New energy positive and negative sequence voltage coordinated optimization method adaptive to high-power fault impact

Abstract

The invention discloses a new energy positive and negative sequence voltage coordinated optimization method adaptive to high-power fault impact, which comprises the following steps of: constructing an optimal unbalanced voltage suppression model of a doubly-fed wind turbine generator under an asymmetric fault of a power grid, and taking the minimum voltage unbalance degree of a grid-connected point as a target function by the model to suppress the optimal unbalanced voltage of the doubly-fed wind turbine generator under the asymmetric fault of the power grid; the capacity constraint of the rotor side converter and the synchronous stability constraint of the system are included; based on the optimal unbalanced voltage suppression model, reconstructing a double-sequence synchronous rotating coordinate system; carrying out convexity processing on the reconstructed model, relaxing the capacity constraint of the rotor-side converter into a second-order cone form, and introducing an auxiliary variable to convert a target function and related constraints into a standard second-order cone programming problem; and solving the second-order cone programming problem, obtaining an optimal voltage dynamic control instruction, and controlling the new energy generator set. According to the method, the inter-sequence coupling effect can be accurately considered, so that the optimal suppression of the voltage imbalance is realized on the premise of ensuring the safety and stability of a machine-network system.

Description

Technical Field

[0001] This invention belongs to the field of new energy grid connection control technology, specifically involving a new energy positive and negative sequence voltage coordination optimization method to adapt to high-power fault impacts, which is used to improve the voltage dynamic performance of new energy generator sets, represented by doubly fed induction generators, when subjected to high-power impacts such as grid asymmetric faults. Background Technology

[0002] As the proportion of renewable energy in the global power system rapidly increases, grid guidelines in various countries, especially those concerning asymmetrical fault ride-through, are becoming increasingly stringent in order to address the challenges posed by high-power surges such as grid asymmetric faults. When asymmetrical faults such as single-phase grounding or two-phase short circuits occur in the grid, renewable energy generators (such as wind turbines) not only cannot disconnect from the grid, but also need to actively inject positive-sequence and negative-sequence currents into the grid to support positive-sequence voltage and suppress negative-sequence voltage, thereby reducing voltage imbalance at the grid connection point.

[0003] Doubly fed induction generators (DFIGs) dominate the wind power market due to their economic advantages, such as small converter capacity (typically 30% of rated power). However, this advantage also presents significant challenges under asymmetrical fault conditions.

[0004] Existing technologies have many shortcomings in solving this problem. Many control strategies exist, such as negative sequence current suppression (reference ① [J. Hu, Y. He, L. Xu and B. Williams, “Improved Control of DFIG Systems During Network Unbalance Using PI–R Current Regulators,” IEEE Trans. Ind. Electron., vol. 56, no. 2, pp. 439-451, Feb. 2009.]), torque ripple suppression (reference ② [J. Hu, H. Xu and Y. He, “Coordinated Control of DFIG's RSC and GSC Under Generalized Unbalanced and Distorted Grid Voltage Conditions,” IEEE Trans. Ind. Electron., vol. 60, no. 7, pp. 2808-2819, July 2013.]), or power ripple suppression (reference ③ [L. Li, H. Nian, L. Ding and B. Zhou, “Direct Power Control of DFIG System Without Phase-Locked Loop Under Unbalanced]). [and Harmonically Distorted Voltage, IEEE Trans. Energy Convers., vol. 33, no. 1, pp. 395-405, March 2018.], etc., mainly focus on improving the operating state of the DFIG itself, but fail to fully fulfill its responsibility of supporting the power grid and suppressing voltage imbalance. Some strategies aimed at suppressing voltage imbalance, although considering the injection of positive and negative sequence currents into the power grid, are often based on simplified models and ignore a key physical phenomenon, the inter-sequence coupling effect.

[0005] Inter-sequence coupling refers to the phenomenon where, under asymmetrical fault conditions, the positive-sequence and negative-sequence networks of the power grid are no longer independent due to the boundary conditions at the fault point. Positive-sequence current affects negative-sequence voltage, and vice versa. This coupling effect is particularly pronounced when the fault point is close to a renewable energy power plant. Ignoring this effect in control strategy design can lead to the following serious consequences: 1) Inaccurate or even worsened voltage imbalance suppression; 2) Misjudgment of the unit's stable operating boundary, potentially resulting in overcurrent or overmodulation at theoretically safe operating points; 3) Loss of synchronization between the phase-locked loop (PLL) and the unit and the grid.

[0006] Furthermore, achieving optimal voltage imbalance suppression requires solving for the optimal current command within a complex system with multiple constraints (such as converter capacity and synchronous stability). The inherent nonlinear electromagnetic coupling between the stator and rotor of the DFIG, coupled with the inter-sequence coupling effect under asymmetric faults, makes this optimization problem highly nonlinear and nonconvex. Traditional rule-based or linear control-based methods struggle to find the optimal solution, while directly solving the nonconvex optimization problem is computationally complex and cannot guarantee global optimality. Summary of the Invention

[0007] The purpose of this invention is to provide a new energy positive and negative sequence voltage coordination optimization method that adapts to the impact of high-power faults. It can accurately take into account the inter-sequence coupling effect and effectively solve the problems of poor voltage imbalance suppression, high operational risk and difficulty in non-convex optimization solutions caused by ignoring the inter-sequence coupling effect in the prior art. Thus, it can achieve optimal suppression of voltage imbalance while ensuring the safety and stability of the machine-grid system.

[0008] To achieve the above objectives, the solution of the present invention is:

[0009] A method for coordinating and optimizing the positive and negative sequence voltages of new energy sources to adapt to high-power fault impacts includes the following steps:

[0010] Step 1: Construct the optimal unbalanced voltage suppression model for doubly fed wind turbine generators under asymmetrical grid faults. The model takes minimizing the voltage imbalance at the grid connection point as the objective function and includes the capacity constraints of the rotor-side converter and the synchronous stability constraints of the system.

[0011] Step 2: Based on the optimal unbalanced voltage suppression model, unify the d-axis of the positive-sequence and negative-sequence synchronous rotating coordinate systems to the grid voltage orientation mode, and reconstruct the dual-sequence synchronous rotating coordinate system;

[0012] Step 3: Perform convexity processing on the reconstructed model, relax the capacity constraint of the rotor-side converter into a second-order cone form, and introduce auxiliary variables to transform the objective function and related constraints into a standard second-order cone programming problem.

[0013] Step 4: Solve the second-order cone programming problem to obtain the optimal voltage dynamic control command, and control the new energy generator set according to the command.

[0014] In step 1 above, the objective function, which is to minimize the voltage imbalance at the grid connection point, is expressed as follows:

[0015] obj:minU s- / U s+

[0016] Where min represents the minimum value, U s+ U is the stator positive sequence voltage.s- This is the stator negative sequence voltage;

[0017] The stator positive and negative sequence voltages are represented as follows:

[0018]

[0019] Among them, U g K represents the grid voltage. + K is the positive sequence voltage equivalent coefficient. - Z is the equivalent coefficient for negative sequence voltage. 1+ Z 2+ Z 1- Z 2- I represents the equivalent sequence impedance in the positive and negative sequence voltage equations; S+ For the stator positive sequence current, I S- This represents the stator negative sequence current; * indicates the conjugate of the vector.

[0020] The stator positive and negative sequence voltage equations are transformed by Park to obtain the following equation.

[0021]

[0022] Where, r 1+ ,x 1+ Z 1+ The real and imaginary parts, r 2+ ,x 2+ Z 2+ The real and imaginary parts, r 1- ,x 1- Z 1- The real and imaginary parts, r 2- ,x 2- Z 2- The real and imaginary parts, S +(-) I represents the transient stability coefficient corresponding to the positive and negative order. sd+(-) I represents the stator positive and negative sequence d-axis current. sq+(-) δ represents the stator positive and negative q-axis currents. +(-) This represents the difference between the stator positive and negative sequence voltages and the grid voltage.

[0023] In step 1 above, the capacity constraint of the rotor-side converter includes rotor current constraint and rotor voltage constraint;

[0024] The rotor current constraint is expressed as follows:

[0025]

[0026] Where, x s x represents the stator inductive reactance of a doubly-fed wind turbine. m I represents the mutual inductance of a doubly-fed wind turbine. rmaxIndicates the upper limit of the rotor current; U s+ U is the stator positive sequence voltage. s- Stator negative sequence voltage; I sd+(-) I represents the stator positive and negative sequence d-axis current. sq+(-) Indicates the positive and negative q-axis currents of the stator;

[0027] The rotor voltage constraint is expressed as follows:

[0028]

[0029] Where, x r U represents the stator inductive reactance of the doubly-fed induction generator (DFIG), s represents the slip of the DFIG, σ represents the leakage flux coefficient of the DFIG, and U represents the stator inductive reactance of the DFIG. rmax This indicates the upper limit of the rotor voltage.

[0030] In step 2 above, the stator voltage equation in the reconstructed dual-sequence synchronous rotating coordinate system is expressed as follows:

[0031]

[0032] Among them, U sd+ U represents the stator positive d-axis voltage. sd- U represents the stator negative d-axis voltage. sq+ U represents the stator positive-sequence q-axis voltage. sq- K represents the stator negative-sequence q-axis voltage; 1+ ,K 2+ K + The real and imaginary parts, K 1- ,K 2- K - The real and imaginary parts of ; ^ represents the reconstructed state components in the bi-order coordinate system.

[0033] In step 3 above, the stator voltage equations in the reconstructed dual-sequence synchronous coordinate system are obtained by second-order cone relaxation.

[0034]

[0035] Based on Cauchy's inequality, a second-order cone relaxation of the rotor current constraint is performed, resulting in...

[0036]

[0037] By performing second-order cone relaxation on the rotor voltage constraint, we obtain:

[0038]

[0039] Among them, I rd+(-) I represents the positive and negative d-axis currents of the rotor. rq+(-) I represents the positive and negative q-axis currents of the rotor. rmaxIndicates the upper limit of the rotor current; U rd+(-) U represents the positive and negative d-axis currents of the rotor. rq+(-) U represents the positive and negative q-axis voltages of the rotor. rmax ^ indicates the upper limit of the rotor voltage; ^ indicates the state components in the reconstructed dual-sequence coordinate system.

[0040] In step 3 above, the auxiliary variables introduced include auxiliary solution variables and auxiliary intermediate variables;

[0041]

[0042] v, w, m, e, t, o are auxiliary variables for solving the problem;

[0043] h = wK 1+ U g +r 1+ mx 1+ e+r 2+ tx 2+ o

[0044] l=wK 2+ U g +x 1+ m+r 1+ e+x 2+ t+r 2+ o

[0045] h and l are auxiliary intermediate variables;

[0046] (l j+1 -l j )h+(h j -h i+1 )l+h j+1 l j -h j l j+1 >0

[0047] h i(i+1) and l j(j+1) It is an inscribed vertex of a polygon inscribed in a unit circle.

[0048] In step 4 above, the second-order cone programming problem to be solved is:

[0049]

[0050] A new energy positive and negative sequence voltage coordination and optimization device adapted to high-power fault impact, comprising,

[0051] The model building module is configured to construct an optimal unbalanced voltage suppression model for doubly fed wind turbine generators under asymmetrical grid faults. The model takes minimizing the voltage imbalance at the grid connection point as the objective function and includes capacity constraints of the rotor-side converter and synchronous stability constraints of the system.

[0052] The coordinate system reconstruction module is configured to reconstruct the dual-sequence synchronous rotating coordinate system by unifying the d-axis of the positive-sequence and negative-sequence synchronous rotating coordinate systems to the grid voltage orientation mode based on the optimal unbalanced voltage suppression model.

[0053] The model convexity module is configured to perform convexity processing on the reconstructed model, relaxing the capacity constraints of the rotor-side converter into a second-order cone form, and introducing auxiliary variables to transform the objective function and related constraints into a standard second-order cone programming problem; and,

[0054] The optimization solution module is configured to solve the second-order cone programming problem, obtain the optimal voltage dynamic control command, and control the new energy generator set according to the command.

[0055] An electronic device includes 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 aforementioned method for coordinating and optimizing positive and negative sequence voltages of new energy sources to adapt to high-power fault impacts.

[0056] A computer-readable storage medium storing computer instructions that, when executed by a processor, implement the steps of the previously described method for coordinating and optimizing positive and negative sequence voltages of new energy sources to adapt to high-power fault impacts.

[0057] By adopting the above scheme, this invention, for the first time, comprehensively considers the inter-sequence coupling effect in the optimal unbalanced voltage suppression model of DFIG, fundamentally ensuring the accuracy of the voltage unbalanced suppression strategy and avoiding the degradation of control performance caused by model mismatch. Through a series of innovative mathematical transformations such as coordinate system reconstruction and second-order cone relaxation, a highly nonlinear and non-convex optimization problem is successfully transformed into a standard convex optimization problem, thereby ensuring the global optimality of the solution and maximizing the voltage unbalanced suppression effect.

[0058] The beneficial effects of this invention are reflected in:

[0059] (1) Safe and reliable operation: The model strictly takes into account the RSC capacity constraint and synchronization stability constraint. The resulting control commands naturally meet the safe operation boundary of the unit and effectively avoid the risks of overcurrent, overmodulation and loss of synchronization that are common under asymmetrical faults.

[0060] (2) Computationally efficient and feasible: The transformed second-order cone programming problem can be solved quickly using existing efficient commercial solvers, making the optimization method have the potential for online application and meeting the real-time requirements of dynamic control of power systems. Attached Figure Description

[0061] Figure 1 This is a flowchart of the present invention;

[0062] Figure 2 This is a schematic diagram of the topology and control structure of the doubly fed induction generator system in an embodiment of the present invention;

[0063] Figure 3 This is a schematic diagram of coordinate system reconstruction for decoupling in an embodiment of the present invention;

[0064] Figure 4 This is a geometric schematic diagram of the auxiliary linear constraint used for constraint compaction in an embodiment of the present invention;

[0065] Figure 5 This is a structural block diagram of the optimization device provided in the embodiments of the present invention. Detailed Implementation

[0066] The technical solution and beneficial effects of the present invention will be described in detail below with reference to the accompanying drawings.

[0067] This invention provides a method for coordinating positive and negative sequence voltages of new energy units to adapt to high-power fault impacts. It aims to address issues such as poor voltage imbalance suppression, overcurrent, overmodulation, and loss of synchronization caused by inter-sequence coupling effects under asymmetrical grid faults. First, this invention establishes an optimal positive and negative sequence voltage coordination optimization model that considers inter-sequence coupling effects and aims to minimize the voltage imbalance factor. This model includes key constraints such as converter capacity and synchronous stability. Then, nonlinear coupling terms in the model are eliminated by reconstructing the dual-sequence synchronous coordinate system. Next, techniques such as second-order cone relaxation and the introduction of auxiliary variables are used to transform the original non-convex problem into a standard, efficiently solvable second-order cone programming problem, ensuring the global optimality of the solution. This invention can accurately calculate the optimal control command, effectively suppressing voltage imbalance while ensuring the safe and stable operation of the unit under fault impacts, significantly improving the fault ride-through capability and grid friendliness of new energy units.

[0068] Figure 1 This is a flowchart illustrating a method for coordinating and optimizing the positive and negative sequence voltages of new energy sources to adapt to high-power fault impacts, according to an exemplary embodiment. The method may include the following steps:

[0069] S1: Establish the optimal unbalanced voltage suppression model of the doubly fed wind turbine under asymmetrical grid faults. The model takes minimizing the voltage imbalance at the grid connection point as the objective function and includes the capacity constraints of the rotor side converter (RSC) and the synchronous stability constraints of the system.

[0070] Specifically, Figure 2This is a topology and control block diagram of a doubly fed wind turbine (DFIG) system under asymmetrical fault conditions, according to an embodiment of the present invention. The DFIG control includes a synchronization unit and a dual-sequence current control unit. A commonly used decoupled double synchronous reference frame phase-locked loop (DDSRF-PLL) is used as the synchronization unit. Since the grid-side converter (GSC) primarily maintains a constant DC bus voltage, this invention simplifies the analysis by treating the GSC as an equivalent constant DC voltage.

[0071] During asymmetrical faults, DFIGs not only cannot disconnect from the grid, but also need to actively inject positive and negative sequence currents into the grid to support positive sequence voltage and suppress negative sequence voltage, thereby reducing the voltage imbalance at the grid connection point. The objective function is expressed as follows:

[0072] obj:minU s- / U s+ ;

[0073] Where min represents the minimum value, U s+ U is the stator positive sequence voltage. s- This is the stator negative sequence voltage;

[0074] The stator positive and negative sequence voltages are represented as follows:

[0075]

[0076] U g K represents the grid voltage. + K is the positive sequence voltage equivalent coefficient. - Z is the equivalent coefficient for negative sequence voltage. 1+ Z 2+ Z 1- Z 2- I represents the equivalent sequence impedance in the positive and negative sequence voltage equations; S+(-) The symbol represents the positive and negative sequence currents of the stator, and * represents the conjugate of the vector.

[0077] Based on the orientation angle obtained from DDSRF-PLL, the stator voltage equation can be further transformed using the Park transformation:

[0078]

[0079] r 1+ ,x 1+ Z 1+ The real and imaginary parts, r 2+ ,x 2+ Z 2+ The real and imaginary parts, r1- ,x 1- Z 1- The real and imaginary parts, r 2- ,x 2- Z 2- The real and imaginary parts, S +(-) I represents the transient stability coefficient corresponding to the positive and negative order. sd+(-) I represents the stator positive and negative sequence d-axis current. sq+(-) δ represents the stator positive and negative q-axis currents. +(-) This represents the difference between the stator positive and negative sequence voltages and the grid voltage.

[0080] Throughout the asymmetrical fault, the PLL should remain synchronized with the grid voltage, which means the PLL must have a stable equilibrium point (SEP). Therefore, S + >0 and S - >0 must be considered as a transient stability constraint expression because it is consistent with the existence of SEP.

[0081] During asymmetrical faults, overcurrent must be prevented and RSC protected. Therefore, the rotor current constraint can be expressed as:

[0082]

[0083] Where, x s x represents the stator inductive reactance of a doubly-fed wind turbine. m I represents the mutual inductance of a doubly-fed wind turbine. rmax Indicates the upper limit of the rotor current;

[0084] To ensure the controllability of the DFIG during a fault, the RSC must avoid overmodulation. Therefore, the rotor voltage constraint can be expressed as:

[0085]

[0086] Where, x r The stator inductive reactance of the doubly-fed induction generator (DFIG) is represented by s, the slip of the DFIG is represented by σ, and the leakage flux coefficient of the DFIG is represented by U. rmax Indicates the upper limit of the rotor voltage;

[0087] S2: Based on the optimal unbalanced voltage suppression model, the d-axis of the positive-sequence and negative-sequence synchronous rotating coordinate systems is unified to the grid voltage orientation mode, and the dual-sequence synchronous rotating coordinate system is reconstructed to eliminate the nonlinearity and coupling characteristics caused by the dual-sequence power angle coupling term;

[0088] Specifically, in order to eliminate the effects of coupling and nonlinear characteristics related to power angle coupling terms, considering that the grid voltage source is the source of the equivalent positive and negative sequence sources in the circuit model, the d-axis of the positive and negative sequence synchronous coordinate system is oriented to the grid voltage. Figure 3 This is a schematic diagram of the dual-sequence coordinate system for a DFIG wind turbine. The voltage equation in the reconstructed dual-sequence synchronous coordinate system can be expressed as:

[0089]

[0090] Among them, U sd+ U represents the stator positive d-axis voltage. sd- U represents the stator negative d-axis voltage. sq+ U represents the stator positive-sequence q-axis voltage; sq- K represents the stator negative-sequence q-axis voltage; 1+ ,K 2+ K + The real and imaginary parts, K 1- ,K 2- K - The real and imaginary parts of , where ^ represents the state components in the reconstructed bi-order coordinate system.

[0091] After the dual-sequence synchronous coordinate system is reconstructed, the transient stability constraints are naturally included in the above equations. Since the reconstruction process is merely an equivalent mathematical transformation, the solvability of the reconstruction equations establishes the existence of a stable equilibrium point for the dual-sequence PLL. As long as the optimal unbalanced voltage suppression problem has a feasible solution, then S must be satisfied. + >0 and S - >0, thus ensuring that the PLL has a stable equilibrium point.

[0092] S3: Perform convexity processing on the reconstructed model, relax the capacity constraint of the rotor-side converter into a second-order cone form, and introduce auxiliary variables to transform the objective function and related constraints into a standard second-order cone programming problem;

[0093] The stator voltage equations in the reconstructed dual-sequence synchronous coordinate system are subjected to second-order cone relaxation:

[0094]

[0095] The rotor current constraint is relaxed by a second-order cone relaxation based on Cauchy's inequality, as follows:

[0096]

[0097] Similarly, a second-order cone relaxation is applied to the rotor voltage constraint, as follows:

[0098]

[0099] Among them, Ird+(-) I represents the positive and negative d-axis currents of the rotor. rq+(-) I represents the positive and negative q-axis currents of the rotor. rmax Indicates the upper limit of the rotor current; U rd+(-) U represents the positive and negative d-axis currents of the rotor. rq+(-) U represents the positive and negative q-axis voltages of the rotor. rmax ^ indicates the upper limit of the rotor voltage; ^ indicates the state components in the reconstructed dual-sequence coordinate system.

[0100] By introducing more auxiliary constraints and variables, the entire problem can be transformed into the standard SOCP form. To handle non-convex objective functions and constraints, auxiliary variables are introduced, and the inscribed polygon approximation method is used to linearize the nonlinear constraints. The solution error is controlled within an allowable range by iteratively increasing the number of polygon sides, as shown below:

[0101]

[0102] v, w, m, e, t, o are auxiliary variables for solving the problem.

[0103] During the relaxation process described above, some inequality constraints may have relaxation gaps, leading to the optimal solution not being the solution to the original problem. To address this issue, this invention introduces auxiliary linear constraints. For example... Figure 4 As shown, for a unit circle constraint of the form ,

[0104] h = wK 1+ U g +r 1+ mx 1+ e+r 2+ tx 2+ o

[0105] l=wK 2+ U g +x 1+ m+r 1+ e+x 2+ t+r 2+ o

[0106] h 2 +l 2 ≤1

[0107] We approximate it using an inscribed regular polygon. By increasing the number of sides of the polygon, the relaxation gap can be made arbitrarily small, thus ensuring the accuracy of the solution.

[0108] h and l are auxiliary intermediate variables.

[0109] (l j+1 -l j )h+(h j -h i+1 )l+hj+1 l j -h j l j+1 >0

[0110] h i(i+1) and l j(j+1) It is an inscribed vertex of a polygon inscribed in a unit circle.

[0111] S4: Solve the second-order cone programming (SOCP) problem to obtain the optimal voltage dynamic control command, and control the new energy generator set according to the command.

[0112] After the above transformation, the original non-convex OVUA problem is rigorously transformed into a standard SOCP problem, with the following form:

[0113]

[0114] By embedding the pre-constructed second-order cone programming problem code into the controller and calling commercial solvers such as Gurobi, the global optimum, i.e., the optimal current command, can be obtained in milliseconds. Then, through inverse coordinate transformation, the current reference value in the traditional PLL coordinate system is obtained and fed into... Figure 2 The current controller in the system can then be used to execute the commands.

[0115] Corresponding to the aforementioned embodiment of a method for coordinating and optimizing the positive and negative sequence voltages of new energy sources to adapt to high-power fault impacts, the present invention also provides an embodiment of a device for coordinating and optimizing the positive and negative sequence voltages of new energy sources to adapt to high-power fault impacts.

[0116] Figure 5 This is a block diagram illustrating a new energy positive and negative sequence voltage coordination optimization device adapted to high-power fault impacts, according to an exemplary embodiment. (Refer to...) Figure 5 The device includes:

[0117] Model building module 1 establishes the optimal unbalanced voltage suppression model of the doubly fed wind turbine under grid asymmetric faults. The model takes minimizing the voltage imbalance at the grid connection point as the objective function and includes the capacity constraints of the rotor-side converter and the synchronous stability constraints of the system.

[0118] Coordinate system reconstruction module 2, based on the optimal unbalanced voltage suppression model, unifies the d-axis of the positive-sequence and negative-sequence synchronous rotating coordinate systems to the grid voltage orientation mode, and reconstructs the dual-sequence synchronous rotating coordinate system to eliminate the nonlinearity and coupling characteristics caused by the dual-sequence power angle coupling term;

[0119] Model convexity module 3 performs convexity processing on the reconstructed model, relaxes the capacity constraint of the rotor-side converter into a second-order cone form, and introduces auxiliary variables to transform the objective function and related constraints into a standard second-order cone programming problem.

[0120] The optimization solution module 4 solves the second-order cone programming problem to obtain the optimal voltage dynamic control command, and controls the new energy generator set according to the command.

[0121] Accordingly, the present invention also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; and when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the above-described method for coordinating and optimizing positive and negative sequence voltages of new energy sources to adapt to high-power fault impacts.

[0122] In practical applications, the aforementioned processor includes a Field-Programmable Gate Array (FPGA), and the processor can be a Central Processing Unit (CPU) or a Digital Signal Processor (DSP). It is understood that for different devices, the electronic devices used to implement the functions of the aforementioned processor can also be other types, and this embodiment of the invention does not impose specific limitations.

[0123] The aforementioned memory can be volatile memory, such as random-access memory (RAM); or non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); or a combination of the above types of memory, and provides instructions and data to the processor.

[0124] Accordingly, the present invention also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the above-described method for coordinating and optimizing the positive and negative sequence voltages of new energy sources to adapt to high-power fault impacts.

[0125] Optionally, the computer-readable storage medium can be applied to any of the methods in the embodiments of the present invention, and the computer program causes the computer to execute the corresponding processes implemented by the processor in the various methods of the embodiments of the present invention. For the sake of brevity, these will not be described in detail here.

[0126] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0127] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0128] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0129] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0130] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0131] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for coordinating and optimizing the positive and negative sequence voltages of new energy sources to adapt to high-power fault impacts, characterized in that... Includes the following steps: Step 1: Construct the optimal unbalanced voltage suppression model for doubly fed wind turbine generators under grid asymmetric faults. The model takes minimizing the voltage imbalance at the grid connection point as the objective function and includes the capacity constraints of the rotor-side converter and the synchronous stability constraints of the system. Step 2: Based on the optimal unbalanced voltage suppression model, unify the d-axis of the positive-sequence and negative-sequence synchronous rotating coordinate systems to the grid voltage orientation mode, and reconstruct the dual-sequence synchronous rotating coordinate system; Step 3: Perform convexity processing on the reconstructed model, relax the capacity constraint of the rotor-side converter into a second-order cone form, and introduce auxiliary variables to transform the objective function and related constraints into a standard second-order cone programming problem. Step 4: Solve the second-order cone programming problem to obtain the optimal voltage dynamic control command, and control the new energy generator set according to the command.

2. The method as described in claim 1, characterized in that: In step 1, the objective function is to minimize the voltage imbalance at the grid connection point, which is expressed as follows: obj:minU s- / U s+ Where min represents the minimum value, U s+ U is the stator positive sequence voltage. s- This is the stator negative sequence voltage; The stator positive and negative sequence voltages are represented as follows: Among them, U g K represents the grid voltage. + K is the positive sequence voltage equivalent coefficient. - Z is the equivalent coefficient for negative sequence voltage. 1+ Z 2+ Z 1- Z 2- I represents the equivalent sequence impedance in the positive and negative sequence voltage equations; S+ For the stator positive sequence current, I S- This represents the stator negative sequence current; * indicates the conjugate of the vector. The stator positive and negative sequence voltage equations are transformed by Park to obtain the following equation. Where, r 1+ ,x 1+ Z 1+ The real and imaginary parts, r 2+ ,x 2+ Z 2+ The real and imaginary parts, r 1- ,x 1- Z 1- The real and imaginary parts, r 2- ,x 2- Z 2- The real and imaginary parts, S +(-) I represents the transient stability coefficient corresponding to the positive and negative order. sd+(-) I represents the stator positive and negative sequence d-axis current. sq+(-) δ represents the stator positive and negative q-axis currents. +(-) This represents the difference between the stator positive and negative sequence voltages and the grid voltage.

3. The method as described in claim 1, characterized in that: In step 1, the capacity constraint of the rotor-side converter includes rotor current constraint and rotor voltage constraint. The rotor current constraint is expressed as follows: Where, x s x represents the stator inductive reactance of a doubly-fed wind turbine. m I represents the mutual inductance of a doubly-fed wind turbine. rmax Indicates the upper limit of the rotor current; U s+ U is the stator positive sequence voltage. s- Stator negative sequence voltage; I sd+(-) I represents the stator positive and negative sequence d-axis current. sq+(-) Indicates the positive and negative q-axis currents of the stator; The rotor voltage constraint is expressed as follows: Where, x r U represents the stator inductive reactance of the doubly-fed induction generator (DFIG), s represents the slip of the DFIG, σ represents the leakage flux coefficient of the DFIG, and U represents the stator inductive reactance of the DFIG. rmax This indicates the upper limit of the rotor voltage.

4. The method as described in claim 2, characterized in that: In step 2, the stator voltage equation in the reconstructed dual-sequence synchronous rotating coordinate system is expressed as follows: Among them, U sd+ U represents the stator positive d-axis voltage. sd- U represents the stator negative d-axis voltage. sq+ U represents the stator positive-sequence q-axis voltage. sq- K represents the stator negative-sequence q-axis voltage; 1+ ,K 2+ K + The real and imaginary parts, K 1- ,K 2- K - The real and imaginary parts of ; ^ represents the reconstructed state components in the bi-order coordinate system.

5. The method as described in claim 4, characterized in that: In step 3, the stator voltage equations in the reconstructed dual-sequence synchronous coordinate system are obtained by second-order cone relaxation. Based on Cauchy's inequality, a second-order cone relaxation of the rotor current constraint is performed, resulting in... By performing second-order cone relaxation on the rotor voltage constraint, we obtain: Among them, I rd+(-) I represents the positive and negative d-axis currents of the rotor. rq+(-) I represents the positive and negative q-axis currents of the rotor. rmax Indicates the upper limit of the rotor current; U rd+(-) U represents the positive and negative d-axis currents of the rotor. rq+(-) U represents the positive and negative q-axis voltages of the rotor. rmax ^ indicates the upper limit of the rotor voltage; ^ indicates the state components in the reconstructed dual-sequence coordinate system.

6. The method as described in claim 5, characterized in that: In step 3, the auxiliary variables introduced include auxiliary solution variables and auxiliary intermediate variables; v, w, m, e, t, o are auxiliary variables for solving the problem; h=wK 1+ U g +r 1+ m-x 1+ e+r 2+ t-x 2+ o l=wK 2+ U g +x 1+ m+r 1+ e+x 2+ t+r 2+ o h and l are auxiliary intermediate variables; (l j+1 -l j )h+(h j -h i+1 )l+h j+1 l j -h j l j+1 >0 h i(i+1) and l j(j+1) It is an inscribed vertex of a polygon inscribed in a unit circle.

7. The method as described in claim 6, characterized in that: In step 4, the second-order cone programming problem to be solved is:

8. A new energy positive and negative sequence voltage coordination and optimization device adapted to high-power fault impact, characterized in that: include, The model building module is configured to construct an optimal unbalanced voltage suppression model for doubly fed wind turbine generators under asymmetrical grid faults. The model takes minimizing the voltage imbalance at the grid connection point as the objective function and includes capacity constraints of the rotor-side converter and synchronous stability constraints of the system. The coordinate system reconstruction module is configured to reconstruct the dual-sequence synchronous rotating coordinate system by unifying the d-axis of the positive-sequence and negative-sequence synchronous rotating coordinate systems to the grid voltage orientation mode based on the optimal unbalanced voltage suppression model. The model convexity module is configured to perform convexity processing on the reconstructed model, relax the capacity constraint of the rotor-side converter into a second-order cone form, and introduce auxiliary variables to transform the objective function and related constraints into a standard second-order cone programming problem. as well as, The optimization solution module is configured to solve the second-order cone programming problem, obtain the optimal voltage dynamic control command, and control the new energy generator set according to the command.

9. An electronic device, characterized in that: include, One or more processors; Memory, used to store 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 new energy positive and negative sequence voltage coordination optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing computer instructions thereon, characterized in that: When the computer instructions are executed by the processor, they implement the steps of the new energy positive and negative sequence voltage coordination optimization method as described in any one of claims 1 to 7 to adapt to high-power fault impacts.

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