State estimation method and device for traction power supply system

Abstract

The invention provides a state estimation method and device for a tractive power supply system, and belongs to the technical field of rail transit power supply, and the method comprises the steps: obtaining measurement data and train position information; judging whether the train position information is missing or has errors or not; when the train position information does not have missing or errors, establishing a state estimation model based on a pre-established traction power supply network model, and solving the state estimation model based on a weighted least square method to obtain an estimated value of each node voltage; and when the train position information is missing or has an error, establishing an augmented state estimation model based on a pre-established traction power supply network model, and solving the augmented state estimation model based on a weighted least square method and an interior point method to obtain an estimated value of each node voltage and an estimated value of the train position. According to the method, the estimated value of the voltage of each node and the estimated value of the train position can be obtained under the condition that the position information and the measurement data have errors, and reliable input is provided for load flow calculation.

Description

Technical Field

[0001] This invention belongs to the field of rail transit power supply technology, and in particular relates to a state estimation method and device for a traction power supply system. Background Technology

[0002] Electrified railways use traction power supply, unlike traditional distribution networks. The load on the metro traction network, i.e., the metro trains, is in a state of high-speed movement, causing the structure and network parameters of the traction network to change constantly, placing higher demands on the accuracy and real-time performance of measurements. Metro systems built earlier and in operation for many years generally suffer from a lack of measurement equipment and outdated measurement methods. Therefore, when performing online power flow calculations on their traction networks, inaccurate locomotive position acquisition, limited measurement data, and measurement errors severely affect the accuracy of the final power flow calculation.

[0003] Since the 1960s, state estimation has been widely applied in power systems. It utilizes the redundancy of real-time measurement systems to improve data accuracy and automatically eliminate errors caused by random interference. State estimation can eliminate measurement errors and estimate system states and network parameters. Traditional state estimation requires known and accurate network parameters, but in engineering practice, network parameters may change. Inaccurate network parameters lead to inaccurate calculated nodal admittance matrices, affecting the accuracy and reliability of state estimation. Meanwhile, rail transit traction power supply networks are a special type of distribution network. Because their loads—metro trains—are in high-speed motion, inaccurate real-time train positions can lead to incorrect network parameters, severely impacting the effectiveness of state estimation. Currently, state estimation research mainly focuses on conventional transmission and distribution networks, and its application to rail transit traction power supply networks is still limited.

[0004] Therefore, there is an urgent need for a traction power supply system state estimation scheme that can obtain estimated values ​​of voltage at each node of the traction network and estimated values ​​of train position even when there are missing or incorrect real-time train position information and measurement errors in the measurement data, so as to provide reliable input for subsequent power flow calculation and improve the accuracy of power flow calculation. Summary of the Invention

[0005] The purpose of this invention is to provide a state estimation method and apparatus for a traction power supply system, which can obtain estimated values ​​of the voltage of each node in the traction network and estimated values ​​of the train position when there are missing or erroneous real-time train position information or measurement errors in the measurement data, thereby providing reliable input for subsequent power flow calculation and improving the accuracy of power flow calculation.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a state estimation method for a traction power supply system, comprising: Acquire measurement data and train position information. The measurement data includes the voltage at each train end, the voltage at each traction station end, the current at each train end, the current at each traction station end, the power at each train end, and the power at each traction station end. Determine if there are any missing or incorrect train location information; When there is no missing or erroneous train position information, a state estimation model is established based on a pre-constructed traction power supply network model. The traction power supply network model is used to characterize the electrical connection relationship between each node in the traction power supply network. The nodes include the nodes corresponding to traction and the nodes corresponding to the train. The state estimation model uses the voltage of each node as the state vector to be estimated and the measurement data as the measurement vector. The state estimation model is solved based on the weighted least squares method to obtain the estimated value of the voltage of each node. When train position information is missing or erroneous, an augmented state estimation model is established based on a pre-constructed traction power supply network model. The augmented state estimation model uses the voltage of each node and the position of each train as the state vector to be estimated, the measured data as the measurement vector, and sets constraints according to the train position. The augmented state estimation model is solved based on the weighted least squares method and the interior point method to obtain the estimated values ​​of the voltage of each node and the estimated values ​​of the train position.

[0007] Furthermore, the state estimation method for the traction power supply system provided by the present invention also includes: Based on the train location information, a topological model of the traction power supply network is obtained. Then, based on this model, the correlation matrix A between the nodes and branches of the traction power supply network is determined. The element in the i-th row and k-th column of the correlation matrix A... The definition is as follows:

[0008] Wherein, the node includes a traction substation node and a train node, and the branch is the line segment between the nodes; Calculate the admittance of each branch according to the following expression:

[0009] in, Indicates the first i Admittance of the branch, Indicates the first i The length of the branch road, Indicates the first i The resistance of the contact wire section Indicates the first i The resistance of a section of rail; Constructing the branch admittance matrix :

[0010] in, This represents the number of branches in the traction network. Constructing the nodal admittance matrix Y : .

[0011] Furthermore, the step of establishing the state estimation model includes: The measurement data are concatenated into a measurement vector z in a preset order:

[0012] in, For each train voltage vector, For each traction station terminal voltage vector, For the current vectors at each train end, Let the current vectors at each traction station end be denoted as . For the power vector at each train end, For each traction station end power vector; Define the voltage at each node as the state vector to be estimated. x : ; Establish the measurement equation: ,in, h For measurement expression, For measurement error, Follows a normal distribution ; The node admittance matrix Y The matrix is ​​divided into three sub-matrices based on the traction sub-node and the train node: the train node and the train node admittance sub-matrix. Admittance coupling submatrix between train nodes and traction sub-nodes Admittance submatrix of traction sub-nodes ; The linear relationship between the current and voltage at each node can be expressed as:

[0013] The relationship between the power of each node and the voltage and current of each node is expressed as follows:

[0014] Transform the measurement equation into a linear form: ,in H For the Jacobian matrix: .

[0015] Furthermore, the steps for solving the state estimation model include: The state vector to be estimated x Using the objective function of weighted least squares as the optimization variable, the state vector to be estimated is obtained. x The estimated value; The objective function is:

[0016] in, This is a weighted diagonal matrix. diagonal element For the first i The variance of measurement error.

[0017] Furthermore, the state estimation method for the traction power supply system provided by the present invention also includes: Determine train nodes j The position is l j ( j =1,2,…, N Train ), N Train For the number of train nodes, and to determine the relationship between the train nodes. j two adjacent nodes j -1 and j The position of +1 is p j-1 , p j+1 ; Determine train nodes j With nodes j The length of the branch k connected to -1 is The train node j With nodes j The length of the branch k connected to +1 is ; Update the branch admittance matrix The updated branch admittance matrix No. k and k+ The diagonal elements are as follows:

[0018] in, and They represent the first k and k+ The resistance of a section of the overhead contact line. and They represent the first k and k+1. Resistance of the railway rail.

[0019] Furthermore, the step of establishing the augmented state estimation model includes: The measurement data are concatenated into a measurement vector z in a preset order:

[0020] in, For the voltage of each train, The voltage at each traction station terminal. For the current at each train end, For the current at each traction station end, For the power of each train end, Power at each traction station end; Define the voltage at each node and the position of each train as the augmented state vector to be estimated. : , , ,in, K This represents the number of trains with missing or incorrect location information. This represents the location of the train node where the Kth location information is missing or incorrect. For the voltage of each node, The position vectors of each train node where the position information is missing or incorrect; Establish the measurement equation: ,in, h For measurement expression, For measurement error, Follows a normal distribution ; The node admittance matrix is ​​divided into three sub-matrices according to the traction sub-node and the train node: the admittance sub-matrix from train node to train node. Admittance coupling submatrix between train nodes and traction sub-nodes Admittance submatrix from traction station node to traction station node ; The linear relationship between the current and voltage at each node can be expressed as:

[0021] The relationship between the power of each node and the voltage and current of each node is expressed as follows:

[0022] Transform the measurement equation into a linear form: ,in To augment the Jacobian matrix: .

[0023] Furthermore, the method of solving the augmented state estimation model based on weighted least squares and interior point method includes: The augmented state vector to be estimated Using the weighted least squares augmented objective function as the optimization variable, a constrained augmented state estimation optimization model is constructed:

[0024] in, This is a weighted diagonal matrix. diagonal element For the first i The variance of the measurement error, the first K The locations of the two adjacent nodes of a train node with missing or incorrect location information are as follows: and ; The augmented state estimation optimization model is rearranged into a quadratic programming form:

[0025] Where G is the gain matrix, ; The augmented state estimation optimization model of the quadratic programming form is solved using the interior-point method to obtain the augmented state vector to be estimated. The estimated value.

[0026] In a first aspect, the present invention provides a state estimation device for a traction power supply system, comprising: The measurement data acquisition module is used to acquire measurement data and train position information. The measurement data includes the voltage of each train end, the voltage of each traction station end, the current of each train end, the current of each traction station end, the power of each train end, and the power of each traction station end. The judgment module is used to determine whether there is missing or incorrect train position information based on the train position information. The state estimation module is used to establish a state estimation model based on a pre-constructed traction power supply network model when there is no missing or erroneous train position information. The traction power supply network model is used to characterize the electrical connection relationship between each node in the traction power supply network. The nodes include the nodes corresponding to traction and the nodes corresponding to the train. The state estimation model uses the voltage of each node as the state vector to be estimated and the measurement data as the measurement vector. The state estimation model is solved based on the weighted least squares method to obtain the estimated value of the voltage of each node. The augmented state estimation module is used to establish an augmented state estimation model based on a pre-constructed traction power supply network model when there is a lack or error in train position information. The augmented state estimation model uses the voltage of each node and the position of each train as the state vector to be estimated, the measured data as the measurement vector, and sets constraints according to the train position. The augmented state estimation model is solved based on the weighted least squares method and the interior point method to obtain the estimated values ​​of the voltage of each node and the estimated values ​​of the train position.

[0027] In a third aspect, the present invention provides an electronic device including a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the state estimation method for the traction power supply system provided by the present invention.

[0028] In a fourth aspect, the present invention provides a computer-readable storage medium storing at least one instruction that, when executed by a processor, implements the state estimation method for a traction power supply system provided by the present invention.

[0029] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention provides a state estimation device, electronic device, and computer-readable storage medium for a traction power supply system, which also solves the problems raised in the background section.

[0030] (1) The state estimation method for the traction power supply system provided by the present invention first determines whether the train position information is missing or erroneous, and then adaptively switches between two types of models. When the position information is reliable, conventional state estimation is used, and when the position is missing or erroneous, augmented state estimation is switched. This integrated process can still stably output the voltage of each node in engineering scenarios where measurement noise and positioning anomalies coexist; when necessary, it can also estimate the train position at the same time, providing reliable input for subsequent power flow calculation, and significantly improving the accuracy and availability of online calculation of the traction network.

[0031] (2) The state estimation method for the traction power supply system provided by this invention constructs a unified traction network topology and parameter model based on the train position: an association matrix, a branch admittance matrix, and a node admittance matrix. The node admittance matrix is ​​divided into three parts: train-train, train-traction substation, and substation-traction substation. The measurement vector z is spliced ​​together by the voltage, current, and power of each train or substation in a preset order. Based on this, the measurement equation is linearized to obtain the Jacobian matrix H and solved using weighted least squares. The advantages of this design are: the topology, parameters, and measurements are strictly aligned, making it easy to implement and maintain in engineering; the linear relationship is established directly using voltage or current measurements, resulting in low computational load, fast convergence, and avoidance of reliance on nonlinear power flow iteration.

[0032] (3) The state estimation method for the traction power supply system provided by this invention, when the train position information is missing or incorrect: first, the equivalent admittance of the corresponding branch is updated based on the positions of two adjacent nodes; then, the voltage of each node plus the position of the train to be estimated is combined to form an augmented state vector, and interval constraints and physical constraints of not crossing adjacent nodes are introduced. The weighted least squares objective is organized into a quadratic programming problem and solved using the interior point method. This can maintain the consistency of the network topology and avoid model mismatch caused by incorrect positions; the voltage and train position are output synchronously, which significantly reduces the impact of missing positions on state estimation; it can be naturally extended to the case where multiple trains are missing or incorrect at the same time, the solution is stable and reliable, and the gain matrix G can quantitatively reflect the observability and effect of the measurement. Attached Figure Description

[0033] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of the state estimation method for the traction power supply system according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the power supply system of a subway train according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the lumped parameter model of the traction power supply network according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a simplified model of the traction power supply network according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the state estimation device for the traction power supply system according to an embodiment of the present invention; Figure 6 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0034] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0035] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0036] Example 1 This invention provides a state estimation method for a traction power supply system, such as... Figure 1 As shown, it includes the following steps S1 to S3.

[0037] Before step S1, a model of the subway traction power supply network is first established. The traction substation and the traction power supply network together constitute the traction power supply system, which is the power supply system for subway trains. Its main components are as follows: Figure 2 As shown. The feeder and return lines are the two terminal lines at the traction port of the traction substation. The overhead contact line is a special type of power transmission line; the train obtains electrical energy through friction between the pantograph and the contact line. The rails are both the path for the train's movement and the conductor; due to the conductivity between the rails and the ground, the rails and the earth form a rail-to-ground circuit, receiving the traction current from the train.

[0038] If the train positioning data is accurate, the traction power supply network can be modeled as a lumped parameter model, such as... Figure 3 As shown in the diagram, G represents the traction substation and T represents the train. r qi Indicates the first i The resistance of the contact wire section r hi Indicates the first i The resistance of a section of rail, y i , y (i+1) Indicates the first i The electrical conductivity of the rail section to the ground. When the injected current into the train is positive, the train absorbs power for traction; when the injected current into the train is negative, the train outputs power for energy feeding.

[0039] Stray currents are negligible compared to traction currents, and feeder currents and return currents are equal in magnitude and opposite in direction. This means that at the same location, the rail current and the contact wire current are equal in magnitude and opposite in direction, so the rail-to-ground conductance can be ignored. Furthermore, when performing power flow calculations or state estimations, the contact wire resistance and rail resistance can be considered together to obtain a simplified model, such as... Figure 4 As shown. The electrical quantities of the traction substation and the train can be measured in real time, and therefore can be considered as ideal power sources.

[0040] Based on the simplified model construction method described above, a topological model of the traction power supply network is obtained by performing a topological modeling of the traction power supply network according to the train location information. Stray currents and rail-to-ground conductance are ignored in the model. The correlation matrix A between the nodes and branches of the traction power supply network is determined based on the traction power supply network model. The element in the i-th row and k-th column of the correlation matrix A... The definition is as follows:

[0041] Among them, nodes include traction substation nodes and train nodes, and branches are line segments between nodes.

[0042] Calculate the admittance of each branch according to the following expression:

[0043] in, Indicates the first i Admittance of the branch, Indicates the first i The length of the branch road, Indicates the first i The resistance of the contact wire section Indicates the first i The resistance of a section of railway track.

[0044] Constructing the branch admittance matrix :

[0045] in, Let be the number of branches in the traction network. The branch admittance matrix is ​​a diagonal square matrix composed of the admittances of each branch.

[0046] Constructing the nodal admittance matrix Y : .

[0047] In step S1, measurement data and train position information are acquired. The measurement data includes the voltage at each train end, the voltage at each traction station end, the current at each train end, the current at each traction station end, the power at each train end, and the power at each traction station end.

[0048] In step S2, it is determined whether the train location information is missing or erroneous. In practical engineering, due to the possibility of lost or incorrect positioning data, the train location information may be missing or erroneous. In such cases, only the approximate range of the train is known, but its exact location cannot be determined. Therefore, the specific network parameters cannot be determined, and the constant-form node admittance matrix cannot be obtained. Y This severely affects the power flow calculation results.

[0049] Therefore, in this step, it is determined whether there is missing or incorrect train position information based on the train position information, and then different state estimation models are constructed in subsequent steps.

[0050] In step S3, when there is no missing or erroneous train position information, a state estimation model is established based on the pre-constructed traction power supply network model. The traction power supply network model is used to characterize the electrical connection relationship between each node in the traction power supply network. The nodes include the nodes corresponding to traction and the nodes corresponding to the train. The state estimation model uses the voltage of each node as the state vector to be estimated and the measurement data as the measurement vector. The state estimation model is solved based on the weighted least squares method to obtain the estimated value of the voltage of each node.

[0051] When train position information is missing or erroneous, an augmented state estimation model is established based on a pre-constructed traction power supply network model. The augmented state estimation model uses the voltage of each node and the position of each train as the state vector to be estimated, and the measurement data as the measurement vector. Constraints are set according to the train position. The augmented state estimation model is solved based on the weighted least squares method and the interior point method to obtain the estimated values ​​of the voltage of each node and the estimated values ​​of the train position.

[0052] In this embodiment of the invention, when there is no missing or erroneous train position information, the step of establishing a state estimation model includes: The measurement data are concatenated into a measurement vector z in a preset order:

[0053] in, For each train voltage vector, For each traction station terminal voltage vector, For the current vectors at each train end, Let the current vectors at each traction station end be denoted as . For the power vector at each train end, This represents the power vector at each traction station end.

[0054] Define the voltage at each node as the state vector to be estimated. x : .

[0055] Establish the measurement equation: ,in, h For measurement expression, For measurement error, Follows a normal distribution .

[0056] node admittance matrix Y The matrix is ​​divided into three sub-matrices based on the traction sub-node and the train node: the train node and the train node admittance sub-matrix. Admittance coupling submatrix between train nodes and traction sub-nodes Admittance submatrix of traction sub-nodes .

[0057] Since the voltage and current values ​​of each traction substation and each train can be directly measured, there is no need to use power flow calculations to solve for node voltages. Therefore, the linear power flow equations can be directly written, that is, the linear relationship between the node current and the node voltage can be expressed as:

[0058] The relationship between the power of each node and the voltage and current of each node is expressed as follows:

[0059] Combining the above two equations, the measurement expression can be derived. h With the state vector to be estimated x There exists a linear relationship, which means converting the measurement equation into a linear form: ,in H For the Jacobian matrix: .

[0060] In this embodiment of the invention, the weighted least squares method is used to solve the above state estimation model. The weighted least squares method is the most user-friendly, convenient, and fundamental algorithm for state estimation, and its theoretical basis is maximum likelihood estimation in probability theory. The least squares method assumes that measurement noise follows a normal distribution and aims to minimize the weighted sum of squared residuals, solving the problem iteratively.

[0061] The steps to solve the state estimation model include: The state vector to be estimated x Using the objective function of weighted least squares as the optimization variable, the state vector to be estimated is obtained. x The estimated value.

[0062] The objective function is:

[0063] in, This is a weighted diagonal matrix. diagonal element For the first i The variance of measurement error.

[0064] In this embodiment of the invention, when there is a lack or error in the train position information, augmented state estimation is used to include the train position as a state variable to be estimated in the state estimation solution. There may be one or more trains with missing or error in position information.

[0065] Determine train nodes j The position is l j ( j =1,2,…, N Train ), N Train For the number of train nodes, and to determine the relationship between the train nodes. j two adjacent nodes j -1 and j The position of +1 is p j-1 , p j+1 . Determine train nodesj With nodes j The length of the branch k connected to -1 is The train node j With nodes j The length of the branch k connected to +1 is .

[0066] Update branch admittance matrix The updated branch admittance matrix No. k and k+ The diagonal elements are as follows:

[0067] in, and They represent the first k and k+ The resistance of a section of the overhead contact line. and They represent the first k and k+ 1. Resistance of the railway rail.

[0068] Based on the updated branch admittance matrix The node admittance matrix is ​​constructed for subsequent construction of the augmented state model.

[0069] In this embodiment of the invention, the steps for establishing the augmented state estimation model include: The measurement data are concatenated into a measurement vector z in a preset order:

[0070] in, For the voltage of each train, The voltage at each traction station terminal. For the current at each train end, For the current at each traction station end, For the power of each train end, This refers to the power at each traction station end.

[0071] Define the voltage at each node and the position of each train as the augmented state vector to be estimated. : , , ,in, K This represents the number of trains with missing or incorrect location information. For the first K The location of a train node where location information is missing or inaccurate. For the voltage of each node, This represents the position vectors of each train node whose position information is missing or incorrect. The number of trains with missing or incorrect position information can be one or more; when there is only one, .

[0072] Establish the measurement equation: ,in, h For measurement expression, For measurement error, Follows a normal distribution .

[0073] The node admittance matrix is ​​divided into three sub-matrices based on the traction sub-nodes and train nodes: train node-to-train node admittance sub-matrices. Admittance coupling submatrix between train nodes and traction sub-nodes Admittance submatrix from traction station node to traction station node .

[0074] The linear relationship between the current and voltage at each node can be expressed as:

[0075] The relationship between the power of each node and the voltage and current of each node is expressed as follows:

[0076] Transform the measurement equation into a linear form: , To augment the Jacobian matrix: .

[0077] In this embodiment of the invention, the process of solving the augmented state estimation model based on the weighted least squares method and the interior point method includes: when using the weighted least squares method for augmented state estimation, the train position state quantity is different from the node voltage state quantity, and there are constraints: the train position has an interval range, and the estimated train position cannot change the network topology (i.e., the train position at node j cannot exceed its two adjacent nodes). This leads to the constraint that, during the solution process, the augmented state vector to be estimated... Using the weighted least squares augmented objective function as the optimization variable, a constrained augmented state estimation optimization model is constructed:

[0078] in, This is a weighted diagonal matrix. diagonal element For the first i The variance of the measurement error, the first KThe locations of the two adjacent nodes of a train node with missing or incorrect location information are as follows: and .

[0079] The augmented state estimation optimization model is rearranged into a quadratic programming form, which allows for convenient solution using the interior point method:

[0080] Where G is the gain matrix, The more measurement vectors there are, the larger the diagonal elements of G, and the better the estimation result. When the measurement expression... There is no state vector in it x i At that time, the augmented Jacobian matrix The i If all elements in the column are 0, and the diagonal elements of G have 0 values, the system cannot estimate them.

[0081] The augmented objective function in the form of a quadratic programming problem is solved using the interior-point method to obtain the augmented state vector to be estimated. The estimated value.

[0082] Example 2 Based on the same inventive concept as the above embodiments, the present invention also provides a state estimation device for a traction power supply system, such as... Figure 5 As shown, it includes: The measurement data acquisition module is used to acquire measurement data and train position information. The measurement data includes the voltage at each train end, the voltage at each traction station end, the current at each train end, the current at each traction station end, the power at each train end, and the power at each traction station end.

[0083] The judgment module is used to determine whether there is any missing or incorrect train position information based on the train position information.

[0084] The state estimation module is used to establish a state estimation model based on a pre-built traction power supply network model when there is no missing or erroneous train position information. The traction power supply network model is used to characterize the electrical connection relationship between each node in the traction power supply network. The nodes include the nodes corresponding to traction and the nodes corresponding to the train. The state estimation model uses the voltage of each node as the state vector to be estimated and the measurement data as the measurement vector. The state estimation model is solved based on the weighted least squares method to obtain the estimated value of the voltage of each node.

[0085] The augmented state estimation module is used to establish an augmented state estimation model based on a pre-built traction power supply network model when there is a lack of train position information or errors. The augmented state estimation model uses the voltage of each node and the position of each train as the state vector to be estimated, and the measurement data as the measurement vector. It sets constraints according to the train position and solves the augmented state estimation model based on the weighted least squares method and the interior point method to obtain the estimated values ​​of the voltage of each node and the estimated values ​​of the train position.

[0086] Example 3 like Figure 6 As shown, the present invention also provides an electronic device 100 for implementing a state estimation method for a traction power supply system; The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0087] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the state estimation method of the traction power supply system of Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0088] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0089] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0090] The memory 101 in the electronic device 100 stores multiple instructions to implement a state estimation method for a traction power supply system. The processor 102 can execute multiple instructions to achieve: acquiring measurement data and train position information, wherein the measurement data includes voltage at each train end, voltage at each traction station end, current at each train end, current at each traction station end, power at each train end, and power at each traction station end. Determine if there are any missing or incorrect train location information; When there is no missing or erroneous train position information, a state estimation model is established based on a pre-constructed traction power supply network model. The traction power supply network model is used to characterize the electrical connection relationship between each node in the traction power supply network. The nodes include the nodes corresponding to traction and the nodes corresponding to the train. The state estimation model uses the voltage of each node as the state vector to be estimated and the measurement data as the measurement vector. The state estimation model is solved based on the weighted least squares method to obtain the estimated value of the voltage of each node. When train position information is missing or erroneous, an augmented state estimation model is established based on a pre-constructed traction power supply network model. The augmented state estimation model uses the voltage of each node and the position of each train as the state vector to be estimated, the measured data as the measurement vector, and sets constraints according to the train position. The augmented state estimation model is solved based on the weighted least squares method and the interior point method to obtain the estimated values ​​of the voltage of each node and the estimated values ​​of the train position.

[0091] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0092] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0094] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0095] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0096] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A state estimation method for a traction power supply system, characterized in that, include: Acquire measurement data and train position information. The measurement data includes the voltage at each train end, the voltage at each traction station end, the current at each train end, the current at each traction station end, the power at each train end, and the power at each traction station end. Determine if there are any missing or incorrect train location information; When there is no missing or erroneous train position information, a state estimation model is established based on a pre-constructed traction power supply network model. The traction power supply network model is used to characterize the electrical connection relationship between each node in the traction power supply network. The nodes include the nodes corresponding to traction and the nodes corresponding to the train. The state estimation model uses the voltage of each node as the state vector to be estimated and the measurement data as the measurement vector. The state estimation model is solved based on the weighted least squares method to obtain the estimated value of the voltage of each node. When train position information is missing or erroneous, an augmented state estimation model is established based on a pre-constructed traction power supply network model. The augmented state estimation model uses the voltage of each node and the position of each train as the state vector to be estimated, the measured data as the measurement vector, and sets constraints according to the train position. The augmented state estimation model is solved based on the weighted least squares method and the interior point method to obtain the estimated values ​​of the voltage of each node and the estimated values ​​of the train position.

2. The state estimation method for the traction power supply system according to claim 1, characterized in that, Also includes: Based on the train location information, a topological model of the traction power supply network is obtained. Then, based on this model, the correlation matrix A between the nodes and branches of the traction power supply network is determined. The element in the i-th row and k-th column of the correlation matrix A... The definition is as follows: Wherein, the node includes a traction substation node and a train node, and the branch is the line segment between the nodes; Calculate the admittance of each branch according to the following expression: in, Indicates the first i Admittance of the branch, Indicates the first i The length of the branch road, Indicates the first i The resistance of the contact wire section Indicates the first i The resistance of a section of rail; Constructing the branch admittance matrix : in, This represents the number of branches in the traction network. Constructing the nodal admittance matrix Y : 。 3. The state estimation method for the traction power supply system according to claim 2, characterized in that, The steps for establishing the state estimation model include: The measurement data are concatenated into a measurement vector z in a preset order: in, For each train voltage vector, For each traction station terminal voltage vector, For the current vectors at each train end, Let the current vectors at each traction station end be denoted as . For the power vector at each train end, For each traction station end power vector; Define the voltage at each node as the state vector to be estimated. x : ; Establish the measurement equation: ,in, h For measurement expression, For measurement error, Follows a normal distribution ; The node admittance matrix Y The matrix is ​​divided into three sub-matrices based on the traction sub-node and the train node: the train node and the train node admittance sub-matrix. Admittance coupling submatrix between train nodes and traction sub-nodes Admittance submatrix of traction sub-nodes ; The linear relationship between the current and voltage at each node can be expressed as: The relationship between the power of each node and the voltage and current of each node is expressed as follows: Transform the measurement equation into a linear form: ,in H For the Jacobian matrix: 。 4. The state estimation method for the traction power supply system according to claim 3, characterized in that, The steps for solving the state estimation model include: The state vector to be estimated x Using the objective function of weighted least squares as the optimization variable, the state vector to be estimated is obtained. x The estimated value; The objective function is: in, This is a weighted diagonal matrix. diagonal element For the first i The variance of measurement error.

5. The state estimation method for a traction power supply system according to claim 2, characterized in that, Also includes: Determine train nodes j The position is l j ( j =1,2,…, N Train ), N Train For the number of train nodes, and to determine the relationship between the train nodes. j two adjacent nodes j -1 and j The position of +1 is p j-1 , p j+1 ; Determine train nodes j With nodes j The length of the branch k connected to -1 is The train node j With nodes j The length of the branch k connected to +1 is ; Update the branch admittance matrix The updated branch admittance matrix No. k and k+ The diagonal elements are as follows: in, and They represent the first k and k+ The resistance of a section of the overhead contact line. and They represent the first k and k+ 1. Resistance of the railway rail.

6. The state estimation method for a traction power supply system according to claim 5, characterized in that, The steps for establishing the augmented state estimation model include: The measurement data are concatenated into a measurement vector z in a preset order: in, For the voltage of each train, The voltage at each traction station terminal. For the current at each train end, For the current at each traction station end, For the power of each train end, Power at each traction station end; Define the voltage at each node and the position of each train as the augmented state vector to be estimated. : , , ,in, K This represents the number of trains with missing or incorrect location information. For the first K The location of a train node where location information is missing or inaccurate. For the voltage of each node, The position vectors of each train node where the position information is missing or incorrect; Establish the measurement equation: ,in, h For measurement expression, For measurement error, Follows a normal distribution ; The node admittance matrix is ​​divided into three sub-matrices according to the traction sub-node and the train node: the admittance sub-matrix from train node to train node. Admittance coupling submatrix between train nodes and traction sub-nodes Admittance submatrix from traction station node to traction station node ; The linear relationship between the current and voltage at each node can be expressed as: The relationship between the power of each node and the voltage and current of each node is expressed as follows: Transform the measurement equation into a linear form: ,in To augment the Jacobian matrix: 。 7. The state estimation method for a traction power supply system according to claim 6, characterized in that, The method for solving the augmented state estimation model based on weighted least squares and interior point method includes: The augmented state vector to be estimated Using the weighted least squares augmented objective function as the optimization variable, a constrained augmented state estimation optimization model is constructed: in, This is a weighted diagonal matrix. diagonal element For the first i The variance of the measurement error, the first K The locations of the two adjacent nodes of a train node with missing or incorrect location information are as follows: and ; The augmented state estimation optimization model is then rearranged into a quadratic programming form: Where G is the gain matrix, ; The augmented state estimation optimization model of the quadratic programming form is solved using the interior-point method to obtain the augmented state vector to be estimated. The estimated value.

8. A state estimation device for a traction power supply system, characterized in that, include: The measurement data acquisition module is used to acquire measurement data and train position information. The measurement data includes the voltage of each train end, the voltage of each traction station end, the current of each train end, the current of each traction station end, the power of each train end, and the power of each traction station end. The judgment module is used to determine whether there is missing or incorrect train position information based on the train position information. The state estimation module is used to establish a state estimation model based on a pre-constructed traction power supply network model when there is no missing or erroneous train position information. The traction power supply network model is used to characterize the electrical connection relationship between each node in the traction power supply network. The nodes include the nodes corresponding to traction and the nodes corresponding to the train. The state estimation model uses the voltage of each node as the state vector to be estimated and the measurement data as the measurement vector. The state estimation model is solved based on the weighted least squares method to obtain the estimated value of the voltage of each node. The augmented state estimation module is used to establish an augmented state estimation model based on a pre-constructed traction power supply network model when there is a lack or error in train position information. The augmented state estimation model uses the voltage of each node and the position of each train as the state vector to be estimated, the measured data as the measurement vector, and sets constraints according to the train position. The augmented state estimation model is solved based on the weighted least squares method and the interior point method to obtain the estimated values ​​of the voltage of each node and the estimated values ​​of the train position.

9. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the state estimation method for the traction power supply system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the state estimation method for the traction power supply system as described in any one of claims 1 to 7.

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