CN104020813A - MPPT hysteresis control algorithm based on FIR filter prediction - Google Patents
MPPT hysteresis control algorithm based on FIR filter prediction Download PDFInfo
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- 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 provides an MPPT hysteresis control algorithm based on FIR filter prediction, and relates to the technical field of MPPT algorithm. According to the MPPT hysteresis control algorithm based on FIR filter prediction, hysteresis control and a self-adaptive prediction mechanism are added on the basis of a conventional disturbance observation method, the disturbance direction is determined by judging disturbance rules on the basis that photovoltaic array output power at the next moment is predicted, the tracking velocity is increased, meanwhile consideration is given to precision of a control system, and losses are reduced. The new algorithm can compensate for the defects of the conventional disturbance observation method in terms of tracking velocity and stability precision, MPPT control can be implemented rapidly and stably, and the probability of oscillation and misjudgement of the maximum power point is greatly reduced. In addition, the MPPT hysteresis control algorithm is easy to control, and facilitates implementation of software programming.
Description
Technical field
The present invention relates to MPPT algorithmic technique field, be specifically related to a kind of stagnant ring control algolithm of MPPT based on the prediction of FIR wave filter.
Background technology
At present, the MPPT algorithm of conventionally using is thanksed for your hospitality moving observation, fuzzy control.Disturbance observation is also referred to as climbing method, and its basic thought is: the output voltage of disturbance photovoltaic cell first, then observe the variation of the output power from photovoltaic cells, and the trend changing according to power continuously changes disturbance voltage direction.Due to the restriction of reality detection and control accuracy, and the step-length of voltage disturbance is certain, is bound to so occur concussion problem, and again because external environment condition is constantly to change, the P-U family curve of photovoltaic cell changes constantly, just likely judges by accident.Fuzzy control is to take Fuzzy Set Theory as basic a kind of emerging control device, its basic thought is: the data that system obtains sampling are through computing, determine the position relationship between working point and maximum power point, automatic calibration quiescent potential value, makes working point be tending towards maximum power point.Fuzzy control process need to be sampled and be obtained data, then calculate, obfuscation again, fuzzy reasoning computing, sharpening, finally obtain a result again, the method process more complicated, the CPU of the higher arithmetic speed of needs, the linguistic variable of obfuscation is chosen and is wanted suitably, sharpening calculating relatively send out assorted, so more difficult realization in actual applications.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of stagnant ring control algolithm of MPPT based on the prediction of FIR wave filter, not only follow the trail of speed, control accuracy and erroneous judgement and concussion and be better than existing disturbance observation method, and this method is easy to programming realization.
Technical matters to be solved by this invention realizes by the following technical solutions:
The stagnant ring control algolithm of MPPT based on the prediction of FIR wave filter,
1), first according to adaptive prediction mechanism principle, set
Y (n) is desired output;
for results of prediction and calculation;
E (n) is the error between desired output and results of prediction and calculation, that is:
2) self-adaptation adjustment
The coefficient vector of fallout predictor has determined the precision of prediction of adaptation mechanism, therefore in order to guarantee the adaptation of prediction algorithm to system time variation and environmental uncertainty, must be to the online rolling optimization of predictor coefficient, i.e. and the self-adaptation adjustment of coefficient;
The self-adaptation adjustment of so-called predictor coefficient is exactly according to predicated error, takes certain criterion on-line optimization coefficient, and the Optimality Criteria of general employing is that to take the mean square value minimum of predicated error be benchmark, and Typical Representative is LMS algorithm:
definition ε (n) is e
2(n) expectation value, i.e. square error: ε (n)=E[e
2(n)];
Substitution can obtain: ε (n)=E[y (n)-H '
nx
n(n)]
2(1)
In order to make ε (n) minimum, must obtain one group of h
k(n) (k=0,1 ..., N-1), it is met the demands;
Can pass through formula ε (n)=E[y (n)-H '
nx
n(n)]
2adopt differential zero setting method to obtain N equation, solve and can obtain predictor coefficient:
In formula, p
n: the simple crosscorrelation amount of y (n) and X (n); R
nN: the autocorrelation matrix of X (n);
That is: p
n=E[y (n) X (n)], R
nN=E[X (n) X '
n(n)];
In general, the coefficient adjustment of fallout predictor can be obtained by above formula, but when N is larger, calculated amount is larger, is unfavorable for that on digital processing unit, programming realizes.
In practical application, can adopt recursion mode to solve, conventional steepest gradient:
H
N(n+1)＝H
N(n)+2μ[p
N-R
NNH
N(n)]，
In formula, μ: step factor, the each iteration of its size impact is in steepest direction length of travel;
Provable, as long as μ value is appropriate, from any H
n(0) set out, can make predictor coefficient converge to
But adopt this process of iteration to calculate, make the predictor coefficient of square error minimum vectorial
time, still need to calculate p
nand R
nN, there is more complicated matrix operation;
Therefore, in order to reduce calculated amount, Widrow has proposed a kind of effective short-cut method, and derives the approximate way of realization of steepest gradient process of iteration: H
n(n+1)=H
n(n)+2 μ e (n) X
n(n).
Here it is Widrow-HoffLMS algorithm, predictor coefficient being carried out to after initial value arranges, just can pass through online updating iteration, makes error mean square value minimum;
According to the adaptive prediction mechanism of foregoing description, the adaptive prediction algorithm step of designing based on FIR model is:
1) according to concrete application, initialization H
n(0), μ;
2) input signal is through adaptive prediction mechanism, output
3) signal
through a time delay process z
-1, output
4) calculate
5, calculate H
n(n+1)=H
n(n)+2 μ e (n) X
n(n).
So far a rolling optimization has calculated, and the new iterative computation cycle, while arriving, is returned to second step, starts new rolling optimization and calculates;
In stagnant ring is controlled, PA is current time photovoltaic array output power, and PB is next moment maximum point power predicting, and PC is a upper moment photovoltaic array output power,
Definition: during PA>PC, be designated as "+", during PB>PA, be designated as "+", otherwise all note is done "-",
By the relatively judgement of power between 3 o'clock, can show that the voltage disturbance rule based on stagnant ring is as follows:
Rule 1: if the power ratio of twice disturbance is "+", voltage keeps former direction disturbance;
Rule 2: if the power ratio of twice disturbance is "-", magnitude of voltage disturbance in the other direction;
Rule 3: if the power ratio of twice disturbance has "+" to have "-", may reach maximum power point or outside irradiation and change very soon, voltage is constant;
Setting p (n) is this photovoltaic array output power constantly, and p (n-1) is the output power of a upper moment photovoltaic array, and d (n) is output duty cycle;
Input voltage signal is through the output power of the photovoltaic array in adaptive prediction mechanism next moment of prediction, enter stagnant ring and control judgement perturbation direction, thereby determine dutycycle, after dutycycle and triangular wave compare, generate the pwm pulse signal of driving switch device, realize dynamic adjustments load, finally realize MPPT maximum power point tracking and control.
The present invention combines the MPPT algorithm of prediction algorithm and the control of stagnant ring, by prediction algorithm, predict next power of maximum power point constantly, and by stagnant ring, control the direction of judging disturbance next time, the method has improved follows the trail of the speed of maximum power point and has improved tracking precision, reduces the disturbance in maximum power point position.
The invention has the beneficial effects as follows:
Accompanying drawing explanation
Fig. 1 is the theory diagram of adaptive prediction mechanism of the present invention;
Fig. 2 is that the stagnant ring of the present invention is controlled output power record diagram;
Fig. 3 is the MPPT control block diagram of forecasting mechanism of the present invention;
Fig. 4 is software flow pattern of the present invention.
Embodiment
For technological means, creation characteristic that the present invention is realized, reach object and effect is easy to understand, below in conjunction with concrete diagram, further set forth the present invention.
The stagnant ring control algolithm of MPPT based on the prediction of FIR wave filter,
1), first according to adaptive prediction mechanism principle, as shown in Figure 1, set
Y (n) is desired output;
for results of prediction and calculation;
E (n) is the error between desired output and results of prediction and calculation, that is:
2) self-adaptation adjustment
The coefficient vector of fallout predictor has determined the precision of prediction of adaptation mechanism, therefore in order to guarantee the adaptation of prediction algorithm to system time variation and environmental uncertainty, must be to the online rolling optimization of predictor coefficient, i.e. and the self-adaptation adjustment of coefficient;
The self-adaptation adjustment of so-called predictor coefficient is exactly according to predicated error, takes certain criterion on-line optimization coefficient, and the Optimality Criteria of general employing is that to take the mean square value minimum of predicated error be benchmark, and Typical Representative is LMS algorithm:
definition ε (n) is e
2(n) expectation value, i.e. square error: ε (n)=E[e
2(n)];
Substitution can obtain: ε (n)=E[y (n)-H '
nx
n(n)]
2(1)
In order to make ε (n) minimum, must obtain one group of h
k(n) (k=0,1 ..., N-1), it is met the demands;
Can pass through formula ε (n)=E[y (n)-H '
nx
n(n)]
2adopt differential zero setting method to obtain N equation, solve and can obtain predictor coefficient:
In formula, p
n: the simple crosscorrelation amount of y (n) and X (n); R
nN: the autocorrelation matrix of X (n);
That is: p
n=E[y (n) X (n)], R
nN=E[X (n) X '
n(n)];
In general, the coefficient adjustment of fallout predictor can be obtained by above formula, but when N is larger, calculated amount is larger, is unfavorable for that on digital processing unit, programming realizes.
In practical application, can adopt recursion mode to solve, conventional steepest gradient:
H
N(n+1)＝H
N(n)+2μ[p
N-R
NNH
N(n)]，
In formula, μ: step factor, the each iteration of its size impact is in steepest direction length of travel;
Provable, as long as μ value is appropriate, from any H
n(0) set out, can make predictor coefficient converge to
But adopt this process of iteration to calculate, make the predictor coefficient of square error minimum vectorial
time, still need to calculate p
nand R
nN, there is more complicated matrix operation;
Therefore, in order to reduce calculated amount, Widrow has proposed a kind of effective short-cut method, and derives the approximate way of realization of steepest gradient process of iteration: H
n(n+1)=H
n(n)+2 μ e (n) X
n(n).
Here it is Widrow-HoffLMS algorithm, predictor coefficient being carried out to after initial value arranges, just can pass through online updating iteration, makes error mean square value minimum;
According to the adaptive prediction mechanism of foregoing description, the adaptive prediction algorithm step of designing based on FIR model is:
1) according to concrete application, initialization H
n(0), μ;
2) input signal is through adaptive prediction mechanism, output
3) signal
through a time delay process z
-1, output
4) calculate
5, calculate H
n(n+1)=H
n(n)+2 μ e (n) X
n(n).
So far a rolling optimization has calculated, and the new iterative computation cycle, while arriving, is returned to second step, starts new rolling optimization and calculates;
In stagnant ring is controlled, PA is current time photovoltaic array output power, and PB is next moment maximum point power predicting, and PC is a upper moment photovoltaic array output power,
Definition: during PA>PC, be designated as "+", during PB>PA, be designated as "+", otherwise all note is done "-", as shown in Figure 2:
By the relatively judgement of power between 3 o'clock, can show that the voltage disturbance rule based on stagnant ring is as follows:
Rule 1: if the power ratio of twice disturbance is "+", voltage keeps former direction disturbance;
Rule 2: if the power ratio of twice disturbance is "-", magnitude of voltage disturbance in the other direction;
Rule 3: if the power ratio of twice disturbance has "+" to have "-", may reach maximum power point or outside irradiation and change very soon, voltage is constant;
As shown in Figure 3, setting p (n) is this photovoltaic array output power constantly, and p (n-1) is the output power of a upper moment photovoltaic array, and d (n) is output duty cycle; Input voltage signal is through the output power of the photovoltaic array in adaptive prediction mechanism next moment of prediction, enter stagnant ring and control judgement perturbation direction, thereby determine dutycycle, after dutycycle and triangular wave compare, generate the pwm pulse signal of driving switch device, realize dynamic adjustments load, finally realize MPPT maximum power point tracking and control.
As shown in Figure 1, system is first by the output power P (n) of the current solar cell of A/D unit inspection, and the solar cell output power P (n-1) in a upper moment, then enter adaptive prediction mechanism, predict the output power P (n+1) of next solar cell constantly, then by the stagnant ring control law relation between next solar cell power constantly of previous moment, current time, prediction relatively, obtain perturbation direction, then strengthen accordingly, reduce or keep current dutycycle.
This stagnant ring control algolithm of MPPT based on the prediction of FIR wave filter, on the basis of conventional disturbance observation method, add stagnant ring to control and adaptive prediction mechanism, predicting on the basis of next photovoltaic array output power constantly, by the judgement of disturbance rule, deterministic disturbances direction, has taken into account the precision of control system having improved simultaneously, and has reduced loss in tracking velocity.Novel algorithm can make up the deficiency of conventional disturbance observation method on tracking velocity and stable state accuracy, reaches fast and stable and implements MPPT control, and greatly reduced concussion and the erroneous judgement at maximum power point.In addition, this algorithm is controlled simple, is easy to software programming and realizes.
More than show and described ultimate principle of the present invention and principal character and advantage of the present invention.The technician of the industry should understand; the present invention is not restricted to the described embodiments; that in above-described embodiment and instructions, describes just illustrates principle of the present invention; without departing from the spirit and scope of the present invention; the present invention also has various changes and modifications, and these changes and improvements all fall in the claimed scope of the invention.The claimed scope of the present invention is defined by appending claims and equivalent thereof.
Claims (1)
1. the stagnant ring control algolithm of MPPT of predicting based on FIR wave filter, is characterized in that:
1), first according to adaptive prediction mechanism principle, set
Y (n) is desired output;
for results of prediction and calculation;
E (n) is the error between desired output and results of prediction and calculation, that is:
2) self-adaptation adjustment
The coefficient vector of fallout predictor has determined the precision of prediction of adaptation mechanism, therefore in order to guarantee the adaptation of prediction algorithm to system time variation and environmental uncertainty, must be to the online rolling optimization of predictor coefficient, i.e. and the self-adaptation adjustment of coefficient;
The self-adaptation adjustment of so-called predictor coefficient is exactly according to predicated error, takes certain criterion on-line optimization coefficient, and the Optimality Criteria of employing is that to take the mean square value minimum of predicated error be benchmark, employing LMS algorithm:
definition ε (n) is e
2(n) expectation value, i.e. square error: ε (n)=E[e
2(n)];
Substitution can obtain: ε (n)=E[y (n)-H '
nx
n(n)]
2
In order to make ε (n) minimum, must obtain one group of h
k(n) (k=0,1 ..., N-1), it is met the demands;
By to formula ε (n)=E[y (n)-H '
nx
n(n)]
2adopt differential zero setting method to obtain N equation, solve and can obtain predictor coefficient:
In formula, p
n: the simple crosscorrelation amount of y (n) and X (n); R
nN: the autocorrelation matrix of X (n);
That is: p
n=E[y (n) X (n)], R
nN=E[X (n) X '
n(n)];
3) according to the adaptive prediction mechanism of foregoing description, the adaptive prediction algorithm step of designing based on FIR model is:
(1) according to concrete application, initialization H
n(0), μ;
(2) input signal is through adaptive prediction mechanism, output
(3) signal
through a time delay process z
-1, output
(4) calculate
5, calculate H
n(n+1)=H
n(n)+2 μ e (n) X
n(n).
So far a rolling optimization has calculated, and the new iterative computation cycle, while arriving, is returned to second step, starts new rolling optimization and calculates;
In stagnant ring is controlled, PA is current time photovoltaic array output power, and PB is next moment maximum point power predicting, and PC is a upper moment photovoltaic array output power,
Definition: during PA>PC, be designated as "+", during PB>PA, be designated as "+", otherwise all note is done "-",
By the relatively judgement of power between 3 o'clock, can show that the voltage disturbance rule based on stagnant ring is as follows:
Rule 1: if the power ratio of twice disturbance is "+", voltage keeps former direction disturbance;
Rule 2: if the power ratio of twice disturbance is "-", magnitude of voltage disturbance in the other direction;
Rule 3: if the power ratio of twice disturbance has "+" to have "-", may reach maximum power point or outside irradiation and change very soon, voltage is constant;
Setting p (n) is this photovoltaic array output power constantly, and p (n-1) is the output power of a upper moment photovoltaic array, and d (n) is output duty cycle;
Input voltage signal is through the output power of the photovoltaic array in adaptive prediction mechanism next moment of prediction, enter stagnant ring and control judgement perturbation direction, thereby determine dutycycle, after dutycycle and triangular wave compare, generate the pwm pulse signal of driving switch device, realize dynamic adjustments load, finally realize MPPT maximum power point tracking and control.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104881077A (en) * | 2015-04-23 | 2015-09-02 | 燕宏伟 | Tracking control method of maximum power point in photovoltaic system |
| CN106125816A (en) * | 2016-08-04 | 2016-11-16 | 安徽省安泰科技股份有限公司 | MPPT Hysteresis control algorithm based on the prediction of modified model FIR filter |
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| US20110282600A1 (en) * | 2010-05-12 | 2011-11-17 | General Electric Company | System and method for photovoltaic plant power curve measurement and health monitoring |
| JP2012169447A (en) * | 2011-02-14 | 2012-09-06 | Tabuchi Electric Co Ltd | Photovoltaic power generation system |
| CN102801363A (en) * | 2011-05-24 | 2012-11-28 | 中山大学 | Photovoltaic system MPPT (maximum power point tracking) control method based on adaptive prediction |
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Cited By (2)
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| CN104881077A (en) * | 2015-04-23 | 2015-09-02 | 燕宏伟 | Tracking control method of maximum power point in photovoltaic system |
| CN106125816A (en) * | 2016-08-04 | 2016-11-16 | 安徽省安泰科技股份有限公司 | MPPT Hysteresis control algorithm based on the prediction of modified model FIR filter |
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Application publication date: 20140903 |