CN113110024B - Design method of wind turbine blade vibration controller based on elliptic focus radius improved gull algorithm - Google Patents
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Abstract
The invention relates to a wind turbine blade vibration controller design method based on an elliptic focal radius improved gull algorithm. Aiming at the problems of multi-degree-of-freedom vibration, system nonlinearity and drive saturation of a blade vibration system, a blade fractional order PID vibration controller is designed by combining the traditional PID and a fractional order theory. Meanwhile, an improved gull (EFR-SOA) algorithm based on the elliptic focal radius principle is designed for searching for an optimal fractional order vibration control parameter. The EFR-SOA algorithm is based on the traditional gull algorithm, introduces the geometric elliptic focal radius principle and is used for dynamically adjusting the path and the speed of the gull spiral movement, thereby improving the global optimization precision, accelerating the convergence speed and reducing the calculation cost. Compared with the traditional optimal PID control, the method can obviously improve the dynamic characteristic of the multi-free vibration suppression of the blade, improve the driving saturation resistance and shorten the optimal calculation time of the control parameter.
Description
Technical Field
The invention belongs to a wind turbine blade vibration control technology, and particularly relates to a wind turbine blade vibration controller design method based on an elliptic focus radius improved gull algorithm.
Background
The PID controller is the most widely used controller in industry, and has the advantages of simple structure, strong robustness and the like. The PID controller is also one of the traditional controllers of the wind turbine blade vibration system, however, in practical application, the following problems still remain to be improved: 1) the dynamic characteristic of the multi-degree-of-freedom vibration suppression of the blade is not ideal; 2) the drive saturation problem can deteriorate the blade vibration control effect; 3) PID vibration control parameters are difficult to set, and an empirical method and a trial and error method are mostly adopted. In recent years, it has been studied to adjust blade vibration control parameters by using an intelligent optimization algorithm, and although the control effect is improved, these algorithms are not designed for the blade vibration control problem, and it is difficult to obtain a global optimum value.
With the development of the fractional order theory, people recognize that many practical industrial systems have the fractional order characteristic, and compared with the traditional integer order PID controller, the fractional order PID controller increases two fractional order control parameters, namely the fractional order lambda and mu, so that the fractional order PID controller has better flexibility and can further improve the stability, the dynamic performance and the anti-saturation performance of the system. The parameter setting of the fractional order PID controller comprises a plurality of methods: a method based on a given amplitude margin and a given phase margin, a control parameter setting method based on Z-N, a setting method based on an internal model principle, a setting method based on an intelligent optimization algorithm and the like. The method based on the intelligent optimization algorithm has the advantages of high efficiency, good effect, high intelligent degree, non-model-based and the like, but because the parameters to be set of the fractional order PID are more, the calculation and setting difficulty of the control parameters is higher when the complex system object is faced, and the advanced intelligent optimization algorithm with high performance and high applicability is needed to search the global optimum and improve the setting effect.
The gull algorithm is a new intelligent optimization algorithm, is proposed by Gaurav Dhiman and Vijay Kumar in 2019, carries out global search and local search mainly by simulating the migration motion and the attack motion of the gull, and approaches the global optimum by utilizing group experience and individual experience. At present, the gull algorithm is applied to the fields of industrial design, feature extraction and classification and the like.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects of the prior art, realize vibration suppression and drive saturation resistance control and obtain excellent dynamic characteristics aiming at multi-degree-of-freedom vibration of the wind turbine blade, the invention provides a wind turbine blade vibration controller design method based on an elliptic focal radius improved gull algorithm.
The technical scheme is as follows:
a design method of a wind turbine blade vibration controller based on an elliptic focus radius improved gull algorithm comprises the following steps:
s1: designing a fractional order PID vibration controller of the blade according to the vibration quantity and the driving control quantity of the wind turbine blade;
s2: designing an improved gull algorithm based on the elliptic focal radius;
s3: s1 vaneA fractional order PID vibration controller with a parameter to be set of K P 、K I 、K D λ, μ, wherein K P To proportional gain, K I To integrate the gain, K D And setting a target function for the differential gain, wherein lambda is an integral fractional order parameter, mu is a differential fractional order parameter, and the parameters are adjusted by using the improved gull algorithm of S2.
Further, in S1, the wind turbine blade vibration controller is designed according to the following formula:
β(t)＝K P y(t)+K I D -λ y(t)+K D D μ y(t)
wherein, K P To proportional gain, K I To integrate the gain, K D In the differential gain, λ is an integral fractional order parameter, μ is a differential fractional order parameter, D is a differential sign, y (t) is a blade vibration quantity, and β (t) is a drive control quantity.
Further, the improved gull algorithm based on the elliptic focal radius in S2 includes the following steps:
s2.1, setting algorithm parameters including defining individual dimension D, gull population S, maximum iteration number G and frequency parameter f c Defining ellipse-related parameters: a is 0 E, wherein, a 0 Is the ellipse major radius, e is the ellipse eccentricity; defining the adjusting parameters of the elliptical fillet: p is a radical of 1 ,p 2 ,p 3 Taking values according to experience;
s2.2, initializing a population: initializing a value range, and randomly initializing the position P (i) of each gull in the value range according to the algorithm parameters of S2.1;
s2.3, evaluating an objective function: calculating the fitness of each gull position according to the objective function, updating the optimal position of each gull, and updating the optimal position P of the population bs (i)；
S2.4 migration movement: in migration movement, gulls move from one position to another according to collision avoidance principles, towards P of S2.3 bs (i) Move and maintain at P bs (i) Near, the new position of gull after movement is D s (i)；
S2.5, adjusting sea gull spiral movement parameters based on the elliptic focal radius: in the iteration process, based on the elliptic focal radius principle, the gull spiral movement parameters u and v are dynamically adjusted, so that the path and the speed of the gull spiral movement are changed; specifically, the sea gull spiral movement parameter is adjusted according to the following formula:
u＝|MF 1 |＝|e×x 0 -a 0 |＝|e×a 0 ×cos(θ e )-a 0 |
v＝|MF 2 |＝|e×x 0 +a 0 |＝|e×a 0 ×cos(θ e )+a 0 |
wherein u and v are parameters of spiral movement of gull, M is a movable point on ellipse, and x is the abscissa 0 ,θ e The ellipse radius corresponding to the M point, | MF1| is the length of the right focal radius of the ellipse, | MF2| is the length of the left focal radius of the ellipse, a0 is the major radius of the ellipse, e is the eccentricity of the ellipse, and cos is a cosine function;
ellipse fillet theta e Changes occur as the point M moves on the ellipse to adjust the rate of change of u, v, defined according to the following equation:
wherein p is 1 ,p 2 ,p 3 Adjustment parameter for elliptical fillet, t k Is the ratio of the number of iterations i to the maximum number of iterations G, θ e Is an elliptical fillet with the range of 0-180 degrees;
s2.6, attack movement: the gull performs attack movement according to the formula in S2.5 and moves the position, and the update position of each gull after the attack movement is P s (i)；
S2.7, keep iterating: and returning to S2.3 and keeping iteration until a maximum iteration number G is reached, and outputting a final calculation result.
Further, the parameter to be set in S3 is defined as p (i) ═ K for each gull position p pi ,K Ii ,K di ,λ i ,μ i ]The fractional order PID vibration controller parameter is used as the fractional order PID vibration controller parameter, and the step S2 is combined with the wind turbine blade vibration controllerPerforming a blade vibration control simulation test to obtain blade vibration quantity y (t); the objective function is defined as:
s.t.β min <β<β max
wherein, J ITAE Is an objective function, t f For a given simulation time, β is the amount of drive control, β min Is a lower limit of beta, beta max An upper limit of β;
and (4) optimizing and calculating parameters of the fractional order PID vibration controller of the blade in the S1 according to a target function by utilizing an EFR-SOA algorithm designed in the S2, and realizing the setting of the parameters by the improved gull algorithm.
Further, step S2.4 includes the following sub-steps:
s2.4.1 to avoid collisions between adjacent gulls, the position of each gull is updated by an additional variable defined as:
wherein P (i) is gull position, C s (i) To avoid updated positions after collision, i is the number of iterations, A is an additional variable, f c The frequency coefficient is A, the value of A is linearly reduced to 0 in the iteration process, G is the maximum iteration frequency, and Max represents a maximum function;
s2.4.2-for adequate food intake, gulls are oriented toward the population optimum position P after avoiding collisions bs (i) Moving, updating location:
M s (i)＝B×(P bs (i)-P(i))，B＝2×A 2 ×rd
wherein M is s (i) For updated positions after moving towards the best position of the population, B is a variable related to A, rd is [0,1 ]]A from step S2.4.1;
s2.4.3 Laribacter moves and keeps near the best position of the population, the update position:
D s (i)＝|C s (i)+M s (i)|
wherein D is s (i) Updating position after sea gull migration, C s (i) From step S2.4.1, M s (i) From step S2.4.2.
The invention has the beneficial effects that:
the traditional PID control and the fractional order theory are combined and used for a wind turbine blade vibration system, and an EFR-SOA algorithm is designed by combining an elliptic focal radius principle and a gull algorithm and used for optimizing and setting parameters of a blade fractional order PID vibration controller, so that the path and the speed of gull spiral movement are dynamically adjusted, and the optimizing precision, the convergence speed and the calculation cost of the traditional gull algorithm are improved. The method can realize multi-degree-of-freedom vibration suppression in a blade vibration system, and has excellent dynamic characteristics aiming at the problems of system nonlinearity and drive saturation.
The design method provided by the invention can be stored on a storage medium as a computer program at the same time, and comprises the following technical scheme:
an electronic device, comprising:
one or more processors; and a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the wind turbine blade vibration controller design method based on the elliptic focal radius modified gull algorithm.
And:
a computer readable medium, on which a computer program is stored, which when executed by a processor implements the above-mentioned wind turbine blade vibration controller design method based on the elliptic focal radius modified gull algorithm.
Drawings
Fig. 1 is a block diagram of a blade vibration control system based on a modified gull algorithm and a fractional order PID controller.
Fig. 2 is a flow chart of a designed improved gull algorithm.
FIG. 3 is a comparison graph of the wind turbine blade vibration control method (EFR-SOA-FOPID) based on the elliptic focal radius improved gull algorithm and fractional PID and the optimization process of the control parameter to be set in the prior art. (the existing control technologies comprise a gull algorithm-based fractional order PID control method (SOA-FOPID), a gull algorithm-based PID control method (SOA-PID), and a Grey wolf algorithm-based PID control method (GWO-PID))
FIG. 4 is a graph of vibration control response versus prior art control and the method of the present invention.
FIG. 5 is a comparison graph of the dynamic time domain response curves of the system of the prior art control and the method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples.
As shown in FIG. 1, the invention provides a design method of a fractional order PID controller of a blade vibration system based on an ellipse focal radius improved gull optimization algorithm, which introduces an ellipse focal radius principle into the gull algorithm, optimizes and sets parameters of the fractional order PID controller of the blade vibration system, improves convergence rate and optimizing precision, and comprises the following specific steps:
step S1: and designing a blade fractional order PID vibration controller according to the vibration quantity and the driving control quantity of the wind turbine blade.
The blade vibration system model is shown in fig. 3, where U is the wind speed, ρ is the air density, h is the flapping vibration amount, θ is the torsional vibration amount, β is the driving control amount, c is the blade airfoil chord length, b is the half chord length, a is the ratio of the distance between the airfoil elastic point and the midpoint of the chord length to the half chord length, and x is θ b is the ratio of the distance between the elastic point and the center of gravity of the wing profile to the half chord length, k h To impart structural rigidity, k θ For torsional structural rigidity, c h To wave damping ratio coefficient, c θ Is the torsional damping ratio coefficient, m t Is the mass of the airfoil, m w To support structural quality, I θ Is moment of inertia, c l,θ As coefficient of lift, c m,θ Is the moment coefficient, c l,β Coefficient of lift for drive control, c m,β Is a moment coefficient of drive control.
The invention designs a blade fractional order PID vibration controller according to the following formula:
β(t)＝K P y(t)+K I D -λ y(t)+K D D μ y(t)，y＝θ
wherein, K P ,K I ,K D The variable-gain driving method comprises the steps of proportional gain, integral gain and differential gain, wherein lambda is an integral fractional order parameter, mu is a differential fractional order parameter, D is a differential sign, y (t) is equal to the torsional vibration quantity theta of the blade, and beta (t) is a driving control quantity.
Step S2: an improved gull (EFR-SOA) algorithm based on the elliptic focal radius is designed.
As shown in fig. 2, the designed EFR-SOA algorithm performs optimization calculation by the following steps:
s2.1, setting algorithm parameters including defining individual dimension D, gull population S, maximum iteration number G and frequency parameter f c Defining ellipse-related parameters (a, b, e), defining regulation parameters (p) of the elliptical corners 1 ,p 2 ,p 3 )；
S2.2, initializing a population: initializing a value range, and randomly initializing the position P (i) of each gull in the value range according to the algorithm parameters of S2.1
S2.3, evaluating an objective function: calculating the fitness of each gull position according to the objective function, updating the optimal position of each gull, and updating the optimal position P of the population bs (i)；
S2.4 migration movement: in migration motion, gulls move from one position to another according to collision avoidance principles, towards P of S2.3 bs (i) Moved and held in the vicinity thereof, and the new position of gull after the movement is D s (i)；
S2.4.1 to avoid collisions between adjacent gulls, the position of each gull is updated by an additional variable defined as:
wherein P (i) is gull position, C s (i) To avoid after collisionI is the number of iterations, A is an additional variable, f c The value of A is linearly reduced to 0 in the iterative process for the frequency coefficient of A, G is the maximum iterative times, and Max represents the maximum function.
S2.4.2-for adequate food intake, gulls are oriented toward the population optimum position P after avoiding collisions bs (i) Moving, updating location:
M s (i)＝B×(P bs (i)-P(i))，B＝2×A 2 ×rd
wherein M is s (i) For updated positions after moving towards the best position of the population, B is a variable related to A, rd is [0,1 ]]A from step S2.4.1.
S2.4.3 Laribacter moves and keeps near the best position of the population, the update position:
D s (i)＝|C s (i)+M s (i)|
wherein D is s (i) Updating position after sea gull migration, C s (i) From step S2.4.1, M s (i) From step S2.4.2.
S2.5, adjusting the sea gull spiral movement parameters based on the elliptical focal radius: in the iteration process, the gull spiral movement parameters u and v are dynamically adjusted based on the elliptic focal radius principle, so that the path and the speed of the gull spiral movement are changed. Specifically, the sea gull spiral movement parameter is adjusted according to the following formula:
u＝|MF 1 |＝|e×x 0 -a 0 |＝|e×a 0 ×cos(θ e )-a 0 |
v＝|MF 2 |＝|e×x 0 +a 0 |＝|e×a 0 ×cos(θ e )+a 0 |
wherein u and v are parameters of spiral movement of gull, M is a movable point on the ellipse, and the abscissa is x 0 ,θ e Is an elliptical fillet corresponding to the M point, | MF 1 L is the length of the right focal radius of the ellipse, | MF 2 I is the length of the focal radius on the left side of the ellipse, a 0 Is the ellipse major radius, e is the ellipse eccentricity, and cos is the cosine function.
Ellipse fillet theta e With point M in the ellipseIs used to adjust the rate of change of u, v, defined according to the following equation:
wherein p is 1 ,p 2 ,p 3 Adjustment parameter for elliptical fillet, t k Is the ratio of the number of iterations i to the maximum number of iterations G, θ e Is an elliptical fillet, and the range is 0-180 degrees.
S2.6, attack movement: the gull performs attack movement according to the formula in S2.5 and moves the position, and the update position of each gull after the attack movement is P s (i)。
Wherein the content of the first and second substances,
u, v are the gull spiral movement position in (X, Y, Z) coordinates from step 2.5, r is the radius of the gull spiral movement, exp is an exponential function, P bs (i) For the best position of the population, D s (i) From step 2.3.4, P s (i) Updating position after gull attack movement.
S2.7, keep iterating: and returning to S2.3 and keeping iteration until the maximum iteration number G is reached, and outputting a final calculation result.
Step S3: the blade fractional order PID vibration controller of the step S1 has a parameter to be set as (K) p ,K I ,K D λ, μ), an objective function is set, and the parameters are adjusted by the EFR-SOA algorithm of step S2.
Defining the parameter to be set of the fractional order PID vibration controller as each gull position P (i) ═ K p ,K i ,K d ,λ,μ]The fractional order PID vibration controller parameter is used in combination with the wind turbine blade vibration controller in step S2, that is, β (t) ═ K P y(t)+K I D -λ y(t)+K D D μ And y (t), wherein y is theta, and a blade vibration control simulation test is carried out to obtain a blade vibration quantity y (t). The objective function is defined as:
s.t.β min <β<β max
wherein, J ITAE Is an objective function, t f For a given simulation time, β is the amount of drive control, β min Is a lower limit of beta, beta max The upper limit of β.
And (4) optimizing and calculating parameters of the blade fractional order PID vibration controller in the S1 according to the objective function by using the EFR-SOA algorithm designed in the step S2, and realizing the setting of the parameters by the EFR-SOA algorithm. .
PREFERRED EMBODIMENTS
The process according to the invention is illustrated below by way of an example:
in order to verify the effectiveness of the EFR-SOA algorithm designed by the invention and the vibration control method provided by the invention, a two-degree-of-freedom nonlinear blade vibration system is taken as an example, and system model parameters are shown in Table 1. And optimizing and setting parameters of the fractional order PID vibration controller of the blade by adopting an EFR-SOA algorithm so as to realize blade vibration control, wherein the values of the algorithm parameters are shown in a table 2.
TABLE 1 System model parameters
TABLE 2 EFR-SOA Algorithm parameters
| Seagull |
30 |
| |
2 |
| Major radius of ellipse a 0 | 0.5 |
| Elliptical eccentricity e | 0.6 |
| Adjustment parameter p of elliptical fillet 1 ,p 2 ,p 3 | [-131.87,0,311.87] |
| Maximum number of |
30 |
Setting the wind speed to be 16m/s, the limit range of the driving control quantity to be [ -pi/3, pi/3 ], calculating the ITAE value of the objective function of the optimization individual, and obtaining a convergence optimization curve as shown in FIG. 4, a system dynamic time domain response curve as shown in FIG. 5, and a system dynamic characteristic analysis result as shown in Table 3. In order to verify the superiority of the method, the method (EFR-SOA-FOPID) of the invention is compared with a PID control method (SOA-PID) based on a gull algorithm in the prior art, a PID control method (GWO-PID) based on a wolf algorithm in the prior art and a fractional order PID control method (SOA-FOPID) based on a gull algorithm in the prior art.
As can be seen from FIG. 4, compared with GWO-PID and SOA-PID, the PID control parameter obtained by the seagull algorithm is better; compared with SOA-PID and SOA-FOPID, the fractional order PID improves the PID control effect under the same algorithm optimization; compared with the SOA-FOPID and the method, the fractional order control parameters set by the EFR-SOA algorithm designed by the invention are better under the same fractional order controller.
As can be seen from fig. 5 and table 3, the PID control method based on the intelligent optimization algorithm (GWO, SOA) is difficult to overcome the problem of drive saturation, resulting in a large ITAE value for drive control, a large overshoot of the flapping vibration amount, a long stabilization time of the torsional vibration amount, and a poor blade vibration control effect; the fractional order PID control based on the SOA obviously improves the effect of the traditional PID control; compared with SOA-FOPID, the method provided by the invention has the advantages that the stabilization time of the torsional vibration suppression is accelerated by 28%, the ITAE performance of the flap vibration suppression is improved by 36%, and the ITAE performance of the driving loss is reduced by 34.3%.
By using the classical SOA algorithm and the EFR-SOA algorithm designed by the invention, the time for optimally calculating the fractional order PID control parameter is 198.6 seconds and 181 seconds respectively.
TABLE 3 System dynamic analysis results under different control methods
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (5)
1. A wind turbine blade vibration controller design method based on an elliptical focal radius improved gull algorithm is characterized by comprising the following steps:
s1: designing a fractional order PID vibration controller of the blade according to the vibration quantity and the driving control quantity of the wind turbine blade;
s2: designing an improved gull algorithm based on the elliptic focal radius;
s3: s1 fractional order PID vibration controller of blade, the parameter to be set is K P 、K I 、K D λ, μ, wherein K P To proportional gain, K I To integrate the gain, K D Setting a target function for differential gain, wherein lambda is an integral fractional order parameter, mu is a differential fractional order parameter, and the parameters are adjusted by utilizing an improved gull algorithm of S2; the improved gull algorithm based on the elliptic focal radius in S2 includes the following steps:
s2.1, setting algorithm parameters including defining individual dimension D, gull population S, maximum iteration number G and frequency parameter f c Defining ellipse-related parameters: a is 0 E, wherein, a 0 Is the ellipse major radius, e is the ellipse eccentricity; defining the adjusting parameters of the elliptical fillet: p is a radical of 1 ,p 2 ,p 3 Taking values according to experience;
s2.2, initializing a population: initializing a value range, and randomly initializing the position P (i) of each gull in the value range according to the algorithm parameters of S2.1;
s2.3, evaluating an objective function: calculating the fitness of each gull position according to the objective function, updating the optimal position of each gull, and updating the optimal position P of the population bs (i)；
S2.4 migration movement: in migration movement, gulls move from one position to another according to collision avoidance principles, towards P of S2.3 bs (i) Move and maintain at P bs (i) Near, the new position of gull after movement is D s (i)；
S2.5, adjusting the sea gull spiral movement parameters based on the elliptical focal radius: in the iteration process, based on the elliptic focal radius principle, the gull spiral movement parameters u and v are dynamically adjusted, so that the path and the speed of the gull spiral movement are changed; specifically, the sea gull spiral movement parameter is adjusted according to the following formula:
u＝|MF 1 |＝|e×x 0 -a 0 |＝|e×a 0 ×cos(θ e )-a 0 |
v＝|MF 2 |＝|e×x 0 +a 0 |＝|e×a 0 ×cos(θ e )+a 0 |
wherein u and v are parameters of spiral movement of gull, M is a movable point on ellipse, and x is the abscissa 0 ,θ e Is the ellipse fillet corresponding to the M point, | MF1| is the length of the right focal radius of the ellipse, | MF2| is the length of the left focal radius of the ellipse, a 0 Is the ellipse major radius, e is the ellipse eccentricity, cos is the cosine function;
ellipse fillet theta e Changes occur as the point M moves on the ellipse to adjust the rate of change of u, v, defined according to the following equation:
wherein p is 1 ,p 2 ,p 3 Adjustment parameter for elliptical fillet, t k Is the ratio of the number of iterations i to the maximum number of iterations G, θ e Is an elliptical fillet with the range of 0-180 degrees;
s2.6, attack movement: the gull performs attack movement according to the formula in S2.5 and moves the position, and the update position of each gull after the attack movement is P s (i)；
S2.7, keep iterating: returning to S2.3 and keeping iteration until a maximum iteration number G is reached, and outputting a final calculation result; in step S2.4, the method comprises the following sub-steps:
s2.4.1 to avoid collisions between adjacent gulls, the position of each gull is updated by an additional variable defined as:
i＝0,1,2,...,G
wherein P (i) is gull position, C s (i) To avoid updated positions after collision, i is the number of iterations, A is an additional variable, f c The frequency coefficient of A is the linear decrease of A to 0 in the iteration process, and G is the maximum iteration numberMax represents a maximum function;
s2.4.2-for adequate food intake, gulls are oriented toward the population optimum position P after avoiding collisions bs (i) Moving, updating the position:
M s (i)＝B×(P bs (i)-P(i))，B＝2×A 2 ×rd
wherein M is s (i) For updated positions after moving towards the best position of the population, B is a variable related to A, rd is [0,1 ]]A from step S2.4.1;
s2.4.3 Laribacter moves and keeps near the best position of the population, the update position:
D s (i)＝|C s (i)+M s (i)|
wherein D is s (i) For updating the position of the sea gull after its migration, C s (i) From step S2.4.1, M s (i) From step S2.4.2.
2. The method for designing a wind turbine blade vibration controller based on the elliptic focal radius improved gull algorithm as claimed in claim 1, wherein in S1, the wind turbine blade vibration controller is designed according to the following formula:
β(t)＝K P y(t)+K I D -λ y(t)+K D D μ y(t)
wherein, K P To proportional gain, K I To integrate the gain, K D In the differential gain, λ is an integral fractional order parameter, μ is a differential fractional order parameter, D is a differential sign, y (t) is a blade vibration quantity, and β (t) is a drive control quantity.
3. The method as claimed in claim 1, wherein the parameter to be set in S3 is defined as each gull position p (i) ═ K pi ,K Ii ,K di ,λ i ,μ i ]The fractional order PID vibration controller parameter is used as the fractional order PID vibration controller parameter, and the wind turbine blade vibration controller in the step S2 is combined to carry out the blade vibration control simulation test to obtain the blade vibrationAmount y (t); the objective function is defined as:
s.t.β min ＜β＜β max
wherein, J ITAE Is an objective function, t f For a given simulation time, β is the amount of drive control, β min Is a lower limit of beta, beta max An upper limit of β;
and (4) optimizing and calculating parameters of the fractional order PID vibration controller of the blade in the S1 according to a target function by utilizing an EFR-SOA algorithm designed in the S2, and realizing the setting of the parameters by the improved gull algorithm.
4. An electronic device, comprising:
one or more processors; and storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method according to any one of claims 1 to 3.
5. A computer-readable medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 1 to 3.
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