PMU and PQMD hybrid harmonic state estimation method based on measurement data adaptive correction

Abstract

The invention provides a PMU and PQMD hybrid harmonic state estimation method based on measurement data adaptive correction, which comprises the following steps of: taking a PMU sampling moment as an estimation moment, introducing an optimization factor into a maximum value and a minimum value of PQMD statistical data, representing harmonic data of a PQMD measurement point as a bounded uncertainty variable, and constructing a PMU and PQMD hybrid harmonic measurement set. Establishing a hybrid harmonic state estimation model by taking the optimizing factor and the harmonic state quantity as optimization variables and taking the minimum standardized measurement residual sum of squares as an optimization target; solving a linear feasible region of the harmonic state quantity by adopting a linear programming algorithm based on iterative operation, and simplifying a harmonic state estimation model into a convex optimization model with a linear constraint condition; and solving the simplified harmonic state estimation model by adopting an interior point method, searching an optimal solution of an optimization target in an optimization variable feasible region, and adaptively correcting harmonic data of a PQMD measurement point by utilizing an optimization factor in the solving process so as to estimate a whole-network harmonic state quantity at a PMU sampling moment.

Description

Technical Field

[0001] The present invention belongs to the technical field of harmonic state estimation, and in particular relates to a PMU and PQMD hybrid harmonic state estimation method based on self-adaptive correction of measurement data. Background Art

[0002] With the large number of distributed energy sources and power electronic equipment connected to the grid, the harmonic pollution of the power system has become increasingly severe, showing a trend of decentralization and full grid. Harmonics in the power system will lead to a decline in power quality. In severe cases, it may cause system resonance and cause accidents such as burning of power grid equipment. In order to ensure the safe and stable operation of the power system, it is necessary to monitor and control harmonics. Harmonic state estimation (HSE) can estimate the harmonic state of the entire network based on limited measurement data, providing technical support for harmonic control.

[0003] Accurate harmonic state estimation is an important prerequisite for efficient and stable operation of power grids. The harmonic measurement data in current power systems mainly come from synchronous phasor measurement units (PMUs) and power quality monitoring devices (PQMDs). Among them, PMUs can provide high-quality synchronous measurement data for harmonic state estimation, but their high cost makes it difficult to meet the global observability of harmonic state estimation. PQMDs have lower costs and a wider deployment range, but they only record statistical data within the sampling period. How to effectively integrate the synchronous measurement data of PMUs and the statistical data of PQMDs to achieve accurate harmonic state estimation requires further research. Summary of the invention

[0004] Considering the global observability of the power grid is an important prerequisite for achieving harmonic state estimation. The current PMU is limited by factors such as technology and cost and cannot meet the global observability. Using the statistical data of PQMD as a data supplement source for PMU can meet the observability requirements of harmonic state estimation. However, statistical data does not have time-scale synchronization. Traditional state estimation methods are difficult to suppress the time-scale asynchronous deviation of statistical data, and directly applying it to harmonic state estimation cannot obtain accurate estimation results.

[0005] Therefore, the present invention proposes a PMU and PQMD hybrid harmonic state estimation method based on adaptive correction of measurement data.

[0006] In this plan:

[0007] (1) Taking the PMU sampling time as the estimation time, an optimization factor is introduced into the maximum and minimum values ​​of the PQMD statistical data, the harmonic data of the PQMD measurement point is characterized as a bounded uncertainty variable, and a hybrid harmonic measurement set of PMU and PQMD is constructed to achieve global observability of state estimation;

[0008] (2) The optimization factor and the harmonic state quantity are taken as optimization variables, the minimum normalized measurement residual sum of squares is taken as the optimization target, and the zero injection power constraint in the system is taken into account to establish a hybrid harmonic state estimation model. In order to ensure the global optimality of the model solution, a linear programming algorithm based on iterative operation is used to solve the linear feasible domain of the harmonic state quantity, and the harmonic state estimation model is simplified into a convex optimization model with linear constraints;

[0009] (3) The simplified harmonic state estimation model is solved by the interior point method to search for the optimal solution of the optimization objective within the feasible domain of the optimization variables. During the solution process, the harmonic data of the PQMD measurement points are adaptively corrected using the optimization factor, thereby accurately estimating the harmonic state of the entire network at the PMU sampling time.

[0010] The technical solution specifically adopted by the present invention to solve the technical problem is:

[0011] A hybrid harmonic state estimation method of PMU and PQMD based on adaptive correction of measurement data:

[0012] Taking the PMU sampling time as the estimation time, the optimization factor is introduced into the maximum and minimum values ​​of the PQMD statistical data, the harmonic data of the PQMD measurement point is characterized as a bounded uncertainty variable, and a hybrid harmonic measurement set of PMU and PQMD is constructed to achieve the global observability of state estimation.

[0013] Then, the optimization factor and the harmonic state quantity are taken as optimization variables together, the minimum normalized measurement residual sum of squares is taken as the optimization target, and the zero injection power constraint in the system is taken into account to establish a hybrid harmonic state estimation model; a linear programming algorithm based on iterative operation is used to solve the linear feasible domain of the harmonic state quantity, and the harmonic state estimation model is simplified into a convex optimization model with linear constraints;

[0014] Afterwards, the simplified harmonic state estimation model is solved by the interior point method to search for the optimal solution of the optimization objective within the feasible domain of the optimization variables. During the solution process, the optimization factor is used to adaptively correct the harmonic data of the PQMD measuring point to estimate the harmonic state of the entire network at the PMU sampling time.

[0015] Furthermore, the PMU measurement data Z1 at the estimation time is:

[0016]

[0017] Where: superscript h represents the harmonic order, subscript i and j represent the node number, They are the harmonic voltage amplitude and phase angle measurement data respectively. They are the branch harmonic current amplitude and phase angle measurement data respectively;

[0018] The minimum and maximum values ​​of the PQMD statistical data at the sampling period covering the estimated time are:

[0019]

[0020] Where: subscripts d and u represent the minimum and maximum values ​​respectively, Z 2,d , Z 2,u are the minimum and maximum values ​​of the PQMD statistical data in the sampling period covering the estimated time, They are the node harmonic active power and reactive power measurement data respectively. They are the branch harmonic active power and reactive power measurement data respectively;

[0021] Convert the PMU branch harmonic current measurement into branch harmonic power measurement:

[0022]

[0023] Where: They are the measured data of branch harmonic active power and reactive power after transformation respectively;

[0024] Thus, a hybrid harmonic measurement set of PMU and PQMD is constructed.

[0025] Furthermore, the construction method of the hybrid harmonic state estimation model is:

[0026] The optimization factor is introduced into the maximum and minimum values ​​of the statistical data, and the harmonic data of the PQMD measurement point is characterized as a bounded uncertainty variable:

[0027]

[0028] Where: Z2 is the harmonic data of the PQMD measurement point at the estimation time, λ is the optimization factor;

[0029] Construct a hybrid harmonic measurement set of PMU and PQMD:

[0030]

[0031] Where: Z is the mixed harmonic measurement set at the estimation time, Z 1eq is the transformed PMU harmonic measurement data;

[0032] Taking the minimum normalized measurement residual sum of squares as the optimization objective and taking into account the zero injection power constraint in the system, a hybrid harmonic state estimation model is established:

[0033]

[0034] Where: J(λ,x) is the optimization objective function, x is the harmonic state quantity, h(x) is the measurement function, σ is the standard deviation of the measurement data, h1(x), h2(x), and c(x) are the measurement functions corresponding to the PMU measurement data, PQMD measurement data, and zero injection power data, respectively.

[0035] Furthermore, the solution of the hybrid harmonic state estimation model includes the following steps:

[0036] First, set the initial feasible domain of the harmonic state quantity to x d(0) ≤x≤x u(0) , define the midpoint of the initial feasible region of the harmonic state quantity:

[0037]

[0038] Where: x m(0) is the midpoint of the initial feasible region of the harmonic state quantity, x d(0) 、x u(0) are the lower and upper bounds of the initial feasible region of the harmonic state quantity respectively;

[0039] In x m(0) Perform first-order Taylor expansion on the measurement functions of PMU measurement data, PQMD measurement data, and zero injection power data:

[0040]

[0041] Where: H1, H2, C are h1(x), h2(x), c(x) at x m(0) The Jacobian matrix at ;

[0042] Define the correction value Δx = xx m(0) , the harmonic state quantity is expressed as an uncertainty set with linear constraints as follows:

[0043]

[0044] Let ΔZ1=Z 1eq -h1(x m(0) ), ΔZ 2,d =Z 2,d -h2(x m(0) ), ΔZ 2,u =Z 2,u -h2(x m(0) ), ΔZ0=-c(x m(0)), then the linear feasible domain solution problem is transformed into the following two linear optimization problems:

[0045]

[0046]

[0047] Where: Δx i,d , Δx i,u They are the correction values ​​Δx i The lower and upper bounds of , n is the number of harmonic state quantities;

[0048] The linear programming algorithm is used to solve equations (10)-(11) to obtain the lower and upper bounds of the correction amount, and then the first approximate solution of the upper and lower bounds of the linear feasible domain is obtained:

[0049]

[0050] x d(1) and x u(1) Replace x d(0) and x u(0) , update to get x m(1) as follows:

[0051]

[0052] x m(1) Replace x m(0) A new round of iterative calculation is performed until convergence or the number of iterations reaches the set upper limit. The convergence conditions for the end of iteration are as follows:

[0053]

[0054] In the formula: || means taking the absolute value, k is the number of iterations, and h is the set convergence threshold;

[0055] After the iteration, the linear feasible domain x of the harmonic state quantity is output. d ≤x≤x u , the harmonic state estimation model is simplified to a convex optimization model with linear constraints as follows:

[0056]

[0057] Where: x m is the midpoint of the linear feasible region of the harmonic state quantity, H m is h(x) at x m The Jacobian matrix at ;

[0058] The interior point method is used to solve equation (15) to obtain the estimated result of the harmonic state quantity.

[0059] Furthermore, the process of hybrid harmonic state estimation includes the following steps:

[0060] S1: Taking the PMU sampling time as the estimation time, the measurement data of PMU and PQMD are selected according to equations (1) to (3);

[0061] S2: According to formula (4), the optimization factor is introduced to characterize the harmonic data of the PQMD measurement point as a bounded uncertainty variable;

[0062] S3: Construct the hybrid harmonic measurement set of PMU and PQMD according to formula (5);

[0063] S4: Establish a hybrid harmonic state estimation model according to formula (6);

[0064] S5: Establish an estimation model of the linear feasible domain of harmonic state quantity according to equations (7) to (11);

[0065] S6: Use the linear programming algorithm to iteratively solve the linear feasible domain of the harmonic state quantity, and obtain a new linear feasible domain according to formula (12);

[0066] S7: Set the convergence condition according to formula (14) to determine whether the convergence condition is met. If the convergence condition is met or the number of iterations reaches the set upper limit, the calculation is terminated and the linear feasible domain of the harmonic state quantity is output; otherwise, k=k+1 is set and the latest feasible domain is used instead of the initial feasible domain to re-execute steps S6-S7;

[0067] S8: According to formula (15), the state estimation model is simplified into a convex optimization model with linear constraints;

[0068] S9: Use the interior point method to solve equation (15) and output the harmonic state quantity of the entire network at the estimated time.

[0069] And, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of a PMU and PQMD hybrid harmonic state estimation method based on adaptive correction of measurement data as described above are implemented.

[0070] A non-transitory computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a PMU and PQMD hybrid harmonic state estimation method based on adaptive correction of measurement data as described above.

[0071] Compared with the prior art, the present invention and its preferred solution take the PMU sampling time as the estimation time, introduce the optimization factor into the statistical data of PQMD, characterize the harmonic data of the PQMD measurement point as a bounded uncertainty variable, and fuse it with the PMU measurement data to construct a hybrid harmonic measurement set. This method can meet the observability conditions of harmonic state estimation. Secondly, the optimization factor and the harmonic state quantity are used as optimization variables together, the minimum standardized measurement residual square sum is used as the optimization target, and the zero injection power constraint in the system is taken into account to establish a hybrid harmonic state estimation model. This method can adaptively correct the harmonic data of the PQMD measurement point through the optimization factor to improve the accuracy of the estimation result. Afterwards, a linear programming algorithm based on iterative operation is used to solve the linear feasible domain of the harmonic state quantity, and the harmonic state estimation model is simplified to a convex optimization model with linear constraints. This method can ensure the global optimality of the model solution. Finally, the simplified harmonic state estimation model is solved by the interior point method, and the optimal solution in the feasible domain of the optimization variables is searched under certain constraints, which can more accurately provide the harmonic state quantity of the entire network at the PMU sampling time. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0073] Figure 1 The figure is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0074] In order to make the features and advantages of this patent more obvious and easy to understand, the following embodiments are specifically described in detail as follows:

[0075] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0076] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0077] 1. Selection of PMU and PQMD measurement data

[0078] Select the PMU measurement data at the estimated time:

[0079]

[0080] Where: Z1 is the PMU harmonic measurement data at the estimation time, the superscript h represents the harmonic order, and the subscripts i and j represent the node numbers. They are the harmonic voltage amplitude and phase angle measurement data respectively. They are the branch harmonic current amplitude and phase angle measurement data respectively.

[0081] Select the minimum and maximum values ​​of the PQMD statistical data in the sampling period covering the estimated time:

[0082]

[0083] Where: subscripts d and u represent the minimum and maximum values ​​respectively, Z 2,d , Z 2,u are the minimum and maximum values ​​of the PQMD statistical data in the sampling period covering the estimated time, They are the node harmonic active power and reactive power measurement data respectively. They are the branch harmonic active power and reactive power measurement data respectively.

[0084] Convert the PMU branch harmonic current measurement into branch harmonic power measurement:

[0085]

[0086] Where: They are the transformed branch harmonic active power and reactive power measurement data respectively.

[0087] 2 Construction of hybrid harmonic state estimation model

[0088] The optimization factor is introduced into the maximum and minimum values ​​of the statistical data, and the harmonic data of the PQMD measurement point is characterized as a bounded uncertainty variable:

[0089]

[0090] Where: Z2 is the harmonic data of the PQMD measurement point at the estimation time, and λ is the optimization factor.

[0091] Construct a hybrid harmonic measurement set of PMU and PQMD:

[0092]

[0093] Where: Z is the mixed harmonic measurement set at the estimation time, Z 1eq is the transformed PMU harmonic measurement data.

[0094] Taking the minimum normalized measurement residual sum of squares as the optimization objective and taking into account the zero injection power constraint in the system, a hybrid harmonic state estimation model is established:

[0095]

[0096] Where: J(λ,x) is the optimization objective function, x is the harmonic state quantity, h(x) is the measurement function, σ is the standard deviation of the measurement data, h1(x), h2(x), c(x) are the measurement functions corresponding to PMU measurement data, PQMD measurement data, and zero injection power data, respectively. 3 Solution of hybrid harmonic state estimation model

[0097] First, set the initial feasible domain of the harmonic state quantity to x d(0) ≤x≤x u(0) , define the midpoint of the initial feasible region of the harmonic state quantity:

[0098]

[0099] Where: x m(0) is the midpoint of the initial feasible region of the harmonic state quantity, x d(0) 、x u(0) They are the lower and upper bounds of the initial feasible domain of the harmonic state quantity respectively.

[0100] In x m(0) Perform first-order Taylor expansion on the measurement functions of PMU measurement data, PQMD measurement data, and zero injection power data:

[0101]

[0102] Where: H1, H2, C are h1(x), h2(x), c(x) at x m(0) The Jacobian matrix at .

[0103] Define the correction value Δx = xx m(0) , the harmonic state quantity is expressed as an uncertainty set with linear constraints as follows:

[0104]

[0105] Let ΔZ1=Z 1eq -h1(x m(0) ), ΔZ 2,d =Z 2,d -h2(x m(0) ), ΔZ 2,u =Z 2,u -h2(x m(0) ), ΔZ0=-c(x m(0) ), then the linear feasible domain solution problem is transformed into the following two linear optimization problems:

[0106]

[0107]

[0108] Where: Δx i,d , Δx i,u They are the correction values ​​Δx i are the lower and upper bounds of , and n is the number of harmonic state quantities.

[0109] By using the linear programming algorithm to solve equations (10)-(11), we can obtain the lower and upper bounds of the correction amount, and then obtain the first approximate solution of the upper and lower bounds of the linear feasible domain:

[0110]

[0111] x d(1) and x u(1) Replace x d(0) and x u(0) , update to get x m(1) as follows:

[0112]

[0113] x m(1) Replace x m(0) A new round of iterative calculation is performed until convergence or the number of iterations reaches the set upper limit. The convergence conditions for the end of iteration are as follows:

[0114]

[0115] In the formula: || represents the absolute value, k is the number of iterations, and h is the set convergence threshold, which is 10 -5 .

[0116] After the iteration, the linear feasible domain x of the harmonic state quantity is output. d ≤x≤x u , the harmonic state estimation model is simplified to a convex optimization model with linear constraints as follows:

[0117]

[0118] Where: x m is the midpoint of the linear feasible region of the harmonic state quantity, H m is h(x) at x m The Jacobian matrix at .

[0119] The interior point method (IPM) is used to solve equation (15) and obtain the estimated results of the harmonic state quantity.

[0120] Based on the above design, Figure 1 As shown, the main process of PMU and PQMD hybrid harmonic state estimation based on adaptive correction of measurement data provided by the embodiment of the present invention is:

[0121] S1: Taking the PMU sampling time as the estimation time, the measurement data of PMU and PQMD are selected according to equations (1)-(3);

[0122] S2: According to formula (4), the optimization factor is introduced to characterize the harmonic data of the PQMD measurement point as a bounded uncertainty variable;

[0123] S3: Construct the hybrid harmonic measurement set of PMU and PQMD according to formula (5);

[0124] S4: Establish a hybrid harmonic state estimation model according to formula (6);

[0125] S5: Establish an estimation model of the linear feasible domain of harmonic state quantities according to equations (7)-(11);

[0126] S6: Use the linear programming algorithm to iteratively solve the linear feasible region of the harmonic state quantity, and obtain a new linear feasible region according to formula (12);

[0127] S7: Set the convergence condition according to formula (14) to determine whether the convergence condition is met. If the convergence condition is met or the number of iterations reaches the set upper limit, the calculation is terminated and the linear feasible domain of the harmonic state quantity is output; otherwise, k=k+1 is set and the latest feasible domain is used instead of the initial feasible domain to re-execute steps S6-S7;

[0128] S8: According to formula (15), the state estimation model is simplified into a convex optimization model with linear constraints;

[0129] S9: Use the interior point method to solve equation (15) and output the harmonic state quantity of the entire network at the estimated time.

[0130] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0131] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0134] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0135] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

[0136] This patent is not limited to the above-mentioned optimal implementation mode. Anyone can derive various other forms of a PMU and PQMD hybrid harmonic state estimation method based on adaptive correction of measurement data under the inspiration of this patent. All equal changes and modifications made according to the scope of the patent application of the present invention should be covered by this patent.

Claims

1. A hybrid harmonic state estimation method of PMU and PQMD based on adaptive correction of measurement data, characterized by: Taking the PMU sampling time as the estimation time, an optimization factor is introduced into the maximum and minimum values ​​of the PQMD statistical data, the harmonic data of the PQMD measurement point is represented as a bounded uncertainty variable, and a hybrid harmonic measurement set of PMU and PQMD is constructed to achieve global observability of state estimation. Then, the optimization factor and the harmonic state quantity are used as optimization variables, the minimum normalized measurement residual sum of squares is taken as the optimization objective, and the zero injection power constraint in the system is taken into account to establish a hybrid harmonic state estimation model. A linear programming algorithm based on iterative operation is used to solve the linear feasible domain of the harmonic state quantity, and the harmonic state estimation model is simplified into a convex optimization model with linear constraints. Afterwards, the interior point method is used to solve the simplified harmonic state estimation model, and the optimal solution of the optimization objective within the feasible domain of the optimization variables is searched. During the solution process, the harmonic data of the PQMD measurement points are adaptively corrected using the optimization factor to estimate the harmonic state of the entire network at the PMU sampling time.

2. The method for estimating harmonic state of a hybrid PMU and PQMD based on adaptive correction of measurement data according to claim 1, characterized in that: The PMU measurement data Z1 at the estimated time is: Where: superscript h represents the harmonic order, subscript i and j represent the node number, are the harmonic voltage amplitude and phase angle measurement data respectively, They are the branch harmonic current amplitude and phase angle measurement data respectively; The minimum and maximum values ​​of the PQMD statistical data in the sampling period covering the estimated time are: Where: subscripts d and u represent the minimum and maximum values ​​respectively, Z 2,d , Z 2,u are the minimum and maximum values ​​of the PQMD statistical data in the sampling period covering the estimated time, P i h 、 They are the node harmonic active power and reactive power measurement data respectively, They are the branch harmonic active power and reactive power measurement data respectively; Convert the PMU's branch harmonic current measurement to branch harmonic power measurement: Where: They are the transformed branch harmonic active power and reactive power measurement data respectively; Thus, a hybrid harmonic measurement set of PMU and PQMD is constructed.

3. The method for hybrid harmonic state estimation of PMU and PQMD based on adaptive correction of measurement data according to claim 2, characterized in that: The construction method of the hybrid harmonic state estimation model is: An optimization factor is introduced into the maximum and minimum values ​​of statistical data, and the harmonic data of the PQMD measurement point is characterized as a bounded uncertainty variable: Where: Z2 is the harmonic data of the PQMD measurement point at the estimation time, λ is the optimization factor; Construct a hybrid harmonic measurement set of PMU and PQMD: Where: Z is the mixed harmonic measurement set at the estimation time, Z 1eq is the transformed PMU harmonic measurement data; Taking the minimum normalized measurement residual sum of squares as the optimization objective and taking into account the zero injection power constraint in the system, a hybrid harmonic state estimation model is established: Where: J(λ,x) is the optimization objective function, x is the harmonic state variable, h(x) is the measurement function, σ is the standard deviation of the measurement data, h1(x), h2(x), and c(x) are the measurement functions corresponding to PMU measurement data, PQMD measurement data, and zero injection power data, respectively.

4. The method for hybrid harmonic state estimation of PMU and PQMD based on adaptive correction of measurement data according to claim 3, characterized in that: Solving the hybrid harmonic state estimation model includes the following steps: First, set the initial feasible region of the harmonic state quantity to x d(0) ≤x≤x u(0) , define the midpoint of the initial feasible region of the harmonic state quantity: Where: x m(0) is the midpoint of the initial feasible region of the harmonic state quantity, x d(0) 、x u(0) are the lower and upper bounds of the initial feasible region of the harmonic state quantity respectively; In x m(0) Perform first-order Taylor expansion on the measurement functions of PMU measurement data, PQMD measurement data, and zero injection power data: Where: H1, H2, C are h1(x), h2(x), c(x) at x m(0) The Jacobian matrix at ; Define the correction amount Δx = xx m(0) , the harmonic state quantity is expressed as an uncertainty set with linear constraints as follows: Let ΔZ1=Z 1eq -h1(x m(0) ), ΔZ 2,d =Z 2,d -h2(x m(0) ), ΔZ 2,u =Z 2,u -h2(x m(0) ), ΔZ0=-c(x m(0) ), then the problem of solving the linear feasible region is transformed into the following two linear optimization problems: Where: Δx i,d , Δx i,u They are correction amounts Δx i The lower and upper bounds of , n is the number of harmonic state quantities; The linear programming algorithm is used to solve Equation (10)-Equation (11) to obtain the lower and upper bounds of the correction amount, and then the first approximate solution of the upper and lower bounds of the linear feasible region is obtained: x d(1) and x u(1) Replace x d(0) and x u(0) , update to get x m(1) as follows: x m(1) Replace x m(0) A new round of iterative calculation is performed until convergence or the number of iterations reaches the set upper limit. The convergence conditions for the end of iteration are as follows: Where: || represents the absolute value, k is the number of iterations, and h is the set convergence threshold; After the iteration, the linear feasible domain x of the harmonic state quantity is output. d ≤x≤x u , the harmonic state estimation model is simplified into a convex optimization model with linear constraints as follows: Where: x m is the midpoint of the linear feasible region of harmonic state quantity, H m is h(x) at x m The Jacobian matrix at ; The interior point method is used to solve equation (15) to obtain the estimated results of the harmonic state quantity.

5. The method for hybrid harmonic state estimation of PMU and PQMD based on adaptive correction of measurement data according to claim 4, characterized in that: The hybrid harmonic state estimation process includes the following steps: S1: Taking the PMU sampling time as the estimation time, the measurement data of PMU and PQMD are selected according to equations (1) to (3); S2: According to formula (4), the optimization factor is introduced to characterize the harmonic data of the PQMD measurement point as a bounded uncertainty variable; S3: Construct the hybrid harmonic measurement set of PMU and PQMD according to formula (5); S4: Establish a hybrid harmonic state estimation model according to formula (6); S5: Establish an estimation model of the linear feasible region of harmonic state quantities according to equations (7) to (11); S6: Use linear programming algorithm to iteratively solve the linear feasible region of harmonic state quantity, and obtain the new linear feasible region according to formula (12); S7: Set the convergence condition according to formula (14) and judge whether the convergence condition is met. If the convergence condition is met or the number of iterations reaches the set upper limit, the calculation is terminated and the linear feasible domain of the harmonic state quantity is output; otherwise, k=k+1 is set and the latest feasible domain is used instead of the initial feasible domain to re-execute steps S6-S7; S8: According to formula (15), the state estimation model is simplified into a convex optimization model with linear constraints; S9: Use the interior point method to solve equation (15) and output the harmonic state quantity of the entire network at the estimated time.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the PMU and PQMD hybrid harmonic state estimation method based on adaptive correction of measurement data as described in any one of claims 1 to 5 are implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for estimating a hybrid harmonic state of PMU and PQMD based on adaptive correction of measurement data as described in any one of claims 1 to 5 are implemented.

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