CN112147900A - Finite time self-adaptive fuzzy tracking control method of full-state constraint power system - Google Patents

Finite time self-adaptive fuzzy tracking control method of full-state constraint power system Download PDF

Info

Publication number
CN112147900A
CN112147900A CN202011060561.2A CN202011060561A CN112147900A CN 112147900 A CN112147900 A CN 112147900A CN 202011060561 A CN202011060561 A CN 202011060561A CN 112147900 A CN112147900 A CN 112147900A
Authority
CN
China
Prior art keywords
time
power system
formula
unknown
equation
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011060561.2A
Other languages
Chinese (zh)
Other versions
CN112147900B (en
Inventor
李泽
许王瑶
崔国增
郝万君
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Suzhou University of Science and Technology
Original Assignee
Suzhou University of Science and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Suzhou University of Science and Technology filed Critical Suzhou University of Science and Technology
Priority to CN202011060561.2A priority Critical patent/CN112147900B/en
Publication of CN112147900A publication Critical patent/CN112147900A/en
Application granted granted Critical
Publication of CN112147900B publication Critical patent/CN112147900B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention relates to a finite time self-adaptive fuzzy tracking control method of a full-state constraint power system, which is characterized in that a controller is designed based on a self-adaptive backstepping method, and a fuzzy logic system is utilized to approximate unknown parameters and external disturbance in the power system; the virtual control signal is filtered by utilizing a finite time command filter, so that the problem of 'calculation explosion' in the traditional backstepping method is solved; under the condition that the state variables do not violate the constraint conditions, the power angle of the generator in the power system reaches the neighborhood near the expected value within a limited time, and all variables in the closed-loop system are bounded; in consideration of the uncertainty of the parameters of the power system and the possibility of external unknown interference in the operation process, the fuzzy logic system is utilized to model the unknown nonlinear part in the power system, so that the control effect is improved; and under the condition that the state variable does not violate the constraint condition, the power angle, the rotating speed and the equivalent reactance of the generator are ensured to reach the neighborhood around the expected value in a limited time.

Description

Finite time self-adaptive fuzzy tracking control method of full-state constraint power system
Technical Field
The invention relates to a finite time self-adaptive fuzzy tracking control method of a full-state constraint power system.
Background
Along with the continuous progress of society and national economy, people have more and more requirements on electric energy, and the requirements on electric energy quality are also continuously improved. The power system is developed rapidly, the scale is enlarged continuously, and the complexity is deepened continuously. If the power system fails, a huge loss will be caused. Therefore, the research on the stability of the power system is of great significance.
A Flexible Alternating Current Transmission System (FACTS) is used as a new regulation control means, and the stability of a power system and the transmission capability of a transmission line can be effectively improved. Static Var Compensator (SVC) is a kind of FACTS device, can come quick reactive power regulation of going on through changing reactance, can carry out reactive compensation simultaneously at any time to maintain system voltage at the constant state, improved the stability of remote transmission line voltage.
The traditional SVC control method is to convert a power system with nonlinear characteristics into a linear system and then design a controller by applying a linear control theory. This type of approach can solve some problems, but also has significant drawbacks. During the process of converting the system into linearity, the nonlinear characteristics are lost, and the expected control effect cannot be obtained in the actual design process.
The reverse deduction method is a nonlinear control method which is widely applied, can keep the nonlinear characteristic of the power system, and is simple in design process and clear in structure. The controller can be combined with various control methods such as adaptive control, fuzzy control, sliding mode control and the like in the design process to improve the control efficiency.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a finite time self-adaptive fuzzy tracking control method of a full-state constraint power system.
The purpose of the invention is realized by the following technical scheme:
the finite time self-adaptive fuzzy tracking control method of the full-state constraint power system is characterized in that:
the state equation of a single infinite power system comprising the SVC is as follows:
Figure BDA0002712223960000021
in formula (1): d is a power unit damping coefficient, and H is a generator inertia time constant with the unit of s; t issvcIs the inertial time constant of the SVC; y issvcFor the admittance of the system, the initial value is ysvc0(ii) a Is the power angle of the generator, with unit of rad, and initial value of0；
Figure BDA0002712223960000022
H1And H2Unknown disturbances superimposed on the generator rotor and system admittance;
the controller is designed as follows:
firstly, coordinate conversion is carried out
Figure BDA0002712223960000023
In the formula (2), zi(i ═ 1,2,3) is the tracking error, πi(i-2, 3) is the command filter output value;
error compensation signals are introduced in the design process of the controller in consideration of errors generated by the use of a command filteri，(i＝1,2,3)；
The tracking error after further compensation is as follows:
vi＝zi-i，i＝1,2,3. (3)
designed virtual control signal alpha1，α2And control input u ═ alpha3The following were used:
Figure BDA0002712223960000031
Figure BDA0002712223960000032
Figure BDA0002712223960000033
in formulae (4) to (6): k is a radical ofi,gi,hi(i ═ 1,2,3) are all positive constants;
Figure BDA0002712223960000034
τi>0 is a constant to be designed;
designed error compensation signali(i ═ 1,2,3) as follows:
Figure BDA0002712223960000035
Figure BDA0002712223960000036
Figure BDA0002712223960000037
the designed adaptation law Θ is as follows:
Figure BDA0002712223960000038
in formula (10): Θ { | | W { [ max { | ] { [ max { ] { [ W ]i||2},i＝1,2，WiFor unknown weight vectors in a fuzzy logic system,
Figure BDA0002712223960000039
is an estimate of Θ; siI is 1,2 is a vector of basis functions in the fuzzy logic system; λ, γ, ai(i ═ 1,2) is a positive constant.
Further, the finite time adaptive fuzzy tracking control method of the full-state constraint power system specifically comprises the following steps:
step 1: designing the first Lyapunov function
Figure BDA0002712223960000041
To V1Derived by derivation
Figure BDA0002712223960000042
Substituting the intermediate virtual control variable alpha1And compensation signal1Equation (12) is further calculated as
Figure BDA0002712223960000043
Step 2: design a second Lyapunov function
Figure BDA0002712223960000044
To V2Derived by derivation
Figure BDA0002712223960000045
Setting the complex part, the unknown part and the external interference part in the formula (15) as an unknown nonlinear function f due to the existence of an uncertain part and external unknown disturbance in the system1(x1,x2)＝θx2+a0-kysvc0sin(x1+0)+H1(ii) a Using fuzzy logic system FLS pair f1(x1,x2) Performing an approximation
f1(x1,x2)＝W1 ΤS1+1 (16)
By young's inequality, equation (15) is further calculated as:
Figure BDA0002712223960000051
substituting the intermediate virtual control variable alpha2And compensation signal2Equation (17) is further calculated as
Figure BDA0002712223960000052
And step 3: design the third Lyapunov function
Figure BDA0002712223960000053
To V3Derived by derivation
Figure BDA0002712223960000054
Setting the complex part, the unknown part and the external interference part in the formula (20) as an unknown nonlinear function f due to the existence of an uncertain part and an external unknown disturbance in the system2(x2)＝-(1/Tsvc)x3+H2(ii) a Using FLS to f2(x2) Performing an approximation
f1(x1,x2)＝W2 ΤS2+2 (21)
Equation (21) is further calculated as
Figure BDA0002712223960000061
Substituting control signal u and compensation signal3Equation (22) is further calculated as
Figure BDA0002712223960000062
Designing a fourth Lyapunov function according to the error compensation signal
Figure BDA0002712223960000063
To VMake a derivation
Figure BDA0002712223960000064
Using the Young's inequality, equation (25) is further derived as
Figure BDA0002712223960000065
In the formula (26), the reaction mixture is,
Figure BDA0002712223960000066
Figure BDA0002712223960000067
according to zi＝vi+iIt can be seen that by ensuring viAndiconverge to the target region within a limited time such that ziConverge to a region near the origin within a limited time; estimation error taking into account the adaptation law
Figure BDA0002712223960000071
Figure BDA0002712223960000072
Design the fifth Lyapunov function
Figure BDA0002712223960000073
Derivation of V
Figure BDA0002712223960000074
Substituted into the adaptation law, equation (28) is further calculated as
Figure BDA0002712223960000075
In formula (29):
Figure BDA0002712223960000076
Figure BDA0002712223960000077
is a constant to be designed, and
Figure BDA0002712223960000078
Figure BDA0002712223960000079
when in use
Figure BDA00027122239600000710
Figure BDA00027122239600000711
When in use
Figure BDA00027122239600000712
Figure BDA00027122239600000713
At this time, equation (29) is calculated as
Figure BDA00027122239600000714
In view of
Figure BDA00027122239600000715
Equation (30) is further calculated as
Figure BDA0002712223960000081
In formula (31):1＝min{2ki,2M,2κ}，2＝min{hi2(1+μ)/2,gi/(μ+1)2(1+μ)/2,(2κ)(1+μ)/2}，
Figure BDA0002712223960000082
in this case, the formula (31) is rewritten into the following two forms
Figure BDA0002712223960000083
Figure BDA0002712223960000084
In formulae (32) and (33): η ∈ (0, 1);
when V is>3/(1(1-. eta.)), the formula (32) is calculated as
Figure BDA0002712223960000085
Convergence time of
Figure BDA0002712223960000086
In formula (35):
Figure BDA0002712223960000087
Figure BDA0002712223960000088
the convergence time of the filter is commanded for two times respectively;
at this time, the signal vi，i，
Figure BDA0002712223960000089
At a finite time t1Inner convergence into the following neighborhoods;
Figure BDA00027122239600000810
when V is(1+μ)/2>3/(2(1-. eta.)), the formula (36) is calculated as
Figure BDA00027122239600000811
Convergence time of
Figure BDA0002712223960000091
At this time, the signal vi，i，
Figure BDA0002712223960000092
At a finite time t2Inner convergence into the following neighborhoods;
Figure BDA0002712223960000093
at this time, it can be obtained
Figure BDA0002712223960000094
Figure BDA0002712223960000095
Further obtain
Figure BDA0002712223960000096
Figure BDA0002712223960000097
Get
Figure BDA0002712223960000098
Then
Figure BDA0002712223960000099
Then, can obtain
Figure BDA00027122239600000910
The tracking error may converge within a limited time to a small area near the origin, and all signals in a closed loop system converge within a limited time to a bounded area.
Further, in order to further prove the constraint of the full-state variable, the finite-time adaptive fuzzy tracking control method of the full-state constraint power system comprises the following steps:
get
Figure BDA00027122239600000911
Theni|≤A；
Because of x1＝v1+1So | x1|≤|v1|+|1|≤T1+AGet it
Figure BDA00027122239600000912
Then there is
Figure BDA00027122239600000913
And because of alpha1By a variable v1，1Composition of so that1Is bounded, further gets pi2Is bounded, i.e. there is a normal amount
Figure BDA00027122239600000914
Satisfy the requirement of
Figure BDA00027122239600000915
The above prove similar, x2＝v2+2+π2Therefore, it is
Figure BDA0002712223960000101
Get
Figure BDA0002712223960000102
Then there is
Figure BDA0002712223960000103
Because of alpha2By a variable v2，2Theta constitutes, so α2Is bounded, further results are bounded, i.e. there is a normal amount
Figure BDA0002712223960000104
Satisfy the requirement of
Figure BDA0002712223960000105
x3＝v3+3+π3Therefore, it is
Figure BDA0002712223960000106
Get
Figure BDA0002712223960000107
Then there is
Figure BDA0002712223960000108
Further, according to the finite time adaptive fuzzy tracking control method of the full-state constraint power system, a single infinite power system including an SVC is built in a Matlab/Simulink simulation environment, and system simulation parameters are as follows:
0＝314.159°，ω0＝57.3rad/s，ysvc0＝0.4p.u.，Vs＝1.0，H＝5.9，D＝1.0，Tsvc＝0.02，X1＝0.84p.u.，X2＝0.52p.u.，BL+BC＝0.3，m＝2，b1r＝0.6，b2r＝-0.55；
the initial value of the system state variable is x1＝0.15，x2＝0.5，x30.2; taking unknown external disturbances as H respectively1＝e-2tsin (2t) sin (4t) and H2＝e-3tcos (3t) cos (6t), and let the disturbance act on the controlled system at time t;
the finite time command filter gain parameters are as follows: delta1＝10，△210; the fuzzy logic system has fuzzy logic rule number of 10 and width of 6, and [ -3,3 ] is selected]×[-3,3]×…×[-3,3]As the center of the basis function;
the controller parameters are designed as follows: tau is1＝0.25，τ2＝0.55，τ3＝0.45，ki＝6，hi＝2，li＝1，i＝1,2,3，μ＝0.6，a1＝a2＝1，λ＝1，γ＝1。
Compared with the prior art, the invention has obvious advantages and beneficial effects, and is embodied in the following aspects:
the invention designs a finite time tracking control method of an infinite electric power system with an SVC single machine, and the controller design is carried out based on a self-adaptive back-stepping method; firstly, an unknown parameter in the power system is approximated to external disturbance by using a Fuzzy Logic System (FLS); then, a Finite Time Command Filter (FTCF) is used for filtering the virtual control signal, so that the problem of 'calculation explosion' in the traditional backstepping method is solved; under the condition that the state variables do not violate the constraint conditions, the control scheme of the invention realizes that the power angle of the generator in the power system reaches the neighborhood near the expected value within a limited time, and all variables in the closed-loop system are bounded;
secondly, providing a self-adaptive tracking control scheme for a single-machine infinite power system with SVC, and modeling an unknown nonlinear part in the power system by using a fuzzy logic system in consideration of the uncertainty of parameters of the power system and the possibility of external unknown interference in the operation process so as to improve the control effect;
due to the fact that dead zones exist in the actual operation of the inverter in the power system, the amplitude of the output voltage fundamental wave is reduced, and the working efficiency of the converter is reduced; meanwhile, the generated harmonic waves weaken the alternating current excitation effect and directly influence the performance of the power system; the control scheme of the invention effectively solves the dead zone problem of the excitation system;
and fourthly, under the condition that the state variable does not violate the constraint condition, the control scheme ensures that the power angle, the rotating speed and the equivalent reactance of the generator reach the neighborhood near the expected value in a limited time, and all variables in the closed-loop system are bounded.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof.
Drawings
FIG. 1: a single machine infinite power system schematic diagram containing SVC;
FIG. 2: the control flow of the invention is shown schematically;
FIG. 3: a power angle change curve diagram of the generator;
FIG. 4: a generator speed variation curve chart;
FIG. 5: an equivalent susceptance change curve graph;
FIG. 6: state variable x1A variation graph;
FIG. 7: state variable x2A variation graph;
FIG. 8: state variable x3A variation graph;
FIG. 9: a change curve graph of the adaptive law theta;
FIG. 10: the dead band control input u varies from graph to graph.
Detailed Description
In order to more clearly understand the technical features, objects, and effects of the present invention, specific embodiments will now be described in detail.
The invention provides a finite time tracking control method of an infinite electric power system with an SVC single machine, which is based on a self-adaptive backstepping method to design a controller, and firstly, a Fuzzy Logic System (FLS) is utilized to approximate unknown parameters and external disturbance in the electric power system; then, the virtual control signal is filtered by using a Finite Time Command Filter (FTCF), and the problem of 'computing explosion' in the traditional backstepping method is solved. In the case that the state variables do not violate the constraints, the control scheme of the invention achieves that the generator power angle in the power system reaches within the neighborhood around the desired value within a limited time, and that all variables in the closed-loop system are bounded.
Referring to fig. 1, a single infinite power system including an SVC includes two parts, one part is a classic second-order generator model, and the other part is an SVC model including a fixed capacitor and a thyristor controlled reactor (TCR-FC).
As shown in fig. 2, a finite time adaptive fuzzy tracking control method of a full-state constraint power system,
the state equation of a single infinite power system comprising the SVC is as follows:
Figure BDA0002712223960000121
in formula (1): d is a power unit damping coefficient, and H is a generator inertia time constant with the unit of s; t issvcIs the inertial time constant of the SVC; y issvcFor the admittance of the system, the initial value is ysvc0(ii) a Is the power angle of the generator, with unit of rad, and initial value of0；
Figure BDA0002712223960000131
H1And H2Unknown disturbances superimposed on the generator rotor and system admittance;
the controller is designed as follows:
firstly, coordinate conversion is carried out
Figure BDA0002712223960000132
In the formula (2), zi(i ═ 1,2,3) is the tracking error, πi(i-2, 3) is the command filter output value;
error compensation signals are introduced in the design process of the controller in consideration of errors generated by the use of a command filteri，(i＝1,2,3)；
The tracking error after further compensation is as follows:
vi＝zi-i，i＝1,2,3. (3)
designed virtual control signal alpha1，α2And control input u ═ alpha3The following were used:
Figure BDA0002712223960000133
Figure BDA0002712223960000134
Figure BDA0002712223960000135
in formulae (4) to (6): k is a radical ofi,gi,hi(i ═ 1,2,3) are all positive constants;
Figure BDA0002712223960000136
τi>0 is a constant to be designed;
designed error compensation signali(i ═ 1,2,3) as follows:
Figure BDA0002712223960000141
Figure BDA0002712223960000142
Figure BDA0002712223960000143
the designed adaptation law Θ is as follows:
Figure BDA0002712223960000144
in formula (10): Θ { | | W { [ max { | ] { [ max { ] { [ W ]i||2},i＝1,2，WiFor unknown weight vectors in a fuzzy logic system,
Figure BDA0002712223960000145
is an estimate of Θ; siI is 1,2 is a vector of basis functions in the fuzzy logic system; λ, γ, ai(i ═ 1,2) is a positive constant.
The method comprises the following specific steps:
step 1: designing the first Lyapunov function
Figure BDA0002712223960000146
To V1Derived by derivation
Figure BDA0002712223960000147
Substituting the intermediate virtual control variable alpha1And compensation signal1Equation (12) is further calculated as
Figure BDA0002712223960000148
Step 2: design a second Lyapunov function
Figure BDA0002712223960000149
To V2Derived by derivation
Figure BDA0002712223960000151
Setting the complex part, the unknown part and the external interference part in the formula (15) as an unknown nonlinear function f due to the existence of an uncertain part and external unknown disturbance in the system1(x1,x2)＝θx2+a0-kysvc0sin(x1+0)+H1(ii) a Using fuzzy logic system FLS pair f1(x1,x2) Performing an approximation
f1(x1,x2)＝W1 ΤS1+1 (16)
By young's inequality, equation (15) is further calculated as:
Figure BDA0002712223960000152
substituting the intermediate virtual control variable alpha2And compensation signal2Equation (17) is further calculated as
Figure BDA0002712223960000153
And step 3: design the third Lyapunov function
Figure BDA0002712223960000154
To V3Derived by derivation
Figure BDA0002712223960000161
Setting the complex part, the unknown part and the external interference part in the formula (20) due to the existence of the uncertain part and the external unknown disturbance in the systemFor an unknown non-linear function f2(x2)＝-(1/Tsvc)x3+H2(ii) a Using FLS to f2(x2) Performing an approximation
f1(x1,x2)＝W2 ΤS2+2 (21)
Equation (21) is further calculated as
Figure BDA0002712223960000162
Substituting control signal u and compensation signal3Equation (22) is further calculated as
Figure BDA0002712223960000163
Designing a fourth Lyapunov function according to the error compensation signal
Figure BDA0002712223960000164
To VMake a derivation
Figure BDA0002712223960000171
Using the Young's inequality, equation (25) is further derived as
Figure BDA0002712223960000172
In the formula (26), the reaction mixture is,
Figure BDA0002712223960000173
Figure BDA0002712223960000174
according to zi＝vi+iIt can be seen that by ensuring viAndiconverge to the target region within a limited time such that ziConverge to a region near the origin within a limited time; estimation error taking into account the adaptation law
Figure BDA0002712223960000175
Figure BDA0002712223960000176
Design the fifth Lyapunov function
Figure BDA0002712223960000177
Derivation of V
Figure BDA0002712223960000178
Substituted into the adaptation law, equation (28) is further calculated as
Figure BDA0002712223960000181
In formula (29):
Figure BDA0002712223960000182
Figure BDA0002712223960000183
is a constant to be designed, and
Figure BDA0002712223960000184
Figure BDA0002712223960000185
when in use
Figure BDA0002712223960000186
Figure BDA0002712223960000187
When in use
Figure BDA0002712223960000188
Figure BDA0002712223960000189
At this time, equation (29) is calculated as
Figure BDA00027122239600001810
In view of
Figure BDA00027122239600001811
Equation (30) is further calculated as
Figure BDA00027122239600001812
In formula (31):1＝min{2ki,2M,2κ}，2＝min{h i2(1+μ)/2,gi/(μ+1)2(1+μ)/2,(2κ)(1+μ)/2}，
Figure BDA00027122239600001813
in this case, the formula (31) is rewritten into the following two forms
Figure BDA00027122239600001814
Figure BDA00027122239600001815
In formulae (32) and (33): η ∈ (0, 1);
when V is>3/(1(1-. eta.)), the formula (32) is calculated as
Figure BDA0002712223960000191
Convergence time of
Figure BDA0002712223960000192
In formula (35):
Figure BDA0002712223960000193
Figure BDA0002712223960000194
the convergence time of the filter is commanded for two times respectively;
at this time, the signal vi，i，
Figure BDA0002712223960000195
At a finite time t1Inner convergence into the following neighborhoods;
Figure BDA0002712223960000196
when V is(1+μ)/2>3/(2(1-. eta.)), the formula (36) is calculated as
Figure BDA0002712223960000197
Convergence time of
Figure BDA0002712223960000198
At this time, the signal vi，i，
Figure BDA0002712223960000199
At a finite time t2Inner convergence into the following neighborhoods;
Figure BDA00027122239600001910
at this time, it can be obtained
Figure BDA00027122239600001911
Figure BDA00027122239600001912
Further obtain
Figure BDA00027122239600001913
Figure BDA00027122239600001914
Get
Figure BDA0002712223960000201
Then
Figure BDA0002712223960000202
Then, can obtain
Figure BDA0002712223960000203
The tracking error may converge within a limited time to a small area near the origin, and all signals in a closed loop system converge within a limited time to a bounded area.
To further demonstrate the constraint of the all-state variables:
get
Figure BDA0002712223960000204
Theni|≤A；
Because of x1＝v1+1So | x1|≤|v1|+|1|≤T1+AGet it
Figure BDA0002712223960000205
Then there is
Figure BDA0002712223960000206
And because of alpha1By a variable v1，1Composition of so that1Is bounded, further gets pi2Is bounded, i.e. there is a normal amount
Figure BDA0002712223960000207
Satisfy the requirement of
Figure BDA0002712223960000208
The above prove similar, x2＝v2+2+π2Therefore, it is
Figure BDA0002712223960000209
Get
Figure BDA00027122239600002010
Then there is
Figure BDA00027122239600002011
Because of alpha2By a variable v2，2Theta constitutes, so α2Is bounded, further results are bounded, i.e. there is a normal amount
Figure BDA00027122239600002012
Satisfy the requirement of
Figure BDA00027122239600002013
x3＝v3+3+π3Therefore, it is
Figure BDA00027122239600002014
Get
Figure BDA00027122239600002015
Then there is
Figure BDA00027122239600002016
A single infinite power system containing SVC is built in a Matlab/Simulink simulation environment, and system simulation parameters are as follows:
0＝314.159°，ω0＝57.3rad/s，ysvc0＝0.4p.u.，Vs＝1.0，H＝5.9，D＝1.0，Tsvc＝0.02，X1＝0.84p.u.，X2＝0.52p.u.，BL+BC＝0.3，m＝2，b1r＝0.6，b2r＝-0.55；
the initial value of the system state variable is x1＝0.15，x2＝0.5，x30.2; taking unknown external disturbances as H respectively1＝e-2tsin (2t) sin (4t) and H2＝e-3tcos (3t) cos (6t), and let the disturbance act on the controlled system at time t;
the finite time command filter gain parameters are as follows: delta1＝10，△210; the fuzzy logic system has fuzzy logic rule number of 10 and width of 6, and [ -3,3 ] is selected]×[-3,3]×…×[-3,3]As the center of the basis function;
the controller parameters are designed as follows: tau is1＝0.25，τ2＝0.55，τ3＝0.45，ki＝6，hi＝2，li＝1，i＝1,2,3，μ＝0.6，a1＝a2＝1，λ＝1，γ＝1。
Simulation results are shown in fig. 3-10, and fig. 3-5 show that the control scheme (FTFAB) provided by the invention has good transient response, can reach a state of small fluctuation within a limited time, and has a high convergence rate; in addition, it can be seen that the FTFAB control scheme effectively constrains the amplitude of the generator-related signals within a desired range, effectively balancing the active power of the system.
6-8, the system state variables can reach the bounded region near the origin in a shorter time without violating the constraint conditions.
As can be seen from fig. 9 and 10, the adaptive law signals and control inputs are bounded.
The invention designs a finite time tracking control method of an infinite power system with an SVC single machine, which is based on a self-adaptive back-stepping method to design a controller; firstly, an unknown parameter in the power system is approximated to external disturbance by using a Fuzzy Logic System (FLS); then, the virtual control signal is filtered by using a Finite Time Command Filter (FTCF), so that the problem of 'computing explosion' in the traditional backstepping method is solved. In the case that the state variables do not violate the constraints, the control scheme of the invention enables the generator power angle in the power system to reach within the neighborhood around the desired value within a limited time, and all variables in the closed-loop system are bounded.
Aiming at a single-machine infinite power system with SVC, a self-adaptive tracking control scheme is provided, and in consideration of uncertainty of parameters (such as damping coefficient) of the power system and the influence of external unknown interference (such as superposition disturbance of system admittance and the influence of circuit element aging on a generator rotor) in the operation process, a fuzzy logic system is utilized to model an unknown nonlinear part in the power system, so that the control effect is improved.
Because of the dead zone existing in the actual operation of the inverter in the power system, the amplitude of the output voltage fundamental wave is reduced, and the working efficiency of the converter is reduced; meanwhile, the generated harmonic waves weaken the alternating current excitation effect and directly influence the performance of the power system; the control scheme of the invention effectively solves the dead zone problem of the excitation system.
Under the condition that the state variables do not violate the constraint conditions, the control scheme of the invention ensures that the power angle, the rotating speed and the equivalent reactance of the generator reach the neighborhood around the expected value in a limited time, and all variables in the closed-loop system are bounded.
It should be noted that: the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention; while the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.

Claims (4)

1. The finite time self-adaptive fuzzy tracking control method of the full-state constraint power system is characterized by comprising the following steps:
the state equation of a single infinite power system comprising the SVC is as follows:
Figure FDA0002712223950000011
in formula (1): d is a power unit damping coefficient, and H is a generator inertia time constant with the unit of s; t issvcIs the inertial time constant of the SVC; y issvcFor the admittance of the system, the initial value is ysvc0(ii) a Is the power angle of the generator, with unit of rad, and initial value of0；
Figure FDA0002712223950000012
Figure FDA0002712223950000014
m>0,b1r>0,b2r<0；
H1And H2Unknown disturbances superimposed on the generator rotor and system admittance;
the controller is designed as follows:
firstly, coordinate conversion is carried out
Figure FDA0002712223950000013
In the formula (2), zi(i ═ 1,2,3) is the tracking error, πi(i-2, 3) is the command filter output value;
considering that the use of a command filter generates errorsError compensation signal is introduced in the design process of difference controlleri，(i＝1,2,3)；
The tracking error after further compensation is as follows:
vi＝zi-i，i＝1,2,3. (3)
designed virtual control signal alpha1，α2And control input u ═ alpha3The following were used:
Figure FDA0002712223950000021
Figure FDA0002712223950000022
Figure FDA0002712223950000023
in formulae (4) to (6): k is a radical ofi,gi,hi(i ═ 1,2,3) are all positive constants;
Figure FDA0002712223950000024
τi>0 is a constant to be designed;
designed error compensation signali(i ═ 1,2,3) as follows:
Figure FDA0002712223950000025
Figure FDA0002712223950000026
Figure FDA0002712223950000027
the designed adaptation law Θ is as follows:
Figure FDA0002712223950000028
in formula (10): Θ { | | W { [ max { | ] { [ max { ] { [ W ]i||2},i＝1,2，WiFor unknown weight vectors in a fuzzy logic system,
Figure FDA0002712223950000029
is an estimate of Θ; siI is 1,2 is a vector of basis functions in the fuzzy logic system; λ, γ, ai(i ═ 1,2) is a positive constant.
2. The finite-time adaptive fuzzy tracking control method of the full-state constraint power system according to claim 1, characterized in that: the method specifically comprises the following steps:
step 1: designing the first Lyapunov function
Figure FDA0002712223950000031
To V1Derived by derivation
Figure FDA0002712223950000032
Substituting the intermediate virtual control variable alpha1And compensation signal1Equation (12) is further calculated as
Figure FDA0002712223950000033
Step 2: design a second Lyapunov function
Figure FDA0002712223950000034
To V2Derived by derivation
Figure FDA0002712223950000035
Setting the complex part, the unknown part and the external interference part in the formula (15) as an unknown nonlinear function f due to the existence of an uncertain part and external unknown disturbance in the system1(x1,x2)＝θx2+a0-kysvc0sin(x1+0)+H1(ii) a Using fuzzy logic system FLS pair f1(x1,x2) Performing an approximation
f1(x1,x2)＝W1 ΤS1+1 (16)
By young's inequality, equation (15) is further calculated as:
Figure FDA0002712223950000041
substituting the intermediate virtual control variable alpha2And compensation signal2Equation (17) is further calculated as
Figure FDA0002712223950000042
And step 3: design the third Lyapunov function
Figure FDA0002712223950000043
To V3Derived by derivation
Figure FDA0002712223950000044
Setting the complex part, the unknown part and the external interference part in the formula (20) as an unknown nonlinear function f due to the existence of an uncertain part and an external unknown disturbance in the system2(x2)＝-(1/Tsvc)x3+H2(ii) a Using FLS to f2(x2) Performing an approximation
Figure FDA0002712223950000045
Equation (21) is further calculated as
Figure FDA0002712223950000046
Substituting control signal u and compensation signal3Equation (22) is further calculated as
Figure FDA0002712223950000051
Designing a fourth Lyapunov function according to the error compensation signal
Figure FDA0002712223950000052
To VMake a derivation
Figure FDA0002712223950000053
Using the Young's inequality, equation (25) is further derived as
Figure FDA0002712223950000054
In the formula (26), the reaction mixture is,
Figure FDA0002712223950000055
Figure FDA0002712223950000056
according to zi＝vi+iIt can be seen that by ensuring viAndiconverge to the target region within a limited time such that ziConverge to a region near the origin within a limited time; estimation error taking into account the adaptation law
Figure FDA0002712223950000057
Design the fifth Lyapunov function
Figure FDA0002712223950000058
Derivation of V
Figure FDA0002712223950000061
Substituted into the adaptation law, equation (28) is further calculated as
Figure FDA0002712223950000062
In formula (29):
Figure FDA0002712223950000063
Figure FDA0002712223950000064
is a constant to be designed, and
Figure FDA0002712223950000065
Figure FDA0002712223950000066
when in use
Figure FDA0002712223950000067
When in use
Figure FDA0002712223950000068
Figure FDA0002712223950000069
At this time, equation (29) is calculated as
Figure FDA00027122239500000610
In view of
Figure FDA00027122239500000611
Equation (30) is further calculated as
Figure FDA00027122239500000612
In formula (31):1＝min{2ki,2M,2κ}，2＝min{hi2(1+μ)/2,gi/(μ+1)2(1+μ)/2,(2κ)(1+μ)/2}，
Figure FDA0002712223950000071
in this case, the formula (31) is rewritten into the following two forms
Figure FDA0002712223950000072
Figure FDA0002712223950000073
In formulae (32) and (33): η ∈ (0, 1);
when V is>3/(1(1-. eta.)), the formula (32) is calculated as
Figure FDA0002712223950000074
Convergence time of
Figure FDA0002712223950000075
In formula (35):
Figure FDA0002712223950000076
Figure FDA0002712223950000077
the convergence time of the filter is commanded for two times respectively;
at this time, the signal vi，i，
Figure FDA0002712223950000078
At a finite time t1Inner convergence into the following neighborhoods;
Figure FDA0002712223950000079
when V is(1+μ)/2>3/(2(1-. eta.)), the formula (36) is calculated as
Figure FDA00027122239500000710
Convergence time of
Figure FDA00027122239500000711
At this time, the signal vi，i，
Figure FDA00027122239500000712
At a finite time t2Inner convergence into the following neighborhoods;
Figure FDA00027122239500000713
at this time, it can be obtained
Figure FDA0002712223950000081
Figure FDA0002712223950000082
Further obtain
Figure FDA0002712223950000083
Figure FDA0002712223950000084
Get
Figure FDA0002712223950000085
Then
Figure FDA0002712223950000086
Then, can obtain
Figure FDA0002712223950000087
The tracking error may converge within a limited time to a small area near the origin, and all signals in a closed loop system converge within a limited time to a bounded area.
3. The finite-time adaptive fuzzy tracking control method of the full-state constraint power system according to claim 1, characterized in that: to further demonstrate the constraint of the all-state variables:
get
Figure FDA0002712223950000088
Theni|≤A；
Because of x1＝v1+1So | x1|≤|v1|+|1|≤T1+AGet it
Figure FDA0002712223950000089
Then there is
Figure FDA00027122239500000810
And because of alpha1By a variable v1，1Composition of so that1Is bounded, further gets pi2Is bounded, i.e. there is a normal amount
Figure FDA00027122239500000811
Satisfy the requirement of
Figure FDA00027122239500000812
The above prove similar, x2＝v2+2+π2Therefore, it is
Figure FDA00027122239500000813
Get
Figure FDA00027122239500000814
Then there is
Figure FDA00027122239500000815
Because of alpha2By a variable v2，2Theta constitutes, so α2Is bounded, further results are bounded, i.e. there is a normal amount
Figure FDA00027122239500000816
Satisfy the requirement of
Figure FDA00027122239500000817
x3＝v3+3+π3Therefore, it is
Figure FDA00027122239500000818
Get
Figure FDA0002712223950000091
Then there is
Figure FDA0002712223950000092
4. The finite-time adaptive fuzzy tracking control method of the full-state constraint power system according to claim 1, characterized in that:
a single infinite power system containing SVC is built in a Matlab/Simulink simulation environment, and system simulation parameters are as follows:
0＝314.159°，ω0＝57.3rad/s，ysvc0＝0.4p.u.，Vs＝1.0，H＝5.9，D＝1.0，Tsvc＝0.02，X1＝0.84p.u.，X2＝0.52p.u.，BL+BC＝0.3，m＝2，b1r＝0.6，b2r＝-0.55；
the initial value of the system state variable is x1＝0.15，x2＝0.5，x30.2; taking unknown external disturbances as H respectively1＝e- 2tsin (2t) sin (4t) and H2＝e-3tcos (3t) cos (6t), and let the disturbance act on the controlled system at time t;
the finite time command filter gain parameters are as follows: delta1＝10，△210; the fuzzy logic system has fuzzy logic rule number of 10 and width of 6, and [ -3,3 ] is selected]×[-3,3]×…×[-3,3]As the center of the basis function;
the controller parameters are designed as follows: tau is1＝0.25，τ2＝0.55，τ3＝0.45，ki＝6，hi＝2，li＝1，i＝1,2,3，μ＝0.6，a1＝a2＝1，λ＝1，γ＝1。
CN202011060561.2A 2020-09-30 2020-09-30 Finite time self-adaptive fuzzy tracking control method of full-state constraint power system Active CN112147900B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011060561.2A CN112147900B (en) 2020-09-30 2020-09-30 Finite time self-adaptive fuzzy tracking control method of full-state constraint power system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011060561.2A CN112147900B (en) 2020-09-30 2020-09-30 Finite time self-adaptive fuzzy tracking control method of full-state constraint power system

Publications (2)

Publication Number Publication Date
CN112147900A true CN112147900A (en) 2020-12-29
CN112147900B CN112147900B (en) 2022-04-26

Family

ID=73951287

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011060561.2A Active CN112147900B (en) 2020-09-30 2020-09-30 Finite time self-adaptive fuzzy tracking control method of full-state constraint power system

Country Status (1)

Country Link
CN (1) CN112147900B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113791543A (en) * 2021-10-15 2021-12-14 南京师范大学 Finite time quantization control method of static var compensator based on disturbance observer

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106788086A (en) * 2017-02-20 2017-05-31 青岛大学 Consider the asynchronous machine command filtering finite time fuzzy control method of input saturation
CN108717266A (en) * 2018-05-30 2018-10-30 迪比（重庆）智能科技研究院有限公司 Neural adaptive tracking control method of the wind field power of fan based on disturbance observer
CN108988361A (en) * 2018-08-22 2018-12-11 中国矿业大学 The quick suppressing method of two-shipper interconnected electric power system chaotic oscillation
CN109240088A (en) * 2018-10-24 2019-01-18 闽江学院 A kind of estimation of electric power networks communication delay and compensation finite-time control method
CN110112738A (en) * 2019-05-15 2019-08-09 青岛大学 A kind of direct current transportation current transformer fuzzy control method based on command filtering
CN110336505A (en) * 2019-07-10 2019-10-15 青岛大学 Asynchronous motor command filtering fuzzy control method based on state constraint
CN110466597A (en) * 2019-07-26 2019-11-19 江苏大学 A kind of electric car EPS AC magnetoelectric machine energy optimal control system
CN110501912A (en) * 2019-04-01 2019-11-26 东北电力大学 Meet the multi-machine power system adaptive fuzzy dynamic surface sliding-mode control of default output tracking performance
CN110500239A (en) * 2019-04-12 2019-11-26 苏州科技大学 A kind of adaptive inverting sliding formwork optimal control method of Wind turbines based on High-Order Sliding Mode observer
CN111371112A (en) * 2020-04-15 2020-07-03 苏州科技大学 Distributed finite time control method for island microgrid heterogeneous battery energy storage system

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106788086A (en) * 2017-02-20 2017-05-31 青岛大学 Consider the asynchronous machine command filtering finite time fuzzy control method of input saturation
CN108717266A (en) * 2018-05-30 2018-10-30 迪比（重庆）智能科技研究院有限公司 Neural adaptive tracking control method of the wind field power of fan based on disturbance observer
CN108988361A (en) * 2018-08-22 2018-12-11 中国矿业大学 The quick suppressing method of two-shipper interconnected electric power system chaotic oscillation
CN109240088A (en) * 2018-10-24 2019-01-18 闽江学院 A kind of estimation of electric power networks communication delay and compensation finite-time control method
CN110501912A (en) * 2019-04-01 2019-11-26 东北电力大学 Meet the multi-machine power system adaptive fuzzy dynamic surface sliding-mode control of default output tracking performance
CN110500239A (en) * 2019-04-12 2019-11-26 苏州科技大学 A kind of adaptive inverting sliding formwork optimal control method of Wind turbines based on High-Order Sliding Mode observer
CN110112738A (en) * 2019-05-15 2019-08-09 青岛大学 A kind of direct current transportation current transformer fuzzy control method based on command filtering
CN110336505A (en) * 2019-07-10 2019-10-15 青岛大学 Asynchronous motor command filtering fuzzy control method based on state constraint
CN110466597A (en) * 2019-07-26 2019-11-19 江苏大学 A kind of electric car EPS AC magnetoelectric machine energy optimal control system
CN111371112A (en) * 2020-04-15 2020-07-03 苏州科技大学 Distributed finite time control method for island microgrid heterogeneous battery energy storage system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
YOUYI WANG等: "Robust nonlinear coordinated generator excitation and SVC control for power systems", 《ELECTRICAL POWER AND ENERGY SYSTEMS》 *
倪骏康等: "电力系统混沌振荡的等效快速终端模糊滑模控制", 《物理学报》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113791543A (en) * 2021-10-15 2021-12-14 南京师范大学 Finite time quantization control method of static var compensator based on disturbance observer
CN113791543B (en) * 2021-10-15 2024-03-01 南京师范大学 Finite time quantitative control method of static var compensator based on interference observer

Also Published As

Publication number Publication date
CN112147900B (en) 2022-04-26

Similar Documents

Publication Publication Date Title
US10333390B2 (en) Systems and methods for providing vector control of a grid connected converter with a resonant circuit grid filter
WO2024021206A1 (en) Method and system for energy storage system control based on grid-forming converter, storage medium, and device
CN108334679B (en) Global sliding mode control method of active power filter
CN109100937B (en) Active power filter global sliding mode control method based on double-hidden-layer recurrent neural network
CN107240921A (en) Based on the SVC sliding-mode controls for integrating adaptive backstepping
CN106849182B (en) Inverter grid-connected control method based on fuzzy control and virtual synchronous generator
CN108199396A (en) The virtual excitation closed-loop control system of energy storage inverter and its design method
CN107453362A (en) Active Power Filter-APF inverting global sliding mode fuzzy control based on neutral net
CN109103884A (en) Active Power Filter-APF back stepping control method based on metacognition fuzzy neural network
CN105305422A (en) Fuzzy sliding mode self-adaption-based UPFC (unified power flow controller) control method
CN112147900B (en) Finite time self-adaptive fuzzy tracking control method of full-state constraint power system
CN110266044B (en) Microgrid grid-connected control system and method based on energy storage converter
CN115940683A (en) Photovoltaic grid-connected inverter control method based on improved sliding mode control
Ahmed et al. Improved damping control method for grid-forming converters using lqr and optimally weighted feedback control loops
CN113224797B (en) PI parameter configuration method for voltage and current double closed-loop control system of inverter
Tan et al. Intelligent controlled dynamic voltage restorer for improving transient voltage quality
CN113328440A (en) Active filtering control method for PLC circuit of electric vehicle charging station
Zou et al. Eigen-Structure Assignment-Based Differential Evolution Algorithm for TS Fuzzy Control Tuning Applied to Water-Turbine Governing System
Malla et al. Supplementary control for virtual synchronous machine based on adaptive dynamic programming
Kanchanaharuthai et al. Adaptive finite-time synergetic control design for power systems with static var compensator
CN117498429A (en) Photovoltaic inverter control method and system based on virtual synchronous generator
CN116581770B (en) Micro-grid system VSG double-droop control method based on self-adaptive neural network
CN109617073A (en) A kind of harmonic wave damped impedance design method applied to virtual synchronous machine
CN110571784B (en) Second-order inertia disturbance rejection control method for direct-current power supply system
Ghazi et al. Adaptive fuzzy sliding mode control of SVC and TCSC for improving the dynamic performance of power systems

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant