A method and system for controlling power of large-scale wind turbine groups under full wind range conditions

Abstract

The present invention discloses a method and system for controlling the power of a large-scale wind turbine group under full wind conditions. The method comprises: collecting wind farm parameters and wind turbine group parameters under full wind conditions; establishing, based on the parameters, a linearized model for wind energy utilization assessment coefficients, a linearized model for wind turbine group kinetic energy storage assessment coefficients, and a linearized model for converter operation risk assessment coefficients; and constructing a full wind power controller for the wind farm based on the linearized models: in the low wind speed range, the wind turbine group output power is optimized to improve wind energy capture efficiency and reduce terminal voltage fluctuations; in the high wind speed range, the wind turbine group output power and weak magnetic current are optimized to reduce terminal voltage deviation and converter operation risk coefficients. The present invention can effectively improve wind energy capture capability in the low wind speed range, reduce terminal voltage deviation and converter operation risk coefficients in the high wind speed range, ensure the stability of wind farm operation in the full wind range, and achieve optimal power control for the full wind range.

Description

Technical Field

[0001] The present invention mainly relates to the field of wind power technology, and in particular to a method and system for controlling the power of a large-scale wind turbine group under full wind range 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 face different control challenges in different wind speed ranges. The randomness and volatility of wind speed pose significant challenges to the stability and economic efficiency of wind turbine and wind farm operations. In the low wind speed range, improving the wind turbine's wind energy capture capability and economic efficiency are key issues. In the high wind speed range, however, the main challenges are the severe fluctuations in the wind turbine's terminal voltage and the increased risk factor for converter operation.

[0003] Traditional large-scale wind farm power control methods do not consider the impact of different wind speed ranges on the operating performance of wind turbines and wind farms. Therefore, it is urgent to study how to achieve large-scale wind farm power control methods across the entire wind domain based on the response differences of wind turbines and wind farms to different wind speed ranges. Summary of the Invention

[0004] In response to the technical problems existing in the prior art, the present invention provides a method and system for controlling the power of large-scale wind turbines under full wind domain conditions, which can effectively improve the wind energy capture capability of wind turbines in the low wind speed range, reduce the voltage deviation at the wind farm terminal and the converter operation risk coefficient in the high wind speed range, ensure the stability and economy of the operation of large-scale wind farms in the entire wind domain, and realize optimal power control of large-scale wind farms in the entire wind domain.

[0005] In order to solve the above technical problems, the technical solution proposed by the present invention is:

[0006] A method for controlling the power of a large-scale wind turbine group under full wind range conditions comprises the following steps:

[0007] 1) Collect wind farm parameters and wind turbine parameters under full wind range conditions;

[0008] 2) Based on the wind farm parameters and wind turbine parameters under full wind conditions and the wind turbine control method, a linearized model for the wind energy utilization assessment coefficient, a linearized model for the wind turbine kinetic energy storage assessment coefficient, and a linearized model for the converter operation risk assessment coefficient under full wind conditions is established;

[0009] 3) Based on the linearized models and the different control requirements of wind turbines in low and high wind speed ranges, a wind farm power controller for the entire wind area is constructed. Specifically:

[0010] 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;

[0011] When the incoming wind speed is in the low wind speed range, the wind energy utilization coefficient-rotor speed coupling characteristics of the wind turbine are considered. Based on the linearization model of the wind turbine wind energy utilization evaluation coefficient, the wind turbine output power is optimized to improve the wind energy capture efficiency and reduce the voltage fluctuation of the wind turbine terminal.

[0012] When the incoming wind speed is in the high wind speed range, the voltage deviation at the wind turbine terminal and the converter operation risk coefficient are reduced by optimizing the wind turbine output power and magnetic weakening current, taking into account the boundary coupling characteristics of the wind turbine terminal voltage and rotor speed.

[0013] The low wind speed interval corresponds to a first preset wind speed interval, and the high wind speed interval corresponds to a second preset wind speed interval; the second preset wind speed interval is greater than the first preset wind speed interval.

[0014] Preferably, in step 1), the wind farm parameters include wind farm line impedance R g 、 inductance L g , virtual damping D ; Wind turbine parameters include incoming 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 .

[0015] Preferably, in step 2), the specific process of establishing the linearization model of wind energy utilization evaluation coefficient of wind turbines in the entire wind domain is as follows:

[0016] Establishing a wind energy utilization evaluation coefficient model for wind turbines , specifically:

[0017] ;

[0018] Where, 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;

[0019] By linearizing near the sampling point, a linear model of wind energy utilization evaluation coefficient of wind turbines is established:

[0020] ;

[0021] , ;

[0022] , , ;

[0023] Where, is the moment of inertia of the wind turbine, is the wind turbine rotor speed, is the initial value of the wind turbine rotor speed, and is the mechanical power and active power output of the wind turbine. and is the initial value of the wind turbine output mechanical power and active power, is the pitch angle of the wind turbine, is the initial value of the wind energy capture coefficient of the wind turbine; 0 represents the initial value, Indicates increment.

[0024] Preferably, when the incoming wind speed is in the low wind speed range, the specific process of improving the wind energy capture efficiency and reducing the wind turbine terminal voltage fluctuation by optimizing the wind turbine output power based on the wind energy utilization evaluation coefficient linearization model of the wind turbine is considered and the wind energy utilization coefficient-rotor speed coupling characteristics are considered:

[0025] Establish a state space model of a large-scale wind farm at low wind speeds:

[0026] ;

[0027] In the formula, the state variable ; is the first-order derivative of the state variable;

[0028] Input variables , output variable ;

[0029] , , ;

[0030] , ;

[0031] ;

[0032] Where, , are the control time constants corresponding to the mechanical power and pitch angle of the wind turbine, , are the control time constants of the active power and reactive power of the wind turbine in the low wind speed range, and is the increment of the active power reference value and the increment of the reactive power reference value of the wind turbine, and is the wind energy utilization evaluation coefficient and reactive power output increment of wind turbine; ref Indicates reference quantity.

[0033] Preferably, the first control objective of the wind farm power controller in the low wind speed range is to maximize the wind energy capture capability of the wind turbine generator set, specifically:

[0034] ;

[0035] in, N p is the controller step size, N W is the number of wind turbines, is the wind energy capture capability weight parameter, For controller k Wind energy utilization assessment coefficient of Buxia wind turbine;

[0036] 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 voltage deviation of the wind turbine terminal. Specifically:

[0037] ;

[0038] in, is the reactive power tracking weight parameter at low wind speed; and For controller k Step down wind turbine reactive power output and reactive power reference;

[0039] The control quantity constraints of the wind farm power controller in the low wind speed range are specifically:

[0040] ;

[0041] in, and For the i Reference values ​​of active power and reactive power output of typhoon turbines, and For the i The maximum allowable values ​​of active power and reactive power output of typhoon turbines.

[0042] Preferably, the specific process of establishing the linearization model of the wind turbine kinetic energy storage evaluation coefficient in step 2) is as follows:

[0043] First, a kinetic energy storage evaluation coefficient model for wind turbines is constructed. :

[0044] ;

[0045] Where, The maximum rotor speed limit for wind turbines;

[0046] The wind turbine kinetic energy storage evaluation coefficient model is linearized to obtain the wind turbine kinetic energy storage evaluation coefficient linearization model:

[0047] ;

[0048] in, 、 、 、 They are the increment 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; 、 、 、 、 They are 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 weak magnetic current of the wind turbine, and the initial value of the q-axis current of the wind turbine side converter; 、 For wind turbine weak magnetic current and machine-side converter q Shaft current; is the number of wind turbine pole pairs, is the magnetic flux of the wind turbine, is the initial value of the maximum rotor speed limit of the wind turbine, 、 The maximum rotor speed limit of the wind turbine sets affects the wind turbine set’s field weakening current and the machine-side converter. q Shaft current sensitivity coefficient.

[0049] Preferably, the specific construction process of the linearization model of the wind turbine converter operation risk assessment coefficient in step 2) is as follows:

[0050] First, a wind turbine converter operation risk assessment coefficient model is constructed. :

[0051]

[0052] Where, is the wind turbine converter temperature, The maximum temperature rise limit of the wind turbine converter;

[0053] The wind turbine converter operation risk assessment coefficient model is linearized to obtain the wind turbine converter operation risk assessment coefficient linearization model, which is specifically:

[0054] ;

[0055] in , , They are the wind turbine rotor speed increment, wind turbine active power increment, and wind turbine reactive power increment, respectively. , , , They are the initial value of wind turbine rotor speed, the initial value of wind turbine terminal voltage, the initial value of wind turbine field weakening current, and the initial value of wind turbine active power; and The wind turbine converter operation risk assessment coefficient is the wind turbine grid side converter d 、 q Shaft current sensitivity coefficient.

[0056] Preferably, in step 3), when the incoming wind speed is in the high wind speed range, 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 weak magnetic current is as follows:

[0057] Establish a state space model of a large-scale wind farm under high wind speed:

[0058] ;

[0059] In the formula, the state variable ; Input variables ; Output variables ;

[0060] , ; , , ;

[0061] ;

[0062] ;

[0063] Where, is the time constant of the wind turbine field weakening current control, is the wind turbine controller sampling period, , are the control time constants of the active power and reactive power of the wind turbine in the high wind speed range respectively.

[0064] Preferably, in the high wind speed range, the first control objective of the wind farm power controller is to maximize the kinetic energy storage capacity of the wind turbine through adaptive adjustment, specifically:

[0065]

[0066] in, and is the maximum kinetic energy storage control weight parameter, and For controller k Step down the maximum rotor speed of wind turbine and the reference value of maximum rotor speed, For controller k Kinetic energy storage evaluation coefficient of Buxia wind turbine;

[0067] In the high wind speed range, the second control goal of the wind farm power controller is to reduce the operating loss of the wind turbine converter, specifically:

[0068]

[0069] in, is the weight parameter of the wind turbine converter operation risk coefficient, For controller k Step-down wind turbine converter operation risk assessment coefficient;

[0070] In 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 voltage deviation of the wind turbine terminal. Specifically:

[0071]

[0072] in, is the reactive power tracking weight parameter under high wind speed, and For controller k Step down the reactive power output and reactive power reference value of the wind turbine.

[0073] The present invention also discloses a large-scale wind turbine group power control system under full wind range working conditions, including an interconnected memory and a processor, wherein the memory stores a computer program, and the computer program executes the steps of the above method when executed by the processor.

[0074] Compared with the prior art, the advantages of the present invention are:

[0075] The present invention provides a large-scale wind turbine group power control method and system under full wind range operating conditions. The method and system collect wind farm parameters and wind turbine group parameters under full wind range operating conditions. Based on the parameters, a linearized model of wind energy utilization assessment coefficient, a linearized model of wind turbine group kinetic energy storage assessment coefficient, and a linearized model of converter operation risk assessment coefficient are established. Based on the linearized models, a full wind range wind farm power controller is constructed. In the low wind speed range, the wind turbine group output power is optimized to improve wind energy capture efficiency and reduce terminal voltage fluctuations. In the high wind speed range, the wind turbine group output power and weak magnetic current are optimized to reduce terminal voltage deviation and converter operation risk coefficient. The present invention can effectively improve the wind turbine group wind energy capture capability in the low wind speed range, reduce the wind farm terminal voltage deviation and converter operation risk coefficient in the high wind speed range, ensure the stability and economy of the operation of large-scale wind farms in the full wind range, and achieve optimal power control of large-scale wind farms in the full wind range. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 This is a flow chart of the power control method for a large-scale wind farm in the entire wind area of ​​the present invention.

[0077] Figure 2 This is a simulation diagram of the wind energy capture ratio of wind turbines in the low wind speed range under different control methods in the present invention.

[0078] Figure 3 This is a simulation diagram of the voltage fluctuation at the wind turbine terminal in the low wind speed range under different control methods in the present invention.

[0079] Figure 4 This is a simulation diagram of the risk coefficient of wind turbine converter operation in the high wind speed range under different control methods in the present invention.

[0080] Figure 5 This is a simulation diagram of the voltage fluctuation at the wind turbine terminal in the high wind speed range under different control methods in the present invention.

[0081] 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. DETAILED DESCRIPTION

[0082] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0083] like Figure 1 As shown, the embodiment of the present invention provides a method for controlling the power of a large-scale wind turbine group under full wind range conditions, comprising the steps of:

[0084] 1) Collect wind farm parameters and wind turbine parameters under full wind range conditions; specifically, wind farm parameters include wind farm line resistance R g、 inductance L g , node voltage V W etc.; Wind turbine parameters include incoming 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 wait;

[0085] 2) Based on the wind farm parameters and wind turbine parameters under the full wind domain conditions and the wind turbine control method, a linearized model for the wind energy utilization assessment coefficient of the wind turbine, a linearized model for the kinetic energy storage assessment coefficient of the wind turbine, and a linearized model for the converter operation risk assessment coefficient under the full wind domain conditions are established;

[0086] 3) Based on the linearized models and the different control requirements of wind turbines in low and high wind speed ranges, a wind farm power controller for the entire wind area is constructed. Specifically:

[0087] Obtain the incoming wind speed and determine whether the incoming wind speed belongs to the first preset wind speed interval (low wind speed interval) or the second preset wind speed interval (high wind speed interval). Specifically, in this embodiment, a wind speed of 3 m / s is used as the cut-in wind speed, a wind speed of 11 m / s is used as the minimum value of the high wind speed area, and a wind speed of 15 m / s is used as the cut-out wind speed. 0 m / s ≤ wind turbine shutdown interval < 3 m / s, 3 m / s ≤ low wind speed interval < 11 m / s, and 11 m / s ≤ high wind speed interval ≤ 15 m / s.

[0088] When the incoming wind speed is in the low wind speed range, the wind energy utilization coefficient-rotor speed coupling characteristics of the wind turbine are considered. Based on the linearization model of the wind turbine wind energy utilization evaluation coefficient, the wind turbine output power is optimized to improve the wind energy capture efficiency and reduce the voltage fluctuation of the wind turbine terminal.

[0089] When the incoming wind speed is in the high wind speed range, considering the boundary coupling characteristics of wind turbine terminal voltage and rotor speed, based on the linearization model of wind turbine kinetic energy storage evaluation coefficient and the linearization model of converter operation risk assessment coefficient, the wind turbine terminal voltage deviation and converter operation risk coefficient are reduced by optimizing the wind turbine output power and weak magnetic current.

[0090] In step 2), a wind energy utilization evaluation coefficient model for wind turbines is established. , specifically:

[0091] ;

[0092] Where, 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;

[0093] By linearizing near the sampling point, a linear model of wind energy utilization evaluation coefficient of wind turbines is established:

[0094] ;

[0095] , ;

[0096] , , ;

[0097] Where, is the moment of inertia of the wind turbine, is the wind turbine rotor speed, is the initial value of the wind turbine rotor speed, and is the mechanical power and active power output of the wind turbine. and is the initial value of the wind turbine output mechanical power and active power, is the pitch angle of the wind turbine, is the initial value of the wind energy capture coefficient of the wind turbine; 0 represents the initial value, Indicates increment.

[0098] In step 3), the wind energy utilization coefficient-rotor speed coupling characteristics of the wind turbine are considered. 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 voltage fluctuation at the wind turbine terminal is reduced by optimizing the wind turbine output power. The specific process is as follows:

[0099] Establish a state space model of a large-scale wind farm at low wind speeds:

[0100] ;

[0101] In the formula, the state variable ; is the first-order derivative of the state variable;

[0102] Input variables , output variable ;

[0103] , , ;

[0104] , ;

[0105] ;

[0106] Where, , are the control time constants corresponding to the mechanical power and pitch angle of the wind turbine, , are the control time constants of the active power and reactive power of the wind turbine in the low wind speed range, and is the increment of the active power reference value and the increment of the reactive power reference value of the wind turbine, and is the wind energy utilization evaluation coefficient and reactive power output increment of wind turbine; ref Indicates reference quantity.

[0107] The first control objective of the power controller for large-scale wind farms at low wind speeds is to maximize the wind energy capture capability of the wind turbines. Specifically:

[0108] ;

[0109] in, N p is the controller step size, N W is the number of wind turbines, is the wind energy capture capability weight parameter, For controller k Wind energy utilization assessment coefficient of Buxia wind turbine;

[0110] The second control objective of the power controller for large-scale wind farms at low wind speeds is to track the reactive power command to reduce the voltage deviation at the wind turbine terminals. Specifically:

[0111] ;

[0112] in, is the reactive power tracking weight parameter at low wind speed; and For controller k Step down the reactive power output and reactive power reference of the wind turbine.

[0113] The control quantity constraints of the power controller of a large-scale wind farm at low wind speed are specifically:

[0114] ;

[0115] in, and For the i Reference values ​​of active power and reactive power output of typhoon turbines, and For the i The maximum allowable values ​​of active power and reactive power output of typhoon turbines.

[0116] Based on the optimal gradient descent method, the iterative process of wind turbine reactive power optimization is:

[0117] ;

[0118] in , is the gradient optimization parameter, For iteration k Secondary wind turbine reactive power reference value, is the voltage gradient function.

[0119] The specific process of establishing the linearization model of the wind turbine kinetic energy storage evaluation coefficient in step 2) is as follows:

[0120] First, build the wind turbine kinetic energy storage evaluation coefficient model:

[0121] In the high wind speed range, the reactive power and voltage support capabilities of wind turbines are improved by maximizing the kinetic energy storage capacity. The kinetic energy storage evaluation coefficient model of wind turbines can be expressed as:

[0122] ;

[0123] Where, The maximum rotor speed limit for wind turbines;

[0124] The above model is linearized as follows:

[0125] ;

[0126] in, 、 、 、 They are the increment 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; 、 、 、 、 They are 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 weak magnetic current of the wind turbine, and the initial value of the q-axis current of the wind turbine side converter; 、 For wind turbine weak magnetic current and machine-side converter q Shaft current; is the number of wind turbine pole pairs, is the magnetic flux of the wind turbine, is the initial value of the maximum rotor speed limit of the wind turbine, 、 The maximum rotor speed limit of the wind turbine sets affects the wind turbine set’s field weakening current and the machine-side converter. q Shaft current sensitivity coefficient.

[0127] In the high wind speed range, the maximum rotor speed boundary is adaptively adjusted based on the wind turbine terminal voltage. The maximum rotor speed boundary reference value can be expressed as:

[0128] ;

[0129] in, is the terminal voltage-rotor speed boundary coupling coefficient, is the DC bus voltage of the wind turbine, is the wind turbine terminal voltage, is the reference value of the wind turbine terminal voltage, 、 is the d-axis and q-axis inductance of the wind turbine side converter, is the high wind speed-rotor boundary adjustment coefficient of the wind turbine.

[0130] The specific construction process of the linearization model of the wind turbine converter operation risk assessment coefficient in step 2) is as follows:

[0131] In the high wind speed range, the active power and reactive power output of the wind turbine are high, the converter temperature rises, and the operation risk factor is high, which can be expressed as:

[0132]

[0133] Where, is the wind turbine converter temperature, The maximum temperature rise limit of the wind turbine converter;

[0134] The above model is linearized as follows:

[0135] ;

[0136] in , , They are the wind turbine rotor speed increment, wind turbine active power increment, and wind turbine reactive power increment, respectively. , , , They are the initial value of wind turbine rotor speed, the initial value of wind turbine terminal voltage, the initial value of wind turbine weak magnetic current, and the initial value of wind turbine active power. and The wind turbine converter operation risk assessment coefficient is the wind turbine grid side converter d 、 q Shaft current sensitivity coefficient.

[0137] In step 3), in the high wind speed range, considering the boundary coupling characteristics of wind turbine terminal voltage and rotor speed, based on the linearized model of wind turbine kinetic energy storage assessment coefficient and the linearized model of converter operation risk assessment coefficient, the wind turbine terminal voltage deviation and converter operation risk coefficient are reduced by optimizing the wind turbine output power and weak magnetic current. The specific process is as follows:

[0138] Establish a state space model of a large-scale wind farm under high wind speed:

[0139] ;

[0140] In the formula, the state variable ; Input variables ; Output variables ;

[0141] , ; ; , ;

[0142] ;

[0143] ;

[0144] Where, is the time constant of the wind turbine field weakening current control, is the wind turbine controller sampling period, , are the control time constants of the active power and reactive power of the wind turbine in the high wind speed range respectively.

[0145] The first control objective of the large-scale wind farm controller at high wind speeds is to maximize the kinetic energy storage capacity of the wind turbines through adaptive regulation. Specifically:

[0146]

[0147] in, and is the maximum kinetic energy storage control weight parameter, and For controller kStep down the maximum rotor speed of wind turbine and the reference value of maximum rotor speed, For controller k Kinetic energy storage evaluation coefficient of Buxia wind turbine;

[0148] The second control objective of the large-scale wind farm controller at high wind speeds is to reduce the operating losses of the wind turbine converter. Specifically:

[0149]

[0150] in, is the weight parameter of the wind turbine converter operation risk coefficient, For controller k Step down wind turbine converter operation risk assessment coefficient.

[0151] The third control objective of the large-scale wind farm controller under high wind speed is to track the reactive power command to reduce the voltage deviation of the wind turbine terminal. Specifically:

[0152]

[0153] in, is the reactive power tracking weight parameter under high wind speed, and For controller k Step down the reactive power output and reactive power reference value of the wind turbine.

[0154] The control quantity constraints of large-scale wind farm controllers under high wind speed are specifically:

[0155]

[0156] in, , and For the i Typhoon turbine generator set active power, reactive power and weak magnetic current output reference values, For the i Maximum current of typhoon turbine generator set.

[0157] Based on the above state-space equations, objective functions and constraints, in the low wind speed range, the wind turbine is controlled to maximize the wind energy capture capability of the wind turbine and track the reactive power command to reduce the voltage deviation at the wind turbine terminal. Based on the state-space model of a large-scale wind farm under low wind speed, a quadratic programming mathematical model is established to obtain the output by weighting the input in the low wind speed range and the low wind speed control target. Through model predictive control rolling optimization, the optimal control solution of the wind turbine under real-time changes in the low wind speed range is obtained, thereby achieving optimal power control of the wind turbine in the low wind speed range.

[0158] In the high wind speed range, the wind turbine is controlled with the objectives of adaptively adjusting and maximizing the kinetic energy storage capacity of the wind turbine, reducing the operating loss of the wind turbine converter, and tracking the reactive power command. Based on the state space model of a large-scale wind farm under high wind speed, a quadratic programming mathematical model is established to obtain the output of the high wind speed range input-high wind speed control target weightedly. Through model predictive control rolling optimization, the optimal control solution of the wind turbine under real-time changes in the high wind speed range is obtained, thereby achieving optimal power control of the wind turbine in the high wind speed range.

[0159] The present invention can effectively improve the wind energy capture capability of wind turbines in the low wind speed range, reduce the voltage deviation at the wind farm end and the converter operation risk coefficient in the high wind speed range, ensure the stability and economy of large-scale wind farm operation in the entire wind domain, and realize optimal power control of large-scale wind farms in the entire wind domain.

[0160] Figure 2 This is a simulation diagram of the wind energy capture ratio of the wind turbine in the low wind speed range under different control methods in the present invention. Compared with the existing control method, the control method proposed in the present invention effectively enhances the wind energy capture capability of the wind turbine in the low wind speed range by optimizing the active power reference value of the wind turbine.

[0161] Figure 3 This is a simulation diagram of the voltage fluctuation at the wind turbine terminal in the low wind speed range under different control methods in the present invention. Compared with the existing control method, the control method proposed in the present invention effectively suppresses the voltage fluctuation at the wind turbine terminal in the low wind speed range by optimizing the reactive power reference value of the wind turbine.

[0162] Figure 4 This is a simulation diagram of the operating risk coefficient of the wind turbine converter 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 operating risk coefficient of the wind turbine converter in the high wind speed range by optimizing and adjusting the output power and weak magnetic current of the wind turbine.

[0163] Figure 5 This is a simulation diagram of the voltage fluctuation at the wind turbine terminal 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 voltage deviation at the wind turbine terminal in the high wind speed range by maximizing the kinetic energy storage capacity.

[0164] Figure 6 This is a 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. 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 weak magnetic current, thereby improving the reactive power and voltage support capacity of the wind turbine in the high wind speed range and reducing wind energy loss.

[0165] The present invention also discloses a large-scale wind farm power control system for the entire wind area, comprising a memory and a processor connected to each other. The memory stores a computer program that, when executed by the processor, performs the steps of the above-described method. The system of the present invention corresponds to the above-described method and similarly possesses the advantages described above.

[0166] The present invention can implement all or part of the process steps in the above-described method embodiments through hardware associated with computer program instructions. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-described method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. Computer-readable storage media include any entity or device capable of carrying computer program code, recording media, USB flash drives, removable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Memory is used to store computer programs and / or modules. The processor implements various functions by running or executing the computer programs and / or modules stored in the memory and accessing data stored in the memory. The memory may include a high-speed random access memory and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0167] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for controlling the power of a large-scale wind turbine group under full wind range conditions, characterized in that: Including steps: 1) Collect wind farm parameters and wind turbine parameters under full wind range conditions; 2) Based on the wind farm parameters and wind turbine parameters under full wind conditions and the wind turbine control method, a linearized model for the wind energy utilization assessment coefficient, a linearized model for the wind turbine kinetic energy storage assessment coefficient, and a linearized model for the converter operation risk assessment coefficient under full wind conditions is established; 3) Based on the linearized models and the different control requirements of wind turbines in low and high wind speed ranges, a wind farm power controller for the entire wind area is constructed. 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, the wind energy utilization coefficient-rotor speed coupling characteristics of the wind turbine are considered. Based on the linearization model of the wind turbine wind energy utilization evaluation coefficient, the wind turbine output power is optimized to improve the wind energy capture efficiency and reduce the voltage fluctuation of the wind turbine terminal. When the incoming wind speed is in the high wind speed range, the voltage deviation at the wind turbine terminal and the converter operation risk coefficient are reduced by optimizing the wind turbine output power and magnetic weakening current, taking into account the boundary coupling characteristics of the wind turbine terminal voltage and rotor speed. The low wind speed interval corresponds to the first preset wind speed interval, and the high wind speed interval corresponds to the second preset wind speed interval; The second preset wind speed range is greater than the first preset wind speed range.

2. The method for controlling the power of a large-scale wind turbine group under full wind range conditions according to claim 1 is characterized in that: In step 1), the wind farm parameters include the wind farm line impedance R g 、 inductance L g , virtual damping D ; Wind turbine parameters include incoming 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 .

3. The method for controlling the power of a large-scale wind turbine group under full wind range conditions according to claim 1 or 2, characterized in that: In step 2), the specific process of establishing the linear model of wind energy utilization evaluation coefficient of wind turbines in the entire wind domain is as follows: Establishing a wind energy utilization evaluation coefficient model for wind turbines , specifically: ； Where, 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, a linear model of wind energy utilization evaluation coefficient of wind turbines is established: ； ， ； ， ， ； Where, is the moment of inertia of the wind turbine, is the wind turbine rotor speed, is the initial value of the wind turbine rotor speed, and is the mechanical power and active power output of the wind turbine. and is the initial value of the wind turbine output mechanical power and active power, is the pitch angle of the wind turbine, is the initial value of the wind energy capture coefficient of the wind turbine; 0 represents the initial value, Indicates increment.

4. The method for controlling the power of a large-scale wind turbine group under full wind range conditions according to claim 3 is characterized in that: When the incoming wind speed is in the low wind speed range, considering the wind turbine wind energy utilization coefficient-rotor speed coupling characteristics, based on the wind turbine wind energy utilization evaluation coefficient linearization model, the specific process of optimizing the wind turbine output power to improve wind energy capture efficiency and reduce wind turbine terminal voltage fluctuation is as follows: Establish a state space model of a large-scale wind farm at low wind speeds: ； In the formula, the state variable ; is the first-order derivative of the state variable; Input variables , output variable ; ， ， ； ， ； ； Where, , are the control time constants corresponding to the mechanical power and pitch angle of the wind turbine, , are the control time constants of the active power and reactive power of the wind turbine in the low wind speed range, and is the increment of the active power reference value and the increment of the reactive power reference value of the wind turbine, and is the wind energy utilization evaluation coefficient and reactive power output increment of wind turbine; ref Indicates reference quantity.

5. The method for controlling the power of a large-scale wind turbine group under full wind range conditions according to claim 4 is characterized in that: The first control objective of the wind farm power controller in the low wind speed range is to maximize the wind energy capture capability of the wind turbine, specifically: ； in, N p is the controller step size, N W is the number of wind turbines, is the wind energy capture capability weight parameter, For controller k Wind energy utilization assessment coefficient of Buxia wind turbine; 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 voltage deviation of the wind turbine terminal. Specifically: ； in, is the reactive power tracking weight parameter at low wind speed; and For controller k Step down wind turbine reactive power output and reactive power reference; The control quantity constraints of the wind farm power controller in the low wind speed range are specifically: ； in, and For the i Reference values ​​of active power and reactive power output of typhoon turbines, and For the i The maximum allowable values ​​of active power and reactive power output of typhoon turbines.

6. The method for controlling the power of a large-scale wind turbine group under full wind range conditions according to claim 5 is characterized in that: The specific process of establishing the linearization model of the wind turbine kinetic energy storage evaluation coefficient in step 2) is as follows: First, a kinetic energy storage evaluation coefficient model for wind turbines is constructed. : ； Where, The maximum rotor speed limit for wind turbines; The wind turbine kinetic energy storage evaluation coefficient model is linearized to obtain the wind turbine kinetic energy storage evaluation coefficient linearization model: ； in, 、 、 、 They are the increment 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; 、 、 、 、 They are 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 weak magnetic current of the wind turbine, and the initial value of the q-axis current of the wind turbine side converter; 、 For wind turbine weak magnetic current and machine-side converter q Shaft current; is the number of wind turbine pole pairs, is the magnetic flux of the wind turbine, is the initial value of the maximum rotor speed limit of the wind turbine, 、 The maximum rotor speed limit of the wind turbine sets affects the wind turbine set’s field weakening current and the machine-side converter. q Shaft current sensitivity coefficient.

7. The method for controlling the power of a large-scale wind turbine group under full wind range conditions according to claim 6 is characterized in that: The specific construction process of the linearization model of the wind turbine converter operation risk assessment coefficient in step 2) is as follows: First, a wind turbine converter operation risk assessment coefficient model is constructed. : Where, is the wind turbine converter temperature, The maximum temperature rise limit of the wind turbine converter; The wind turbine converter operation risk assessment coefficient model is linearized to obtain the wind turbine converter operation risk assessment coefficient linearization model, which is specifically: ； in , , They are the wind turbine rotor speed increment, wind turbine active power increment, and wind turbine reactive power increment, respectively. , , , They are the initial value of wind turbine rotor speed, the initial value of wind turbine terminal voltage, the initial value of wind turbine field weakening current, and the initial value of wind turbine active power; and The wind turbine converter operation risk assessment coefficient is the wind turbine grid side converter d 、 q Shaft current sensitivity coefficient.

8. The method for controlling the power of a large-scale wind turbine group under full wind range conditions according to claim 7 is characterized in that: In step 3), when the incoming wind speed is in the high wind speed range, considering the boundary coupling characteristics of the wind turbine terminal voltage and rotor speed, based on the linearized model of the wind turbine kinetic energy storage assessment coefficient and the linearized model of the converter operation risk assessment 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 weak magnetic current is as follows: Establish a state space model of a large-scale wind farm under high wind speed: ； In the formula, the state variable ; Input variables ; Output variables ; ， ， ； ， ； ； ； Where, is the time constant of the wind turbine field weakening current control, is the wind turbine controller sampling period, , are the control time constants of the active power and reactive power of the wind turbine in the high wind speed range respectively.

9. The method for controlling the power of a large-scale wind turbine group under full wind range conditions according to claim 8, characterized in that: In the high wind speed range, the first control objective of the wind farm power controller is to maximize the kinetic energy storage capacity of the wind turbine through adaptive adjustment. Specifically: in, and is the maximum kinetic energy storage control weight parameter, and For controller k Step down the maximum rotor speed of wind turbine and the reference value of maximum rotor speed, For controller k Kinetic energy storage evaluation coefficient of Buxia wind turbine; In the high wind speed range, the second control goal of the wind farm power controller is to reduce the operating loss of the wind turbine converter, specifically: in, is the weight parameter of the wind turbine converter operation risk coefficient, For controller k Step-down wind turbine converter operation risk assessment coefficient; In 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 voltage deviation of the wind turbine terminal. Specifically: in, is the reactive power tracking weight parameter under high wind speed, and For controller k Step down the reactive power output and reactive power reference value of the wind turbine.

10. A large-scale wind turbine group power control system under full wind range conditions, comprising a memory and a processor connected to each other, wherein a computer program is stored in the memory, characterized in that: When the computer program is executed by a processor, the computer program performs the steps of the method according to any one of claims 1 to 9.

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