Method and device for estimating line parameters of power distribution network based on synchronous phasor measurement

Abstract

The invention discloses a synchronous phasor measurement-based power distribution network line parameter estimation method and device, a medium and a terminal, and the method comprises the steps: introducing a model comprising a system error and a random error when a single-branch measurement model is built, and comprehensively considering uncertain factors; therefore, the accuracy and practicability of the multi-branch line measurement model established by the single-branch measurement model are improved, and in addition, the weighted least square method is selected for iteration in iteration, so that the estimation accuracy is further improved. The method comprises the following steps: establishing a single-branch measurement model according to a single-branch pi model and a measurement error; the single-branch pi model is used for representing a single-branch line in the power distribution network line; establishing a multi-branch line measurement model on the basis of the single-branch measurement model; and based on the multi-branch line measurement model, estimating the line impedance of the power distribution network by adopting an iterative weighted least square method.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network line parameters, and particularly to an estimation method, device, medium and terminal for distribution network line parameters based on synchronized phasor measurement. Background Art

[0002] In a power system, the effective estimation of line impedance is crucial for state estimation and protection relay setting, etc. Traditionally, these estimations mainly rely on static or off-line measurements and empirical formulas or models, and these methods are often limited by measurement accuracy and real-time performance. In recent years, with the emergence of synchronized phasor measurement units (PMUs), especially μPMUs designed for distribution systems, synchronized phasor measurement in the power system has become more accurate and real-time. A PMU can provide synchronized voltage and current phasor measurements, and these measurement data can be time-aligned and coordinated based on Coordinated Universal Time (UTC), so as to achieve consistency and accuracy within the network.

[0003] Traditional measurement methods usually complete the setting of network parameters through theoretical calculations and off-line measurements, which cannot meet the requirements of accuracy and real-time performance. Moreover, the topological structure of the distribution system is complex and changeable, which further increases the difficulty of network parameter estimation. Although PMUs can provide accurate measurement data in comparison, there are still certain errors and uncertainties in their measurements, which will all affect the final estimation results.

[0004] Therefore, how to reduce measurement errors and uncertainties has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides an estimation method, device, medium and terminal for distribution network line parameters based on synchronized phasor measurement. In this method, when establishing a single-branch measurement model, a model including systematic errors and random errors is introduced, comprehensively considering uncertainty factors, thereby improving the accuracy and practicability of the multi-branch line measurement model established from the single-branch measurement model. In addition, the weighted least squares method is selected for iteration during iteration to further improve the accuracy of estimation.

[0006] According to one aspect of the present invention, there is provided an estimation method for distribution network line parameters based on synchronized phasor measurement. The distribution network line parameters include the impedance of the distribution network line. The method includes:

[0007] Establish a single-branch measurement model according to the single-branch π model and measurement errors. The single-branch π model is used to represent a single-branch line in the distribution network line.

[0008] Based on the single-branch measurement model, establish a multi-branch line measurement model.

[0009] Based on the multi-branch line measurement model, the iterative weighted least squares method is used to estimate the impedance of the distribution network line.

[0010] In some embodiments, the measurement error includes systematic error and random error. The systematic error includes the systematic error of the sensor and the systematic error of the phasor measurement unit. The random error includes the random error of the sensor and the random error of the phasor measurement unit. Among them, the phasor measurement unit is a device that processes the data collected by the sensor to obtain the amplitude and phase of the voltage and current. The sensor is a device that measures the instantaneous signals of the voltage and current in the distribution network line.

[0011] The steps of establishing a single-branch measurement model according to the single-branch π model and the measurement error include:

[0012] According to the single-branch π model, establish the initial voltage phasors at both ends of the single-branch line and the initial current phasor flowing through the single-branch line.

[0013] Utilize the measurement error to adjust the initial voltage phasors at both ends and the initial current phasor, and correspondingly, obtain the voltage phasors at both ends of the single-branch line and the current phasor flowing through the single-branch line.

[0014] According to the voltage phasors at both ends of the single-branch line and the current phasor flowing through the single-branch line, determine the single-branch measurement model.

[0015] In some embodiments, the steps of establishing a multi-branch line measurement model based on the single-branch measurement model include:

[0016] According to the single-branch measurement model, construct an overdetermined system of equations and integrate the prior information of the overdetermined system of equations.

[0017] According to the overdetermined system of equations, determine the initial multi-branch line measurement model.

[0018] For the initial multi-branch line measurement model, add injection node constraints and zero injection constraints to determine the multi-branch line measurement model.

[0019] In some embodiments, the steps of constructing an overdetermined system of equations according to the single-branch measurement model include:

[0020] According to the single-branch measurement model, establish a branch voltage drop equation.

[0021] According to the branch voltage drop equation, determine the nonlinear measurement model.

[0022] According to the nonlinear measurement model, construct an overdetermined system of equations.

[0023] In some embodiments, the step of determining the non-linear measurement model according to the branch voltage drop equation includes:

[0024] Rearranging the branch voltage drop equation to obtain the real part and the imaginary part;

[0025] Using the real part and the imaginary part to determine the non-linear measurement model, where the non-linear measurement model includes the system error, the random error, and the equivalent measurement value based on the voltage and current synchronized phasor data collected by the phasor measurement unit.

[0026] According to another aspect of the present invention, there is provided an estimation device for distribution network line parameters based on synchronized phasor measurement. The distribution network line parameters include the impedance of the distribution network line. The device includes:

[0027] A first establishment unit for establishing a single-branch measurement model according to the single-branch π model and the measurement error. The single-branch π model is used to represent a single-branch line in the distribution network line;

[0028] A second establishment unit for establishing a multi-branch line measurement model based on the single-branch measurement model;

[0029] An estimation unit for estimating the impedance of the distribution network line by using the iterative weighted least squares method based on the multi-branch line measurement model.

[0030] In some embodiments, the first establishment unit includes:

[0031] A third establishment unit for establishing the initial two-end voltage phasor of the single-branch line and the initial current phasor flowing through the single-branch line according to the single-branch π model;

[0032] An adjustment unit for adjusting the initial two-end voltage phasor and the initial current phasor by using the measurement error. Correspondingly, the two-end voltage phasor of the single-branch line and the current phasor flowing through the single-branch line are obtained;

[0033] A first determination unit for determining the single-branch measurement model according to the two-end voltage phasor of the single-branch line and the current phasor flowing through the single-branch line.

[0034] In some embodiments, the second establishment unit includes:

[0035] A first construction unit for constructing an overdetermined equation set according to the single-branch measurement model and integrating the prior information of the overdetermined equation set;

[0036] A second determination unit for determining the initial multi-branch line measurement model according to the overdetermined equation set;

[0037] An adding unit is configured to add an injection node constraint and a zero injection constraint to the initial multi-branch line measurement model to determine a multi-branch line measurement model.

[0038] According to another aspect of the present invention, there is provided a storage medium storing at least one executable instruction, and the executable instruction causes a processor to execute the method for estimating distribution network line parameters based on synchronized phasor measurement.

[0039] According to another aspect of the present invention, there is provided a terminal, including: a processor, a memory, a communication interface and a communication bus, and the processor, the memory and the communication interface complete communication with each other through the communication bus;

[0040] The memory is configured to store at least one executable instruction, and the executable instruction causes the processor to execute the method for estimating distribution network line parameters based on synchronized phasor measurement.

[0041] The present invention provides a method, a device, a medium and a terminal for estimating distribution network line parameters based on synchronized phasor measurement. In this method, a model including systematic error and random error is introduced when establishing a single-branch measurement model, comprehensively considering uncertainty factors, thereby improving the accuracy and practicability of the multi-branch line measurement model established from the single-branch measurement model. In addition, the weighted least squares method is selected for iteration to further improve the accuracy of estimation. The method includes: establishing a single-branch measurement model according to a single-branch π model and measurement error; the single-branch π model is used to represent a single-branch line in the distribution network line; based on the single-branch measurement model, establishing a multi-branch line measurement model; and estimating the impedance of the distribution network line by using the iterative weighted least squares method based on the multi-branch line measurement model.

[0042] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention are specifically exemplified below. Description of the Drawings

[0043] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0044] Figure 1The flowchart of an estimation method for distribution network line parameters based on synchronized phasor measurement provided according to some embodiments is exemplarily shown;

[0045] Figure 2 The distribution network line including a single-branch π model provided according to some embodiments is exemplarily shown;

[0046] Figure 3 The flowchart of another estimation method for distribution network line parameters based on synchronized phasor measurement provided according to some embodiments is exemplarily shown;

[0047] Figure 4 The flowchart of another estimation method for distribution network line parameters based on synchronized phasor measurement provided according to some embodiments is exemplarily shown;

[0048] Figure 5 The flowchart of another estimation method for distribution network line parameters based on synchronized phasor measurement provided according to some embodiments is exemplarily shown;

[0049] Figure 6 The structural schematic diagram of an estimation device for distribution network line parameters of a dog based on synchronized phasor measurement provided according to some embodiments is exemplarily shown;

[0050] Figure 7 The structural schematic diagram of a terminal provided according to an embodiment of the present invention is shown. Detailed implementation manners

[0051] Hereinafter, the exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be completely conveyed to those skilled in the art.

[0052] In a power system, the effective estimation of line impedance is crucial for state estimation and protection relay settings, etc. Traditionally, these estimations mainly rely on static or offline measurements and empirical formulas or models, which are often limited by measurement accuracy and real-time performance. In recent years, with the emergence of synchronized phasor measurement units (PMUs), especially μPMUs designed for distribution systems, synchronized phasor measurement in power systems has become more accurate and real-time. PMUs can provide synchronized voltage and current phasor measurements, and these measurement data can be time-aligned and coordinated based on Coordinated Universal Time (UTC), thus achieving consistency and accuracy within the network. The traditional measurement methods usually complete the network parameter settings through theoretical calculations and offline measurements, which cannot meet the requirements of accuracy and real-time performance. Moreover, the topological structure of the distribution system is complex and changeable, further increasing the difficulty of network parameter estimation. Although PMUs can provide accurate measurement data in comparison, there are still certain errors and uncertainties in their measurements, which will all affect the final estimation results. Therefore, how to reduce measurement errors and uncertainties has become a technical problem that needs to be urgently solved by those skilled in the art.

[0053] To solve the above technical problems, an embodiment of the present application provides a method for estimating distribution network line parameters based on synchronized phasor measurement. In this method, a model including systematic error and random error is introduced when establishing a single-branch measurement model, comprehensively considering uncertainty factors, thereby improving the accuracy and practicality of the multi-branch line measurement model established from the single-branch measurement model. Additionally, the weighted least squares method iteration is selected in the iteration to further improve the accuracy of the estimation.

[0054] Figure 1 An exemplary flowchart of a method for estimating distribution network line parameters based on synchronized phasor measurement according to some embodiments is shown.

[0055] In an embodiment of the present application, the distribution network line parameters include the impedance of the distribution network line. This embodiment of the present application estimates the impedance of the distribution network line, and the method includes S100 - S300.

[0056] S100. Establish a single-branch measurement model according to the single-branch π model and measurement error; the single-branch π model is used to represent a single-branch line in the distribution network line.

[0057] In an embodiment of the present application, according to the topological structure of the distribution network, a single-branch measurement model including measurement error is established. This model considers the systematic error and random error introduced by sensors and PMUs (phasor measurement units), and uses the single-branch π model to represent a single-branch line in the distribution network line. Figure 2Exemplarily shown is a distribution network line including a single-branch π model provided according to some embodiments. It should be noted that the phasor measurement unit is a device that processes the data collected by the sensor to obtain the amplitudes and phases of voltage and current, and the sensor is a device that measures the instantaneous signals of voltage and current in the distribution network line. There will be certain systematic errors and random errors in the use of both the sensor and the phasor measurement unit.

[0058] In the embodiments of the present application, the phasor measurement unit is used to measure electrical quantities such as synchronous voltage and current phasors in the power system. To obtain these electrical quantities (including the amplitudes and phases of voltage and current), it is necessary to rely on a sensor, which can be built into the phasor measurement unit. The sensor converts the high voltage and large current in the power system (i.e., the original electrical quantity signal) into low voltage and small current signals suitable for measurement by the phasor measurement unit. The phasor measurement unit collects and processes based on these converted signals to obtain synchronous phasor data of voltage and current.

[0059] In some embodiments, the measurement error includes systematic error and random error. The systematic error includes the systematic error of the sensor and the systematic error of the phasor measurement unit, and the random error includes the random error of the sensor and the random error of the phasor measurement unit; wherein, the phasor measurement unit is a device that processes the data collected by the sensor to obtain the amplitudes and phases of voltage and current, and the sensor is a device that measures the instantaneous signals of voltage and current in the distribution network line;

[0060] Figure 3 Exemplarily shown is a flowchart of another method for estimating the parameters of a distribution network line based on synchronous phasor measurement provided according to some embodiments. The steps of establishing a single-branch measurement model according to the single-branch π model and the measurement error include S101 - S103.

[0061] S101. According to the single-branch π model, establish the initial two-end voltage phasors of the single-branch line and the initial current phasor flowing through the single-branch line.

[0062] In this embodiment, the initial two-end voltage phasors of the single-branch line (such as Figure 2 the branch (i, j) shown) and the initial current phasor flowing through this branch can both be expressed as functions of amplitude and phase according to Euler's formula. The specific formulas are as follows:

[0063]

[0064] where, v i and v j are the initial two-end voltage phasors of the single-branch line. Among them, v i is the initial voltage phasor at i, and v jis the initial voltage phasor at j, i ij is the initial current phasor flowing through branch (i, j); V i and V j are the amplitudes of the voltages measured by the phasor measurement unit at i and j respectively; Φ i and Φ j are the phases of the voltages measured by the phasor measurement unit at i and j respectively; I ij and θ ij are the amplitude and phase of the current measured by the phasor measurement unit for branch (i, j) respectively.

[0065] S102. Use the measurement error to adjust the initial two-terminal voltage phasor and the initial current phasor. Correspondingly, obtain the two-terminal voltage phasor of the single-branch line and the current phasor flowing through the single-branch line.

[0066] Specifically, use the measurement error to adjust the initial two-terminal voltage phasor to obtain the two-terminal voltage phasor of the single-branch line. Use the measurement error to adjust the initial current phasor to obtain the current phasor flowing through the single-branch line.

[0067] In this embodiment, considering that the phasor is affected by the amplitude and phase errors introduced by the sensor and the PMU itself, the initial two-terminal voltage phasor and the initial current phasor are adjusted. The specific functional representation is:

[0068]

[0069] Among them, and are the two-terminal voltage phasor of the single-branch line and the current phasor flowing through the single-branch line, where is the voltage vector at i, is the voltage vector at j; is the current phasor flowing through branch (i, j), V i R and V j R and Φ i R and Φ j R are the actual amplitudes and phases of the voltages measured by the phasor measurement unit at i and j respectively, I ij R and θ ij R are the actual amplitudes and phases of the current measured by the phasor measurement unit for branch (i, j) respectively. ξ i and ξ j and η ijSystem errors caused by the measured voltage amplitudes at locations i and j by the sensor and the measured current amplitude of branch (i,j), α i and α j and ψ ij are system errors caused by the measured voltage phases at locations i and j by the sensor and the measured current phase of branch (i,j), respectively. ξ i PMU and ξ j PMU and η ij PMU are random errors caused by the measured voltage amplitudes at locations i and j by the phasor measurement unit and the measured current amplitude of branch (i,j), respectively, α i PMU and α j PMU and ψ ij PMU are random errors caused by the measured voltage phases at locations i and j by the phasor measurement unit and the measured current phase of branch (i,j), respectively.

[0070] S103. Determine the single-branch measurement model according to the voltage phasors at both ends of the single-branch line and the current phasor flowing through the single-branch line.

[0071] In the embodiments of the present application, the single-branch measurement model includes the voltage phasors at both ends of the single-branch line and the current phasor flowing through the single-branch line, and the single-branch measurement model can be represented by the voltage phasors at both ends of the single-branch line and the current phasor flowing through the single-branch line.

[0072] S200. Based on the single-branch measurement model, establish a multi-branch line measurement model.

[0073] In the embodiments of the present application, in order to comprehensively represent the parameters of the distribution network line (including current, voltage, resistance, reactance, etc.), a multi-branch line measurement model is established based on the single-branch measurement model.

[0074] In some embodiments, Figure 4 exemplarily shows a flowchart of another method for estimating the parameters of a distribution network line based on synchronous phasor measurement according to some embodiments. The step of establishing a multi-branch line measurement model based on the single-branch measurement model includes S201-S203.

[0075] S201. According to the single-branch measurement model, construct an overdetermined system of equations and integrate the prior information of the overdetermined system of equations;

[0076] S202. Determine the initial multi-branch line measurement model according to the overdetermined system of equations;

[0077] S203. Add injection node constraints and zero injection constraints to the initial multi-branch line measurement model to determine the multi-branch line measurement model.

[0078] In some embodiments, Figure 5 Exemplarily shown is a flowchart of another method for estimating distribution network line parameters based on synchronized phasor measurement provided according to some embodiments. S201. The steps of constructing an overdetermined system of equations according to the single-branch measurement model include S2011 - S2013.

[0079] S2011. Establish a branch voltage drop equation according to the single-branch measurement model.

[0080] Specifically, the branch voltage drop equation is expressed as:

[0081]

[0082] where Z ij 0 is the per-unit value of the impedance of branch (i,j), R ij 0 and X ij 0 are the per-unit values of the resistance and reactance of branch (i,j) respectively. γ ij and β ij are the relative deviations of the line resistance and reactance with respect to the per-unit value, and are also correction parameters. V i R , V j R and Φ i R , Φ j R are the actual amplitudes and phases of the voltages measured by the phasor measurement unit at i and j respectively, I ij R and θ ij R are the actual amplitudes and phases of the branch (i,j) measurement current measured by the phasor measurement unit respectively. ξ i , ξ j and η ij are the system errors caused by the sensor for the measured voltage amplitudes at i and j and the measured current amplitude of branch (i,j) respectively, α i , α j and ψ ij are the system errors caused by the sensor for the measured voltage phases at i and j and the measured current phase of branch (i,j) respectively. ξ i PMU , ξ j PMU and η ijPMU Random errors caused by the phasor measurement unit for the measured voltage amplitudes at locations i and j and the measured current amplitude of branch (i, j), respectively, α i PMU 、α j PMU and ψ ij PMU Random errors caused by the phasor measurement unit for the measured voltage phases at locations i and j and the measured current phase of branch (i, j), respectively.

[0083] S2012. Determine a non - linear measurement model according to the branch voltage drop equation.

[0084] In some embodiments, the step of determining a non - linear measurement model according to the branch voltage drop equation includes:

[0085] Arrange the branch voltage drop equation to obtain the real part and the imaginary part;

[0086] Specifically, the process of arranging the branch voltage drop equation includes:

[0087] Expanding the exponential of the branch voltage drop equation gives:

[0088]

[0089]

[0090] Separate the real part and the imaginary part and simplify to get:

[0091] The real part is:

[0092] The imaginary part is:

[0093] Where:

[0094] V i and Φ i Are the amplitude and phase of the measured voltage at location i, respectively, I ij and θ ij Are the amplitude and phase of the measured current of branch (i, j), respectively.

[0095] Use the real part and the imaginary part to determine the non - linear measurement model, and the non - linear measurement model includes the system error, the random error, and the equivalent measurement value based on the voltage and current synchronous phasor data collected by the phasor measurement unit.

[0096] In this embodiment, based on the refined relationship presented by the real and imaginary part formulas, further integration and abstraction are performed to obtain a non-linear measurement model expression that comprehensively covers the relationship between the error vector and the equivalent measurement value of the voltage / current synchronous phasor data collected by the phasor measurement unit (PMU) as follows:

[0097]

[0098] y ij is the equivalent measurement value of the voltage / current synchronous phasor data collected by the phasor measurement unit, and y ij is the set of voltage and current synchronous phasor data collected by the phasor measurement unit, including the real and imaginary parts of the measured voltage and the real and imaginary parts of the measured current; h ij is a function of the measurement error and the equivalent measurement value of the voltage / current synchronous phasor data collected by the phasor measurement unit, x ij is the state vector to be estimated, including system error-related parameters, and e ij is the equivalent measurement random error vector.

[0099] The relative difference γ ij and β ij of the actual value of the line parameter relative to its rated value generally cannot be considered close to 1, so linear processing is not possible. Therefore, the latter term of y ij is split according to the system error and the random error, and there is:

[0100]

[0101] where f ij is the measurement vector function, and ε ij is the error vector.

[0102] S2013. Construct an overdetermined equation set according to the non-linear measurement model.

[0103] To estimate the branch network parameters and the system error, multiple PMU measurements of the same physical quantity are required. These measurements will introduce multiple equations involving the same unknowns, and then an overdetermined equation set is constructed as follows:

[0104]

[0105] where y ij tot is the measurement value group composed of the real and imaginary parts of the voltage and current phasors obtained by the PMU for the branch (i, j) at multiple moments; N t is the number of timestamps considered; y ij r (t k ) is at the moment tk The real part of the measured value, y ij x (t k ) is at time t k The imaginary part of the measured value, k ∈ [1, 2, 3…, N t ; f ij tk (x ij ) is at time t k The measurement function vector related to branch (i, j), x ij is the state vector to be estimated, including parameters related to systematic errors in branch measurements, such as α i 、ξ i etc.; ε ij tk (γ ij， β i， e ij tk ) is the equivalent measurement random error vector of the branch at time t k , γ ij and β ij are the correction parameters of resistance and reactance on branch (i, j) respectively, e ij tk is at time t k The random error vector related to branch (i, j), including random errors introduced by PMU measurements, such as ξ i PMU 、α i PMU etc.

[0106] In some embodiments, the process of integrating the prior information of the overdetermined equations includes:

[0107] The proposed estimator (overdetermined equations), that is, the overdetermined equations composed of multiple equations involving the same unknowns introduced by multiple PMU measurements of the same physical quantity, also needs to define the uncertainty model in detail, so the prior information of the unknown parameters needs to be obtained. The prior values of the network correction parameters and sensor errors can be integrated into the algorithm. In the absence of other information, the nominal values of the network parameters represent the best prior assumptions, so the prior values of the former are usually zero. Similarly, since only the error range of the sensor is usually known, the prior values of the latter are also usually zero. Therefore, the constraint conditions are as follows:

[0108]

[0109] where 0 represents a all-zero vector, I 2Mij is the 2M ij × 2M ij identity matrix, and ε ij prioris a vector containing prior knowledge about the error. x ij The dimension of ij , i.e., the number of unknowns, is 2M ij : Two of the constraints correspond to impedance, and the other two respectively correspond to the voltage and current PMU measurement channels related to the branch (i,j). Therefore, ε ij prior is a random error vector with a mean of zero, and its uncertainty can be derived from the prior information about the maximum deviation of the unknown parameters.

[0110] In some embodiments, according to the overdetermined system of equations, an initial multi-branch line measurement model is determined, and the initial multi-branch line measurement model is as follows:

[0111]

[0112] where f ij tot (x) and ε ij tot (γ,β,e) respectively represent the measurement function vector and the error vector of the branch (i,j) at all time points, and N br is the number of branches involved.

[0113] In some embodiments, the process of adding injection node constraints and zero injection constraints to the initial multi-branch line measurement model to determine the multi-branch line measurement model includes:

[0114] In the above formula, injection node constraints and zero injection constraints are further added.

[0115] The injection node constraint is expressed as:

[0116]

[0117] where and are respectively the real part and the imaginary part of the injected current phasor at node j; Γ j is the set of nodes adjacent to node j, k is the adjacent node; x is the state vector to be estimated; and are respectively the real part and the imaginary part of the measured current phasor of the branch (j,n); and are respectively the equivalent measured random error vectors of the real part and the imaginary part of the injected current at node j; η j and ψ j are respectively the amplitude and phase systematic errors of the overall current at node j; η jk and ψ jk are respectively the amplitude and phase systematic errors of the measured current of the branch (j,n), and The random error components of the real and imaginary parts of the injected current at node j.

[0118]

[0119] where ε j (γ, β, e) is the equivalent measured random error vector of the injected current at node j, and are the random error components of the magnitude and phase of the overall current at node j, respectively, and are the random error components of the magnitude and phase of the measured current in branch (j, n), respectively. The random error caused by the current injection PMU is considered together with the branch current error. Therefore, each node current measurement gives two additional constraints, which have two equivalent measured values and can be added to the global estimation model.

[0120] The zero injection constraint is expressed as:

[0121]

[0122] ε j-zero (γ, β, e) is the equivalent measured random error vector of the current at node j when considering zero injection nodes, and are the random error components of the real and imaginary parts of the current measurement at node j when considering zero injection nodes. When considering zero injection nodes, since there is no injection measurement, the relationship is simplified and there are no additional unknowns. This makes the new equivalent random error pair (denoted by "j-zero") associated with the current balance at zero injection nodes and the branch measurement error.

[0123] S300. Based on the multi-branch line measurement model, the impedance of the distribution network line is estimated by using the iterative weighted least squares method.

[0124] In the embodiment of the present application, based on the synchronous phasor measurement data obtained by the phasor measurement unit (PMU), the impedance of the line is estimated by using the iterative weighted least squares method (WLS). This algorithm takes into account the estimation of both line parameters and measurement errors.

[0125] The process is as follows:

[0126] Step (1): Define the measurement model. The model expression is as follows:

[0127] y = f(x) + ε

[0128] where y includes all equivalent measured values (related to branches, injections, and zero injections), f is a non-linear measurement vector function, ε represents all equivalent measured random errors, and x is the state vector to be estimated.

[0129] Step (2): Set the iteration goal, that is, to find the optimal state vector to be estimated. It is expressed by the formula:

[0130]

[0131] where argmin x represents finding the value of x that makes the following expression reach the minimum, that is, the optimal state vector to be estimated; represents the Mahalanobis distance norm, is the weighting matrix, which gives different weights according to the error characteristics of different measurements, and minimizes this norm to make the state vector to be estimated closer to the true value.

[0132] Step (3): Perform iterative calculations, taking the q-th iteration as an example.

[0133] First, calculate the residual vector r based on the current state vector to be estimated q , and the formula is expressed as:

[0134]

[0135] Secondly, calculate the value of the Jacobian matrix of the function f(x) with respect to xat , and the formula is as follows:

[0136]

[0137] Then, calculate the covariance matrix , and the formula is expressed as follows:

[0138]

[0139] where is the Jacobian matrix of the error function at the q-th iteration, and ∑ e is the covariance matrix of the measurement error vector e.

[0140] Finally, update the state vector x to be estimated, and the expression is as follows:

[0141]

[0142] where represents the increment of the state vector x to be estimated at the (q + 1)-th iteration step, that is, the change in the state vector x to be estimated from the q-th step to the (q + 1)-th step; and are the estimated values of the state vector x to be estimated at the (q + 1)-th iteration step and the q-th iteration step, respectively.

[0143] Step (4): Determine whether the iteration terminates according to the following:

[0144]

[0145] where δ = 10 -7 , when the condition is satisfied, the iteration terminates; when not satisfied, return to step (3) to continue the iteration.

[0146] Step (5): The finally output estimated vector And calculate its covariance matrix with the following formula:

[0147]

[0148] where is the Jacobian matrix of the error function, and ∑ prior is the covariance matrix related to the prior information. On the one hand, the covariance matrix can evaluate the uncertainty of the estimation result and measure the estimation reliability; on the other hand, it can be further used for subsequent analysis and decision-making.

[0149] Through the iterative weighted least squares method, the PMU measurement data can be processed online and the accuracy of the estimation can be improved.

[0150] The embodiment of the present application provides a method, device, medium and terminal for estimating the parameters of a distribution network line based on synchronized phasor measurement. In this method, when establishing a single-branch measurement model, a model including systematic error and random error is introduced, comprehensively considering uncertainty factors, thereby improving the accuracy and practicability of the multi-branch line measurement model established from the single-branch measurement model. In addition, the weighted least squares method is selected for iteration during iteration to further improve the accuracy of the estimation.

[0151] Further, as an implementation of the method shown above Figure 1 , the embodiment of the present invention provides an apparatus for estimating the parameters of a distribution network line based on synchronized phasor measurement, where the parameters of the distribution network line include the impedance of the distribution network line; Figure 6 Exemplarily shows a schematic structural diagram of an apparatus for estimating the parameters of a distribution network line based on synchronized phasor measurement according to some embodiments. The apparatus includes a first establishment unit 601, a second establishment unit 602 and an estimation unit 603.

[0152] The first establishment unit 601 is configured to establish a single-branch measurement model according to the single-branch π model and the measurement error; the single-branch π model is used to represent a single-branch line in the distribution network line;

[0153] The second establishment unit 602 is configured to establish a multi-branch line measurement model based on the single-branch measurement model;

[0154] An estimation unit 603, configured to estimate the impedance of the distribution network line by using the iterative weighted least squares method based on the multi-branch line measurement model.

[0155] In some embodiments, the measurement error includes a systematic error and a random error. The systematic error includes the systematic error of the sensor and the systematic error of the phasor measurement unit, and the random error includes the random error of the sensor and the random error of the phasor measurement unit. Wherein, the phasor measurement unit is a device that processes the data collected by the sensor to obtain the amplitude and phase of the voltage and current, and the sensor is a device that measures the instantaneous signals of the voltage and current in the distribution network line. The first establishing unit includes:

[0156] A third establishing unit, configured to establish an initial two-terminal voltage phasor of the single-branch line and an initial current phasor flowing through the single-branch line according to the single-branch π model.

[0157] An adjustment unit, configured to adjust the initial two-terminal voltage phasor and the initial current phasor by using the measurement error, and correspondingly, obtain the two-terminal voltage phasor of the single-branch line and the current phasor flowing through the single-branch line.

[0158] A first determining unit, configured to determine the single-branch measurement model according to the two-terminal voltage phasor of the single-branch line and the current phasor flowing through the single-branch line.

[0159] In some embodiments, the second establishing unit includes:

[0160] A first constructing unit, configured to construct an overdetermined system of equations according to the single-branch measurement model and integrate the prior information of the overdetermined system of equations.

[0161] A second determining unit, configured to determine an initial multi-branch line measurement model according to the overdetermined system of equations.

[0162] An adding unit, configured to add an injection node constraint and a zero injection constraint to the initial multi-branch line measurement model to determine the multi-branch line measurement model.

[0163] In some embodiments, the first constructing unit includes:

[0164] A fourth establishing unit, configured to establish a branch voltage drop equation according to the single-branch measurement model.

[0165] A third determining unit, configured to determine a non-linear measurement model according to the branch voltage drop equation.

[0166] A second constructing unit, configured to construct an overdetermined system of equations according to the non-linear measurement model.

[0167] In some embodiments, the third determination unit is specifically configured to:

[0168] Arrange the branch voltage drop equation to obtain the real part and the imaginary part;

[0169] Use the real part and the imaginary part to determine a non-linear measurement model, where the non-linear measurement model includes the system error, the random error, and an equivalent measurement value based on the voltage and current synchronous phasor data collected by the phasor measurement unit.

[0170] According to an embodiment of the present invention, there is provided a storage medium storing at least one executable instruction, and the computer executable instruction can execute the management method for power extraction of a transmission line in any of the above method embodiments.

[0171] Figure 7 The schematic structural diagram of a terminal provided according to an embodiment of the present invention is shown. The specific implementation of the terminal in the specific embodiments of the present invention is not limited.

[0172] As Figure 7 shown, the terminal may include: a processor 402, a communication interface 404, a memory 406, and a communication bus 408.

[0173] Wherein: the processor 402, the communication interface 404, and the memory 406 communicate with each other through the communication bus 408.

[0174] The communication interface 404 is used to communicate with network elements of other devices such as clients or other servers.

[0175] The processor 402 is configured to execute a program 410, and specifically may execute relevant steps in the above embodiments of the management method for power extraction of a transmission line.

[0176] Specifically, the program 410 may include program codes, and the program codes include computer operation instructions.

[0177] The processor 402 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention. One or more processors included in the terminal may be of the same type of processor, such as one or more CPUs; or may be of different types of processors, such as one or more CPUs and one or more ASICs.

[0178] A memory 406 for storing a program 410. The memory 406 may include high-speed RAM memory and may also include non-volatile memory, such as at least one disk memory.

[0179] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed over a network composed of multiple computing devices. Optionally, they can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a sequence different from here, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. In this way, the present invention is not limited to any specific combination of hardware and software.

[0180] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An estimation method for distribution network line parameters based on synchronized phasor measurement, characterized in that, The distribution network line parameters include the impedance of the distribution network line; the method includes: Establishing a single-branch measurement model according to the single-branch π model and measurement errors; the single-branch π model is used to represent a single-branch line in the distribution network line; Establishing a multi-branch line measurement model based on the single-branch measurement model; Based on the multi-branch line measurement model, using the iterative weighted least squares method to estimate the impedance of the distribution network line.

2. The method according to claim 1, wherein The measurement errors include systematic errors and random errors. The systematic errors include the systematic errors of sensors and the systematic errors of phasor measurement units. The random errors include the random errors of sensors and the random errors of phasor measurement units. Among them, the phasor measurement unit is a device that processes the data collected by sensors to obtain the amplitudes and phases of voltage and current, and the sensor is a device that measures the instantaneous voltage and current signals in the distribution network line; The step of establishing a single-branch measurement model according to the single-branch π model and measurement errors includes: According to the single-branch π model, establishing the initial voltage phasors at both ends of the single-branch line and the initial current phasor flowing through the single-branch line; Using the measurement errors to adjust the initial voltage phasors at both ends and the initial current phasor, and correspondingly, obtaining the voltage phasors at both ends of the single-branch line and the current phasor flowing through the single-branch line; Determining the single-branch measurement model according to the voltage phasors at both ends of the single-branch line and the current phasor flowing through the single-branch line.

3. The method according to claim 2, characterized in that, The step of establishing a multi-branch line measurement model based on the single-branch measurement model includes: Constructing an overdetermined system of equations according to the single-branch measurement model and integrating the prior information of the overdetermined system of equations; Determining the initial multi-branch line measurement model according to the overdetermined system of equations; Adding injection node constraints and zero injection constraints to the initial multi-branch line measurement model to determine the multi-branch line measurement model.

4. The method according to claim 3, characterized in that, The step of constructing an overdetermined system of equations according to the single-branch measurement model includes: Establishing a branch voltage drop equation according to the single-branch measurement model; Determining a non-linear measurement model according to the branch voltage drop equation; Constructing an overdetermined system of equations according to the non-linear measurement model.

5. The method according to claim 4, characterized in that, The step of determining a non-linear measurement model according to the branch voltage drop equation includes: Rearranging the branch voltage drop equation to obtain the real part and the imaginary part; Using the real part and the imaginary part to determine the non-linear measurement model, and the non-linear measurement model includes the systematic error, the random error, and the equivalent measurement value based on the synchronized phasor data of voltage and current collected by the phasor measurement unit.

6. An estimation device for distribution network line parameters based on synchronized phasor measurement, characterized in that, The distribution network line parameters include the impedance of the distribution network line; the device includes: A first establishing unit for establishing a single-branch measurement model according to the single-branch π model and measurement errors; the single-branch π model is used to represent a single-branch line in the distribution network line; A second establishing unit for establishing a multi-branch line measurement model based on the single-branch measurement model; An estimation unit for estimating the impedance of the distribution network line based on the multi-branch line measurement model by using the iterative weighted least squares method.

7. The device according to claim 6, characterized in that, The first establishment unit includes: A third establishment unit for establishing the initial voltage phasors at both ends of the single-branch line and the initial current phasor flowing through the single-branch line according to the single-branch π model; An adjustment unit for adjusting the initial voltage phasors at both ends and the initial current phasor by using the measurement error, and correspondingly, obtaining the voltage phasors at both ends of the single-branch line and the current phasor flowing through the single-branch line; A first determination unit for determining the single-branch measurement model according to the voltage phasors at both ends of the single-branch line and the current phasor flowing through the single-branch line; 8. The device according to claim 7, characterized in that, The second establishment unit includes: A first construction unit for constructing an overdetermined system of equations according to the single-branch measurement model and integrating the prior information of the overdetermined system of equations; A second determination unit for determining the initial multi-branch line measurement model according to the overdetermined system of equations; An addition unit for adding an injection node constraint and a zero injection constraint to the initial multi-branch line measurement model to determine the multi-branch line measurement model.

9. A storage medium storing at least one executable instruction, and the executable instruction causes the processor to execute the method for estimating the parameters of the distribution network line based on synchronous phasor measurement according to any one of claims 1-5.

10. A terminal, comprising: A processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete communication with each other through the communication bus; The memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the method for estimating the parameters of the distribution network line based on synchronous phasor measurement according to any one of claims 1-5.

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