CN105913161A - Method of acquiring maximum power point of photovoltaic system based on multi-objective optimization - Google Patents
Method of acquiring maximum power point of photovoltaic system based on multi-objective optimization Download PDFInfo
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Abstract
The invention discloses a method of acquiring the maximum power point of a photovoltaic system based on multi-objective optimization. The method is characterized by comprising the following steps: 1, the photovoltaic system maximum power point tracking problem is converted into a multi-objective optimization problem shown as in a formula (1); and 2, the multi-objective optimization method is used for optimizing the multi-objective optimization problem to obtain voltage and current corresponding to the maximum power point. The maximum power point of the photovoltaic system in the environment can be acquired by only detecting the external environment, and the purpose of improving the precision of the maximum power point can be achieved.
Description
Technical field
The present invention relates to photovoltaic system maximum power point technical field, a kind of photovoltaic system based on multiple-objection optimization
The acquisition methods of system maximum power point.
Background technology
Photovoltaic generating system under certain environmental conditions, only exists a peak power output point.Once its operating point is deviateed
High-power, the output of solaode will strongly reduce.Therefore, in order to utilize photovoltaic generating system to greatest extent
Energy, improve system efficiency it is necessary to constantly adjust the running voltage of photovoltaic system according to the change of external environment so that
Photovoltaic generating system always works near maximum power point, and this technology is referred to as MPPT maximum power point tracking.
Along with developing rapidly of photovoltaic generation, the effect of MPPT maximum power point tracking algorithm seems and is even more important.At present, peak power
Point track algorithm is broadly divided into two categories below:
(1) approximation method of adjustment based on engineer applied.The Typical Representative of this kind of method is constant voltage tracing: owing to not sharing the same light
Some constant magnitude of voltage U always it is approximately close to according to the maximum power point under intensitymax, so according to the structure of photovoltaic cell
U is calculated with parametermax, and make photovoltaic system become a manostat, just can stabilize it and be operated near maximum power point.
Constant voltage tracing control method is easily achieved, and control system is simple.But its steady-state error is bigger, the merit of photovoltaic system loss
Rate also compares many.
(2) Mathematical Method based on output characteristics.This kind of method mainly includes disturbance observational method, conductance increment method etc., is
Use a widest class MPPT maximum power point tracking algorithm at present.This type of method by the output voltage disturbance in addition to photovoltaic array,
Observe the change of its output characteristics to judge current duty, thus determine the direction of disturbance next time, finally make system stability
Operate near maximum power point.But the disturbance step-length that this kind of MPPT maximum power point tracking algorithm is selected is for tracking accuracy and sound
Answering speed to take into account, disturbance step-length is the biggest, and speed is the fastest, but precision is the poorest;Vice versa.
Summary of the invention
The present invention is for avoiding the weak point existing for above-mentioned prior art, it is provided that a kind of photovoltaic system based on multiple-objection optimization
The acquisition methods of maximum power point, to only needing to detect the external environment of photovoltaic system, can trace into photovoltaic system effectively
Maximum power point, and reach to improve the purpose of maximum power point precision.
In order to achieve the above object, the technical solution adopted in the present invention is:
The feature of the acquisition methods of a kind of photovoltaic system maximum power point based on multiple-objection optimization of the present invention is to enter as follows
OK:
Step 1, the tracking problem of described photovoltaic system maximum power point is converted to the multi-objective optimization question as shown in formula (1):
In formula (1), VrAnd IrRepresent the accessed voltage and current corresponding to maximum power point respectively;VmaxAnd ImaxRespectively
Represent given illumination and at a temperature of maximum voltage and maximum current;f1X () expression minimizes power function, and have:
f1(x)=Vmax×Imax-Vr×Ir (2)
In formula (1), f2X () represents current error function, and have:
In formula (3), IPVFor the photovoltaic electric current generated by illumination;I0For diode saturation current;Q is the quantity of electric charge of electronics;k
For Boltzmann constant;T is the absolute temperature of solaode;A is desirability figure, A ∈ [1,2];RSFor series equivalent electricity
Resistance;RshFor parallel equivalent resistance;
Step 2, utilize Multipurpose Optimal Method that described multi-objective optimization question is optimized, obtain corresponding to maximum power point
Voltage VrWith electric current Ir。
The feature of acquisition methods of the present invention lies also in,
Described Multipurpose Optimal Method be preference multi-objective optimization algorithm based on weight vector be to carry out as follows:
Step 2.1, definition select probability are δ;Definition cyclic variable is L;Definition maximum cycle is Lmax;Delimiting period
For T;Defined variable m, initializes m=1;
In m-th cycle TmUnder, produce initial population P0M () and corresponding initial weight vector set w, be designated asW={w1,w2,…,wi,…,wn};Represent the
I initial individuals, andWithRepresent i-th initial individuals respectivelyInitial voltage and initial current;wiRepresent i-th individuality piThe initial weight vector of (m);And wi=(wi1,wi2);
wi1And wi2Represent i-th individuality p respectivelyiFirst weights of (m) and the second weights;
Step 2.2, initialization L=1;Binary search sampling site method is utilized to reduce initial population P0The region of search [0, V of (m)max] and
[0,Imax], thus obtain the interval [V of Optimizing Searchk-1,Vk+1] and [Ik-1,Ik+1];
Step 2.3, to arrange preference point be p (m)=(p1(m),p2(m));p1M () is m-th cycle TmUnder minimize power
Function f1The expected value of (x), p2M () is m-th cycle TmLower current error function f2The expected value of (x);Preference neighborhood is set
B=(b1,b2);b1And b2Represent length and the width of preference neighborhood respectively;
Step 2.4, utilize formula (4) by m-th cycle TmFollowing i-th initial individualsInitial weight vector wiReflect
It is mapped to the i-th initial individuals in the preference neighborhood b of preference point p (m), after being mappedWeight vector wi', and
wi'=(wi′1,wi′2);Thus the weight vector set w '={ w after being mapped1′,w′2,…,wi′,…,wn' }:
wia'=2 × ba×wia+pa(m)-ba, a=1,2 (4)
Step 2.5, utilize formula (5) by i-th initial individualsWeight vector wi' be converted to i-th initial individualsThe weight vector λ with preference informationi, thus obtain the weight vector set with preference information
λ={ λ1,λ2,…,λi,…,λn}:
Step 2.5, calculating i-th initial individualsInitial weight vector wiAt the beginning of individual with remaining n-1 respectively
Euclidean distance between beginning weight vector, obtains i-th initial individualsEuclidean distance set, from described Euclidean distance
Set is chosen minimum front M apart from corresponding individuality as i-th initial individualsM nearest-neighbors
Individual;
Step 2.6, individual from described i-th with described select probability δM nearest-neighbors individuality in choose three
Individual or probability with 1-δ is from described initial population PL-1M () is chosen three individualities, and utilize differential evolution operator to selected
Three individualities taken carry out cross and variation, thus produce m-th cycle TmThe i-th of lower the L time circulation is the most individual
Step 2.7, repetition step 2.6 are to population PL-1M in (), each individuality carries out cross and variation, thus produce the L time circulation
N the new individual new population constituted
Step 2.8, the new population Q that the L time is circulatedL(m) and the population P of the L-1 time circulationL-1M () merges, obtain
M-th cycle TmThe merging population R of lower the L time circulationL(m);
Step 2.9, utilize Chebyshev's formula from described the L time circulation merging population RLM () selects and there is preference information
Mate most n of weight vector set λ individual constitute the population P circulated the L timeL(m);
Step 2.10, L+1 is assigned to L, and judges L > LmaxWhether set up, if setting up, then it represents that obtain the m-th cycle
TmLower LmaxThe population P of secondary circulationLmax(m), and from described population PLmaxM () is chosen maximum power point, with described maximum work
Voltage and current corresponding to rate point is m-th cycle T to be obtainedmUnder voltage Vr(m) and electric current Ir(m);If no
Set up, then return step 2.6;
Step 2.11, by voltage Vr(m) and electric current IrM () is as VrAnd IrSubstitution minimizes power function f1In (x), obtain
f1' (x), it is judged that f1' (x) < p1M whether () set up, if setting up, then by f1' (x) is as the m+1 cycle Tm+1Under power
Littleization expected value;Again by m-th cycle TmUnder LmaxThe population P of secondary circulationLmaxM () is as the m+1 cycle Tm+1Under
Initial population P0(m+1);
Step 2.12, make m+1 be assigned to m, and return step 2.2.
Described binary search sampling site method is to carry out as follows:
Step 2.2.1, to [0, Vmax] carry out uniform sampling, and after calculating the output of each sampled point, choose maximum work output
Employing point corresponding to rate is designated as Vk;
Step 2.2.2, choose [Vk-1,Vk+1] and [I of correspondencek-1,Ik+1] interval as Optimizing Search.
Compared with the prior art, beneficial effects of the present invention is embodied in:
1, the present invention only needs to detect the environmental change that photovoltaic system is extraneous, can get the maximum power point of photovoltaic system, it is easy to
Operation realizes, and can realize the peak power output point of off-line tracking photovoltaic system, it is to avoid because oscillating around at maximum power point
And cause energy loss;Meanwhile, external environment generation cataclysm or photovoltaic array be positioned at local shelter from heat or light when, the present invention
Also the maximum power point of photovoltaic system can be acquired.
2, the existing method under external environment generation transition or photovoltaic array are positioned at situation of locally sheltering from heat or light that the present invention is directed to obtains maximum work
The deficiency of rate point, it is proposed that the method for multiple-objection optimization optimizes maximum power point problem, thus improves the precision of peak power
With its accuracy.
3, the present invention by propose binary search sampling site method search the hunting zone gone to the lavatory so that algorithm obtain one less and more smart
True scope, improves the time efficiency of algorithm.
4, the present invention incorporates decisionmaker's preference information in search procedure, and the preference that final Search Results is limited in policymaker is adjacent
In territory so that it is the constraint of photovoltaic system model can be met more accurately.
5, the present invention is by the most dynamically updating the preference information of policymaker, improves the precision of algorithm.
6, the present invention is by the most dynamically Population Regeneration, accelerates the convergence rate of population in algorithm so that the speed of service of algorithm
Faster, the operation time of algorithm is decreased.
Accompanying drawing explanation
Fig. 1 is the equivalent model of photovoltaic cell in prior art;
Fig. 2 is the structural representation of the present invention;
Fig. 3 is weight vector mapping graph of the present invention;
Fig. 4 is the flow chart of present invention preference based on weight vector Multipurpose Optimal Method.
Detailed description of the invention
In the present embodiment, the acquisition methods of a kind of photovoltaic system maximum power point based on multiple-objection optimization is to carry out as follows:
Step 1, the tracking problem of described photovoltaic system maximum power point is converted to the multi-objective optimization question as shown in formula (1):
In formula (1), VrAnd IrRepresent the accessed voltage and current corresponding to maximum power point respectively;VmaxAnd ImaxRespectively
Represent given illumination and at a temperature of maximum voltage and maximum current;f1X () expression minimizes power function, and have:
f1(x)=Vmax×Imax-Vr×Ir (2)
In formula (1), f2X () represents current error function, and have:
In formula (3), IPVFor the photovoltaic electric current generated by illumination;I0For diode saturation current;Q is the quantity of electric charge of electronics;K is
Boltzmann constant;T is the absolute temperature of solaode;A is desirability figure, A ∈ [1,2];RSFor series equivalent resistance;
RshFor parallel equivalent resistance;
According to the equivalent model of the photovoltaic cell shown in Fig. 1, its output characteristic equation can be drawn:
The purpose of the maximum power point of tracking photovoltaic system seeks to find (V, the I) that make V*I value maximum right, and to make simultaneously
The real current I' obtained by the output characteristic equation of model by running voltage V meets photovoltaic cell with the electric current I traced into
The error constraints of model.Therefore, the peak power of tracking photovoltaic system should consider power, considers the current error of model again,
This meets the character of multi-objective optimization question, therefore can be converted into the tracking problem of the maximum power point of photovoltaic system such as formula
(1) multi-objective optimization question shown in:
Step 2, utilize Multipurpose Optimal Method that the multi-objective optimization question in embodiment is optimized, obtain maximum power point
Corresponding voltage VrWith electric current Ir。
In being embodied as, Multipurpose Optimal Method be preference multi-objective optimization algorithm based on weight vector be to carry out as follows:
As in figure 2 it is shown, after maximum power point problem is converted to the multi-objective optimization question shown in formula (1), the present embodiment use based on
The preference multi-objective optimization algorithm of weight vector optimizes this multi-objective optimization question, can get the power points of correspondence.
Step 2.1, definition select probability are δ;Definition cyclic variable is L;Definition maximum cycle is Lmax;Delimiting period
For T;Defined variable m, initializes m=1;
In m-th cycle TmUnder, produce initial population P0M () and corresponding initial weight vector set w, be designated asW={w1,w2,…,wi,…,wn};Represent the
I initial individuals, andRepresent i-th initial individuals respectivelyInitial voltage and initial current;wiRepresent i-th individuality piThe initial weight vector of (m);And wi=(wi1,wi2);
wi1And wi2Represent i-th individuality p respectivelyiFirst weights of (m) and the second weights;
Step 2.2, initialization L=1;Binary search sampling site method is utilized to reduce initial population P0The region of search [0, V of (m)max] and
[0,Imax], thus obtain the interval [V of Optimizing Searchk-1,Vk+1] and [Ik-1,Ik+1];
Step 2.2.1, to [0, Vmax] carry out uniform sampling, and after calculating the output of each sampled point, choose maximum work output
Employing point corresponding to rate is designated as Vk;
Step 2.2.2, choose [Vk-1,Vk+1] and [I of correspondencek-1,Ik+1] interval as Optimizing Search.
Output characteristic equation according to model, error function F (V, I) builds formula (5), and the purpose of this function is to find applicable photovoltaic
(V, the I) of system model is right;
OrderThis external voltage V is constant, it therefore follows that the function about Z as shown in formula (6);
Z in formula (6) is carried out derivation such as formula (7), it is possible to find F ' (Z) >=0, shows that function F (Z) is a monotonically increasing function,
As Z=I=V=0, can obtainTherefore because finding Z > 0 to meet F (Z) > 0.
Analytical formula (6) understands, as Z > 0, and I0(exp (Z)-1) > 0;Therefore, if metTime, just can meet F (Z) > 0;That is be appreciated that when Z meetsTime, F (Z) > 0;Meanwhile, F (Z) is a monotonically increasing function, interval [0, Zx]
Have and an only zero root, therefore binary search is sent out, and to can be used for searching for (V, I) right;
Therefore, under given external environment, as I=0 and F (Z)=0, can get photovoltaic system by binary search
Big running voltage Vmax.Work as VmaxAfter determining, the hunting zone of the initial solution in Multipurpose Optimal Method is fixed on [0, Vmax], for
Obtain approximate optimal solution, can be right by choosing optimal candidate (V, I) from some row solutions, to this, the present embodiment is in interval
[0,Vmax] to choose 50 (V, I) uniformly right, is defined as (Vi,Ii), i=1,2 ..., 50;Corresponding output Pi=Vi*Ii,
Assume that peak power output is Pnear_opt=Pk, corresponding (V, I) is to for (Vk,Ik), therefore optimum running voltage is in interval
[Vk-1,Vk+1] (the P-V curve of photovoltaic system is unimodal curve), corresponding electric current interval is [Ik-1,Ik+1], wherein electric current can be with
The increase of voltage and reduce;Therefore, by the decision space [0, V of initial multi-objective optimization questionmax]×[0,Imax] reduce
[Vk-1,Vk+1]×[Ik-1,Ik+1];So can reduce the search volume of solution, obtain a less and more accurate scope, improve
The time efficiency of algorithm.
Step 2.3, to arrange preference point be p (m)=(p1(m),p2(m));p1M () is m-th cycle TmUnder minimize power
Function f1The expected value of (x), p2M () is m-th cycle TmLower current error function f2The expected value of (x);Preference neighborhood is set
B=(b1,b2);b1And b2Represent length and the width of preference neighborhood respectively;
Step 2.4, utilize formula (8) by m-th cycle TmFollowing i-th initial individualsInitial weight vector wiReflect
It is mapped to the i-th initial individuals in the preference neighborhood b of preference point p (m), after being mappedWeight vector wi', and
wi'=(wi′1,wi′2);Thus the weight vector set w '={ w after being mapped1′,w′2,…,wi′,…,wn' }:
wia'=2 × ba×wia+pa(m)-ba, a=1,2 (8)
The weight vector that Fig. 3 gives in two objective optimisation problems is mapped in the neighborhood of preference point p=(0.5,0.2) by formula 8,
Weight vector after being mapped, wherein Size of Neighborhood is b=(0.25,0.25).
Step 2.5, utilize formula (9) by i-th initial individualsWeight vector wi' be converted to i-th initial individualsThe weight vector λ with preference informationi, thus obtain the weight vector set with preference information
λ={ λ1,λ2,…,λi,…,λn}:
Step 2.5, calculating i-th initial individualsInitial weight vector wiAt the beginning of individual with remaining n-1 respectively
Euclidean distance between beginning weight vector, obtains i-th initial individualsEuclidean distance set, from described Euclidean distance
Set is chosen minimum front M apart from corresponding individuality as i-th initial individualsM nearest-neighbors
Individual;
Step 2.6, individual from described i-th with described select probability δM nearest-neighbors individuality in choose three
Individual or probability with 1-δ is from described initial population PL-1M () is chosen three individualities, and utilize differential evolution operator to selected
Three individualities taken carry out cross and variation, thus produce m-th cycle TmThe i-th of lower the L time circulation is the most individual
Step 2.7, repetition step 2.6 are to population PL-1M in (), each individuality carries out cross and variation, thus produce the L time circulation
N the new individual new population constituted
Step 2.8, the new population Q that the L time is circulatedL(m) and the population P of the L-1 time circulationL-1M () merges, obtain
M-th cycle TmThe merging population R of lower the L time circulationL(m);
Step 2.9, utilize Chebyshev's formula from described the L time circulation merging population RLM () selects and there is preference information
Mate most n of weight vector set λ individual constitute the population P circulated the L timeL(m);
Step 2.10, L+1 is assigned to L, and judges L > LmaxWhether set up, if setting up, then it represents that obtain the m-th cycle
TmLower LmaxThe population P of secondary circulationLmax(m), and from described population PLmaxM () is chosen maximum power point, with described maximum work
Voltage and current corresponding to rate point is m-th cycle T to be obtainedmUnder voltage Vr(m) and electric current Ir(m);If no
Set up, then return step 2.6;
Step 2.11, by voltage Vr(m) and electric current IrM () is as VrAnd IrSubstitution minimizes power function f1In (x), obtain
f1' (x), it is judged that f1' (x) < p1M whether () set up, if setting up, then by f1' (x) is as the m+1 cycle Tm+1Under power
Littleization expected value;So can constantly update the preference point of initial setting up in algorithm, dynamically updating of preference information improves this reality
Execute the precision of example.Again by m-th cycle TmUnder LmaxThe population P of secondary circulationLmaxM () is as the m+1 cycle Tm+1Under
Initial population P0(m+1);Owing to the number of times of environmental transients extraneous in a day is little, therefore the kind under a upper moment maximum power point
Group has directive significance to subsequent time, therefore in the present embodiment using the final population in a upper moment as the initial population of subsequent time.
The convergence rate of population during dynamically more novel species accelerates algorithm so that the speed of service of algorithm faster, when decreasing the operation of algorithm
Between;
Step 2.12, make m+1 be assigned to m, and return step 2.2.
Fig. 4 gives the preference multi-objective optimization algorithm based on weight vector algorithm flow chart under a cycle.
Claims (3)
1. an acquisition methods for photovoltaic system maximum power point based on multiple-objection optimization, is characterized in that carrying out as follows:
Step 1, the tracking problem of described photovoltaic system maximum power point is converted to the multi-objective optimization question as shown in formula (1):
In formula (1), VrAnd IrRepresent the accessed voltage and current corresponding to maximum power point respectively;VmaxAnd ImaxRespectively
Represent given illumination and at a temperature of maximum voltage and maximum current;f1X () expression minimizes power function, and have:
f1(x)=Vmax×Imax-Vr×Ir (2)
In formula (1), f2X () represents current error function, and have:
In formula (3), IPVFor the photovoltaic electric current generated by illumination;I0For diode saturation current;Q is the quantity of electric charge of electronics;k
For Boltzmann constant;T is the absolute temperature of solaode;A is desirability figure, A ∈ [1,2];RSFor series equivalent electricity
Resistance;RshFor parallel equivalent resistance;
Step 2, utilize Multipurpose Optimal Method that described multi-objective optimization question is optimized, obtain corresponding to maximum power point
Voltage VrWith electric current Ir。
2. according to the acquisition methods described in claim, it is characterized in that, described Multipurpose Optimal Method is based on weight vector inclined
Good multi-objective optimization algorithm, and carry out as follows:
Step 2.1, definition select probability are δ;Definition cyclic variable is L;Definition maximum cycle is Lmax;Delimiting period
For T;Defined variable m, initializes m=1;
In m-th cycle TmUnder, produce initial population P0M () and corresponding initial weight vector set w, be designated asW={w1,w2,…,wi,…,wn};Represent the
I initial individuals, and WithRepresent i-th initial individuals respectivelyInitial voltage and initial current;wiRepresent i-th individuality piThe initial weight vector of (m);And wi=(wi1,wi2);
wi1And wi2Represent i-th individuality p respectivelyiFirst weights of (m) and the second weights;
Step 2.2, initialization L=1;Binary search sampling site method is utilized to reduce initial population P0The region of search [0, V of (m)max] and
[0,Imax], thus obtain the interval [V of Optimizing Searchk-1,Vk+1] and [Ik-1,Ik+1];
Step 2.3, to arrange preference point be p (m)=(p1(m),p2(m));p1M () is m-th cycle TmUnder minimize power
Function f1The expected value of (x), p2M () is m-th cycle TmLower current error function f2The expected value of (x);Preference neighborhood is set
B=(b1,b2);b1And b2Represent length and the width of preference neighborhood respectively;
Step 2.4, utilize formula (4) by m-th cycle TmFollowing i-th initial individualsInitial weight vector wiReflect
It is mapped to the i-th initial individuals in the preference neighborhood b of preference point p (m), after being mappedWeight vector w 'i, and
w′i=(w 'i1,w′i2);Thus the weight vector set w '={ w ' after being mapped1,w′2,…,w′i,…,w′n}:
wia'=2 × ba×wia+pa(m)-ba, a=1,2 (4)
Step 2.5, utilize formula (5) by i-th initial individualsWeight vector w 'iBe converted to i-th initial individualsThe weight vector λ with preference informationi, thus obtain the weight vector set with preference information
λ={ λ1,λ2,…,λi,…,λn}:
Step 2.5, calculating i-th initial individualsInitial weight vector wiAt the beginning of individual with remaining n-1 respectively
Euclidean distance between beginning weight vector, obtains i-th initial individualsEuclidean distance set, from described Euclidean distance
Set is chosen minimum front M apart from corresponding individuality as i-th initial individualsM nearest-neighbors
Individual;
Step 2.6, individual from described i-th with described select probability δM nearest-neighbors individuality in choose three
Individual or probability with 1-δ is from described initial population PL-1M () is chosen three individualities, and utilize differential evolution operator to selected
Three individualities taken carry out cross and variation, thus produce m-th cycle TmThe i-th of lower the L time circulation is the most individual
Step 2.7, repetition step 2.6 are to population PL-1M in (), each individuality carries out cross and variation, thus produce the L time circulation
N the new individual new population constituted
Step 2.8, the new population Q that the L time is circulatedL(m) and the population P of the L-1 time circulationL-1M () merges, obtain
M-th cycle TmThe merging population R of lower the L time circulationL(m);
Step 2.9, utilize Chebyshev's formula from described the L time circulation merging population RLM () selects and there is preference information
Mate most n of weight vector set λ individual constitute the population P circulated the L timeL(m);
Step 2.10, L+1 is assigned to L, and judges L > LmaxWhether set up, if setting up, then it represents that obtain the m-th cycle
TmLower LmaxThe population P of secondary circulationLmax(m), and from described population PLmaxM () is chosen maximum power point, with described maximum work
Voltage and current corresponding to rate point is m-th cycle T to be obtainedmUnder voltage Vr(m) and electric current Ir(m);If no
Set up, then return step 2.6;
Step 2.11, by voltage Vr(m) and electric current IrM () is as VrAnd IrSubstitution minimizes power function f1In (x), obtain
f1' (x), it is judged that f1' (x) < p1M whether () set up, if setting up, then by f1' (x) is as the m+1 cycle Tm+1Under power
Littleization expected value;Again by m-th cycle TmUnder LmaxThe population P of secondary circulationLmaxM () is as the m+1 cycle Tm+1Under
Initial population P0(m+1);
Step 2.12, make m+1 be assigned to m, and return step 2.2.
3. according to the acquisition methods described in claim, it is characterized in that, described binary search sampling site method is to carry out as follows:
Step 2.2.1, to [0, Vmax] carry out uniform sampling, and after calculating the output of each sampled point, choose maximum work output
Employing point corresponding to rate is designated as Vk;
Step 2.2.2, choose [Vk-1,Vk+1] and [I of correspondencek-1,Ik+1] interval as Optimizing Search.
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