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 present invention relates to a kind of fuzzy neural overall situation fast terminal sliding-mode controls of photovoltaic combining inverter, belong to inverse
Become device technical field of control method, and in particular to fuzzy neural network, TSM control.
Background technique
Support energetically with country to photovoltaic power generation industry, photovoltaic power generation technology study more and more burning hoter, photovoltaic electric energy
Gradually in electric power energy dispensing in occupation of important function, inverter is the indispensable a part of photovoltaic generating system, light
The characteristics of volt system is influenced vulnerable to environmental change, proposes higher requirement for inverter control.
Inverter is a kind of power equipment by converting direct-current power into alternating-current power, and currently used control strategy is current mode
Control strategy, by control grid-connected current and network voltage with the same phase of frequency.In recent years, scholar proposes voltage mode control strategy,
Its target is control inverter output voltage, is consistent it with network voltage, including size, amplitude and phase.Traditional
The control effects such as control mode such as PID, PR, stagnant ring, sagging control are unsatisfactory, and system robustness is weaker.
Sliding formwork control is a kind of nonlinear control method, and the design of sliding mode is unrelated with image parameter and disturbance, sliding
Mould control is insensitive to Parameters variation and disturbance, and physics realization is simple.Conventional sliding formwork control can not be such that tracking error is having
Zero is converged in the time of limit.Terminal sliding mode strategy enables system tracking error by introducing nonlinear terms in sliding-mode surface
Zero is converged within the limited time.Ordinary terminal sliding formwork control has faster convergence rate when far from equalization point, but leans on
Convergence rate is slack-off when nearly equalization point, and global fast terminal sliding formwork control can have global rapidity.Fuzzy neural network is comprehensive
Fuzzy logic and neural network structure have very strong adaptive learning ability.
Summary of the invention
Purpose: in order to overcome the deficiencies in the prior art, the control of global fast terminal is introduced into inverter, so
Fuzzy neural network online compensation systematic uncertainty is used afterwards, and the fuzzy neural for providing a kind of photovoltaic combining inverter is global fast
Fast TSM control method improves grid-connected performance so that inverter robustness greatly enhances.
Technical solution: in order to solve the above technical problems, the technical solution adopted by the present invention are as follows:
A kind of fuzzy neural overall situation fast terminal sliding-mode control of photovoltaic combining inverter, comprising the following steps:
Step 1: establishing photovoltaic combining inverter mathematical model according to Circuit Theorem；
Step 2: considering external interference and uncertainty, photovoltaic combining inverter mathematical model is modified；
Step 3: designing global fast terminal sliding-mode surface；
Step 4: design control law, and for uncertainty present in system, it is carried out using fuzzy neural network online
It approaches；
Step 5: being compensated to network approximate error in step 4；
Step 6: design adaptive law, obtains the control law equation of fuzzy neural overall situation fast terminal sliding mode controller；
Step 7: utilizing gained control law equation, control signal is generated, each power switch pipe of inverter is controlled.
Photovoltaic combining inverter mathematical model in a cycle in the step 1 are as follows:
Wherein, udcFor DC side voltage of converter, uacFor grid-connected inverters voltage, D is on inverter in diagonal relationship
Switching tube S1、S4Duty ratio, Cac、LacRespectively inverter ac lateral capacitance and inductance, RLTo exchange lateral load.
Revised photovoltaic combining inverter mathematical model in the step 2 are as follows:
Wherein, g (t) is lumped parameter, indicates systematic uncertainty and external interference, hereinafter referred to as uncertain.
In the step 3, global fast terminal sliding-mode surface are as follows:
Wherein, e=uac-uacrFor valtage following, uacFor inverter output voltage, uacrFor power grid reference voltage；α,
β is normal number, and p, q (p > q) are positive odd number.
In the step 4, the control law of sliding mode controller are as follows:
Wherein, g is systematic uncertainty, and ks is linear compensation item, and k is a normal number；
It is uncertain using fuzzy neural network approximation system, specifically:
Wherein,For the estimated value of uncertain g, w is network connection power, function of the ξ from network input layer to rules layer.
In the step 5, network approximate error is compensated, specifically:
-εmsgn(s) (6)
Wherein, εmFor the network approximate error upper bound, sgn is sign function.
The adaptive law are as follows:
Wherein,For the derivative of the estimated value of optimal connection weight, r is a normal number, and s is sliding formwork function, and ξ is network inputs
Layer arrives the functional vector of rules layer；
The control law of final fuzzy neural overall situation fast terminal sliding mode controller are as follows:
WhereinFor the estimated value of uncertain g, whereinFor the estimated value of optimal connection weight, ξ is from network
Function of the input layer to rules layer；εmFor the network approximate error upper bound, sgn is sign function.
The step 7 includes the following steps: according to duty ratio D obtained by step 6, by generating 4 compared with triangular carrier
Road PWM wave controls signal, wherein S1,S4Duty ratio is D, S2,S3Duty ratio is 1-D, controls the on-off of 4 switching tubes of inverter,
Realize that DC-AC is converted and completed grid-connected.
The utility model has the advantages that the fuzzy neural overall situation fast terminal sliding formwork control side of photovoltaic combining inverter provided by the invention
Method, due to having used global fast terminal sliding formwork control, so that inverter receives network voltage tracking error in finite time
It holds back to zero；Fuzzy neural network online compensation systematic uncertainty, enhances system robustness, and system chatter is small；In addition, simultaneously
Net voltage is insensitive to the variation of DC voltage, stable DC side voltage need not be thus carried out using bulky capacitor, to save
Cost.
Detailed description of the invention
Fig. 1 show a kind of main circuit structure schematic diagram of specific implementation of the present invention.
Fig. 2 show a kind of method structural schematic diagram of specific implementation of the present invention.
Fig. 3 show contravarianter voltage tracking effect figure.
Fig. 4 show valtage following figure.
Fig. 5 show control amount D variation diagram.
Fig. 6 is shown with grid-connected inverters voltage spectrum figure.
In Fig. 1, S1-S4- power switch pipe, udc- DC voltage, uac- inverter output voltage, uacr- power grid ginseng
Examine voltage, Cdc- DC bus capacitor, Lac- inverter ac side inductance, Cac- inverter ac lateral capacitance, RL- load.
Specific embodiment
The present invention will be further explained with reference to the accompanying drawing.
Referring to Fig. 1, inverter control structures of the invention use H bridge structure, by 4 power switch pipe S1、S2、S3、S4Structure
At.CdcFor DC bus capacitor, Cac、LacRespectively inverter ac lateral capacitance and inductance, RLTo exchange lateral load.Pass through control
Power switch pipe duty ratio completes tracking of the inverter output voltage to network voltage.
Referring to fig. 2, the fuzzy neural overall situation fast terminal sliding-mode control of a kind of photovoltaic combining inverter, including it is following
Step:
Step 1: establishing photovoltaic combining inverter mathematical model according to Circuit Theorem；
Step 2: considering external interference and uncertainty, photovoltaic combining inverter mathematical model is modified；
Step 3: designing global fast terminal sliding-mode surface；
Step 4: design control law, and for uncertainty present in system, it is carried out using fuzzy neural network online
It approaches；
Step 5: being compensated to network approximate error in step 4；
Step 6: design adaptive law, obtains the control law equation of fuzzy neural overall situation fast terminal sliding mode controller；
Step 7: utilizing gained control law equation, control signal is generated, each power switch pipe of inverter is controlled.
Embodiment one
(1) according to Circuit theory, circuit equation when two groups of switching tubes are respectively turned on, adoption status space average are established
Method establishes inverter and is averaged mathematical model:
Wherein, uacFor grid-connected inverters voltage, udcFor DC voltage, D is the switching tube on inverter in diagonal relationship
S1、S4Duty ratio, Cac、LacRespectively inverter ac lateral capacitance and inductance, RLTo exchange lateral load.
It, need to be to the grid-connected of (1) formula since in actual motion, inverter is influenced by modeling error and external interference
The amendment of inverter mathematical model.Consider the actual inverter mathematical model of systematic uncertainty and external interference are as follows:
Wherein, g (t) is systematic uncertainty and external interference.
(2) global fast terminal sliding formwork control ratio is designed
Defining global fast terminal sliding-mode surface is
Wherein, e=uac-uacrFor valtage following, uacFor inverter output voltage, uacrFor power grid reference voltage.α,
β is normal number, and p, q (p > q) are positive odd number.
In order to guarantee the stability of system, defining Lyapunov function is
According to Lyapunov stability theorem, stablize control system, need
Designing the control law based on Lyapunov stability theorem is
Wherein, g is systematic uncertainty, and ks is linear compensation item, and k is a normal number.At this time
System is stablized.
(3) fuzzy neural network compensation system is uncertain
Since systematic uncertainty can not be known, i.e., the control law in formula (4) cannot achieve, therefore use neural fuzzy system
Systematic uncertainty is estimated.
Wherein,For the estimated value of uncertain g, w is network connection power, function of the ξ from network input layer to rules layer.
(4) network approximate error is compensated, specifically:
-εmsgn(s) (6)
Wherein, εmFor the network approximate error upper bound, sgn is sign function.
(5) adaptive law based on Lyapunov stability theorem is designed
For proof system stability, redefining Lyapunov function is
WhereinFor weights estimation error, w*For network best initial weights,For the estimation of optimal power, r is one just
Constant.
Designing the adaptive law based on Lyapunov stability theorem is
Wherein,For the derivative of the estimated value of optimal connection weight, r is a normal number, and s is sliding formwork function, and ξ is network inputs
Layer arrives the functional vector of rules layer；
(6) the control law equation of final fuzzy neural overall situation fast terminal sliding mode controller are as follows:
WhereinFor the estimated value of uncertain g, whereinFor the estimated value of optimal connection weight, ξ is from network
Function of the input layer to rules layer.εmFor the network approximate error upper bound, sgn is sign function.At this point,
Known by Lyapunov stability theorem, control system is stablized.
(7) according to step 6 gained duty ratio D, by compared with triangular carrier, generating 4 tunnel PWM waves control signal (wherein
S1,S4Duty ratio is D, S2,S3Duty ratio is 1-D), the on-off of 4 switching tubes of inverter is controlled, realizes that DC-AC is converted and completed
It is grid-connected.
(8) by emulation, verifying is invented:
Simulation model is established in Matlab/Simulink, circuit model is as shown in Figure 1, inverter direct-flow side connects photovoltaic
System, simulation result is as shown in Figures 3 to 6,
Shown in Fig. 3, inverter output voltage tracks network voltage soon, and is overlapped with its waveform.
Shown in Fig. 4, contravarianter voltage tracking error converges to 0 quickly.
Shown in Fig. 5, control amount D waveform is smooth, buffets small.
Shown in Fig. 6, by grid-connected inverters voltage spectrum figure, it will be seen that THD is only 0.03%, and harmonic content is very low.
In summary:
The present invention is due to having used global fast terminal sliding formwork control, so that contravarianter voltage tracking error converges to quickly
0；Using fuzzy neural network online compensation systematic uncertainty, system robustness is enhanced, and system chatter is small；In addition, simultaneously
Net voltage is insensitive to the variation of DC voltage, just need not carry out stable DC side voltage using bulky capacitor in this way, to save
Cost.
The above is only a preferred embodiment of the present invention, it should be pointed out that: for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (1)
1. a kind of fuzzy neural overall situation fast terminal sliding-mode control of photovoltaic combining inverter, it is characterised in that: including with
Lower step:
Step 1: establishing photovoltaic combining inverter mathematical model according to Circuit Theorem；
Step 2: considering external interference and uncertainty, photovoltaic combining inverter mathematical model is modified；
Step 3: designing global fast terminal sliding-mode surface；
Step 4: design control law, and for uncertainty present in system, it is forced online using fuzzy neural network
Closely；
Step 5: being compensated to network approximate error in step 4；
Step 6: design adaptive law, obtains the control law equation of fuzzy neural overall situation fast terminal sliding mode controller；
Step 7: utilizing gained control law equation, control signal is generated, each power switch pipe of inverter is controlled；
Photovoltaic combining inverter mathematical model in a cycle in the step 1 are as follows:
Wherein, udcFor DC side voltage of converter, uacFor grid-connected inverters voltage, D is the switch on inverter in diagonal relationship
Pipe S1、S4Duty ratio, Cac、LacRespectively inverter ac lateral capacitance and inductance, RLTo exchange lateral load；
Revised photovoltaic combining inverter mathematical model in the step 2 are as follows:
Wherein, g (t) is lumped parameter, indicates systematic uncertainty and external interference, hereinafter referred to as uncertain；
In the step 3, global fast terminal sliding-mode surface are as follows:
Wherein, e=uac-uacrFor valtage following, uacFor inverter output voltage, uacrFor power grid reference voltage；α, β are positive
Constant, p, q (p > q) are positive odd number；
In the step 4, the control law of sliding mode controller are as follows:
Wherein, g is systematic uncertainty, and ks is linear compensation item, and k is a normal number；
It is uncertain using fuzzy neural network approximation system, specifically:
Wherein,For the estimated value of uncertain g, w is network connection power, function of the ξ from network input layer to rules layer；
In the step 5, network approximate error is compensated, specifically:
-εmsgn(s) (6)
Wherein, εmFor the network approximate error upper bound, sgn is sign function；
The adaptive law are as follows:
Wherein,For the derivative of the estimated value of optimal connection weight, r is a normal number, and s is sliding formwork function, and ξ arrives for network input layer
The functional vector of rules layer；
The control law of final fuzzy neural overall situation fast terminal sliding mode controller are as follows:
WhereinFor the estimated value of uncertain g, whereinFor the estimated value of optimal connection weight, ξ is from network inputs
Layer arrives the function of rules layer；εmFor the network approximate error upper bound, sgn is sign function；
The step 7 includes the following steps: according to duty ratio D obtained by step 6, by generating 4 tunnels compared with triangular carrier
PWM wave controls signal, wherein S1,S4Duty ratio is D, S2,S3Duty ratio is 1-D, controls the on-off of 4 switching tubes of inverter, real
Existing DC-AC is converted and is completed grid-connected.
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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 |
| CN108923430B (en) * | 2018-07-16 | 2021-09-24 | 河海大学常州校区 | Active power filter neural network terminal sliding mode control method and computing equipment |
| CN111682589A (en) * | 2020-06-23 | 2020-09-18 | 国网山西省电力公司电力科学研究院 | High-permeability photovoltaic power station grid-connected characteristic research method based on self-adaptive sliding mode control strategy |
| 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 |
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