Weighted minimum absolute value robust state estimation method and device for power distribution network

Abstract

The invention relates to a weighted minimum absolute value robust state estimation method and device for a power distribution network, and the method comprises the steps: adding a slack variable to eliminate an absolute value in a target function, and obtaining a WLAV robust estimation model; based on the WLAV robust estimation model, constructing a Lagrange function corresponding to the state estimation model, and under the condition that the Lagrange function meets a preset minimum condition, performing Taylor expansion on a KKT condition of the Lagrange function to obtain a modified equation so as to obtain the state estimation model of the WLAV; and solving a robust state estimation result of the power distribution network by using a filter line search interior point method based on the state estimation model of the WLAV. Therefore, the problems of complex method model, low calculation efficiency and complex engineering application value caused by high calculation complexity and sensitive initial value in related technologies are solved.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network, and in particular to a weighted minimum absolute value robust state estimation method and device for a distribution network. Background Art

[0002] As the core of the Energy Management System (EMS), the distribution network state estimation obtains the optimal estimate of the state quantity by processing the raw data. The state estimation algorithm based on the traditional weighted least squares (WLS) is simple and fast, but this method is the optimal estimation under the assumption that the error data obeys the Gaussian distribution. However, the distribution of error data in the actual measurement system does not necessarily satisfy the Gaussian distribution, which will have a destructive effect on the state estimation results.

[0003] In the related art, one method is to design a robust estimator that can suppress the influence of bad data. The robust estimator has high robustness. When bad data appears in the measurement system, it can minimize its influence and obtain the optimal estimate of the state quantity. Weighted Least Absolute Value (WLAV) estimation is a widely studied robust estimation method in power system state estimation. In solving large-scale optimization problems, the interior point method is insensitive to initial values ​​and has good robustness, so it is widely used in power system optimization problems. Clements et al. proposed a WLAV state estimation solution based on the interior point method in 1991. In the WLAV robust estimation model, the absolute value in the objective function is eliminated by adding slack variables and constraints, thereby converting it into an optimization problem that can be solved using the interior point method.

[0004] However, in the related technologies, the traditional interior point method needs to calculate a large number of matrix operations and linear system solutions in each iteration step, which leads to high algorithm complexity. Especially for large-scale problems, the computational cost is very expensive. At the same time, the interior point method is sensitive to the selection of the initial point. Different initial points may lead to convergence to different local optimal solutions. Therefore, the initial point needs to be selected with caution. Summary of the invention

[0005] The present application provides a weighted minimum absolute value robust state estimation method and device for a distribution network to solve the problems in the related technology of high computational complexity and initial value sensitivity resulting in complex method models, low computational efficiency, and limited engineering application value.

[0006] The first aspect of the present application provides a weighted minimum absolute value robust state estimation method for a distribution network, comprising the following steps: adding slack variables to eliminate the absolute value in the objective function to obtain a WLAV robust estimation model; based on the WLAV robust estimation model, constructing a Lagrangian function corresponding to the state estimation model, and when the Lagrangian function satisfies a preset minimum condition, performing a KKT conditional Taylor expansion of the Lagrangian function to obtain a correction equation to obtain the WLAV state estimation model; based on the WLAV state estimation model, using a filter line search interior point method to solve the robust state estimation result of the distribution network.

[0007] Optionally, in one embodiment of the present application, the WLAV robust estimation model is:

[0008]

[0009] Among them, l and u are slack variables, w is the m-dimensional weight vector of the quantity measurement z, and w T is the transpose of w, z is the m (m>n) dimensional measurement vector, and h(x) is the nonlinear function vector.

[0010] Optionally, in one embodiment of the present application, the Lagrangian function is:

[0011] L=w T (+u)-η T (-h()+lu)-α T l-β T u,

[0012] Wherein, l and u are both slack variables, w is the m-dimensional weight vector of the quantity measurement z, and η, α, and β are all Lagrange multipliers.

[0013] Optionally, in one embodiment of the present application, the state estimation model of the WLAV is:

[0014]

[0015] Wherein, w is the m-dimensional weight vector of the quantity measurement z, and ε is the measurement error vector.

[0016] Optionally, in one embodiment of the present application, the state estimation model based on the WLAV uses the filter line search interior point method to solve the robust state estimation result of the distribution network, including: based on the primal-dual barrier algorithm, the obstacle parameter sequence that meets the preset decreasing condition converges to zero, and the primal-dual equation is solved by Newton's method to obtain a solution result; based on the solution result, the solution of the symmetric linear system that meets the preset conditions and the block rows that meet the preset elimination conditions are solved, and the solution and the block rows are derived from the primal-dual equation to obtain a derived result; the search direction and step size are calculated according to the derived result to obtain an iterative result, and whether the preset step size acceptance condition is met according to the test point and the iterative result; when the preset step size acceptance condition is met, a second-order correction is calculated, and a new corrected search direction is obtained according to the second-order correction, and the robust state estimation result of the distribution network is obtained according to the new corrected search direction.

[0017] The second aspect of the present application provides a weighted minimum absolute value robust state estimation device for a distribution network, including: an acquisition module, used to add slack variables to eliminate the absolute value in the objective function to obtain a WLAV robust estimation model; a construction module, used to construct a Lagrangian function corresponding to a state estimation model based on the WLAV robust estimation model, and when the Lagrangian function meets a preset minimum condition, perform a KKT conditional Taylor expansion of the Lagrangian function to obtain a correction equation to obtain the WLAV state estimation model; an estimation module, used to solve the robust state estimation result of the distribution network based on the WLAV state estimation model using a filter line search interior point method.

[0018] Optionally, in one embodiment of the present application, the WLAV robust estimation model is:

[0019]

[0020] Among them, l and u are slack variables, w is the m-dimensional weight vector of the quantity measurement z, and w T is the transpose of w, z is the m (m>n) dimensional measurement vector, and h(x) is the nonlinear function vector.

[0021] Optionally, in one embodiment of the present application, the Lagrangian function is:

[0022] L=w T (+u)-η T (-h()+lu)-α T l-β T u,

[0023] Wherein, l and u are both slack variables, w is the m-dimensional weight vector of the quantity measurement z, and η, α, and β are all Lagrange multipliers.

[0024] Optionally, in one embodiment of the present application, the state estimation model of the WLAV is:

[0025]

[0026] Wherein, w is the m-dimensional weight vector of the quantity measurement z, and ε is the measurement error vector.

[0027] Optionally, in one embodiment of the present application, the estimation module includes: a solution unit, which is used to converge the obstacle parameter sequence that satisfies the preset decreasing condition to zero based on the primal-dual barrier algorithm, and solve the primal-dual equation using Newton's method to obtain a solution result; a derivation unit, which is used to solve the solution of the symmetric linear system that satisfies the preset condition and eliminate the block rows that satisfy the preset elimination condition based on the solution result, and derive the solution and the block rows from the primal-dual equation to obtain a derivation result; a judgment unit, which is used to calculate the search direction and step size according to the derivation result, obtain an iterative result, and judge whether the preset step size acceptance condition is met according to the test point and the iterative result; an estimation unit, which is used to calculate the second-order correction when the preset step size acceptance condition is met, and obtain a new corrected search direction according to the second-order correction, and obtain the anti-difference state estimation result of the distribution network according to the new corrected search direction.

[0028] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the weighted minimum absolute value robust state estimation method for the distribution network as described in the above embodiment.

[0029] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the weighted minimum absolute value robust state estimation method for the distribution network as described above.

[0030] The fifth aspect of the present application provides a computer program product, which stores a computer program that, when executed by a processor, implements the weighted minimum absolute value robust state estimation method for a distribution network as described above.

[0031] The embodiment of the present application can improve the calculation speed of WLAV state estimation, use filter line search to correct obstacle parameters, improve the correction accuracy in the Newton direction, thereby reducing the number of iterations, and adopt a primal-dual interior point algorithm for nonlinear programming with filter line search method, including the feasibility recovery stage of the filtering method, the second-order correction and the inertia correction of the KKT matrix. Thus, the problems of high computational complexity, sensitivity to initial values, complex method models, low computational efficiency, and complex engineering application value are solved.

[0032] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0034] Figure 1 A flow chart of a weighted minimum absolute value robust state estimation method for a distribution network provided according to an embodiment of the present application;

[0035] Figure 2 It is a flow chart of a weighted minimum absolute value robust state estimation method for a distribution network according to an embodiment of the present application;

[0036] Figure 3 A schematic diagram of the structure of a weighted minimum absolute value robust state estimation device for a distribution network provided according to an embodiment of the present application;

[0037] Figure 4 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0038] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0039] The following describes the weighted minimum absolute value robust state estimation method and device for the distribution network of the embodiment of the present application with reference to the accompanying drawings. In view of the problem that the method model is complex and the computational efficiency is low due to the high computational complexity and initial value sensitivity in the related technology mentioned in the background technology center, which limits the complexity of its engineering application value, the present application provides a weighted minimum absolute value robust state estimation method for a distribution network, in which the calculation speed of WLAV state estimation can be improved, the obstacle parameters are corrected by filtering line search, the correction accuracy in the Newton direction is improved, thereby reducing the number of iterations, and a primal-dual interior point algorithm of nonlinear programming with filtering line search method is adopted, including the feasibility recovery stage of the filtering method, the second-order correction and the inertia correction of the KKT matrix. As a result, the problems that the method model is complex and the computational efficiency is low due to the high computational complexity and initial value sensitivity in the related technology, which limits the complexity of its engineering application value, are solved.

[0040] Specifically, Figure 1 A flow chart of a weighted minimum absolute value robust state estimation method for a distribution network provided in an embodiment of the present application.

[0041] like Figure 1 As shown, the weighted minimum absolute value robust state estimation method of the distribution network includes the following steps:

[0042] In step S101, slack variables are added to eliminate the absolute value in the objective function to obtain a WLAV robust estimation model.

[0043] In the actual implementation process, the embodiment of the present application can add relaxation variables l and u to eliminate the absolute value in the objective function and obtain a WLAV robust estimation model, thereby providing support for improving the calculation speed of WLAV state estimation.

[0044] In step S102, based on the WLAV robust estimation model, a Lagrangian function corresponding to the state estimation model is constructed, and when the Lagrangian function meets the preset minimum condition, the KKT conditional Taylor expansion of the Lagrangian function is performed to obtain a correction equation to obtain the WLAV state estimation model.

[0045] It can be understood that the preset minimum condition in the embodiment of the present application can be that the Lagrangian function L is minimum.

[0046] As a possible implementation method, the embodiment of the present application can construct a Lagrangian function corresponding to the state estimation model based on the WLAV robust estimation model, and when the Lagrangian function is minimum, perform the KKT conditional Taylor expansion of the Lagrangian function to obtain the correction equation, and finally complete the state estimation modeling of WLAV to obtain the state estimation model of WLAV.

[0047] Among them, the WLAV robust estimation model is:

[0048]

[0049] Among them, l and u are slack variables, w is the m-dimensional weight vector of the quantity measurement z, and w T is the transpose of w, z is the m (m>n) dimensional measurement vector, and h(x) is the nonlinear function vector.

[0050] Among them, the Lagrangian function is:

[0051] L=w T (+u)-η T (-h()+lu)-α T l-β T u,

[0052] Among them, l and u are slack variables, w is the m-dimensional weight vector of the quantity measurement z, and η, α, and β are all Lagrange multipliers.

[0053] Furthermore, when the Lagrangian function L is minimum, the KKT conditional Taylor expansion of the Lagrangian function L obtains the corrected equation:

[0054]

[0055] -Dη-Dα=-L i ,

[0056] Dη-Dβ=-L u ,

[0057]

[0058]

[0059] BDu+UDβ=-L β ,

[0060] in, The rest are deduced by analogy; μ is any positive number; A, B, L, U are diagonal matrices with α, β, l, u as diagonal elements, respectively. is the second-order gradient of h(x).

[0061] Among them, the state estimation model of WLAV is:

[0062]

[0063] Among them, w is the m-dimensional weight vector of the measurement z, and ε is the measurement error vector.

[0064] It should be noted that the preset minimum condition can be set by those skilled in the art according to actual conditions and is not specifically limited here.

[0065] In step S103, based on the WLAV state estimation model, the filter line search interior point method is used to solve the robust state estimation result of the distribution network.

[0066] It can be understood that the embodiments of the present application can be based on the state estimation model of WLAV, and the filtered line search interior point method for solving the nonlinear constrained optimization problem. The filtered line search interior point method can be used to transform the nonlinear constrained optimization problem with non-negative constraints into a barrier problem, and solve the robust state estimation result of the distribution network. The primal-dual interior point algorithm of nonlinear programming with the filtered line search method solves the problem of complex method model caused by high computational complexity and initial value sensitivity of the traditional interior point method.

[0067] Among them, in one embodiment of the present application, based on the WLAV state estimation model, the filter line search interior point method is used to solve the robust state estimation result of the distribution network, including: based on the primal-dual barrier algorithm, the obstacle parameter sequence that meets the preset decreasing condition converges to zero, and the primal-dual equation is solved by Newton's method to obtain a solution result; based on the solution result, the solution of the symmetric linear system that meets the preset conditions and the block rows that meet the preset elimination conditions are solved, and the solution and the block rows are derived from the primal-dual equation to obtain a derived result; the search direction and step size are calculated according to the derived result to obtain the iterative result, and whether the preset step size acceptance condition is met is judged according to the test point and the iterative result; when the preset step size acceptance condition is met, the second-order correction is calculated, and a new corrected search direction is obtained according to the second-order correction, and the robust state estimation result of the distribution network is obtained according to the new corrected search direction.

[0068] Specifically, the embodiments of the present application can study the primal-dual obstacle method for general nonlinear optimization problems:

[0069]

[0070] Among them, f(x) is the objective function, c(x) is the equality constraint, and x L 、x U are the upper and lower limits of the decision variable x.

[0071] Using the primal-dual barrier method, the above method can be simplified to:

[0072]

[0073] The proposed algorithm computes an (approximate) solution to the general obstacle problem:

[0074]

[0075] stc(x)=0,

[0076] For a decreasing barrier parameter sequence μ converges to zero. Again, this can be interpreted as the KKT condition for the primal-dual equation:

[0077]

[0078] Among them, λ is the Lagrange multiplier, e is the identity matrix, and μ is the obstacle parameter.

[0079] For a given fixed value μ j The obstacle problem is solved by Newton's method for the primal-dual equation:

[0080]

[0081] in, W k is the Hessen matrix, the Lagrangian function

[0082] This method does not solve the asymmetric linear system directly, but rather computes the solution equivalently by first solving a smaller symmetric linear system;

[0083]

[0084] and Derived from the primal-dual equations solved using Newton's method by eliminating the last block row. The vector have to:

[0085]

[0086] Calculate the search direction and step size So that the next iteration is:

[0087]

[0088] Furthermore, consider that during the backtracking line search, if the test point If it results in sufficient progress towards any one of the goals compared to the current iteration, then it is accepted:

[0089]

[0090] When θ k ≤θ min ,θ min ∈(0,∞] and satisfies:

[0091]

[0092] Where δ>0,s θ >1,

[0093] That is, the Armijo condition is satisfied and x k (α k, )>0,

[0094]

[0095] Accept step size α k, .

[0096] Furthermore, the embodiment of the present application may define a filter set The initial filter set is where θ max ∈(θ(x0),∞], if for the test point x k (α k, ) satisfies, then Therefore, the filter expansion criterion is expressed as:

[0097] and

[0098] Minimum step size:

[0099]

[0100] Furthermore, in the embodiment of the present application, the step length α can be tried in the first attempt. k, is rejected if θ(x k (α k, ))≥θ(x k ), then calculate the second-order correction (For step length satisfy:

[0101]

[0102] Get the new corrected search direction:

[0103]

[0104] The embodiment of the present application can transform the nonlinear constrained optimization problem with non-negative constraints into an obstacle problem according to the filter line search interior point method for solving the nonlinear constrained optimization problem, and then use Newton's method to solve this optimization problem to obtain the search direction. The objective function used in the corresponding filtering method is also transformed into the objective function of the obstacle function. Finally, the step length is corrected with a second-order correction step to obtain a corrected Newton step, thereby improving the correction accuracy in the Newton direction, thereby reducing the number of iterations, and solving the problem of complex method model caused by high computational complexity and initial value sensitivity of the traditional interior point method.

[0105] The specific implementation methods of this application are as follows:

[0106] Given the initial value (x0,λ0,z0), where the initial value of the barrier parameter μ0>0; set the constant:

[0107] s max ≥1; κ ∈ >0;

[0108] κ μ ∈(0,1);θ μ ∈(1,2);

[0109] τ min ∈(0,1);κ Σ >1;

[0110]

[0111] δ>0;γ α ∈(0,1);

[0112]

[0113] Step 1: Initialization, the inner iteration is k, the outer iteration is j, and the initial values ​​are all 0:

[0114]

[0115] τ0=max{τ min ,1-μ0},

[0116] Step 2: Check whether the entire problem converges. If the following equation is met, it converges and the iteration terminates:

[0117]

[0118] Step 3: Check whether the obstacle problem converges. Perform the following operations.

[0119] 3.1: Update μ, τ, and j to j+1 for the next iteration.

[0120] 3.2: Reinitialize the filter:

[0121]

[0122] 3.3: If k=0, repeat step 3, otherwise go to step 4.

[0123] Step 4: Calculate the search direction and calculate In this calculation, δ w ,δ c Take 0 first.

[0124] No. 5: Backtracking linear search.

[0125] 5.1: Initialize linear search, let

[0126]

[0127] 5.2: Calculate new test points,

[0128] 5.3: Check filter acceptability if If the test is rejected, proceed to step 5.5.

[0129] 5.4: Check if there is enough reduction relative to the current iteration.

[0130] The first case: θ(x k )≤θ min .

[0131] satisfy If satisfied Acceptance test step x k+1 :=x k (α k, ), go to step 6; otherwise, go to 5.5.

[0132] The second case: θ(x k )>θ min .

[0133] Dissatisfied If θ(x k (α k, ))≤(1-γ θ )θ(x k ), Acceptance test step x k+1 :=x k (α k, ), proceed to step 6; otherwise, proceed to step 5.5.

[0134] 5.5: Initialize the second-order correction.

[0135] 5.6: Compute the second-order correction.

[0136] 5.7: Check the acceptability of the filter (in SOC), if Reject the test step and select the test points.

[0137] 5.8: Check for sufficient reduction (in SOC) relative to the current iteration.

[0138] First case:

[0139] θ(x k)≤θ min ,satisfy If satisfied Acceptance test step x k+1 :=x k ( k,l ), go to step 6; otherwise, go to step 5.5.

[0140] Second case:

[0141] θ(x k )> min , not satisfied If θ(x k ( k,l ))≤(1-γ θ )( k ), Acceptance test step x k+1 :=x k ( k,l ), go to step 6; otherwise, go to step 5.5.

[0142] 5.9: Next second-order correction.

[0143] 5.10: Select test points.

[0144] No. 6: Accept the test step size, α k :=α k,l or α k :=α SOC ,

[0145]

[0146] Update using the above formula:

[0147]

[0148] Step 7: Add filters if necessary. If θ k ≤θ min ,θ min ∈(0,∞],

[0149] And meet or if the Armijo condition is satisfied and x k (α k, )>0, To α k,l If it does not hold, then use the added filter, otherwise keep the filter unchanged.

[0150] Step 8: Continue to the next iteration. Increase the iteration counter k=k+1 and return to step 2.

[0151] Step 9: Feasibility recovery phase. Using the enhancement filter, calculate the new iteration x by reducing the infeasibility measure θ(x) k+1 , so that x k+1 is acceptable for the enhancement filter, and the normal iterations in step 8 continue.

[0152] Specifically, it can be combined Figure 2 As shown, the working principle of the weighted minimum absolute value robust state estimation method for the distribution network in the embodiment of the present application is described in detail with a specific embodiment.

[0153] like Figure 2 As shown, the embodiment of the present application may include the following steps:

[0154] Step S201: state estimation modeling of WLAV.

[0155] Step S202: solving the state estimation based on the filter line search interior point method.

[0156] According to the weighted minimum absolute value robust state estimation method of the distribution network proposed in the embodiment of the present application, the calculation speed of the WLAV state estimation can be improved, the obstacle parameters are corrected by filtering line search, the correction accuracy in the Newton direction is improved, thereby reducing the number of iterations, and a primal-dual interior point algorithm of nonlinear programming with filtering line search method is adopted, including the feasibility recovery stage of the filtering method, the second-order correction and the inertia correction of the KKT matrix. Thus, the problem of high computational complexity and initial value sensitivity in the related technology, resulting in complex method models and low computational efficiency, which limits the complexity of its engineering application value, is solved.

[0157] Next, a weighted minimum absolute value robust state estimation device for a distribution network proposed according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0158] Figure 3 It is a block diagram of a weighted minimum absolute value robust state estimation device for a distribution network according to an embodiment of the present application.

[0159] like Figure 3 As shown, the weighted minimum absolute value robust state estimation device 10 of the distribution network includes: an acquisition module 100, a construction module 200 and an estimation module 300.

[0160] Specifically, the acquisition module 100 is used to add slack variables to eliminate the absolute value in the objective function to obtain the WLAV robust estimation model.

[0161] The construction module 200 is used to construct a Lagrangian function corresponding to the state estimation model based on the WLAV robust estimation model, and when the Lagrangian function meets the preset minimum condition, the KKT conditional Taylor expansion of the Lagrangian function is performed to obtain a correction equation to obtain the WLAV state estimation model.

[0162] The estimation module 300 is used to solve the robust state estimation result of the distribution network by using the filter line search interior point method based on the WLAV state estimation model.

[0163] Optionally, in one embodiment of the present application, the WLAV robust estimation model is:

[0164]

[0165] Among them, l and u are slack variables, w is the m-dimensional weight vector of the quantity measurement z, and w T is the transpose of w, z is the m (m>n) dimensional measurement vector, and h(x) is the nonlinear function vector.

[0166] Optionally, in one embodiment of the present application, the Lagrangian function is:

[0167] L=w T (+u)-η T (-h()+lu)-α T l-β T u,

[0168] Among them, l and u are slack variables, w is the m-dimensional weight vector of the quantity measurement z, and η, α, and β are all Lagrange multipliers.

[0169] Optionally, in one embodiment of the present application, the state estimation model of WLAV is:

[0170]

[0171] Among them, w is the m-dimensional weight vector of the measurement z, and ε is the measurement error vector.

[0172] Optionally, in one embodiment of the present application, the estimation module 300 includes: a solving unit, a deriving unit, a judging unit and an estimating unit.

[0173] Among them, the solving unit is used to converge the obstacle parameter sequence that meets the preset decreasing condition to zero based on the primal-dual obstacle algorithm, and solve the primal-dual equation using Newton's method to obtain the solution result.

[0174] The derivation unit is used to solve the solution of the symmetric linear system that meets the preset conditions and eliminate the block rows that meet the preset elimination conditions based on the solution results, and derive the solution and the block rows from the original-dual equation to obtain the derived results.

[0175] The judgment unit is used to calculate the search direction and step length according to the derived results, obtain the iterative results, and judge whether the preset step length acceptance condition is met according to the test points and the iterative results.

[0176] The estimation unit is used to calculate the second-order correction under the condition of satisfying the preset step acceptance condition, obtain a new correction search direction according to the second-order correction, and obtain the robustness state estimation result of the distribution network according to the new correction search direction.

[0177] It should be noted that the aforementioned explanation of the embodiment of the weighted minimum absolute value robust state estimation method for a distribution network is also applicable to the weighted minimum absolute value robust state estimation device for a distribution network of this embodiment, and will not be repeated here.

[0178] According to the weighted minimum absolute value robust state estimation device for the distribution network proposed in the embodiment of the present application, the calculation speed of the WLAV state estimation can be improved, the obstacle parameters are corrected by filtering line search, the correction accuracy in the Newton direction is improved, thereby reducing the number of iterations, and a primal-dual interior point algorithm of nonlinear programming with filtering line search method is adopted, including the feasibility recovery stage of the filtering method, the second-order correction and the inertia correction of the KKT matrix. Thus, the problem of high computational complexity and initial value sensitivity in the related technology, which leads to complex method models and low computational efficiency, and limits its complex engineering application value is solved.

[0179] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0180] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0181] When the processor 402 executes the program, the weighted minimum absolute value robust state estimation method for the distribution network provided in the above embodiment is implemented.

[0182] Furthermore, the electronic device further comprises:

[0183] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0184] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0185] The memory 401 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0186] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0187] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0188] The processor 402 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0189] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the weighted minimum absolute value robust state estimation method for a distribution network as described above.

[0190] An embodiment of the present application also provides a computer program product, on which a computer program is stored, and when the program is executed by a processor, the weighted minimum absolute value robust state estimation method for the distribution network as described above is implemented.

[0191] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0192] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0193] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0194] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0195] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0196] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0197] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0198] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A weighted minimum absolute value robust state estimation method for a distribution network, characterized in that: The following steps are involved: Add slack variables to eliminate the absolute value in the objective function and obtain the WLAV robust estimation model; Based on the WLAV robust estimation model, a Lagrangian function corresponding to the state estimation model is constructed, and when the Lagrangian function satisfies a preset minimum condition, a KKT conditional Taylor expansion of the Lagrangian function is performed to obtain a correction equation to obtain a state estimation model of the WLAV; Based on the WLAV state estimation model, the filter line search interior point method is used to solve the robust state estimation result of the distribution network.

2. The method according to claim 1, characterized in that The WLAV robust estimation model is: Among them, l and u are slack variables, w is the m-dimensional weight vector of the quantity measurement z, and w T is the transpose of w, z is the m (m>n) dimensional measurement vector, and h(x) is the nonlinear function vector.

3. The method according to claim 2, characterized in that The Lagrangian function is: L=w T (l+u)-η T (zh(x)+lu)-a T l-b T you, Wherein, l and u are both slack variables, w is the m-dimensional weight vector of the quantity measurement z, and η, α, and β are all Lagrange multipliers.

4. The method according to claim 3, characterized in that The state estimation model of the WLAV is: Wherein, w is the m-dimensional weight vector of the quantity measurement z, and ε is the measurement error vector.

5. The method according to claim 1, characterized in that The state estimation model based on the WLAV uses a filter line search interior point method to solve the robust state estimation result of the distribution network, including: Based on the primal-dual barrier algorithm, the barrier parameter sequence that meets the preset decreasing condition converges to zero, and the primal-dual equation is solved by Newton's method to obtain the solution result; Based on the solution result, solving the solution of the symmetric linear system that meets the preset conditions and eliminating the block rows that meet the preset elimination conditions, and deriving the solution and the block rows from the primal-dual equation to obtain a derived result; Calculating the search direction and step length according to the derived result to obtain an iterative result, and judging whether a preset step length acceptance condition is met according to the test point and the iterative result; When the preset step acceptance condition is met, a second-order correction is calculated, and a new correction search direction is obtained according to the second-order correction, and a robustness state estimation result of the distribution network is obtained according to the new correction search direction.

6. A weighted minimum absolute value robust state estimation device for a distribution network, characterized in that: include: The acquisition module is used to add slack variables to eliminate the absolute value in the objective function and obtain the WLAV robust estimation model; A construction module, for constructing a Lagrangian function corresponding to a state estimation model based on the WLAV robust estimation model, and performing a KKT conditional Taylor expansion of the Lagrangian function to obtain a correction equation when the Lagrangian function satisfies a preset minimum condition, so as to obtain a state estimation model of the WLAV; The estimation module is used to solve the robust state estimation result of the distribution network by using the filter line search interior point method based on the state estimation model of the WLAV.

7. The device according to claim 6, characterized in that The WLAV robust estimation model is: Among them, l and u are slack variables, w is the m-dimensional weight vector of the quantity measurement z, and w T is the transpose of w, z is the m (m>n) dimensional measurement vector, and h(x) is the nonlinear function vector.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the weighted minimum absolute value robust state estimation method for a distribution network as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the weighted minimum absolute value robust state estimation method for a distribution network as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the weighted minimum absolute value robust state estimation method for a distribution network as described in any one of claims 1 to 5.

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