CN108803771A - Maximum power point tracing method based on Adaptive Fuzzy Control - Google Patents
Maximum power point tracing method based on Adaptive Fuzzy Control Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05F—SYSTEMS FOR REGULATING ELECTRIC OR MAGNETIC VARIABLES
- G05F1/00—Automatic systems in which deviations of an electric quantity from one or more predetermined values are detected at the output of the system and fed back to a device within the system to restore the detected quantity to its predetermined value or values, i.e. retroactive systems
- G05F1/66—Regulating electric power
- G05F1/67—Regulating electric power to the maximum power available from a generator, e.g. from solar cell
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- G—PHYSICS
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- 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
- G05B13/041—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 in which a variable is automatically adjusted to optimise the performance
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- 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
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Abstract
The invention discloses a kind of maximum power point tracing methods based on Adaptive Fuzzy Control, include the following steps：1) input variable and output variable of fuzzy controller are determined；2) conclude and summarize the control rule of fuzzy controller；3) blurring and anti fuzzy method method are determined；4) relevant parameters such as division and the quantizing factor of domain are determined.Adaptive Fuzzy Control MPPT method tracking velocities are fast, and dynamic response performance is good, reach after stable state almost without fluctuation, can stablize, efficiently tracking photovoltaic array maximum power point；In the case of the system parameter disturbances such as intensity of illumination, environment temperature, new operating point can be quickly found, system is kept to stablize；The anti-extraneous factor interference performance of solar energy photovoltaic system is strengthened, systematic parameter determines simply, reduces energy waste.
Description
Technical field
The invention belongs to field of new energy generation, more particularly to it is a kind of based on the maximum power point of Adaptive Fuzzy Control with
Track method.
Background technology
In recent years, under global energy shortage and the serious dual-pressure of environmental pollution, it is current to seek regenerative resource
Hot issue, solar energy has many advantages, such as that distributional region is wide, pollution-free as a kind of regenerative resource of green, thus
Favor by masses.A kind of important transition form that electric energy is solar energy is converted solar energy into, photoelectric conversion technique exists at present
Constantly develop, have reached can large-scale application level.Under the overall background for walking sustainable development path, photovoltaic cell
There is good development prospect.Although photovoltaic power generation technology has a extensive future, but there are still certain problems.It remains high always
Photovoltaic cell price be restrict its industrialization and scale one of principal element.How the conversion of photovoltaic generating system is improved
Efficiency becomes photovoltaic generation one of key problem urgently to be resolved hurrily.
In photovoltaic generating system, maximum power point tracing method is the important means for improving system effectiveness.It is common at present
Maximum power point tracing method have perturbation observation method, conductance increment method etc., but these methods are after reaching stable state that there are one
Fixed fluctuation, this has resulted in the waste of the energy, meanwhile, in the case of the system parameter disturbances such as intensity of illumination, environment temperature,
New operating point can not be quickly found, dynamic property is poor.
Invention content
Present invention aim to address the dynamics of current common maximum power of photovoltaic cell point-tracking method and stability
The poor problem of energy proposes a kind of maximum power point tracing method based on Adaptive Fuzzy Control.
Realize that the technical solution of the object of the invention is：A kind of MPPT maximum power point tracking based on Adaptive Fuzzy Control
Method includes the following steps：
Step 1: determining the input variable and output variable of fuzzy controller；The input quantity of fuzzy control is set as photovoltaic
Change rate e (k) and e (k) variable quantity of the output power of battery for output voltageIts fuzzy subset is respectively divided into
It is negative big, it is negative small, zero, just small, honest fuzzy concept.The output quantity of fuzzy controller is set as amount of duty cycle adjustment Dc, mould
Paste subset, which is equally divided into, to be born greatly, negative small, and zero, it is just small, it is honest.In order to improve the stable state accuracy of fuzzy controller, drawn fuzzy
Timesharing, bear big, honest range obtain it is sufficiently large, bear it is small, zero-sum just it is small take it is smaller.This is because basic fuzzy subset is discussing
Uniformly divide on domain, it is suppressed that the nonlinear characteristic of fuzzy controller, but be a kind of limit to the stable state accuracy for improving fuzzy controller
System, it is difficult to reach higher control accuracy, especially be become apparent when Discrete Finite domain designs.
Step 2: concluding and summarizing the control rule of fuzzy controller；Determining for fuzzy rule considers following two aspects：
(1) duty ratio D is adjusted according to the size and Orientation of e (k)c, current power value distance is illustrated when e (k) is larger most
High-power point is farther out, it should increase and adjust step-length, keep it rapid close to maximum power point；E (k) should make adjusting when being larger negative value
Step-length is reversed and takes large values.
(2) if ec (k) is smaller positive value, and when e (k) be the higher value born, illustrates that intensity of illumination is reduced rapidly or environment temperature
Degree is rapid to be increased, and it is negative value to cause output power variation.At this point, present operating point is still located on the left of power curve, should continue to
Originally step-length direction optimizing, to prevent erroneous judgement.
Step 3: determining blurring and anti fuzzy method method；Mamdani models are chosen as fuzzy controller model,
" friendship " method is min, and " simultaneously " method is max, inference method min, clustering method max, and reverse formulating method is attached most importance to heart method.
Step 4: determining the relevant parameters such as division and the quantizing factor of domain；Set the discrete of the linguistic variable of input quantity
Domain is 17 grades, sets the discrete domain of output quantity as 7 grades, by quantizing factor by output quantity DcIncorporate domain into
In.
Compared with prior art, the present invention its remarkable advantage is：(1) Adaptive Fuzzy Control MPPT methods tracking velocity
Soon, dynamic response performance is good, reaches after stable state almost without fluctuation, can stablize, efficiently tracking photovoltaic array maximum work
Rate point；(2) in the case of the system parameter disturbances such as intensity of illumination, environment temperature, new operating point, maintainer can quickly be found
System is stablized；(3) the anti-extraneous factor interference performance of solar energy photovoltaic system is strengthened, systematic parameter determines simply, reduces energy
Source wastes.
Description of the drawings
Fig. 1 is the flow chart of the maximum power point tracing method the present invention is based on Adaptive Fuzzy Control.
Fig. 2 is photovoltaic array P-V characteristics of the present invention with intensity of illumination change curve.
Fig. 3 is photovoltaic array I-V characteristic of the present invention with intensity of illumination change curve.
Fig. 4 is that photovoltaic array I-V characteristic of the present invention varies with temperature curve graph.
Fig. 5 is that photovoltaic array P-V characteristics of the present invention vary with temperature curve graph.
Fig. 6 is the changed power figure of conventional maximum power point tracing method of the invention.
Fig. 7 is the changed power figure of the maximum power point tracing method of Adaptive Fuzzy Control of the present invention.
Representative meaning is numbered in figure is：1 is the input variable and output variable for determining fuzzy controller, and 2 be conclusion
With the control rule for summarizing fuzzy controller, 3 is determine blurring and anti fuzzy method method, and 4 be division and the amount for determining domain
Change the relevant parameters such as the factor.
Specific implementation mode
A kind of maximum power point tracing method based on Adaptive Fuzzy Control of the present invention, includes the following steps：
Step 1, the input variable and output variable for determining fuzzy controller；The input quantity of fuzzy control includes：Photovoltaic electric
Change rate e (k) and e (k) variable quantity of the output power in pond for output voltageIts fuzzy subset is respectively divided into negative
Greatly, negative small, zero, it is just small, it is honest；The output variable of fuzzy controller is set as amount of duty cycle adjustment Dc, fuzzy subset's division
It is to bear greatly, it is negative small, zero, it is just small, it is honest.
Step 2, the control rule for determining fuzzy controller；Fuzzy rule is specially：
(1) duty ratio D is adjusted according to the size and Orientation of e (k)c, current power value distance is illustrated when e (k) is larger most
High-power point farther out, increases and adjusts step-length, keeps it rapid close to maximum power point；E (k) should make adjusting step-length when being larger negative value
Reversely and take large values；
(2) if ec (k) is smaller positive value, and when e (k) be the higher value born, illustrates that intensity of illumination is reduced rapidly or environment temperature
Degree is rapid to be increased, and it is negative value to cause output power variation；At this point, present operating point is still located on the left of power curve, should continue to
Originally step-length direction optimizing.
Step 3 determines blurring and anti fuzzy method method；Mamdani models are chosen as fuzzy controller model, " friendship "
Method is min, and " simultaneously " method is max, inference method min, clustering method max, and reverse formulating method is attached most importance to heart method.
Step 4, the division for determining domain and quantizing factor these relevant parameters complete the tracking to maximum power point.If
The discrete domain for determining the linguistic variable of input quantity is 17 grades, sets the discrete domain of output quantity as 7 grades, passes through quantization
The factor is by output quantity DcIt incorporates into domain.
It is described in more detail below.
The present invention proposes a kind of maximum power point tracing method based on Adaptive Fuzzy Control, includes the following steps：
Step 1: determining the input variable and output variable of fuzzy controller, including the determination of variable and the selection of number；
The input quantity of fuzzy control is set as change rate e (k) of the output power for output voltage=[P (k)-P (k- of photovoltaic cell
1)]/[U (k)-U (k-1)] and e (k) variable quantitiesIts fuzzy subset is respectively divided as follows：
E (k)={ NB NM NS Z PS PM PB }；
Wherein NB, NS, Z, PS, PB indicate negative big respectively, negative small, zero, just small, the fuzzy concepts such as honest.
The output quantity of fuzzy controller is set as amount of duty cycle adjustment Dc, fuzzy subset divides as follows：
Dc={ NB NM NS Z PS PM PB }
In order to improve the stable state accuracy of fuzzy controller, in fuzzy division, the range of NB, PB obtain sufficiently large, NS, Z
It is taken with PS smaller.This is because basic fuzzy subset uniformly divides on domain, it is suppressed that the nonlinear characteristic of fuzzy controller,
But the stable state accuracy to improving fuzzy controller is a kind of limitation, it is difficult to reach higher control accuracy, especially have discrete
It is become apparent when limit domain design.Triangular function and trapezoidal function, input, output are used for the membership function of input quantity
Triangular function, elsewhere is applied to apply trapezoidal function at NM, NS, Z, PS, PM of amount.
The required fuzzy control rule of control performance is realized Step 2: determining；The output characteristics of photovoltaic cell is generally full
Sufficient following three points：
(1) when environmental condition (intensity of illumination, environment temperature an etc.) timing, output power is with voltage (or duty ratio)
Change and similar downward parabolic as shown in Figure 2 is presented, there are a maximal points, i.e. maximum power point.
(2) as shown in Figure 2,3, short circuit current and maximum power are directly proportional to intensity of illumination variation, and open-circuit voltage is with illumination
Intensity changes in logarithmic parabola.
(3) as shown in Figure 4,5, short circuit current is increased with environment temperature and is slightly increased, and open-circuit voltage can be reduced seriously, most
High-power value can also decrease.
According to the above analysis, the formulation of fuzzy rule should consider following two aspects：
(1) duty ratio D is adjusted according to the size and Orientation of e (k)c, current power value distance is illustrated when e (k) is larger most
High-power point is farther out, it should increase and adjust step-length, keep it rapid close to maximum power point；E (k) should make adjusting when being larger negative value
Step-length is reversed and takes large values.
(2) if ec (k) is smaller positive value, and when e (k) be the higher value born, illustrates that intensity of illumination is reduced rapidly or environment temperature
Degree is rapid to be increased, and it is negative value to cause output power variation.At this point, present operating point is still located on the left of power curve, should continue to
Originally step-length direction optimizing, to prevent erroneous judgement.
According to mentioned above principle, can obtain as fuzzy control rule table is as shown in table 1.
1 fuzzy control rule table of table
Step 3: blurring and the anti fuzzy method method of the system of determination, more preferable performance can be obtained according to system performance determination
Method；It determines blurring and the anti fuzzy method method of system, chooses Mamdani models as fuzzy controller model, " friendship "
Method is min, and " simultaneously " method is max, inference method min, clustering method max, and reverse formulating method is attached most importance to heart method.
Step 4: determining the relevant parameters such as division and the quantizing factor of domain；Determine division and the quantizing factor etc. of domain
Relevant parameter, the basic domain for setting the linguistic variable of input quantity are all [- 8,8], and corresponding discrete domain is 17 grades.
The basic domain of output quantity is set as [- 3,3], practical DcVariation range be [- 0.03,0.03], by quantizing factor by he
Incorporate into domain.
Adaptive Fuzzy Control MPPT method tracking velocities are fast, and dynamic response performance is good, reach stable state after almost without
Fluctuation can stablize, efficiently tracking photovoltaic array maximum power point.
Further detailed description is done to the present invention with reference to embodiment.
Embodiment
A kind of maximum power point tracing method based on Adaptive Fuzzy Control, includes the following steps：
Step 1: the input variable and output variable of fuzzy controller are determined, by voice variable E and EcAnd output Δ U
It is respectively defined as 7 fuzzy subsets, wherein E and EcDP/dU and Δ dP/dU are respectively represented, i.e.,
E={ NB NM NS Z PS PM PB }；
Ec={ NB NM NS Z PS PM PB }；
Dc={ NB NM NS Z PS PM PB }.
It is 17 and 11 grades that their domain, which is respectively provided, i.e.,
E={-8-7-6-5-4-3-2-1 012345678 }；
Ec={-8-7-6-5-4-3-2-1 012345678 }；
Dc={-3-2-1 0123 }.
Step 2: determining fuzzy control rule table, 49 control rules shown in table 1 are inputted, according to of fuzzy subset
Number can determine the item number of fuzzy control rule, due to fuzzy controller input quantity there are two, and each input quantity divides
For 7 fuzzy subsets, i.e. fuzzy control rule can be determined as 7*7=49 items.
Step 3: blurring and the anti fuzzy method method of the system of determination, choose Mamdani models as fuzzy controller mould
Type, " friendship " method are min, and " simultaneously " method is max, inference method min, clustering method max, and reverse formulating method is attached most importance to the heart
Method.
Step 4: the basic domain of the linguistic variable of setting input quantity is all [- 8,8], corresponding discrete domain is 17
A grade.The basic domain of output quantity is set as [- 3,3], practical DcVariation range be [- 0.03,0.03], by quantization because
Son incorporates them in domain into.
It is respectively the power change that the maximum power point tracing method based on Adaptive Fuzzy Control obtains as shown in Figure 6, Figure 7
The changed power figure that change figure and conventional maximum power point tracing method obtain, it can be seen that conventional maximum power point tracing method
Simulated response speed is slow, and reaches after stable state there are certain fluctuation, causes the waste of the energy, and Adaptive Fuzzy Control
The tracking velocity of method is fast, and dynamic response performance is good, almost without fluctuation after arrival stable state, shows that the method for the present invention solves
Problem above, shows good control performance.
Claims (5)
1. a kind of maximum power point tracing method based on Adaptive Fuzzy Control, which is characterized in that include the following steps：
Step 1, the input variable and output variable for determining fuzzy controller；
Step 2, the control rule for determining fuzzy controller；
Step 3 determines blurring and anti fuzzy method method；
Step 4, the division for determining domain and quantizing factor these relevant parameters complete the tracking to maximum power point.
2. the maximum power point tracing method based on Adaptive Fuzzy Control as described in claim 1, which is characterized in that step 1
In, the input quantity of fuzzy control includes：The output power of photovoltaic cell changes the change rate e (k) and e (k) of output voltage
AmountIts fuzzy subset, which is respectively divided into, to be born greatly, negative small, and zero, it is just small, it is honest；The output variable of fuzzy controller is set as
Amount of duty cycle adjustment Dc, fuzzy subset, which is divided into, to be born greatly, negative small, and zero, it is just small, it is honest.
3. the maximum power point tracing method based on Adaptive Fuzzy Control as described in claim 1, which is characterized in that step 2
In, fuzzy rule is specially：
(1) duty ratio D is adjusted according to the size and Orientation of e (k)c, illustrate current power value apart from maximum power when e (k) is larger
Point farther out, increases and adjusts step-length, keeps it rapid close to maximum power point；E (k) adjusting step-length should be made reversed when being larger negative value and
It takes large values；
(2) if ec (k) be smaller positive value, and e (k) be bear higher value when illustrate that intensity of illumination is reduced rapidly or environment temperature is fast
Speed increases, and it is negative value to cause output power variation；At this point, present operating point is still located on the left of power curve, should continue to original
The optimizing of step-length direction.
4. the maximum power point tracing method based on Adaptive Fuzzy Control as described in claim 1, which is characterized in that step 3
In, Mamdani models are chosen as fuzzy controller model, and " friendship " method is min, and " simultaneously " method is max, and inference method is
Min, clustering method max, reverse formulating method are attached most importance to heart method.
5. the maximum power point tracing method based on Adaptive Fuzzy Control as described in claim 1, which is characterized in that step 4
In, the discrete domain of the linguistic variable of input quantity is set as 17 grades, is set the discrete domain of output quantity as 7 grades, is led to
Quantizing factor is crossed by output quantity DcIt incorporates into domain.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109508062A (en) * | 2019-01-28 | 2019-03-22 | 吉林建筑大学 | A kind of photovoltaic power generation control method and system based on fuzzy conductance |
| CN110737302A (en) * | 2019-11-14 | 2020-01-31 | 华能海南发电股份有限公司 | MPPT control method based on photovoltaic power generation system resistance matching |
| CN112180731A (en) * | 2020-10-13 | 2021-01-05 | 天津大学 | Energy equipment operation control method and system |
| CN113623126A (en) * | 2021-06-23 | 2021-11-09 | 湖南大学 | Direct-drive permanent magnet hydroelectric power generation system control method, system, terminal and readable storage medium based on fuzzy control |
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Application publication date: 20181113 |