CN111244942A - Photovoltaic power generation system dynamic output feedback control method based on difference operator dispersion - Google Patents

Photovoltaic power generation system dynamic output feedback control method based on difference operator dispersion Download PDF

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CN111244942A
CN111244942A CN202010063717.6A CN202010063717A CN111244942A CN 111244942 A CN111244942 A CN 111244942A CN 202010063717 A CN202010063717 A CN 202010063717A CN 111244942 A CN111244942 A CN 111244942A
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matrix
power generation
fuzzy
photovoltaic power
output feedback
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CN111244942B (en
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汪星一
钟智雄
黄修丹
黄伟雄
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Minjiang University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

Abstract

The invention relates to a dynamic output feedback control method of a photovoltaic power generation system based on difference operator discretization, which comprises the steps of firstly, building a physical model of the solar photovoltaic power generation system, and considering that the traditional forward displacement operator discretization method can lead the discretization system to tend to be unstable during high-speed sampling; by adopting the method of the difference operator discretization, the photovoltaic power generation continuous system and the discrete system can be researched under a unified framework. Then, a T-S fuzzy method is adopted to express the nonlinear dynamics of the system, a dynamic output feedback controller is provided, and the stable operation of the system of the photovoltaic power generation system under the condition of high-speed sampling are realized
Figure DEST_PATH_IMAGE002
And (4) performance. The invention takes the situation of high-speed sampling into consideration, designs the dynamic output feedback controller, ensures the stable operation of the system and ensures the stable operation of the system
Figure 908245DEST_PATH_IMAGE002
And (4) performance.

Description

Photovoltaic power generation system dynamic output feedback control method based on difference operator dispersion
Technical Field
The invention relates to the field of photovoltaic power generation system dynamic output feedback control, in particular to a photovoltaic power generation system dynamic output feedback control method based on difference operator dispersion.
Background
Photovoltaic power generation systems are power generation systems that directly convert light energy into electrical energy without the need for a thermal process. The device has the characteristics of high reliability, long service life, no environmental pollution, independent power generation and grid-connected operation. Output feedback control is one of the main forms of linear system control, and when the state of the system is not measurable, the output feedback can realize the control of the system under certain conditions. Dynamic output feedback has better properties and control effects. When a continuous system and a discrete system of photovoltaic power generation are researched under a unified framework, the discrete system tends to be unstable due to the consideration that under the condition of high-speed sampling, the discrete method of the traditional forward displacement operator is adopted.
Disclosure of Invention
In view of the above, the present invention provides a feedback control method for dynamic output of a photovoltaic power generation system based on difference operator discretization, which realizes stable operation of the system of the photovoltaic power generation system under the condition of high-speed sampling and
Figure BDA0002375311690000011
and (4) performance.
In order to achieve the purpose, the invention adopts the following technical scheme:
a photovoltaic power generation system dynamic output feedback control method based on difference operator dispersion comprises the following steps:
step S1, constructing a solar photovoltaic power generation system;
s2, expressing the nonlinear dynamics of the system by a T-S fuzzy method, and establishing a fuzzy system model of the solar photovoltaic power generation system;
step S3, designing the model with difference based on the stability principle of Lyapunov function according to the obtained fuzzy system modelThe dynamic output feedback controller of the division operator realizes the stable operation of the photovoltaic power generation system under the condition of high-speed sampling
Figure BDA0002375311690000012
And (4) performance.
Further, the solar photovoltaic power generation system comprises a photovoltaic cell, a DC bus, a DC/DC module, a dynamic output feedback controller and a direct current load; the DC/DC module is respectively connected with the photovoltaic cell, the dynamic output feedback controller and the direct current load; and the dynamic output feedback controller is connected to a connecting line of the DC/DC module and the load through a DC bus.
Further, the step S2 is specifically:
step S21, establishing a physical model of the solar photovoltaic power generation system, as shown in formula (1):
Figure BDA0002375311690000021
in the formula, phiPV，vPV，φLAnd V0Respectively representing the output current of the photovoltaic cell, the output voltage of the photovoltaic cell, the current flowing through the inductor L and the regulated output voltage; cPVA capacitance on the photovoltaic power generation side; r0，RLAnd RMAre respectively through a capacitor C0An inductor L, a resistor on the power field effect transistor; vDA forward voltage of the power diode; phi is a0Is a measurable load current; and u is a control signal of the switch pulse width of the power field effect transistor.
Step S22: definition e0＝V0-VrefSo that the output voltage V is0Close to the reference output voltage VrefAnd, under the condition of high sampling rate, defining fuzzy antecedent variable based on difference operator model theory method
Figure BDA0002375311690000022
θ3＝vPV,θ4＝φLThe following fuzzy rule is selected:
if theta is greater than theta1(t) is Mi1，θ2(t) is Mi2，θ3(t) is Mi3，θ4(t) is Mi4Establishing a fuzzy model system as follows:
Figure BDA0002375311690000023
wherein x (t) ═ vPVφLe0]T，
Figure BDA0002375311690000028
u (t) and ω (t) are the control input and the disturbance input, respectively; the regulated output z (t) being the output voltage V0；MijIs a fuzzy set, r is a fuzzy rule; delta CiIs a matrix of uncertainty parameters, Δ C, of the signal outputdiIs an uncertainty parameter matrix of the delayed output of the signal, DωiIs an interference parameter matrix of the output signal; b isωiIs a disturbance parameter matrix of the system. Theta (t) ([ theta ])1(t)，θ2(t)，θ3(t)，θ4(t)]Is a front-part variable; t is a sampling period, n (T) represents a delayed sampling period of the system (1) and satisfies the following:
nm≤n(t)≤nM， (3)
in the formula, nmAnd nMRespectively representing minimum and maximum delay sample periods; phi (t) is the sequence [ -n ]M0]The real-valued initial condition above;
Figure BDA0002375311690000024
is the difference operator of x (t), defined as follows:
Figure BDA0002375311690000025
furthermore, Ai，Adi，Bi，BωiThe system parameter matrix of (2):
Figure BDA0002375311690000026
Figure BDA0002375311690000027
Ci，Cdi，Fiand DiIs a known constant matrix with appropriate dimensions, uncertainty matrix Δ Ai，ΔAdiAnd Δ CdiThe form of (A) is as follows:
Figure BDA0002375311690000031
in the formula M1i，M2i，E1iAnd E2iIs a known matrix of appropriate dimensions, Δ (t) is an unknown real variant matrix that satisfies:
Figure BDA0002375311690000032
and is
Figure BDA0002375311690000033
Where J is a constant matrix of known appropriate dimensions,
Figure BDA0002375311690000034
an unknown real-valued time-varying matrix function that satisfies:
Figure BDA0002375311690000035
step S23: by binding fuzzy rules, a fuzzy model based on the difference operator photovoltaic nonlinear system (1) is obtained as follows:
Figure BDA0002375311690000036
wherein the content of the first and second substances,
Figure BDA0002375311690000037
in the formula (I), the compound is shown in the specification,
Figure BDA0002375311690000038
and is
Figure BDA0002375311690000039
Figure BDA00023753116900000310
Middle Mij(θj(t)) represents MijMiddle thetaj(t) membership function, hereinafter by λi(θ (t)) is simply denoted as λi。。
Further, the step S3 is specifically:
and step S31, introducing the following fuzzy dynamic output feedback controller based on the difference operator discrete method according to the photovoltaic fuzzy system model in the step (10):
Figure BDA00023753116900000311
wherein
Figure BDA0002375311690000041
Is a state variable of the dynamic output feedback controller,
Figure BDA0002375311690000042
Figure BDA0002375311690000043
Figure BDA0002375311690000044
is the gain of the dynamic output feedback controller to be designed, the closed-loop fuzzy control system which can be expanded by the formulas (10) and (12) is as follows:
Figure BDA0002375311690000045
wherein the content of the first and second substances,
Figure BDA0002375311690000046
and:
Figure BDA0002375311690000047
step S32: given a matrix of 0<P＝PT，
Figure BDA0002375311690000048
0<Q＝QT，
Figure BDA0002375311690000049
Figure BDA00023753116900000410
And a forward scalar ε, establishing a Lyapunov function as follows:
V(t)＝V1(t)+V2(t)+V3(t)+V4(t)+V5(t)， (15)
wherein:
Figure BDA00023753116900000411
Figure BDA00023753116900000412
Figure BDA00023753116900000413
Figure BDA00023753116900000414
Figure BDA00023753116900000415
by
Figure BDA00023753116900000416
Respectively obtaining the products of the first step and the second step,
Figure BDA00023753116900000417
and
Figure BDA00023753116900000418
Figure BDA00023753116900000419
step S33: calculating V (t) by using a difference operator discrete method for the track of the closed-loop fuzzy system (13), and according to the attribute of an increment operator: for any function of time x (t) and
Figure BDA00023753116900000420
where t is the sampling period, there are:
Figure BDA00023753116900000421
obtaining:
Figure BDA00023753116900000422
Figure BDA0002375311690000051
Figure BDA0002375311690000052
Figure BDA0002375311690000053
for any constant semi-positive definite symmetric matrix W, two positive integers r and r0R is more than or equal to r0Not less than 1, the following equation holds,
Figure BDA0002375311690000054
calculating V by using difference operator discrete method5(t), and according to equations (17) and (22), one can obtain:
Figure BDA0002375311690000055
for a positive definite symmetric matrix P, it can be obtained from equation (13):
Figure BDA0002375311690000056
0< γ <1 is defined and the following indices are considered:
Figure BDA0002375311690000057
in the formula (I), the compound is shown in the specification,
Figure BDA0002375311690000058
Figure BDA0002375311690000061
wherein the content of the first and second substances,
Figure BDA0002375311690000062
Figure BDA0002375311690000063
Figure BDA0002375311690000064
let Ψ in equation (25)1(t)<0 applying the cone-complement theorem, the following matrix inequality holds:
Ψ(t)<0， (28)
wherein the content of the first and second substances,
Figure BDA0002375311690000065
Figure BDA0002375311690000066
Figure BDA0002375311690000067
Figure BDA0002375311690000068
Figure BDA0002375311690000069
by Ψ1(t)<0 can give
Figure BDA00023753116900000610
Step S34: given a matrix of 0<X＝XT，0<Y＝YT，
Figure BDA0002375311690000071
Figure BDA0002375311690000072
Matrix array
Figure BDA0002375311690000073
And a positive scalar e1，∈2I, j ═ 1, 2.., r; by using a matrix decoupling technology, the nonlinear matrix inequality in the formula (28) is converted into a linear matrix inequality, and the inequality of the formula (28) is rewritten as follows:
Figure BDA0002375311690000074
given a matrix of appropriate size Q, H, E and R ═ RT>0, and Q and R are symmetrical, and R>0, having:
Q+HF(t)E+ETFT(t)HT<0， (31)
for F(t) satisfies FT(t) F (t). ltoreq.R, there being one and only one scalar ε>0, the following results are obtained:
Figure BDA0002375311690000075
according to the formulas (31), (32), the method is applied to psiii<0, the following inequality can be obtained:
Figure BDA0002375311690000076
Ψii<0, and a plurality of groups consisting of,
Figure BDA0002375311690000077
Figure BDA0002375311690000078
Figure BDA0002375311690000079
Figure BDA00023753116900000710
Figure BDA0002375311690000081
Figure BDA0002375311690000082
p is divided into the following:
Figure BDA0002375311690000083
definition of
Figure BDA0002375311690000084
And a slave SWTIs not I-XY, canObtaining:
Figure BDA0002375311690000085
definition Γ ═ diag { H1，H1，H1，H1，H1I, I }, left-and right-multiplying Γ for equation (41)TAnd Γ, can be obtained in formula (33)
Figure BDA0002375311690000086
The variables of (c) are:
Figure BDA0002375311690000087
Figure BDA0002375311690000088
step S35: the same process can obtain psiijAnd Ψji. The following matrix inequality holds:
Figure BDA0002375311690000089
and is provided with a plurality of groups of the materials,
Figure BDA00023753116900000810
Figure BDA00023753116900000811
wherein the content of the first and second substances,
Figure BDA00023753116900000812
Figure BDA0002375311690000091
Figure BDA0002375311690000092
Figure BDA0002375311690000093
Figure BDA0002375311690000094
Figure BDA0002375311690000095
Figure BDA0002375311690000096
Figure BDA0002375311690000097
Figure BDA0002375311690000098
Figure BDA0002375311690000099
robust dynamic output feedback controller
Figure BDA00023753116900000910
As formula (10), the parameters are:
Figure BDA00023753116900000911
Figure BDA0002375311690000101
Figure BDA0002375311690000102
Figure BDA0002375311690000103
where S and W are two non-singular matrices, the following equation is satisfied:
SW＝I-XY-1。 (42)
compared with the prior art, the invention has the following beneficial effects:
the invention takes the situation of high-speed sampling into consideration, designs the dynamic output feedback controller, ensures the stable operation of the system and ensures the stable operation of the system
Figure BDA0002375311690000104
Performance of
Drawings
FIG. 1 is a schematic view of a photovoltaic system in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart of the method of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
Referring to fig. 2, the invention provides a photovoltaic power generation system dynamic output feedback control method based on difference operator dispersion, which includes the following steps:
step S1, constructing a solar photovoltaic power generation system; as shown in fig. 1, the solar photovoltaic power generation system includes a photovoltaic cell, a DC bus, a DC/DC module, a dynamic output feedback controller, and a DC load; the DC/DC module is respectively connected with the photovoltaic cell, the dynamic output feedback controller and the direct current load; and the dynamic output feedback controller is connected to a connecting line of the DC/DC module and the load through a DC bus.
S2, expressing the nonlinear dynamics of the system by a T-S fuzzy method, and establishing a fuzzy system model of the solar photovoltaic power generation system;
step S3, designing a dynamic output feedback controller with a difference operator based on the Lyapunov function stability principle according to the obtained fuzzy system model, and realizing the stable operation of the system of the photovoltaic power generation system under the condition of high-speed sampling and
Figure BDA0002375311690000105
and (4) performance.
In this embodiment, the step S2 specifically includes:
step S21, establishing a physical model of the solar photovoltaic power generation system, as shown in formula (1):
Figure BDA0002375311690000111
in the formula, phiPV，vPV，φLAnd V0Respectively representing the output current of the photovoltaic cell, the output voltage of the photovoltaic cell, the current flowing through the inductor L and the regulated output voltage; cPVA capacitance on the photovoltaic power generation side; r0，RLAnd RMAre respectively through a capacitor C0An inductor L, a resistor on the power field effect transistor; vDA forward voltage of the power diode; phi is a0Is a measurable load current; and u is a control signal of the switch pulse width of the power field effect transistor.
Step S22: definition e0＝V0-VrefSo that the output voltage V is0Close to the reference output voltage VrefAnd, under the condition of high sampling rate, defining fuzzy antecedent variable based on difference operator model theory method
Figure BDA0002375311690000112
θ3＝vPV,θ4＝φLThe following fuzzy rule is selected:
if theta is greater than theta1(t) is Mi1，θ2(t) is Mi2，θ3(t) is Mi3，θ4(t) is Mi4Establishing a fuzzy model system as follows:
Figure BDA0002375311690000113
wherein x (t) ═ vPVφLe0]T，
Figure BDA0002375311690000114
u (t) and ω (t) are the control input and the disturbance input, respectively; the regulated output z (t) being the output voltage V0；MijIs a fuzzy set, r is a fuzzy rule; delta CiIs a matrix of uncertainty parameters, Δ C, of the signal outputdiIs an uncertainty parameter matrix of the delayed output of the signal, DωiIs an interference parameter matrix of the output signal; b isωiIs a disturbance parameter matrix of the system. Theta (t) ([ theta ])1(t)，θ2(t)，θ3(t)，θ4(t)]Is a front-part variable; t is a sampling period, n (T) represents a delayed sampling period of the system (1) and satisfies the following:
nm≤n(t)≤nM， (3)
in the formula, nmAnd nMRespectively representing minimum and maximum delay sample periods; phi (t) is the sequence [ -n ]M0]The real-valued initial condition above;
Figure BDA0002375311690000115
is the difference operator of x (t), defined as follows:
Figure BDA0002375311690000116
furthermore, Ai，Adi，Bi，BωiThe system parameter matrix of (2):
Figure BDA0002375311690000117
Figure BDA0002375311690000118
Ci，Cdi，Fiand DiIs a known constant matrix with appropriate dimensions, uncertainty matrix Δ Ai，ΔAdiAnd Δ CdiThe form of (A) is as follows:
Figure BDA0002375311690000121
in the formula M1i，M2i，E1iAnd E2iIs a known matrix of appropriate dimensions, Δ (t) is an unknown real variant matrix that satisfies:
Figure BDA0002375311690000122
and is
Figure BDA0002375311690000123
Where J is a constant matrix of known appropriate dimensions,
Figure BDA0002375311690000124
an unknown real-valued time-varying matrix function that satisfies:
Figure BDA0002375311690000125
step S23: by binding fuzzy rules, a fuzzy model based on the difference operator photovoltaic nonlinear system (1) is obtained as follows:
Figure BDA0002375311690000126
wherein the content of the first and second substances,
Figure BDA0002375311690000127
in the formula (I), the compound is shown in the specification,
Figure BDA0002375311690000128
and is
Figure BDA0002375311690000129
Figure BDA00023753116900001210
Middle Mij(θj(t)) represents MijMiddle thetaj(t) membership function, hereinafter by λi(θ (t)) is simply denoted as λi。。
Further, the step S3 is specifically:
and step S31, introducing the following fuzzy dynamic output feedback controller based on the difference operator discrete method according to the photovoltaic fuzzy system model in the step (10):
Figure BDA00023753116900001211
wherein
Figure BDA0002375311690000131
Is a state variable of the dynamic output feedback controller,
Figure BDA0002375311690000132
Figure BDA0002375311690000133
Figure BDA0002375311690000134
is the gain of the dynamic output feedback controller to be designed, the closed-loop fuzzy control system which can be expanded by the formulas (10) and (12) is as follows:
Figure BDA0002375311690000135
wherein the content of the first and second substances,
Figure BDA0002375311690000136
and:
Figure BDA0002375311690000137
step S32: given a matrix of 0<P＝PT，
Figure BDA0002375311690000138
0<Q＝QT，
Figure BDA0002375311690000139
Figure BDA00023753116900001310
And a forward scalar ε, establishing a Lyapunov function as follows:
V(t)＝V1(t)+V2(t)+V3(t)+V4(t)+V5(t)， (15)
wherein:
Figure BDA00023753116900001311
Figure BDA00023753116900001312
Figure BDA00023753116900001313
Figure BDA00023753116900001314
Figure BDA00023753116900001315
by
Figure BDA00023753116900001316
Respectively obtaining the products of the first step and the second step,
Figure BDA00023753116900001317
and
Figure BDA00023753116900001318
Figure BDA00023753116900001319
step S33:calculating V (t) by using a difference operator discrete method for the track of the closed-loop fuzzy system (13), and according to the attribute of an increment operator: for any function of time x (t) and
Figure BDA00023753116900001320
where t is the sampling period, there are:
Figure BDA00023753116900001321
obtaining:
Figure BDA00023753116900001322
Figure BDA0002375311690000141
Figure BDA0002375311690000142
Figure BDA0002375311690000143
for any constant semi-positive definite symmetric matrix W, two positive integers r and r0R is more than or equal to r0Not less than 1, the following equation holds,
Figure BDA0002375311690000144
calculating V by using difference operator discrete method5(t), and according to equations (17) and (22), one can obtain:
Figure BDA0002375311690000145
for a positive definite symmetric matrix P, it can be obtained from equation (13):
Figure BDA0002375311690000146
0< γ <1 is defined and the following indices are considered:
Figure BDA0002375311690000147
in the formula (I), the compound is shown in the specification,
Figure BDA0002375311690000148
Figure BDA0002375311690000151
wherein the content of the first and second substances,
Figure BDA0002375311690000152
Figure BDA0002375311690000153
Figure BDA0002375311690000154
let Ψ in equation (25)1(t)<0 applying the cone-complement theorem, the following matrix inequality holds:
Ψ(t)<0， (28)
wherein the content of the first and second substances,
Figure BDA0002375311690000155
Figure BDA0002375311690000156
Figure BDA0002375311690000157
Figure BDA0002375311690000158
Figure BDA0002375311690000159
by Ψ1(t)<0 can give
Figure BDA00023753116900001510
Step S34: given a matrix of 0<X＝XT，0<Y＝YT，
Figure BDA0002375311690000161
Figure BDA0002375311690000162
Matrix array
Figure BDA0002375311690000163
And a positive scalar e1，∈2I, j ═ 1, 2.., r; by using a matrix decoupling technology, the nonlinear matrix inequality in the formula (28) is converted into a linear matrix inequality, and the inequality of the formula (28) is rewritten as follows:
Figure BDA0002375311690000164
given a matrix of appropriate size Q, H, E and R ═ RT>0, and Q and R are symmetrical, and R>0, having:
Q+HF(t)E+ETFT(t)HT<0， (31)
for F (t) satisfies FT(t) F (t). ltoreq.R, there being one and only one scalar ε>0, the following results are obtained:
Figure BDA0002375311690000165
according to the formulas (31), (32), the method is applied to psiii<0, the following inequality can be obtained:
Figure BDA0002375311690000166
Ψii<0, and a plurality of groups consisting of,
Figure BDA0002375311690000167
Figure BDA0002375311690000168
Figure BDA0002375311690000169
Figure BDA00023753116900001610
Figure BDA0002375311690000171
Figure BDA0002375311690000172
p is divided into the following:
Figure BDA0002375311690000173
definition of
Figure BDA0002375311690000174
And a slave SWTI-XY, available:
Figure BDA0002375311690000175
definition Γ ═ diag { H1，H1，H1，H1，H1I, I }, left-and right-multiplying Γ for equation (41)TAnd Γ, can be obtained in formula (33)
Figure BDA0002375311690000176
The variables of (c) are:
Figure BDA0002375311690000177
step S35: the same process can obtain psiijAnd Ψji. The following matrix inequality holds:
Figure BDA0002375311690000178
and is provided with a plurality of groups of the materials,
Figure BDA0002375311690000179
Figure BDA00023753116900001710
wherein the content of the first and second substances,
Figure BDA00023753116900001711
Figure BDA0002375311690000181
Figure BDA0002375311690000182
Figure BDA0002375311690000183
Figure BDA0002375311690000184
Figure BDA0002375311690000185
Figure BDA0002375311690000186
Figure BDA0002375311690000187
Figure BDA0002375311690000188
Figure BDA0002375311690000189
robust dynamic output feedback controller
Figure BDA00023753116900001810
As formula (10), the parameters are:
Figure BDA00023753116900001811
Figure BDA0002375311690000191
Figure BDA0002375311690000192
Figure BDA0002375311690000193
where S and W are two non-singular matrices, the following equation is satisfied:
SW＝I-XY-1。 (42)
the above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.

Claims (4)

1. A photovoltaic power generation system dynamic output feedback control method based on difference operator dispersion is characterized by comprising the following steps:
step S1: constructing a solar photovoltaic power generation system;
step S2: expressing the nonlinear dynamics of the system by a T-S fuzzy method, and establishing a fuzzy system model of the solar photovoltaic power generation system;
step S3: according to the obtained fuzzy system model, based on the Lyapunov function stability principle, a dynamic output feedback controller with a difference operator is designed to realize the stable operation of the system of the photovoltaic power generation system under the condition of high-speed sampling
Figure FDA0002375311680000015
And (4) performance.
2. The differential operator discretization-based photovoltaic power generation system dynamic output feedback control method according to claim 1, characterized in that: the solar photovoltaic power generation system comprises a photovoltaic cell, a DC bus, a DC/DC module, a dynamic output feedback controller and a direct current load; the DC/DC module is respectively connected with the photovoltaic cell, the dynamic output feedback controller and the direct current load; and the dynamic output feedback controller is connected to a connecting line of the DC/DC module and the load through a DC bus.
3. The differential operator discretization-based photovoltaic power generation system dynamic output feedback control method according to claim 1, wherein the step S2 specifically comprises:
step S21, establishing a physical model of the solar photovoltaic power generation system, as shown in formula (1):
Figure FDA0002375311680000011
in the formula, phiPV，vPV，φLAnd V0Respectively representing the output current of the photovoltaic cell, the output voltage of the photovoltaic cell, the current flowing through the inductor L and the regulated output voltage; cPVA capacitance on the photovoltaic power generation side; r0，RLAnd RMAre respectively through a capacitor C0Inductor L, power field effect transistorA resistance on the tube; vDA forward voltage of the power diode; phi is a0Is a measurable load current; and u is a control signal of the switch pulse width of the power field effect transistor.
Step S22: definition e0＝V0-VrefSo that the output voltage V is0Close to the reference output voltage VrefAnd, under the condition of high sampling rate, defining fuzzy antecedent variable based on difference operator model theory method
Figure FDA0002375311680000012
θ3＝vPV,θ4＝φLThe following fuzzy rule is selected:
if theta is greater than theta1(t) is Mi1，θ2(t) is Mi2，θ3(t) is Mi3，θ4(t) is Mi4Establishing a fuzzy model system as follows:
Figure FDA0002375311680000013
wherein x (t) ═ vPVφLe0]T，
Figure FDA0002375311680000014
u (t) and ω (t) are the control input and the disturbance input, respectively; the regulated output z (t) being the output voltage V0；MijIs a fuzzy set, r is a fuzzy rule; delta CiIs a matrix of uncertainty parameters, Δ C, of the signal outputdiIs an uncertainty parameter matrix of the delayed output of the signal, DωiIs an interference parameter matrix of the output signal; b isωiIs a disturbance parameter matrix of the system. Theta (t) ([ theta ])1(t)，θ2(t)，θ3(t)，θ4(t)]Is a front-part variable; t is a sampling period, n (T) represents a delayed sampling period of the system (1) and satisfies the following:
nm≤n(t)≤nM， (3)
in the formula, nmAnd nMRespectively representing minimum and maximum delay sample periods; phi (t) is the sequence [ -n ]M0]The real-valued initial condition above;
Figure FDA0002375311680000021
is the difference operator of x (t), defined as follows:
Figure FDA0002375311680000022
furthermore, Ai，Adi，Bi，BωiThe system parameter matrix is as follows:
Figure FDA0002375311680000023
Figure FDA0002375311680000024
Ci，Cdi，Fiand DiIs a known constant matrix with appropriate dimensions, uncertainty matrix Δ Ai，ΔAdiAnd Δ CdiThe form of (A) is as follows:
Figure FDA0002375311680000025
in the formula M1i，M2i，E1iAnd E2iIs a known matrix of appropriate dimensions, Δ (t) is an unknown real variant matrix that satisfies:
Figure FDA0002375311680000026
and is
Figure FDA0002375311680000027
Wherein J is aGiven the matrix of constants of the appropriate dimensions,
Figure FDA0002375311680000028
an unknown real-valued time-varying matrix function that satisfies:
Figure FDA0002375311680000029
step S23: by binding fuzzy rules, a fuzzy model based on the difference operator photovoltaic nonlinear system (1) is obtained as follows:
Figure FDA00023753116800000210
wherein the content of the first and second substances,
Figure FDA0002375311680000031
in the formula (I), the compound is shown in the specification,
Figure FDA0002375311680000032
and is
Figure FDA0002375311680000033
Figure FDA0002375311680000034
Middle Mij(θj(t)) represents MijMiddle thetaj(t) membership function, hereinafter by λi(θ (t)) is simply denoted as λi。
4. The differential operator discretization-based photovoltaic power generation system dynamic output feedback control method according to claim 1, wherein the step S3 specifically comprises:
step S31: according to the photovoltaic fuzzy system model in (10), introducing the following fuzzy dynamic output feedback controller based on a difference operator discrete method:
Figure FDA0002375311680000035
wherein
Figure FDA0002375311680000036
Is a state variable of the dynamic output feedback controller,
Figure FDA0002375311680000037
Figure FDA0002375311680000038
is the gain of the dynamic output feedback controller to be designed, the closed-loop fuzzy control system which can be expanded by the formulas (10) and (12) is as follows:
Figure FDA0002375311680000039
wherein the content of the first and second substances,
Figure FDA00023753116800000310
and:
Figure FDA00023753116800000311
step S32: given a matrix of 0<P＝PT，
Figure FDA00023753116800000312
0<Q＝QT，
Figure FDA00023753116800000313
Figure FDA00023753116800000314
And a forward scalar ε, establishing a Lyapunov function as follows:
V(t)＝V1(t)+V2(t)+V3(t)+V4(t)+V5(t)， (15)
wherein:
Figure FDA00023753116800000315
Figure FDA00023753116800000316
Figure FDA00023753116800000317
Figure FDA0002375311680000041
Figure FDA0002375311680000042
by
Figure FDA0002375311680000043
Respectively obtaining the products of the first step and the second step,
Figure FDA0002375311680000044
and
Figure FDA0002375311680000045
Figure FDA0002375311680000046
step S33: calculating V (t) by using a difference operator discrete method for the track of the closed-loop fuzzy system (13), and according to the attribute of an increment operator: for any function of time x (t) and
Figure FDA0002375311680000047
where t is the sampling period, there are:
Figure FDA0002375311680000048
obtaining:
Figure FDA0002375311680000049
Figure FDA00023753116800000410
Figure FDA00023753116800000411
Figure FDA00023753116800000412
for any constant semi-positive definite symmetric matrix W, two positive integers r and r0R is more than or equal to r0Not less than 1, the following equation holds,
Figure FDA0002375311680000051
calculating V by using difference operator discrete method5(t), and according to equations (17) and (22), one can obtain:
Figure FDA0002375311680000052
for a positive definite symmetric matrix P, it can be obtained from equation (13):
Figure FDA0002375311680000053
0< γ <1 is defined and the following indices are considered:
Figure FDA0002375311680000054
in the formula (I), the compound is shown in the specification,
Figure FDA0002375311680000055
Figure FDA0002375311680000056
wherein the content of the first and second substances,
Figure FDA0002375311680000057
Figure FDA0002375311680000058
Figure FDA0002375311680000059
let Ψ in equation (25)1(t)<0 applying the cone-complement theorem, the following matrix inequality holds:
Ψ(t)<0， (28)
wherein the content of the first and second substances,
Figure FDA0002375311680000061
Figure FDA0002375311680000062
Figure FDA0002375311680000063
Figure FDA0002375311680000064
Figure FDA0002375311680000065
by Ψ1(t)<0 can getGo out
Figure FDA0002375311680000066
Step S34: given a matrix of 0<X＝XT，0<Y＝YT，
Figure FDA0002375311680000067
Figure FDA0002375311680000068
Matrix array
Figure FDA0002375311680000069
And a positive scalar e1，∈2I, j ═ 1, 2.., r; by using a matrix decoupling technology, the nonlinear matrix inequality in the formula (28) is converted into a linear matrix inequality, and the inequality of the formula (28) is rewritten as follows:
Figure FDA00023753116800000610
given a matrix of appropriate size Q, H, E and R ═ RT>0, and Q and R are symmetrical, and R>0, having:
Q+HF(t)E+ETFT(t)HT<0， (31)
for F (t) satisfies FT(t) F (t). ltoreq.R, there being one and only one scalar ε>0, the following results are obtained:
Figure FDA00023753116800000611
according to the formulas (31), (32), the method is applied to psiii<0, the following inequality can be obtained:
Figure FDA0002375311680000071
Ψii<0, and a plurality of groups consisting of,
Figure FDA0002375311680000072
Figure FDA0002375311680000073
Figure FDA0002375311680000074
Figure FDA0002375311680000075
Figure FDA0002375311680000076
Figure FDA0002375311680000077
p is divided into the following:
Figure FDA0002375311680000078
definition of
Figure FDA0002375311680000079
And a slave SWTI-XY, available:
Figure FDA00023753116800000710
definition Γ ═ diag { H1，H1，H1，H1，H1I, I }, left-and right-multiplying Γ for equation (41)TAnd Γ, can be obtained in formula (33)
Figure FDA00023753116800000711
Variation of variable (2)Comprises the following steps:
Figure FDA00023753116800000712
Figure FDA0002375311680000081
step S35: the same process can obtain psiijAnd Ψji. The following matrix inequality holds:
Figure FDA0002375311680000082
and is provided with a plurality of groups of the materials,
Figure FDA0002375311680000083
Figure FDA0002375311680000084
wherein the content of the first and second substances,
Figure FDA0002375311680000085
Figure FDA0002375311680000086
Figure FDA0002375311680000087
Figure FDA0002375311680000088
Figure FDA0002375311680000089
Figure FDA00023753116800000810
Figure FDA0002375311680000091
Figure FDA0002375311680000092
Figure FDA0002375311680000093
Figure FDA0002375311680000094
robust dynamic output feedback controller
Figure FDA0002375311680000095
As formula (10), the parameters are:
Figure FDA0002375311680000096
Figure FDA0002375311680000097
Figure FDA0002375311680000098
Figure FDA0002375311680000099
where S and W are two non-singular matrices, the following equation is satisfied:
SW＝I-XY-1(42)。
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