Optimal Load Reduction Control Method and System for Large-Scale Wind Farms with Adaptive Kinetic Energy Energy Storage

Abstract

The present invention discloses an optimal load shedding control method and system for a large-scale wind farm with adaptive kinetic energy energy storage. The method of the present invention includes: obtaining wind turbine units and their demand parameters, determining the optimal rotor speed reference value of the wind turbine units during load shedding and the kinetic energy storage coefficient of each wind turbine unit; constructing a dynamic model of the wind farm node voltage, and obtaining the optimal reactive power reference value of the wind turbine units through optimization iteration based on the gradient projection method; constructing a wind turbine unit model based on model predictive control, and solving for the optimal power and weak magnetic current reference values to control each wind turbine unit with the goals of maximizing the kinetic energy storage coefficient, adaptively adjusting the maximum rotor speed boundary of the wind turbine units, and following the optimal reactive power reference value. The purpose of the present invention is 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 units, suppress the voltage fluctuation of the wind farm nodes, and enhance the load shedding capacity of the large-scale wind farm.

Description

Technical Field

[0001] The present 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 the power system. As the penetration rate of wind power in the power system 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 pitch angle change and the control method of adding 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 method does not consider the maximum kinetic energy storage boundary restricted by the converter current and voltage and the 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 present invention: Aiming at the above problems of the prior art, an optimal load shedding control method and system for a large-scale wind farm with adaptive kinetic energy storage are provided. The present 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 solution adopted by the present invention is as follows:

[0005] An optimal load shedding control method for a large-scale wind farm with adaptive kinetic energy storage, comprising the following steps:

[0006] S1, obtaining the wind turbine unit parameters and the wind turbine unit demand parameters;

[0007] S2, according to the wind turbine unit parameters and the wind turbine unit demand parameters, combining the converter current and voltage and the modulation ratio limit 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;

[0008] 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;

[0009] S4, perform 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 regulate each wind turbine.

[0010] Optionally, the calculation function expression for the maximum rotor speed boundary of the wind turbine in step S2 is:

[0011] ,

[0012] 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 machine-side converter respectively;

[0013] The calculation function expression for the optimal rotor speed reference value of the wind turbine during the load shedding period in step S2 is:

[0014] ,

[0015] ,

[0016] In the above formula, is the optimal rotor speed reference value of the wind turbine during the load shedding period, is the field-weakening current parameter for frequency modulation, 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;

[0017] The calculation function expression for the kinetic energy storage coefficient of each wind turbine in step S2 is:

[0018] ,

[0019] 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.

[0020] Optionally, step S3 includes:

[0021] S3.1, Construct the dynamic model of the node voltage of the wind farm:

[0022] ,

[0023] In the above formula, is the node voltage, is the topology matrix of the wind farm, is transpose of, is the topology matrix of the wind farm inverse matrix of, the superscript T represents the transpose operation, is the node reactance, is the reactive power injected at the nodes of the wind farm, is the node resistance, is the active power injected at the nodes of the wind farm, is the amplitude of the grid connection 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;

[0024] S3.2, Construct the objective function of the first-stage control aiming to reduce the node voltage deviation of the wind farm:

[0025] ,

[0026] 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;

[0027] S3.3, Through the gradient projection method, disperse the global voltage optimization problem to the local controller to optimize and iterate to obtain the optimal reactive power reference value of the wind turbine:

[0028] ,

[0029] In the above formula, and are the optimal reactive power reference values obtained in the (K + 1)-th and K-th iterations respectively, and are the voltage optimization parameters, represents gradient of, the superscript " " represents the projection function of the voltage gradient in the reactive power range.

[0030] Optionally, step S4 includes:

[0031] S4.1, construct the continuous-time state equation of the wind turbine model based on model predictive control:

[0032] ,

[0033] ,

[0034] wherein, is the state variable, is the derivative of, 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:

[0035] ,

[0036] ,

[0037] ,

[0038] ,

[0039] , ,

[0040] , ,

[0041] wherein, is the increment of the wind turbine rotor speed, is the increment of the wind turbine mechanical power, is the increment of the wind turbine electromagnetic power, 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 wind turbine active power reference value, is the increment of the wind turbine field-weakening current reference value, is the increment of the wind turbine reactive power reference value, is the increment of the wind turbine kinetic energy storage coefficient, is the increment of the wind turbine maximum rotor speed, is the model predictive control sampling time, is the wind turbine active power control time constant, is the wind turbine field-weakening current control time constant, 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:

[0042]

[0043] Among them, and are the sensitivity coefficients of the maximum rotor speed to the converter d-axis current , q-axis current respectively, is the initial value of the q-axis current , is the number of rotor pole pairs, is the synchronous generator magnetic flux, 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 synchronous generator moment of inertia, is the initial value of the rotor speed of the wind turbine generator set;

[0044] S4.2, set the sampling interval , discretize the continuous-time state equation of the wind turbine generator set model to obtain the discrete-time state equation of the wind turbine generator set model:

[0045] ,

[0046] ,

[0047] In the above formula, and are the state variables at the (k + 1)-th moment and the k-th moment 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:

[0048] , ,

[0049] In the above formula, is the sampling interval, t is time;

[0050] S4.3. Set the objective function and constraints 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, 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.

[0051] S4.4. Solve the discrete-time state equation of the wind turbine model according to the set objective function and constraints 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.

[0052] Optionally, the functional expression of the first objective function is:

[0053] ,

[0054] 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.

[0055] Optionally, the functional expression of the second objective function is:

[0056] ,

[0057] 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 shedding period at the k-th step.

[0058] Optionally, the functional expression of the third objective function is:

[0059] ,

[0060] 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 reactive power of the i-th wind turbine at the k-th step. is the reference value of the reactive power of the i-th wind turbine during the load shedding period at the k-th step.

[0061] Optionally, the calculation function expression of the objective function is:

[0062] ,

[0063] In the above formula, is the objective function, is the first objective function, is the second objective function, is the third objective function.

[0064] Optionally, the function expression of the constraint condition is:

[0065] ,

[0066] ,

[0067] ,

[0068] In the above formula, is the reference value of the active power of the wind turbine, is the available capacity of the active power of the wind turbine, is the reference value of the field weakening current of the wind turbine, is the maximum value of the converter current of the wind turbine, is the field weakening current of the wind turbine, is the available capacity of the reactive power of the wind turbine, is the reference value of the reactive power of the wind turbine, , is the number of wind turbines.

[0069] 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.

[0070] 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 to adaptively adjust 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 control, and the optimal power and field-weakening current reference values are calculated to control 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

[0071] Figure 1 It is a schematic diagram of the basic process of the method in the embodiment of the present invention.

[0072] Figure 2 It is a simulation diagram of the system frequency under different control methods in the embodiment of the present invention.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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

[0077] 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 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.

[0078] 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:

[0079] S1. Obtain the wind turbine parameters and wind turbine demand parameters;

[0080] S2. According to the wind turbine parameters and wind turbine demand parameters, combine the current voltage and modulation ratio limits of the wind turbine converter 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;

[0081] 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;

[0082] 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.

[0083] 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 .

[0084] The calculation function expression of the maximum rotor speed boundary of the wind turbine in step S2 of this embodiment is:

[0085] ,

[0086] In the above formula, is the maximum rotor speed boundary of the wind turbine generator set, 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.

[0087] In this embodiment, the calculation function expression of the optimal rotor speed reference value of the wind turbine generator set during the load shedding period in step S2 is:

[0088] ,

[0089] ,

[0090] In the above formula, is the optimal rotor speed reference value of the wind turbine generator set 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 generator set, is the frequency deviation, is the system frequency, is the system frequency reference value.

[0091] In this embodiment, the calculation function expression of the kinetic energy storage coefficient of each wind turbine generator set in step S2 is:

[0092] ,

[0093] In the above formula, is the kinetic energy storage coefficient of the i-th wind turbine generator set, is the rotational speed of the i-th wind turbine generator set, is the initial value of the maximum rotor speed boundary of the i-th wind turbine generator set, , is the number of wind turbine generator sets.

[0094] 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 generator set is obtained through iterative optimization of 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:

[0095] S3.1, constructing a dynamic model of the wind farm node voltage:

[0096] ,

[0097] In the above formula, is the node voltage, is the wind farm topology matrix, is the transpose of, is the wind farm topology matrix the inverse matrix of, and the superscript T represents the transpose operation, is the node reactance, is the reactive power injected at the wind farm node, is the node resistance, is the active power injected at the wind farm node, is the amplitude of the grid connection point voltage of the wind farm, is the coefficient of the wind farm topology matrix, is the reactive power sensitivity, is the active power-voltage coefficient;

[0098] S3.2. Construct the objective function of the first-stage control aiming to reduce the node voltage deviation of the wind farm:

[0099] ,

[0100] 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;

[0101] S3.3. Through the gradient projection method, the global voltage optimization problem is decentralized to the local controller to optimize and iterate to obtain the optimal reactive power reference value of the wind turbine:

[0102] ,

[0103] In the above formula, and are respectively the optimal reactive power reference values obtained in the K +1 and K th iterations, and are the voltage optimization parameters, represents the gradient of, and the superscript " " represents the projection function of the voltage gradient in the reactive power range.

[0104] The second-stage control of step S4 in this embodiment is specifically implemented based on a second-stage controller. In the second-stage controller, the active power, reactive power, and field-weakening current of the wind turbine are optimized for adaptive regulation 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 instructions 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:

[0105] S4.1, constructing a continuous-time state equation of a wind turbine model based on model predictive control:

[0106] ,

[0107] ,

[0108] where, is the state variable, is the derivative of, is the input variable, is the output variable, is the state matrix, is the input matrix, is the output matrix, and are coefficient matrices, and there are:

[0109] ,

[0110] ,

[0111] ,

[0112] ,

[0113] , ,

[0114] , ,

[0115] where, is the increment of the wind turbine rotor speed, is the increment of the wind turbine mechanical power, is the increment of the wind turbine electromagnetic power, is the increment of the field-weakening current, is the increment of the reactive power, and the superscript T represents the transpose operation, is the increment of the wind turbine active power reference value, is the increment of the wind turbine field-weakening current reference value, is the increment of the reactive power reference value 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:

[0116]

[0117] Among them, and are respectively the sensitivity coefficients of the maximum rotor speed to the converter d-axis current , q-axis current , is the initial quantity of the q-axis current (the subscript 0 represents the initial quantity), is the number of rotor poles, is the synchronous generator magnetic flux, is the initial quantity of the mechanical power of the wind turbine generator set, is the initial quantity of the mechanical power of the wind turbine generator set, is the synchronous generator moment of inertia, is the initial quantity of the rotor speed of the wind turbine generator set;

[0118] S4.2. Set the sampling interval , and discretize the continuous-time state equation of the wind turbine generator set model to obtain the discrete-time state equation of the wind turbine generator set model:

[0119] ,

[0120] ,

[0121] 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:

[0122] , ,

[0123] In the above formula, is the sampling interval, and t is the time;

[0124] 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;

[0125] 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.

[0126] In this embodiment, the functional expression of the first objective function is:

[0127] ,

[0128] 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.

[0129] In this embodiment, the functional expression of the second objective function is:

[0130] ,

[0131] 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 shedding period at the k-th step.

[0132] In this embodiment, the functional expression of the third objective function is:

[0133] ,

[0134] 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 reactive power of the i-th wind turbine at the k-th step, is the reference value of the reactive power of the i-th wind turbine during the load shedding period at the k-th step.

[0135] In this embodiment, the calculation function expression of the objective function is:

[0136] ,

[0137] In the above formula, is the objective function, is the first objective function, is the second objective function, is the third objective function.

[0138] In this embodiment, the function expression of the constraint condition is:

[0139] ,

[0140] ,

[0141] ,

[0142] In the above formula, is the reference value of the active power of the wind turbine, is the available capacity of the active power of the wind turbine, is the reference value of the field weakening current of the wind turbine, is the maximum value of the converter current of the wind turbine, is the field weakening current of the wind turbine, is the available capacity of the reactive power of the wind turbine, is the reference value of the reactive power of the wind turbine, , is the number of wind turbines, where the subscript i represents the initial quantity, the subscript max represents the maximum value, the superscript avi represents the maximum allowable value, and the superscript ref represents the reference value.

[0143] In order to verify the optimal load shedding control method for a large-scale wind farm 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.

[0144] Figure 2 This is the system frequency simulation diagram under different control methods during the load reduction of the wind turbine in this embodiment. Refer to Figure 2 It can be seen that, compared with the existing distributed control method ("Distributed") and the maximum power tracking control method ("Maximum Power Tracking"), the method proposed in this embodiment ("Proposed Method") effectively reduces the system frequency fluctuation during load reduction by optimizing the active power and the field-weakening current.

[0145] Figure 3 This is the terminal voltage simulation diagram under different control methods during the load reduction of the wind turbine in this embodiment. Refer to Figure 3 It can be seen that, compared with the existing distributed control method ("Distributed") and the 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 by optimizing the reactive power.

[0146] Figure 4 and Figure 5 This is the active power and reactive power output simulation diagram under different control methods during the transient voltage of the wind turbine in this embodiment. Refer to Figure 4 and Figure 5 It can be seen that, compared with the existing distributed control method ("Distributed") and the 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 reduction of the wind farm by optimizing the active power and reactive power, and enhances the load reduction ability of the wind farm.

[0147] Figure 6 This is the kinetic energy storage coefficient simulation diagram under different control methods during the transient voltage of the wind turbine in this embodiment. Refer to Figure 6 It can be seen that, compared with the existing distributed control method ("Distributed") and the maximum power tracking control method ("Maximum Power Tracking"), the method proposed in this embodiment ("Proposed Method") makes full use of the kinetic energy storage ability by optimizing the active power and the field-weakening current, enhances the load reduction ability of the wind farm, and reduces the wind energy loss.

[0148] It can be seen that the optimal load reduction control method for large-scale wind farms with adaptive kinetic energy storage in this embodiment studies the maximum rotor speed boundary of wind turbines by analyzing the feasible operating region of converter current and the modulation ratio limit, defines the kinetic energy storage coefficient to quantify the load reduction ability of wind turbines, adaptively adjusts the maximum kinetic energy storage boundary based on the system frequency deviation and the available active power of wind turbines during load reduction, reduces the system frequency deviation by making full use of the kinetic energy storage ability of the wind farm, and optimizes the reactive power of wind turbines, which can suppress the node voltage fluctuation of the wind farm and enhance the load reduction ability of large-scale wind farms.

[0149] 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 the large-scale wind farm with adaptive kinetic energy energy storage.

[0150] Those skilled in the art should understand that the technical solutions 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 code. 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 flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented 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 means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 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 implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 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 steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0151] 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 within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art in this technical field, several improvements and refinements made without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. An optimal load shedding control method for large-scale wind farms with adaptive kinetic energy energy storage, characterized in that, Including the following steps: S1. Obtain the wind turbine parameters and the wind turbine demand parameters; S2. According to the wind turbine parameters and the wind turbine demand parameters, combine the current, voltage and modulation ratio limits of the wind turbine converter 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 of the wind turbine during the load shedding period, and the kinetic energy storage coefficient of each wind turbine; the kinetic energy storage coefficient is the ratio of the rotational speed of the wind turbine to the initial value of the maximum rotor speed boundary; 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 to control each wind turbine 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; The function expression of the first objective function for maximizing the kinetic energy storage coefficient 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; The function expression of the second objective function for adaptively adjusting the maximum rotor speed boundary of the wind turbine is: ， In the above formula, is the second objective function, is the optimization parameter for the maximum kinetic energy storage boundary, 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 shedding period at the k-th step; The function expression of the third objective function for following the optimal reactive power reference value 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 reactive power of the i-th wind turbine at the k-th step, is the reference value of the reactive power of the i-th wind turbine during the load shedding period at the k-th step.

2. The optimal load shedding control method for a large-scale wind farm with adaptive kinetic energy energy storage according to claim 1, 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 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 of 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 reference value of the optimal rotor speed of the wind turbine during the load shedding period, is the parameter of the frequency regulation weak magnetic current, 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 reference value of the system frequency; 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.

3. The optimal load shedding control method for a large-scale wind farm with adaptive kinetic energy energy storage according to claim 1, characterized in that, Step S3 includes: S3.

1. Construct a dynamic model of the wind farm node voltage: ， In the above formula, is the node voltage, is the wind farm topology matrix, is the transpose of, is the wind farm topology matrix the inverse matrix of, where the superscript T represents the transpose operation, is the node reactance, is the reactive power injected into the wind farm nodes, is the node resistance, is the active power injected into the wind farm nodes, is the magnitude of the grid connection point voltage of the wind farm, is the coefficient of the wind farm topology matrix, is the reactive power sensitivity, is the active power-voltage coefficient; S3.

2. Construct an objective function for the first-stage control with the goal of reducing the wind farm node voltage deviation: ， In the above formula, is the objective function for 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 the optimal reactive power reference values obtained from the (K + 1)-th and K-th iterations respectively, and are voltage optimization parameters, denotes the gradient of, and the superscript " " represents the projection function of the voltage gradient in the reactive power range.

4. The optimal load shedding control method for a large-scale wind farm with adaptive kinetic energy energy storage according to claim 1, characterized in that Step S4 includes: S4.

1. Construct a continuous-time state equation of the wind turbine model based on model predictive control: ， ， wherein, is a state variable, is the derivative of, is an input variable, is an output variable, is a state matrix, is an input matrix, is an output matrix, and are coefficient matrices, and there is: ， ， ， ， ， ， ， ， Among them, 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: Among them, and are the maximum rotor speeds for the converter d-axis current , q-axis current sensitivity coefficients, is the initial value of the q-axis current , is the number of rotor pole pairs, is the synchronous generator magnetic flux, is the initial value of the wind turbine mechanical power , is the initial value of the wind turbine mechanical power , is the synchronous generator moment of inertia, 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 there is: ， ， 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 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, reactive power reference value, and field-weakening current reference value of the wind turbine for regulating each wind turbine. and the reactive power reference value of the wind turbine and the field-weakening current reference value to regulate each wind turbine.

5. The optimal load shedding control method for a large-scale wind farm with adaptive kinetic energy energy storage according to claim 4, wherein 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.

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

7. An optimal load shedding control system for a large-scale wind farm with adaptive kinetic energy energy storage, comprising a microprocessor and a memory connected to each other, characterized in that, The microprocessor is programmed or configured to execute the optimal load shedding control method for a large-scale wind farm with adaptive kinetic energy storage according to any one of claims 1 to 6.

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