A Method and System for Optimizing Control Parameters of Wind Turbine Units Based on Intelligent Algorithms

Abstract

The present invention discloses a method and system for optimizing the control parameters of a wind turbine generator set based on an intelligent algorithm, comprising: obtaining a state space equation, i.e., a linearized model of the wind turbine generator set, by linearizing the wind turbine generator set, the model comprising an input vector, an output vector, a state vector, and a coefficient matrix; forming a closed-loop system with the linearized model of the wind turbine generator set and a variable pitch controller, taking a step wind speed as input, obtaining the output response of the closed-loop system's impeller speed and the output response of the displacement in the front-rear direction of the cabin, and defining the objective function as: the weighted sum of the integral of the absolute deviation of the impeller speed and the maximum displacement in the front-rear direction of the cabin; optimizing the control parameters is to find the optimal solution in the feasible domain of the parameter space, and quickly find the position of the optimal solution in the parameter space through an intelligent optimization algorithm. The present invention does not rely on the engineering experience of engineers, does not increase hardware costs, and realizes automatic optimization of control parameters, thereby realizing online real-time control parameter optimization of the wind turbine generator set.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine control, and in particular to a method and system for optimizing wind turbine control parameters based on an intelligent algorithm. Background Art

[0002] As the most important green and clean energy, wind power generation converts wind energy into electrical energy without consuming fossil energy or producing greenhouse gases. Therefore, it has developed rapidly around the world. In the process of converting wind energy into electrical energy, the control of wind turbines is particularly important. The wind energy utilization efficiency, stable output of electrical energy, stable rotor speed and load of wind turbines in the process of energy conversion are all determined by control. At present, the control parameters of wind turbines are set by engineering and technical personnel based on experimental methods, which is related to the experience and theoretical level of engineering and technical personnel, and it is difficult to obtain the optimal control parameters. Summary of the invention

[0003] The first purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and to provide a method for optimizing the control parameters of a wind turbine set based on an intelligent algorithm. By setting the objective function, parameter space, feasible domain for optimization and initial solution set, the optimal position point, i.e., the optimal control parameters, is iteratively obtained using a swarm intelligence optimization method.

[0004] The second object of the present invention is to provide a wind turbine control parameter optimization system based on an intelligent algorithm.

[0005] The first object of the present invention is achieved by the following technical solution: A method for optimizing control parameters of a wind turbine generator system based on an intelligent algorithm, comprising:

[0006] Wind turbine linearization: By linearizing the wind turbine, the state space equation is obtained, that is, the linearized model of the wind turbine. The linearized model includes input vectors, output vectors, state vectors and coefficient matrices.

[0007] Objective function of parameter optimization: The linearized model of the wind turbine and the pitch controller form a closed-loop system, and the step wind speed is used as input to obtain the output response of the closed-loop system's impeller speed and the output response of the nacelle's front-to-back displacement. The objective function is defined as the weighted sum of the integral of the impeller speed absolute deviation and the maximum displacement of the nacelle in the front-to-back direction.

[0008] Feasible domain of parameter space: The optimization of control parameters is to find the optimal control parameters in the parameter space. The parameter space needs to set a search range, that is, the feasible domain of the parameter space;

[0009] Intelligent optimization algorithm for parameter optimization: The optimization of control parameters is to find the optimal solution in the feasible domain of parameter space. The intelligent optimization algorithm can quickly find the location of the optimal solution in the parameter space.

[0010] Furthermore, the linearized model expression of the wind turbine is as follows:

[0011]

[0012] In the above formula, represents the input vector at time t; represents the output variable at time t; represents the state vector at time t; represents the rate of change of the state vector at time t; A, B, C, D are coefficient matrices;

[0013] Input Vector It should contain at least three variables, namely, pitch angle command, average wind speed of wind rotor and generator torque command. The specific expression is as follows:

[0014]

[0015] In the above formula, Indicates pitch angle command; Indicates the average wind speed of the wind rotor; Represents the generator torque command; input vector is a column vector. The superscript T in the expression indicates transposition, which converts the row vector into a column vector.

[0016] Output vector It should contain at least two variables, namely the wind rotor speed and the nacelle front-to-back displacement. The specific expression is as follows:

[0017]

[0018] In the above formula, ω r Indicates the wind wheel speed; x fa Represents the displacement of the cabin in the front and rear directions; output vector is a column vector. The superscript T in the expression indicates transposition, which converts the row vector into a column vector.

[0019] State Vector It should at least include the low-order modes of the blades, the low-order modes of the tower, the low-order modes of the transmission chain, the speed of the wind rotor and the speed change rate of the wind rotor. The specific expressions are as follows:

[0020]

[0021] In the above formula, represents the modal vector of blade 1; represents the modal vector of blade 2; represents the modal vector of blade 3; represents the modal vector of the tower; ωr Indicates the wind wheel speed; Indicates the rate of change of wind rotor speed; state vector is a column vector. The superscript T in the expression represents transposition, which converts the row vector into a column vector.

[0022] Furthermore, the modal vector of the blade 1 is The expression is as follows:

[0023]

[0024] In the above formula, represents the first-order flapping mode displacement of blade 1; represents the first-order swing modal displacement of blade 1; represents the first-order flapping mode velocity of blade 1; represents the first-order swing mode velocity of blade 1; represents the second-order flapping mode displacement of blade 1; represents the second-order swing mode displacement of blade 1; represents the second-order flapping mode velocity of blade 1; represents the second-order swing mode velocity of blade 1;

[0025] The modal vector of blade 2 The expression is as follows:

[0026]

[0027] In the above formula, represents the first-order flapping mode displacement of blade 2; represents the first-order swing modal displacement of blade 2; represents the first-order flapping mode velocity of blade 2; represents the first-order swing modal velocity of blade 2; represents the second-order flapping mode displacement of blade 2; represents the second-order swing mode displacement of blade 2; represents the second-order flapping mode velocity of blade 2; represents the second-order swing mode velocity of blade 2;

[0028] The modal vector of the blade 3 The expression is as follows:

[0029]

[0030] In the above formula, represents the first-order flapping mode displacement of blade 3; represents the first-order swing modal displacement of blade 3; represents the first-order flapping modal velocity of blade 3; represents the first-order swing modal velocity of blade 3; represents the second-order flapping mode displacement of blade 3; represents the second-order swing mode displacement of blade 3; represents the second-order flapping mode velocity of blade 3; represents the second-order swing mode velocity of blade 3;

[0031] The modal vectors of the tower are The expression is as follows:

[0032]

[0033] In the above formula, represents the first-order lateral modal displacement of the tower; represents the first-order fore-aft modal displacement of the tower; represents the first-order lateral modal velocity of the tower; represents the first-order fore-aft modal velocity of the tower; represents the second-order lateral modal displacement of the tower; represents the second-order fore-aft modal displacement of the tower; represents the second-order lateral modal velocity of the tower; represents the second-order fore-and-aft modal velocity of the tower.

[0034] Furthermore, the coefficient matrices A, B, C, and D can be outputted through the linearization module of the wind turbine modeling software. Since the coefficient matrix is ​​related to the average wind speed, it is necessary to select several reference points in the range from the rated wind speed to the cut-out wind speed and output the coefficient matrix at each reference point.

[0035] Furthermore, the pitch controller of the closed-loop system adopts proportional-integral control, and its transfer function is as follows:

[0036]

[0037] In the above formula, C(s) represents the controller transfer function; k p represents the proportional gain of the pitch controller; T i represents the integral constant of the pitch controller; s represents the independent variable of the complex plane after Laplace transformation;

[0038] The specific expression of the objective function is as follows:

[0039]

[0040] In the above formula, f([k p ,T i ]) represents the objective function, which is k p and T i Function; k1 represents the speed weighting coefficient; ωr Indicates the wind wheel speed; Indicates the wind wheel speed setting value; Indicates the absolute deviation of the wind rotor speed; represents the integral calculation; t1 represents the maximum integral time interval; k2 represents the weighted coefficient of cabin displacement; σ xfa represents the maximum absolute displacement of the cabin; the maximum absolute displacement of the cabin is expressed as follows:

[0041]

[0042] In the above formula, x fa represents the displacement of the cabin in the front and rear directions; |·| represents the absolute value; It means taking the maximum value in the time interval 0≤t≤t2; t2 represents the time interval with the maximum displacement of the cabin.

[0043] Furthermore, the parameter space is a two-dimensional space, and each point in the parameter space constitutes a set of control parameters, namely, the pitch control proportional gain and the pitch control integral constant. Each point in the parameter space is expressed as:

[0044] s j =[k p ,T i ]

[0045] In the above formula, s j represents a point in the parameter space, which is a set of control parameters; k p represents the proportional gain of the pitch controller; T i represents the integral constant of the pitch controller;

[0046] The feasible region of parameter space is defined as follows:

[0047] S={[k p ,T i ]|0≤k p ≤k pmax ,1≤T i ≤T imax}

[0048] In the above formula, S represents the feasible domain of the parameter space, which is a set, and the internal elements are points s j ;k pmax represents the upper boundary of the proportional gain of the pitch controller, the proportional gain k p The value range is 0 to k pmax ; T imax Indicates the upper boundary of the integral constant of the pitch controller, the integral constant T i The value range is 1 to T imax .

[0049] Furthermore, the intelligent optimization algorithm comprises the following steps:

[0050] 1) In the feasible region of the parameter space, randomly generate several points, each point s j is a set of optional control parameters [k p ,T i ],k p represents the proportional gain of the pitch controller, T i represents the integral constant of the pitch controller, and the set of randomly generated initial control parameters is expressed as follows:

[0051] {s1,s2,…,s j ,…,s N |s j =[k p ,T i ],1≤j≤N}

[0052] In the above formula, s j represents a point in the feasible region of the parameter space, representing a set of control parameters; N represents the number of points in the set; in 0≤k p ≤k pmax Randomly generate k in range p , when 1≤T i ≤T imax Randomly generate T within the range i ;

[0053] 2) Based on the objective function of parameter optimization, calculate each point s in the parameter space j The objective function value of is:

[0054] {f1,f2,…,f j ,…,f N |f j =f(s j ),1≤j≤N}

[0055] In the above formula, f j represents the point s in the feasible region j The objective function value is expressed as f(s j ); The objective function value reflects the quality of each point. The lower the objective function value, the better the point.

[0056] 3) Calculate and update N points in the parameter space. Based on the objective function value of each point in the parameter space, update the position of the N points in the parameter space, eliminate inferior points, and move toward the optimal point:

[0057] The update equation for each point is as follows:

[0058]

[0059] In the above formula, sj (t+1) means at time t+1 point s j Location; j (t) represents the time point s at t j The position of; C1 represents the local optimal weighting coefficient; C2 represents the global optimal weighting coefficient; σ represents a random number between 0 and 1; Indicates point s j The best position you have ever experienced; * Indicates that N points experience the optimal position;

[0060] 4) Determine whether the number of loop calculations has reached the maximum value. If it has reached the maximum number of loops, output the optimal position s * As the optimal control parameter; otherwise, return to step 2) to calculate.

[0061] The second object of the present invention is achieved by the following technical solution: A wind turbine control parameter optimization system based on an intelligent algorithm, comprising:

[0062] A linearization processing module obtains a state space equation, i.e., a linearization model of the wind turbine generator set, by linearizing the wind turbine generator set. The linearization model includes an input vector, an output vector, a state vector, and a coefficient matrix.

[0063] The objective function module of parameter optimization forms a closed-loop system with the linearized model of the wind turbine and the pitch controller, takes the step wind speed as input, and obtains the output response of the closed-loop system's impeller speed and the output response of the nacelle's front-to-back displacement. The objective function is defined as the weighted sum of the integral of the impeller speed absolute deviation and the maximum displacement of the nacelle in the front-to-back direction.

[0064] Intelligent optimization module for parameter optimization. The optimization of control parameters is to find the optimal control parameters in the parameter space. The parameter space needs to set a search range, that is, the feasible domain of the parameter space. Through this intelligent optimization module, the position of the optimal solution in the parameter space can be quickly found.

[0065] Furthermore, the linearized model expression of the wind turbine is as follows:

[0066]

[0067] In the above formula, represents the input vector at time t; represents the output variable at time t; represents the state vector at time t; represents the rate of change of the state vector at time t; A, B, C, D are coefficient matrices;

[0068] Input Vector It should contain at least three variables, namely, pitch angle command, average wind speed of wind rotor and generator torque command. The specific expression is as follows:

[0069]

[0070] In the above formula, Indicates pitch angle command; Indicates the average wind speed of the wind rotor; Represents the generator torque command; input vector is a column vector. The superscript T in the expression indicates transposition, which converts the row vector into a column vector.

[0071] Output vector It should contain at least two variables, namely the wind rotor speed and the nacelle front-to-back displacement. The specific expression is as follows:

[0072]

[0073] In the above formula, ω r Indicates the wind wheel speed; x fa Represents the displacement of the cabin in the front and rear directions; output vector is a column vector. The superscript T in the expression indicates transposition, which converts the row vector into a column vector.

[0074] State Vector It should at least include the low-order modes of the blades, the low-order modes of the tower, the low-order modes of the transmission chain, the speed of the wind rotor and the speed change rate of the wind rotor. The specific expressions are as follows:

[0075]

[0076] In the above formula, represents the modal vector of blade 1; represents the modal vector of blade 2; represents the modal vector of blade 3; represents the modal vector of the tower; ω r Indicates the wind wheel speed; Represents the rate of change of wind rotor speed; state vector is a column vector. The superscript T in the expression represents transposition, which converts the row vector into a column vector.

[0077] Furthermore, the modal vector of the blade 1 is The expression is as follows:

[0078]

[0079] In the above formula, represents the first-order flapping mode displacement of blade 1; represents the first-order swing mode displacement of blade 1; represents the first-order flapping mode velocity of blade 1; represents the first-order swing modal velocity of blade 1; represents the second-order flapping mode displacement of blade 1; represents the second-order swing mode displacement of blade 1; represents the second-order flapping mode velocity of blade 1; represents the second-order swing mode velocity of blade 1;

[0080] The modal vector of blade 2 The expression is as follows:

[0081]

[0082] In the above formula, represents the first-order flapping mode displacement of blade 2; represents the first-order swing modal displacement of blade 2; represents the first-order flapping mode velocity of blade 2; represents the first-order swing modal velocity of blade 2; represents the second-order flapping mode displacement of blade 2; represents the second-order swing mode displacement of blade 2; represents the second-order flapping mode velocity of blade 2; represents the second-order swing mode velocity of blade 2;

[0083] The modal vector of the blade 3 The expression is as follows:

[0084]

[0085] In the above formula, represents the first-order flapping mode displacement of blade 3; represents the first-order swing modal displacement of blade 3; represents the first-order flapping modal velocity of blade 3; represents the first-order swing modal velocity of blade 3; represents the second-order flapping mode displacement of blade 3; represents the second-order swing mode displacement of blade 3; represents the second-order flapping mode velocity of blade 3; represents the second-order swing mode velocity of blade 3;

[0086] The modal vectors of the tower are The expression is as follows:

[0087]

[0088] In the above formula, represents the first-order lateral modal displacement of the tower; represents the first-order fore-aft modal displacement of the tower; represents the first-order lateral modal velocity of the tower; represents the first-order fore-aft modal velocity of the tower; represents the second-order lateral modal displacement of the tower; represents the second-order fore-aft modal displacement of the tower; represents the second-order lateral modal velocity of the tower; represents the second-order fore-and-aft modal velocity of the tower.

[0089] Furthermore, the coefficient matrices A, B, C, and D can be outputted through a linearization module of a wind turbine modeling software. Since the coefficient matrix is ​​related to the average wind speed, it is necessary to select several reference points in the range from rated wind speed to cut-out wind speed and output the coefficient matrix at each reference point.

[0090] Furthermore, the pitch controller of the closed-loop system adopts proportional-integral control, and its transfer function is as follows:

[0091]

[0092] In the above formula, C(s) represents the controller transfer function; k p represents the proportional gain of the pitch controller; T i represents the integral constant of the pitch controller; s represents the independent variable of the complex plane after Laplace transformation;

[0093] The specific expression of the objective function is as follows:

[0094]

[0095] In the above formula, f([k p ,T i ]) represents the objective function, which is k p and T i Function; k1 represents the speed weighting coefficient; ω r Indicates the wind wheel speed; Indicates the wind wheel speed setting value; Indicates the absolute deviation of the wind wheel speed; represents the integral calculation; t1 represents the maximum integral time interval; k2 represents the weighted coefficient of cabin displacement; σ xfa represents the maximum absolute displacement of the cabin; the maximum absolute displacement of the cabin is expressed as follows:

[0096]

[0097] In the above formula, x fa represents the displacement of the cabin in the front and rear directions; |·| represents the absolute value; It means taking the maximum value in the time interval 0≤t≤t2; t2 represents the time interval with the maximum displacement of the cabin.

[0098] Furthermore, the parameter space is a two-dimensional space, and each point in the parameter space constitutes a set of control parameters, namely, the pitch control proportional gain and the pitch control integral constant. Each point in the parameter space is expressed as:

[0099] s j =[k p ,T i ]

[0100] In the above formula, s j represents a point in the parameter space, which is a set of control parameters; k p represents the proportional gain of the pitch controller; T i represents the integral constant of the pitch controller;

[0101] The feasible region of parameter space is defined as follows:

[0102] S={[k p ,T i ]|0≤k p ≤k pmax ,1≤T i ≤T imax}

[0103] In the above formula, S represents the feasible domain of the parameter space, which is a set, and the internal elements are points s j ;k pmax represents the upper boundary of the proportional gain of the pitch controller, the proportional gain k p The value range is 0 to k pmax ; T imax Indicates the upper boundary of the integral constant of the pitch controller, the integral constant T i The value range is 1 to T imax .

[0104] Furthermore, the intelligent optimization module specifically performs the following operations:

[0105] 1) In the feasible region of the parameter space, randomly generate several points, each point s j is a set of optional control parameters [k p ,T i ],k p represents the proportional gain of the pitch controller, T i represents the integral constant of the pitch controller, and the set of randomly generated initial control parameters is expressed as follows:

[0106] {s1,s2,…,s j ,…,s N |s j=[k p ,T i ],1≤j≤N}

[0107] In the above formula, s j represents a point in the feasible region of the parameter space, representing a set of control parameters; N represents the number of points in the set; in 0≤k p ≤k pmax Randomly generate k in range p , when 1≤T i ≤T imax Randomly generate T within the range i ;

[0108] 2) Based on the objective function of parameter optimization, calculate each point s in the parameter space j The objective function value of is:

[0109] {f1,f2,…,f j ,…,f N |f j =f(s j ),1≤j≤N}

[0110] In the above formula, f j represents the point s in the feasible region j The objective function value is expressed as f(s j ); The objective function value reflects the quality of each point. The lower the objective function value, the better the point.

[0111] 3) Calculate and update N points in the parameter space. Based on the objective function value of each point in the parameter space, update the position of the N points in the parameter space, eliminate inferior points, and move toward the optimal point:

[0112] The update equation for each point is as follows:

[0113]

[0114] In the above formula, s j (t+1) means at time t+1 point s j Location; j (t) represents the time point s at t j The position of; C1 represents the local optimal weighting coefficient; C2 represents the global optimal weighting coefficient; σ represents a random number between 0 and 1; Indicates point s j The best position you have ever experienced; * Indicates that N points experience the optimal position;

[0115] 4) Determine whether the number of loop calculations has reached the maximum value. If it has reached the maximum number of loops, output the optimal position s *As the optimal control parameter; otherwise, return to step 2) to calculate.

[0116] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0117] 1. The wind turbine control parameter optimization method and system proposed in the present invention can quickly find the optimal control parameters without relying on the engineering experience of engineers, and realize automatic optimization of control parameters.

[0118] 2. The wind turbine control parameter optimization method and system proposed in the present invention do not require the addition of additional measurement sensors and do not increase hardware costs, and can achieve online real-time optimization of wind turbine control parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0119] Figure 1 This is the logic block diagram of the closed-loop system.

[0120] Figure 2 Schematic diagram of the system of the present invention. DETAILED DESCRIPTION

[0121] The present invention is further described in detail below in conjunction with embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0122] Example 1

[0123] This embodiment discloses a method for optimizing wind turbine control parameters based on an intelligent algorithm, including:

[0124] Wind turbine linearization: By linearizing the wind turbine, the state space equation is obtained, that is, the linearized model of the wind turbine. The linearized model includes input vectors, output vectors, state vectors and coefficient matrices.

[0125] Objective function of parameter optimization: The linearized model of the wind turbine and the pitch controller form a closed-loop system, and the step wind speed is used as input to obtain the output response of the closed-loop system's impeller speed and the output response of the nacelle's front-to-back displacement. The objective function is defined as the weighted sum of the integral of the impeller speed absolute deviation and the maximum displacement of the nacelle in the front-to-back direction.

[0126] Feasible domain of parameter space: The optimization of control parameters is to find the optimal control parameters in the parameter space. The parameter space needs to set a search range, that is, the feasible domain of the parameter space;

[0127] Intelligent optimization algorithm for parameter optimization: The optimization of control parameters is to find the optimal solution in the feasible domain of parameter space. The intelligent optimization algorithm can quickly find the location of the optimal solution in the parameter space.

[0128] Specifically, the linearized model expression of the wind turbine is as follows:

[0129]

[0130] In the above formula, represents the input vector at time t; represents the output variable at time t; represents the state vector at time t; represents the rate of change of the state vector at time t; A, B, C, D are coefficient matrices;

[0131] Input Vector It should contain at least three variables, namely, pitch angle command, average wind speed of wind rotor and generator torque command. The specific expression is as follows:

[0132]

[0133] In the above formula, Indicates pitch angle command; Indicates the average wind speed of the wind rotor; Represents the generator torque command; input vector is a column vector. The superscript T in the expression indicates transposition, which converts the row vector into a column vector.

[0134] Output vector It should contain at least two variables, namely the wind rotor speed and the nacelle front-to-back displacement. The specific expression is as follows:

[0135]

[0136] In the above formula, ω r Indicates the wind wheel speed; x fa Represents the displacement of the cabin in the front and rear directions; output vector is a column vector. The superscript T in the expression indicates transposition, which converts the row vector into a column vector.

[0137] State Vector It should at least include the low-order modes of the blades, the low-order modes of the tower, the low-order modes of the transmission chain, the speed of the wind rotor and the speed change rate of the wind rotor. The specific expressions are as follows:

[0138]

[0139] In the above formula, represents the modal vector of blade 1; represents the modal vector of blade 2; represents the modal vector of blade 3; represents the modal vector of the tower; ω r Indicates the wind wheel speed; Represents the rate of change of wind rotor speed; state vector is a column vector. The superscript T in the expression represents transposition, which converts the row vector into a column vector.

[0140] The modal vector of blade 1 The expression is as follows:

[0141]

[0142] In the above formula, represents the first-order flapping mode displacement of blade 1; represents the first-order swing modal displacement of blade 1; represents the first-order flapping mode velocity of blade 1; represents the first-order swing mode velocity of blade 1; represents the second-order flapping mode displacement of blade 1; represents the second-order swing mode displacement of blade 1; represents the second-order flapping mode velocity of blade 1; represents the second-order swing mode velocity of blade 1;

[0143] The modal vector of blade 2 The expression is as follows:

[0144]

[0145] In the above formula, represents the first-order flapping mode displacement of blade 2; represents the first-order swing modal displacement of blade 2; represents the first-order flapping mode velocity of blade 2; represents the first-order swing modal velocity of blade 2; represents the second-order flapping mode displacement of blade 2; represents the second-order swing mode displacement of blade 2; represents the second-order flapping mode velocity of blade 2; represents the second-order swing mode velocity of blade 2;

[0146] The modal vector of the blade 3 The expression is as follows:

[0147]

[0148] In the above formula, represents the first-order flapping mode displacement of blade 3; represents the first-order swing modal displacement of blade 3; represents the first-order flapping modal velocity of blade 3; represents the first-order swing modal velocity of blade 3; represents the second-order flapping mode displacement of blade 3; represents the second-order swing mode displacement of blade 3; represents the second-order flapping mode velocity of blade 3; represents the second-order swing mode velocity of blade 3;

[0149] The modal vectors of the tower are The expression is as follows:

[0150]

[0151] In the above formula, represents the first-order lateral modal displacement of the tower; represents the first-order fore-aft modal displacement of the tower; represents the first-order lateral modal velocity of the tower; represents the first-order fore-aft modal velocity of the tower; represents the second-order lateral modal displacement of the tower; represents the second-order fore-aft modal displacement of the tower; represents the second-order lateral modal velocity of the tower; represents the second-order fore-and-aft modal velocity of the tower.

[0152] Specifically, the coefficient matrices A, B, C, and D can be output through the linearization module of the wind turbine modeling software. Since the coefficient matrix is ​​related to the average wind speed, it is necessary to select several reference points in the range from rated wind speed to cut-out wind speed. Generally, five reference points are selected, and the coefficient matrix is ​​output at each reference point.

[0153] Specifically, the logic block diagram of the closed-loop system is as follows: Figure 1 As shown, the pitch controller of the closed-loop system adopts proportional-integral control, and its transfer function is as follows:

[0154]

[0155] In the above formula, C(s) represents the controller transfer function; k p represents the proportional gain of the pitch controller; T i represents the integral constant of the pitch controller; s represents the independent variable of the complex plane after Laplace transformation;

[0156] The specific expression of the objective function is as follows:

[0157]

[0158] In the above formula, f([k p ,T i ]) represents the objective function, which is k p and T i Function; k1 represents the speed weighting coefficient; ω rIndicates the wind wheel speed; Indicates the wind wheel speed setting value; Indicates the absolute deviation of the wind rotor speed; represents the integral calculation; t1 represents the maximum integral time interval; k2 represents the weighted coefficient of cabin displacement; σ xfa represents the maximum absolute displacement of the cabin; the maximum absolute displacement of the cabin is expressed as follows:

[0159]

[0160] In the above formula, x fa represents the displacement of the cabin in the front and rear directions; |·| represents the absolute value; It means taking the maximum value in the time interval 0≤t≤t2; t2 represents the time interval with the maximum displacement of the cabin.

[0161] Specifically, the parameter space is a two-dimensional space, and each point in the parameter space constitutes a set of control parameters, namely, the pitch control proportional gain and the pitch control integral constant. Each point in the parameter space is expressed as:

[0162] s j =[k p ,T i ]

[0163] In the above formula, s j represents a point in the parameter space, which is a set of control parameters; k p represents the proportional gain of the pitch controller; T i represents the integral constant of the pitch controller;

[0164] The feasible region of parameter space is defined as follows:

[0165] S={[k p ,T i ]|0≤k p ≤k pmax ,1≤T i ≤T imax}

[0166] In the above formula, S represents the feasible domain of the parameter space, which is a set, and the internal elements are points s j ;k pmax represents the upper boundary of the proportional gain of the pitch controller, the proportional gain k p The value range is 0 to k pmax ; T imax Indicates the upper boundary of the integral constant of the pitch controller, the integral constant T i The value range is 1 to T imax .

[0167] Specifically, the intelligent optimization algorithm includes the following steps:

[0168] 1) In the feasible region of the parameter space, randomly generate several points, each point s j is a set of optional control parameters [k p ,T i ],k p represents the proportional gain of the pitch controller, T i represents the integral constant of the pitch controller, and the set of randomly generated initial control parameters is expressed as follows:

[0169] {s1,s2,…,s j ,…,s N |s j =[k p ,T i ],1≤j≤N}

[0170] In the above formula, s j represents a point in the feasible region of the parameter space, representing a set of control parameters; N represents the number of points in the set; in 0≤k p ≤k pmax Randomly generate k in range p , when 1≤T i ≤T imax Randomly generate T within the range i ;

[0171] 2) Based on the objective function of parameter optimization, calculate each point s in the parameter space j The objective function value of is:

[0172] {f1,f2,…,f j ,…,f N |f j =f(s j ),1≤j≤N}

[0173] In the above formula, f j represents the point s in the feasible region j The objective function value is expressed as f(s j ); The objective function value reflects the quality of each point. The lower the objective function value, the better the point.

[0174] 3) Calculate and update N points in the parameter space. Based on the objective function value of each point in the parameter space, update the position of the N points in the parameter space, eliminate inferior points, and move toward the optimal point:

[0175] The update equation for each point is as follows:

[0176]

[0177] In the above formula, s j(t+1) means at time t+1 point s j Location; j (t) represents the time point s at t j The position of; C1 represents the local optimal weighting coefficient; C2 represents the global optimal weighting coefficient; σ represents a random number between 0 and 1; Indicates point s j The best position you have ever experienced; * Indicates that N points experience the optimal position;

[0178] 4) Determine whether the number of loop calculations has reached the maximum value. If it has reached the maximum number of loops, output the optimal position s * As the optimal control parameter; otherwise, return to step 2) to calculate.

[0179] Example 2

[0180] This embodiment discloses a wind turbine control parameter optimization system based on an intelligent algorithm. Figure 2 As shown, the system includes the following functional modules:

[0181] A linearization processing module obtains a state space equation, i.e., a linearization model of the wind turbine generator set, by linearizing the wind turbine generator set. The linearization model includes an input vector, an output vector, a state vector, and a coefficient matrix.

[0182] The objective function module of parameter optimization forms a closed-loop system with the linearized model of the wind turbine and the pitch controller, takes the step wind speed as input, and obtains the output response of the closed-loop system's impeller speed and the output response of the nacelle's front-to-back displacement. The objective function is defined as the weighted sum of the integral of the impeller speed absolute deviation and the maximum displacement of the nacelle in the front-to-back direction.

[0183] Intelligent optimization module for parameter optimization. The optimization of control parameters is to find the optimal control parameters in the parameter space. The parameter space needs to set a search range, that is, the feasible domain of the parameter space. Through this intelligent optimization module, the position of the optimal solution in the parameter space can be quickly found.

[0184] Specifically, the linearized model expression of the wind turbine is as follows:

[0185]

[0186] In the above formula, represents the input vector at time t; represents the output variable at time t; represents the state vector at time t; represents the rate of change of the state vector at time t; A, B, C, D are coefficient matrices;

[0187] Input Vector It should contain at least three variables, namely, pitch angle command, average wind speed of wind rotor and generator torque command. The specific expression is as follows:

[0188]

[0189] In the above formula, Indicates pitch angle command; Indicates the average wind speed of the wind rotor; Represents the generator torque command; input vector is a column vector. The superscript T in the expression indicates transposition, which converts the row vector into a column vector.

[0190] Output vector It should contain at least two variables, namely the wind rotor speed and the nacelle front-to-back displacement. The specific expression is as follows:

[0191]

[0192] In the above formula, ω r Indicates the wind wheel speed; x fa Represents the displacement of the cabin in the front and rear directions; output vector is a column vector. The superscript T in the expression indicates transposition, which converts the row vector into a column vector.

[0193] State Vector It should at least include the low-order modes of the blades, the low-order modes of the tower, the low-order modes of the transmission chain, the speed of the wind rotor and the speed change rate of the wind rotor. The specific expressions are as follows:

[0194]

[0195] In the above formula, represents the modal vector of blade 1; represents the modal vector of blade 2; represents the modal vector of blade 3; represents the modal vector of the tower; ω r Indicates the wind wheel speed; Indicates the rate of change of wind rotor speed; state vector is a column vector. The superscript T in the expression represents transposition, which converts the row vector into a column vector.

[0196] The modal vector of blade 1 The expression is as follows:

[0197]

[0198] In the above formula, represents the first-order flapping mode displacement of blade 1; represents the first-order swing mode displacement of blade 1; represents the first-order flapping mode velocity of blade 1; represents the first-order swing modal velocity of blade 1; represents the second-order flapping mode displacement of blade 1; represents the second-order swing mode displacement of blade 1; represents the second-order flapping mode velocity of blade 1; represents the second-order swing mode velocity of blade 1;

[0199] The modal vector of blade 2 The expression is as follows:

[0200]

[0201] In the above formula, represents the first-order flapping mode displacement of blade 2; represents the first-order swing modal displacement of blade 2; represents the first-order flapping mode velocity of blade 2; represents the first-order swing modal velocity of blade 2; represents the second-order flapping mode displacement of blade 2; represents the second-order swing mode displacement of blade 2; represents the second-order flapping mode velocity of blade 2; represents the second-order swing mode velocity of blade 2;

[0202] The modal vector of the blade 3 The expression is as follows:

[0203]

[0204] In the above formula, represents the first-order flapping mode displacement of blade 3; represents the first-order swing modal displacement of blade 3; represents the first-order flapping modal velocity of blade 3; represents the first-order swing modal velocity of blade 3; represents the second-order flapping mode displacement of blade 3; represents the second-order swing mode displacement of blade 3; represents the second-order flapping mode velocity of blade 3; represents the second-order swing mode velocity of blade 3;

[0205] The modal vectors of the tower are The expression is as follows:

[0206]

[0207] In the above formula, represents the first-order lateral modal displacement of the tower; represents the first-order fore-aft modal displacement of the tower; represents the first-order lateral modal velocity of the tower; represents the first-order fore-aft modal velocity of the tower; represents the second-order lateral modal displacement of the tower; represents the second-order fore-aft modal displacement of the tower; represents the second-order lateral modal velocity of the tower; represents the second-order fore-and-aft modal velocity of the tower.

[0208] Specifically, the coefficient matrices A, B, C, and D can be obtained by outputting the linearization module of the wind turbine modeling software. Since the coefficient matrix is ​​related to the average wind speed, it is necessary to select several reference points in the range from rated wind speed to cut-out wind speed. Generally, five reference points are selected, and the coefficient matrix is ​​output at each reference point.

[0209] Specifically, the pitch controller of the closed-loop system adopts proportional-integral control, and its transfer function is as follows:

[0210]

[0211] In the above formula, C(s) represents the controller transfer function; k p represents the proportional gain of the pitch controller; T i represents the integral constant of the pitch controller; s represents the independent variable of the complex plane after Laplace transformation;

[0212] The specific expression of the objective function is as follows:

[0213]

[0214] In the above formula, f([k p ,T i ]) represents the objective function, which is k p and T i Function; k1 represents the speed weighting coefficient; ω r Indicates the wind wheel speed; Indicates the wind wheel speed setting value; Indicates the absolute deviation of the wind wheel speed; represents the integral calculation; t1 represents the maximum integral time interval; k2 represents the weighted coefficient of cabin displacement; σ xfa represents the maximum absolute displacement of the cabin; the maximum absolute displacement of the cabin is expressed as follows:

[0215]

[0216] In the above formula, x farepresents the displacement of the cabin in the front and rear directions; |·| represents the absolute value; It means taking the maximum value in the time interval 0≤t≤t2; t2 represents the time interval with the maximum displacement of the cabin.

[0217] Specifically, the parameter space is a two-dimensional space, and each point in the parameter space constitutes a set of control parameters, namely, the pitch control proportional gain and the pitch control integral constant. Each point in the parameter space is expressed as:

[0218] s j =[k p ,T i ]

[0219] In the above formula, s j represents a point in the parameter space, which is a set of control parameters; k p represents the proportional gain of the pitch controller; T i represents the integral constant of the pitch controller;

[0220] The feasible region of parameter space is defined as follows:

[0221] S={[k p ,T i ]|0≤k p ≤k pmax ,1≤T i ≤T imax}

[0222] In the above formula, S represents the feasible domain of the parameter space, which is a set, and the internal elements are points s j ;k pmax represents the upper boundary of the proportional gain of the pitch controller, the proportional gain k p The value range is 0 to k pmax ; T imax Indicates the upper boundary of the integral constant of the pitch controller, the integral constant T i The value range is 1 to T imax .

[0223] Specifically, the intelligent optimization module performs the following operations:

[0224] 1) In the feasible region of the parameter space, randomly generate several points, each point s j is a set of optional control parameters [k p ,T i ],k p represents the proportional gain of the pitch controller, T i represents the integral constant of the pitch controller, and the set of randomly generated initial control parameters is expressed as follows:

[0225] {s1,s2,…,s j,…,s N |s j =[k p ,T i ],1≤j≤N}

[0226] In the above formula, s j represents a point in the feasible region of the parameter space, representing a set of control parameters; N represents the number of points in the set; in 0≤k p ≤k pmax Randomly generate k in range p , when 1≤T i ≤T imax Randomly generate T within the range i ;

[0227] 2) Based on the objective function of parameter optimization, calculate each point s in the parameter space j The objective function value of is:

[0228] {f1,f2,…,f j ,…,f N |f j =f(s j ),1≤j≤N}

[0229] In the above formula, f j represents the point s in the feasible region j The objective function value is expressed as f(s j ); The objective function value reflects the quality of each point. The lower the objective function value, the better the point.

[0230] 3) Calculate and update N points in the parameter space. Based on the objective function value of each point in the parameter space, update the position of the N points in the parameter space, eliminate inferior points, and move toward the optimal point:

[0231] The update equation for each point is as follows:

[0232]

[0233] In the above formula, s j (t+1) means at time t+1 point s j Location; j (t) represents the time point s at t j The position of; C1 represents the local optimal weighting coefficient; C2 represents the global optimal weighting coefficient; σ represents a random number between 0 and 1; Indicates point s j The best position you have ever experienced; * Indicates that N points experience the optimal position;

[0234] 4) Determine whether the number of loop calculations has reached the maximum value. If it has reached the maximum number of loops, output the optimal position s * As the optimal control parameter; otherwise, return to step 2) to calculate.

[0235] Example 3

[0236] This embodiment discloses a storage medium storing a program. When the program is executed by a processor, the method for optimizing wind turbine control parameters based on an intelligent algorithm described in Embodiment 1 is implemented.

[0237] The storage medium in this embodiment can be a disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), a USB flash drive, a mobile hard disk, or other media.

[0238] Example 4

[0239] This embodiment discloses a computing device, including a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the wind turbine control parameter optimization method based on the intelligent algorithm described in Example 1 is implemented.

[0240] The computing device described in this embodiment may be a desktop computer, a laptop computer, a smart phone, a PDA handheld terminal, a tablet computer, a programmable logic controller (PLC), or other terminal devices with a processor function.

[0241] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A method for optimizing wind turbine control parameters based on intelligent algorithms, characterized in that: include: Wind turbine linearization: By linearizing the wind turbine, the state space equation is obtained, that is, the linearized model of the wind turbine. The linearized model includes input vectors, output vectors, state vectors and coefficient matrices. Objective function of parameter optimization: The linearized model of the wind turbine and the pitch controller form a closed-loop system, and the step wind speed is used as input to obtain the output response of the closed-loop system's impeller speed and the output response of the nacelle's front-to-back displacement. The objective function is defined as the weighted sum of the integral of the impeller speed absolute deviation and the maximum displacement of the nacelle in the front-to-back direction. Feasible domain of parameter space: The optimization of control parameters is to find the optimal control parameters in the parameter space. The parameter space needs to set a search range, that is, the feasible domain of the parameter space; Intelligent optimization algorithm for parameter optimization: The optimization of control parameters is to find the optimal solution in the feasible domain of parameter space. The intelligent optimization algorithm can quickly find the location of the optimal solution in the parameter space. The linearized model expression of the wind turbine is as follows: In the above formula, represents the input vector at time t; represents the output variable at time t; represents the state vector at time t; represents the rate of change of the state vector at time t; A, B, C, D are coefficient matrices; Input Vector It should contain at least three variables, namely, pitch angle command, average wind speed of wind rotor and generator torque command. The specific expression is as follows: In the above formula, Indicates pitch angle command; Indicates the average wind speed of the wind rotor; Represents the generator torque command; input vector is a column vector. The superscript T in the expression indicates transposition, which converts the row vector into a column vector. Output vector It should contain at least two variables, namely the wind rotor speed and the nacelle front-to-back displacement. The specific expression is as follows: In the above formula, ω r Indicates the wind wheel speed; x fa Represents the displacement of the cabin in the front and rear directions; output vector is a column vector. The superscript T in the expression indicates transposition, which converts the row vector into a column vector. State Vector It should at least include the low-order modes of the blades, the low-order modes of the tower, the low-order modes of the transmission chain, the speed of the wind rotor and the speed change rate of the wind rotor. The specific expressions are as follows: In the above formula, represents the modal vector of blade 1; represents the modal vector of blade 2; represents the modal vector of blade 3; represents the modal vector of the tower; ω r Indicates the wind wheel speed; Represents the rate of change of wind rotor speed; state vector is a column vector. The superscript T in the expression represents transposition, which converts the row vector into a column vector.

2. The method for optimizing wind turbine control parameters based on intelligent algorithm according to claim 1, characterized in that: The modal vector of blade 1 The expression is as follows: In the above formula, represents the first-order flapping mode displacement of blade 1; represents the first-order swing modal displacement of blade 1; represents the first-order flapping mode velocity of blade 1; represents the first-order swing mode velocity of blade 1; represents the second-order flapping mode displacement of blade 1; represents the second-order swing mode displacement of blade 1; represents the second-order flapping mode velocity of blade 1; represents the second-order swing mode velocity of blade 1; The modal vector of blade 2 The expression is as follows: In the above formula, represents the first-order flapping mode displacement of blade 2; represents the first-order swing modal displacement of blade 2; represents the first-order flapping mode velocity of blade 2; represents the first-order swing modal velocity of blade 2; represents the second-order flapping mode displacement of blade 2; represents the second-order swing mode displacement of blade 2; represents the second-order flapping mode velocity of blade 2; represents the second-order swing mode velocity of blade 2; The modal vector of the blade 3 The expression is as follows: In the above formula, represents the first-order flapping mode displacement of blade 3; represents the first-order swing modal displacement of blade 3; represents the first-order flapping modal velocity of blade 3; represents the first-order swing modal velocity of blade 3; represents the second-order flapping mode displacement of blade 3; represents the second-order swing mode displacement of blade 3; represents the second-order flapping mode velocity of blade 3; represents the second-order swing mode velocity of blade 3; The modal vectors of the tower are The expression is as follows: In the above formula, represents the first-order lateral modal displacement of the tower; represents the first-order fore-aft modal displacement of the tower; represents the first-order lateral modal velocity of the tower; represents the first-order fore-aft modal velocity of the tower; represents the second-order lateral modal displacement of the tower; represents the second-order fore-aft modal displacement of the tower; represents the second-order lateral modal velocity of the tower; represents the second-order fore-and-aft modal velocity of the tower.

3. The method for optimizing wind turbine control parameters based on intelligent algorithm according to claim 1, characterized in that: The coefficient matrices A, B, C, and D can be outputted through the linearization module of the wind turbine modeling software. Since the coefficient matrix is ​​related to the average wind speed, it is necessary to select several reference points in the range from the rated wind speed to the cut-out wind speed, and output the coefficient matrix at each reference point.

4. The method for optimizing wind turbine control parameters based on intelligent algorithm according to claim 1, characterized in that: The pitch controller of the closed-loop system adopts proportional-integral control, and its transfer function is as follows: In the above formula, C(s) represents the controller transfer function; k p represents the proportional gain of the pitch controller; T i represents the integral constant of the pitch controller; s represents the independent variable of the complex plane after Laplace transformation; The specific expression of the objective function is as follows: In the above formula, f([k p ,T i ]) represents the objective function, which is k p and T i Function; k1 represents the speed weighting coefficient; ω r Indicates the wind wheel speed; Indicates the wind wheel speed setting value; Indicates the absolute deviation of the wind wheel speed; represents the integral calculation; t1 represents the maximum integral time interval; k2 represents the weighted coefficient of cabin displacement; σ xfa represents the maximum absolute displacement of the cabin; the maximum absolute displacement of the cabin is expressed as follows: In the above formula, x fa represents the displacement of the cabin in the front and rear directions; |·| represents the absolute value; It means taking the maximum value in the time interval 0≤t≤t2; t2 represents the time interval with the maximum displacement of the cabin.

5. The method for optimizing wind turbine control parameters based on intelligent algorithm according to claim 1, characterized in that: The parameter space is a two-dimensional space. Each point in the parameter space constitutes a set of control parameters, namely, the pitch control proportional gain and the pitch control integral constant. Each point in the parameter space is expressed as: s j =[k p ,T i ] In the above formula, s j represents a point in the parameter space, which is a set of control parameters; k p represents the proportional gain of the pitch controller; T i represents the integral constant of the pitch controller; The feasible region of parameter space is defined as follows: S={[k p ,T i ]|0≤k p ≤k pmax ,1≤T i ≤T imax } In the above formula, S represents the feasible domain of the parameter space, which is a set, and the internal elements are points s j ;k pmax represents the upper boundary of the proportional gain of the pitch controller, the proportional gain k p The value range is 0 to k pmax ; T imax Indicates the upper boundary of the integral constant of the pitch controller, the integral constant T i The value range is 1 to T imax .

6. The method for optimizing wind turbine control parameters based on intelligent algorithm according to claim 1, characterized in that: The intelligent optimization algorithm comprises the following steps: 1) In the feasible region of the parameter space, randomly generate several points, each point s j is a set of optional control parameters [k p ,T i ],k p represents the proportional gain of the pitch controller, T i represents the integral constant of the pitch controller, and the set of randomly generated initial control parameters is expressed as follows: {s1,s2,…,s j ,…,s N |s j =[k p ,T i ],1≤j≤N} In the above formula, s j represents a point in the feasible region of the parameter space, representing a set of control parameters; N represents the number of points in the set; in 0≤k p ≤k pmax Randomly generate k in range p , when 1≤T i ≤T imax Randomly generate T within the range i ; 2) Based on the objective function of parameter optimization, calculate each point s in the parameter space j The objective function value of is: {f1,f2,…,f j ,…,f N |f j =f(s j ),1≤j≤N} In the above formula, f j represents the point s in the feasible region j The objective function value is expressed as f(s j ); The objective function value reflects the quality of each point. The lower the objective function value, the better the point. 3) Calculate and update N points in the parameter space. Based on the objective function value of each point in the parameter space, update the position of the N points in the parameter space, eliminate inferior points, and move toward the optimal point: The update equation for each point is as follows: In the above formula, s j (t+1) means at time t+1 point s j Location; j (t) represents the time point s at t j The position of; C1 represents the local optimal weighting coefficient; C2 represents the global optimal weighting coefficient; σ represents a random number between 0 and 1; Indicates point s j The best position you have ever experienced; * Indicates that N points experience the optimal position; 4) Determine whether the number of loop calculations has reached the maximum value. If it has reached the maximum number of loops, output the optimal position s * As the optimal control parameter; otherwise, return to step 2) to calculate.

7. A wind turbine control parameter optimization system based on intelligent algorithm, characterized in that: include: A linearization processing module obtains a state space equation, i.e., a linearization model of the wind turbine generator set, by linearizing the wind turbine generator set. The linearization model includes an input vector, an output vector, a state vector, and a coefficient matrix. The objective function module of parameter optimization forms a closed-loop system with the linearized model of the wind turbine and the pitch controller, takes the step wind speed as input, and obtains the output response of the closed-loop system's impeller speed and the output response of the nacelle's front-to-back displacement. The objective function is defined as the weighted sum of the integral of the impeller speed absolute deviation and the maximum displacement of the nacelle in the front-to-back direction. Intelligent optimization module for parameter optimization. The optimization of control parameters is to find the optimal control parameters in the parameter space. The parameter space needs to set a search range, that is, the feasible domain of the parameter space. Through this intelligent optimization module, the position of the optimal solution in the parameter space can be quickly found; The linearized model expression of the wind turbine is as follows: In the above formula, represents the input vector at time t; represents the output variable at time t; represents the state vector at time t; represents the rate of change of the state vector at time t; A, B, C, D are coefficient matrices; Input Vector It should contain at least three variables, namely, pitch angle command, average wind speed of wind rotor and generator torque command. The specific expression is as follows: In the above formula, Indicates pitch angle command; Indicates the average wind speed of the wind rotor; Represents the generator torque command; input vector is a column vector. The superscript T in the expression indicates transposition, which converts the row vector into a column vector. Output vector It should contain at least two variables, namely the wind rotor speed and the nacelle front-to-back displacement. The specific expression is as follows: In the above formula, ω r Indicates the wind wheel speed; x fa Represents the displacement of the cabin in the front and rear directions; output vector is a column vector. The superscript T in the expression indicates transposition, which converts the row vector into a column vector. State Vector It should at least include the low-order modes of the blades, the low-order modes of the tower, the low-order modes of the transmission chain, the speed of the wind rotor and the speed change rate of the wind rotor. The specific expressions are as follows: In the above formula, represents the modal vector of blade 1; represents the modal vector of blade 2; represents the modal vector of blade 3; represents the modal vector of the tower; ω r Indicates the wind wheel speed; Represents the rate of change of wind rotor speed; state vector is a column vector. The superscript T in the expression represents transposition, which converts the row vector into a column vector.

8. The wind turbine control parameter optimization system based on intelligent algorithm according to claim 7 is characterized in that: The modal vector of blade 1 The expression is as follows: In the above formula, represents the first-order flapping mode displacement of blade 1; represents the first-order swing mode displacement of blade 1; represents the first-order flapping mode velocity of blade 1; represents the first-order swing modal velocity of blade 1; represents the second-order flapping mode displacement of blade 1; represents the second-order swing mode displacement of blade 1; represents the second-order flapping mode velocity of blade 1; represents the second-order swing mode velocity of blade 1; The modal vector of blade 2 The expression is as follows: In the above formula, represents the first-order flapping mode displacement of blade 2; represents the first-order swing modal displacement of blade 2; represents the first-order flapping mode velocity of blade 2; represents the first-order swing modal velocity of blade 2; represents the second-order flapping mode displacement of blade 2; represents the second-order swing mode displacement of blade 2; represents the second-order flapping mode velocity of blade 2; represents the second-order swing mode velocity of blade 2; The modal vector of the blade 3 The expression is as follows: In the above formula, represents the first-order flapping mode displacement of blade 3; represents the first-order swing modal displacement of blade 3; represents the first-order flapping modal velocity of blade 3; represents the first-order swing modal velocity of blade 3; represents the second-order flapping mode displacement of blade 3; represents the second-order swing mode displacement of blade 3; represents the second-order flapping mode velocity of blade 3; represents the second-order swing mode velocity of blade 3; The modal vectors of the tower are The expression is as follows: In the above formula, represents the first-order lateral modal displacement of the tower; represents the first-order fore-aft modal displacement of the tower; represents the first-order lateral modal velocity of the tower; represents the first-order fore-aft modal velocity of the tower; represents the second-order lateral modal displacement of the tower; represents the second-order fore-aft modal displacement of the tower; represents the second-order lateral modal velocity of the tower; represents the second-order fore-and-aft modal velocity of the tower.

9. The wind turbine control parameter optimization system based on intelligent algorithm according to claim 7, characterized in that: The coefficient matrices A, B, C, and D can be outputted through the linearization module of the wind turbine modeling software. Since the coefficient matrix is ​​related to the average wind speed, it is necessary to select several reference points in the range from the rated wind speed to the cut-out wind speed, and output the coefficient matrix at each reference point.

10. The wind turbine control parameter optimization system based on intelligent algorithm according to claim 7, characterized in that: The pitch controller of the closed-loop system adopts proportional-integral control, and its transfer function is as follows: In the above formula, C(s) represents the controller transfer function; k p represents the proportional gain of the pitch controller; T i represents the integral constant of the pitch controller; s represents the independent variable of the complex plane after Laplace transformation; The specific expression of the objective function is as follows: In the above formula, f([k p ,T i ]) represents the objective function, which is k p and T i Function; k1 represents the speed weighting coefficient; ω r Indicates the wind wheel speed; Indicates the wind wheel speed setting value; Indicates the absolute deviation of the wind wheel speed; represents the integral calculation; t1 represents the maximum integral time interval; k2 represents the weighted coefficient of cabin displacement; σ xfa represents the maximum absolute displacement of the cabin; the maximum absolute displacement of the cabin is expressed as follows: In the above formula, x fa represents the displacement of the cabin in the front and rear directions; |·| represents the absolute value; It means taking the maximum value in the time interval 0≤t≤t2; t2 represents the time interval with the maximum displacement of the cabin.

11. The wind turbine control parameter optimization system based on intelligent algorithm according to claim 7, characterized in that: The parameter space is a two-dimensional space. Each point in the parameter space constitutes a set of control parameters, namely, the pitch control proportional gain and the pitch control integral constant. Each point in the parameter space is expressed as: s j =[k p ,T i ] In the above formula, s j represents a point in the parameter space, which is a set of control parameters; k p represents the proportional gain of the pitch controller; T i represents the integral constant of the pitch controller; The feasible region of parameter space is defined as follows: S={[k p ,T i ]|0≤k p ≤k pmax ,1≤T i ≤T imax } In the above formula, S represents the feasible domain of the parameter space, which is a set, and the internal elements are points s j ;k pmax represents the upper boundary of the proportional gain of the pitch controller, the proportional gain k p The value range is 0 to k pmax ; T imax Indicates the upper boundary of the integral constant of the pitch controller, the integral constant T i The value range is 1 to T imax .

12. The wind turbine control parameter optimization system based on intelligent algorithm according to claim 7, characterized in that: The intelligent optimization module specifically performs the following operations: 1) In the feasible region of the parameter space, randomly generate several points, each point s j is a set of optional control parameters [k p ,T i ],k p represents the proportional gain of the pitch controller, T i represents the integral constant of the pitch controller, and the set of randomly generated initial control parameters is expressed as follows: {s1,s2,…,s j ,…,s N |s j =[k p ,T i ],1≤j≤N} In the above formula, s j represents a point in the feasible region of the parameter space, representing a set of control parameters; N represents the number of points in the set; in 0≤k p ≤k pmax Randomly generate k in range p , when 1≤T i ≤T imax Randomly generate T within the range i ; 2) Based on the objective function of parameter optimization, calculate each point s in the parameter space j The objective function value of is: {f1,f2,…,f j ,…,f N |f j =f(s j ),1≤j≤N} In the above formula, f j represents the point s in the feasible region j The objective function value is expressed as f(s j ); The objective function value reflects the quality of each point. The lower the objective function value, the better the point. 3) Calculate and update N points in the parameter space. Based on the objective function value of each point in the parameter space, update the position of the N points in the parameter space, eliminate inferior points, and move toward the optimal point: The update equation for each point is as follows: In the above formula, s j (t+1) means at time t+1 point s j Location; j (t) represents the time point s at t j The position of; C1 represents the local optimal weighting coefficient; C2 represents the global optimal weighting coefficient; σ represents a random number between 0 and 1; Indicates point s j The best position you have ever experienced; * Indicates that N points experience the optimal position; 4) Determine whether the number of loop calculations has reached the maximum value. If it has reached the maximum number of loops, output the optimal position s * As the optimal control parameter; otherwise, return to step 2) to calculate.

Images