Distribution automation state estimation method based on branch power

Abstract

The invention relates to a power distribution automation state estimation method based on branch power, which comprises the following steps of: 1, aiming at sufficient measurement configuration of a power distribution network, performing one-time forward-backward substitution load flow calculation on the power distribution network by using node power measurement and power pseudo measurement by adopting the power distribution network state estimation method based on the branch power, performing state estimation by using equivalent power conversion after obtaining an initial voltage value; 2, aiming at relatively insufficient measurement configuration of the power distribution network, adopting a power distribution network state estimation method based on branch node power estimation and utilizing a forward / backward substitution estimation method based on single-branch WLS estimation to carry out state estimation on the power distribution network; the method has the advantages that the estimation accuracy is high, the observability is improved, the calculation complexity is effectively reduced, and the estimation precision is high.

Description

Technical Field

[0001] The present invention belongs to the technical field of power distribution networks, and in particular relates to a distribution automation state estimation method based on branch power. Background Art

[0002] In recent years, with the continuous improvement of the automation level of distribution systems, distribution network state estimation, as the basic module of distribution automation systems, has become increasingly important for the convergence, robustness and accuracy of its algorithms to complete advanced analysis, control and scheduling functions of distribution automation systems and distribution network management systems. Due to the significant differences between distribution networks and transmission networks in terms of structure and measurement configuration, such as: the distribution network is radial, three-phase unbalanced, measurement redundancy is low, and the line R / X is relatively large, directly applying the traditional transmission system state estimation method to the distribution network will result in problems such as numerical stability and unobservable information matrix, making it impossible to directly adopt the state estimation method suitable for the transmission network. Currently, there are mainly two methods based on measurement. Quantity transformation, methods using branch current, branch power, etc. as state quantities. These methods are for the entire distribution network in each iteration of estimation, but the correctness of the root node voltage will seriously affect the calculation results, and the existing methods cannot use voltage measurement, which affects the accuracy of the algorithm. In addition, the existing algorithms do not consider the situation of insufficient measurement configuration of the distribution system, and cannot use branch current amplitude measurement and node voltage amplitude measurement, which can easily lead to low credibility of state estimation results, or even unobservable state estimation calculations. Therefore, it is very necessary to provide a distribution automation state estimation method based on branch power with high estimation accuracy, improved observability, effective reduction of computational complexity, and high estimation accuracy. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a distribution automation state estimation method based on branch power, which has high estimation accuracy, improves observability, effectively reduces computational complexity and has high estimation precision.

[0004] The object of the present invention is achieved by: a distribution automation state estimation method based on branch power, the method comprising the following steps:

[0005] Step 1: In view of the sufficient measurement configuration of the distribution network, a distribution network state estimation method based on branch power is adopted. The node power measurement and power pseudo-measurement are used to perform a forward-backward power flow calculation on the distribution network. After obtaining the initial voltage value, the state estimation is performed using equivalent power conversion;

[0006] Step 2: In view of the relatively insufficient measurement configuration of the distribution network, a distribution network state estimation method based on branch node power estimation is adopted, and the forward / backward estimation method based on single-branch WLS estimation is used to estimate the state of the distribution network.

[0007] The step 1 specifically comprises the following steps:

[0008] Step 1.1: Measurement transformation;

[0009] Step 1.2: Measurement transformation model of distribution network system;

[0010] Step 1.3: Expression of distribution system measurement model in measurement transformation.

[0011] The measurement transformation in step 1.1 is specifically as follows: the mathematical expression of the measurement transformation is: z′=A(x)z+B(x)(1), where A(x) and B(x) are the measurement transformation coefficient matrix and vector respectively, both of which are functions of the state quantity; z′ is the new quantity measurement after the transformation, and the corresponding linear measurement function is: h′(x)=A′x+B′(2), where A′ and B′ are constant matrices and constant vectors respectively; the iterative formula for solving the state estimation problem after the measurement transformation is: (k+1) =(G′) -1 (A′) T W′[z′(x (k) )-B′](3), where G′=(A′) T W′A′ is the constant information matrix after measurement transformation; W′ is the weight matrix of equivalent measurement z′:

[0012] The measurement transformation model of the distribution network system in step 1.2 includes the following steps:

[0013] Step 1.21: Equivalent transformation of each measurement: including branch head end power measurement, branch end power measurement, load power measurement, root node injection measurement, current amplitude measurement and voltage amplitude measurement;

[0014] Step 1.22: Equivalent measurement function: including branch head end power equivalent measurement function, branch end power equivalent measurement function, load power equivalent measurement function, root node injection equivalent measurement function, current amplitude equivalent measurement function and voltage amplitude equivalent measurement function.

[0015] The expression of the distribution system measurement model in step 1.3 in measurement transformation is specifically: after the measurement transformation, the measurement functions are all linear functions of the state quantity, and the active and reactive quantities are decoupled: Combining (2), we know that A′ P , A′ Q It is a sparse constant coefficient matrix containing only 1 and -1. When active measurement and reactive measurement appear in pairs, we have: A′ P =A′ Q, the constant vector B′ is 0; combining equations (5)-(10) and observing equation (1), we can find that A(x) is a unit matrix. Therefore, the weight matrix W′ of the equivalent measurement in equation (4) is strictly equal to the weight matrix W of the actual measurement. W is divided into W P , W Q , then according to (3), the iterative formula for the equivalent transformation state estimation of the distribution system is obtained: Among them, the equivalent information matrix is:

[0016] The distribution network state estimation method based on branch estimation in step 2 specifically includes the following steps:

[0017] Step 2.1: Single branch state estimation;

[0018] Step 2.2: Distribution network structure numbering and calculation;

[0019] Step 2.3: Node power estimation;

[0020] Step 2.4: Selection of weights.

[0021] The single-branch state estimation in step 2.1 is specifically as follows: the state of the single-branch system consisting of the starting node i (as the reference node) and the terminal node j is estimated, and the elements of the measurement vector Z are V i ,V j ,P ij ,Q ij ,P ji ,Q ji ,P j ,Q j ,I ij ,I ji ,I j For some quantities in the equation, the weighted least squares WLS state estimation is the solution to the following problem: Where m is the number of measurements; w k is the weight of the kth quantity measurement; h k (x) is the measurement function corresponding to the kth measurement; x is the state vector; the solution of equation (20) is performed using the following iterative formula, with l representing the iteration number: In the formula, is the measurement Jacobian matrix; W∈R m×m is the diagonal matrix of measurement weight coefficients, with the diagonal elements being w k (k=1,2,...,m) composition; using As the convergence criterion; after obtaining the state value, the estimated power of the starting branch can be obtained by the following formula: If V i In the estimation, the state quantity is used. Replace Vi .

[0022] The node power estimation in step 2.3 is as follows: According to Kirchhoff's theorem, the injection / output power of any node in the distribution network should be balanced. For node i, the direction marked in the figure is the positive direction, then According to the principle of WLS state estimation, these quantities are estimated using the constraints of equation (24), that is, the following problem is solved: Where k = k1, k2, ..., k l ,j, the solution is: If there is no measurement P mij and Q mij , then in the first forward branch-by-branch estimation process, the node does not perform power estimation, but in subsequent estimations, the previous estimated value is used as a pseudo measurement to participate in the node power estimation; when estimating branch i, branches k1 to k l The estimated power of the branch start point has been estimated. When estimating the power of node i, the estimated start point power is used. Instead of the corresponding branches k1 to k in equations (27) and (28), l ; however, if the estimated starting power of each branch has a large deviation from the measured power of the corresponding branch, the corresponding node active or reactive power estimation is not performed, and the sum of the subsequent branch starting power estimate and the load power of node i is directly used as the new measurement to participate in the estimation of branch i.

[0023] The selection of the weights in step 2.4 comprises the following steps:

[0024] Step 2.41: Selection of weights in node power estimation;

[0025] Step 2.42: Selection of weights in branch estimation.

[0026] Beneficial effects of the invention: The invention is a distribution automation state estimation method based on branch power, which can adapt to the distribution network with insufficient measurement redundancy and a small proportion of power measurement that is often encountered in actual distribution systems. In use, the invention aims at the problem of low measurement redundancy of the distribution network, and proposes to use node power measurement and power pseudo-measurement to perform a forward-backward flow calculation on the distribution network to obtain the initial value of the state estimation, and then use all measurement types to perform state estimation on the system through measurement transformation, that is, a method of using equivalent power transformation to perform state estimation after obtaining the initial voltage value. The method of the invention effectively overcomes the disadvantage that the traditional state estimation algorithm is difficult to use current measurement, and improves the observability and calculation accuracy of the state estimation; the invention fully utilizes Using the radiation characteristics of the distribution network and based on the current measurement configuration of the distribution network, a push-forward / back-substitution estimation method based on single-branch WLS estimation is used to estimate the state of the distribution network. In the push-forward branch-by-branch estimation process, the method of the present invention conditionally adds the terminal node power estimation before estimating the branch with subsequent branches, that is, using the node injection / outflow power balance constraint condition, estimates and corrects the terminal node power to improve the estimation accuracy as much as possible, and proposes certain principles through analysis to reasonably select the weights. The method of the present invention can use various available quantity measurements and is insensitive to r / x values. The push-forward / back-substitution process effectively reduces the complexity of calculation; the present invention has the advantages of high estimation accuracy, improved observability, effective reduction of calculation complexity, and high estimation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Schematic diagram of a typical radial distribution network of the present invention.

[0028] Figure 2 It is a schematic diagram of a single branch system of the present invention.

[0029] Figure 3 It is a schematic diagram of the radiation network and numbering of the present invention.

[0030] Figure 4 It is a schematic diagram of the local radiation network of the present invention. DETAILED DESCRIPTION

[0031] The present invention will be further described below in conjunction with the accompanying drawings.

[0032] Example 1

[0033] like Figure 1-4 As shown, a distribution automation state estimation method based on branch power comprises the following steps:

[0034] Step 1: In view of the sufficient measurement configuration of the distribution network, a distribution network state estimation method based on branch power is adopted. The node power measurement and power pseudo-measurement are used to perform a forward-backward power flow calculation on the distribution network. After obtaining the initial voltage value, the state estimation is performed using equivalent power conversion;

[0035] In this embodiment, step 1 specifically includes the following steps:

[0036] Step 1.1: Measurement transformation;

[0037] Specifically, the mathematical expression of the measurement transformation is: z′=A(x)z+B(x)(1), where A(x) and B(x) are the measurement transformation coefficient matrix and vector, respectively, and both are functions of the state quantity; z′ is the new quantity after the transformation, and the corresponding linear measurement function is: h′(x)=A′x+B′(2), where A′ and B′ are constant matrices and constant vectors, respectively; the iterative formula for solving the state estimation problem after the measurement transformation is: (k+1) =(G′) -1 (A′) T W′[z′(x (k) )-B′](3), where G′=(A′) T W′A′ is the constant information matrix after measurement transformation; W′ is the weight matrix of equivalent measurement z′:

[0038] Step 1.2: The measurement transformation model of the distribution network system includes the following steps:

[0039] Step 1.21: Equivalent transformation of each measurement: The typical radial structure of the distribution network is as follows: Figure 1 As shown, the measurement transformation is performed according to the actual measurement information, P ij , Q ij Indicates the power at the head end of the branch, P ji , Q ji Indicates the branch end power, P m , Q m Indicates actual quantity measurement; P me , Q me Indicates equivalent quantity measurement, with the direction indicated by the arrow in the figure as the positive direction, specifically: ① Branch head end power measurement: ②Power measurement at the end of branch: Among them, ΔP ij , ΔQ ij For line L ij The power loss is a nonlinear function of the state variables; ③ Load power measurement: ④Root node injection measurement: ⑤Current amplitude measurement: in, is the actual branch current amplitude measurement; I ij is the estimated value of the branch current amplitude obtained in the previous iteration; ⑥ Voltage amplitude measurement: in, is the actual node voltage amplitude measurement; V j is the estimated value of the node voltage amplitude obtained in the previous iteration;

[0040] Step 1.22: Equivalent measurement function, taking the branch head end power as the state quantity, the measurement function h′ of the equivalent measurement z′ after measurement transformation is specifically as follows: ① Branch head end power equivalent measurement function: ② Branch end power equivalent measurement function: ③ Load power equivalent measurement function: Among them, C j is the set of child nodes of node j; ④ The root node injects the equivalent measurement function: Among them, C r is the set of child nodes of node j; ⑤Equivalent measurement function of current amplitude: ⑥ Voltage amplitude equivalent measurement function:

[0041] Step 1.3: Expression of distribution system measurement model in measurement transformation.

[0042] Specifically, it can be seen from equations (11)-(16) that after the measurement transformation, the measurement functions are all linear functions of the state quantity, and the active and reactive quantities are decoupled: Combining (2), we know that A′ P , A′ Q It is a sparse constant coefficient matrix containing only 1 and -1. When active measurement and reactive measurement appear in pairs, we have: A′ P =A′ Q , the constant vector B′ is 0; combining equations (5)-(10) and observing equation (1), we can find that A(x) is a unit matrix. Therefore, the weight matrix W′ of the equivalent measurement in equation (4) is strictly equal to the weight matrix W of the actual measurement. W is divided into W P , W Q , then according to (3), the iterative formula for the equivalent transformation state estimation of the distribution system is obtained: Among them, the equivalent information matrix is:

[0043] In this embodiment, the algorithm flow of the embodiment of the present invention is as follows: (1) state estimation initialization: including network topology analysis; adding load pseudo measurement and zero virtual load measurement; forming a coefficient matrix A′ according to equations (11)-(16) P , A′ Q ; Joint weight matrix W P , WQ Calculate the information matrix G′ P =G′ Q And factorize it; iterative variable k = 0;

[0044] (2) Perform a forward-backward power flow iteration on all nodes based on the node load measurement and pseudo-measurement to obtain the initial values ​​of the voltage and state quantity of each node:

[0045] (3) From the state quantity Node voltage According to equations (5)-(10), we can transform z′ into P , z′ Q ;

[0046] (4) According to formula (18), the new branch head end power is obtained

[0047] (5) By Starting from the root node, the voltage of each node is obtained through a back-calculation;

[0048] (6) Determine the maximum value of the modulus component of the voltage difference between two adjacent iterations Is it less than the convergence precision? If so, stop the calculation; otherwise, k+1, jump to step (2).

[0049] Step 2: In view of the relatively insufficient measurement configuration of the distribution network, a distribution network state estimation method based on branch node power estimation is adopted, and the forward / backward estimation method based on single-branch WLS estimation is used to estimate the state of the distribution network.

[0050] The present invention is a distribution automation state estimation method based on branch power, which can adapt to the distribution network with insufficient measurement redundancy and a small proportion of power measurement that is often encountered in actual distribution systems. In use, the present invention aims at the problem of low measurement redundancy of the distribution network, proposes to use node power measurement and power pseudo-measurement to perform a forward-backward flow calculation on the distribution network to obtain the initial value of the state estimation, and then use all measurement types to perform state estimation on the system through measurement transformation, that is, a method of using equivalent power transformation to perform state estimation after obtaining the initial voltage value. The method of the present invention effectively overcomes the disadvantage that the traditional state estimation algorithm is difficult to use current measurement, and improves the observability and calculation accuracy of the state estimation; the present invention makes full use of the distribution network The method of the present invention has the characteristics of radiation, based on the current measurement configuration of the distribution network, and adopts the forward / back-substitution estimation method based on the single-branch WLS estimation to estimate the state of the distribution network. In the forward branch-by-branch estimation process, the terminal node power estimation is conditionally added before the estimation of the branch with subsequent branches, that is, the node injection / outflow power balance constraint is used to estimate and correct the terminal node power to improve the estimation accuracy as much as possible, and certain principles are proposed through analysis to reasonably select the weights. The method of the present invention can use various available quantity measurements and is insensitive to the r / x value. The forward / back-substitution process effectively reduces the complexity of the calculation; the present invention has the advantages of high estimation accuracy, improved observability, effective reduction of calculation complexity, and high estimation accuracy.

[0051] Example 2

[0052] like Figure 1-4 As shown, a distribution automation state estimation method based on branch power comprises the following steps:

[0053] Step 1: In view of the sufficient measurement configuration of the distribution network, a distribution network state estimation method based on branch power is adopted. The node power measurement and power pseudo-measurement are used to perform a forward-backward power flow calculation on the distribution network. After obtaining the initial voltage value, the state estimation is performed using equivalent power conversion;

[0054] Step 2: In view of the relatively insufficient measurement configuration of the distribution network, a distribution network state estimation method based on branch node power estimation is adopted, and the forward / backward estimation method based on single-branch WLS estimation is used to estimate the state of the distribution network.

[0055] In this embodiment, the distribution network state estimation method based on branch estimation specifically includes the following steps:

[0056] Step 2.1: Single branch state estimation;

[0057] Specifically: Figure 2 As shown in the figure, the state estimation is performed on a single-branch system consisting of the starting node i (as the reference node) and the terminal node j. The elements of the measurement vector Z are Vi ,V j ,P ij ,Q ij ,P ji ,Q ji ,P j ,Q j ,I ij ,I ji ,I j For some quantities in the equation, the weighted least squares WLS state estimation is the solution to the following problem: Where m is the number of measurements; w k is the weight of the kth quantity measurement; h k (x) is the measurement function corresponding to the kth measurement; x is the state vector, including V j ,δ j , the equivalent measurement is V i When m>2, V i , let the dimension of x be n, then n = 2 or 3; the solution of formula (20) is performed using the following iterative formula (l represents the iteration number): In the formula, is the measurement Jacobian matrix; W∈R m×m is the diagonal matrix of measurement weight coefficients, with the diagonal elements being w k (k=1,2,...,m) composition; using As the convergence criterion; it can be seen that H T (x)WH(x)∈R n×n , that is, R 2×2 or R 3×3 ; In practice, m is small, usually m < 10, so it becomes simple to perform the above iterative solution; after obtaining the state value, the estimated power of the starting branch can be obtained by the following formula: If V i In the estimation, the state quantity is used. Replace V i .

[0058] Step 2.2: Distribution network structure numbering and calculation;

[0059] Specifically: Figure 3The principle of numbering is: regard the radiation network as a tree, the root node (usually corresponding to the substation bus in the distribution network) is numbered 0, and then advance to the leaf node layer by layer, the node number increases in sequence, and the node number of the lower layer starts after all the nodes in each layer are numbered, until all the nodes are numbered; the branch number is the node number of the branch end (close to the node end); starting from the branch with the largest branch number, a single branch estimation is performed on each branch in descending order of branch number. For branches with subsequent branches (i.e. branches where non-leaf nodes are located), the estimated branch start power of all subsequent branches is used to estimate the node power of the branch end node before estimation, and then the estimated result is used as the quantity measurement to estimate the branch with the selected weight until the first branch is estimated, which is a forward branch-by-branch estimation process; then starting from the root node, the estimated root node voltage and the start power of each branch are used to back-substitute to obtain the voltage of each node, which is used as a back-substitution process; the forward / back-substitution process is repeated until the maximum voltage difference between two adjacent times is less than the given accuracy.

[0060] Step 2.3: Node power estimation;

[0061] Specifically: According to Kirchhoff's theorem, the injection / output power of any node in the distribution network should be balanced, such as Figure 4 For the local radiation network shown in the figure, for node i, the direction marked in the figure is the positive direction, then there should be According to the principle of WLS state estimation, these quantities are estimated using the constraints of equation (24), that is, the following problem is solved: Where k = k1, k2, ..., k l ,j, the solution is: If there is no measurement P mij and Q mij , then in the first forward branch-by-branch estimation process, the node does not perform power estimation, but in subsequent estimations, the previous estimated value is used as a pseudo measurement to participate in the node power estimation; when estimating branch i, branches k1 to k l The estimated power of the branch start point has been estimated. When estimating the power of node i, the estimated start point power is used. Instead of the corresponding branches k1 to k in equations (27) and (28), l However, if the estimated power at the beginning of each branch has a large deviation from the measured power at the beginning of the corresponding branch, or Consider branch k hThere may be bad data or too large estimation error in the measurement. In order to reduce its impact on other data, the corresponding node active or reactive power estimation is not performed, and the sum of the subsequent branch start power estimation value and the load power of node i is directly used as the new measurement to participate in the estimation of branch i.

[0062] Step 2.4: The selection of weights includes the following steps:

[0063] Step 2.41: Selection of weights in node power estimation;

[0064] Specifically: Figure 4 Taking the node i as an example, when estimating the node power, the branch k1 to k l The estimation has been completed, and the power at the beginning of their branch has been estimated. When participating in the node power estimation, it is considered that its accuracy is higher than other quantities because it has been estimated. This is equivalent to the effect of other quantity measurements in the branch being accumulated to the power at the beginning of the branch. Considering that in addition to active power, reactive power and voltage amplitude, there may also be current measurement in branch measurement, the node power estimation weight selection principle in equations (25) and (26) is given as follows: w pi 、w pij 、w qi 、w qij Take 1 for both.

[0065] Step 2.42: Selection of weights in branch estimation.

[0066] Specifically: for Figure 4 After the branch i shown in the figure estimates the power of node i, the sum of the initial power of each subsequent branch and the node load power is no longer used as the quantity measurement of the branch, that is, it is not allowed to participate in the branch estimation, but its effect is accumulated to the terminal power of branch i. At this time, the weights for estimating branch i are selected as follows: The other measurement weights are 1; in addition, the measurement weights of all the measurements of the branches where each leaf node is located are 1.

[0067] In this embodiment, the algorithm flow of the embodiment of the present invention is as follows: 1) Initialization, each node voltage is set to the root node voltage, the number of iterations k=0, the branch number i=n (n is the total number of branches);

[0068] 2) Determine whether branch i has subsequent branches (i.e., whether node i is a leaf node). If not, go to 6);

[0069] 3) Determine whether branch i has terminal power measurement, if yes, go to 5);

[0070] 4) If k=0, add the power at the beginning of the subsequent branch and the load power of node i as the pseudo measurement, and go to 6); if k>0, use the terminal power of branch i calculated last time as the pseudo measurement;

[0071] 5) Perform node power estimation according to the above method, and use the terminal power estimation value of branch i in the result as the measurement value, and do not use the sum of the subsequent branch start power estimation value and the node i load power estimation value as the measurement value, but add its effect to the branch terminal power through the adjustment of the weight to participate in the branch estimation;

[0072] 6) Select weights according to the above principles to perform branch estimation;

[0073] 7)i=i-1, if i>0i=i-1, go to 2);

[0074] 8) Using the estimated power at the beginning of each branch, the voltage of each node is calculated by back substitution from the root node.

[0075] 9) Determine the maximum value of the modulus component of the voltage difference between two adjacent iterations Is it less than the given convergence index ε? If so, stop the calculation; otherwise, k=k+1, i=n, ​​go to 2).

[0076] The present invention is a distribution automation state estimation method based on branch power, which can adapt to the distribution network with insufficient measurement redundancy and a small proportion of power measurement that is often encountered in actual distribution systems. In use, the present invention aims at the problem of low measurement redundancy of the distribution network, proposes to use node power measurement and power pseudo-measurement to perform a forward-backward flow calculation on the distribution network to obtain the initial value of the state estimation, and then use all measurement types to perform state estimation on the system through measurement transformation, that is, a method of using equivalent power transformation to perform state estimation after obtaining the initial voltage value. The method of the present invention effectively overcomes the disadvantage that the traditional state estimation algorithm is difficult to use current measurement, and improves the observability and calculation accuracy of the state estimation; the present invention makes full use of the distribution network The method of the present invention has the characteristics of radiation, based on the current measurement configuration of the distribution network, and adopts the forward / back-substitution estimation method based on the single-branch WLS estimation to estimate the state of the distribution network. In the forward branch-by-branch estimation process, the terminal node power estimation is conditionally added before the estimation of the branch with subsequent branches, that is, the node injection / outflow power balance constraint is used to estimate and correct the terminal node power to improve the estimation accuracy as much as possible, and certain principles are proposed through analysis to reasonably select the weights. The method of the present invention can use various available quantity measurements and is insensitive to the r / x value. The forward / back-substitution process effectively reduces the complexity of the calculation; the present invention has the advantages of high estimation accuracy, improved observability, effective reduction of calculation complexity, and high estimation accuracy.

Claims

1. A distribution automation state estimation method based on branch power, characterized in that: The method comprises the following steps: Step 1: In view of the sufficient measurement configuration of the distribution network, a distribution network state estimation method based on branch power is adopted. The node power measurement and power pseudo-measurement are used to perform a forward-backward power flow calculation on the distribution network. After obtaining the initial voltage value, the state estimation is performed using equivalent power conversion; Step 2: In view of the relatively insufficient measurement configuration of the distribution network, a distribution network state estimation method based on branch node power estimation is adopted, and the forward / backward estimation method based on single-branch WLS estimation is used to estimate the state of the distribution network.

2. A distribution automation state estimation method based on branch power as claimed in claim 1, characterized in that: The step 1 specifically comprises the following steps: Step 1.1: Measurement transformation; Step 1.2: Measurement transformation model of distribution network system; Step 1.3: Expression of distribution system measurement model in measurement transformation.

3. A distribution automation state estimation method based on branch power as claimed in claim 2, characterized in that: The measurement transformation in step 1.1 is specifically: the mathematical expression of the measurement transformation is: z′=A(x)z+B(x)(1), where A(x) and B(x) are the measurement transformation coefficient matrix and vector respectively, both of which are functions of the state quantity; z′ is the new quantity measurement after transformation, and the corresponding linear measurement function is: h′(x)=A′x+B′(2), where A′ and B′ are constant matrices and constant vectors respectively; the iterative formula for solving the state estimation problem after measurement transformation is: (k+1) =(G′) -1 (A′) T W′[z′(x (k) )-B′](3), where G′=(A′) T W′A′ is the constant information matrix after measurement transformation; W′ is the weight matrix of equivalent measurement z′:

4. A distribution automation state estimation method based on branch power as claimed in claim 2, characterized in that: The measurement transformation model of the distribution network system in step 1.2 includes the following steps: Step 1.21: Equivalent transformation of each measurement: including branch head end power measurement, branch end power measurement, load power measurement, root node injection measurement, current amplitude measurement and voltage amplitude measurement; Step 1.22: Equivalent measurement function: including branch head end power equivalent measurement function, branch end power equivalent measurement function, load power equivalent measurement function, root node injection equivalent measurement function, current amplitude equivalent measurement function and voltage amplitude equivalent measurement function.

5. A distribution automation state estimation method based on branch power as claimed in claim 2, characterized in that: The expression of the distribution system measurement model in step 1.3 in measurement transformation is specifically: after the measurement transformation, the measurement functions are all linear functions of the state quantity, and the active and reactive quantities are decoupled: Combining (2), we know that A′ P , A′ Q It is a sparse constant coefficient matrix containing only 1 and -1. When active measurement and reactive measurement appear in pairs, we have: P =A′ Q , the constant vector B′ is 0; combining equations (5)-(10) and observing equation (1), we can find that A(x) is a unit matrix. Therefore, the weight matrix W′ of the equivalent measurement in equation (4) is strictly equal to the weight matrix W of the actual measurement. W is divided into W P , W Q , then according to (3), the iterative formula for the equivalent transformation state estimation of the distribution system is obtained: Among them, the equivalent information matrix is:

6. A distribution automation state estimation method based on branch power according to claim 1, characterized in that: The distribution network state estimation method based on branch estimation in step 2 specifically includes the following steps: Step 2.1: Single branch state estimation; Step 2.2: Distribution network structure numbering and calculation; Step 2.3: Node power estimation; Step 2.4: Selection of weights.

7. A distribution automation state estimation method based on branch power as claimed in claim 6, characterized in that: The single-branch state estimation in step 2.1 is specifically as follows: the state of the single-branch system consisting of the starting node i (as the reference node) and the terminal node j is estimated, and the elements of the measurement vector Z are V i ,V j ,P ij ,Q ij ,P ji ,Q ji ,P j ,Q j ,I ij ,I ji ,I j For some quantities in the equation, the weighted least squares WLS state estimation is the solution to the following problem: Where m is the number of measurements; w k is the weight of the kth quantity measurement; h k (x) is the measurement function corresponding to the kth measurement; x is the state vector; the solution of equation (20) is performed using the following iterative formula, with l representing the iteration number: In the formula, is the measurement Jacobian matrix; W∈R m×m is the diagonal matrix of measurement weight coefficients, with the diagonal elements being w k (k=1,2,...,m) composition; using As the convergence criterion; after obtaining the state value, the estimated power of the starting branch can be obtained by the following formula: If V i In the estimation, the state quantity is used. Replace V i .

8. A distribution automation state estimation method based on branch power as claimed in claim 6, characterized in that: The node power estimation in step 2.3 is as follows: According to Kirchhoff's theorem, the injection / output power of any node in the distribution network should be balanced. For node i, the direction marked in the figure is the positive direction, then According to the principle of WLS state estimation, these quantities are estimated using the constraints of equation (24), that is, the following problem is solved: Where k = k1, k2, ..., k l ,j, the solution is: If there is no measurement P mij and Q mij , then in the first forward branch-by-branch estimation process, the node does not perform power estimation, but in subsequent estimations, the previous estimated value is used as a pseudo measurement to participate in the node power estimation; when estimating branch i, branches k1 to k l The estimated power of the branch start point has been estimated. When estimating the power of node i, the estimated start point power is used. Instead of the corresponding branches k1 to k in equations (27) and (28), l ; however, if the estimated starting power of each branch has a large deviation from the measured power of the corresponding branch, the corresponding node active or reactive power estimation is not performed, and the sum of the subsequent branch starting power estimate and the load power of node i is directly used as the new measurement to participate in the estimation of branch i.

9. A method for estimating distribution automation state based on branch power according to claim 6, characterized in that: The selection of the weights in step 2.4 comprises the following steps: Step 2.41: Selection of weights in node power estimation; Step 2.42: Selection of weights in branch estimation.

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