阅读说明：本技术 一种智能变桨距机电控制优化方法 (Intelligent variable-pitch electromechanical control optimization method ) 是由 陈健云 徐强 李静 苑晨阳 于 2019-11-13 设计创作，主要内容包括：本发明公开了一种智能变桨距机电控制优化方法,属于风力发电机技术领域。所述的智能变桨距机电控制优化方法的主要创新在于：对于传统的PID机电控制方法,将其比例系数作为基于塔架顶部位移响应和风轮转速反馈的实时变化的优化参数,将基于发电功率稳定性的单目标函数机电控制,扩展为同时考虑塔架顶部振动位移和发电功率稳定性的多目标机电控制,通过与智能优化方法相结合提出了智能变桨矩机电控制方法。数值结果表明,该智能方法不仅能够降低发电控制波动性,而且可以降低传动轴的转矩和塔架底部的弯矩,降低疲劳振动荷载。(The invention discloses an intelligent variable-pitch electromechanical control optimization method, and belongs to the technical field of wind driven generators. The main innovation of the intelligent variable pitch electromechanical control optimization method is as follows: for the traditional PID electromechanical control method, the proportionality coefficient is used as an optimization parameter based on the real-time change of tower top displacement response and wind wheel rotating speed feedback, the single objective function electromechanical control based on the power generation stability is expanded into multi-objective electromechanical control considering the tower top vibration displacement and the power generation stability at the same time, and the intelligent pitch-variable electromechanical control method is provided by combining with an intelligent optimization method. The numerical result shows that the intelligent method not only can reduce the power generation control fluctuation, but also can reduce the torque of a transmission shaft and the bending moment at the bottom of the tower and reduce the fatigue vibration load.)

1. An intelligent electromechanical pitch control optimization method is characterized in that a proportional coefficient K of PID electromechanical control is used_P、K_I、K_DOptimized as target parameters, characterised by the packageThe method comprises the following steps:

step one, establishing a fitness function

Establishing an objective function f_objAs shown in formula (1):

in the formula, t is an independent variable,

optimizing the target parameters to make the target function obtain the minimum value, and always selecting a larger fitness value based on an intelligent algorithm during searching, so that the reciprocal of the target function is taken as the fitness function, as shown in formula (2):

step two, determining an initial value

Determining the maximum search frequency L and the maximum cycle frequency C;

giving K according to formula (3)_jAnd (4) assignment:

x_ij＝x_min,j+rand(0,1)(x_max,j-x_min,j) (3)

in the formula: i is a positive integer, j is P, I, D, x_ijRepresents K_jOf the random parameter variation space of (2) the ith solution, x_min,jAnd x_max,jAre each K_jUpper and lower bounds within the value range; rand (0,1) is a random number in the range of (0, 1);

step three, performing parameter neighborhood search

Performing a parameter neighborhood search according to equation (4):

in the above formula: l, x ═ 1,2_n' representation is based on x_ijThe n parameter neighborhood search is carried out to obtain the domain parameters, k is a positive integer and is not equal to i,is [ -1,1 [ ]]A random number of (c);

step four, calculating the current optimal solution

The selection probability is calculated according to equation (5):

wherein, the positive integer z is the number of f in the corresponding range;

according to formula (4) to x_ijPerforming parameter neighborhood search to obtain field parameter x'₁And calculating the associated fitness f 'according to formulas (2) and (5)'₁And selection probability p'₁；

If p is₁' > p, the current optimal solution is: x is the number of_ij ^d＝x₁'; wherein: p represents x_ijA corresponding selection probability;

if p is₁' is less than or equal to p, and the maximum searching times is not reached, the equations (2), (4) and (5) are repeated to search the field again and compare the selection probabilities, and the current optimal solution is determined;

if p is₁' < p, and the maximum number of searches is reached, then x_ij ^d＝x_ij；

Repeating step four to calculate all x_ijCurrent optimal solution x of_ij ^d；

Step five, calculating the optimal solution

Calculating all current optimal solutions x according to formula (2) and formula (5)_ij ^dCorresponding fitness f^dAnd a selection probability p^d(ii) a Comparing all selection profilesRate p^dWill select the probability p^dMaximum current optimal solution x_ij ^dAnd (3) as an optimal solution and outputting: k_j＝x_ij ^d。

Technical Field

The invention belongs to the technical field of wind driven generators, and particularly relates to an intelligent variable pitch electromechanical control optimization method.

Background

The wind power generation realizes the control of the generated power through electromechanical control, and realizes the target of rated power generation through controlling the blade pitch angle when the wind speed exceeds the rated wind speed. Except for the traditional PI control method, a neural network, a fuzzy control method and the like are applied to electromechanical control.

The traditional electromechanical control method generally has some defects, for example, the PI control method causes the generated power of the fan to have large fluctuation when the parameter is not properly selected, so that the power grid is impacted, and the frequent adjustment of the blade pitch angle by the electromechanical control also increases the fatigue vibration of the fan structure. At present, the method is usually combined with artificial intelligence methods such as a neural network and the like, so that the fatigue load of a fan structure is reduced while the electromechanical control effect is improved. However, the existing intelligent control method usually needs to have better prior knowledge and has larger application limitation, and meanwhile, the optimization objective function only takes the output power as a single target, so that the vibration control effect is poor.

Therefore, from the perspective of improving the fatigue life of the fan in the whole life cycle process of operation, on the basis of taking the improvement of the stability of the generated power as the target of electromechanical control, the reduction of the structural fatigue vibration is taken as the target, and the research of the multi-target electromechanical control method based on the intelligent method is very necessary.

Disclosure of Invention

In order to improve the defect of the current electromechanical control on the structural fatigue vibration control, the invention provides an intelligent variable-pitch electromechanical control method for reducing the fatigue vibration of a wind power structure while improving the stability of output power. The method fully utilizes the original PID electromechanical control method, combines an intelligent algorithm with the PID method, takes the vibration displacement of the tower top and the rated rotating speed of the wind wheel as control targets, and determines the PID optimal parameters through a multi-target control function. Compared with the existing intelligent electromechanical control method, the intelligent variable-pitch electromechanical control optimization method provided by the invention has the advantages that the minimization of the vibration displacement at the tower top and the overshoot of the rotating speed of the wind wheel is used as a multi-objective control function to optimize the control parameters, so that the fluctuation of the output power of the fan is reduced, and the fatigue vibration response of the tower structure is reduced.

In order to achieve the purpose, the technical scheme adopted by the invention is as follows:

an intelligent electromechanical pitch control optimization method is characterized in that a proportional coefficient K of PID electromechanical control is used_P、K_I、K_DThe optimization is carried out as a target parameter, and the optimization method comprises the following steps:

step one, establishing a fitness function

Establishing an objective function f_objAs shown in formula (1):

in the formula, t is an independent variable,

for historical accumulated error, b [ R ]_Rover+R_Tover]For the current control error, e_T(t) is the displacement at the top of the column, e_R(t) is the difference between the rotational speed of the wind wheel and the rated rotational speed, R_ToverAnd R_RoverB is the weight of historical accumulated absolute error and current control error;

optimizing the target parameters to make the target function obtain the minimum value, and always selecting a larger fitness value based on an intelligent algorithm during searching, so that the reciprocal of the target function is taken as the fitness function, as shown in formula (2):

step two, determining an initial value

Determining the maximum search frequency L and the maximum cycle frequency C;

giving K according to formula (3)_jAnd (4) assignment:

x_ij＝x_min，j+rand(0，1)(x_max，j-x_min，j) (3)

in the formula: i is a positive integer, j is P, I, D, x_ijRepresents K_jOf the random parameter variation space of (2) the ith solution, x_min,jAnd x_max,jAre each K_jUpper and lower bounds within the value range; range of rand (0,1) is (0,1)The random number of (2);

step three, performing parameter neighborhood search

Performing a parameter neighborhood search according to equation (4):

in the above formula: l, x ═ 1,2_n' representation is based on x_ijThe n parameter neighborhood search is carried out to obtain the domain parameters, k is a positive integer and is not equal to i,

is [ -1,1 [ ]]A random number of (c);

step four, calculating the current optimal solution

The selection probability is calculated according to equation (5):

wherein, the positive integer z is the number of f in the corresponding range;

according to formula (4) to x_ijPerforming parameter neighborhood search to obtain a domain parameter x₁', and calculating the associated fitness f according to equations (2) and (5)₁' and selection probability p₁'；

If p is₁' > p, the current optimal solution is: x is the number of_ij ^d＝x₁'; wherein: p represents x_ijA corresponding selection probability;

if p is₁' is less than or equal to p, and the maximum searching times is not reached, the equations (2), (4) and (5) are repeated to search the field again and compare the selection probabilities, and the current optimal solution is determined;

if p is₁' < p, and the maximum number of searches is reached, then x_ij ^d＝x_ij；

Repeating step four to calculate all x_ijCurrent optimal solution x of_ij ^d；

Step five, calculating the optimal solution

According to formula (2) andequation (5) calculates all current optimal solutions x_ij ^dCorresponding fitness f^dAnd a selection probability p^d(ii) a Comparing all selection probabilities p^dWill select the probability p^dMaximum current optimal solution x_ij ^dAnd (3) as an optimal solution and outputting: k_j＝x_ij ^d。

The invention has the beneficial effects that:

the invention controls the proportionality coefficient K of PID electromechanical_P、K_I、K_DThe method is optimized as a target parameter, multi-target intelligent control with tower top vibration and wind wheel rotating speed as control quantities is realized, the stability of the generated power is improved, and the engine torque and tower vibration are effectively reduced.

Drawings

FIG. 1 is a flow chart of a technical solution for pitch intelligent control;

FIG. 2 is a block diagram of a pitch intelligent control system;

FIG. 3 is a graph of iterative convergence times;

FIG. 4 is a comparison of output power under intelligent optimization control;

FIG. 5 is a comparison of engine torque under smart optimization control;

fig. 6 is a comparison of tower top displacement under intelligent optimization control.

Detailed Description

In order to make the objects, features and advantages of the present invention more obvious and understandable, the present invention is further described below with reference to the accompanying drawings in combination with the embodiments so that those skilled in the art can implement the present invention by referring to the description, and the scope of the present invention is not limited to the embodiments. It is to be understood that the embodiments described below are only some embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

An intelligent pitch-controlled electromechanical control optimization method as shown in fig. 1, and fig. 2 is a control system structure thereofThe invention relates to a proportional coefficient K of PID electromechanical control_P、K_I、K_DAs optimization parameters. The invention will now be further explained using the NREL5MW fan as an example, with a wind wheel diameter of 126 meters and a hub height of 90 meters. The average wind speed was set to 20 m/s.

Three proportionality coefficients K to be optimized_P、K_I、K_DThe value ranges are respectively 0.001-1.0, 0.001-0.3 and 0.0001-0.2; the weight coefficient a of the fitness function is 5, b is 1,

setting the maximum search frequency as 50 and the maximum cycle frequency as 100; and then parameter optimization is carried out according to the method provided by the technical scheme.

The parameter field searching process is shown in fig. 3, and it can be seen from fig. 3 that for the parameter proportional coefficient controlled by PID, the proposed optimization method can achieve the optimal solution by 8 searches, has very high searching efficiency, and can ensure real-time change and control of PID adaptive parameters.

The control effects of the fan output power, the engine torque and the tower top displacement are shown in the graphs 4, 5 and 6, and it can be seen that through the proportional coefficient of PID electromechanical control, through increasing the tower top vibration feedback and the tower top vibration control target, the single target control based on the stability of the power generation power is realized.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.