A method and device for estimating line parameters of a medium voltage distribution network

Abstract

The application discloses a kind of medium voltage distribution network line parameter estimation method and device.The medium voltage distribution network line parameter estimation method is suitable for the intelligent terminal configured on any power supply section of the medium voltage distribution network, the method comprises: in each preset period, local power flow data is collected, and the power flow data collected by adjacent intelligent terminal of the intelligent terminal is obtained as off-site power flow data;Based on the π-type equivalent circuit of the line between the intelligent terminal and the adjacent intelligent terminal, a line parameter estimation model is constructed;According to the local power flow data and the off-site power flow data, the line parameter estimation model is solved, and the line parameter estimation value of the line is obtained.The application can accurately estimate the line parameters of the medium voltage distribution network according to the local power flow data and the power flow data of the adjacent intelligent terminal.

Description

Technical Field

[0001] This invention relates to the field of power distribution network line parameter estimation technology, and in particular to a method and apparatus for estimating medium-voltage power distribution network line parameters. Background Technology

[0002] Medium-voltage distribution networks serve as the link between power supply systems and users, and are an important component of urban infrastructure. With rapid economic development, the scale and structure of medium-voltage distribution networks are becoming increasingly large and complex to meet the rapidly growing load demands of users. Furthermore, the randomness and dispersion of distributed power sources such as photovoltaics have led to increasingly complex and variable power flow distributions, requiring the monitoring of more and more objects. In the future, medium-voltage distribution networks will develop towards distributed collaborative control, which involves configuring distribution terminals with intelligent decision-making capabilities at key distribution ring points. Through interactive communication between adjacent intelligent terminals, monitoring and judgment information is exchanged, enabling various control, protection, and regulation functions of the medium-voltage distribution network in a zoned autonomous and collaborative manner.

[0003] Distributed control and decision-making based on smart terminals typically require information such as line parameters of the medium-voltage distribution network. Due to external environmental influences, the line parameters of the medium-voltage distribution network will change during actual operation. If the initial parameters and distances at the factory are consistently used as references, it will be difficult to accurately assess the line's operating status, leading to deviations in distributed control or decision-making. Therefore, it is necessary to accurately estimate the line parameters of the medium-voltage distribution network at regular intervals. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, the present invention provides a method and apparatus for estimating the line parameters of a medium-voltage distribution network, which can accurately estimate the line parameters of a medium-voltage distribution network at regular intervals based on local power flow data and power flow data from adjacent smart terminals.

[0005] To address the aforementioned technical problems, in a first aspect, an embodiment of the present invention provides a method for estimating line parameters in a medium-voltage distribution network, applicable to intelligent terminals configured on any power supply section of the medium-voltage distribution network, the method comprising:

[0006] Within each preset period, local power flow data is collected, and power flow data collected by adjacent smart terminals of the smart terminal is obtained as off-site power flow data.

[0007] Based on the π-type equivalent circuit of the line between the smart terminal and the adjacent smart terminal, a line parameter estimation model is constructed.

[0008] Based on the local power flow data and the remote power flow data, the line parameter estimation model is solved to obtain the estimated values ​​of the line parameters.

[0009] Furthermore, the step of collecting local power flow data and acquiring power flow data collected by neighboring smart terminals as remote power flow data within each preset period includes:

[0010] Within each preset cycle, the bus voltage of the power supply section where the smart terminal is located and the active power and reactive power of each distribution line are collected as the local power flow data.

[0011] Within each preset period, based on the topology of the medium-voltage distribution network, the adjacent smart terminals of the smart terminal are determined, and the bus voltage of the power supply section where the adjacent smart terminal is located and the active power and reactive power of each distribution line are obtained as the off-site power flow data.

[0012] Furthermore, the line parameter estimation model is as follows:

[0013]

[0014] Where R is the resistance of the line, X is the reactance of the line, and B... C P1 is the equivalent capacitance to ground of the line; Q1 is the active power at the beginning of the line; P2 is the active power at the end of the line; Q2 is the reactive power at the end of the line; U1 is the voltage at the beginning of the line; and U2 is the voltage at the end of the line.

[0015] Further, the step of solving the line parameter estimation model based on the local power flow data and the remote power flow data to obtain the estimated line parameters specifically involves:

[0016] Based on the local power flow data and the remote power flow data, the Newton-Raphson iteration method is used to solve the line parameter estimation model until the estimated line parameters are obtained when the estimation error is less than the preset error.

[0017] Furthermore, the estimation error is:

[0018]

[0019] Among them, R (i+1) R is the resistance of the circuit obtained in the (i+1)th iteration. (i) X is the resistance of the circuit obtained in the i-th iteration; (i+1) X is the reactance of the line obtained in the (i+1)th iteration. (i) B is the reactance of the line obtained in the i-th iteration; C (i+1) B is the reactance of the line obtained in the (i+1)th iteration. C (i)The reactance of the line is obtained in the i-th iteration.

[0020] Secondly, one embodiment of the present invention provides a medium-voltage distribution network line parameter estimation device, applicable to intelligent terminals configured on any power supply section of the medium-voltage distribution network, the device comprising:

[0021] The data acquisition module is used to collect local power flow data in each preset period and obtain power flow data collected by adjacent smart terminals of the smart terminal as off-site power flow data.

[0022] The model building module is used to build a line parameter estimation model based on the π-type equivalent circuit of the line between the smart terminal and the adjacent smart terminal.

[0023] The parameter estimation module is used to solve the line parameter estimation model based on the local power flow data and the remote power flow data to obtain the estimated values ​​of the line parameters.

[0024] Furthermore, the data acquisition module includes:

[0025] The local power flow data acquisition unit is used to acquire the bus voltage of the power supply section where the smart terminal is located and the active power and reactive power of each distribution line as the local power flow data in each preset period.

[0026] The off-site power flow data acquisition unit is used to determine the adjacent smart terminals of the smart terminal based on the topology of the medium-voltage distribution network within each preset period, and to acquire the bus voltage of the power supply section where the adjacent smart terminals are located and the active power and reactive power of each distribution line as the off-site power flow data.

[0027] Furthermore, the line parameter estimation model is as follows:

[0028]

[0029] Where R is the resistance of the line, X is the reactance of the line, and B... C P1 is the equivalent capacitance to ground of the line; Q1 is the active power at the beginning of the line; P2 is the active power at the end of the line; Q2 is the reactive power at the end of the line; U1 is the voltage at the beginning of the line; and U2 is the voltage at the end of the line.

[0030] Furthermore, the parameter estimation module is specifically used to solve the line parameter estimation model using the Newton-Raphson iteration method based on the local power flow data and the remote power flow data, until the estimated value of the line parameters is obtained when the estimation error is less than a preset error.

[0031] Furthermore, the estimation error is:

[0032]

[0033] Among them, R (i+1) R is the resistance of the circuit obtained in the (i+1)th iteration. (i) X is the resistance of the circuit obtained in the i-th iteration; (i+1) X is the reactance of the line obtained in the (i+1)th iteration. (i) B is the reactance of the line obtained in the i-th iteration; C (i+1) B is the reactance of the line obtained in the (i+1)th iteration. C (i) The reactance of the line is obtained in the i-th iteration.

[0034] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0035] By configuring an intelligent terminal in each power supply section of the medium-voltage distribution network, the intelligent terminal collects local power flow data in each preset cycle and obtains power flow data collected by neighboring intelligent terminals as remote power flow data. Based on the π-type equivalent circuit of the line between the intelligent terminal and the neighboring intelligent terminals, a line parameter estimation model is constructed. Based on the local power flow data and the remote power flow data, the line parameter estimation model is solved to obtain the estimated value of the line parameters. It can accurately estimate the line parameters of the medium-voltage distribution network at regular intervals based on the local power flow data and the power flow data of the neighboring intelligent terminals. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating a method for estimating the parameters of a medium-voltage distribution network line according to the first embodiment of the present invention.

[0037] Figure 2 This is a schematic diagram of the structure of a medium-voltage distribution network with three interconnected feedback lines, as exemplified in the first embodiment of the present invention;

[0038] Figure 3 This is a schematic diagram of the π-type equivalent circuit of the line between adjacent smart terminals SAm and SAn in the first embodiment of the present invention.

[0039] Figure 4 This is a phasor diagram of the voltage across the line between adjacent smart terminals SAm and SAn, as exemplified in the first embodiment of the present invention.

[0040] Figure 5 This is a schematic diagram of the structure of a 10kV distribution network with 13 nodes and a single feedback line, as exemplified in the first embodiment of the present invention.

[0041] Figure 6 This is a schematic diagram of a medium-voltage distribution network line parameter estimation device according to the second embodiment of the present invention. Detailed Implementation

[0042] The technical solutions of this invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0043] It should be noted that the step numbers in this document are only for the convenience of explaining the specific embodiments and are not intended to limit the order in which the steps are executed. The method provided in this embodiment can be executed by relevant terminal devices, and the following description uses a smart terminal configured on any power supply section of a medium-voltage distribution network as the execution subject.

[0044] like Figure 1 As shown, the first embodiment provides a method for estimating line parameters in a medium-voltage distribution network, applicable to intelligent terminals configured on any power supply section of a medium-voltage distribution network. The method includes steps S1 to S3:

[0045] S1. In each preset cycle, collect local power flow data and obtain power flow data collected by adjacent smart terminals of the smart terminal as off-site power flow data.

[0046] S2. Based on the π-type equivalent circuit of the line between the smart terminal and adjacent smart terminals, construct a line parameter estimation model;

[0047] S3. Based on local and remote power flow data, solve the line parameter estimation model to obtain the estimated values ​​of the line parameters.

[0048] It should be noted that each power supply section of the medium-voltage distribution network is equipped with a smart terminal, and each smart terminal monitors the power flow data of its respective power supply section.

[0049] As an example, in step S1, the smart terminal collects local power flow data in each preset period, including the bus voltage of the power supply section where the smart terminal is located, the active power and reactive power of each distribution line, and interacts with its adjacent smart terminals, i.e., the adjacent smart terminals, to obtain the power flow data collected by the adjacent smart terminals as off-site power flow data, including the bus voltage of the power supply section where the adjacent smart terminal is located, the active power and reactive power of the distribution lines connected to the distribution lines in the power supply section where the smart terminal is located.

[0050] In step S2, the line between the smart terminal and the adjacent smart terminal is equivalent to a π-type equivalent circuit. Based on the π-type equivalent circuit of this line, a line parameter estimation model is constructed.

[0051] In step S3, based on local power flow data and remote power flow data, the power flow data at both ends of the line between the smart terminal and the adjacent smart terminal is determined. The power flow data at both ends of the line is input into the line parameter estimation model, the line parameter estimation model is solved, and the estimated value of the line parameters is obtained.

[0052] This embodiment can accurately estimate the line parameters of the medium-voltage distribution network at regular intervals based on local power flow data and power flow data from adjacent smart terminals.

[0053] In a preferred embodiment, the step of collecting local power flow data and acquiring power flow data collected by neighboring smart terminals as remote power flow data within each preset period includes: collecting the bus voltage of the power supply section where the smart terminal is located and the active and reactive power of each distribution line as local power flow data within each preset period; and determining the neighboring smart terminals based on the topology of the medium-voltage distribution network within each preset period, and acquiring the bus voltage of the power supply section where the neighboring smart terminals are located and the active and reactive power of each distribution line as remote power flow data.

[0054] As an example, suppose the medium-voltage distribution network is a medium-voltage distribution network with three interconnected feedback lines, and its topology is as follows: Figure 2 As shown.

[0055] It is understandable that medium-voltage lines with interconnected topologies are called a feeder group. When any fault occurs within a feeder group, downstream loads and distributed power sources of the faulty section can be switched to other feeders for power supply via tie switches.

[0056] A smart terminal is configured on each power supply section of the medium-voltage distribution network. Each smart terminal is responsible for monitoring an isolated section. Generally, smart terminals are configured on a unit of one ring network point.

[0057] Smart terminals are divided into two categories. One category consists of smart terminals located at the feeder outgoing lines on the substation side, identified as ST. Each feeder outgoing line terminal on the substation outgoing line side has a unique number, such as... Figure 2 The first type is ST1, ST2, and ST3; the second type is the intelligent terminal corresponding to each power supply section of the medium-voltage distribution network, denoted as SA, such as... Figure 2 SA1, SA2, SA3, SA4, SA5, SA6, SA7 in .

[0058] The intelligent terminal (SA) configured on each power supply section of the medium-voltage distribution network will periodically sample the bus voltage of the ring network point and the current of each distribution line in its power supply section, and calculate the magnitude and direction of the active and reactive power of each distribution line.

[0059] Within each preset cycle, each SA samples the bus voltage and current flowing through the distribution lines in its power supply section, calculates the power frequency RMS value, and calculates the magnitude and direction of the active and reactive power flowing through the distribution lines to obtain local power flow data.

[0060] by Figure 2 Taking SA1 as an example, SA1 collects the bus voltage of its power supply section and the current of the distribution lines where k1, k2, and k3 are located, and calculates the power frequency effective value of each current. Based on the bus voltage and the power frequency effective value of the current of the distribution lines where k1, k2, and k3 are located, the magnitude and direction of the active power and reactive power of the distribution lines where k1, k2, and k3 are located are calculated to obtain local power flow data.

[0061] Specifically, in each preset cycle, the measured quantities of SA1 include: the fundamental effective value of the ring network bus voltage V(SA1); the fundamental effective value of the current flowing through the distribution line I(SA1-k1), I(SA1-k2), I(SA1-k3); the active power of the distribution line P(SA1-k1), P(SA1-k2), P(SA1-k3), with the power flowing into the bus being positive and flowing out being negative; and the reactive power of the distribution line Q(SA1-k1), Q(SA1-k2), Q(SA1-k3), with the power flowing into the bus being positive and flowing out being negative.

[0062] After identifying the topology of the medium-voltage distribution network and obtaining the upstream and downstream relationships of each SA, each SA sends the locally measured fundamental effective value of the voltage and the calculated active and reactive power flowing through the distribution line to the adjacent upstream and downstream SAs, and receives the relevant power flow data sent to it by the adjacent upstream and downstream SAs.

[0063] Taking SA1 as an example, after measuring the effective value of the fundamental wave voltage of the ring network bus V(SA1), the active power of the distribution line P(SA1-k1), P(SA1-k2), P(SA1-k3), and the reactive power of the distribution line Q(SA1-k1), Q(SA1-k2), Q(SA1-k3), SA1 sends V(SA1), P(SA1-k1), and Q(SA1-k1) to the upstream SA2, V(SA1), P(SA1-k2), and Q(SA1-k2) to the downstream SA4, and V(SA1), P(SA1-k3), and Q(SA1-k3) to the downstream SA6.

[0064] At this time, in addition to sending the corresponding power flow data to the adjacent SA, SA1 also receives V(SA2), P(SA2-k2), and Q(SA2-k2) sent by upstream SA2, V(SA4), P(SA4-k2), and Q(SA4-k2) sent by downstream SA4, and V(SA6), P(SA6-k3), and Q(SA6-k3) sent by downstream SA6.

[0065] In a preferred embodiment, the line parameter estimation model is as follows:

[0066]

[0067] Where R is the resistance of the line, X is the reactance of the line, and B... C P1 is the equivalent capacitance to ground of the line; Q1 is the active power at the beginning of the line; P2 is the active power at the end of the line; Q2 is the reactive power at the end of the line; U1 is the voltage at the beginning of the line; and U2 is the voltage at the end of the line.

[0068] As an example, for a line upstream of this SA, the power at the end of the line is the power measured by this SA, and the power at the beginning of the line is the negative value of the power measured by the upstream SA; for a line downstream of this SA, the power at the beginning of the line is the negative value of the power measured by this SA, and the power at the end of the line is the negative value of the power measured by the downstream SA. For example, for a line between SA2 and SA1, the active power at the beginning of the line is -P(SA2-k2), and the reactive power is -Q(SA2-k2); the active power at the end of the line is P(SA1-k1), and the reactive power is Q(SA1-k1).

[0069] Let the power at the beginning of the line between adjacent smart terminals SAm and SAn be P1+jQ1, the power at the end of the line be P2+jQ2, the voltage at the beginning of the line be U1, the voltage at the end of the line be U2, the resistance of the line be R, the reactance of the line be X, and the equivalent capacitance to ground of the line be B. C The π-type equivalent circuit of the line between adjacent smart terminals SAm and SAn is as follows: Figure 3 As shown.

[0070] The phasor diagram of the voltages at both ends of the line is as follows: Figure 4 As shown, The voltage phasors at the beginning and end of the line under test are: They are respectively The voltage drop longitudinal component, They are respectively The voltage drop transverse component, ψ is the voltage phasor. The included angle.

[0071] Let U1 and U2 be respectively The effective values, ΔU1 and ΔU2 are respectively The effective values, δU1 and δU2 are respectively Valid value.

[0072] From the voltage drop formula, we can obtain:

[0073]

[0074]

[0075]

[0076]

[0077] Let the phase angle difference between the voltages at both ends be ψ, then we have:

[0078] ΔU1=U1-U2cosψ (6);

[0079] ΔU2=U1cosψ-U2 (7);

[0080] δU1=U2sinψ (8);

[0081] δU2=U1sinψ (9);

[0082] Combining formulas (2) to (8), and using the trigonometric formula sin 2 ψ+cos 2 ψ = 1, therefore:

[0083]

[0084]

[0085] Eliminating the sine and cosine respectively yields:

[0086]

[0087]

[0088] Therefore, a system of three quadratic equations can be constructed as the equation system for estimating line parameters, i.e., the line parameter estimation model:

[0089]

[0090] In equation (1), R is the resistance of the line, X is the reactance of the line, and B... C P1 is the equivalent capacitance to ground of the line; Q1 is the active power at the beginning of the line; P2 is the active power at the end of the line; Q2 is the reactive power at the end of the line; U1 is the voltage at the beginning of the line; and U2 is the voltage at the end of the line.

[0091] In a preferred embodiment, the step of solving the line parameter estimation model based on local power flow data and remote power flow data to obtain the estimated value of the line parameters specifically involves: using the Newton-Raphson iteration method to solve the line parameter estimation model based on local power flow data and remote power flow data until the estimated value of the line parameters is obtained when the estimation error is less than a preset error.

[0092] In the preferred embodiment, the estimation error is:

[0093]

[0094] Among them, R (i+1) R is the resistance of the circuit obtained in the (i+1)th iteration. (i) X is the resistance of the circuit obtained in the i-th iteration; (i+1) Let X be the reactance of the line obtained in the (i+1)th iteration. (i) B is the reactance of the line obtained in the i-th iteration; C (i+1) Let B be the reactance of the line obtained in the (i+1)th iteration. C (i) Let be the reactance of the line obtained in the i-th iteration.

[0095] As an example, there are two methods for solving the line parameter estimation model: analytical solution and numerical solution. The analytical solution of the three-variable quadratic equation system of equation (1) is complex, so the Newton iteration method can be used to solve the three-variable quadratic equation system of equation (1).

[0096] The variables to be solved in the system of three quadratic equations in equation (1) are R, X, and B. C / 2, that is:

[0097]

[0098]

[0099]

[0100] Then we have:

[0101]

[0102]

[0103]

[0104] Therefore, we have the Jacobian matrix J, that is:

[0105]

[0106] Let the initial value for Newton's iteration method be... The value of the i-th iteration is Then we have:

[0107]

[0108]

[0109] In equation (14), R (i+1) R is the resistance of the circuit obtained in the (i+1)th iteration. (i) X is the resistance of the circuit obtained in the i-th iteration; (i+1) Let X be the reactance of the line obtained in the (i+1)th iteration. (i) B is the reactance of the line obtained in the i-th iteration; C (i+1) Let B be the reactance of the line obtained in the (i+1)th iteration. C (i) Let be the reactance of the line obtained in the i-th iteration.

[0110] Based on the above derivation process, it can be seen that the process of solving the line parameter estimation model using the Newton iteration method is as follows: After the intelligent terminal reads the power and voltage fundamental effective values ​​at both ends of the line, it gives the initial values ​​of the line impedance and equivalent ground capacitance to be solved, and uses equation (22) to iteratively solve the variables to be solved, and compares the sum of squares of the difference between the solution value of this iteration and the solution value of the previous iteration, that is, after each iteration, the ε value of equation (14) is solved. If the ε value is greater than or equal to the given preset error, then the next iteration is performed to solve equation (22). If the ε value is less than the given preset error, then the iteration is stopped, and the variable estimate value of the current period is obtained, which is the line parameter estimate value of the line.

[0111] Regarding the selection of initial values, in the absence of historical values ​​for line parameter estimation, an approximate estimate of the line impedance can be obtained based on the basic parameters of the line configuration as the initial values ​​of the line resistance and reactance, while the initial value of the line-to-ground capacitance can be set to 0. In the presence of historical values, the estimated line parameters obtained in the previous period are used as the initial values ​​of the variables to estimate the line parameters for the current period.

[0112] This embodiment uses the Newton-Raphson iteration method to solve the line parameter estimation model, which simplifies the solution process and improves the efficiency of line parameter estimation in medium-voltage distribution networks.

[0113] To more clearly illustrate the method for estimating medium-voltage distribution network line parameters provided in the first embodiment, as follows: Figure 5 A 13-node 10kV distribution network with a single feedback line is shown as a case study to verify the correctness of the method. Among them, Figure 5Node 3 is connected to a 250kW photovoltaic power source.

[0114] Table 1 shows a comparison between the actual and estimated values ​​of the line parameters.

[0115] Table 1 Comparison of Actual Values ​​and Estimated Values

[0116]

[0117] This method only requires measuring the power magnitude, direction, and voltage amplitude at both ends of the line under test, reducing the complexity of the measurement. Furthermore, experimental results demonstrate that the absolute value of its maximum relative error does not exceed 0.1%, indicating that this method can accurately estimate the π-type equivalent circuit of the line. Therefore, this method can provide accurate line parameters for distributed control and decision-making based on intelligent terminals.

[0118] Based on the same inventive concept as the first embodiment, the second embodiment provides as follows: Figure 6 The device shown is a medium-voltage distribution network line parameter estimation device, applicable to smart terminals configured on any power supply section of a medium-voltage distribution network. The device includes: a data acquisition module 21, used to acquire local power flow data in each preset period, and to acquire power flow data acquired by adjacent smart terminals as remote power flow data; a model building module 22, used to construct a line parameter estimation model based on the π-type equivalent circuit of the line between the smart terminal and adjacent smart terminals; and a parameter estimation module 23, used to solve the line parameter estimation model based on the local power flow data and the remote power flow data to obtain the estimated values ​​of the line parameters.

[0119] In a preferred embodiment, the data acquisition module 21 includes: a local power flow data acquisition unit, used to acquire the bus voltage of the power supply section where the smart terminal is located and the active power and reactive power of each distribution line as local power flow data in each preset period; and a remote power flow data acquisition unit, used to determine the adjacent smart terminals of the smart terminal based on the topology of the medium-voltage distribution network in each preset period, and acquire the bus voltage of the power supply section where the adjacent smart terminals are located and the active power and reactive power of each distribution line as remote power flow data.

[0120] In a preferred embodiment, the line parameter estimation model is as follows:

[0121]

[0122] Where R is the resistance of the line, X is the reactance of the line, and B... C P1 is the equivalent capacitance to ground of the line; Q1 is the active power at the beginning of the line; P2 is the active power at the end of the line; Q2 is the reactive power at the end of the line; U1 is the voltage at the beginning of the line; and U2 is the voltage at the end of the line.

[0123] In a preferred embodiment, the parameter estimation module 23 is specifically used to solve the line parameter estimation model using the Newton-Raphson iteration method based on local power flow data and remote power flow data, until the estimated line parameters are obtained when the estimation error is less than a preset error.

[0124] In the preferred embodiment, the estimation error is:

[0125]

[0126] Among them, R (i+1) R is the resistance of the circuit obtained in the (i+1)th iteration. (i) X is the resistance of the circuit obtained in the i-th iteration; (i+1) Let X be the reactance of the line obtained in the (i+1)th iteration. (i) B is the reactance of the line obtained in the i-th iteration; C (i+1) Let B be the reactance of the line obtained in the (i+1)th iteration. C (i) Let be the reactance of the line obtained in the i-th iteration.

[0127] In summary, implementing the embodiments of the present invention has the following beneficial effects:

[0128] By configuring an intelligent terminal in each power supply section of the medium-voltage distribution network, the intelligent terminal collects local power flow data in each preset cycle and obtains power flow data collected by neighboring intelligent terminals as remote power flow data. Based on the π-type equivalent circuit of the line between the intelligent terminal and the neighboring intelligent terminals, a line parameter estimation model is constructed. Based on the local power flow data and the remote power flow data, the line parameter estimation model is solved to obtain the estimated value of the line parameters. It can accurately estimate the line parameters of the medium-voltage distribution network at regular intervals based on the local power flow data and the power flow data of the neighboring intelligent terminals.

[0129] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

[0130] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

Claims

1. A method for estimating parameters of medium-voltage distribution network lines, characterized in that, The method is applicable to smart terminals configured on any power supply section of the medium-voltage distribution network, and includes: Within each preset period, local power flow data is collected, and power flow data collected by adjacent smart terminals of the smart terminal is obtained as off-site power flow data. Based on the π-type equivalent circuit of the line between the smart terminal and the adjacent smart terminal, a line parameter estimation model is constructed. Based on the local power flow data and the remote power flow data, the route parameter estimation model is solved to obtain the estimated values ​​of the route parameters. The route parameter estimation model is as follows: ； Where R is the resistance of the line, X is the reactance of the line, and B... C Let P1 be the active power at the beginning of the line and Q1 be the reactive power at the beginning of the line; P2 be the active power at the end of the line and Q2 be the reactive power at the end of the line; U1 be the voltage at the beginning of the line and U2 be the voltage at the end of the line.

2. The method for estimating medium-voltage distribution network line parameters as described in claim 1, characterized in that, The step of collecting local power flow data and acquiring power flow data collected by neighboring smart terminals as off-site power flow data within each preset period includes: Within each preset cycle, the bus voltage of the power supply section where the smart terminal is located and the active power and reactive power of each distribution line are collected as the local power flow data. Within each preset period, based on the topology of the medium-voltage distribution network, the adjacent smart terminals of the smart terminal are determined, and the bus voltage of the power supply section where the adjacent smart terminal is located and the active power and reactive power of each distribution line are obtained as the off-site power flow data.

3. The method for estimating medium-voltage distribution network line parameters as described in claim 1, characterized in that, The step of solving the route parameter estimation model based on the local power flow data and the remote power flow data to obtain the estimated route parameters is as follows: Based on the local power flow data and the remote power flow data, the Newton-Raphson iteration method is used to solve the line parameter estimation model until the estimated line parameters are obtained when the estimation error is less than the preset error.

4. The method for estimating medium-voltage distribution network line parameters as described in claim 3, characterized in that, The estimation error is: ； Among them, R (i+1) R is the resistance of the circuit obtained in the (i+1)th iteration. (i) X is the resistance of the circuit obtained in the i-th iteration; (i+1) X is the reactance of the line obtained in the (i+1)th iteration. (i) B is the reactance of the line obtained in the i-th iteration; C (i+1) B is the reactance of the line obtained in the (i+1)th iteration. C (i) The reactance of the line is obtained in the i-th iteration.

5. A device for estimating parameters of a medium-voltage distribution network line, characterized in that, The device is applicable to intelligent terminals configured on any power supply section of the medium-voltage distribution network, and includes: The data acquisition module is used to collect local power flow data in each preset period and obtain power flow data collected by adjacent smart terminals of the smart terminal as off-site power flow data. The model building module is used to build a line parameter estimation model based on the π-type equivalent circuit of the line between the smart terminal and the adjacent smart terminal. The parameter estimation module is used to solve the line parameter estimation model based on the local power flow data and the remote power flow data to obtain the estimated values ​​of the line parameters, wherein the line parameter estimation model is: ； Where R is the resistance of the line, X is the reactance of the line, and B... C Let P1 be the active power at the beginning of the line and Q1 be the reactive power at the beginning of the line; P2 be the active power at the end of the line and Q2 be the reactive power at the end of the line; U1 be the voltage at the beginning of the line and U2 be the voltage at the end of the line.

6. The medium-voltage distribution network line parameter estimation device as described in claim 5, characterized in that, The data acquisition module includes: The local power flow data acquisition unit is used to acquire the bus voltage of the power supply section where the smart terminal is located and the active power and reactive power of each distribution line as the local power flow data in each preset period. The off-site power flow data acquisition unit is used to determine the adjacent smart terminals of the smart terminal based on the topology of the medium-voltage distribution network within each preset period, and to acquire the bus voltage of the power supply section where the adjacent smart terminals are located and the active power and reactive power of each distribution line as the off-site power flow data.

7. The medium-voltage distribution network line parameter estimation device as described in claim 5, characterized in that, The parameter estimation module is specifically used to solve the line parameter estimation model using the Newton-Raphson iteration method based on the local power flow data and the remote power flow data, until the estimated line parameters are obtained when the estimation error is less than a preset error.

8. The medium-voltage distribution network line parameter estimation device as described in claim 7, characterized in that, The estimation error is: ； Among them, R (i+1) R is the resistance of the circuit obtained in the (i+1)th iteration. (i) X is the resistance of the circuit obtained in the i-th iteration; (i+1) X is the reactance of the line obtained in the (i+1)th iteration. (i) B is the reactance of the line obtained in the i-th iteration; C (i+1) B is the reactance of the line obtained in the (i+1)th iteration. C (i) The reactance of the line is obtained in the i-th iteration.

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