a higher-order node representation, which is the second node representation after the iterations. As the first feature vector; Step 105: Replace the covariance matrix at the first moment with the instantaneous covariance matrix, continue running the unscented Kalman filter algorithm, and then obtain the extended state estimate at the third moment. Decode the dimension of the extended state estimate representing the location of the fault point on the transmission line to obtain the location of the fault point. The first moment and the third moment are separated by a second moment. The first moment, the second moment and the third moment are three adjacent moments, and they are ordered from front to back in time.

2. The method for locating transmission line faults based on BeiDou timing and positioning technology according to claim 1, characterized in that, Fault types include: lightning strikes, lightning backflashovers, tree obstructions, wildfires, hanging objects, icing, wind deflection, and construction equipment touching power lines.

3. The method for locating transmission line faults based on BeiDou timing and positioning technology according to claim 1, characterized in that, The state-space model of a uniform transmission line of length l is represented as follows: Equations of state: ； Measurement equation: ； In the state equation and measurement equation: It is the state vector at time k, and the state vector includes multiple dimensions; It is the control input vector at time k; It is the measurement vector at time k; It is a nonlinear state transition function; It is a nonlinear measurement function; It is process noise, with a mean of 0 and a covariance of . Gaussian distribution; It is a measurement noise, with a mean of 0 and a covariance of . Gaussian distribution; The extended state vector is: ； Accordingly, the state equation and measurement equation are modified as follows: ； Failure distance ratio refers to: ； in: It is the fault distance ratio, with a value range of [0, 1]. Indicates the distance from the fault point to the line terminal; l represents the total length of the entire transmission line.

4. The method for locating transmission line faults based on BeiDou timing and positioning technology according to claim 3, characterized in that, The state vector includes the state variables of voltage and current on all measured line segments.

5. The method for locating transmission line faults based on BeiDou timing and positioning technology according to claim 3, characterized in that, The measurement vector includes voltage and current measurements at both ends of all line segments.

6. The method for locating transmission line faults based on BeiDou timing and positioning technology according to claim 3, characterized in that, The steps of the unscented Kalman filter algorithm are as follows: initialization: Initial mean: ; Initial state covariance: ; for Perform the following steps: Calculate 2n+1 sigma points: ； in , It is a scaling parameter; Updated in time: State prediction: ； State prediction covariance: ； in and As weight, satisfying ; Measurement Update: Measurement and prediction: ； Measure the predicted covariance: ； Cross covariance: ； Kalman gain: ； State and covariance updates: ； ； This represents the covariance obtained at time k after the update.

7. A power transmission line fault location system based on BeiDou timing and positioning technology, characterized in that, include: Signal detection terminal, main station system and mobile handheld terminal; The signal detection terminal is installed on the conductors or towers of the transmission line, with one set installed every 20-30km. It detects the traveling wave signal on the conductor in real time and sends it to the main station system via GPRS. Main site system: Software system: used to execute the power transmission line fault location method based on BeiDou timing and positioning technology as described in any one of claims 1-6; Hardware system: application server, data server, data network communication equipment, and human-computer interaction equipment; It also includes a BeiDou timing module, which uses BeiDou satellite timing to synchronize the clocks of all signal detection devices.

8. A computer storage medium, characterized in that, It stores computer-readable instructions, and when a computer reads the computer-readable instructions, it can execute the power transmission line fault location method based on BeiDou timing and positioning technology as described in any one of claims 1-6.

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