A method for improving the active support capability of doubly-fed wind turbines under asymmetric faults

Abstract

This invention discloses a method for improving the active support capability of a doubly-fed wind turbine under asymmetric faults. This method establishes an optimization model for a doubly-fed wind turbine with the goal of minimizing voltage imbalance at the common connection point. The optimization problem is delimited into two sub-optimization problems: one in which the negative-sequence voltage can be eliminated and the other in which it cannot. By using the Karush-Kuhn-Tucker condition and geometric analysis to shrink the constraints, an analytical solution for the rotor's positive and negative sequence current commands is obtained. This method overcomes the problem of insufficient unbalanced voltage suppression capability under asymmetric faults in doubly-fed wind turbines, thereby significantly reducing voltage imbalance at the common connection point under asymmetric faults. This method provides a reference for improving the active support capability of doubly-fed wind turbines under asymmetric faults.

Description

Technical Field

[0001] The present application relates to the field of wind power generation control technology, and in particular to a method for improving the active support capability of a doubly-fed wind turbine generator set under asymmetric faults. Background Art

[0002] Wind power, as a core component of renewable energy generation, accounts for a significant share of the new power system. However, in recent years, voltage imbalances have frequently occurred due to unexpected factors such as asymmetric grid faults. This poses a severe challenge to the stability of wind power grid integration and, in turn, threatens the safe and stable operation of the entire power system.

[0003] Current grid standards require wind farms to inject positive and negative sequence currents into the grid when a wind turbine detects an asymmetric fault in the grid to suppress voltage imbalance at the point of common coupling (PCC). Doubly fed induction generators (DFIGs), the primary wind turbine type, are crucial for improving wind power systems' adaptability to grid integration by ensuring they can support the grid while operating without disconnecting from the grid.

[0004] At present, the following research has been conducted on asymmetric fault ride-through of DFIG at home and abroad: ① Reference [[6]Xu Lie. Enhanced Control and Operation of DFIG-Based Wind Farms During Network Unbalance[J], IEEE Transactions on Energy Conversion, 2008, 23(4):1073-1081] proposed a positive and negative sequence current control strategy with the goal of eliminating the double frequency oscillation of electromagnetic torque under unbalanced voltage, but it increases the risk of overmodulation of the rotor-side converter. = 2 \* GB3 ② Reference [Xu Hailiang, Zhang Yufeng,Li Zhi, et al. Reactive current constraints and coordinated control of DFIG'sRSC and GSC during asymmetric grid condition[J]. IEEE Access, 2020,8(1):184339-184349] On the basis of eliminating the double frequency oscillation of electromagnetic torque on the generator side, the controllable current margin of the grid-side converter is used to collaboratively suppress the voltage unbalance factor (VUF) of the PCC. Reference = 3 \* GB3 ③[Chang Yuanzhu, Ilhan Kocar, Hu Jiabing, et al. Coordinated Control of DFIGConverters to Comply with Reactive Current Requirements in Emerging GridCodes[J]. Journal of Modern Power Systems and Clean Energy, 2022, 10(2):502-514]From the perspective of the positive and negative sequence requirements of the power grid, a DFIG positive and negative sequence instruction allocation strategy based on priority current limiting (positive sequence reactive power, negative sequence reactive power, positive sequence active power) is proposed.

[0005] These instructions are mostly obtained based on droop control using voltage deviation or PI control using pre-set values. They do not fully utilize the current and voltage capacity of the rotor side and cannot achieve the optimal voltage unbalance factor attenuation (OVUFA) of the DFIG. Summary of the Invention

[0006] The purpose of the embodiments of the present application is to provide a method for improving the active support capability of a doubly-fed wind turbine generator system under asymmetric faults, so as to solve the technical problem in the related art that the current and voltage capacity of the DFIG rotor side cannot be fully utilized to achieve optimal voltage imbalance suppression.

[0007] According to a first aspect of an embodiment of the present application, a method for improving the active support capability of a doubly-fed wind turbine generator system under an asymmetric fault is provided, comprising:

[0008] Establish an optimization model for doubly fed wind turbines with the voltage imbalance at the common connection point minimized;

[0009] Based on the doubly-fed wind turbine optimization model, the negative sequence voltage suppression capability of the doubly-fed wind turbine is judged, and the optimization problem is delimited into a sub-optimization problem in which the negative sequence voltage can be completely eliminated and a sub-optimization problem in which the negative sequence voltage cannot be completely eliminated;

[0010] For the negative sequence voltage full elimination sub-optimization problem, the necessary conditions are obtained using the Karush-Kuhn-Tucker conditions, and the current command is further solved;

[0011] For the sub-optimization problem that the negative sequence voltage cannot be completely eliminated, the geometric analysis method is used to shrink the constraints, determine the distribution boundary of the optimal solution, and obtain an analytical solution.

[0012] According to a second aspect of an embodiment of the present application, a device for improving the active support capability of a doubly-fed wind turbine generator system under an asymmetric fault is provided, comprising:

[0013] A model building module is used to build a doubly-fed wind turbine optimization model with the voltage imbalance at the common connection point minimized;

[0014] a delimiting module for judging the negative sequence voltage suppression capability of the doubly fed wind turbine based on the doubly fed wind turbine optimization model, and delimiting the optimization problem into a sub-optimization problem in which the negative sequence voltage can be completely eliminated and a sub-optimization problem in which the negative sequence voltage cannot be completely eliminated;

[0015] A first solving module is configured to obtain necessary conditions for the negative sequence voltage full elimination sub-optimization problem using the Karush-Kuhn-Tucker condition and further solve the current command;

[0016] The second solving module is used to shrink the constraints of the sub-optimization problem that the negative sequence voltage cannot be completely eliminated by using a geometric analysis method, determine the distribution boundary of the optimal solution, and obtain an analytical solution.

[0017] According to a third aspect of an embodiment of the present application, there is provided an electronic device, characterized by comprising:

[0018] one or more processors;

[0019] a memory for storing one or more programs;

[0020] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in the first aspect.

[0021] The technical solutions provided by the embodiments of the present application may have the following beneficial effects:

[0022] It can be seen from the above embodiments that since the present application establishes a doubly fed wind turbine optimization model with the voltage imbalance at the common connection point minimized, and delimits the optimization problem into two sub-optimization problems in which the negative sequence voltage can be eliminated and cannot be eliminated, the constraints are contracted by using the Karush-Kuhn-Tucker condition and the geometric analysis method, and the analytical solution form of the rotor positive and negative sequence current instructions is obtained, the problem of insufficient unbalanced voltage suppression capability of the doubly fed wind turbine under asymmetric faults is overcome, thereby achieving the effect of greatly reducing the voltage imbalance at the common connection point under asymmetric faults.

[0023] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0025] Figure 1 The present invention is a flow chart showing a method for improving the active support capability of a doubly-fed wind turbine generator system under an asymmetric fault according to an exemplary embodiment.

[0026] Figure 2 The figure is a topology diagram of a doubly-fed wind power generation system according to an exemplary embodiment.

[0027] Figure 3 is a diagram showing the relationship between voltage imbalance and rotor positive and negative sequence currents according to an exemplary embodiment.

[0028] Figure 4 is a diagram showing the relationship between voltage imbalance and rotor positive and negative sequence voltages according to an exemplary embodiment.

[0029] Figure 5 FIG. 4 is a schematic diagram showing geometric characteristics under rotor current constraints according to an exemplary embodiment.

[0030] Figure 6 FIG. 4 is a schematic diagram showing geometric characteristics of a rotor under voltage constraints according to an exemplary embodiment.

[0031] Figure 7FIG. 1 is a schematic diagram of geometric characteristics under dual constraints of rotor current and voltage according to an exemplary embodiment.

[0032] Figure 8 The present invention is a block diagram of a device for improving the active support capability of a doubly-fed wind turbine generator system under an asymmetric fault condition according to an exemplary embodiment. DETAILED DESCRIPTION

[0033] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0034] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0035] Figure 1 FIG. 1 is a flow chart showing a method for improving the active support capability of a doubly-fed wind turbine generator system under an asymmetric fault according to an exemplary embodiment. Figure 1 As shown, the method may include the following steps:

[0036] S1: Establish a doubly fed wind turbine optimization model with the voltage imbalance at the common connection point minimized;

[0037] Specifically, Figure 2 This diagram illustrates the topology of a doubly-fed wind turbine system according to an exemplary embodiment. The DFIG is controlled by an RSC, and the stator is directly connected to the external grid. A decoupled dual synchronous phase-locked loop (SPL) separates the positive and negative sequence components, and a flux observer measures the transient flux. Based on this measured state, a control signal is generated using the method proposed in this application. After modulation, it is input into the RSC to control the DFIG.

[0038] When an asymmetric fault occurs in the power grid, positive-sequence and negative-sequence flux are induced on the stator side of the DFIG due to the excitation of external positive-sequence and negative-sequence sources. Due to the slip characteristics caused by the DFIG motor structure, the circuit relationship between the positive-sequence current and voltage on the rotor side can be expressed as:

[0039] ;

[0040] ;

[0041] In the formula L s is the stator inductance, L r is the rotor inductance, L m For mutual induction, σ =1− L 2 m / L s L r is the magnetic flux leakage coefficient; s is the slip rate; ω 1 is the grid synchronization angular velocity, I r+ and I r- are the positive and negative sequence currents of the rotor respectively, U r+ and U r- are the positive and negative sequence voltages of the rotor respectively, U s+ and U s- are the positive and negative sequence voltages of the stator respectively; bold indicates vector.

[0042] The positive and negative sequence synchronous rotating coordinate systems are oriented by the positive and negative sequence components of the stator side voltage respectively. The circuit constraints can be expressed as:

[0043] ;

[0044] ;

[0045] Where, L g is the grid inductance; U g+ and U g- are the positive and negative sequence voltages of the grid respectively; I sd+ and I sd- are the positive and negative sequence active currents of the stator respectively; I sq+ and I sq- are the positive and negative sequence reactive currents of the stator respectively.

[0046] Under asymmetric faults, the DFIG should first ensure its own safety, and then inject positive and negative sequence currents into the grid to support the grid voltage. Therefore, the DFIG should avoid overcurrent and overmodulation, that is, meet the rotor-side current and voltage stress constraints. The optimization model can be expressed as follows:

[0047] ;

[0048] Where VUF represents the voltage unbalance, obj Indicates the optimization target, min indicates the minimum, I r+ and I r- are the positive and negative sequence currents of the rotor respectively, U r+ and U r- are the positive and negative sequence voltages of the rotor respectively, max and rmax They are the current and voltage capacity limits of the rotor respectively.

[0049] Establishing an optimization model for minimum voltage imbalance can maximize the unbalanced voltage suppression capability of DFIG.

[0050] S2: Based on the doubly-fed wind turbine optimization model, the negative sequence voltage suppression capability of the doubly-fed wind turbine is judged, and the optimization problem is delimited into a sub-optimization problem in which the negative sequence voltage can be completely eliminated and a sub-optimization problem in which the negative sequence voltage cannot be completely eliminated;

[0051] Specifically, the negative-sequence voltage suppression capability of the DFIG is judged based on the rotor current and voltage capacity that the DFIG can allocate, as well as the negative-sequence voltage and grid strength. The optimization problem is then divided into two sub-optimization problems: when the negative-sequence voltage can be completely eliminated and when the negative-sequence voltage cannot be completely eliminated.

[0052] If the grid voltage imbalance is low, the negative sequence voltage can be completely eliminated and the VUF can be reduced to 0. The rotor side capacity required for the optimization problem should meet the following requirements:

[0053] ;

[0054] ;

[0055] In the formula I b Indicates the defined rotor current limits.

[0056] On the contrary, if the grid voltage imbalance is high and the negative sequence voltage cannot be completely eliminated, the above conditions are not met.

[0057] By dividing the optimization problem into sub-optimization problems, the computational accuracy can be improved and the global optimality of the solution can be guaranteed.

[0058] S3: For the negative sequence voltage full elimination sub-optimization problem, the necessary conditions are obtained using the Karush-Kuhn-Tucker conditions, and the current command is further solved;

[0059] Specifically, this problem degenerates into a positive sequence support problem with negative sequence constraints, which requires fully supporting the positive sequence voltage of the power grid under the premise of eliminating the negative sequence voltage, that is:

[0060] ;

[0061] In the formula, max means maximum, U g+ and U g- are the positive and negative sequence voltages of the grid respectively; U sd is the positive sequence d-axis voltage of the stator, U sq+ is the positive sequence q-axis voltage of the stator, I sd is the positive sequence d-axis current of the stator, I sq+ is the positive sequence q-axis current of the stator; I rd+ is the positive sequence d-axis current of the rotor, I rq+ is the positive sequence q-axis current of the rotor; U rd+ is the sequence d axis voltage of the rotor, U rq+ is the positive sequence q-axis voltage of the rotor.

[0062] This optimization problem is non-convex due to the presence of square roots and quadratic terms. Classical convex optimization problem solving methods cannot guarantee the optimality of the solution. Since the constraints are time-varying, in order to ensure the rapidity of the solution, it is necessary to avoid using heuristic algorithms to search for iterative solutions. The KKT condition is used to further shrink the boundary and simplify the problem, but it should be noted that the KKT solution only obtains necessary conditions, not the necessary and sufficient conditions for global optimization. The Lagrangian function of the optimization problem is obtained, and the solution is that the necessary condition for the optimal positive sequence voltage support is I rd+ =0, that is, the DFIG does not output positive sequence active power. Therefore, the solution to the negative sequence voltage can completely eliminate the sub-optimization problem can be further solved.

[0063] ;

[0064] ;

[0065] ;

[0066] Where, L The Lagrangian function representing the optimization problem, f represents the objective function, g 1 and g2 represent the constraints, λ1 and λ2 represent the Lagrange coefficients, L re is the equivalent inductance. I rq- are the positive and negative sequence q-axis currents of the rotor respectively.

[0067] By further shrinking the constraints through KKT conditions, the computational difficulty of the solution can be reduced while ensuring the global optimality of the solution.

[0068] S4: For the sub-optimization problem that the negative sequence voltage cannot be completely eliminated, the geometric analysis method is used to shrink the constraints, determine the distribution boundary of the optimal solution, and obtain an analytical solution.

[0069] Specifically, if the negative sequence voltage cannot be completely eliminated, the optimization problem can be further expressed as:

[0070] ;

[0071] Where, U sd- is the negative sequence d-axis voltage of the stator, U sq- is the negative sequence q-axis voltage of the stator; I sd- is the negative sequence d-axis current of the stator; I sq- is the negative sequence q-axis current of the stator; I rd- are the negative sequence d-axis current of the rotor, I rq- are the negative sequence q-axis current of the rotor respectively; U rd- are the negative sequence d-axis voltage of the rotor, U rq- are the negative sequence q-axis voltages of the rotor respectively.

[0072] Compared with the sub-optimization problem in which the negative-sequence voltage can be completely eliminated, the existence of the VUF quotient form and the introduction of negative-sequence variables make the problem more nonlinear, the dimension higher, and the solution more difficult. Therefore, geometric analysis is used to locate the distribution of the optimal solution and further shrink the constraint boundary. Figure 3 The relationship between the rotor positive and negative sequence currents and VUF is given. Figure 4 The relationship between the rotor's positive and negative sequence voltages and VUF is presented. Although VUF varies differently with rotor-side current and voltage margin, it can be concluded that the minimum VUF is closely related to the remaining current and voltage margin after RSC demagnetization. Within the optimal value neighborhood, the two are inversely proportional: the greater the current and voltage capacity, the smaller the VUF. The optimal solution falls within the current or voltage constraint boundary, converting the inequality constraint into an equality constraint.

[0073] The KKT condition was previously used to establish the necessary condition for optimal support of positive-sequence voltage: zero positive-sequence active current. Similarly, the necessary condition for optimal suppression of negative-sequence voltage can also be derived from the KKT condition: zero negative-sequence active current. Because VUF is defined as the quotient of negative-sequence voltage and positive-sequence voltage, minimizing VUF implies simultaneously achieving optimal positive-sequence voltage support and negative-sequence voltage suppression under specific constraints. Therefore, the DFIG's lack of positive and negative-sequence active power output can also be considered a necessary condition for the optimization problem, further narrowing the constraints.

[0074] ① If the rotor-side current stress is the primary constraint for limiting the DFIG's ability to suppress unbalanced voltage, and the rotor-side voltage margin is sufficient, that is, the current constraint is a tight constraint and the voltage constraint is a non-tight constraint. The inequality constraint for the rotor-side current stress limit is converted into an equality constraint. In this case, VUF can be expressed as:

[0075] ;

[0076] Figure 5 The relationship between VUF and negative sequence current amplitude under current constraint is given. To better describe the geometric characteristics under current constraint, the feasible region is expanded (such as the dotted line part). The solid line part is the actual limit of the rotor side current. Figure 5 It can be seen that under the rotor side current constraint, in order to achieve the optimal suppression of VUF, the entire current capacity should be used for negative sequence voltage suppression. At this time, the rotor positive and negative sequence current instructions are calculated by the following formula:

[0077] ;

[0078] = 2 \* GB3 ② If the rotor-side voltage stress serves as the primary constraint for limiting the DFIG's ability to suppress unbalanced voltage, and the rotor-side current margin is sufficient, that is, the voltage constraint is a tight constraint and the current constraint is a non-tight constraint. The inequality constraint for the rotor-side voltage stress limit is transformed into an equality constraint.

[0079] Figure 6 The relationship between VUF and rotor negative sequence voltage under rotor side voltage constraint is given. Figure 6 It can be seen that VUF and the rotor negative-sequence voltage amplitude do not correspond one-to-one. This is because as the degree of support for the grid-connected point negative-sequence voltage increases, Ur− shifts from being in phase with the negative-sequence EMF to being in phase with it. Under rotor-side voltage constraints, the condition for optimal VUF suppression is that the rotor negative-sequence voltage is zero. At this point, the rotor positive and negative sequence current commands are calculated using the following equations:

[0080] ;

[0081] = 3 \* GB3 ③ If the rotor side current and voltage constraints are both tight constraints, the rotor side current and voltage limits are simultaneously transformed from inequality constraints to equality constraints.

[0082] In order to more directly reflect the geometric characteristics under the rotor side current and voltage constraints, variable dimension reduction analysis is performed. Taking the rotor current as the variable, the rotor side voltage constraint is re-expressed, and its feasible region is Figure 7 As shown in , their intersection is the projection of VUF on the feasible domain surface. Figure 7 It can be seen that the minimum value of VUF falls on the boundary of the rotor-side current and voltage. However, the feasible region boundary of the rotor-side current and voltage has two intersection points. To ensure the optimality of OVUFA, the feasible solutions are verified one by one. It is found that the minimum value of VUF is achieved at the intersection point with a larger negative-sequence current. The corresponding rotor positive and negative sequence current commands are given by the following equations.

[0083] ;

[0084] As can be seen from the above examples, this application establishes a doubly-fed wind turbine optimization model that minimizes voltage imbalance at the common connection point. The optimization problem is delimited into two sub-optimization problems: those in which the negative-sequence voltage can be eliminated and those in which it cannot. By using the Karush-Kuhn-Tucker condition and geometric analysis to shrink the constraints, an analytical solution for the rotor's positive and negative sequence current commands is obtained. This overcomes the problem of insufficient unbalanced voltage suppression under asymmetric faults in doubly-fed wind turbines, thereby significantly reducing voltage imbalance at the common connection point under asymmetric faults. This is the method we provide.

[0085] Corresponding to the aforementioned embodiment of the method for improving the active support capability of a doubly-fed wind turbine generator set under asymmetric faults, the present application also provides an embodiment of a device for improving the active support capability of a doubly-fed wind turbine generator set under asymmetric faults.

[0086] Figure 8 This is a block diagram of a device for improving the active support capability of a doubly-fed wind turbine generator system under an asymmetric fault according to an exemplary embodiment. Figure 8 , the device comprises:

[0087] Model building module 1, used to establish a doubly fed wind turbine optimization model with the voltage imbalance at the common connection point minimized;

[0088] A definition module 2 is used to judge the negative sequence voltage suppression capability of the doubly fed wind turbine based on the doubly fed wind turbine optimization model, and define the optimization problem into a sub-optimization problem in which the negative sequence voltage can be completely eliminated and a sub-optimization problem in which the negative sequence voltage cannot be completely eliminated;

[0089] A first solving module 3 is configured to obtain necessary conditions for the negative sequence voltage full elimination sub-optimization problem using the Karush-Kuhn-Tucker condition and further solve the current command;

[0090] The second solving module 4 is used for shrinking the constraints of the sub-optimization problem that the negative sequence voltage cannot be completely eliminated by using a geometric analysis method, determining the distribution boundary of the optimal solution, and obtaining an analytical solution.

[0091] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0092] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0093] Accordingly, the present application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method for improving the active support capability of a doubly-fed wind turbine generator set under asymmetric faults as described above.

[0094] Accordingly, the present application also provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implements the method for improving the active support capability of a doubly-fed wind turbine generator system under asymmetric faults as described above.

[0095] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the contents disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered merely as exemplary, and the true scope and spirit of the present application are indicated by the claims.

[0096] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for improving the active support capability of a doubly-fed wind turbine generator system under asymmetric fault conditions, characterized in that: include: Establish an optimization model for doubly fed wind turbines with the voltage imbalance at the common connection point minimized; Based on the doubly-fed wind turbine optimization model, the negative sequence voltage suppression capability of the doubly-fed wind turbine is judged, and the optimization problem is delimited into a sub-optimization problem in which the negative sequence voltage can be completely eliminated and a sub-optimization problem in which the negative sequence voltage cannot be completely eliminated; For the negative sequence voltage full elimination sub-optimization problem, the necessary conditions are obtained using the Karush-Kuhn-Tucker conditions, and the current command is further solved; For the sub-optimization problem where the negative sequence voltage cannot be completely eliminated, the geometric analysis method is used to shrink the constraints, determine the distribution boundary of the optimal solution, and obtain an analytical solution; Among them, for the negative sequence voltage full elimination sub-optimization problem, the necessary conditions are obtained using the Karush-Kuhn-Tucker condition, and the Lagrangian function of the optimization problem is obtained. The necessary conditions for the optimal positive sequence voltage support are solved as follows: I rd+ =0, that is, the doubly fed wind turbine does not output positive sequence active power; further solving the negative sequence voltage can completely eliminate the solution of the sub-optimization problem: ； In the formula L s is the stator inductance, L r is the rotor inductance, L m For mutual induction, σ =1− L 2 m / L s L r is the magnetic flux leakage coefficient; s is the slip rate; ω 1 is the grid synchronization angular velocity, L g is the grid inductance; U g+ and U g- are the positive and negative sequence voltages of the grid respectively; I rq+ and I rq- are the positive and negative sequence q-axis currents of the rotor respectively; L g is the equivalent inductance; max and rmax They are the current and voltage capacity limits of the rotor respectively; Among them, for the sub-optimization problem where the negative sequence voltage cannot be completely eliminated, the geometric analysis method is used to shrink the constraints, determine the distribution boundary of the optimal solution, and obtain an analytical solution; if the negative sequence voltage cannot be completely eliminated, the optimization problem is further expressed as: ； In the formula obj Indicates the optimization target, min indicates the minimum, st Indicates constraints; L s is the stator inductance, L r is the rotor inductance, L m For mutual induction, σ =1− L 2 m / L s L r is the magnetic flux leakage coefficient; s is the slip rate; ω 1 is the grid synchronization angular velocity, L g is the grid inductance; U g+ and U g- are the positive and negative sequence voltages of the grid respectively; U sd+ and U sd- are the positive and negative sequence d-axis voltages of the stator, U sq+ and U sq- are the positive and negative sequence q-axis voltages of the stator, I sd+ and I sd- are the positive and negative sequence d-axis currents of the stator respectively; I sq+ and I sq- are the positive and negative sequence q-axis currents of the stator respectively; I rd+ and I rd- are the positive and negative sequence d-axis currents of the rotor respectively, I rq+ and I rq- are the positive and negative sequence q-axis currents of the rotor respectively, U rd+ and U rd- are the positive and negative sequence d-axis voltages of the rotor respectively, U rq+ and U rq- are the positive and negative sequence q-axis voltages of the rotor respectively, max and rmax They are the current and voltage capacity limits of the rotor respectively; By using geometric analysis to locate the distribution of the optimal solution and further shrinking the constraint boundary, it is concluded that the minimum VUF value is closely related to the remaining current and voltage margin after demagnetization of the rotor-side converter. VUF and the rotor converter capacity margin are inversely proportional within the neighborhood of the optimal value, that is, the larger the current and voltage capacity, the smaller the VUF. The optimal solution falls on the current or voltage constraint boundary, converting the inequality constraint into an equality constraint.

2. The method according to claim 1, wherein: The doubly fed wind turbine optimization model is as follows: ； Where, obj Indicates the optimization target, min indicates the minimum, st Indicates constraint conditions; VUF indicates voltage unbalance; I r+ and I r- are the positive and negative sequence currents of the rotor respectively; U r+ and U r- are the positive and negative sequence voltages of the rotor respectively, max and rmax They are the current and voltage capacity limits of the rotor respectively.

3. The method according to claim 1, wherein: The optimization problem is delimited into the sub-optimization problem of negative sequence voltage being completely eliminated and the sub-optimization problem of negative sequence voltage not being completely eliminated. The delimitation conditions are as follows: ； ； In the formula L s is the stator inductance, L r is the rotor inductance, L m For mutual induction, σ =1− L 2 m / L s L r is the magnetic flux leakage coefficient; s is the slip rate; ω 1 is the grid synchronization angular velocity, L g is the grid inductance; max and rmax are the current and voltage capacity limits of the rotor, I b is the defined current boundary.

4. The method according to claim 1, wherein If the rotor side current stress is used as the primary constraint to limit the unbalanced voltage of the doubly fed wind turbine, the inequality constraint of the rotor side current stress is transformed into an equality constraint. At this time, VUF is expressed as: ； Where VUF represents the voltage unbalance; L s is the stator inductance, L r is the rotor inductance, L m For mutual induction; s is the slip rate; ω 1 is the grid synchronization angular velocity, L g is the grid inductance; U g+ and U g- are the positive and negative sequence voltages of the grid respectively; max and rmax They are the current and voltage capacity limits of the rotor respectively; Under the rotor-side current constraint, in order to achieve optimal VUF suppression, the entire current capacity should be used for negative-sequence voltage suppression. At this time, the rotor-side current command is calculated by the following formula: ； In the formula I rq+ and I rq- are the positive and negative sequence q-axis currents of the rotor respectively, max This is the current capacity limit of the rotor.

5. The method according to claim 1, wherein If the rotor side voltage stress is used as the primary constraint to limit the DFIG to suppress the unbalanced voltage, the inequality constraint of the rotor side voltage stress limitation is transformed into an equality constraint; VUF and the rotor negative sequence voltage amplitude are not one-to-one corresponding, because as the degree of support for the grid-connected point negative sequence voltage increases, U r− The negative sequence EMF will change from the same phase to the opposite phase. Under the voltage constraint on the rotor side, the condition for achieving optimal VUF suppression is that the rotor negative sequence voltage is zero. At this time, the command is calculated by the following formula: ； In the formula L s is the stator inductance, L r is the rotor inductance, L m For mutual induction; s is the slip rate; ω 1 is the grid synchronization angular velocity, L g is the grid inductance, L re is the equivalent inductance; U g+ and U g- are the positive and negative sequence voltages of the grid respectively; rmax This is the voltage capacity limit of the rotor.

6. The method according to claim 1, characterized in that If the rotor-side current and voltage constraints are simultaneously used as tight constraints, the rotor-side current and voltage limits are simultaneously converted from inequality constraints to equality constraints. The minimum value of VUF falls on the rotor-side current and voltage boundaries, but the feasible region boundaries of the rotor-side current and voltage have two intersection points. To ensure the optimality of OVUFA, the feasible solutions are checked one by one. It is found that the minimum value of VUF is obtained at the intersection point with a larger negative-sequence current. The corresponding current command is given by the following formula: ； In the formula L s is the stator inductance, L r is the rotor inductance, L m For mutual induction, L re is the equivalent inductance; s is the slip rate; ω 1 is the grid synchronization angular velocity, L g is the grid inductance; U g+ and U g- are the positive and negative sequence voltages of the grid respectively; I rq+ and I rq- are the positive and negative sequence q-axis currents of the rotor respectively; max and rmax They are the current and voltage capacity limits of the rotor respectively.

7. A device for improving the active support capability of a doubly-fed wind turbine generator system under asymmetric fault conditions, characterized in that: include: A model building module is used to build a doubly-fed wind turbine optimization model with the voltage imbalance at the common connection point minimized; a delimiting module for judging the negative sequence voltage suppression capability of the doubly fed wind turbine based on the doubly fed wind turbine optimization model, and delimiting the optimization problem into a sub-optimization problem in which the negative sequence voltage can be completely eliminated and a sub-optimization problem in which the negative sequence voltage cannot be completely eliminated; A first solving module is configured to obtain necessary conditions for the negative sequence voltage full elimination sub-optimization problem using the Karush-Kuhn-Tucker condition and further solve the current command; The second solving module is used for determining the distribution boundary of the optimal solution and obtaining an analytical solution for the sub-optimization problem in which the negative sequence voltage cannot be completely eliminated by using a geometric analysis method to shrink the constraints; Among them, for the negative sequence voltage full elimination sub-optimization problem, the necessary conditions are obtained using the Karush-Kuhn-Tucker condition, and the Lagrangian function of the optimization problem is obtained. The necessary conditions for the optimal positive sequence voltage support are solved as follows: I rd+ =0, that is, the doubly fed wind turbine does not output positive sequence active power; further solving the negative sequence voltage can completely eliminate the solution of the sub-optimization problem: ； In the formula L s is the stator inductance, L r is the rotor inductance, L m For mutual induction, σ =1− L 2 m / L s L r is the magnetic flux leakage coefficient; s is the slip rate; ω 1 is the grid synchronization angular velocity, L g is the grid inductance; U g+ and U g- are the positive and negative sequence voltages of the grid respectively; I rq+ and I rq- are the positive and negative sequence q-axis currents of the rotor respectively; L g is the equivalent inductance; max and rmax They are the current and voltage capacity limits of the rotor respectively; Among them, for the sub-optimization problem where the negative sequence voltage cannot be completely eliminated, the geometric analysis method is used to shrink the constraints, determine the distribution boundary of the optimal solution, and obtain an analytical solution; if the negative sequence voltage cannot be completely eliminated, the optimization problem is further expressed as: ； In the formula obj Indicates the optimization target, min indicates the minimum, st Indicates constraints; L s is the stator inductance, L r is the rotor inductance, L m For mutual induction, σ =1− L 2 m / L s L r is the magnetic flux leakage coefficient; s is the slip rate; ω 1 is the grid synchronization angular velocity, L g is the grid inductance; U g+ and U g- are the positive and negative sequence voltages of the grid respectively; U sd+ and U sd- are the positive and negative sequence d-axis voltages of the stator, U sq+ and U sq- are the positive and negative sequence q-axis voltages of the stator, I sd+ and I sd- are the positive and negative sequence d-axis currents of the stator respectively; I sq+ and I sq- are the positive and negative sequence q-axis currents of the stator respectively; I rd+ and I rd- are the positive and negative sequence d-axis currents of the rotor respectively, I rq+ and I rq- are the positive and negative sequence q-axis currents of the rotor respectively, U rd+ and U rd- are the positive and negative sequence d-axis voltages of the rotor respectively, U rq+ and U rq- are the positive and negative sequence q-axis voltages of the rotor respectively, max and rmax They are the current and voltage capacity limits of the rotor respectively; By using geometric analysis to locate the distribution of the optimal solution and further shrinking the constraint boundary, it is concluded that the minimum VUF value is closely related to the remaining current and voltage margin after demagnetization of the rotor-side converter. VUF and the rotor converter capacity margin are inversely proportional within the neighborhood of the optimal value, that is, the larger the current and voltage capacity, the smaller the VUF. The optimal solution falls on the current or voltage constraint boundary, converting the inequality constraint into an equality constraint.

8. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

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