CN106707763B - The fuzzy neural overall situation fast terminal sliding-mode control of photovoltaic combining inverter - Google Patents
The fuzzy neural overall situation fast terminal sliding-mode control of photovoltaic combining inverter Download PDFInfo
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Abstract
The invention discloses a kind of fuzzy neural overall situation fast terminal sliding-mode controls of photovoltaic combining inverter, initially set up inverter mathematical model, then consider the interference and uncertainty of physical presence in inverter, are modified to inverter model.The control target of voltage-controlled type gird-connected inverter is no error following of the inverter output voltage to power grid reference voltage, and in order to make tracking error converge to zero within the limited time, the present invention is using global fast terminal sliding mode control strategy.The present invention carries out online compensation for uncertainty present in system, using Fuzzy Neural Network System, so that inverter has certain adaptability to external interference, greatly strengthens the robustness of system.The adaptive law based on Lyapunov is designed, ensure that the stability of system.The present invention controls gird-connected inverter using fuzzy neural overall situation fast terminal sliding mode control strategy, so that system robustness enhances, and control law buffeting is small.
Description
Technical Field
The invention relates to a fuzzy neural global fast terminal sliding mode control method for a photovoltaic grid-connected inverter, belongs to the technical field of inverter control methods, and particularly relates to a fuzzy neural network and terminal sliding mode control.
Background
With the strong support of the state on the photovoltaic power generation industry, the photovoltaic power generation technology is researched to be more and more hot, the photovoltaic electric energy gradually plays an important role in the distribution of electric energy, the inverter is an indispensable part of a photovoltaic power generation system, the photovoltaic system is easy to be influenced by environmental changes, and higher requirements are provided for the control of the inverter.
The inverter is a power device for converting direct current into alternating current, the current common control strategy is a current type control strategy, and the grid-connected current and the grid voltage are controlled to have the same frequency and phase. In recent years, voltage-type control strategies have been proposed by scholars, the aim of which is to control the inverter output voltage so as to be consistent with the grid voltage, including magnitude, amplitude and phase. The traditional control modes such as PID, PR, hysteresis loop, droop control and the like have unsatisfactory control effects, and the system robustness is weak.
The sliding mode control is a nonlinear control method, the design of the sliding mode is independent of object parameters and disturbance, the sliding mode control is insensitive to parameter change and disturbance, and the physical realization is simple. Conventional sliding mode control does not allow the tracking error to converge to zero in a limited time. The terminal sliding mode strategy enables the system tracking error to be converged to zero in a limited time by introducing a nonlinear term into a sliding mode surface. The common terminal sliding mode control has a faster convergence speed when being far away from a balance point, but the convergence speed becomes slower when being close to the balance point, and the global fast terminal sliding mode control can have global rapidity. The fuzzy neural network integrates fuzzy logic and a neural network structure and has strong self-adaptive learning capacity.
Disclosure of Invention
The purpose is as follows: in order to overcome the defects in the prior art, the global fast terminal control is introduced into the inverter, and then the fuzzy neural network is used for compensating the uncertainty of the system on line, the fuzzy neural global fast terminal sliding mode control method of the photovoltaic grid-connected inverter is provided, so that the robustness of the inverter is greatly enhanced, and the grid-connected performance is improved.
The technical scheme is as follows: in order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a fuzzy neural global fast terminal sliding mode control method of a photovoltaic grid-connected inverter comprises the following steps:
firstly, establishing a mathematical model of a photovoltaic grid-connected inverter according to a circuit theorem;
step two, correcting the mathematical model of the photovoltaic grid-connected inverter by considering external interference and uncertainty;
step three, designing a global fast terminal sliding mode surface;
designing a control law, and aiming at the uncertainty existing in the system, carrying out online approximation by adopting a fuzzy neural network;
step five, compensating the network approximation error in the step four;
designing a self-adaptive law to obtain a control law equation of the fuzzy neural global fast terminal sliding mode controller;
and seventhly, generating a control signal by using the obtained control law equation, and controlling each power switch tube of the inverter.
The mathematical model of the photovoltaic grid-connected inverter in one period in the step one is as follows:
wherein u isdcIs the DC side voltage of the inverter uacFor inverter grid-connected voltage, D is a switch tube S in diagonal relation on the inverter1、S4Duty ratio, Cac、LacRespectively, an AC side capacitor and an inductor, RLIs an AC side load.
The corrected mathematical model of the photovoltaic grid-connected inverter in the second step is as follows:
wherein g (t) is a lumped parameter, which represents the uncertainty of the system and the external interference, hereinafter referred to as uncertainty.
In the third step, the overall rapid terminal sliding mode surface is as follows:
wherein e ═ uac-uacrFor voltage tracking error, uacFor the inverter output voltage uacrIs a grid reference voltage; alpha and beta are positive integers, and p and q (p > q) are positive odd numbers.
In the fourth step, the control law of the sliding mode controller is as follows:
wherein g is the system uncertainty, k · s is the linear compensation term, and k is a normal number;
the method adopts a fuzzy neural network to approximate the system uncertainty, and specifically comprises the following steps:
wherein,as an estimate of uncertainty g, w is the network connection weight, ξ is a function of the network input layer to the rule layer.
In the fifth step, the network approximation error is compensated, specifically:
-εmsgn(s) (6)
wherein epsilonmFor net approximation error upper bound, sgn is a sign function.
The adaptive law is as follows:
wherein,the derivative of the estimated value of the optimal connection weight is r is a normal number, s is a sliding mode function, and xi is a function vector from the network input layer to the regular layer;
the control law of the final fuzzy neural global fast terminal sliding mode controller is as follows:
whereinEstimation for uncertainty gThe values of, among others,xi is a function from the network input layer to the rule layer as an estimated value of the optimal connection weight; epsilonmFor net approximation error upper bound, sgn is a sign function.
The seventh step comprises the following steps: generating 4 paths of PWM wave control signals by comparing with a triangular carrier according to the duty ratio D obtained in the step six, wherein S1,S4Duty ratio of D, S2,S3The duty ratio is 1-D, the on-off of 4 switching tubes of the inverter is controlled, the DC-AC conversion is realized, and the grid connection is completed.
Has the advantages that: according to the fuzzy neural global fast terminal sliding mode control method of the photovoltaic grid-connected inverter, due to the fact that global fast terminal sliding mode control is used, the inverter can converge to zero on a power grid voltage tracking error within a limited time; the fuzzy neural network compensates the uncertainty of the system on line, the robustness of the system is enhanced, and the buffeting of the system is small; in addition, grid-connected voltage is insensitive to the change of direct current side voltage, so that large capacitor is not needed to stabilize the direct current side voltage, and cost is saved.
Drawings
Fig. 1 is a schematic diagram of a main circuit structure according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of a method according to an embodiment of the present invention.
Fig. 3 is a graph showing the effect of inverter voltage tracking.
Fig. 4 shows a voltage tracking error graph.
Fig. 5 is a graph showing a change in the control amount D.
Fig. 6 shows a grid-connected voltage spectrum diagram of the inverter.
In FIG. 1, S1-S4Power switching tube, udc-DC side voltage uac-inverter output voltage uacrGrid reference voltage, CdcDC side capacitance, LacAC side inductance of inverter, Cac-AC side capacitance of inverter, RLNegativeAnd (4) loading.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
Referring to fig. 1, the inverter control structure of the present invention uses an H-bridge structure consisting of 4 power switching transistors S1、S2、S3、S4And (4) forming. CdcIs a DC side capacitor, Cac、LacRespectively, an AC side capacitor and an inductor, RLIs an AC side load. And the tracking of the output voltage of the inverter to the voltage of the power grid is completed by controlling the duty ratio of the power switching tube.
Referring to fig. 2, a fuzzy neural global fast terminal sliding mode control method for a photovoltaic grid-connected inverter includes the following steps:
firstly, establishing a mathematical model of a photovoltaic grid-connected inverter according to a circuit theorem;
step two, correcting the mathematical model of the photovoltaic grid-connected inverter by considering external interference and uncertainty;
step three, designing a global fast terminal sliding mode surface;
designing a control law, and aiming at the uncertainty existing in the system, carrying out online approximation by adopting a fuzzy neural network;
step five, compensating the network approximation error in the step four;
designing a self-adaptive law to obtain a control law equation of the fuzzy neural global fast terminal sliding mode controller;
and seventhly, generating a control signal by using the obtained control law equation, and controlling each power switch tube of the inverter.
Example one
(1) According to a circuit theory, a circuit equation when two groups of switching tubes are respectively conducted is established, and an average mathematical model of the inverter is established by adopting a state space average method:
wherein u isacFor inverter grid-connected voltage udcIs DC side voltage, and D is a switch tube S in diagonal relation on the inverter1、S4Duty ratio, Cac、LacRespectively, an AC side capacitor and an inductor, RLIs an AC side load.
In actual operation, the inverter is influenced by modeling errors and external interference, and the mathematical model of the photovoltaic grid-connected inverter in the formula (1) needs to be corrected. The actual inverter mathematical model considering the system uncertainty and the external interference is as follows:
wherein g (t) is the system uncertainty and the external interference.
(2) Design global fast terminal sliding mode control law
Defining a global fast terminal sliding mode surface as
Wherein e ═ uac-uacrFor voltage tracking error, uacFor the inverter output voltage uacrIs the grid reference voltage. Alpha and beta are positive integers, and p and q (p > q) are positive odd numbers.
To ensure the stability of the system, the Lyapunov function is defined as
According to the Lyapunov stability theorem, the control system needs to be stabilized
The control law based on the Lyapunov stability theorem is designed as
Wherein g is the system uncertainty, k · s is the linear compensation term, and k is a normal number. At this timeThe system is stable.
(3) Fuzzy neural network compensation system uncertainty
Since the system uncertainty is not known, i.e., the control law in equation (4) cannot be realized, the system uncertainty is estimated using a fuzzy neural system.
Wherein,as an estimate of uncertainty g, w is the network connection weight, ξ is a function of the network input layer to the rule layer.
(4) Compensating the network approximation error, specifically:
-εmsgn(s) (6)
wherein epsilonmFor net approximation error upper bound, sgn is a sign function.
(5) Self-adaptive law designed based on Lyapunov stability theorem
To demonstrate system stability, the Lyapunov function is redefined as
WhereinFor weight estimation error, w*The weight value of the network is optimized,for the best weight estimation, r is a normal number.
The self-adaptive law based on the Lyapunov stability theorem is designed as
Wherein,the derivative of the estimated value of the optimal connection weight is r is a normal number, s is a sliding mode function, and xi is a function vector from the network input layer to the regular layer;
(6) the final control law equation of the fuzzy neural global fast terminal sliding mode controller is as follows:
whereinIs an estimate of the uncertainty, g, where,ξ is a function of the network input layer to the rule layer for an estimate of the optimal connection weights. EpsilonmFor net approximation error upper bound, sgn is a sign function. At this time, the process of the present invention,
the control system is stable according to the Lyapunov stability theorem.
(7) Generating 4 paths of PWM wave control signals (wherein S is the same as the PWM wave control signals) by comparing with the triangular carrier according to the duty ratio D obtained in the step 61,S4Duty ratio of D, S2,S3The duty ratio is 1-D), the on-off of 4 switching tubes of the inverter is controlled, the DC-AC conversion is realized, and the grid connection is completed.
(8) Through simulation, the invention is verified:
a simulation model is established in Matlab/Simulink, a circuit model is shown in figure 1, an inverter direct current side is connected with a photovoltaic system, simulation results are shown in figures 3 to 6,
as shown in fig. 3, the inverter output voltage quickly tracks the upper grid voltage and coincides with its waveform.
As shown in fig. 4, the inverter voltage tracking error converges to 0 quickly.
As shown in fig. 5, the waveform of the control amount D is smooth and the chattering is small.
As shown in fig. 6, from the frequency spectrum of the inverter grid-connected voltage, it is seen that the THD is only 0.03%, and the harmonic content is very low.
In summary, the following steps:
according to the invention, as global fast terminal sliding mode control is used, the voltage tracking error of the inverter is quickly converged to 0; the fuzzy neural network is used for compensating the uncertainty of the system on line, the robustness of the system is enhanced, and the buffeting of the system is small; in addition, grid-connected voltage is insensitive to the change of direct current side voltage, so that large capacitor is not needed to stabilize the direct current side voltage, and cost is saved.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (1)
1. A fuzzy neural global fast terminal sliding mode control method of a photovoltaic grid-connected inverter is characterized by comprising the following steps: the method comprises the following steps:
firstly, establishing a mathematical model of a photovoltaic grid-connected inverter according to a circuit theorem;
step two, correcting the mathematical model of the photovoltaic grid-connected inverter by considering external interference and uncertainty;
step three, designing a global fast terminal sliding mode surface;
designing a control law, and aiming at the uncertainty existing in the system, carrying out online approximation by adopting a fuzzy neural network;
step five, compensating the network approximation error in the step four;
designing a self-adaptive law to obtain a control law equation of the fuzzy neural global fast terminal sliding mode controller;
seventhly, generating a control signal by using the obtained control law equation, and controlling each power switching tube of the inverter;
the mathematical model of the photovoltaic grid-connected inverter in one period in the step one is as follows:
wherein u isdcIs the DC side voltage of the inverter uacFor inverter grid-connected voltage, D is a switch tube S in diagonal relation on the inverter1、S4Duty ratio, Cac、LacRespectively, an AC side capacitor and an inductor, RLIs an AC side load;
the corrected mathematical model of the photovoltaic grid-connected inverter in the second step is as follows:
wherein g (t) is a lumped parameter, which represents the uncertainty of the system and the external interference, hereinafter referred to as uncertainty;
in the third step, the overall rapid terminal sliding mode surface is as follows:
wherein e ═ uac-uacrFor voltage tracking error, uacFor the inverter output voltage uacrIs a grid reference voltage; alpha and beta are positive integers, and p and q (p is more than q) are positive odd numbers;
in the fourth step, the control law of the sliding mode controller is as follows:
wherein g is the system uncertainty, k · s is the linear compensation term, and k is a normal number;
the method adopts a fuzzy neural network to approximate the system uncertainty, and specifically comprises the following steps:
wherein,is an estimated value of uncertainty g, w is the network connection weight, ξ is a function from the network input layer to the rule layer;
in the fifth step, the network approximation error is compensated, specifically:
-εmsgn(s) (6)
wherein epsilonmFor the upper bound of the network approximation error, sgn is a sign function;
the adaptive law is as follows:
wherein,the derivative of the estimated value of the optimal connection weight is r is a normal number, s is a sliding mode function, and xi is a function vector from the network input layer to the regular layer;
the control law of the final fuzzy neural global fast terminal sliding mode controller is as follows:
whereinIs an estimate of the uncertainty, g, where,xi is a function from the network input layer to the rule layer as an estimated value of the optimal connection weight; epsilonmFor the upper bound of the network approximation error, sgn is a sign function;
the seventh step comprises the following steps: generating 4 paths of PWM wave control signals by comparing with a triangular carrier according to the duty ratio D obtained in the step six, wherein S1,S4Duty ratio of D, S2,S3The duty ratio is 1-D, the on-off of 4 switching tubes of the inverter is controlled, the DC-AC conversion is realized, and the grid connection is completed.
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| CN107102553B (en) * | 2017-06-16 | 2019-10-29 | 国网重庆市电力公司电力科学研究院 | Control method of grid-connected inverter based on Time-delay Robust model algorithm |
| CN107482677B (en) * | 2017-08-15 | 2020-04-28 | 河海大学常州校区 | Fuzzy sliding mode control method for photovoltaic grid-connected inverter based on disturbance observer |
| CN108429475B (en) * | 2018-02-11 | 2020-02-18 | 东南大学 | Grid-connected inverter control method for wave power generation system |
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| CN111812984B (en) * | 2020-07-20 | 2022-06-03 | 温州大学 | Model-based robust filtering method for inverter control system |
| CN111948946B (en) * | 2020-08-24 | 2022-05-17 | 淮阴工学院 | Robust evaluation inversion control system based on HJI theory and design method thereof |
| CN113359938B (en) * | 2021-06-23 | 2022-08-12 | 安徽工业大学 | Photovoltaic system maximum power point tracking method based on nonsingular rapid terminal sliding mode control |
| CN115833140A (en) * | 2022-11-15 | 2023-03-21 | 中科芯集成电路有限公司 | Self-adaptive droop control method for parallel inverter system |
| CN116125803B (en) * | 2022-12-28 | 2024-06-11 | 淮阴工学院 | Inverter backstepping fuzzy neural network control method based on extreme learning machine |
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