Optimal load shedding control method and system for self-adaptive kinetic energy storage large-scale wind power plant

Abstract

The invention discloses an adaptive kinetic energy storage large-scale wind power plant optimal load shedding control method and system, and the method comprises the steps: obtaining wind turbine generators and demand parameters thereof, and determining the optimal rotor speed reference value of the wind turbine generators during load shedding and the kinetic energy storage coefficient of each wind turbine generator; constructing a wind power plant node voltage dynamic model, and performing optimization iteration based on a gradient projection method to obtain an optimal reactive power reference value of a wind turbine generator; and constructing a wind turbine generator model based on model prediction control, and solving to obtain the optimal power and weak magnetic current reference value by taking maximization of the kinetic energy storage coefficient, self-adaptive adjustment of the maximum rotor rotating speed boundary of the wind turbine generator and following of the optimal reactive power reference value as targets so as to regulate and control each wind turbine generator. The invention aims to fully utilize the kinetic energy storage capacity of the wind power plant to reduce the frequency deviation of the system, optimize the reactive power of the wind turbine generator, inhibit the node voltage fluctuation of the wind power plant and enhance the load shedding capacity of the large-scale wind power plant.

Description

Technical Field

[0001] The invention belongs to the technical field of wind power generation, and particularly relates to an optimal load shedding control method and system for a large-scale wind farm with adaptive kinetic energy storage. Background Art

[0002] The randomness and intermittency of wind energy pose great challenges to the stable operation of power systems. As the penetration rate of wind power in power systems is getting higher and higher, wind farms need to provide fast frequency regulation and dynamic voltage support capabilities. In actual wind turbines, wind turbine units usually operate in the maximum power point tracking mode. When the system load is cut off, the wind turbine should quickly respond to the system frequency and voltage fluctuations and perform load shedding control. Compared with the control methods of variable pitch angle and additional energy storage devices, kinetic energy storage control can avoid frequent pitch angle fluctuations and additional operating costs. However, the existing kinetic energy storage load shedding control methods do not consider the maximum kinetic energy storage boundary restricted by the converter current, voltage and modulation ratio, and the load shedding capacity of the wind farm is not fully utilized. Summary of the Invention

[0003] The technical problem to be solved by the invention: Aiming at the above problems of the prior art, the invention provides an optimal load shedding control method and system for a large-scale wind farm with adaptive kinetic energy storage. The invention aims to make full use of the kinetic energy storage capacity of the wind farm to reduce the system frequency deviation, optimize the reactive power of the wind turbine unit, suppress the voltage fluctuation of the wind farm node, and enhance the load shedding capacity of the large-scale wind farm.

[0004] In order to solve the above technical problems, the technical scheme adopted by the invention is as follows: An optimal load shedding control method for a large-scale wind farm with adaptive kinetic energy storage, comprising the following steps: S1, obtaining the wind turbine unit parameters and the wind turbine unit demand parameters; S2, according to the wind turbine unit parameters and the wind turbine unit demand parameters, combining the converter current, voltage and modulation ratio limits of the wind turbine unit to determine the maximum rotor speed boundary of the wind turbine unit, combining the system frequency deviation and the available active power capacity of the wind turbine unit to determine the optimal rotor speed reference value of the wind turbine unit during load shedding, and the kinetic energy storage coefficient of each wind turbine unit; S3, performing the first-level control: constructing a wind farm node voltage dynamic model, and dispersing the global voltage optimization problem to the local controller through the gradient projection method to optimize and iterate to obtain the optimal reactive power reference value of the wind turbine unit; S4, performing the second-level control: constructing a wind turbine unit model based on model predictive control, and solving to obtain the optimal power and weak magnetic current reference values with the goal of maximizing the kinetic energy storage coefficient, adaptively adjusting the maximum rotor speed boundary of the wind turbine unit, and following the optimal reactive power reference value to regulate each wind turbine unit.

[0005] Optionally, the calculation function expression for the maximum rotor speed boundary of the wind turbine in step S2 is: , In the above formula, is the maximum rotor speed boundary of the wind turbine, is the DC bus voltage, is the number of rotor pole pairs, and are the d-axis and q-axis inductances of the stator respectively, and are the d-axis and q-axis currents of the machine-side converter respectively; The calculation function expression for the optimal rotor speed reference value of the wind turbine during the load shedding period in step S2 is: , , In the above formula, is the optimal rotor speed reference value of the wind turbine during the load shedding period, is the frequency modulation weak magnetic current parameter, is the frequency droop coefficient, is the available active power capacity of the wind turbine, is the frequency deviation, is the system frequency, is the system frequency reference value; The calculation function expression for the kinetic energy storage coefficient of each wind turbine in step S2 is: , In the above formula, is the kinetic energy storage coefficient of the i-th wind turbine, is the rotational speed of the i-th wind turbine, is the initial value of the maximum rotor speed boundary of the i-th wind turbine, , is the number of wind turbines.

[0006] Optionally, step S3 includes: S3.1, constructing a dynamic model of the wind farm node voltage: , In the above formula, is the node voltage, is the wind farm topology matrix, is transpose of, is the wind farm topology matrix inverse matrix of, the superscript T represents the transpose operation, is the node reactance, Inject reactive power into the nodes of the wind farm, is the node resistance, is the active power injected into the nodes of the wind farm, is the voltage amplitude at the grid connection point of the wind farm, is the topology matrix coefficient of the wind farm, is the reactive power sensitivity, is the active power-voltage coefficient; S3.2. Construct the objective function of the first-stage control aiming to reduce the node voltage deviation of the wind farm: , In the above formula, is the objective function of the first-stage control, is the node voltage, is the node voltage reference value, is the voltage optimization parameter; S3.3. Optimize and iterate through the gradient projection method to disperse the global voltage optimization problem to the local controller to obtain the optimal reactive power reference value of the wind turbine: , In the above formula, and are the optimal reactive power reference values obtained from the (K + 1)-th and K-th iterations respectively, and are the voltage optimization parameters, represents gradient, and the superscript " " represents the projection function of the voltage gradient in the reactive power range.

[0007] Optionally, step S4 includes: S4.1. Construct the continuous-time state equation of the wind turbine model based on model predictive control: , , Among them, is the state variable, is the derivative, is the input variable, is the output variable, is the state matrix, is the input matrix, is the output matrix, and are the coefficient matrices, and there are: , , , , , , , , wherein, is the increment of the rotor speed of the wind turbine generator set, is the increment of the mechanical power of the wind turbine generator set, is the increment of the electromagnetic power of the wind turbine generator set, is the increment of the field-weakening current, is the increment of the reactive power. The superscript T represents the transpose operation, is the increment of the reference value of the active power of the wind turbine generator set, is the increment of the reference value of the field-weakening current of the wind turbine generator set, is the increment of the reference value of the reactive power of the wind turbine generator set, is the increment of the kinetic energy storage coefficient of the wind turbine generator set, is the increment of the maximum rotor speed of the wind turbine generator set, is the sampling time of the model predictive control, is the active power control time constant of the wind turbine generator set, is the field-weakening current control time constant of the wind turbine generator set, is the reactive power control time constant of the wind turbine generator set, is the initial value of the rotor speed of the wind turbine generator set, ~ are coefficients, and there are:

[0008] wherein, and are the sensitivity coefficients of the maximum rotor speed to the d-axis current and the q-axis current of the converter, is the initial value of the q-axis current , is the number of rotor poles, is the magnetic flux of the synchronous generator, is the initial value of the mechanical power of the wind turbine generator set, is the initial value of the mechanical power of the wind turbine generator set, is the moment of inertia of the synchronous generator, is the initial value of the rotor speed of the wind turbine generator set; S4.2. Set the sampling interval , discretize the continuous-time state equation of the wind turbine model to obtain the discrete-time state equation of the wind turbine model: , , In the above formula, and are the state variables at time k + 1 and time k respectively, is the input variable at time k, is the output variable at time k + 1, is the discretized state matrix, is the discretized input matrix, and there are: , , In the above formula, is the sampling interval, t is the time; S4.3. Set the objective function and constraint conditions for the discrete-time state equation of the wind turbine model, where the objective function includes the first objective function for maximizing the kinetic energy storage coefficient, the second objective function for adaptively adjusting the maximum rotor speed boundary of the wind turbine, and the third objective function for following the optimal reactive power reference value; S4.4. Solve the discrete-time state equation of the wind turbine model according to the set objective function and constraint conditions to obtain the optimal active power reference value of the wind turbine, the reactive power reference value of the wind turbine, and the field-weakening current reference value to control each wind turbine.

[0009] Optionally, the functional expression of the first objective function is: , In the above formula, is the first objective function, is the kinetic energy storage optimization parameter, is the step size, is the number of wind turbines, is the kinetic energy storage coefficient of the i-th wind turbine at the k-th step.

[0010] Optionally, the functional expression of the second objective function is: , In the above formula, is the second objective function, is the maximum kinetic energy storage boundary optimization parameter, is the step size, is the number of wind turbines, is the maximum rotor speed boundary of the i-th wind turbine in the k-th step. is the optimal rotor speed reference value of the i-th wind turbine during the load shedding period in the k-th step.

[0011] Optionally, the functional expression of the third objective function is: , In the above formula, is the third objective function, is the reactive power optimization parameter, is the step size, is the number of wind turbines, is the maximum rotor speed boundary of the i-th wind turbine in the k-th step, is the optimal rotor speed reference value of the i-th wind turbine during the load shedding period in the k-th step.

[0012] Optionally, the calculation functional expression of the objective function is: , In the above formula, is the objective function, is the first objective function, is the second objective function, is the third objective function.

[0013] Optionally, the functional expression of the constraint condition is: , , , In the above formula, is the active power reference value of the wind turbine, is the available capacity of the active power of the wind turbine, is the weak magnetic current reference value of the wind turbine, is the maximum value of the converter current of the wind turbine, is the weak magnetic current of the wind turbine, is the available capacity of the reactive power of the wind turbine, is the reactive power reference value of the wind turbine, , is the number of wind turbines.

[0014] In addition, the present invention also provides an optimal load shedding control system for a large-scale wind farm with adaptive kinetic energy energy storage, including a microprocessor and a memory connected to each other, and the microprocessor is programmed or configured to execute the optimal load shedding control method for the large-scale wind farm with adaptive kinetic energy energy storage.

[0015] Compared with the prior art, the present invention mainly has the following advantages: The optimal load shedding control method for a large-scale wind farm with adaptive kinetic energy storage of the present invention includes: obtaining wind turbine parameters and demand parameters; according to the obtained wind turbine parameters and wind turbine demand parameters, considering the current amplitude and modulation ratio limitations of the converter, analyzing the kinetic energy storage boundary, and quantifying the maximum load shedding capacity of the wind turbine; in the first-stage control, based on the gradient projection method, the global voltage optimization problem is decentralized to the local controller, and the optimal reactive power reference value of the wind turbine is obtained through the optimization iteration of the wind farm line impedance parameters and reactive power, and the optimal reactive power reference value is input into the second-stage control; in the second-stage control, the active power, reactive power, and field-weakening current of the wind turbine are optimized for adaptive adjustment and to maximize the kinetic energy storage boundary. Considering the delay characteristics of the wind turbine, the reactive power is further optimized to follow the optimization instruction of the first-stage control, and the optimal power and field-weakening current reference values are calculated to regulate each wind turbine. The present invention can effectively reduce the node voltage and frequency deviation of the wind farm under system load fluctuations, and maximize the kinetic energy storage capacity of the wind turbine to reduce wind energy loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the basic process of the method according to the embodiment of the present invention.

[0017] Figure 2 It is a simulation diagram of the system frequency under different control methods in the embodiment of the present invention.

[0018] Figure 3 It is a simulation diagram of the terminal voltage of the wind turbine under different control methods in the embodiment of the present invention.

[0019] Figure 4 It is a simulation diagram of the active power of the wind turbine under different control methods in the embodiment of the present invention.

[0020] Figure 5 It is a simulation diagram of the reactive power of the wind turbine under different control methods in the embodiment of the present invention.

[0021] Figure 6 It is a simulation diagram of the kinetic energy storage coefficient of the wind turbine under different control methods in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] As Figure 1 shown, the optimal load shedding control method for a large-scale wind farm with adaptive kinetic energy energy storage in this embodiment includes the following steps: S1. Obtain the wind turbine parameters and wind turbine demand parameters; S2. According to the wind turbine parameters and wind turbine demand parameters, combine the current and voltage of the wind turbine converter and the modulation ratio limit to determine the maximum rotor speed boundary of the wind turbine, combine the system frequency deviation and the available active power capacity of the wind turbine to determine the optimal rotor speed reference value during load shedding, and the kinetic energy storage coefficient of each wind turbine; S3. Execute the first-level control: construct a dynamic model of the wind farm node voltage, and disperse the global voltage optimization problem to the local controller through the gradient projection method to optimize and iterate to obtain the optimal reactive power reference value of the wind turbine; S4. Execute the second-level control: construct a wind turbine model based on model predictive control, and solve for the optimal power and field-weakening current reference values with the goal of maximizing the kinetic energy storage coefficient, adaptively adjusting the maximum rotor speed boundary of the wind turbine, and following the optimal reactive power reference value to control each wind turbine.

[0024] In step S1 of this embodiment, the wind turbine parameters include the generator resistance R s and inductance L s , DC bus voltage V dc , rotor speed ω r , active power P W , reactive power Q W , field-weakening current i sd ; the wind turbine demand parameters include wind speed v , wind farm line resistance R g , reactance L g , node voltage V W , system frequency f .

[0025] The calculation function expression of the maximum rotor speed boundary of the wind turbine in step S2 of this embodiment is: , In the above formula, is the maximum rotor speed boundary of the wind turbine, is the DC bus voltage, is the number of rotor poles, and are the d-axis and q-axis inductances of the stator respectively, and are the d-axis and q-axis currents of the grid-side converter respectively.

[0026] In this embodiment, the calculation function expression of the optimal rotor speed reference value of the wind turbine during the load shedding in step S2 is: , , In the above formula, is the optimal rotor speed reference value of the wind turbine during the load shedding, is the frequency modulation weak magnetic current parameter, is the frequency droop coefficient, is the available active power capacity of the wind turbine, is the frequency deviation, is the system frequency, is the system frequency reference value.

[0027] In this embodiment, the calculation function expression of the kinetic energy storage coefficient of each wind turbine in step S2 is: , In the above formula, is the kinetic energy storage coefficient of the i-th wind turbine, is the rotational speed of the i-th wind turbine, is the initial value of the maximum rotor speed boundary of the i-th wind turbine, , is the number of wind turbines.

[0028] In this embodiment, a two-stage controller is established. The first-stage control in step S3 of this embodiment is specifically implemented based on the first-stage controller. In the first-stage controller, the global voltage optimization problem is decentralized to the local controller based on the gradient projection method, and the optimal reactive power reference value of the wind turbine is obtained through iterative optimization based on the wind farm line impedance parameters and reactive power, and the optimal reactive power reference value is input into the second-stage control. Step S3 of this embodiment includes: S3.1, constructing a dynamic model of the wind farm node voltage: , In the above formula, is the node voltage, is the wind farm topology matrix, is transpose of, is the wind farm topology matrix inverse matrix of, and the superscript T represents the transpose operation, is the node reactance, Inject reactive power into the nodes of the wind farm, is the node resistance, is the active power injected into the nodes of the wind farm, is the amplitude of the grid-connected point voltage of the wind farm, is the topology matrix coefficient of the wind farm, is the reactive power sensitivity, is the active power-voltage coefficient; S3.2, construct the objective function of the first-stage control aiming to reduce the node voltage deviation of the wind farm: , In the above formula, is the objective function of the first-stage control, is the node voltage, is the reference value of the node voltage, is the voltage optimization parameter; S3.3, disperse the global voltage optimization problem to the local controller through the gradient projection method to optimize and iterate to obtain the optimal reactive power reference value of the wind turbine: , In the above formula, and are respectively the optimal reactive power reference values obtained in the K +1-th and K -th iterations, and are the voltage optimization parameters, represents 's gradient, and the superscript " " represents the projection function of the voltage gradient in the reactive power range.

[0029] The second-stage control of step S4 in this embodiment is specifically implemented based on the second-stage controller. In the second-stage controller, the active power, reactive power, and field-weakening current of the wind turbine are optimized to adaptively regulate and maximize the kinetic energy storage boundary. Considering the delay characteristics of the wind turbine, the reactive power is further optimized to follow the optimization instruction of the first-stage controller, and the optimal power and field-weakening current reference values are calculated to control each wind turbine. Step S4 in this embodiment includes: S4.1, construct the continuous-time state equation of the wind turbine model based on model predictive control: , , Among them, is the state variable, is 's derivative, is the input variable, is the output variable, is the state matrix, is the input matrix, is the output matrix, and is the coefficient matrix, and there are: , , , , , , , , where, is the increment of the rotor speed of the wind turbine, is the increment of the mechanical power of the wind turbine, is the increment of the electromagnetic power of the wind turbine, is the increment of the field-weakening current, is the increment of the reactive power. The superscript T represents the transpose operation, is the increment of the reference value of the active power of the wind turbine, is the increment of the reference value of the field-weakening current of the wind turbine, is the increment of the reference value of the reactive power of the wind turbine, is the increment of the kinetic energy storage coefficient of the wind turbine, is the increment of the maximum rotor speed of the wind turbine, is the sampling time of the model predictive control, is the active power control time constant of the wind turbine, is the field-weakening current control time constant of the wind turbine, is the reactive power control time constant of the wind turbine, is the initial value of the rotor speed of the wind turbine, ~ are coefficients, and there are:

[0030] where, and are the sensitivity coefficients of the maximum rotor speed to the d-axis current 、q-axis current of the converter, is the initial value of the q-axis current (the subscript 0 represents the initial value), is the number of rotor poles, is the magnetic flux of the synchronous generator, is the initial value of the mechanical power of the wind turbine ; is the initial value of the mechanical power of the wind turbine ; is the moment of inertia of the synchronous generator, is the initial value of the rotor speed of the wind turbine; S4.2, set the sampling interval , and discretize the continuous-time state equation of the wind turbine model to obtain the discrete-time state equation of the wind turbine model: , , In the above formula, and are the state variables at the (k + 1)-th and k-th moments respectively, is the input variable at the k-th moment, is the output variable at the (k + 1)-th moment, is the discretized state matrix, is the discretized input matrix, and there are: , , In the above formula, is the sampling interval, and t is the time; S4.3, set the objective function and constraint conditions for the discrete-time state equation of the wind turbine model, where the objective function includes a first objective function for maximizing the kinetic energy storage coefficient to fully utilize the kinetic energy storage capacity of the wind turbine, a second objective function for adaptively adjusting the maximum rotor speed boundary (maximum kinetic energy storage boundary) of the wind turbine to reduce the system frequency deviation, and a third objective function for following the optimal reactive power reference value to improve the reactive power response rate; S4.4, solve the discrete-time state equation of the wind turbine model according to the set objective function and constraint conditions to obtain the optimal active power reference value of the wind turbine, the reactive power reference value of the wind turbine, and the field-weakening current reference value to control each wind turbine.

[0031] In this embodiment, the functional expression of the first objective function is: , In the above formula, is the first objective function, is the kinetic energy storage optimization parameter, is the step size, is the number of wind turbines, is the kinetic energy storage coefficient of the i-th wind turbine at the k-th step.

[0032] In this embodiment, the functional expression of the second objective function is: , In the above formula, is the second objective function, is the maximum kinetic energy storage boundary optimization parameter, is the step size, is the number of wind turbines, is the maximum rotor speed boundary of the i-th wind turbine at the k-th step, is the optimal rotor speed reference value of the i-th wind turbine during the load reduction period at the k-th step.

[0033] In this embodiment, the functional expression of the third objective function is: , In the above formula, is the third objective function, is the reactive power optimization parameter, is the step size, is the number of wind turbines, is the maximum rotor speed boundary of the i-th wind turbine at the k-th step, is the optimal rotor speed reference value of the i-th wind turbine during the load reduction period at the k-th step.

[0034] In this embodiment, the calculation functional expression of the objective function is: , In the above formula, is the objective function, is the first objective function, is the second objective function, is the third objective function.

[0035] In this embodiment, the functional expression of the constraint condition is: , , , In the above formula, is the active power reference value of the wind turbine, is the available capacity of the active power of the wind turbine, is the weak magnetic current reference value of the wind turbine, is the maximum value of the converter current of the wind turbine, is the weak magnetic current of the wind turbine, is the available reactive power capacity of the wind turbine generator set, is the reactive power reference value of the wind turbine generator set, , is the number of wind turbine generator sets, where the subscript i represents the initial quantity, and the subscript max represents the maximum value, and the superscript avi represents the maximum allowable value, and the superscript ref represents the reference value.

[0036] In order to verify the optimal load shedding control method for large-scale wind farms with adaptive kinetic energy energy storage in this embodiment, the method of this embodiment is verified and the results are as Figures 2 - 6 shown.

[0037] Figure 2 is the system frequency simulation diagram under different control methods during the load shedding of the wind turbine generator set in this embodiment. Refer to Figure 2 It can be seen that compared with the existing distributed control method ("distributed") and maximum power tracking control method ("maximum power tracking"), the method proposed in this embodiment ("proposed method") effectively reduces the system frequency fluctuation during load shedding by optimizing the active power and weak magnetic current.

[0038] Figure 3 is the terminal voltage simulation diagram under different control methods during the load shedding of the wind turbine generator set in this embodiment. Refer to Figure 3 It can be seen that compared with the existing distributed control method ("distributed") and maximum power tracking control method ("maximum power tracking"), the method proposed in this embodiment ("proposed method") effectively reduces the terminal voltage deviation of the wind turbine generator set by optimizing the reactive power.

[0039] Figure 4 and Figure 5 are the active power and reactive power output simulation diagrams under different control methods during the transient voltage of the wind turbine generator set in this embodiment. Refer to Figure 4 and Figure 5 It can be seen that compared with the existing distributed control method ("distributed") and maximum power tracking control method ("maximum power tracking"), the method proposed in this embodiment ("proposed method") reduces the frequency and node voltage fluctuations during the load shedding of the wind farm by optimizing the active power and reactive power, and enhances the load shedding ability of the wind farm.

[0040] Figure 6 is the kinetic energy storage coefficient simulation diagram under different control methods during the transient voltage of the wind turbine generator set in this embodiment. Refer to Figure 6It can be seen that, compared with the existing distributed control method ("distributed") and maximum power point tracking control method ("maximum power tracking"), the method proposed in this embodiment ("the proposed method") makes full use of the kinetic energy storage capacity by optimizing the active power and field weakening current, enhances the load shedding capacity of the wind farm, and reduces wind energy loss.

[0041] It can be seen that the optimal load shedding control method for a large-scale wind farm with adaptive kinetic energy energy storage in this embodiment studies the maximum rotor speed boundary of the wind turbine by analyzing the feasible operating region of the converter current and the modulation ratio limit, defines the kinetic energy storage coefficient to quantify the load shedding capacity of the wind turbine, adaptively adjusts the maximum kinetic energy storage boundary based on the system frequency deviation and the available active power of the wind turbine during load shedding, reduces the system frequency deviation by making full use of the kinetic energy storage capacity of the wind farm, and optimizes the reactive power of the wind turbine, which can suppress the node voltage fluctuation of the wind farm and enhance the load shedding capacity of the large-scale wind farm.

[0042] In addition, this embodiment also provides an optimal load shedding control system for a large-scale wind farm with adaptive kinetic energy energy storage, including a microprocessor and a memory connected to each other, and the microprocessor is programmed or configured to execute the optimal load shedding control method for a large-scale wind farm with adaptive kinetic energy energy storage.

[0043] Those skilled in the art should understand that the technical solution provided by the present invention can be in the form of a method, a system, or a computer program product. Therefore, the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be implemented in the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes. The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device realizes the functions in the process Figure 1 one process or multiple processes and / or blocksFigure 1 The functions specified in one or more boxes. These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide for implementing in the process Figure 1 One process or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes.

[0044] The above is only the preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. An optimal load shedding control method for a large-scale wind farm with adaptive kinetic energy storage, characterized in that: The steps include: S1, obtaining wind turbine parameters and wind turbine demand parameters; S2, according to the wind turbine parameters and the wind turbine demand parameters, combined with the wind turbine converter current voltage and modulation ratio limit, determine the wind turbine maximum rotor speed boundary, combined with the system frequency deviation and the wind turbine available active power capacity to determine the wind turbine optimal rotor speed reference value during load reduction, as well as the kinetic energy storage coefficient of each wind turbine; S3, execute the first level control: construct a dynamic model of wind farm node voltage, and distribute the global voltage optimization problem to the local controller based on the gradient projection method to optimize and iterate to obtain the optimal reactive power reference value of the wind turbine; S4, execute the second level control: construct a wind turbine model based on model predictive control, with the goal of maximizing the kinetic energy storage coefficient, adaptively adjusting the maximum rotor speed boundary of the wind turbine, and following the optimal reactive power reference value to obtain the optimal power and weak magnetic current reference value to regulate each wind turbine.

2. The large-scale wind farm optimal load shedding control method with adaptive kinetic energy storage according to claim 1 is characterized in that: The calculation function expression of the maximum rotor speed boundary of the wind turbine in step S2 is: ， In the above formula, is the maximum rotor speed limit of the wind turbine, is the DC bus voltage, is the number of rotor pole pairs, and are the d-axis and q-axis inductances of the stator, and are the d-axis and q-axis currents of the machine-side converter respectively; The calculation function expression of the optimal rotor speed reference value of the wind turbine during the load reduction period in step S2 is: ， ， In the above formula, is the optimal rotor speed reference value of the wind turbine during load shedding, is the frequency-modulated weak magnetic current parameter, is the frequency droop coefficient, is the available active power capacity of the wind turbine, is the frequency deviation, is the system frequency, is the system frequency reference value; The calculation function expression of the kinetic energy storage coefficient of each wind turbine in step S2 is: ， In the above formula, is the kinetic energy storage coefficient of the i-th wind turbine, is the speed of the i-th wind turbine, is the initial value of the maximum rotor speed boundary of the wind turbine of the i-th wind turbine, , is the number of wind turbines.

3. The large-scale wind farm optimal load shedding control method with adaptive kinetic energy storage according to claim 1 is characterized in that: Step S3 includes: S3.1, construct the wind farm node voltage dynamic model: ， In the above formula, is the node voltage, is the wind farm topology matrix, for The transpose of is the wind farm topology matrix The inverse matrix of , the superscript T indicates the transpose operation, is the node reactance, Inject reactive power into wind farm nodes, is the node resistance, Inject active power into wind farm nodes, is the voltage amplitude at the wind farm grid connection point, is the wind farm topology matrix coefficient, is the reactive power sensitivity, is the active power-voltage coefficient; S3.2, construct the objective function of the first stage control with the goal of reducing the voltage deviation of wind farm nodes: ， In the above formula, is the objective function of the first stage control, is the node voltage, is the node voltage reference value, Optimize parameters for voltage; S3.3, by distributing the global voltage optimization problem to the local controller based on the gradient projection method, the optimal reactive power reference value of the wind turbine is obtained by optimization iteration: ， In the above formula, and The optimal reactive power reference value is obtained for the K+1th and Kth iterations respectively, and is the voltage optimization parameter, express The gradient of ” represents the projection function of voltage gradient in the reactive power range.

4. The optimal load shedding control method for a large-scale wind farm with adaptive kinetic energy storage according to claim 1 is characterized in that: Step S4 includes: S4.1, construct the continuous-time state equation of the wind turbine model based on model predictive control: ， ， in, is the state variable, for The derivative of is the input variable, is the output variable, is the state matrix, is the input matrix, is the output matrix, and is a coefficient matrix, and has: ， ， ， ， ， ， ， ， in, is the wind turbine rotor speed increment, is the mechanical power increment of the wind turbine, is the electromagnetic power increment of the wind turbine, is the field weakening current increment, is the reactive power increment, and the superscript T indicates the transposition operation. is the reference value increment of active power of wind turbine, is the reference value increment of the weak magnetic current of the wind turbine, is the reactive power reference value increment of the wind turbine, is the increment of kinetic energy storage coefficient of wind turbine, is the maximum rotor speed increment of the wind turbine, Control sampling time for model prediction, is the active power control time constant of the wind turbine, is the time constant of the weak magnetic current control of the wind turbine, is the reactive power control time constant of the wind turbine, is the initial value of the wind turbine rotor speed, ~ is the coefficient, and: in, and The maximum rotor speed Converter D-axis current , q-axis current The sensitivity coefficient, is the q-axis current The initial amount of is the number of rotor pole pairs, is the synchronous generator magnetic flux, is the mechanical power of the wind turbine The initial amount of is the mechanical power of the wind turbine The initial amount of is the moment of inertia of the synchronous generator, is the initial value of the wind turbine rotor speed; S4.2, set the sampling interval , discretize the continuous-time state equation of the wind turbine model to obtain the discrete-time state equation of the wind turbine model: ， ， In the above formula, and are the state variables at time k+1 and time k respectively, is the input variable at time k, is the output variable at time k+1, is the discretized state matrix, is the discretized input matrix, and: ， ， In the above formula, is the sampling interval, t is the time; S4.3, setting objective functions and constraints for the discrete time state equation of the wind turbine model, wherein the objective functions include a first objective function for maximizing the kinetic energy storage coefficient, a second objective function for adaptively adjusting the maximum rotor speed boundary of the wind turbine, and a third objective function for following the optimal reactive power reference value; S4.4, according to the set objective function and constraints, solve the discrete time state equation of the wind turbine model to obtain the optimal active power reference value of the wind turbine , wind turbine reactive power reference value and field weakening current reference value To control each wind turbine.

5. The large-scale wind farm optimal load shedding control method with adaptive kinetic energy storage according to claim 4 is characterized in that: The functional expression of the first objective function is: ， In the above formula, is the first objective function, Optimizing parameters for kinetic energy storage, is the step length, is the number of wind turbines, is the kinetic energy storage coefficient of the i-th wind turbine in the k-th step.

6. The optimal load shedding control method for large-scale wind farms with adaptive kinetic energy storage according to claim 4 is characterized in that: The function expression of the second objective function is: ， In the above formula, is the second objective function, Optimize parameters for maximum kinetic energy storage boundary, is the step length, is the number of wind turbines, is the maximum rotor speed boundary of the wind turbine of the i-th wind turbine in the k-th step, is the optimal rotor speed reference value of the wind turbine during the load reduction period of the i-th wind turbine in the k-th step.

7. The large-scale wind farm optimal load shedding control method with adaptive kinetic energy storage according to claim 4 is characterized in that: The functional expression of the third objective function is: ， In the above formula, is the third objective function, Optimize the parameters for reactive power, is the step length, is the number of wind turbines, is the maximum rotor speed boundary of the wind turbine of the i-th wind turbine in the k-th step, is the optimal rotor speed reference value of the wind turbine during the load reduction period of the i-th wind turbine in the k-th step.

8. The large-scale wind farm optimal load shedding control method with adaptive kinetic energy storage according to claim 4 is characterized in that: The calculation function expression of the objective function is: ， In the above formula, is the objective function, is the first objective function, is the second objective function, is the third objective function.

9. The large-scale wind farm optimal load shedding control method with adaptive kinetic energy storage according to claim 4 is characterized in that: The function expression of the constraint condition is: ， ， ， In the above formula, is the active power reference value of the wind turbine, is the available capacity of active power of wind turbines, is the reference value of the weak magnetic current of the wind turbine, is the maximum current of the wind turbine converter, is the weak magnetic current of the wind turbine, is the available reactive power capacity of the wind turbine, is the reactive power reference value of the wind turbine, , is the number of wind turbines.

10. A large-scale wind farm optimal load shedding control system with adaptive kinetic energy storage, comprising an interconnected microprocessor and a memory, characterized in that: The microprocessor is programmed or configured to execute the large-scale wind farm optimal load reduction control method for adaptive kinetic energy storage as described in any one of claims 1 to 9.

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