Large-scale wind turbine group power control method and system under full-wind-domain working condition

Abstract

The invention discloses a large-scale wind turbine group power control method and system under a full-wind-domain working condition. The method comprises the steps that wind power plant parameters and wind turbine generator set parameters under the full-wind-domain working condition are collected; according to the parameters, establishing a wind power utilization evaluation coefficient linearization model, a wind turbine generator kinetic energy storage evaluation coefficient linearization model and a converter operation risk evaluation coefficient linearization model; constructing a whole-wind-domain wind power plant power controller based on each linearization model: in a low-wind-speed interval, optimizing the output power of a wind turbine generator to improve the wind energy capture efficiency and reduce the terminal voltage fluctuation; and in a high wind speed interval, the terminal voltage deviation and the operation risk coefficient of the converter are reduced by optimizing the output power and the flux weakening current of the wind turbine generator. The method can effectively improve the wind energy capturing capability in a low-wind-speed interval, reduce the terminal voltage deviation and the converter operation risk coefficient in a high-wind-speed interval, guarantee the operation stability of the whole-wind-domain wind power plant, and achieve the optimal power control of the whole-wind-domain wind power plant.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of wind power, and specifically relates to a method and system for controlling the power of a large-scale wind turbine group under all-wind-region working conditions. Background Art

[0002] The stable operation of wind turbines and large-scale wind farms is closely related to the fluctuation and range of wind speed. Wind turbines and wind farms will face different control problems in different wind speed intervals. The randomness and volatility of wind speed have brought great challenges to the stability and economy of the operation of wind turbines and wind farms. In the low wind speed interval, the main problem is to improve the wind energy capture ability and economy of wind turbines; while in the high wind speed interval, the main problems are the severe fluctuation of the terminal voltage of wind turbines and the increase in the operation risk coefficient of converters.

[0003] Traditional large-scale wind farm power control methods do not consider the influence of different wind speed intervals on the operation performance of wind turbines and wind farms. Therefore, it is urgent to study how to realize the power control method of large-scale wind farms in the whole wind region according to the response differences of wind turbines and wind farms in different wind speed intervals. Summary of the Invention

[0004] In view of the technical problems existing in the prior art, the present invention provides a method and system for controlling the power of a large-scale wind turbine group under all-wind-region working conditions, which can effectively improve the wind energy capture ability of wind turbines in the low wind speed interval, reduce the terminal voltage deviation of the wind farm and the operation risk coefficient of the converter in the high wind speed interval, ensure the stability and economy of the operation of large-scale wind farms in the whole wind region, and realize the optimal power control of large-scale wind farms in the whole wind region.

[0005] To solve the above technical problems, the technical solution proposed by the present invention is as follows: A method for controlling the power of a large-scale wind turbine group under all-wind-region working conditions, comprising the steps of: 1) Collecting the parameters of the wind farm and the wind turbines under all-wind-region working conditions; 2) Based on the parameters of the wind farm and the wind turbines under all-wind-region working conditions and the wind turbine control method, establishing a linearized model of the wind energy utilization evaluation coefficient of the wind turbines in the whole wind region, a linearized model of the kinetic energy storage evaluation coefficient of the wind turbines, and a linearized model of the converter operation risk evaluation coefficient; 3) Based on each linearized model, according to the different control requirements of wind turbines in the low wind speed interval and the high wind speed interval, constructing a power controller for the wind farm in the whole wind region. Specifically: Obtaining the incoming flow wind speed and determining whether the incoming flow wind speed belongs to the low wind speed interval or the high wind speed interval; When the incoming flow wind speed is in the low wind speed range, considering the coupling characteristics of the wind energy utilization coefficient-rotor speed of the wind turbine, based on the linearized model of the wind energy utilization evaluation coefficient of the wind turbine, the wind energy capture efficiency is improved and the terminal voltage fluctuation of the wind turbine is reduced by optimizing the output power of the wind turbine; When the incoming flow wind speed is in the high wind speed range, considering the boundary coupling characteristics of the terminal voltage-rotor speed of the wind turbine, based on the linearized model of the kinetic energy storage evaluation coefficient of the wind turbine and the linearized model of the converter operation risk evaluation coefficient, the terminal voltage deviation of the wind turbine and the converter operation risk coefficient are reduced by optimizing the output power and field weakening current of the wind turbine; Among them, the low wind speed range corresponds to the first preset wind speed range, and the high wind speed range corresponds to the second preset wind speed range; the second preset wind speed range is greater than the second preset wind speed range.

[0006] Preferably, in step 1), the wind farm parameters include the wind farm line impedance R g 、 inductance L g and virtual damping D ; the wind turbine parameters include the incoming flow wind speed v , DC bus voltage V dc , rotor speed ω r , active power P W , reactive power Q W , field weakening current i sd and converter temperature T .

[0007] Preferably, in step 2), the specific process of establishing the linearized model of the wind energy utilization evaluation coefficient of the wind turbine in the whole wind domain is as follows: Establish a wind energy utilization evaluation coefficient model of the wind turbine , specifically: ; In the formula, is the wind energy capture coefficient of the wind turbine, is the maximum wind energy capture coefficient of the wind turbine at this wind speed; By linearizing near the sampling point, establish a linearized model of the wind energy utilization evaluation coefficient of the wind turbine: ; , ; , , ; In the formula, is the moment of inertia of the wind turbine generator set, is the rotor speed of the wind turbine generator set, is the initial value of the rotor speed of the wind turbine generator set, and are the output mechanical power and electromagnetic power of the wind turbine generator set, and are the initial values of the output mechanical power and electromagnetic power of the wind turbine generator set, is the pitch angle of the wind turbine generator set, is the initial value of the wind energy capture coefficient of the wind turbine generator set; the subscript 0 represents the initial value, represents the increment.

[0008] Preferably, when the incoming flow wind speed is in the low wind speed range, considering the coupling characteristics of the wind energy utilization coefficient - rotor speed of the wind turbine generator set, based on the linearized model of the wind energy utilization evaluation coefficient of the wind turbine generator set, the specific process of improving the wind energy capture efficiency and reducing the terminal voltage fluctuation of the wind turbine generator set by optimizing the output power of the wind turbine generator set is as follows: Establish a state - space model of a large - scale wind farm under low wind speed: ; In the formula, the state variable ; is the first - order derivative of the state variable; The input variable , the output variable ; , , ; , ; ; In the formula, , , and are the control time constants corresponding to the mechanical power, electromagnetic power, pitch angle, and reactive power of the wind turbine generator set respectively, and are the incremental command values of the active power and reactive power of the wind turbine generator set, and are the wind energy utilization evaluation coefficient and the incremental output of reactive power of the wind turbine generator set; the superscript ref represents the reference quantity.

[0009] Preferably, the first control objective of the wind farm power controller in the low wind speed range is to maximize the wind energy capture ability of the wind turbine generator set, specifically: ; Among them, N p is the control step of the controller, N W is the number of wind turbines, is the wind energy capture ability weight parameter, is the k step-down wind energy utilization evaluation coefficient of the wind turbine; The second control objective of the low-wind-speed interval wind farm power controller is to track the reactive power command to reduce the wind turbine terminal voltage deviation, specifically: ; Among them, is the reactive power tracking weight parameter at low wind speed; and are the reactive power output and reactive power reference of the wind turbine at the k step of the controller; The control quantity constraints of the low-wind-speed interval wind farm power controller are specifically: ; Among them, and are the reference values of the active power and reactive power outputs of the i th wind turbine, and are the maximum allowable values of the active power and reactive power outputs of the i th wind turbine.

[0010] Preferably, the specific establishment process of the linearization model of the wind turbine kinetic energy storage evaluation coefficient in step 2) is: First, construct the wind turbine kinetic energy storage evaluation coefficient model : ; In the formula, is the maximum rotor speed limit of the wind turbine; The wind turbine kinetic energy storage evaluation coefficient model is linearized to obtain the linearization model of the wind turbine kinetic energy storage evaluation coefficient: ; Among them, , , , are the increments of the wind turbine kinetic energy storage evaluation coefficient, the increment of the wind turbine rotor speed, the increment of the wind turbine active power output, and the increment of the wind turbine field weakening current, respectively; , , , , They are respectively the initial value of the maximum rotor speed of the wind turbine generator set, the initial value of the rotor speed of the wind turbine generator set, the initial value of the active power of the wind turbine generator set, the initial value of the field-weakening current of the wind turbine generator set, and the initial value of the q-axis current of the machine-side converter of the wind turbine generator set; and is the machine-side converter of the wind turbine generator set d and q axis current; is the number of pole pairs of the wind turbine generator set, is the magnetic flux of the wind turbine generator set, is the initial value of the maximum rotor speed limit of the wind turbine generator set, and are the sensitivity coefficients of the maximum rotor speed limit of the wind turbine generator set to the machine-side converter of the wind turbine generator set d and q axis current.

[0011] Preferably, the specific construction process of the linearization model of the converter operation risk assessment coefficient of the wind turbine generator set in step 2) is as follows: First, construct the converter operation risk assessment coefficient model of the wind turbine generator set :

[0012] In the formula, is the temperature of the converter of the wind turbine generator set, is the maximum temperature rise limit of the converter of the wind turbine generator set; The converter operation risk assessment coefficient model of the wind turbine generator set is linearized to obtain the linearization model of the converter operation risk assessment coefficient of the wind turbine generator set, specifically: ; where , , are respectively the rotor speed increment of the wind turbine generator set, the active power increment of the wind turbine generator set, and the reactive power increment of the wind turbine generator set, , , , are respectively the initial value of the rotor speed of the wind turbine generator set, the initial value of the terminal voltage of the wind turbine generator set, the initial value of the field-weakening current of the wind turbine generator set, and the initial value of the active power of the wind turbine generator set; and are the sensitivity coefficients of the converter operation risk assessment coefficient of the wind turbine generator set to the grid-side converter of the wind turbine generator set d and q axis current.

[0013] Preferably, in step 3), when the incoming flow wind speed is in the high wind speed range, considering the boundary coupling characteristics of the wind turbine terminal voltage-rotor speed, based on the linearized models of the wind turbine kinetic energy storage evaluation coefficient and the converter operation risk evaluation coefficient, the specific process of reducing the wind turbine terminal voltage deviation and the converter operation risk coefficient by optimizing the wind turbine output power and the field weakening current is as follows: Establish a state space model of a large-scale wind farm under high wind speeds: ; In the formula, the state variable ; the input variable ; the output variable ; , ; , , ; ; ; In the formula, is the field weakening current control time constant of the wind turbine, is the sampling period of the wind turbine controller, is the reactive power control time constant of the wind turbine.

[0014] Preferably, under the high wind speed range, the first control objective of the wind farm power controller is to adaptively adjust and maximize the wind turbine kinetic energy storage capacity, specifically:

[0015] Among them, and are the maximum kinetic energy storage control weight parameters, and are the maximum rotor speed and the maximum rotor speed reference value of the wind turbine at the k th step of the controller, is the wind turbine kinetic energy storage evaluation coefficient at the k th step of the controller; Under the high wind speed range, the second control objective of the wind farm power controller is to reduce the wind turbine converter operation loss, specifically:

[0016] Among them, is the wind turbine converter operation risk coefficient weight parameter, is the wind turbine converter operation risk evaluation coefficient at the k th step of the controller; Under high wind speed conditions, the third control objective of the wind farm power controller is to track the reactive power command to reduce the terminal voltage deviation of the wind turbine, specifically as follows:

[0017] Among them, is the reactive power tracking weight parameter under low wind speed, and are the reactive power output and reactive power reference value of the wind turbine at the k th step of the controller.

[0018] The present invention also discloses a large-scale wind turbine group power control system under full wind domain conditions, including a memory and a processor connected to each other. A computer program is stored on the memory, and when the computer program is run by the processor, it executes the steps of the method described above.

[0019] Compared with the prior art, the advantages of the present invention are as follows: The large-scale wind turbine group power control method and system under full wind domain conditions of the present invention collect the wind farm parameters and wind turbine parameters under full wind domain conditions; according to each parameter, establish a linearization model of the wind energy utilization evaluation coefficient, a linearization model of the kinetic energy storage evaluation coefficient of the wind turbine, and a linearization model of the converter operation risk evaluation coefficient; based on each linearization model, construct a full wind domain wind farm power controller: in the low wind speed range, improve the wind energy capture efficiency and reduce the terminal voltage fluctuation by optimizing the output power of the wind turbine; in the high wind speed range, reduce the terminal voltage deviation and the converter operation risk coefficient by optimizing the output power of the wind turbine and the field weakening current. The present invention can effectively improve the wind energy capture ability of the wind turbine in the low wind speed range, reduce the wind farm terminal voltage deviation and the converter operation risk coefficient in the high wind speed range, ensure the stability and economy of the operation of the large-scale wind farm in the full wind domain, and realize the optimal power control of the large-scale wind farm in the full wind domain. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flowchart of the large-scale wind farm power control method under full wind domain conditions of the present invention.

[0021] Figure 2 is a simulation diagram of the wind energy capture ratio of the wind turbine in the low wind speed range under different control methods of the present invention.

[0022] Figure 3 is a simulation diagram of the terminal voltage fluctuation of the wind turbine in the low wind speed range under different control methods of the present invention.

[0023] Figure 4 is a simulation diagram of the converter operation risk coefficient of the wind turbine in the high wind speed range under different control methods of the present invention.

[0024] Figure 5This is the simulation diagram of the terminal voltage fluctuation of the wind turbine in the high wind speed range under different control methods in the present invention.

[0025] Figure 6 This is the simulation diagram of the maximum kinetic energy storage ratio of the wind turbine in the high wind speed range under different control methods in the present invention. Specific Embodiments

[0026] The present invention will be further described below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0027] As Figure 1 shown, the large-scale wind turbine group power control method under the full wind domain working conditions provided by the embodiment of the present invention includes the steps: 1) Collect the wind farm parameters and wind turbine parameters under the full wind domain working conditions; specifically, the wind farm parameters include the wind farm line resistance R g 、 inductance L g , node voltage V W etc.; the wind turbine parameters include the incoming flow wind speed v , DC bus voltage V dc , rotor speed ω r , active power P W , reactive power Q W , weak magnetic current i sd and converter temperature T etc.; 2) Based on the wind farm parameters and wind turbine parameters under the full wind domain working conditions, and based on the wind turbine control method, establish a linearized model of the wind energy utilization evaluation coefficient of the wind turbine under the full wind domain, a linearized model of the kinetic energy storage evaluation coefficient of the wind turbine, and a linearized model of the converter operation risk evaluation coefficient; 3) Based on each linearized model, according to the different control requirements of the wind turbines in the low wind speed range and the high wind speed range, construct a power controller for the full wind domain wind farm, specifically: Obtain the incoming flow wind speed and determine whether the incoming flow wind speed belongs to the first preset wind speed range (low wind speed range) or the second preset wind speed range (high wind speed range); specifically, in this embodiment, the cut-in wind speed is 3 m / s, the minimum value of the high wind speed area is 11 m / s, and the cut-out wind speed is 15 m / s; 0 m / s ≤ wind turbine shutdown interval < 3 m / s, 3 m / s ≤ low wind speed range < 11 m / s, 11 m / s ≤ high wind speed range ≤ 15 m / s; When the incoming flow wind speed is in the low wind speed range, considering the coupling characteristics of the wind energy utilization coefficient - rotor speed of the wind turbine, based on the linearized model of the wind energy utilization evaluation coefficient of the wind turbine, the wind energy capture efficiency is improved and the terminal voltage fluctuation of the wind turbine is reduced by optimizing the output power of the wind turbine; When the incoming flow wind speed is in the high wind speed range, considering the boundary coupling characteristics of the terminal voltage - rotor speed of the wind turbine, based on the linearized model of the kinetic energy storage evaluation coefficient of the wind turbine and the linearized model of the converter operation risk evaluation coefficient, the terminal voltage deviation of the wind turbine and the converter operation risk coefficient are reduced by optimizing the output power and the field weakening current of the wind turbine.

[0028] In step 2), establish a wind energy utilization evaluation coefficient model for the wind turbine , specifically: ; In the formula, is the wind energy capture coefficient of the wind turbine, is the maximum wind energy capture coefficient of the wind turbine at this wind speed; By linearizing near the sampling point, establish a linearized model of the wind energy utilization evaluation coefficient of the wind turbine: ; , ; , , ; In the formula, is the moment of inertia of the wind turbine, is the rotor speed of the wind turbine, is the initial value of the rotor speed of the wind turbine, and are the output mechanical power and electromagnetic power of the wind turbine, and are the initial values of the output mechanical power and electromagnetic power of the wind turbine, is the pitch angle of the wind turbine, is the initial value of the wind energy capture coefficient of the wind turbine; the subscript 0 represents the initial value, represents the increment.

[0029] In step 3), considering the coupling characteristics of the wind energy utilization coefficient - rotor speed of the wind turbine, based on the linearized model of the wind energy utilization evaluation coefficient of the wind turbine, the wind energy capture efficiency is improved and the terminal voltage fluctuation of the wind turbine is reduced by optimizing the output power of the wind turbine. The specific process is as follows: Establish a state - space model of a large - scale wind farm under low wind speeds: ; Wherein, the state variable ; is the first derivative of the state variable; the input variable , the output variable ; , , ; , ; ; Wherein, , , and are respectively the control time constants corresponding to the mechanical power, electromagnetic power, pitch angle, and reactive power of the wind turbine generator set, and are the incremental commands of the active power and reactive power of the wind turbine generator set, and are the wind energy utilization evaluation coefficient and the incremental output of reactive power of the wind turbine generator set; the superscript ref represents the reference quantity.

[0030] The first control objective of the power controller for a large-scale wind farm under low wind speeds is to maximize the wind energy capture ability of the wind turbine generator set, specifically: ; Among them, N p is the control step of the controller, N W is the number of wind turbine generator sets, is the wind energy capture ability weight parameter, is the wind energy utilization evaluation coefficient of the wind turbine generator set at the k th step of the controller; The second control objective of the power controller for a large-scale wind farm under low wind speeds is to track the reactive power command to reduce the terminal voltage deviation of the wind turbine generator set, specifically: ; Among them, is the reactive power tracking weight parameter under low wind speeds; and are the reactive power output and reactive power reference quantity of the wind turbine generator set at the k th step of the controller.

[0031] The control quantity constraints of the power controller for a large-scale wind farm under low wind speeds are specifically: ; Among them, and is the reference value of the active power and reactive power output of the i th typhoon wind turbine generator set, and is the maximum allowable value of the active power and reactive power output of the i th typhoon wind turbine generator set.

[0032] Based on the optimal gradient descent method, the reactive power optimization iteration process of the wind turbine generator set is as follows: ; where , is the gradient optimization parameter, is the reference value of the reactive power of the wind turbine generator set at the k th iteration, is the voltage gradient function.

[0033] The specific establishment process of the linearized model of the kinetic energy storage evaluation coefficient of the wind turbine generator set in step 2) is as follows: First, construct the kinetic energy storage evaluation coefficient model of the wind turbine generator set: In the high wind speed range, the reactive power and voltage support ability of the wind turbine generator set are improved by maximizing the kinetic energy storage ability. The kinetic energy storage evaluation coefficient model of the wind turbine generator set can be expressed as: ; In the formula, is the maximum rotor speed limit of the wind turbine generator set; The above model is linearized as follows: ; where , , , are the increments of the kinetic energy storage evaluation coefficient of the wind turbine generator set, the increment of the rotor speed of the wind turbine generator set, the increment of the active power output of the wind turbine generator set, and the increment of the field weakening current of the wind turbine generator set, respectively; , , , , are the initial values of the maximum rotor speed of the wind turbine generator set, the initial value of the rotor speed of the wind turbine generator set, the initial value of the active power of the wind turbine generator set, the initial value of the field weakening current of the wind turbine generator set, and the initial value of the q-axis current of the machine side converter of the wind turbine generator set, respectively; , are the d , q axis currents of the machine side converter of the wind turbine generator set; is the number of pole pairs of the wind turbine generator set, is the magnetic flux of the wind turbine generator set, is the initial value of the maximum rotor speed limit of the wind turbine generator set, , is the maximum rotor speed limit of the wind turbine generator set for the machine-side converter of the wind turbine generator set d , q axis current sensitivity coefficient.

[0034] In the high wind speed range, the maximum rotor speed boundary is adaptively adjusted based on the terminal voltage of the wind turbine generator set. The reference value of the maximum rotor speed boundary can be expressed as: ; where is the terminal voltage-rotor speed boundary coupling coefficient, is the DC bus voltage of the wind turbine generator set, is the terminal voltage of the wind turbine generator set, is the reference value of the terminal voltage of the wind turbine generator set, , are the d-axis and q-axis inductances of the machine-side converter of the wind turbine generator set, is the high wind speed-rotor boundary adjustment coefficient of the wind turbine generator set.

[0035] The specific construction process of the linearized model of the converter operation risk assessment coefficient of the wind turbine generator set in step 2) is as follows: In the high wind speed range, the active power and reactive power outputs of the wind turbine generator set are relatively high, the converter temperature rises, and the operation risk coefficient is high, which can be expressed as:

[0036] In the formula, is the temperature of the converter of the wind turbine generator set, is the maximum temperature rise limit of the converter of the wind turbine generator set; The above model is linearized, specifically: ; where , , are the rotor speed increment, active power increment, and reactive power increment of the wind turbine generator set respectively, , , , are the initial rotor speed value, initial terminal voltage value, initial field weakening current value, and initial active power value of the wind turbine generator set respectively, and are the axis current sensitivity coefficients of the converter operation risk assessment coefficient of the wind turbine generator set for the grid-side converter of the wind turbine generator set d , q axis.

[0037] In step 3), in the high wind speed range, considering the boundary coupling characteristics of the wind turbine terminal voltage - rotor speed, based on the linearized models of the wind turbine kinetic energy storage evaluation coefficient and the converter operation risk evaluation coefficient, the output power and field weakening current of the wind turbine are optimized to reduce the wind turbine terminal voltage deviation and the converter operation risk coefficient. The specific process is as follows: Establish a state - space model of a large - scale wind farm at high wind speeds: ; Where the state variables ; The input variables ; The output variables ; , ; ; , ; ; ; Where is the field weakening current control time constant of the wind turbine, is the sampling period of the wind turbine controller, is the reactive power control time constant of the wind turbine.

[0038] The first control objective of the large - scale wind farm controller at high wind speeds is to adaptively adjust and maximize the wind turbine kinetic energy storage capacity, specifically:

[0039] Where and are the maximum kinetic energy storage control weight parameters, and are the maximum rotor speed and the maximum rotor speed reference value of the wind turbine at the k th step of the controller, is the wind turbine kinetic energy storage evaluation coefficient at the k th step of the controller; The second control objective of the large - scale wind farm controller at high wind speeds is to reduce the wind turbine converter operation loss, specifically:

[0040] Where is the wind turbine converter operation risk coefficient weight parameter, is the wind turbine converter operation risk evaluation coefficient at the k th step of the controller.

[0041] ​​​​​The third control objective of the large-scale wind farm controller at high wind speeds is to track the reactive power command to reduce the terminal voltage deviation of the wind turbine, specifically:

[0042] Among them, is the reactive power tracking weight parameter at low wind speeds, and are the reactive power output and reactive power reference value of the wind turbine at the k th step of the controller.

[0043] The control quantity constraints of the large-scale wind farm controller at high wind speeds are specifically:

[0044] Among them, , and are the reference values of the active power, reactive power, and field-weakening current output of the i th wind turbine, is the maximum current of the i th wind turbine.

[0045] Based on the above state space equation, objective function, and constraint conditions, in the low wind speed range, the wind turbine takes maximizing the wind energy capture ability of the wind turbine and tracking the reactive power command to reduce the terminal voltage deviation of the wind turbine as the control objective. Based on the large-scale wind farm state space model at low wind speeds, a quadratic programming mathematical model of the input quantity in the low wind speed range - weighted summation of the low wind speed control objectives to obtain the output quantity is established. Through model predictive control rolling optimization, the optimal control solution for the real-time change of the wind turbine in the low wind speed range is obtained, so as to realize the optimal power control of the wind turbine in the low wind speed range.

[0046] In the high wind speed range, the wind turbine takes adaptive adjustment and maximizing the kinetic energy storage ability of the wind turbine, reducing the operation loss of the wind turbine converter, and tracking the reactive power command as the control objective. Based on the large-scale wind farm state space model at high wind speeds, a quadratic programming mathematical model of the input quantity in the high wind speed range - weighted summation of the high wind speed control objectives to obtain the output quantity is established. Through model predictive control rolling optimization, the optimal control solution for the real-time change of the wind turbine in the high wind speed range is obtained, so as to realize the optimal power control of the wind turbine in the high wind speed range.

[0047] The present invention can effectively improve the wind energy capture ability of the wind turbine in the low wind speed range, reduce the terminal voltage deviation of the wind farm and the operation risk coefficient of the converter in the high wind speed range, ensure the stability and economy of the operation of the large-scale wind farm in the whole wind region, and realize the optimal power control of the large-scale wind farm in the whole wind region.

[0048] Figure 2This is a simulation diagram of the wind energy capture ratio of a wind turbine in the low wind speed range under different control methods in the present invention. Compared with the existing control methods, the control method proposed in the present invention effectively enhances the wind energy capture ability of the wind turbine in the low wind speed range by optimizing the active power reference value of the wind turbine.

[0049] Figure 3 This is a simulation diagram of the terminal voltage fluctuation of a wind turbine in the low wind speed range under different control methods in the present invention. Compared with the existing control methods, the control method proposed in the present invention effectively suppresses the terminal voltage fluctuation of the wind turbine in the low wind speed range by optimizing the reactive power reference value of the wind turbine.

[0050] Figure 4 This is a simulation diagram of the converter operation risk coefficient of a wind turbine in the high wind speed range under different control methods in the present invention. Compared with the existing model predictive control and droop control methods, the control method proposed in the present invention effectively reduces the converter operation risk coefficient of the wind turbine in the high wind speed range by optimizing and regulating the output power and field weakening current of the wind turbine.

[0051] Figure 5 This is a simulation diagram of the terminal voltage fluctuation of a wind turbine in the high wind speed range under different control methods in the present invention. Compared with the existing model predictive control and droop control methods, the control method proposed in the present invention effectively reduces the terminal voltage deviation of the wind turbine in the high wind speed range by maximizing the kinetic energy storage capacity.

[0052] Figure 6 This is a simulation diagram of the maximum kinetic energy storage ratio of a wind turbine in the high wind speed range under different control methods in the present invention. Compared with the existing model predictive control and droop control methods, the control method proposed in the present invention fully utilizes the kinetic energy storage capacity by optimizing the active power and field weakening current, improves the reactive power and voltage support ability of the wind turbine in the high wind speed range, and reduces the wind energy loss at the same time.

[0053] The present invention also discloses a full-wind-region large-scale wind farm power control system, which includes a memory and a processor connected to each other. A computer program is stored on the memory, and when the computer program is run by the processor, it executes the steps of the above-mentioned method. The system of the present invention corresponds to the above method and has the same advantages as those of the above method.

[0054] The implementation of all or part of the processes in the above-described embodiment methods of the present invention can also be completed by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable storage medium includes: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. The memory is used to store the computer program and / or module. The processor realizes various functions by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory. The memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices, etc.

[0055] The above are only the preferred embodiments 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 concept 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 the technical field, several improvements and refinements made without departing from the principle of the present invention should be regarded as within the protection scope of the present invention.

Claims

1. A power control method for a large-scale wind turbine group under all wind field conditions, characterized in that, Including the steps: 1) Collect the wind farm parameters and wind turbine parameters under the full wind domain conditions; 2) Based on the wind farm parameters and wind turbine parameters under the full wind domain conditions and the wind turbine control method, establish the linearized models of the wind energy utilization evaluation coefficient, the kinetic energy storage evaluation coefficient, and the converter operation risk evaluation coefficient of the wind turbine under the full wind domain; 3) Based on each linearized model, according to the different control requirements of the wind turbine in the low wind speed range and the high wind speed range, construct the power controller of the wind farm in the full wind domain. Specifically: Obtain the incoming wind speed and determine whether the incoming wind speed belongs to the low wind speed range or the high wind speed range; When the incoming wind speed is in the low wind speed range, considering the coupling characteristic of the wind energy utilization coefficient-rotor speed of the wind turbine, based on the linearized model of the wind energy utilization evaluation coefficient of the wind turbine, improve the wind energy capture efficiency and reduce the terminal voltage fluctuation of the wind turbine by optimizing the output power of the wind turbine; When the incoming wind speed is in the high wind speed range, considering the boundary coupling characteristic of the terminal voltage-rotor speed of the wind turbine, based on the linearized models of the kinetic energy storage evaluation coefficient and the converter operation risk evaluation coefficient of the wind turbine, reduce the terminal voltage deviation and the converter operation risk coefficient of the wind turbine by optimizing the output power and the field weakening current of the wind turbine; Wherein the low wind speed range corresponds to the first preset wind speed range, and the high wind speed range corresponds to the second preset wind speed range; The second preset wind speed range is greater than the second preset wind speed range.

2. The large-scale wind turbine group power control method under the full wind field condition according to claim 1, characterized in that In step 1), the wind farm parameters include the wind farm line impedance R g 、 inductance L g , virtual damping D ; the wind turbine parameters include the incoming wind speed v , DC bus voltage V dc , rotor speed ω r , active power P W , reactive power Q W , field weakening current i sd and converter temperature T .

3. The large-scale wind turbine group power control method under the full wind field condition according to claim 1 or 2, characterized in that, In step 2), the specific process of establishing the linearized model of the wind energy utilization evaluation coefficient of the wind turbine under the full wind domain is as follows: Establish a wind energy utilization evaluation coefficient model for wind turbines , specifically as follows: ； In the formula, is the wind energy capture coefficient of the wind turbine, is the maximum wind energy capture coefficient of the wind turbine at this wind speed; By linearizing near the sampling points, establish the linearized model of the wind energy utilization evaluation coefficient of the wind turbine: ； ， ； ， ， ； In the formula, is the moment of inertia of the wind turbine, is the rotor speed of the wind turbine, is the initial value of the rotor speed of the wind turbine, and are the output mechanical power and electromagnetic power of the wind turbine, and are the initial values of the output mechanical power and electromagnetic power of the wind turbine, is the pitch angle of the wind turbine, is the initial value of the wind energy capture coefficient of the wind turbine; the subscript 0 represents the initial value, represents the increment.

4. The method for controlling the power of a large-scale wind turbine group under the full wind field condition according to claim 3, characterized in that, When the incoming wind speed is in the low wind speed range, considering the coupling characteristic of the wind energy utilization coefficient-rotor speed of the wind turbine, based on the linearized model of the wind energy utilization evaluation coefficient of the wind turbine, the specific process of improving the wind energy capture efficiency and reducing the terminal voltage fluctuation of the wind turbine by optimizing the output power of the wind turbine is as follows: Establish the state space model of the large-scale wind farm under low wind speed: ； In the formula, the state variable ; is the first derivative of the state variable; Input variable , output variable ; ， ， ； ， ； ； Wherein, , , and are respectively the control time constants corresponding to the mechanical power, electromagnetic power, pitch angle, and reactive power of the wind turbine; and are the incremental commands of the active power and reactive power of the wind turbine; and are the wind energy utilization evaluation coefficient and the incremental output of reactive power of the wind turbine; the superscript ref represents the reference quantity.

5. The large-scale wind turbine group power control method under the full wind field condition according to claim 4, wherein The first control objective of the wind farm power controller in the low wind speed range is to maximize the wind energy capture ability of the wind turbine. Specifically: ； Among them, N p is the control step size of the controller, N W is the number of wind turbines, is the weight parameter of wind energy capture ability, is the k wind energy utilization evaluation coefficient of the wind turbine at the The second control objective of the wind farm power controller in the low wind speed range is to track the reactive power command to reduce the terminal voltage deviation of the wind turbine. Specifically: ； Among them, is the reactive power tracking weight parameter at low wind speeds; and are the reactive power output and reactive power reference of the wind turbine at the k th step of the controller; The control quantity constraint of the wind farm power controller in the low wind speed range is specifically: ； Among them, and are the reference values of the active power and reactive power outputs of the i th typhoon wind turbine generator set, and are the maximum allowable values of the active power and reactive power outputs of the i th typhoon wind turbine generator set.

6. The large-scale wind turbine group power control method under the full wind field condition according to claim 5, characterized in that, The specific establishment process of the linearized model of the kinetic energy storage evaluation coefficient of the wind turbine in step 2) is as follows: First, construct a kinetic energy storage evaluation coefficient model for wind turbines : ； In the formula, is the maximum rotor speed limit of the wind turbine; The kinetic energy storage evaluation coefficient model of the wind turbine is linearized to obtain the linearized model of the kinetic energy storage evaluation coefficient of the wind turbine: ； Among them, , , , are respectively the increment of the kinetic energy storage evaluation coefficient of the wind turbine, the increment of the rotor speed of the wind turbine, the increment of the active power output of the wind turbine, and the increment of the field-weakening current of the wind turbine; , , , , are respectively the initial value of the maximum rotor speed of the wind turbine, the initial value of the rotor speed of the wind turbine, the initial value of the active power of the wind turbine, the initial value of the field-weakening current of the wind turbine, and the initial value of the q-axis current of the machine-side converter of the wind turbine; , are the d , q axis currents of the machine-side converter of the wind turbine; is the number of pole pairs of the wind turbine, is the magnetic flux of the wind turbine, is the initial value of the maximum rotor speed limit of the wind turbine, , are the sensitivity coefficients of the maximum rotor speed limit of the wind turbine to the d , q axis currents of the machine-side converter of the wind turbine.

7. The method for controlling the power of a large-scale wind turbine group under the full wind field condition according to claim 6, characterized in that, The specific construction process of the linearized model of the converter operation risk evaluation coefficient of the wind turbine in step 2) is as follows: First, build a risk assessment coefficient model for the operation of the wind turbine converter : Wherein, is the temperature of the converter of the wind turbine generator set; is the maximum temperature rise limit of the converter of the wind turbine generator set; The converter operation risk evaluation coefficient model of the wind turbine is linearized to obtain the linearized model of the converter operation risk evaluation coefficient of the wind turbine. Specifically: ； Among them , , are the rotor speed increment of the wind turbine generator set, the active power increment of the wind turbine generator set, and the reactive power increment of the wind turbine generator set, respectively , , , are the initial value of the rotor speed of the wind turbine generator set, the initial value of the terminal voltage of the wind turbine generator set, the initial value of the field weakening current of the wind turbine generator set, and the initial value of the active power of the wind turbine generator set, respectively and are the sensitivity coefficients of the operation risk assessment coefficient of the wind turbine generator set converter to the grid-side converter of the wind turbine generator set d , q shaft current 8. The large-scale wind turbine group power control method under the full wind field condition according to claim 7, characterized in that, In step 3), when the incoming flow wind speed is in the high wind speed range, considering the boundary coupling characteristics of the wind turbine terminal voltage - rotor speed, based on the linearized model of the wind turbine kinetic energy storage evaluation coefficient and the linearized model of the converter operation risk evaluation coefficient, the specific process of reducing the wind turbine terminal voltage deviation and the converter operation risk coefficient by optimizing the wind turbine output power and the field weakening current is as follows: Establish a state - space model of a large - scale wind farm under high wind speeds: ； In the formula, the state variable ; Input variable ; Output variable ; ， ， ； ， ； ； ； Wherein, is the field-weakening current control time constant of the wind turbine generator set, is the sampling period of the wind turbine generator set controller, is the reactive power control time constant of the wind turbine generator set.

9. The large-scale wind turbine group power control method under the full wind field condition according to claim 8, wherein, Under the high wind speed range, the first control objective of the wind farm power controller is to adaptively adjust and maximize the wind turbine kinetic energy storage capacity, specifically: Among them, and are the maximum kinetic energy storage control weight parameters, and are the maximum rotor speed and the reference value of the maximum rotor speed of the wind turbine at the k th step of the controller, is the kinetic energy storage evaluation coefficient of the wind turbine at the k th step of the controller; Under the high wind speed range, the second control objective of the wind farm power controller is to reduce the wind turbine converter operation loss, specifically: Among them, is the weight parameter of the operation risk coefficient of the wind turbine converter, is the k step-down operation risk assessment coefficient of the wind turbine converter; Under the high wind speed range, the third control objective of the wind farm power controller is to track the reactive power command to reduce the wind turbine terminal voltage deviation, specifically: Among them, is the reactive power tracking weight parameter at low wind speeds, and is the reactive power output and reactive power reference value of the wind turbine at the k th step.

10. A large-scale wind turbine group power control system under all wind field conditions, including a memory and a processor connected to each other, and a computer program is stored on the memory, characterized in that, The computer program, when run by a processor, executes the steps of the method according to any one of claims 1 - 9.

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