CN111812983A - Wind turbine generator primary frequency modulation load shedding control method based on differential flat active disturbance rejection control - Google Patents

Wind turbine generator primary frequency modulation load shedding control method based on differential flat active disturbance rejection control Download PDF

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CN111812983A
CN111812983A CN202010695086.XA CN202010695086A CN111812983A CN 111812983 A CN111812983 A CN 111812983A CN 202010695086 A CN202010695086 A CN 202010695086A CN 111812983 A CN111812983 A CN 111812983A
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wind turbine
disturbance rejection
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王浩霖
郭强
白志刚
崔亚明
韩国强
王进
王雪峰
卢家勇
陈淑琴
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Electric Power Research Institute of State Grid Shanxi Electric Power Co Ltd
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Abstract

The invention belongs to the technical field of wind power generation, and particularly relates to a wind turbine generator primary frequency modulation load shedding control method based on differential flat active disturbance rejection control, which provides a wind turbine generator primary frequency modulation load shedding control method based on differential flat active disturbance rejection control, improves the control performance of a pitch angle controller when the wind turbine generator participates in the primary frequency modulation load shedding control, and automatically optimizes the parameters of the differential flat active disturbance rejection controller by using an improved particle swarm optimization algorithm on the basis of a differential flat active disturbance rejection control model; the method is widely applied to the field of primary frequency modulation load shedding control of the wind turbine generator.

Description

Wind turbine generator primary frequency modulation load shedding control method based on differential flat active disturbance rejection control
Technical Field
The invention belongs to the technical field of wind power generation, and relates to a primary frequency modulation control method of a double-fed wind turbine generator, which is used for improving the control performance of a pitch angle controller when the wind turbine generator participates in primary frequency modulation load shedding control, in particular to a primary frequency modulation load shedding control method of the wind turbine generator based on differential flat active disturbance rejection control.
Background
The traditional fossil energy provides a large amount of energy for human beings, but with the continuous development of global economy, the human beings have more and more requirements on the energy, the traditional fossil energy is increasingly in shortage and causes serious environmental pollution, the development of clean renewable energy becomes a necessary trend, and wind power generation has a wide development space as one of clean energy. In recent years, more and more wind turbines are connected to the grid, and the randomness and the fluctuation of power of the wind turbines bring great challenges to the frequency stability of the power grid. In order to improve the wind power consumption capability of the power grid, the wind turbine generator needs to have a function of participating in primary frequency modulation of the power grid.
In order to enable the wind turbine generator to have the capacity of participating in primary frequency modulation of a power grid, load shedding control is needed, the main purpose of the load shedding control is to reduce the generating power of the wind turbine generator so as to obtain a certain spare capacity, and the method can be divided into overspeed control and pitch angle control. The overspeed control is mostly used when the wind speed is low, the power regulation range of the overspeed control is limited, the power regulation range of the pitch angle control is large, and the overspeed control is suitable for a full wind speed section and is an indispensable part for load shedding control. As shown in FIG. 1, the load shedding by the pitch angle control is realized by knowing the generated power P when the wind turbine generator is unloadedtarThen, the power rotating speed (P-omega) of the wind turbine generator set is usedr) The curve calculates the generating power P of the wind turbinetarTime corresponding target rotational speed omegarefSecondly, a target pitch angle instruction beta is given out through a pitch angle controllerrefAnd then responding to the controller command through a pitch angle actuator, so that the rotating speed of the wind turbine generator is kept at omegarefNearby, and further stabilizing the generating power of the wind turbine generator at the target power PtarNearby。
When load shedding control is performed by pitch angle control, the performance of the pitch angle controller has a great influence on the entire load shedding control process. The pitch control process of the wind turbine generator is nonlinear and has external disturbance, and the required control effect is often difficult to achieve only through a traditional PI controller, so that how to improve the control performance of the pitch angle controller becomes a problem to be solved urgently. Meanwhile, the parameters of the controller have great influence on the control performance of the controller, and the parameters are usually set manually and are often not optimal, so how to optimize the parameters of the controller also becomes a problem to be solved urgently.
The Differential Flat Active Disturbance Rejection Control (DFADRC) defines internal disturbance (perturbation of model parameters) and external disturbance as 'total disturbance', observes and cancels the total disturbance in real time through an extended state observer, has strong robustness and applicability, and can be used for a nonlinear system. The controller needs to adjust three parameters, the parameter adjustment only by experience has certain limitation, and the adjusted parameters are often not optimal.
Disclosure of Invention
The invention overcomes the defects in the prior art, provides a method for the wind turbine generator pitch angle controller based on differential flat active disturbance rejection control, and further provides a method for automatically optimizing parameters of the differential flat active disturbance rejection controller through an improved particle swarm optimization algorithm, so that the control performance of the pitch angle controller when the wind turbine generator participates in primary frequency modulation load shedding control is improved. The invention provides a method for optimizing parameters of differential flat active disturbance rejection control through an improved particle swarm optimization algorithm, and solves the problem of parameter optimization of a controller.
In order to solve the technical problems, the invention adopts the technical scheme that: a wind turbine generator primary frequency modulation load shedding control method based on differential flat active disturbance rejection control comprises the following steps:
designing a pitch angle controller by adopting a differential flat active disturbance rejection control strategy;
the pitch control process of the wind turbine generator is as follows:
Figure BDA0002590732720000021
wherein: y is the output (i.e. speed of rotation omega)r) U is the control quantity signal (i.e. pitch angle β), a1，a0B is an unknown parameter;
assuming that the nominal value of part of the parameters of the controlled object is known
Figure BDA0002590732720000022
When there is an external disturbance, equation (1) can be rewritten as follows:
Figure BDA0002590732720000023
wherein:
Figure BDA0002590732720000024
for total disturbance of the system, b0B is an estimated value, and eta is external disturbance;
is selected by
Figure BDA0002590732720000025
Then equation (2) can be written as:
Figure BDA0002590732720000026
wherein:
Figure BDA0002590732720000027
the extended state observer is:
Figure BDA0002590732720000031
when L is ═ L1l2l3]TWhen the values are appropriate, the estimated quantity can be accurately tracked in real time, namely
Figure BDA0002590732720000032
Figure BDA0002590732720000033
In order to reduce the number of parameter adjustments and ensure the stability of the extended state observer, the root of the observer characteristic equation is configured at-omega by a pole configuration methodoAnd (3) treating the following components:
λ(s)＝|sI-(A-LC)|＝(s+ωo)3(5)
thus, the parameters are:
Figure BDA0002590732720000034
wherein: omegaoTo extend the state observer bandwidth, and ωo>0；
If it is not
Figure BDA0002590732720000035
It is possible to accurately track y in real time,
Figure BDA0002590732720000036
if the feedback control law is selected as:
Figure BDA0002590732720000037
the control system can be simplified to the following form:
Figure BDA0002590732720000038
expected tracking value y given flat output y*Error is e (t) ═ y*(t) -y (t), since the controlled object is a second order differential flat system, the linear feedback control law is:
Figure BDA0002590732720000039
the closed-loop error characteristic equation is:
p(s)＝s2+1s+0＝0 (9)
in order to ensure the stability of the controller, the characteristic root is configured in the left half plane-omega of the s domaincAnd (3) treating the following components:
p(s)＝s2+1s+0＝s2+2ζcωcs+ωc 2(10)
then1＝2ζcωc，0＝ωc 2(ii) a Wherein: omegacFor controller bandwidth, ζcTypically 1;
through the analysis, the parameter needing to be set for the differential flat active disturbance rejection control is the controller bandwidth omegacObserver bandwidth ωoAnd b0。
Under the condition that the differential flat active disturbance rejection control model is taken as a basis, the parameters of the differential flat active disturbance rejection controller are optimized by adopting an improved particle swarm optimization algorithm, and the method specifically comprises the following steps:
step 1: initializing parameters including initial position, speed and the like;
step 2: calculating a fitness value, and recording the individual optimal position pbest and the global optimal position gbest;
and step 3: updating the particle speed and the particle position, which are respectively shown as formulas (11) and (12);
Figure BDA0002590732720000041
Figure BDA0002590732720000042
where w is the inertial weight, c1And c2As learning factors, as shown in formulas (13), (14) and (15) respectively,
Figure BDA0002590732720000043
for the ith individual velocity at the kth iteration,
Figure BDA0002590732720000044
and gbestkRespectively an ith individual optimal position and a global optimal position in the kth iteration,
Figure BDA0002590732720000045
is the ith individual position at the kth iteration;
Figure BDA0002590732720000046
Figure BDA0002590732720000047
Figure BDA0002590732720000048
wherein wmaxIs an initial weight (typically 0.9), wminTo final weight (typically 0.4), ncFor the current number of iterations, nmaxIs the maximum iteration number;
and 4, step 4: updating the individual best pbest and the global best gbest;
and 5: mapping the gbest to [0,1], generating a chaotic sequence through a formula (16), and reflecting the sequence to an original solution space;
zn+1＝μzn(1-zn),n＝0,1,2,… (16)
wherein mu is a control parameter; let z0∈[0,1]The Logistic system is completely in a chaotic state; the method has randomness and ergodicity;
then calculating and comparing the fitness value of the particle to obtain the best particle, and randomly replacing one particle in the original population;
step 6: if the end condition is reached, the optimization is ended, otherwise, the step 3 is carried out.
The fitness function in the improved particle swarm optimization algorithm is specifically as follows:
time-multiplied-error absolute value Integral (ITAE), as shown in equation (17):
Figure BDA0002590732720000051
wherein T ismaxAnd e (t) is the error between the actual rotating speed and the set rotating speed value.
Compared with the prior art, the invention has the beneficial effects that: the invention solves the problem of parameter optimization of the controller by the method for optimizing the parameters of the differential flat active disturbance rejection control through the improved particle swarm optimization algorithm, and realizes the automatic optimization of the parameters of the differential flat active disturbance rejection controller. The control performance of the pitch angle controller when the wind turbine generator participates in primary frequency modulation load shedding control is improved.
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The invention is further described below with reference to the accompanying drawings.
Fig. 1 shows a method for reducing load in conventional pitch angle control.
FIG. 2 is a parameter optimization process of the improved particle swarm optimization algorithm for optimizing the differential flat active disturbance rejection controller.
Detailed Description
The invention is further illustrated in fig. 2.
A wind turbine generator primary frequency modulation load shedding control method based on differential flat active disturbance rejection control comprises the following steps:
designing a pitch angle controller by adopting a differential flat active disturbance rejection control strategy;
generally, a model of a wind turbine needs to be simplified when a pitch variation process of the wind turbine is analyzed, the pitch variation process can be regarded as a second-order process, a pitch angle beta of the wind turbine is input, and the output of the pitch angle beta is a rotor rotating speed omega of the wind turbiner. Because the rotating speed and the power of the wind turbine generator have one-to-one correspondence relation when only pitch control is carried out, the generated power of the wind turbine generator can be adjusted through pitch angle control according to actual requirements.
The pitch control process of the wind turbine generator is as follows:
Figure BDA0002590732720000052
wherein: y is the output (i.e. speed of rotation omega)r) U is the control quantity signal (i.e. pitch angle β), a1,a0And b is an unknown parameter.
In order to utilize the known part of the model and to reduce the tracking pressure of the extended state observer. The invention assumes that the nominal value of part of the parameters of the controlled object is known
Figure BDA0002590732720000053
When there is an external disturbance, equation (1) can be rewritten as follows:
Figure BDA0002590732720000054
wherein:
Figure BDA0002590732720000055
for the total disturbance of the system (including uncertainty of the model parameters and external disturbances), b0Is an estimate of b, and η is the external perturbation.
Is selected by
Figure BDA0002590732720000056
Then equation (2) can be written as:
Figure BDA0002590732720000061
wherein:
Figure BDA0002590732720000062
the extended state observer is:
Figure BDA0002590732720000063
when L is ═ L1l2l3]TWhen the values are appropriate, the estimated quantity can be accurately tracked in real time, namely
Figure BDA0002590732720000064
Figure BDA0002590732720000065
In order to reduce the number of parameter adjustments and ensure the stability of the extended state observer, the root of the observer characteristic equation is usually configured at- ω by a pole configuration methodoAnd (3) treating the following components:
λ(s)＝|sI-(A-LC)|＝(s+ωo)3(5)
thus, the parameters are:
Figure BDA0002590732720000066
wherein: omegaoTo extend the state observer bandwidth, and ωo>0。
If it is not
Figure BDA0002590732720000067
It is possible to accurately track y in real time,
Figure BDA0002590732720000068
if the feedback control law is selected as:
Figure BDA0002590732720000069
the control system can be simplified to the following form:
Figure BDA00025907327200000610
expected tracking value y given flat output y*Error is e (t) ═ y*(t) -y (t), since the controlled object is a second order differential flat system, the linear feedback control law:
Figure BDA00025907327200000611
the closed-loop error characteristic equation is:
p(s)＝s2+1s+0＝0 (9)
to ensure the stability of the controller, the characteristic root can be configured in the left half plane-omega of the s domaincAnd (3) treating the following components:
p(s)＝s2+1s+0＝s2+2ζcωcs+ωc 2(10)
then1＝2ζcωc，0＝ωc 2. Wherein: omegacFor controller bandwidth, ζcTypically 1.
According to the theoretical analysis, the parameter needing to be set for the differential flat active disturbance rejection control is the controller bandwidth omegacObserver bandwidth ωoAnd b0。
Further, optimizing parameters of a differential flat active disturbance rejection controller based on an improved particle swarm optimization algorithm;
the invention improves a standard particle swarm optimization algorithm, and comprises the following steps:
step 1: initializing parameters (initial position and speed, etc.);
step 2: calculating a fitness value, and recording the individual optimal position pbest and the global optimal position gbest;
and step 3: updating the particle speed and the particle position, which are respectively shown as formulas (11) and (12);
Figure BDA0002590732720000071
Figure BDA0002590732720000072
where w is the inertial weight, c1And c2As learning factors, as shown in formulas (13), (14) and (15) respectively,
Figure BDA0002590732720000073
for the ith iterationThe speed of the individual is determined by the speed of the individual,
Figure BDA0002590732720000074
and gbestkRespectively an ith individual optimal position and a global optimal position in the kth iteration,
Figure BDA0002590732720000075
is the ith individual position at the kth iteration.
Figure BDA0002590732720000076
Figure BDA0002590732720000077
Figure BDA0002590732720000078
Wherein wmaxIs an initial weight (typically 0.9), wminTo final weight (typically 0.4), ncFor the current number of iterations, nmaxIs the maximum number of iterations
And 4, step 4: updating individual best pbest and global best gbest
And 5: the gbest is mapped to [0,1], a chaotic sequence is generated through formula (16), and the sequence is reflected to the original solution space.
zn+1＝μzn(1-zn),n＝0,1,2,… (16)
Where μ is the control parameter. Let z0∈[0,1]The Logistic system is completely in a chaotic state. It has randomness and ergodicity.
Then calculating and comparing the fitness value of the particle to obtain the best particle, and randomly replacing one particle in the original population;
step 6: if the end condition is reached, the optimization is ended, otherwise, the step 3 is carried out.
The fitness function in the improved particle swarm optimization algorithm is as follows:
the fitness function in the optimization algorithm is selected as time-multiplied-error absolute value Integral (ITAE), as shown in equation (17):
Figure BDA0002590732720000081
wherein T ismaxAnd e (t) is the error between the actual rotating speed and the set rotating speed value.
The above embodiments are merely illustrative of the principles of the present invention and its effects, and do not limit the present invention. It will be apparent to those skilled in the art that modifications and improvements can be made to the above-described embodiments without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications or changes be made by those skilled in the art without departing from the spirit and technical spirit of the present invention, and be covered by the claims of the present invention.

Claims (3)

1. A wind turbine generator primary frequency modulation load shedding control method based on differential flat active disturbance rejection control is characterized by comprising the following steps:
designing a pitch angle controller by adopting a differential flat active disturbance rejection control strategy;
the pitch control process of the wind turbine generator is as follows:
Figure FDA0002590732710000011
wherein: y is the output (i.e. speed of rotation omega)r) U is the control quantity signal (i.e. pitch angle β), a1，a0B is an unknown parameter;
assuming that the nominal value of part of the parameters of the controlled object is known
Figure FDA0002590732710000012
When there is an external disturbance, equation (1) can be rewritten as follows:
Figure FDA0002590732710000013
wherein:
Figure FDA0002590732710000014
for total disturbance of the system, b0B is an estimated value, and eta is external disturbance;
is selected by
Figure FDA0002590732710000018
Then equation (2) can be written as:
Figure FDA0002590732710000015
wherein:
Figure FDA0002590732710000016
C＝[1 0 0]
the extended state observer is:
Figure FDA0002590732710000017
when L is ═ L1l2l3]TWhen the values are appropriate, the estimated quantity can be accurately tracked in real time, namely
Figure FDA0002590732710000019
Figure FDA00025907327100000110
In order to reduce the number of parameter adjustments and ensure the stability of the extended state observer, the root of the observer characteristic equation is configured at-omega by a pole configuration methodoAnd (3) treating the following components:
λ(s)＝|sI-(A-LC)|＝(s+ωo)3(5)
thus, the parameters are:
Figure FDA0002590732710000021
wherein: omegaoTo extend the state observer bandwidth, and ωo>0；
If it is not
Figure FDA0002590732710000022
It is possible to accurately track y in real time,
Figure FDA0002590732710000028
if the feedback control law is selected as:
Figure FDA0002590732710000023
the control system can be simplified to the following form:
Figure FDA0002590732710000024
expected tracking value y given flat output y*Error is e (t) ═ y*(t) -y (t), since the controlled object is a second order differential flat system, the linear feedback control law is:
Figure FDA0002590732710000025
the closed-loop error characteristic equation is:
p(s)＝s2+1s+0＝0 (9)
in order to ensure the stability of the controller, the characteristic root is configured in the left half plane-omega of the s domaincAnd (3) treating the following components:
p(s)＝s2+1s+0＝s2+2ζcωcs+ωc 2(10)
then1＝2ζcωc，0＝ωc 2(ii) a Wherein: omegacFor controller bandwidth, ζcTypically 1;
through the analysis, the parameter needing to be set for the differential flat active disturbance rejection control is the controller bandwidth omegacObserver bandwidth ωoAnd b0。
2. The wind turbine generator primary frequency modulation load shedding control method based on the differential flat active disturbance rejection control as claimed in claim 1, wherein under the condition that the differential flat active disturbance rejection control model is taken as a basis, a parameter optimization method based on an improved particle swarm optimization algorithm is adopted for the differential flat active disturbance rejection controller, and the specific steps are as follows:
step 1: initializing parameters including initial position, speed and the like;
step 2: calculating a fitness value, and recording the individual optimal position pbest and the global optimal position gbest;
and step 3: updating the particle speed and the particle position, which are respectively shown as formulas (11) and (12);
Figure FDA0002590732710000026
Figure FDA0002590732710000027
where w is the inertial weight, c1And c2Is a learning factor represented by the formulas (13), (14) and (15), respectively, vi kFor the ith individual velocity, pbest, at the kth iterationi kAnd gbestkRespectively the ith individual optimal position and the global optimal position, x, in the kth iterationi kIs the ith individual position at the kth iteration;
Figure FDA0002590732710000031
Figure FDA0002590732710000032
Figure FDA0002590732710000033
wherein wmaxIs an initial weight (typically 0.9), wminTo final weight (typically 0.4), ncFor the current number of iterations, nmaxIs the maximum iteration number;
and 4, step 4: updating the individual best pbest and the global best gbest;
and 5: mapping the gbest to [0,1], generating a chaotic sequence through a formula (16), and reflecting the sequence to an original solution space;
zn+1＝μzn(1-zn),n＝0,1,2,… (16)
wherein mu is a control parameter; let z0∈[0,1]The Logistic system is completely in a chaotic state; the method has randomness and ergodicity;
then calculating and comparing the fitness value of the particle to obtain the best particle, and randomly replacing one particle in the original population;
step 6: if the end condition is reached, the optimization is ended, otherwise, the step 3 is carried out.
3. The wind turbine generator primary frequency modulation load shedding control method based on the differential flat active disturbance rejection control as claimed in claim 2, wherein the fitness function in the improved particle swarm optimization algorithm is specifically:
time-multiplied-error absolute value Integral (ITAE), as shown in equation (17):
Figure FDA0002590732710000034
wherein T ismaxAnd e (t) is the error between the actual rotating speed and the set rotating speed value.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117879412A (en) * 2024-03-12 2024-04-12 宝士达新能源科技(苏州)有限公司 Diesel generator rotating speed self-adaptive lifting control method based on load power change
CN117879412B (en) * 2024-03-12 2024-06-04 宝士达新能源科技(苏州)有限公司 Diesel generator rotating speed self-adaptive lifting control method based on load power change

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Application publication date: 20201023