A power system bad data identification method and system adaptive to multiple types of errors

Abstract

The present application relates to the technical field of power system state estimation, and in particular to a power system bad data identification method and system suitable for multiple types of errors, which comprises preliminary screening of measurement and topology bad data based on the Lagrange multiplier method, screening of suspicious measurement and topology bad data, avoiding low solving efficiency caused by excessive model integer variables, identifying measurement and topology errors based on a MINLP optimization model, realizing identification and correction of measurement and topology bad data, solving the limitations of traditional methods in robustness, and providing technical support for realizing power system multi-source data checking and repairing. The power system bad data identification method provided by the present application can handle measurement and topology errors, effectively avoiding the missed judgment and misjudgment problems that may be caused by traditional methods, correcting and retaining measurement and topology bad data, avoiding loss of system observability, and ensuring the safety and stability of power system operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system state estimation, in particular to a power system bad data identification method and system suitable for multiple types of errors. BACKGROUND

[0002] Due to the insufficient accuracy of measurement equipment meters, communication error codes and other reasons, the telemetry and remote signaling data of the power system may have bad data with large errors, which has a great impact on the accuracy of state estimation, and in serious cases, it may cause the state estimation to fail to converge. The traditional residual identification method identifies bad data based on statistical rules, and is invalid for lever measurement bad data. The optimization identification method has high identification accuracy, but due to the complex nonlinear characteristics of the model, the solving efficiency is a great challenge. Robust state estimation models such as SHGM estimation model and M estimation model can suppress the impact of lever measurement bad data to a certain extent, but cannot eliminate it, and do not have the ability to identify and correct measurement and reference data. For power systems with insufficient observability, the adaptability is insufficient.

[0003] In terms of MINLP problem solving algorithms, for a large number of repeated structures in constraints and objective functions, the traditional automatic differentiation processing is completed by traversing each node in the graph, and the derivative is propagated step by step along the edge. This method is low in computational efficiency. For the processing of integer variables, traditional MINLP solving methods usually rely on the relaxation of integer variables, but in many practical problems, functions are not defined or discontinuous at non-integer points, which makes these methods unable to be applied or difficult to converge.

[0004] Therefore, an effective power system bad data identification method suitable for multiple types of errors is needed, which simultaneously considers automatic differentiation processing to achieve fast solving, has data identification and correction capability for measurement and reference scenarios, and can improve the observability of the power system compared to the traditional "one-size-fits-all" identification and rejection. SUMMARY

[0005] The purpose of the present application is to provide a power system bad data identification method and system suitable for multiple types of errors, which solves the problem that telemetry and remote signaling data are stored in multiple types of business systems during the construction and development of the power system, which may lead to multiple types of data errors and is difficult to identify and correct.

[0006] The present application is achieved by the following technical solutions:

[0007] In a first aspect, the present application provides a power system bad data identification method suitable for multiple types of errors, comprising:

[0008] Obtaining measurement data, preprocessing all the measurement data based on a Lagrange multiplier to obtain a plurality of Lagrange multipliers corresponding to the measurement data, setting a judgment threshold, marking the measurement data with the Lagrange multiplier greater than the judgment threshold and storing in a target set;

[0009] An identification model of discrete multi-type measurement bad data is established, and the identification model for the target set is calculated based on an optimization solver to obtain a bad data identification result;

[0010] Based on the bad data identification result, a power system data correction model of measurement and inverse is established, the power system bad data in the bad data identification result is identified again, and the measurement and inverse of the power system data is corrected, and a bad data list is output.

[0011] Preferably, preprocessing all the measurement data based on a Lagrange multiplier comprises:

[0012] Setting an initial objective function and a constraint condition, the constraint condition comprising a measurement constraint, a structure constraint and an operation constraint;

[0013] Classifying the Lagrange multiplier, the measurement value, the measurement equation, the Jacobian matrix and the covariance matrix based on the constraint condition, and constructing a first Lagrange function based on the constraint condition and the initial objective function.

[0014] Preferably, the initial objective function comprises:

[0015]

[0016] In the formula, is an initial objective function output value, is a measurement residual, and is a covariance matrix of the measurement value and the prior state, and are state variable estimated value and prior value respectively;

[0017] The measurement constraint, the structure constraint and the operation constraint comprise:

[0018]

[0019] In the formula, is a measurement value, is a measurement equation, represents a zero injection power constraint, represents information provided by a switch state.

[0020] Preferably, the first Lagrange function based on the constraint condition and the initial objective function comprises:

[0021]

[0022] wherein, is a Lagrange function, , , are Lagrange multipliers corresponding to the measurement constraint, the structure constraint and the operation constraint respectively, is a set of Lagrange multipliers of all constraints, and T is a transpose.

[0023] Preferably, the establishing the identification model of the discrete multi-type measurement bad data comprises:

[0024]

[0025] wherein, is the number of suspicious measurements, is the number of non-suspicious measurements, is a 0 / 1 integer variable, is a constant, is a residual term of suspicious bad data, is a residual term of non-suspicious bad data, is a set of switch branches, is a node number i, is a node number j, is a binary variable, is a switch topology telecontrol state.

[0026] Preferably, the calculating the identification model for the target set based on the optimization solver comprises:

[0027] defining a second Lagrange function and a penalty function;

[0028] defining an iteration point, and constructing a trust region quadratic subproblem;

[0029] estimating a predicted improvement ratio, and updating a trust region radius according to the predicted improvement ratio;

[0030] updating the iteration point and a Hessian matrix, constructing a convergence condition, and judging whether the convergence condition is met;

[0031] if the convergence condition is met, terminating the iteration, and if the convergence condition is not met, redefining the iteration point, constructing the trust region quadratic subproblem, and continuing the iteration until the convergence condition is met.

[0032] Preferably, the estimating the predicted improvement ratio comprises:

[0033]

[0034] wherein, is the predicted improvement ratio, , are the values of the iteration points, denotes the penalty function at the point denotes the penalty function at the point where and denote the update steps for the continuous variable x and the integer variable y, respectively, denotes the actual improvement of the objective function value at the current solution after neglecting the integer variable y, denotes the predicted improvement of the objective function after considering the update steps, as follows:

[0035]

[0036]

[0037] where , is the first order derivative of the objective function at is the Hessian matrix approximation of the current solution, which is used to represent the quadratic approximation of the objective function, is a parameter to control the penalty strength, is the first order derivative of the constraint at .

[0038] Preferably, the constructing the convergence condition comprises:

[0039]

[0040] where is the convergence threshold, is the negative part of the constraint violation, defined as follows:

[0041]

[0042] where denotes the e-th constraint, is the number of equality constraints.

[0043] Preferably, the establishing the power system data correction model of the measurement mis-reconciliation comprises:

[0044]

[0045] where is a 0 / 1 integer variable to represent whether the measurement is mis-reconciled, is the i-th suspicious measurement bad data.

[0046] ​​In a second aspect, the application also discloses a power system bad data identification system suitable for multiple types of errors, characterized by being used for executing the power system bad data identification method suitable for multiple types of errors, and further comprising:

[0047] The bad data preliminary screening module is configured to acquire measurement data, pre-process all the measurement data based on Lagrange multipliers to obtain Lagrange multipliers corresponding to the measurement data, set a judgment threshold, mark the measurement data with the Lagrange multiplier greater than the judgment threshold, and store the measurement data in a target set;

[0048] The bad data identification module is configured to establish an identification model of discrete multiple types of measurement bad data, calculate the identification model for the target set based on an optimization solver, and obtain a bad data identification result.

[0049] The bad data correction module is configured to establish a power system data correction model of measurement inversion based on the bad data identification result, perform secondary identification on the power system bad data in the bad data identification result, correct the power system data of measurement inversion, and output a bad data list.

[0050] The technical scheme of the application has at least the following advantages and beneficial effects:

[0051] 1. The method provided by the application includes preliminary screening of measurement and topology bad data based on the Lagrange multiplier method, screening of suspicious measurement and topology bad data, avoidance of low solving efficiency caused by too many model integer variables, implementation of identification of measurement and topology errors based on a MINLP optimization model, and implementation of identification and correction of measurement inversion data on the basis of the above steps, solving the limitations of traditional methods in robustness, providing technical support for multi-source data checking and repair of a power system. The power system bad data identification method provided by the method can handle measurement and topology errors, effectively avoids the missed judgment and misjudgment problems that may be caused by traditional methods, corrects and retains bad data of measurement inversion, avoids loss of system observability, and ensures the safety and stability of the operation of the power system.

[0052] 2. The method uses more measurement data, comprehensively identifies and processes multiple types of errors such as measurement inversion, measurement errors and topology errors, has unified identification and processing capability, can greatly improve the quality of state estimation calculation results, the structure-aware automatic differentiation and trust region SQP method can greatly improve the solving efficiency of the algorithm, constructs and solves MIQP sub-problems at integer points, can process non-relaxable MINLP problems, and compared with traditional methods, the algorithm is more robust, has fewer function call times, and is suitable for engineering optimization problems containing integer variables. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be regarded as a limitation to the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0054] Figure 1 Flowchart for bad data identification of the present application;

[0055] Figure 2 Flowchart for structure-aware automatic differentiation of the present application;

[0056] Figure 3 Flowchart for solving process of MINLP problem of the present application. DETAILED DESCRIPTION

[0057] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0058] The independently described modules or sub-modules can be physically separated or not physically separated, can be software implemented or hardware implemented, and part of the modules or sub-modules can be implemented by software, the function of the part of the modules or sub-modules is called by the processor, and the other part of the modules or sub-modules is implemented by hardware, for example, by hardware circuit. In addition, part or all of the modules can be selected to achieve the purpose of the present application scheme according to actual needs.

[0059] Please refer to Figure 1 A bad data identification method for power system adaptive to multiple types of errors, comprising:

[0060] S101: Obtain measurement data, pre-process all the measurement data based on Lagrange multipliers to obtain a plurality of Lagrange multipliers corresponding to the measurement data, and set a judgment threshold, mark the measurement data with a Lagrange multiplier greater than the judgment threshold and store it in a target set;

[0061] The measurement data includes telemetering data and telecontrol data, the measurement and topology bad data are preliminarily screened, all the telemetering and telecontrol data sets are pre-processed based on the Lagrange multiplier method, and the telemetering and telecontrol data with a multiplier index exceeding the threshold are marked and stored in a suspicious target set.

[0062] Specifically, since the number of integer variables affects the solving efficiency of the MINLP model, when the system scale increases, the number of remote signaling and remote measurement also increases, and the solving time of the model is positively correlated with the number of 0-1 integer variables. Therefore, in step S101, the measurement and topology bad data are screened, wherein the topology bad data can be regarded as measurement data with a measurement value of 0, the measurement here is the set of measurement values and topology remote signaling data, the Lagrange multipliers of all measurements and topologies are calculated, the measurements exceeding the threshold are put into the suspicious measurement and topology set, and only the 0 / 1 integer variable is set for the measurements in the suspicious set, so that the number of 0 / 1 variables is reduced and the calculation efficiency is improved.

[0063] S102: An identification model of discrete multi-type bad data is established, and the identification model for the target set is calculated based on an optimization solver to obtain a bad data identification result.

[0064] S103: Based on the bad data identification result, a power system data correction model of measurement and topology is established, the power system bad data in the bad data identification result is identified again, the measurement and topology of the power system data are corrected, and a bad data list is output.

[0065] The method provided by the application includes preliminary screening of measurement and topology bad data based on the Lagrange multiplier method, screening of suspicious measurement and topology bad data, and avoiding low solving efficiency caused by too many integer variables of the model. Then, the identification of measurement and topology errors is realized based on the MINLP optimization model. Finally, based on the above steps, the identification and correction of measurement and topology data are realized, the limitations of traditional methods in robustness are solved, and technical support is provided for the realization of power system multi-source data checking and repair. The power system bad data identification method provided by the method can handle measurement and topology errors, effectively avoid the misjudgment and misjudgment problems that may be caused by traditional methods, correct and retain the bad data of measurement and topology, avoid the loss of system observability, and ensure the safety and stability of the power system operation.

[0066] In an example embodiment of the application, the pre-processing of all the measurement data based on the Lagrange multiplier includes:

[0067] S201: An initial objective function and a constraint condition are set, and the constraint condition includes a measurement constraint, a structure constraint and an operation constraint.

[0068] S202: The Lagrange multiplier, the measurement value, the measurement equation, the Jacobian matrix and the covariance matrix are classified based on the constraint condition, and a first Lagrange function based on the constraint condition and the initial objective function is constructed.

[0069] Specifically, the initial objective function includes:

[0070]

[0071] In the formula, The initial objective function output value, To measure the residual, and Let the covariance matrix be the measured values ​​and the prior state. and These are the estimated values ​​and prior values ​​of the state variables, respectively.

[0072] The measurement constraints, structural constraints, and operational constraints include:

[0073]

[0074] In the formula, For measurement value, For the measurement equation, Represents zero-injection power constraint. This represents the information provided by the switch status.

[0075] Secondly, based on the above three types of constraints, the Lagrange multipliers, measurements, measurement equations, Jacobian matrices, and covariance matrices are classified as follows:

[0076]

[0077] In the formula, For the set of all constrained Lagrange multipliers, , , These are the Lagrange multipliers corresponding to measurement constraints, structural constraints, and operational constraints, respectively. For Jacobian matrices, Let covariance be the set of all constraints. , , These are the covariance matrices corresponding to measurement constraints, structural constraints, and operational constraints, respectively. Specifically, constructing the first Lagrangian function based on the constraints and the initial objective function includes:

[0078]

[0079] In the formula, For Lagrange functions, Let T be a Lagrange multiplier and T be the transpose.

[0080] The KKT conditions for the objective function can be expressed as:

[0081]

[0082] In the formula, denotes the gradient of the Lagrangian function with respect to the variable x, i.e. the first order derivative of the variable x, is the inverse of the prior state covariance matrix.

[0083] The above equation can be equivalently written as:

[0084]

[0085] Substituting the above equation into the equation, we have:

[0086]

[0087] where, denotes the gradient of the Lagrangian function with respect to the variable , i.e. the first order derivative of the variable .

[0088] Therefore, the equality constraint can be rewritten as:

[0089]

[0090] i.e.

[0091]

[0092] Therefore, the first order necessary condition of the objective function can be expressed as:

[0093]

[0094] which can be further expressed in matrix form as:

[0095]

[0096] where, is the measurement value, and the vector is the Lagrange multiplier containing the measurement, operation and structural constraints, which can be used to detect and identify the inconsistency of the mathematical model under state estimation constraints. Since the method has different sensitivities to each parameter, the Lagrange multiplier needs to be normalized. Therefore, the normalized Lagrange multiplier is further defined as:

[0097]

[0098] where, is the diagonal element of the first row of the Lagrangian covariance matrix , is the normalized Lagrange multiplier of the i-th constraint, is the Lagrange multiplier of the i-th constraint, which can be solved by the following equation: ​

[0099]

[0100] In the formula, the dimensions of matrix , and are in one-to-one correspondence. , and are in one-to-one correspondence.

[0101] For the measurement and topology data, when bad data occurs, the NLM value related to the error information is obviously higher than the corresponding value of normal measurement, by setting a threshold, the measurement and topology values greater than the threshold are stored in the suspicious set; for the measurement and topology data whose NLM does not exceed the threshold, it is considered as "good data" or correct topology information, and the 0-1 integer variable is no longer set, thereby reducing the number of 0-1 variables, and finally realizing the improvement of the calculation efficiency.

[0102] In an example embodiment of the application, the Kirchhoff's current law (KCL) and the optimal state matching under the minimum cost of measurement and topology are considered, and a bad data identification model based on MINLP is constructed as follows:

[0103]

[0104] In the formula, is the number of measurements, is a 0 / 1 integer variable, is a constant, is a residual term of suspicious bad data, is a residual term of non-suspicious bad data, is a switch branch set, , are node numbers respectively, is a binary variable, is a switch topology telecontrol state.

[0105]

[0106] In the formula, is an equality constraint set, is an integer variable representing the quality of measurement data, and the value range is , represents bad data, represents good data, represents bad data, is a measurement value in the target set, is the expression of the i-th measurement equation, is the square of the i-th measurement standard deviation, is the square of the j-th measurement standard deviation, for a measurement value in a normal measurement set, for a jth measurement equation expression, for a current flowing into a node i, for a current amplitude variable of a branch ij, for a current of a branch ij, for a phase current of a branch ij, for a phase voltage of a node i, for a phase voltage of a node j, for a phase voltage of a node j, for a phase voltage of a node j, for an admittance of a branch ij, for a measurable set, for a normal measurement set.

[0107] One example embodiment of the present application, as shown in Figure 2 calculating, by the optimization solver, an identification model for the target set comprises:

[0108] The polynomial repetition processing method based on structure-aware automatic differentiation includes the most critical operation for nonlinear problems, which is to calculate the Jacobian matrix of the constraint and the Hessian matrix of the Lagrange function. When there are a large number of repeated structures in the constraints and the objective function in the problem, if the differential calculation is performed by traversing these structures, a large amount of computing power and time will be wasted.

[0109] To solve such problems, the constraints and the objective function can be grouped by structure before solving the problem. The hash value calculated by the hash algorithm is used to determine whether it is the same structure. If it is a hash value that has not been inserted before, it means a new structure, which is used as a representative structure of a group. The automatic differentiation calculation function after that is generated from it, avoiding repeated operations.

[0110] Traditional automatic differentiation processing is completed by traversing each node in the graph, and the derivative is propagated step by step along the edge. Structure-aware automatic differentiation tracks the execution process of reverse automatic differentiation in advance and generates compact binary code to directly calculate the derivative. This is much more efficient than directly traversing the calculation graph.

[0111] When the structure grouping is completed, the representative of each group is generated by generating a CppAD automatic differentiation function structure, and then the binary code of the automatic differentiation function and its pointer are generated by the just-in-time compilation technology and passed to the underlying solver for use. When processing the integer part, it is processed by relaxation and used as a continuous variable to perform automatic differentiation using CppAD, and then the branch and bound method is used to process and approximate the integer solution. Thus, the structure-aware automatic differentiation of the mixed integer nonlinear programming problem is completed.

[0112] The Mixed-Integer Trust Region SQP Method.

[0113] Consider the general optimization problem of minimizing an objective function under nonlinear equality and inequality constraints:

[0114]

[0115] where and are vectors representing continuous and integer variables, respectively. Assume that the problem functions and are continuously differentiable under all where is the lower bound of variable , is the upper bound of variable , is the lower bound of variable , is the upper bound of variable , is the number of equality constraints, is a real number space of dimension , is the dimension of continuous variable , is an integer space of dimension , denotes the dimension of integer variable ,

[0116] As shown in Figure 3 , the solution process for this MINLP problem is as follows:

[0117] Define the second Lagrangian function and penalty function

[0118] Define the Lagrangian function as:

[0119]

[0120] The penalty function is:

[0121]

[0122] where if , otherwise ; is the penalty factor, is the Lagrange multiplier corresponding to constraint , Lagrangian function, penalty function.

[0123] Construct a trust region quadratic subproblem, define the current point as Convert the problem into the following mixed integer quadratic programming problem:

[0124]

[0125]

[0126] where, is the continuous variable direction increment, is the integer variable increment, is the approximate Hessian matrix of the current Lagrangian, and are the trust region radii of the continuous variable and the integer variable respectively, denotes the variable perturbation, denotes the change of the integer variable .

[0127] Estimate the predicted improvement ratio:

[0128]

[0129] where, is the predicted improvement ratio, , are the values of the iteration point, denotes the penalty function at the point , denotes the penalty function at the point , where and denote the update steps of the continuous variable x and the integer variable y respectively, denotes the actual improvement of the objective function value at the current solution , ignoring the integer variable y, denotes the predicted improvement of the objective function after considering the update steps, as follows:

[0130]

[0131]

[0132] where, , is the first order derivative of the objective function at , is the Hessian matrix approximation of the current solution, used to represent the quadratic approximation of the objective function, is a parameter that controls the strength of the penalty. To constrain The first derivative of The lower bound of

[0133] The trust region radius is updated according to the value of :

[0134]

[0135] The trust radius is set to be the integer radius The lower bound of is set to prevent the step size from becoming too small and freezing the integer variable.

[0136] The iteration point and the Hessian matrix are updated:

[0137] If is good enough, accept:

[0138]

[0139] Update using BFGS for the next iteration:

[0140]

[0141] where is the update step size, is the first derivative of the updated Lagrangian function, is the first derivative of the updated Lagrangian function.

[0142] A convergence condition is constructed to determine whether to converge. If the convergence condition is met, the iteration is terminated. If the convergence condition is not met, the iteration point is redefined, the trust region quadratic subproblem is constructed, and the iteration continues until the convergence condition is met.

[0143] The construction of the convergence condition includes:

[0144]

[0145] where is the convergence threshold, is the negative part of the constraint violation, defined as follows:

[0146]

[0147] where represents the e-th constraint, is the number of equality constraints.

[0148] In an embodiment, based on the measurement and the topology identification result in step S102, the measurement bad data is identified again, the bad data of the measurement marker is screened, and correction is performed.

[0149] The power system data correction model of the measurement marker includes:

[0150]

[0151]

[0152]

[0153]

[0154] In the formula, is a measurement residual term corresponding to the second stage suspicious data, is the target set in step S101, is the non-bad data measurement after identification in step S102, the bad data of the measurement marker can be corrected through the above model calculation, and the power system robust state estimation calculation is realized, is a 0 / 1 integer variable representing whether the measurement marker is 0 / 1.

[0155] In summary, the main process of the application is as follows:

[0156] Based on the obtained original measurement information, including power, current, voltage measurement data and telemetry information.

[0157] Based on the Lagrange multiplier method, the suspicious measurement and topology data are screened.

[0158] A unified identification model of measurement and topology bad data is established, and efficient solution is realized based on structure perception automatic differentiation processing and mixed integer trust region SQP algorithm.

[0159] Based on the above process, a data correction model is established, and efficient solution is realized based on structure perception automatic differentiation processing and mixed integer trust region SQP algorithm.

[0160] The bad data list and global electrical quantity information are output.

[0161] The application also provides a power system bad data identification system suitable for multiple types of errors, which is used for executing the power system bad data identification method suitable for multiple types of errors, and further includes:

[0162] The bad data preliminary screening module is configured to obtain measurement data, pre-process all the measurement data based on the Lagrange multiplier, obtain a plurality of Lagrange multipliers corresponding to the measurement data, set a judgment threshold, mark the measurement data with the Lagrange multiplier greater than the judgment threshold, and store the measurement data in the target set.

[0163] The bad data identification module is configured to establish an identification model for discrete multi-type measurement bad data, calculate the identification model for the target set based on the optimization solver, and obtain the bad data identification results.

[0164] The bad data correction module is configured to establish a power system data correction model based on the bad data identification results, perform secondary identification of bad power system data in the bad data identification results, correct the power system data with reversed measurement standards, and output a bad data list.

[0165] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0166] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. This computer software product, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0167] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying bad data in a power system that adapts to multiple types of errors, characterized in that, include: Acquire measurement data, preprocess all the measurement data based on Lagrange multipliers to obtain several Lagrange multipliers corresponding to the measurement data, set a judgment threshold, mark the measurement data whose Lagrange multipliers are greater than the judgment threshold, and store them in the target set; Establish a discrete multi-type measurement bad data identification model, calculate the identification model for the target set based on the optimization solver, and obtain the bad data identification results; Based on the bad data identification results, a power system data correction model with reversed measurement standards is established. The bad data in the power system in the bad data identification results are re-identified, and the power system data with reversed measurement standards is corrected, and a bad data list is output. The preprocessing of all the measurement data based on Lagrange multipliers includes: Set the initial objective function and constraints, including measurement constraints, structural constraints, and operational constraints; Based on the constraints, the Lagrange multipliers, measurements, measurement equations, Jacobian matrix, and covariance matrix are classified, and a first Lagrange function based on the constraints and the initial objective function is constructed. The initial objective function includes: In the formula, The initial objective function output value, To measure the residual, and Let the covariance matrix be the measured values ​​and the prior state. and These are the estimated values ​​and prior values ​​of the state variables, respectively. Measurement constraints, structural constraints, and operational constraints include: In the formula, For measurement value, For the measurement equation, Represents zero-injection power constraint. Information provided by the switch status; The construction of the first Lagrange function based on constraints and an initial objective function includes: In the formula, For Lagrange functions, , , These are the Lagrange multipliers corresponding to measurement constraints, structural constraints, and operational constraints, respectively. Let T be the set of all constrained Lagrange multipliers, and let T be the transpose. The establishment of the identification model for discrete multi-type measurement bad data includes: In the formula, Number of suspicious measurements The number of non-suspicious measurements. An integer variable of 0 / 1 to characterize whether a measurement is suspicious. It is a constant. For residuals of suspected bad data, For residuals that are not suspected bad data, For the set of switch branches, and All are node numbers. For binary variables, This is the remote signaling status of the switch topology; The power system data correction model for establishing the measurement standard includes: In the formula, To characterize whether the measured value is an 0 / 1 integer variable with an inverted scale, This is the i-th suspicious measurement bad data.

2. The method for identifying bad data in a power system adaptable to multiple types of errors according to claim 1, characterized in that, The identification model for the target set calculated based on the optimization solver includes: Define the second Lagrange function and the penalty function; Define the iteration point and construct the trust region quadratic subproblem; Estimate the predicted improvement ratio and update the trust region radius based on the predicted improvement ratio; Update the iteration points and the Hessian matrix, construct the convergence condition, and determine whether convergence has occurred; If the convergence condition is met, the iteration terminates. If the convergence condition is not met, the iteration point is redefined, a trust region quadratic subproblem is constructed, and the iteration continues until the convergence condition is met.

3. The method for identifying bad data in a power system adaptable to multiple types of errors according to claim 2, characterized in that, The estimated prediction improvement rate includes: In the formula, To predict the improvement rate, , These are the values ​​at the iteration points, Indicates at point The penalty function below, Indicates at point The penalty function is given below, where and Let x and y represent the update step sizes for the continuous variable x and the integer variable y, respectively. Indicates the current solution Below, ignoring the integer variable y, the actual improvement in the objective function value is... The improvement in the objective function prediction after considering the updated step size is as follows: In the formula, , For the objective function in The first derivative below, This is the approximation of the Hessian matrix of the current solution, used to represent a second-order approximation of the objective function. Parameters for controlling the intensity of punishment, To constrain exist The first derivative below.

4. The method for identifying bad data in a power system adaptable to multiple types of errors according to claim 3, characterized in that, The convergence conditions include: In the formula, The convergence threshold, To constrain the negative portion of the violation, the following applies: In the formula, This represents the e-th constraint. The number of equality constraints.

5. A power system bad data identification system adaptable to multiple types of errors, characterized in that, A method for identifying bad data in a power system adaptable to multiple types of errors, as described in any one of claims 1-4, includes: The bad data screening module is configured to acquire measurement data, preprocess all the measurement data based on Lagrange multipliers to obtain several Lagrange multipliers corresponding to the measurement data, set a judgment threshold, mark the measurement data whose Lagrange multipliers are greater than the judgment threshold, and store them in the target set. The bad data identification module is configured to establish an identification model for discrete multi-type measurement bad data, calculate the identification model for the target set based on the optimization solver, and obtain the bad data identification results. The bad data correction module is configured to establish a power system data correction model based on the bad data identification results, perform secondary identification of bad power system data in the bad data identification results, correct the power system data with reversed measurement standards, and output a bad data list.

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