CN110836168B - Fan damping self-adaptive control method based on PSO optimization and controller thereof - Google Patents
Fan damping self-adaptive control method based on PSO optimization and controller thereof Download PDFInfo
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Abstract
The invention discloses a fan damping self-adaptive control method based on PSO optimization and a controller thereof, wherein the fan damping self-adaptive control method comprises the following steps: designing a mathematical model of a damping controller, and initializing frequency and a damping ratio; secondly, changing parameters of a damping controller to obtain a damping ratio of the transmission chain; step three, obtaining the particle speed; step four, obtaining the historical optimal position of the particles; step five, obtaining the position of the particles; judging whether the optimal value is reached according to the allowable range of the damping ratio error; the torque increment obtained by the speed filtering is superposed on the set torque value of the generator, so that the effect of increasing the damping ratio of the transmission chain is achieved, the optimal value of the damping controller parameter is found through the PSO algorithm, and finally the damping ratio of the transmission chain is close to the required damping ratio of the transmission chain after the set damping controller is added into a torque ring. The method has the advantages of simple algorithm, easy implementation, good running speed and low calculated amount, and the performance and the stability of the model can be ensured.
Description
Technical Field
The invention relates to the technical field of wind power generation, damping self-adaptive controllers and intelligent control, in particular to a fan damping self-adaptive control method based on PSO optimization and a controller thereof.
Background
The wind generating set has a very complicated mechanical structure, each part has different natural frequencies, and the running state of the natural frequency of the main part is avoided when the fan runs, so that the vibration is reduced, and the stable running is realized. Therefore, the vibration control is particularly important for the design of the wind power controller, and the vibration control of the transmission chain which is an important component of the wind turbine generator is related to important indexes such as fan operation stability and generating capacity, so that the accurate design of the transmission chain damping controller has important significance.
The damping controller is a filter aiming at the rotating speed of the fan, the input of the damping controller is the rotating speed of the generator, the output of the damping controller is torque, and the damping required by the stable operation of the fan is obtained through the setting of frequency and damping. In the traditional technology, the damping controller parameters are generally obtained by manually debugging the damping controller, but the parameters obtained by manually debugging the damping controller need to be obtained by a debugger with abundant experience and need to be tried by utilizing multiple simulation experiments, and finally obtained parameters cannot be guaranteed to be optimal parameters by manually debugging the damping controller. In addition, after the unit runs for a long time, the required damping of the unit can be changed to a certain extent, and parameters of the damping controller need to be debugged again at the moment, so that manpower and material resources are wasted, and the efficiency is low.
Disclosure of Invention
Aiming at the torque loop operation of a wind turbine generator and a damping controller of a transmission chain, the parameter of the damping controller of the transmission chain is optimized through an improved particle swarm optimization algorithm, namely a PSO algorithm, so that the optimal parameter can be obtained with low labor cost.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention discloses a fan damping self-adaptive control method based on PSO optimization, which is characterized by comprising the following steps of:
step one, designing a mathematical model of a damping controller, and initializing frequency and a damping ratio: the mathematical model of the damping controller in the first step is shown as an expression (1):
wherein G(s) is torqueIncrement, s being engine speed, ωfFor damping the controller parameter frequency, xifIs the damping ratio. The principle of control is a wave trap, the damping ratio to be increased is set as a known parameter xi, and the parameter frequency omega of the damping controllerfAnd damping ratio xifAn optimal value can be found through a PSO algorithm;
step two, changing parameters of a damping controller to obtain a damping ratio of the transmission chain: calculating aiming at the damping controller to obtain the damping ratio of the transmission chain after the first group of torque rings are subjected to damping
Step three, obtaining the particle speed: performing parameter optimization based on a PSO algorithm, and establishing a speed updating formula of a particle group and a constraint condition of particle moving speed so as to obtain the particle running speed in particle group optimization;
step four, obtaining the historical optimal position of the particles: establishing an objective function by a control system with a minimized value of the target damping ratio error integral, and establishing an updating regular expression of the historical optimal position of the particles and a definition expression of the historical optimal position according to the objective function;
step five, obtaining the position of the particles: establishing a particle evolution equation expression to obtain the position of the particle;
step six, judging whether the optimal value is reached according to the damping ratio error reaching the allowable range: and if no optimal value is found, returning to the step two and continuing to circulate, otherwise, finding the optimal value and ending all the steps. Aiming at the torque loop operation and the transmission chain damping controller of the wind turbine generator, parameters of the transmission chain damping controller are optimized through an improved particle swarm optimization algorithm, namely a PSO algorithm, and a torque increment obtained through speed filtering is superposed on a set torque value of a generator, so that the effect of increasing the damping ratio of the transmission chain is achieved, and finally the damping ratio of the transmission chain is increased after the set damping controller is added into the torque loop
Near desired drive train damping ratio ξThe method has the advantages of simple algorithm, easy implementation, capability of obtaining the optimal parameters with lower labor cost, good operation speed and lower calculated amount, and capability of ensuring the performance and the stability of the model.
Preferably, the velocity updating formula of the particle group in the step three is expressed by the expression (2):
in the expression (2): vj(t) is the velocity of the particle j in the t-th generation; k is an inertia factor, is a non-negative number and gradually decreases along with the iteration number of the particles; e.g. of the type1Is the cognition factor; r is1Is a random number ranging between (0, 1); p is a radical ofj(t) is the historical optimal position of the particle j; x is the number ofj(t) is the position of the particle j in the t-th generation; e.g. of the type2Is a social coefficient; r is2Is a random number ranging between (0, 1); p is a radical ofg(t) is the population history optimal location. The particle swarm optimization algorithm treats an individual as a particle in an N-dimensional search space, then the particle flies at a certain running speed, and the running speeds of the particle and the swarm can be adjusted in real time.
Preferably, the constraint condition of the moving speed of the particles in the third step is shown in expression (5):
|vjk(t+1)|≤vmax(5) (5)；
in the expression (5): definition vmaxIs the maximum value of velocity, vjk(t +1) is the velocity of the particle j in the t +1 th generation during the decreasing process of k, k is an inertia factor and is a non-negative number, and the k gradually decreases along with the iteration number of the particle. The method aims to limit the moving speed of the particle j and ensure the stability of the particle subgroup algorithm.
Preferably, the calculation formula of k is shown in expression (7):
k＝0.9-0.5(N-1)/(Nmaxlength-1) (7)；
in the expression (7), N is the iteration number, and N ismaxlengthIs the maximum number of iterations.k is a non-negative number, has great influence on the performance of the algorithm, and adopts a gradual decreasing formula to avoid the occurrence of a local optimal value.
Preferably, the objective function established in step four is shown in expression (8):
f＝∫|eξ|dt (8)；
e in the expression (8)ξIn order to be able to correct the damping ratio error,
ξ is the desired drive train damping ratio,
the damping ratio of the transmission chain after the torque ring is added.
Preferably, the updating rule expression of the historical optimal position of the microparticle in the fourth step is shown as expression (3):
the definition expression of the historical optimal position in the fourth step is shown as expression (4):
pg(t)＝argmin{f[pj(t)] |j＝1,2,3,.....n|} (4)；
p in the expression (3) and the expression (4)j(t) is the historical optimum position of the particle j, xj(t) is the position of the particle j in the t-th generation, pg(t) is the historical optimum position of the population, and n is the number of particles contained in the population.
Preferably, the evolutionary equation of the particle j in the fifth step is shown in expression (6):
xj(t+1)＝vj(t+1)+xj(t) (6)；
x in the expression (6)j(t) is the position of the particle j in the t-th generation, xj(t +1) is the position of the particle j in the t +1 th generation, vj(t +1) is the velocity at which the particle j is in the t +1 th generation.
The invention relates to a controller using a fan damping self-adaptive control method based on PSO optimization, which comprises a torque loop controller, a fan, a transmission chain self-adaptive damping controller, a first adder and a second adder, wherein the first adder, the torque loop controller, the second adder and the fan are sequentially connected, the output end of the fan is connected with the input end of the first adder, the other end of the fan is connected with the transmission chain self-adaptive damping controller, one end of the output end of the torque loop controller is connected with the second adder, the other end of the output end of the torque loop controller is connected with the transmission chain self-adaptive damping controller, an externally provided rotating speed given signal and a measured rotating speed signal output by the fan are jointly input to the first adder, the first adder controls and outputs a rotating speed error signal to the input end of the torque loop controller, the torque loop controller outputs a torque given signal to the second adder and the transmission chain self-adaptive damping controller, and the transmission chain self-adaptive damping controller outputs a torque and resistance value signal to a second adder.
Preferably, the transmission chain adaptive damping controller comprises a torque resistance adding filter, a PSO optimizing module, a third adder and a transmission chain damping ratio calculating module, the torque resistance adding filter, the PSO optimizing module, the third adder and the transmission chain damping ratio calculating module are sequentially connected, the input end of the transmission chain damping ratio calculating module is provided with a measured rotating speed signal by a fan, a torque ring controller is provided with a torque given signal, the transmission chain damping ratio calculating module outputs an actual transmission chain damping ratio signal to the input end of the third adder, the outside is provided with an ideal damping ratio signal to the input end of the third adder, the output end of the third adder outputs a transmission chain damping ratio error to the PSO optimizing module, the PSO optimizing module outputs a frequency signal and a damping ratio signal to the torque resistance adding filter, and the measured rotating speed signal output by the fan is also provided to the torque resistance adding filter, the torque adding resistance filter outputs a torque adding resistance value to the input end of the second adder.
The invention has the beneficial effects that: (1) according to the invention, a particle swarm optimization algorithm is introduced into the design of the damping controller of the transmission chain, and the parameters of the damping controller are adaptively optimized by using the improved particle swarm optimization algorithm, so that the optimal parameters can be obtained with low labor cost, the working efficiency is improved, and the conservatism and complexity of the design of the controller are effectively reduced; (2) the method can realize the self-adaptive resistance increasing of the torque ring of the wind turbine generator, can ensure that the torque ring still keeps the optimal resistance increasing state after the wind field runs for a long time, can ensure that the wind turbine generator has good running speed for a long time, and can ensure the performance and the stability of the model.
Drawings
FIG. 1 is a PSO optimization flow chart of the present invention.
Fig. 2 is a block diagram of a damping adaptive controller of the present invention.
In the figure, 1, a first adder, 2, a torque loop controller, 3, a second adder, 4, a fan, 5, a transmission chain self-adaptive damping controller, 6, a torque resistance adding filter, 7, a PSO optimizing module, 8, a third adder and 9, a transmission chain damping ratio calculating module are included.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
A controller using a fan damping adaptive control method based on PSO optimization according to this embodiment, as shown in fig. 2, includes a torque loop controller 2, a fan 4, a transmission chain adaptive damping controller 5, a first adder 1 and a second adder 3, where the first adder 1, the torque loop controller 2, the second adder 3 and the fan 4 are sequentially connected, an output end of the fan 4 is connected to an input end of the first adder 1, and another end is connected to the transmission chain adaptive damping controller 5, one end of an output end of the torque loop controller 2 is connected to the second adder 3, and another end is connected to the transmission chain adaptive damping controller 5, an externally provided rotation speed setting signal and a measured rotation speed signal output by the fan are jointly input to the first adder 1, the first adder 1 controls to output a rotation speed error signal to an input end of the torque loop controller 2, and the torque loop controller 2 outputs a torque setting signal to the second adder 3 and the transmission chain adaptive damping controller And 5, the transmission chain adaptive damping controller 5 outputs a torque and resistance value signal to the second adder 3.
The transmission chain self-adaptive damping controller 5 comprises a torque resistance-adding filter 6, a PSO optimizing module 7, a third adder 8 and a transmission chain damping ratio calculating module 9, wherein the torque resistance-adding filter 6, the PSO optimizing module 7, the third adder 8 and the transmission chain damping ratio calculating module 9 are sequentially connected, the input end of the transmission chain damping ratio calculating module 9 is provided with a measured rotating speed signal by a fan 4 and a torque given signal by a torque loop controller 2, the transmission chain damping ratio calculating module 9 outputs an actual transmission chain damping ratio signal to the input end of the third adder 8, an ideal damping ratio signal is provided to the input end of the third adder 8 from the outside, the output end of the third adder 8 outputs a transmission chain damping ratio error to the PSO optimizing module 7, the PSO optimizing module 7 outputs a frequency signal and a damping ratio signal to the torque resistance-adding filter 6, and the measured rotating speed signal output by the fan is also provided to the torque resistance-adding filter 6, the torque adding resistance filter 6 outputs a torque adding resistance value to the input terminal of the second adder 3.
The working principle is as follows: the external given rotating speed is provided for the first adder 1, and the first adder 1 outputs a rotating speed error to the torque loop controller 2 by processing the measured rotating speed provided by the fan and the external given rotating speed signal. The control input of the torque loop controller 2 is the difference value between the given rotating speed and the measured rotating speed, namely the rotating speed error, the output of the control algorithm is the given torque, one part of the output is output to the transmission chain adaptive damping controller 5, the other part of the output is output to the second adder 3, and the output is input to the fan 4 through the second adder 3. The control input of the fan 4 is the sum of the given torque and the added resistance value of the torque, the output of the control algorithm is the measured rotating speed, one part of the measured rotating speed signal is output to the added resistance filter 6 of the torque, one part of the measured rotating speed signal is fed back to the input end of the first adder 1, the other part of the measured rotating speed signal and the given torque signal provided by the torque loop controller 2 are calculated by the transmission chain damping ratio calculating module 9, the actual transmission chain damping ratio signal is output to the third adder 8, the actual transmission chain damping ratio signal and the ideal damping ratio signal provided by the outside are input to the third adder 8 together, the third adder 8 controls the algorithm to output the transmission chain damping ratio error signal, the transmission chain damping ratio error signal is adaptively optimized through the PSO optimizing module according to the PSO algorithm, the frequency and the damping ratio are output to the added resistance filter 6 of the torque, then the added resistance filter 6 of the torque outputs the added resistance value signal to, finally realizing the set damping ratio of the transmission chain after the damping controller is added into the torque ring
Approaching the desired drive train damping ratio ξ.
In this embodiment, a fan damping adaptive control method based on PSO optimization, as shown in fig. 1, includes the following steps:
step one, designing a mathematical model of a damping controller, and initializing frequency and a damping ratio, wherein the mathematical model of the damping controller is shown as an expression (1):
where G(s) is the torque increment, s is the engine speed, ωfFor damping the controller parameter frequency, xifIs the damping ratio. The principle of control is a wave trap, the damping to be added is set as a known parameter xi, and the parameter frequency omega of a damping controllerfAnd damping xifThe optimum value can be found by the PSO algorithm.
Step two, changing parameters of a damping controller to obtain a damping ratio of the transmission chain: calculating aiming at the damping controller to obtain the damping ratio of the transmission chain after the first group of torque rings are subjected to damping
Step three, obtaining the particle speed: parameter optimization is carried out based on a PSO algorithm, and a speed updating formula of the particle group and a constraint condition of the particle moving speed are established, so that the particle running speed in particle group optimization is obtained.
The particle swarm optimization algorithm treats an individual as a particle in an N-dimensional search space, and the particle flies at a certain running speed. The running speed of the particles and the group can be adjusted in real time, and the speed updating formula of the particle group is as the expression (2):
in the expression (2): vj(t) is the velocity of the particle j in the t-th generation; k is an inertia factor, is a non-negative number and gradually decreases along with the iteration number of the particles; e.g. of the type1Is the cognition factor; r is1Is a random number ranging between (0, 1); p is a radical ofj(t) is the historical optimal position of the particle j; x is the number ofj(t) is the position of the particle j in the t-th generation; e.g. of the type2Is a social coefficient; r is2Is a random number ranging between (0, 1); p is a radical ofg(t) is the population history optimal location. Because the value is gradually close to the optimal value along with the iteration, the particle speed is reduced, and the cognition coefficient e1And social coefficient e2Is a generally positive number or zero, and is usually given by e1＝e2＝2。
To ensure stability of the particle swarm algorithm, v is definedmaxThe maximum speed is used for limiting the moving speed of the particle j, and the constraint condition of the moving speed of the particle is shown in expression (5):
|vjk(t+1)|≤vmax(5)；
in expression (5): v. ofjk(t +1) is the speed of the particle j in the t +1 th generation in the k decreasing process, since k is an inertia factor and is a non-negative number, the speed gradually decreases with the number of particle iterations, which has a great influence on the performance of the algorithm, and in order to avoid occurrence of a local optimal value, the embodiment adopts a gradual decrease formula as shown in expression (7):
k＝0.9-0.5(N-1)/(Nmaxlength-1) (7)；
in the expression (7), N is the iteration number, and N ismaxlengthIs the maximum number of iterations.
Step four, obtaining the historical optimal position of the particles: establishing an objective function by using a control system with a minimized value of target damping ratio error integral, and establishing an updating regular expression of the historical optimal position of the particles and a definition expression of the historical optimal position according to the objective function, wherein the updating regular expression of the historical optimal position of the particles is shown as an expression (3):
the definition expression of the historical optimal position in the fourth step is shown as expression (4):
pg(t)＝argmin{f[pj(t)] |j＝1,2,3,.....n|} (4)；
expression (3) and p in expression (4)j(t) is the historical optimum position of the particle j, xj(t) is the position of the particle j in the t-th generation, pg(t) is the historical optimum position of the population, and n is the number of particles contained in the population.
Step five, obtaining the position of the particles: establishing the expression of particle evolution equation to obtain the position of the particle due to pj(t) is the historical optimal position of the particle j, and for a control system with the minimized target, the smaller the value of the target function, the better the corresponding adaptive value, and the established target function is shown in expression (8):
f＝∫|eξ|dt (8)；
e in expression (8)ξIn order to be able to correct the damping ratio error,
ξ is the desired drive train damping ratio,
the damping ratio of the transmission chain after the torque ring is added.
Step six, judging whether the optimal value is reached according to the damping ratio error reaching the allowable range: if no optimal value is found, returning to the step two and continuing to circulate, otherwise, finding the optimal value, ending all the steps, and finally realizing the set damping ratio of the transmission chain after the damping controller is added into the torque ring
Approaching the desired drive train damping ratio ξ.
In this embodiment, a known nonlinear structural parameter (n) is sety、nu) Taking the number of particles N-30, the number of particle iterations N-40, and ξ -0.005 as an example, the value of the objective function obtained is 100.
The method aims at the torque loop operation of the wind turbine generator and the transmission chain damping controller, utilizes the PSO algorithm to carry out self-adaptive optimization on the parameters of the transmission chain damping controller, can obtain the optimal parameters with lower labor cost, improves the working efficiency, and effectively reduces the conservatism and complexity of the controller design. The adaptive controller aims to obtain a torque increment through speed filtering and superpose the torque increment on a given value of the torque of the generator, so that the effect of increasing the damping ratio of a transmission chain is achieved. The required increase of the specific damping ratio of the transmission chain can be obtained by online identification, which is not described herein. The method can realize the self-adaptive resistance increasing of the torque ring of the wind turbine generator, can ensure that the torque ring still keeps the optimal resistance increasing state after the wind field runs for a long time, can ensure that the wind turbine generator has good running speed for a long time, and can ensure the performance and the stability of the model.
Claims (9)
1. A fan damping self-adaptive control method based on PSO optimization is characterized by comprising the following steps:
step one, designing a mathematical model of a damping controller, and initializing frequency and a damping ratio: the mathematical model of the damping controller in the first step is shown as an expression (1):
wherein G(s) is the torque increment, s2As engine speed, ωfFor damping the controller parameter frequency, xifIs the damping ratio;
step two, changing parameters of a damping controller to obtain a damping ratio of the transmission chain: calculating aiming at the damping controller to obtain the damping ratio of the transmission chain after the first group of torque rings are subjected to damping
Step three, obtaining the particle speed: performing parameter optimization based on a PSO algorithm, and establishing a speed updating formula of a particle group and a constraint condition of particle moving speed so as to obtain the particle running speed in particle group optimization;
step four, obtaining the historical optimal position of the particles: establishing an objective function by a control system with a minimized value of the target damping ratio error integral, and establishing an updating regular expression of the historical optimal position of the particles and a definition expression of the historical optimal position according to the objective function;
step five, obtaining the position of the particles: establishing a particle evolution equation expression to obtain the position of the particle;
step six, judging whether the optimal value is reached according to the damping ratio error reaching the allowable range: and if no optimal value is found, returning to the step two and continuing to circulate, otherwise, finding the optimal value and ending all the steps.
2. The PSO-optimization-based fan damping adaptive control method according to claim 1, wherein the velocity update formula of the particle group in the third step is expressed by the following expression (2):
in the expression (2): vj(t) is the velocity of the particle j in the t-th generation; k is an inertia factor, is a non-negative number and gradually decreases along with the iteration number of the particles; e.g. of the type1Is the cognition factor; r is1Is a random number ranging between (0, 1); p is a radical ofj(t) is the historical optimal position of the particle j; x is the number ofj(t) is the position of the particle j in the t-th generation; e.g. of the type2Is a social coefficient; r is2Is a random number ranging between (0, 1); p is a radical ofg(t) is the population history optimal location.
3. The fan damping adaptive control method based on PSO optimization according to claim 1, wherein the constraint conditions of the moving speed of the particles in the three steps are shown in expression (5):
|vjk(t+1)|≤vmax (5)；
the above-mentionedIn expression (5): definition vmaxIs the maximum value of velocity, vjk(t +1) is the velocity of the particle j in the t +1 th generation during the decreasing process of k, k is an inertia factor and is a non-negative number, and the k gradually decreases along with the iteration number of the particle.
4. The PSO-optimization-based fan damping adaptive control method according to claim 3, wherein the calculation formula of k is shown in expression (7):
k＝0.9-0.5(N-1)/(Nmaxlength-1) (7)；
in the expression (7), N is the iteration number, and N ismaxlengthIs the maximum number of iterations.
5. The PSO-optimization-based fan damping adaptive control method as claimed in claim 1, wherein the objective function established in the fourth step is shown in expression (8):
f＝∫|eξ|dt (8)；
e in the expression (8)ξIn order to be able to correct the damping ratio error,
ξ is the desired drive train damping ratio,
the damping ratio of the transmission chain after the torque ring is added.
6. The PSO-optimization-based fan damping adaptive control method as claimed in claim 1, wherein the updating rule expression of the historical optimal position of the particle in the fourth step is shown in expression (3):
the definition expression of the historical optimal position in the fourth step is shown as expression (4):
pg(t)＝argmin{f[pj(t)] |j＝1,2,3,.....n|} (4)；
p in the expression (3) and the expression (4)j(t) is the historical optimum position of the particle j, xj(t) is the position of the particle j in the t-th generation, pg(t) is the historical optimum position of the population, and n is the number of particles contained in the population.
7. The PSO-optimization-based fan damping adaptive control method as claimed in claim 1, wherein the evolutionary equation of particle j in the fifth step is shown in expression (6):
xj(t+1)＝vj(t+1)+xj(t) (6)；
x in the expression (6)j(t) is the position of the particle j in the t-th generation, xj(t +1) is the position of the particle j in the t +1 th generation, vj(t +1) is the velocity at which the particle j is in the t +1 th generation.
8. A controller using the PSO-based fan damping adaptive control method according to any one of claims 1-7, comprising a torque loop controller (2), a fan (4), a driving chain adaptive damping controller (5), a first adder (1) and a second adder (3), wherein the first adder (1), the torque loop controller (2), the second adder (3) and the fan (4) are connected in sequence, the output end of the fan (4) is connected with the input end of the first adder (1), the other end of the fan is connected with the driving chain adaptive damping controller (5), one end of the output end of the torque loop controller (2) is connected with the second adder (3), the other end of the output end of the torque loop controller is connected with the driving chain adaptive damping controller (5), an externally provided rotating speed given signal and a measured rotating speed signal output by the fan are jointly input to the first adder (1), the first adder (1) controls output of a rotating speed error signal to the input end of the torque loop controller (2), the torque loop controller (2) outputs a torque given signal to the second adder (3) and the transmission chain adaptive damping controller (5), and the transmission chain adaptive damping controller (5) outputs a torque adding resistance value signal to the second adder (3).
9. The controller for the fan damping adaptive control method based on PSO optimization according to claim 8, wherein the driving chain adaptive damping controller (5) comprises a torque adding and resisting filter (6), a PSO optimization module (7), a third adder (8) and a driving chain damping ratio calculation module (9), the torque adding and resisting filter (6), the PSO optimization module (7), the third adder (8) and the driving chain damping ratio calculation module (9) are connected in sequence, the input end of the driving chain damping ratio calculation module (9) is provided with a measuring rotating speed signal by the fan (4), a torque setting signal by the torque ring controller (2), the driving chain damping ratio calculation module (9) outputs an actual driving chain damping ratio signal to the input end of the third adder (8), and an ideal damping ratio signal is provided to the input end of the third adder (8), the output end of the third adder (8) outputs the transmission chain damping ratio error to the PSO optimizing module (7), the PSO optimizing module (7) outputs a frequency signal and a damping ratio signal to the torque resistance adding filter (6), a measured rotating speed signal output by the fan is also provided to the torque resistance adding filter (6), and the torque resistance adding filter (6) outputs a torque resistance adding value to the input end of the second adder (3).
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