A distribution network feeder measurement configuration method and device

Abstract

The present invention discloses a distribution network feeder measurement configuration method and device, which establishes a linearized relationship between the feeder measurement configuration and the state estimation error of the power system based on the least squares method state estimation; uses measurement information to perform state estimation to obtain a state estimation value of the power system; obtains the total square error of the state estimation of each node based on the linearized relationship and the state estimation value, and determines the measurement configuration scheme of the power system based on the total square error of the state estimation of each node, thereby realizing the optimized configuration of the distribution network feeder measurement, effectively taking into account the state estimation accuracy problem of the distribution network, reducing the distribution network state estimation error, and improving the accuracy of dispatching control, thereby improving the efficiency and precision of the measurement configuration.

Description

Technical Field

[0001] The present invention relates to the field of power system distribution optimization, and in particular to a distribution network feeder measurement configuration method and device. Background Art

[0002] In power grid dispatching and management, distribution network state estimation faces serious observability issues. On the one hand, with the development of new power systems and the massive integration of distributed power sources, problems such as reverse flow and voltage over-limit have become prominent. New business models such as virtual power plants rely on state perception. These new characteristics and trends place higher demands on power system state estimation. On the other hand, the current situational awareness capabilities of distribution networks are insufficient. The number, coverage, and timeliness of measurement devices in domestic distribution networks are suboptimal, necessitating efficient deployment of measurement configurations while taking economic factors into consideration. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a distribution network feeder measurement configuration method and device, which can improve the efficiency and accuracy of the measurement configuration.

[0004] In order to solve the above technical problems, a technical solution adopted by the present invention is:

[0005] A distribution network feeder measurement configuration method includes the following steps:

[0006] Based on the least squares state estimation, a linear relationship between the feeder measurement configuration and the state estimation error of the power system is established;

[0007] Using the measurement information to perform state estimation to obtain a state estimation value of the power system;

[0008] A total square error of each node state estimate is obtained according to the linearized relationship and the state estimate value, and a measurement configuration scheme of the power system is determined according to the total square error of each node state estimate.

[0009] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0010] A distribution network feeder measurement configuration device, comprising:

[0011] A data processing module is used to establish a linear relationship between the feeder measurement configuration and the state estimation error of the power system based on the least squares state estimation;

[0012] A state estimation module, configured to perform state estimation using measurement information to obtain a state estimation value of the power system;

[0013] A scheme determination module is used to obtain a total square error of each node state estimate based on the linearized relationship and the state estimation value, and determine a measurement configuration scheme of the power system based on the total square error of each node state estimate.

[0014] Furthermore, the data processing module is further used to:

[0015] Determine the voltage amplitude and phase angle of each node in the power system as state quantities;

[0016] According to the linearization of the state quantity near the state quantity working point, a linear relationship between the quantity measurement and the state quantity is obtained;

[0017] Determining a state estimation error between an estimated value of the state quantity and a true value of the state quantity according to a least squares state estimation method based on the linear relationship;

[0018] Calculating a measurement deviation between an estimated value of the quantity measurement and a true value of the quantity measurement according to the state estimation error;

[0019] An error level of the quantity measurement is determined according to the measurement deviation, where the error level of the quantity measurement is a linearized relationship between a feeder measurement configuration and a state estimation error of the power system.

[0020] Furthermore, the state estimation module is further used to:

[0021] Determining a calculated value of the quantity measurement using Kirchhoff's law based on the state quantity and the admittance matrix;

[0022] determining a residual between a calculated value and a measured value of the quantity measurement, and obtaining a residual vector based on the residual;

[0023] Establishing an objective function based on the residual vector;

[0024] An estimated value of the state of the power system is calculated using an iterative algorithm according to the objective function.

[0025] Furthermore, the solution determination module is further used to:

[0026] Determine the theoretical value of the variance of the load estimate value of each node in the substation according to the linearized relationship and the state estimate value;

[0027] Calculate the expected value of the total square error of the state estimation of each node in the substation area based on the theoretical value of the variance of the load estimation value of each node in the substation area;

[0028] A measurement configuration scheme for the power system is determined according to an expected value of a total square error of the state estimation of each node in the substation area.

[0029] Furthermore, the scheme determination module is further used to determine the measurement configuration scheme of the power system according to the expected value of the total square error estimated by the state of each node in the substation area, including:

[0030] If the power system is a single-feeder measurement configuration, the measurement device configuration positions are traversed until the measurement device configuration position with the smallest expected value of the total square error of the node state estimation is selected, and the measurement configuration scheme of the power system is obtained based on the selected measurement device configuration position.

[0031] Furthermore, it also includes a data update module for:

[0032] Get newly added repeated measurements;

[0033] updating the error level of the quantity measurement according to the newly added repeated quantity measurement to obtain an updated error level of the quantity measurement;

[0034] The error level of the updated quantity measurement is:

[0035]

[0036] q aa =Q (a) ,ii;

[0037] Where Q (a),kk represents the error level of the updated quantity measurement, Q ii represents the diagonal element of row i, Q kk represents the k-th row diagonal element, R a represents the variance matrix of the updated quantity measurement, Q ki Represents the element in row k and column i.

[0038] The beneficial effects of the present invention are as follows: a linearized relationship between the feeder measurement configuration and the state estimation error of the power system is established based on the least squares state estimation, the measurement information is used for state estimation, and the state estimation value of the power system is obtained; the total square error of the state estimation of each node is obtained based on the linearized relationship and the state estimation value; and the measurement configuration scheme of the power system is determined based on the total square error of the state estimation of each node, thereby realizing the optimized configuration of the feeder measurement of the distribution network, effectively taking into account the state estimation accuracy problem of the distribution network, reducing the distribution network state estimation error, and improving the accuracy of dispatching control, thereby improving the efficiency and accuracy of the measurement configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flow chart of the steps of a distribution network feeder measurement configuration method according to an embodiment of the present invention;

[0040] Figure 2This is a structural diagram of a distribution network feeder measurement configuration device according to an embodiment of the present invention;

[0041] Figure 3 This is a single-line diagram of an IEEE 14-node network in a distribution network feeder measurement configuration method according to an embodiment of the present invention;

[0042] Figure 4 Schematic diagram of node error distribution in each substation for different feeder measurement schemes in the distribution network feeder measurement configuration method according to an embodiment of the present invention;

[0043] Figure 5 Schematic diagram of error distribution under different combinations when measuring multiple feeders in the distribution network feeder measurement configuration method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0045] Please refer to Figure 1 , an embodiment of the present invention provides a distribution network feeder measurement configuration method, comprising the steps of:

[0046] Based on the least squares state estimation, a linear relationship between the feeder measurement configuration and the state estimation error of the power system is established;

[0047] Using the measurement information to perform state estimation to obtain a state estimation value of the power system;

[0048] A total square error of each node state estimate is obtained according to the linearized relationship and the state estimate value, and a measurement configuration scheme of the power system is determined according to the total square error of each node state estimate.

[0049] From the above description, it can be seen that the beneficial effects of the present invention are: establishing a linear relationship between the feeder measurement configuration and the state estimation error of the power system based on the least squares state estimation, using measurement information for state estimation to obtain the state estimation value of the power system, obtaining the total square error of the state estimation of each node based on the linearization relationship and the state estimation value, and determining the measurement configuration scheme of the power system based on the total square error of the state estimation of each node, realizing the optimized configuration of the distribution network feeder measurement, which can effectively take into account the state estimation accuracy problem of the distribution network, reduce the distribution network state estimation error, improve the accuracy of dispatching control, and thus improve the efficiency and accuracy of the measurement configuration.

[0050] Furthermore, establishing a linear relationship between feeder measurement configuration and state estimation error of the power system according to the least squares state estimation includes:

[0051] Determine the voltage amplitude and phase angle of each node in the power system as state quantities;

[0052] According to the linearization of the state quantity near the state quantity working point, a linear relationship between the quantity measurement and the state quantity is obtained;

[0053] Determining a state estimation error between an estimated value of the state quantity and a true value of the state quantity according to a least squares state estimation method based on the linear relationship;

[0054] Calculating a measurement deviation between an estimated value of the quantity measurement and a true value of the quantity measurement according to the state estimation error;

[0055] An error level of the quantity measurement is determined according to the measurement deviation, where the error level of the quantity measurement is a linearized relationship between a feeder measurement configuration and a state estimation error of the power system.

[0056] From the above description, it can be seen that by establishing a linear relationship between the feeder measurement configuration and the state estimation error of the power system based on the least squares state estimation, a consistent expression of information such as whether measurement is configured, the measurement configuration location, and the measurement accuracy can be obtained, which is convenient for reducing the distribution network state estimation error while considering the state estimation accuracy problem of the distribution network in the future.

[0057] Furthermore, the linear relationship between the quantity measurement and the state quantity is:

[0058]

[0059] Where, represents the quantity measured, represents the state quantity, represents the measurement error of each quantity, H represents the Jacobian matrix;

[0060] Determining a state estimation error between an estimated value of the state quantity and a true value of the state quantity according to a least squares state estimation method based on the linear relationship includes:

[0061]

[0062] Where, represents the true value of the state quantity, represents the estimated value of the state quantity, and R represents the quantity measurement variance matrix;

[0063] Calculating a measurement deviation between an estimated value of the quantity measurement and a true value of the quantity measurement according to the state estimation error includes:

[0064]

[0065] Where Δz represents the measurement deviation;

[0066] Determining the error level of the measurement according to the measurement deviation includes:

[0067]

[0068] Where Q represents the error level of the measurement, and E() represents the expected value.

[0069] From the above description, it can be seen that the relationship between the state estimation error and the measurement configuration can be determined based on the error level of the measurement. The error of the state estimation is jointly determined by the linearized Jacobian matrix and the measurement variance matrix. When the working point is constant, the Jacobian matrix depends on the network structure of the observed network and the selected measurement point location. The measurement variance matrix depends on the accuracy level of the measurement point. The various measurement points influence each other through electrical connections, and jointly determine the accuracy level of each estimated quantity.

[0070] Furthermore, the using the measurement information to perform state estimation to obtain the state estimation value of the power system includes:

[0071] Determining a calculated value of the quantity measurement using Kirchhoff's law based on the state quantity and the admittance matrix;

[0072] determining a residual between a calculated value and a measured value of the quantity measurement, and obtaining a residual vector based on the residual;

[0073] Establishing an objective function based on the residual vector;

[0074] An estimated value of the state of the power system is calculated using an iterative algorithm according to the objective function.

[0075] It can be seen from the above description that calculating the state estimation value facilitates the subsequent determination of the state estimation error based on the state estimation value.

[0076] Furthermore, the determining the calculated value of the quantity measurement using Kirchhoff's law according to the state quantity and the admittance matrix includes:

[0077]

[0078] Wherein, x represents the state quantity, h() represents the admittance matrix, and h(x) represents the calculated value of the quantity measurement;

[0079] The obtaining of the residual vector according to the residual comprises:

[0080] r(x)=zh(x);

[0081] Where z represents the measured value, r(x) represents the residual vector;

[0082] The establishing of the objective function according to the residual vector comprises:

[0083]

[0084] Where J(x) represents the objective function, R represents the measurement variance matrix, σ i represents the i-th diagonal element of the R matrix, r i represents the measurement error of the i-th quantity, and m represents the matrix order;

[0085] Calculating the state estimation value of the power system using an iterative algorithm according to the objective function includes:

[0086] Δx (l) =[H T (x (l) )R -1 H(x (l) )] -1 H T (x (l) )R -1 r(x (l) );

[0087] x (l+1) =x (l) +Δx (l) ;

[0088] Where Δx (l) Indicates the change in the estimated value of the first round during the iteration, x (l) represents the estimated value of the first round in the iterative process, x (l+1) Represents the l+1th round estimate in the iterative process.

[0089] From the above description, it can be seen that after state estimation, the H matrix and R matrix can be obtained, so as to subsequently determine the theoretical value of the variance of the load estimation value of each node in the substation.

[0090] Furthermore, obtaining a total square error of each node state estimate based on the linearized relationship and the state estimate value, and determining a measurement configuration scheme of the power system based on the total square error of each node state estimate includes:

[0091] Determine the theoretical value of the variance of the load estimate value of each node in the substation according to the linearized relationship and the state estimate value;

[0092] Calculate the expected value of the total square error of the state estimation of each node in the substation area based on the theoretical value of the variance of the load estimation value of each node in the substation area;

[0093] A measurement configuration scheme for the power system is determined according to an expected value of a total square error of the state estimation of each node in the substation area.

[0094] From the above description, it can be seen that the measurement configuration scheme of the power system is determined according to the expected value of the total square error of the state estimation of each node in the substation area, so that the configuration scheme with the lowest error can be determined, thereby improving the efficiency and accuracy of the measurement configuration.

[0095] Furthermore, the calculation of the expected value of the total square error of the state estimation of each node in the substation area according to the theoretical value of the variance of the load estimation value of each node in the substation area includes:

[0096]

[0097] Where Δz i represents the measurement deviation of the i-th quantity, Q ii Represents the variance of each quantity measured.

[0098] From the above description, it can be seen that by calculating the expected value of the total square error of the state estimation of each node in the substation area, it can be used to evaluate the quality of the measurement configuration scheme, thereby determining the optimal measurement configuration scheme.

[0099] Furthermore, the determining of the measurement configuration scheme of the power system according to the expected value of the total square error of the state estimation of each node in the substation area includes:

[0100] If the power system is a single-feeder measurement configuration, the measurement device configuration positions are traversed until the measurement device configuration position with the smallest expected value of the total square error of the node state estimation is selected, and the measurement configuration scheme of the power system is obtained based on the selected measurement device configuration position.

[0101] As can be seen from the above description, when a single feeder measurement configuration is used, the configuration scheme with the lowest error can be selected by traversing the deployment positions of the measurement devices to achieve measurement configuration optimization, thereby improving the efficiency and accuracy of the measurement configuration.

[0102] Furthermore, it also includes:

[0103] Get new incremental measurements;

[0104] updating the error level of the quantity measurement according to the newly added quantity measurement to obtain an updated error level of the quantity measurement;

[0105] The error level of the updated quantity measurement is:

[0106]

[0107] q aa =Q (a) ,ii;

[0108] Where Q (a),kk represents the error level of the updated quantity measurement, Q iiIndicates the variance of each quantity measured corresponding to the diagonal element in the i-th row, Q kk Indicates the variance of each quantity measured corresponding to the diagonal elements in the kth row, R a represents the variance matrix of the updated quantity measurement, Q ki Represents the element in row k and column i.

[0109] From the above description, it can be seen that the degree of influence of the newly added quantity measurement on the accuracy of other quantity measurements depends on the closeness of the electrical connection between the quantity measurements. The closer the electrical connection between the quantity measurements, the greater the degree of mutual influence.

[0110] Please refer to Figure 2 A distribution network feeder measurement configuration device, characterized by comprising:

[0111] A data processing module is used to establish a linear relationship between the feeder measurement configuration and the state estimation error of the power system based on the least squares state estimation;

[0112] A state estimation module, configured to perform state estimation using measurement information to obtain a state estimation value of the power system;

[0113] A scheme determination module is used to obtain a total square error of each node state estimate based on the linearized relationship and the state estimation value, and determine a measurement configuration scheme of the power system based on the total square error of each node state estimate.

[0114] From the above description, it can be seen that the beneficial effects of the present invention are: establishing a linear relationship between the feeder measurement configuration and the state estimation error of the power system based on the least squares state estimation, using measurement information for state estimation to obtain the state estimation value of the power system, obtaining the total square error of the state estimation of each node based on the linearization relationship and the state estimation value, and determining the measurement configuration scheme of the power system based on the total square error of the state estimation of each node, realizing the optimized configuration of the distribution network feeder measurement, which can effectively take into account the state estimation accuracy problem of the distribution network, reduce the distribution network state estimation error, improve the accuracy of dispatching control, and thus improve the efficiency and accuracy of the measurement configuration.

[0115] Furthermore, the data processing module is further used to:

[0116] Determine the voltage amplitude and phase angle of each node in the power system as state quantities;

[0117] According to the linearization of the state quantity near the state quantity working point, a linear relationship between the quantity measurement and the state quantity is obtained;

[0118] Determining a state estimation error between an estimated value of the state quantity and a true value of the state quantity according to a least squares state estimation method based on the linear relationship;

[0119] Calculating a measurement deviation between an estimated value of the quantity measurement and a true value of the quantity measurement according to the state estimation error;

[0120] An error level of the quantity measurement is determined according to the measurement deviation, where the error level of the quantity measurement is a linearized relationship between a feeder measurement configuration and a state estimation error of the power system.

[0121] From the above description, it can be seen that by establishing a linear relationship between the feeder measurement configuration and the state estimation error of the power system based on the least squares state estimation, a consistent expression of information such as whether measurement is configured, the measurement configuration location, and the measurement accuracy can be obtained, which is convenient for reducing the distribution network state estimation error while considering the state estimation accuracy problem of the distribution network in the future.

[0122] Furthermore, the state estimation module is further used to:

[0123] Determining a calculated value of the quantity measurement using Kirchhoff's law based on the state quantity and the admittance matrix;

[0124] determining a residual between a calculated value and a measured value of the quantity measurement, and obtaining a residual vector based on the residual;

[0125] Establishing an objective function based on the residual vector;

[0126] An estimated value of the state of the power system is calculated using an iterative algorithm according to the objective function.

[0127] It can be seen from the above description that calculating the state estimation value facilitates the subsequent determination of the state estimation error based on the state estimation value.

[0128] Furthermore, the solution determination module is further used to:

[0129] Determine the theoretical value of the variance of the load estimate value of each node in the substation according to the linearized relationship and the state estimate value;

[0130] Calculate the expected value of the total square error of the state estimation of each node in the substation area based on the theoretical value of the variance of the load estimation value of each node in the substation area;

[0131] A measurement configuration scheme for the power system is determined according to an expected value of a total square error of the state estimation of each node in the substation area.

[0132] From the above description, it can be seen that the measurement configuration scheme of the power system is determined according to the expected value of the total square error of the state estimation of each node in the substation area, so that the configuration scheme with the lowest error can be determined, thereby improving the efficiency and accuracy of the measurement configuration.

[0133] Furthermore, the scheme determination module is further used to determine the measurement configuration scheme of the power system according to the expected value of the total square error estimated by the state of each node in the substation area, including:

[0134] If the power system is a single-feeder measurement configuration, the measurement device configuration positions are traversed until the measurement device configuration position with the smallest expected value of the total square error of the node state estimation is selected, and the measurement configuration scheme of the power system is obtained based on the selected measurement device configuration position.

[0135] As can be seen from the above description, when a single feeder measurement configuration is used, the configuration scheme with the lowest error can be selected by traversing the deployment positions of the measurement devices to achieve measurement configuration optimization, thereby improving the efficiency and accuracy of the measurement configuration.

[0136] Furthermore, it also includes a data update module for:

[0137] Get newly added repeated measurements;

[0138] updating the error level of the quantity measurement according to the newly added repeated quantity measurement to obtain an updated error level of the quantity measurement;

[0139] The error level of the updated quantity measurement is:

[0140]

[0141] q aa =Q (a) ,ii;

[0142] Where Q (a),kk represents the variance matrix of the updated measurement value, Q ii represents the diagonal element of row i, Q kk represents the k-th row diagonal element, R a represents the variance matrix of the updated quantity measurement, Q ki Represents the element in row k and column i.

[0143] From the above description, it can be seen that the degree of influence of the newly added quantity measurement on the accuracy of other quantity measurements depends on the closeness of the electrical connection between the quantity measurements. The closer the electrical connection between the quantity measurements, the greater the degree of mutual influence.

[0144] The above-mentioned distribution network feeder measurement configuration method and device of the present invention can be applied to the feeder measurement configuration of the distribution network, and are described below through specific implementation methods:

[0145] Example 1

[0146] Please refer to Figure 1 and Figure 3-Figure 5A distribution network feeder measurement configuration method according to this embodiment is characterized by comprising the steps of:

[0147] S1. Establish a linear relationship between the feeder measurement configuration and the state estimation error of the power system based on the least squares state estimation method, specifically including:

[0148] S11. Determine the voltage amplitude and phase angle of each node in the power system as state quantities The observed electrical quantities can be derived using state quantities.

[0149] S12, linearizing the state quantity near a state quantity operating point to obtain a linear relationship between the quantity measurement and the state quantity;

[0150] In an optional embodiment, the dynamics of a system is determined by the value of the state quantity and the dynamic characteristics of the system, and other quantities including quantity measurements can be derived from the state quantity. Therefore, when the power system is stably operating in a certain state, the derived relationship can be linearized near the state quantity operating point to obtain a linear relationship between the state quantity and the measurement value of the quantity measurement, specifically:

[0151]

[0152] Where, A measurement value representing a quantity measured;

[0153] When actually observing and sampling each measurement, there is a deviation between the measured value and the actual value due to factors such as instrument accuracy, errors introduced during digital signal processing, and estimation errors in pseudo-measurement algorithms. For ease of study, it is assumed that this deviation satisfies a normal distribution. The standard deviation of the deviation of different measurements is different and can be roughly determined based on the accuracy level of the measuring instrument. Therefore, the linear relationship between the measurement and the state quantity is:

[0154]

[0155] Where, represents the quantity measured, represents the state quantity, represents the measurement error of each quantity, H represents the Jacobian matrix;

[0156] in, Each element in the equation is subject to a certain standard deviation (σ1σ2…σ n ) is normally distributed, so the measurement variance matrix is ​​defined as

[0157] According to the principle of least squares state estimation, when the initial value of the state quantity is good, the state quantity will converge to the minimum point of the square error determined by the actual value of the measurement with second-order convergence. Due to the existence of measurement error, the estimated result of the state quantity will have a certain deviation from the actual value. Since the algorithm has good convergence and the estimated value of the state quantity is close to the true value, it can be considered that the true value of the state quantity is used as the initial value of the iteration and it can approximately converge to the estimated value after one iteration. The change of the state quantity in this iteration is the estimated error of the state quantity. Therefore, the formula in S13 can be obtained from the iterative process;

[0158] S13. Determine a state estimation error between an estimated value of the state quantity and a true value of the state quantity using a least squares state estimation method based on the linear relationship. Specifically:

[0159]

[0160] Where, represents the true value of the state quantity, represents the estimated value of the state quantity, and R represents the quantity measurement variance matrix;

[0161] S14. Calculate a measurement deviation between the estimated value of the quantity measurement and the true value of the quantity measurement according to the state estimation error, specifically:

[0162]

[0163] Where Δz represents the measurement deviation;

[0164] Since we know the distribution function of v, we can find the distribution of Δz and obtain the measurement errors of each quantity after state estimation. When assuming that v satisfies the Gaussian distribution, the mean value of Δz is 0 according to the above calculation formula.

[0165] S15. Determine an error level of the measurement according to the measurement deviation, where the error level of the measurement is a linearized relationship between the feeder measurement configuration and the state estimation error of the power system, specifically:

[0166]

[0167] Where Q represents the error level of the measurement, and E() represents the expected value.

[0168] It can be seen from this formula that the state estimation error is jointly determined by the linearized Jacobian matrix H and the measurement variance matrix R. Given a fixed operating point, H depends on the network structure of the observed network and the positions of the selected measurement points, while R depends on the accuracy levels of the measurement points. Each measurement point affects the others through electrical connections and jointly determines the accuracy levels of the estimated quantities. Below, the properties of this matrix Q will be explained in combination with its physical meaning:

[0169] diag[H(H T R -1 H) -1 H T <R, the state estimation can improve the accuracy of the measurements and can act as a filter. Adding new measurements will surely have a positive effect on the state estimation. The analysis is as follows:

[0170] In an alternative embodiment, the impact of the newly added measurement on the state estimation is as follows: Assume that the original network has m measurements and n state variables. Considering the case of adding one-dimensional measurement, the Jacobian matrix H after the addition can be written as:

[0171] a

[0172]

[0173] After adding one-dimensional measurement, the error level Q of the measurements increases by one row and one column. Let Q (a) be the original Q matrix, the column vector and row vector that are the extra parts of the Q matrix after adding the measurement, and Q aa be the single element in the lower right corner of the new matrix. The error level Q a of the new measurement can be written as:

[0174]

[0175] In the formula, Q (a) represents the error level of the original measurements, represents the column vector that is the extra part of the error level of the original measurements after adding the measurement, represents the row vector that is the extra part of the error level of the original measurements after adding the measurement, and q aa represents the single element in the lower right corner of the error level of the new measurement;

[0176] According to the definition, the error level Q a of the new measurement is:

[0177]

[0178] In the formula, H a s represents the Jacobian matrix after adding the measurement, and R arepresents the measurement variance matrix after adding the measurement;

[0179] Among them, the error level Q of the new quantity measurement a The i-th diagonal element Q ii for:

[0180]

[0181] Where H i Represents the diagonal element Q in row i ii The corresponding Jacobian matrix;

[0182] because

[0183]

[0184] make

[0185]

[0186]

[0187] Substitute the new measurement error level Q a The error level Q of the original i-th quantity measurement can be obtained by sorting out (a),ii for:

[0188]

[0189] The second half of the above formula, that is, the right side of the minus sign, is a positive definite matrix. Therefore, in the error level of the new quantity measurement, the main diagonal elements of the original estimate become smaller, which improves the accuracy of the state estimation.

[0190] In another optional embodiment, the equivalent expression for the newly added repeated quantity measurement is: when the newly added quantity measurement is the same as the previous quantity measurement, that is, the newly added row already exists in the original H matrix, let the subscript of the row corresponding to the newly added quantity be a, assuming The enhancement of the diagonal elements of the k-th row of the original matrix (corresponding to the variance of the k-th quantity measurement) is as follows:

[0191] Get newly added repeated measurements;

[0192] updating the error level of the quantity measurement according to the newly added repeated quantity measurement to obtain an updated error level of the quantity measurement;

[0193] The error level of the updated quantity measurement is:

[0194]

[0195] q aa =Q (a) ,ii;

[0196] Where Q (a),kk represents the error level of the updated quantity measurement, Q ii represents the diagonal element of row i, Q kk represents the k-th row diagonal element, R a represents the variance matrix of the updated quantity measurement, Q ki Represents the element in row k and column i.

[0197] Due to the advantage of the main diagonal of the Q matrix, the accuracy of the corresponding quantity measurement will be significantly enhanced, while the improvement of the accuracy of other quantity measurements is significantly smaller. The specific improvement depends on the Q matrix. ki The strength of the electrical connection between the two reactions.

[0198] According to the above analysis, the influence of the new quantity measurement on the accuracy of other quantity measurements depends on the closeness of the electrical connection between the quantities measured (through (Reflection), the closer the electrical connection, the higher the degree of mutual influence. When a certain quantity is measured repeatedly, the estimated results of the two quantities are consistent and the error level is the same. Let the standard deviation of the existing measurement be σ1, the new measurement be σ2, and the new matrix H with repeated measurement is T R -1 H is compared with the original matrix, only the Replace with This is equivalent to a standard deviation of The measurement is similar to the parallel effect of resistors. When the standard deviations of the two measurements differ greatly, it is approximately equivalent to performing only one measurement with higher accuracy.

[0199] In another optional embodiment, the equivalent expression for deleting the measurement is similar to the above description. As the standard deviation of the measurement increases, the coefficient of the corresponding item in the R matrix increases. Then it decreases, and H T R -1 The weights of the parts of the H matrix that are related to the measurement are also calculated according to When σ is maximized, the corresponding item in R becomes 0, and the calculation result is consistent with the deleted measurement, that is, no measurement is performed and the measurement is deleted. It is equivalent to setting it to 0. Based on the above equivalence conditions, adding a new measurement can be equivalent to adding a repeated measurement. The variance of the original measurement is extremely large, which provides certain convenience for mathematical analysis.

[0200] S2. Using the measurement information to perform state estimation to obtain a state estimation value of the power system, specifically including:

[0201] The state quantity of a power system is generally taken as the complex voltage at each node. It can be expressed in polar coordinates as the voltage amplitude and phase angle, or in rectangular coordinates as the real and imaginary parts of the voltage. The measured quantities of a power system are generally the active power injected into a node or branch, the reactive power, and the node voltage amplitude. In conventional power flow, if the injected complex power given by each PQ node (nodes with known injected active power P and reactive power Q) and the injected active power and voltage amplitude given by each PV node (nodes with known injected active power P and voltage U) are considered as measurements, then the number of measurements is exactly equal to the number of state quantities. However, in state estimation, the types of measurements include not only the injected complex power at each node, but also the branch complex power and node voltage amplitude. Therefore, the number of measurements in state estimation is generally greater than the number of state quantities.

[0202] The state quantity to be requested It is an n-dimensional vector composed of the voltage amplitude and phase angle of each node:

[0203]

[0204] Where, v n represents the amplitude, θ n represents the phase angle;

[0205] The known conditions for state estimation are m real-time measurements of the power system, for example:

[0206]

[0207] Where V represents the node voltage, P represents the active power of the line or node, Q represents the reactive power of the line or node, and the subscripts of V, P, and Q are all node numbers.

[0208] S21. Determine the calculated value of the quantity measurement using Kirchhoff's law according to the state quantity and the admittance matrix. Specifically:

[0209]

[0210] Wherein, x represents the state quantity, h() represents the admittance matrix, and h(x) represents the calculated value of the quantity measurement;

[0211] According to Kirchhoff's law, we can establish the calculation formula h(x) for measuring various quantities using the unknown state quantity x and the admittance matrix. There are m equations in total.

[0212] S22, determining a residual between a calculated value and a measured value of the quantity measurement, and obtaining a residual vector according to the residual;

[0213] Based on the above, as long as there is a set of state variables x, the calculated value h(x) of the quantity measurement can be obtained. The difference between the given quantity measurement and this calculated value is called the residual. The residuals of each quantity measurement form an m-dimensional residual vector, specifically:

[0214] r(x)=zh(x);

[0215] Where z represents the measured value, r(x) represents the residual vector;

[0216] Due to m>n and measurement errors, it is impossible to find a So the residual vector is:

[0217]

[0218] S23, establishing an objective function according to the residual vector, specifically:

[0219]

[0220] In the formula, J(x) represents the objective function, R represents the is the m×m-order measurement variance matrix of the diagonal elements, σ i represents the i-th diagonal element of the R matrix, r i represents the measurement error of the i-th quantity, m represents the matrix order; R -1 Acts as a weight;

[0221] Since all components are 0, we can expect to obtain a state vector that minimizes the sum of squares of the weighted residuals For this purpose, the objective function as above can be established.

[0222] S24. Calculate the estimated state value of the power system using an iterative algorithm according to the objective function, specifically:

[0223] Δx (l) =[H T (x (l) )R -1 H(x (l) )] -1 H T (x (l) )R -1 r(x (l) );

[0224] x (l+1) =x (l) +Δx (l) ;

[0225] Where Δx (l) Indicates the change in the estimated value of the first round during the iteration, x (l) represents the estimated value of the first round in the iterative process, x(l+1) Represents the l+1th round estimate in the iterative process.

[0226] S3. Obtaining a total square error of each node state estimate based on the linearized relationship and the state estimate value, and determining a measurement configuration scheme for the power system based on the total square error of each node state estimate, specifically comprising:

[0227] S31, determining a theoretical value of the variance of the load estimate value of each node in the substation according to the linearized relationship and the state estimate value;

[0228] Specifically, after the state estimation is performed for each case, the above Q=E(ΔzΔz T )=H(H T R -1 H) -1 H T Obtain the theoretical value of the variance of the load estimate of each node in the substation area;

[0229] S32. Calculate the expected value of the total square error of the state estimation of each node in the substation area based on the theoretical value of the variance of the load estimation value of each node in the substation area, specifically:

[0230]

[0231] Where Δz i represents the measurement deviation of the i-th quantity, Q ii Represents the diagonal elements in row i.

[0232] S33, determining a measurement configuration scheme for the power system according to an expected value of a total square error of the state estimation of each node in the substation area;

[0233] Specifically, if the power system is a single-feeder measurement configuration, the measurement device configuration positions are traversed until the measurement device configuration position with the smallest expected value of the total square error of the node state estimation is selected, and the measurement configuration scheme of the power system is obtained based on the selected measurement device configuration position.

[0234] In an optional implementation, if the power system is configured with multi-feeder measurement, all possible measurement combinations are listed, the accuracy of each measurement combination is evaluated, and the measurement combination with the highest accuracy is determined.

[0235] The above method is applied in a specific example as follows:

[0236] The IEEE 14-node system is used to build the power distribution network. The network single-line diagram is as follows: Figure 3 As shown, Figure 3Where PQ is the generator PQ node, Bus is the bus, π is the line impedance parameter, and the node network parameters are shown in Table 1;

[0237] Table 1 IEEE14 node network parameters

[0238]

[0239]

[0240] (1) Configuring a measuring device on a single feeder branch

[0241] The network model built for this simulation has a total of 13 branches. The situations where branch PQ measurement devices are arranged on each branch are traversed, and state estimation is performed respectively to compare the errors of the load state estimation of the substation. Due to the large dispersion of the results, after the state estimation is performed for each case, the H matrix and R matrix obtained are directly calculated by Q=E(ΔzΔz T )=H(H T R -1 H) -1 H T Obtain the theoretical value of the variance of the load estimate of each node in the substation area;

[0242] Based on Calculate the expected value of the total square error of the state estimation of each node in the substation area;

[0243] The total square error of the estimated PQ values ​​in each area when the measurement device is deployed on each branch is shown in Table 2;

[0244] Table 2 Total square error of PQ value estimation in each area

[0245]

[0246]

[0247] Several typical measurements are used as examples to demonstrate the specific impact of feeder branch measurements on the load node errors in each substation. Figure 4 As shown;

[0248] From Table 2 and Figure 4 It can be found that on branches equidistant from the terminal (measured by the number of nodes reaching the terminal), such as branches 2, 11, 10, and 4 connected to the terminal, the heavier the flow load, the more significant the accuracy improvement brought by configuring the measurement device on this branch. Under the same load, for example, on branches 2, 5, and 7 located on the same line, the load level even gradually decreases from the terminal. The closer the distance to the terminal, the more significant the accuracy improvement brought by configuring the measurement device on this branch.

[0249] Regardless of the configuration scheme adopted, the accuracy of the state estimation of each node is no worse than that without feeder measurement. Figure 4 By comparing the electrical connection locations of the corresponding branches, it can be found that configuring branch measurement devices primarily improves the accuracy of adjacent nodes with close electrical connections. For example, the measurement device on branch 2 connected to node 9 significantly reduces the state estimation error of node 9, making it the measurement configuration with the greatest overall improvement in accuracy.

[0250] Looking at the corresponding rows of the Jacobian matrix H of branch 2 and node 9, we can see

[0251] The two rows are highly parallel and electrically closely connected, which can be approximated as a direct improvement in the measurement accuracy of node 9 (from a 10% error in substation measurements to approximately a 1% error in feeder measurements). In contrast, the Jacobian matrix rows of branch 5 and node 9, which have a weaker electrical connection, are significantly less parallel than those of branch 2. Therefore, the improvement in accuracy of branch 9's state estimation is less than that achieved by placing a measurement device on branch 2.

[0252] (2) Configuring measurement devices on multiple feeder branches

[0253] To verify the configuration method of the measurement combination proposed in the mathematical analysis, all cases of selecting two configuration measurement devices on all branches were traversed, and the solution with the greatest accuracy improvement was selected. The simulation results of the combinations with the highest accuracy are shown in Table 3.

[0254] Table 3: The most accurate measurement configuration combination

[0255]

[0256] Figure 5 Several typical combinations are shown to demonstrate their error distribution across various substations. Combined with Table 3, we can see that combinations 1 and 3 validate two different configuration strategies. Combination 1 achieves an overall improvement in measurement accuracy for heavily loaded areas by combining measurements on adjacent lines. Combination 3 deploys measurements at the ends of two heavily loaded lines, maximizing the estimation accuracy of single-load nodes. Repeated measurements on the same branch, however, yield more limited accuracy improvements. Even for the branch connected to the most heavily loaded node, node 8, the accuracy of repeated measurements ranks only 21st.

[0257] Based on the basic theory and network structure of least squares state estimation, the present invention establishes a linear relationship between feeder measurement configuration and state estimation error, obtains a consistent expression of information such as configuration measurement, measurement configuration location, and measurement accuracy, and uses measurement information state pre-estimation to obtain the approximate operating point of the system, thereby obtaining the error relationship matrix under different measurement configurations, and then calculating the total state estimation error under the configuration scheme, thereby realizing the optimized configuration of distribution network feeder measurement, effectively considering the state estimation accuracy problem of the distribution network, reducing the distribution network state estimation error, and improving the accuracy of dispatching control.

[0258] Example 2

[0259] Please refer to Figure 2 , a distribution network feeder measurement configuration device of this embodiment includes:

[0260] A data processing module is used to establish a linear relationship between the feeder measurement configuration and the state estimation error of the power system based on the least squares state estimation;

[0261] A state estimation module, configured to perform state estimation using measurement information to obtain a state estimation value of the power system;

[0262] A scheme determination module is used to obtain a total square error of each node state estimate based on the linearized relationship and the state estimation value, and determine a measurement configuration scheme of the power system based on the total square error of each node state estimate.

[0263] In an optional embodiment, the data processing module is further configured to:

[0264] Determine the voltage amplitude and phase angle of each node in the power system as state quantities;

[0265] According to the linearization of the state quantity near the state quantity working point, a linear relationship between the quantity measurement and the state quantity is obtained;

[0266] Determining a state estimation error between an estimated value of the state quantity and a true value of the state quantity according to a least squares state estimation method based on the linear relationship;

[0267] Calculating a measurement deviation between an estimated value of the quantity measurement and a true value of the quantity measurement according to the state estimation error;

[0268] An error level of the quantity measurement is determined according to the measurement deviation, where the error level of the quantity measurement is a linearized relationship between a feeder measurement configuration and a state estimation error of the power system.

[0269] In an optional embodiment, the state estimation module is further configured to:

[0270] Determining a calculated value of the quantity measurement using Kirchhoff's law based on the state quantity and the admittance matrix;

[0271] determining a residual between a calculated value and a measured value of the quantity measurement, and obtaining a residual vector based on the residual;

[0272] Establishing an objective function based on the residual vector;

[0273] An estimated value of the state of the power system is calculated using an iterative algorithm according to the objective function.

[0274] In an optional implementation, the solution determination module is further configured to:

[0275] Determine the theoretical value of the variance of the load estimate value of each node in the substation according to the linearized relationship and the state estimate value;

[0276] Calculate the expected value of the total square error of the state estimation of each node in the substation area based on the theoretical value of the variance of the load estimation value of each node in the substation area;

[0277] A measurement configuration scheme for the power system is determined according to an expected value of a total square error of the state estimation of each node in the substation area.

[0278] In an optional embodiment, the scheme determination module is further configured to determine the measurement configuration scheme of the power system according to the expected value of the total square error of the state estimation of each node in the substation area, including:

[0279] If the power system is a single-feeder measurement configuration, the measurement device configuration positions are traversed until the measurement device configuration position with the smallest expected value of the total square error of the node state estimation is selected, and the measurement configuration scheme of the power system is obtained based on the selected measurement device configuration position.

[0280] In an optional embodiment, the system further includes a data updating module for:

[0281] Get newly added repeated measurements;

[0282] updating the error level of the quantity measurement according to the newly added repeated quantity measurement to obtain an updated error level of the quantity measurement;

[0283] The error level of the updated quantity measurement is:

[0284]

[0285] q aa =Q (a) ,ii;

[0286] Where Q (a),kk represents the variance matrix of the updated measurement value, Qii represents the diagonal element of row i, Q kk represents the k-th row diagonal element, R a represents the variance matrix of the updated quantity measurement, Q ki Represents the element in row k and column i.

[0287] In summary, the present invention provides a distribution network feeder measurement configuration method and device, which establishes a linearized relationship between the feeder measurement configuration and the state estimation error of the power system based on the least squares state estimation; uses measurement information to perform state estimation to obtain the state estimation value of the power system; obtains the total square error of the state estimation of each node based on the linearized relationship and the state estimation value, and determines the measurement configuration scheme of the power system based on the total square error of the state estimation of each node. By establishing a linearized relationship between the feeder measurement configuration and the state estimation error of the power system based on the least squares state estimation, a consistent expression of information such as whether measurement is configured, the measurement configuration location, and the measurement accuracy can be obtained, thereby realizing the optimized configuration of the distribution network feeder measurement, effectively taking into account the state estimation accuracy problem of the distribution network, reducing the distribution network state estimation error, and improving the accuracy of dispatching control, thereby improving the efficiency and accuracy of the measurement configuration.

[0288] It will be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0289] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0290] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0291] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0292] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0293] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A distribution network feeder measurement configuration method, characterized in that: Including steps: Based on the least squares state estimation, a linear relationship between the feeder measurement configuration and the state estimation error of the power system is established; Using the measurement information to perform state estimation to obtain a state estimation value of the power system; Obtaining a total square error of each node state estimate based on the linearized relationship and the state estimate value, and determining a measurement configuration scheme of the power system based on the total square error of each node state estimate; The method of establishing a linear relationship between feeder measurement configuration and state estimation error of the power system based on the least squares state estimation includes: Determine the voltage amplitude and phase angle of each node in the power system as state quantities; According to the linearization of the state quantity near the state quantity working point, a linear relationship between the quantity measurement and the state quantity is obtained; Determining a state estimation error between an estimated value of the state quantity and a true value of the state quantity according to a least squares state estimation method based on the linear relationship; Calculating a measurement deviation between an estimated value of the quantity measurement and a true value of the quantity measurement according to the state estimation error; determining an error level of the quantity measurement based on the measurement deviation, wherein the error level of the quantity measurement is a linearized relationship between a feeder measurement configuration and a state estimation error of the power system; Obtaining a total square error of each node state estimate according to the linearized relationship and the state estimate value, and determining a measurement configuration scheme of the power system according to the total square error of each node state estimate includes: Determine the theoretical value of the variance of the load estimate value of each node in the substation according to the linearized relationship and the state estimate value; Calculate the expected value of the total square error of the state estimation of each node in the substation area based on the theoretical value of the variance of the load estimation value of each node in the substation area; A measurement configuration scheme for the power system is determined according to an expected value of a total square error of the state estimation of each node in the substation area.

2. A distribution network feeder measurement configuration method according to claim 1, characterized in that: The linear relationship between the quantity measurement and the state quantity is: ； Where, represents the quantity measured, represents the state quantity, represents the measurement error of each quantity, H represents the Jacobian matrix; Determining a state estimation error between an estimated value of the state quantity and a true value of the state quantity according to a least squares state estimation method based on the linear relationship includes: ； Where, represents the true value of the state quantity, represents the estimated value of the state quantity, and R represents the quantity measurement variance matrix; Calculating a measurement deviation between an estimated value of the quantity measurement and a true value of the quantity measurement according to the state estimation error includes: ； Where, Indicates measurement deviation; Determining the error level of the measurement according to the measurement deviation includes: ； Where Q represents the error level of the measurement, and E() represents the expected value.

3. A distribution network feeder measurement configuration method according to claim 1, characterized in that: The using the measurement information to perform state estimation to obtain the state estimation value of the power system includes: Determining a calculated value of the quantity measurement using Kirchhoff's law based on the state quantity and the admittance matrix; determining a residual between a calculated value and a measured value of the quantity measurement, and obtaining a residual vector based on the residual; Establishing an objective function based on the residual vector; An estimated value of the state of the power system is calculated using an iterative algorithm according to the objective function.

4. A distribution network feeder measurement configuration method according to claim 3, characterized in that: Determining the calculated value of the quantity measurement using Kirchhoff's law according to the state quantity and the admittance matrix includes: ； Wherein, h(x) represents the calculated value of the quantity measured; The obtaining of the residual vector according to the residual comprises: ； Where z represents the measured value, r(x) represents the residual vector; The establishing of the objective function according to the residual vector comprises: ； In the formula, J(x) represents the objective function, R represents the measurement variance matrix, represents the i-th diagonal element of the R matrix, represents the measurement error of the i-th quantity, and m represents the matrix order; Calculating the state estimation value of the power system using an iterative algorithm according to the objective function includes: ； ； Where, represents the change in the estimated value of the first round during the iteration, represents the estimated value of the lth round in the iterative process, Represents the estimated value of the l+1th round in the iterative process.

5. A distribution network feeder measurement configuration method according to claim 1, characterized in that: The method of calculating the expected value of the total square error of the state estimation of each node in the substation area according to the theoretical value of the variance of the load estimation value of each node in the substation area includes: ； Where, represents the measurement deviation of the i-th quantity, Q ii Represents the difference of diagonal elements in row i.

6. A distribution network feeder measurement configuration method according to claim 1, characterized in that: The method of determining the measurement configuration scheme of the power system according to the expected value of the total square error of the state estimation of each node in the substation area includes: If the power system is a single-feeder measurement configuration, the measurement device configuration positions are traversed until the measurement device configuration position with the smallest expected value of the total square error of the node state estimation is selected, and the measurement configuration scheme of the power system is obtained based on the selected measurement device configuration position.

7. A distribution network feeder measurement configuration method according to claim 1, characterized in that: Also includes: Get newly added repeated measurements; updating the error level of the quantity measurement according to the newly added repeated quantity measurement to obtain an updated error level of the quantity measurement; The error level of the updated quantity measurement is: ； ； Where, represents the error level of the updated quantity measurement, Q ii represents the diagonal elements in row i, represents the k-th row diagonal element, represents the variance matrix of the updated quantity measurement, Q ki represents the element in row k and column i, , m represents the matrix order, q aa The single element in the lower right corner represents the error level of the updated quantity measurement, Q (a),ii Represents the error level value of the original i-th quantity measurement.

8. A distribution network feeder measurement configuration device, characterized in that: include: A data processing module is used to establish a linear relationship between the feeder measurement configuration and the state estimation error of the power system based on the least squares state estimation; A state estimation module, configured to perform state estimation using measurement information to obtain a state estimation value of the power system; a scheme determination module, configured to obtain a total square error of the state estimation of each node according to the linearized relationship and the state estimation value, and determine a measurement configuration scheme of the power system according to the total square error of the state estimation of each node; The method of establishing a linear relationship between feeder measurement configuration and state estimation error of the power system based on the least squares state estimation includes: Determine the voltage amplitude and phase angle of each node in the power system as state quantities; According to the linearization of the state quantity near the state quantity working point, a linear relationship between the quantity measurement and the state quantity is obtained; Determining a state estimation error between an estimated value of the state quantity and a true value of the state quantity according to a least squares state estimation method based on the linear relationship; Calculating a measurement deviation between an estimated value of the quantity measurement and a true value of the quantity measurement according to the state estimation error; determining an error level of the quantity measurement based on the measurement deviation, wherein the error level of the quantity measurement is a linearized relationship between a feeder measurement configuration and a state estimation error of the power system; Obtaining a total square error of each node state estimate according to the linearized relationship and the state estimate value, and determining a measurement configuration scheme of the power system according to the total square error of each node state estimate includes: Determine the theoretical value of the variance of the load estimate value of each node in the substation according to the linearized relationship and the state estimate value; Calculate the expected value of the total square error of the state estimation of each node in the substation area based on the theoretical value of the variance of the load estimation value of each node in the substation area; A measurement configuration scheme for the power system is determined according to an expected value of a total square error of the state estimation of each node in the substation area.

Images