CN108398982B - A kind of maximum power tracking method of photovoltaic array under local shadow - Google Patents
A kind of maximum power tracking method of photovoltaic array under local shadow Download PDFInfo
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- G05F—SYSTEMS FOR REGULATING ELECTRIC OR MAGNETIC VARIABLES
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Abstract
The present invention relates to a kind of maximum power tracking methods of photovoltaic array under local shadow, comprising the following steps: S1: according to the characteristic of photovoltaic cell, establishing the photovoltaic array model under the conditions of local shades；S2: using the QPSO innovatory algorithm based on Bloch spherical surface to photovoltaic array model solution, output power is obtained；S3: using the output power of acquisition as fitness function, by iterative search, the MPPT maximum power point tracking of photovoltaic array is realized.The diversity of particle can be still kept in the iteration later period based on the QPSO innovatory algorithm of Bloch spherical surface, the probability for obtaining globally optimal solution can be improved, the present invention can avoid the oscillation near maximum power point when realizing photovoltaic array maximal power tracing, improve steady-state performance.
Description
Technical field
The present invention relates to technical field of photovoltaic power generation, more particularly, to a kind of maximum power of photovoltaic array under local shadow
Tracking.
Background technique
The energy plays an extremely important role in terms of creating new opportunities and boosting economic growth, while world economy
Development and the growth of population have encouraged world energy sources demand in turn.The key problem of China's energy resource structure is shown: first is that
Energy resource structure is based on coal；Second is that Oil Safety problem increasingly significant；Third is that coal-smoke pollution is brought sternly to ecological environment
Weight problem.It can be seen that Optimization of Energy Structure is imperative, slowly increase the ratio of green regenerative energy sources, reduces fossil energy
The use in source.
Solar energy power generating is considered as new energy technology most promising on our times, each developed country
Investment a huge sum of money competitively researchs and develops, and actively pushes forward industrialization process, application of exploiting market energetically.But photovoltaic power generation
Industry also encounters many problems in development: photovoltaic cell is with high costs, incident photon-to-electron conversion efficiency is lower, the danger of partial occlusion
Evil.
MPPT maximum power point tracking is to reduce cost of electricity-generating, improve the most direct effective method of generating efficiency.Existing big portion
Divide maximum power point tracing method is all the uniform illumination that photovoltaic array is subject to using premise, and is had ignored in actual life
In, the probability that photovoltaic array is blocked is very big.When photovoltaic array is by partial occlusion, so that traditional MPPT maximum power point tracking side
Method is easily trapped into local optimum and is difficult to search global optimum.
Perturbation observation method and conductance increment method are relatively early to apply the maximum power tracking method in photovoltaic generating system, quilt
Referred to as traditional maximum power tracking method.Perturbation observation method control thinking is simple, realizes more convenient, it can be achieved that maximum power
The tracking of point, improves the utilization efficiency of system.But since perturbation observation method is only with the output power before and after photovoltaic cell twice
It is studied for object, is not accounted for external environment condition variation to the influence of output power twice before and after photovoltaic array, make
It is easy to appear during " erroneous judgement " of method, " erroneous judgement " increases the tracking time, the delivery efficiency of photovoltaic array is reduced,
The failure for leading to tracking when serious, prevents this method from being accurately tracked by peak power output.
Conductance increment method tracking accuracy is higher, and control effect is good, is not influenced by power time curve.But this method is to biography
Sensor has higher requirement, while the selection of step-length also will affect the performance of algorithm, in the faster feelings of extraneous changes in environmental conditions
Also it will appear " erroneous judgement " under condition.
In recent years, constantly improve with intelligent algorithm, particle swarm algorithm, genetic algorithm, FUZZY ALGORITHMS FOR CONTROL and nerve
Network algorithm etc. is introduced in the maximal power tracing control of photovoltaic generating system.The use of these algorithms, effectively improves
The precision of maximal power tracing, reduces energy loss.But intelligent algorithm often has that control parameter is more, and control thought is multiple
Miscellaneous, to the demanding disadvantage of hardware, this constrains the application to engineering practice of these algorithms to a certain extent, and with light
The running environment of photovoltaic array becomes to become increasingly complex, due to building, trees block or dust etc. causes photovoltaic array surface
The non-uniform situation of the intensity of illumination being subject to occurs often, at this point, power vs. voltage (P-U) characteristic curve of photovoltaic array will go out
Existing multiple peak values.Part intelligent algorithm lacks the ability of global optimizing, is only applicable in as traditional maximum power tracking method
In single peak maximal power tracing system, when tracking to multimodal valve system, tracking failure will cause.Therefore, one is studied
Kind has the maximum power tracking method of global optimizing characteristic very crucial for improving photovoltaic efficiency.
In the application of photovoltaic array, PSO, QPSO and BQPSO algorithm are more commonly used algorithms, but three because particle it is more
Easily there is " precocity " and local convergence problem in sample missing,
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind to be based on Bloch ball
The maximum power tracking method of the QPSO innovatory algorithm in face, raising system stability photovoltaic array under local shadow.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of maximum power tracking method of photovoltaic array under local shadow, comprising the following steps:
(1) according to the characteristic of photovoltaic cell, the photovoltaic array model under the conditions of local shades is established；
(2) photovoltaic array model solution is obtained using the QPSO innovatory algorithm (IBQPSO algorithm) based on Bloch spherical surface
Take output power；
(3) using the output power of acquisition as fitness function, by iterative search, the maximum work of photovoltaic array is realized
The tracking of rate point.
In the step (two), the QPSO innovatory algorithm based on Bloch spherical surface specifically includes the following contents:
1) algorithm parameter is set and initializes particle populations, initializes the phase of particle, each particle includes three positions
Information, specific steps include:
101) under Bloch spherical coordinate, point can by two angle, θs andIt determines, quantum bit Bloch ball
Areal coordinate indicates are as follows:
102) it is encoded using the Bloch spherical coordinate of quantum bit, then i-th of particle P in populationiBloch spherical coordinate
Are as follows:
In formula,θij=π × rand, rand are the random number in [0,1] section；I=1,2 ..., m, m
For population scale；N is the number of optimized variable；In IBQPSO algorithm, each particle takes up space three positions simultaneously, i.e., together
Shi represents three optimization solutions, respectively X solution, Y solution, Z solution:
Piz=(cos θi1,cosθi2,… ,cosθin)；
103) remember i-th of particle PiOn j-th of quantum bit Bloch coordinate be [xij,yij,zij]T, j=1,2 ...
N, n are the number of optimized variable；Optimization problem solution space jth dimension value range beThen by unit space In=
[-1,1]nIt is mapped to the transformation for mula of optimization problem solution space are as follows:
104) terminate initialization, export primary information.
2) fitness value of each particle is calculated；
3) itself and global optimum's phase are updated according to the fitness value of particle；
4) utilize adaptive Quantum rotating gate, two phase parameter θ to the quantum bit of global optimum's phase andInto
Row adjustment, realizes the location updating of particle, and map that solution space；
Adaptive Quantum rotating gate is shown below:
Wherein, the corresponding rotation angle α of current iterationiIs defined as:
In formula, αminIt is minimum rotation angle, takes 0.01 × pi；αmaxIt is maximum rotation angle, takes 0.5 × pi；fiRefer to current
The adaptive value of i-th of particle；fminIt is the minimum adaptive value in contemporary particle；fmaxIt is the maximum adaptation value in contemporary particle；gen
It is current the number of iterations；Maxgen is the maximum number of iterations of algorithm setting.
More new formula is shown below:
5) it calculates each particle fitness value and evaluates, itself is updated according to the fitness of particle, select individual optimum angle,
And obtain global optimum's phase；
According to the fitness value of particle, the initial position of particle is judged, and by the position of this particle and other particles
Position is compared；The highest particle of fitness value is individual optimal particle, and phase is individual optimum angle；
Individual optimal particle is compared with the fitness value of four particles of upper level, if fitness value is greater than upper one
The highest fitness value of grade, then the individual optimal particle is global optimum's particle, and phase is global optimum's phase；If fitness
Value is less than the highest fitness value of upper level, then global optimum's particle is upper level highest fitness value particle, and phase is complete
Office's optimum angle.
6) it saves individual optimum angle and judges whether to reach maximum number of iterations, if not up to, step 7) is gone to, if reaching
It arrives, goes to step 8)；
7) variation particle is selected with mutation probability pa, utilizes two phases of adaptive Quantum rotating gate adjustment quantum bit
Parameter θ andThe variation for realizing particle, calculates the fitness value of new population and evaluation, goes to step 4)；
Adaptive Quantum rotating gate is shown below:
Wherein, the corresponding rotation angle α of current iterationiIs defined as:
To each particle according to mutation probability, two phase parameter θ of adaptive Quantum rotating gate adjustment quantum bit are utilized
WithRealize that particle variations, particle variations formula are shown below:
More new formula is shown below:
8) optimal solution is exported.
In the maximum power tracking method of photovoltaic array under local shadow of the present invention, photovoltaic is indicated using the position of particle
The voltage of array passes through constantly iteration by photovoltaic array output photovoltaic array power and as the fitness function of particle
Search, searches out photovoltaic array maximum power, reaches optimizing purpose.
Compared with prior art, the invention has the following advantages that
1, the method for the present invention is based on Bloch spherical surface, is improved QPSO, and maximum power point of photovoltaic array is being applied to
When tracking, there is faster tracking velocity to maximum power point, improve the dynamic response of system, avoid in maximum power point
Neighbouring oscillation improves the steady-state performance of system；
2, IBQPSO algorithm carries out particle coding using Bloch spherical surface, and each particle represents three positions, in iteration three
A position synchronized update, while more flexible adaptive Quantum rotating gate is utilized in particle variations, thus after iteration
Phase still keeps the diversity of particle, can be improved the ergodic to solution space, to improve the probability for obtaining globally optimal solution；
3, the method for the present invention realizes the update and variation of particle using adaptive Quantum rotating gate, is avoiding falling into part most
While excellent, realize stable steady state power and export, the variation for environment, including under local shades and shade catastrophe
Maximum power point can be found, the tracking ability of system is enhanced.
Detailed description of the invention
Fig. 1 is the flow chart of the method for the present invention；
Fig. 2 is the flow chart of the QPSO innovatory algorithm based on Bloch spherical surface；
Fig. 3 is the photovoltaic array P-U performance diagram under the present embodiment different illumination conditions, wherein Fig. 3 (a) is standard
The P-U characteristic curve of photovoltaic array under illumination, Fig. 3 (b) are the P-U characteristic curve of the photovoltaic array under shadow condition 1,2,3,
Fig. 3 (c) is the P-U characteristic curve of the photovoltaic array under shadow condition 4,5,6；
Fig. 4 be in the embodiment of the present invention photovoltaic array under standard illumination condition using IBQPSO, PSO, QPSO and
The P-T curve simulation result diagram of BQPSO algorithm；
Fig. 5 be in the embodiment of the present invention photovoltaic array under six kinds of shadowed conditions using IBQPSO, PSO, QPSO and
The P-T Dependence Results figure of BQPSO algorithm, wherein Fig. 5 (a) is the P-T curve of the photovoltaic array under shadow condition 1, Fig. 5 (b)
For the P-T curve of the photovoltaic array under shadow condition 2, Fig. 5 (c) is the P-T curve of the photovoltaic array under shadow condition 3, Fig. 5
It (d) is the P-T curve of the photovoltaic array under shadow condition 4, Fig. 5 (e) is the P-T curve of the photovoltaic array under shadow condition 5, figure
5 (f) be the P-T curve of the photovoltaic array under shadow condition 6；
Fig. 6 be in the embodiment of the present invention photovoltaic array under shade catastrophe using IBQPSO, PSO, QPSO and
The P-T curve simulation result diagram of BQPSO algorithm, wherein Fig. 6 (a) is the P-T curve of the photovoltaic array in situation 1, Fig. 6 (b)
For the P-T curve of the photovoltaic array in situation 2, Fig. 6 (c) is the P-T curve of the photovoltaic array in situation 3, and Fig. 6 (d) is situation 4
Under photovoltaic array P-T curve.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
Embodiment
As shown in Figure 1, the present invention relates to a kind of maximum power tracking method of photovoltaic array under local shadow, this method packet
Include following steps:
(1) according to the characteristic of photovoltaic cell, the photovoltaic array model under the conditions of local shades is established；
(2) photovoltaic array model solution is obtained using the QPSO innovatory algorithm (IBQPSO algorithm) based on Bloch spherical surface
Take output power；
(3) using the output power of acquisition as fitness function, by iterative search, the maximum work of photovoltaic array is realized
The tracking of rate point.
As shown in Fig. 2, QPSO innovatory algorithm in the present invention based on Bloch spherical surface specifically includes the following steps:
1) parameter of the QPSO innovatory algorithm based on Bloch spherical surface is set, initializes particle populations, and to the phase of particle
It is initialized；
2) fitness value of each particle is calculated；
3) itself and global optimum's phase are updated according to the fitness value of particle；
4) utilize adaptive Quantum rotating gate, two phase parameter θ to the quantum bit of global optimum's phase andInto
Row adjustment, realizes the location updating of particle, and map that solution space；
5) it calculates each particle fitness value and evaluates, itself is updated according to the fitness of particle, selects individual optimum angle,
And select global optimum's phase；
6) it saves individual optimum angle and judges whether to reach maximum number of iterations, if not up to, step 7) is gone to, if reaching
It arrives, goes to step 8)；
7) selection variation particle, calculates adaptive Quantum rotating gate, carries out adaptive grain using adaptive Quantum rotating gate
After son variation, step 4) is executed；
8) optimal solution is exported, the variation of voltage is obtained according to the particle phase after variation, further obtains output power.
The particular content of step 1) are as follows:
A point under Bloch spherical coordinate can by two angle, θs andIt determines, quantum bit is sat with Bloch spherical surface
Mark indicates are as follows:
It is encoded in BQPSO algorithm using the Bloch spherical coordinate of quantum bit, IBQPSO algorithm of the invention is using identical
Coding mode, it may be assumed that
In formula, PiFor the Bloch spherical coordinate of i-th of particle in population；θij=π × rand,
Rand is the random number in [0,1] section；I=1,2 ..., m, m are population scale；N is the number of optimized variable；It is calculated in IBQPSO
In method, each particle takes up space three positions simultaneously, i.e., represents three optimizations solutions simultaneously, and respectively X solution, Y solution, Z are solved:
Piz=(cos θi1,cosθi2,… ,cosθin)
Remember i-th of particle PiOn j-th of quantum bit Bloch coordinate be [xij,yij,zij]T, j=1,2 ... n, n are
The number of optimized variable；Optimization problem solution space jth dimension value range beThen by unit space In=[- 1,
1]nIt is mapped to the transformation for mula of optimization problem solution space are as follows:
Terminate initialization, exports primary information.
The particular content of step 4) are as follows:
Using adaptive Quantum rotating gate, two phase parameter θ to the quantum bit of global optimum's phase andIt carries out
Adjustment, realizes the location updating of particle, and map that solution space；
Adaptive Quantum rotating gate is shown below:
More new formula is shown below:
Wherein, αiFor the corresponding rotation angle of current iteration.
The corresponding rotation angle α of current iterationiIs defined as:
In formula, αminIt is minimum rotation angle, takes 0.01 × pi；αmaxIt is maximum rotation angle, takes 0.5 × pi；fiRefer to current
The adaptive value of i-th of particle；fminIt is the minimum adaptive value in contemporary particle；fmaxIt is the maximum adaptation value in contemporary particle；gen
It is current the number of iterations；Maxgen is the maximum number of iterations of algorithm setting.
The particular content of step 5) are as follows:
According to the fitness value of particle, the initial position of particle is judged；The position of particle and other all particles is carried out
Fitness value compares, and the highest particle of fitness value is individual optimal particle, and phase is individual optimum angle；
Individual optimal particle is compared with the fitness value of four particles of upper level, if fitness value is greater than upper one
The highest fitness value of grade, then the individual optimal particle is global optimum's particle, and phase is global optimum's phase；If fitness
Value is less than the highest fitness value of upper level, then global optimum's particle is upper level highest fitness value particle, and phase is complete
Office's optimum angle.
To prove that effectiveness of the invention and dominance, the present embodiment have carried out photovoltaic array in difference to IBQPSO algorithm
Under the conditions of emulation, and compared with PSO, QPSO and BQPSO algorithm have carried out simulation result.The present embodiment devises six kinds of yin
Shadow situation compares analysis for the simulation result of six kinds of shadowed conditions and standard illumination condition.
Fig. 3 is photovoltaic array power vs. voltage (P-U) performance diagram under the conditions of local shades, and photovoltaic array is being marked
Agree to do a favour condition, that is, refer to reference temperature and with reference under intensity of illumination, shown in P-U characteristic curve such as Fig. 3 (a).In real life, photovoltaic
The probability that array is blocked is very big.Photovoltaic array can be because surrounding tree shade, black clouds, house etc. block and generate local shades and ask
Topic, the present invention design six kinds of shadow conditions, analyze the P-U characteristic curve of photovoltaic array under local shadow:
1) shadow condition 1: shade is distributed as [3:2:1], and wherein 1B, 1C, 1D irradiation level are 800W/m2, 2C, 2D irradiation
Degree is 600W/m2, 3D irradiation level is 200W/m2。
2) shadow condition 2: shade is distributed as [2:1:0], and wherein 1C, 1D irradiation level are 800W/m2, 2D irradiation level is
600W/m2。
3) shadow condition 3: shade is distributed as [2:0:0], and wherein 1C, 1D irradiation level are 800W/m2, shadow condition 1,2 and 3
Under P-U characteristic curve such as 3 figures (b) shown in:
Analysis is carried out to Fig. 3 (b) to obtain: when intensity of illumination locating for photovoltaic cell is not identical in photovoltaic array, photovoltaic
Multi-peak is presented in the P-U characteristic curve of array.Four peak values, three peaks are presented in P-U characteristic curve under shadow condition 1,2 and 3 respectively
Value and bimodal.
4) shadow condition 4: shade is distributed as [3:2:1], and wherein 1B, 1C, 1D temperature are 50 DEG C, and 2C, 2D temperature are 35
DEG C, 3D temperature is 15 DEG C.
5) shadow condition 5: shade is distributed as [2:1:0], and wherein 1C, 1D temperature are 50 DEG C, and 2D temperature is 35 DEG C.
6) shadow condition 6: shade branch is [2:0:0], and wherein 1C, 1D temperature are 50 DEG C, under shadow condition 4,5 and 6
Shown in P-U characteristic curve such as Fig. 3 (c):
Analysis is carried out to Fig. 3 (c) to obtain: when temperature locating for photovoltaic cell is not identical in photovoltaic array, photovoltaic array
P-U characteristic curve multi-peak can also be presented.To obtain, no matter as intensity of illumination difference or as caused by temperature difference
It requires to carry out maximum power point of photovoltaic array tracking under the conditions of local shades.
Fig. 4 is P-T curve emulation of IBQPSO, PSO, QPSO and BQPSO algorithm under photovoltaic array standard illumination condition
Result figure, analyzing Fig. 4 can obtain: PSO, QPSO and BQPSO algorithm the convergence speed are slow and oscillation early period is serious；IBQPSO is calculated
Method can be realized the steady state power output of fast and stable, improve photovoltaic efficiency significantly.
Fig. 5 is simulation result diagram of IBQPSO, PSO, QPSO and BQPSO algorithm under six kinds of shadowed conditions of photovoltaic array,
In real life, the probability that photovoltaic array is blocked is very big, when the photovoltaic cell in photovoltaic array is strong in different illumination
When under the conditions of degree and temperature, multi-peak characteristic is presented in the P-U curve of photovoltaic array.
Standard illumination condition and the convergence time of four kinds of methods under six kinds of shadowed conditions are as shown in table 1:
1 four kinds of method simulation convergence times of table
Combined standard illumination condition, the simulation result diagram under six kinds of shadow conditions and table 1 carry out analysis and obtain: PSO,
QPSO and BQPSO algorithm the convergence speed is slow and can fall into local optimum in multimodal situation.IBQPSO algorithm the convergence speed is fast, energy
It enough realizes stable steady state power output, improves photovoltaic efficiency significantly.
It being mutated for shade, the present embodiment devises four kinds of catastrophes, specific as follows:
1) standard illumination → 1 → shadow condition of shadow condition, 2 → shadow condition 3
2) 3 → shadow condition of shadow condition, 2 → shadow condition, 1 → standard illumination
3) standard illumination → 4 → shadow condition of shadow condition, 5 → shadow condition 6
4) 6 → shadow condition of shadow condition, 5 → shadow condition, 4 → standard illumination
For above-mentioned four kinds of catastrophes, it is utilized respectively IBQPSO, BQPSO, QPSO and PSO algorithm and is emulated, specifically
Simulation result is as shown in Figure 6.It will be appreciated from fig. 6 that PSO algorithm no matter in intensity of illumination or temperature jump convergence rate it is slow and
It is vibrated seriously at mutation, while local optimum may be fallen into, improve photovoltaic efficiency cannot.QPSO and BQPSO is calculated
Although method convergence rate is faster than PSO algorithm, the oscillation at mutation is less than PSO algorithm, but still may fall into local optimum, makes
Photovoltaic efficiency cannot improve.IBQPSO algorithm the convergence speed is most fast and oscillation is not present near maximum power point, real
Show stable steady state power output, improves photovoltaic efficiency significantly.
In conclusion no matter IBQPSO algorithm can be quickly high in the case where intensity of illumination enhances or weakens suddenly
Converge to global maximum power point to effect.Multi-peaks phenomenon is showed since the variation of temperature also results in photovoltaic array P-U curve.
Obtained by emulation: no matter temperature increases or reduces suddenly in the case where also can quickly and efficiently to converge to the overall situation maximum
Power points.Therefore IBQPSO algorithm can quickly and efficiently realize the tracking of global maximum power point under complicated obstruction conditions, show
It lands and improves photovoltaic efficiency.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
The staff for being familiar with the art in the technical scope disclosed by the present invention, can readily occur in various equivalent modifications or replace
It changes, these modifications or substitutions should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with right
It is required that protection scope subject to.
Claims (3)
1. a kind of maximum power tracking method of photovoltaic array under local shadow, which comprises the following steps:
S1: according to the characteristic of photovoltaic cell, the photovoltaic array model under the conditions of local shades is established；
S2: using the QPSO innovatory algorithm based on Bloch spherical surface to photovoltaic array model solution, output power is obtained；Including with
Lower step:
1) parameter of the QPSO innovatory algorithm based on Bloch spherical surface is set, the phase of particle populations and particle is initialized；
2) fitness value of each particle is calculated；
3) itself and global optimum's phase are updated according to the fitness value of particle；
4) adaptive Quantum rotating gate is utilized, the phase parameter of quantum bit is adjusted, realizes the location updating of particle, and
Map that solution space；
5) it calculates each particle fitness value and evaluates, itself is updated according to the fitness of particle, selects individual optimum angle, and select
Global optimum's phase out；
6) it saves individual optimum angle and judges whether to reach maximum number of iterations, if not up to, going to step 7), if reaching,
Go to step 8)；
7) particle is selected according to mutation probability, calculates adaptive Quantum rotating gate, carried out using adaptive Quantum rotating gate adaptive
After particle variations, step 4) is executed；
8) optimal solution is exported；
In the step 4) and step 7), phase parameter include two phase parameter θ and
The particular content of step 1) are as follows:
101) by phase parameter θ andTwo angles determine the point under Bloch spherical coordinate, quantum bit Bloch spherical coordinate
It indicates are as follows:
102) it is encoded using the Bloch spherical coordinate of quantum bit, then i-th of particle P in populationiBloch spherical coordinate are as follows:
In formula,θij=π × rand, rand are the random number in [0,1] section；I=1,2 ..., m, m are population
Scale；N is the number of optimized variable；In IBQPSO algorithm, each particle takes up space three positions simultaneously, i.e., represents simultaneously
Three optimization solutions, respectively X solution, Y solution, Z solution:
Piz=(cos θi1,cosθi2,…,cosθin)
103) remember i-th of particle PiOn j-th of quantum bit Bloch coordinate be [xij,yij,zij]T, j=1,2 ... n, n are excellent
Change the number of variable；Optimization problem solution space jth dimension value range beThen by unit space In=[- 1,1]n
It is mapped to the transformation for mula of optimization problem solution space are as follows:
104) terminate initialization, export primary information；
The particular content of the step 4) are as follows:
Using adaptive Quantum rotating gate, two phase parameter θ to quantum bit andIt is adjusted, realizes the position of particle
It updates, and maps that solution space；
Adaptive Quantum rotating gate U is shown below:
More new formula is shown below:
Wherein, αiFor the corresponding rotation angle of current iteration；
The corresponding rotation angle α of the current iterationiIs defined as:
In formula, αminIt is minimum rotation angle；αmaxIt is maximum rotation angle；fiRefer to the adaptive value of current i-th of particle；fminIt is the present age
Minimum adaptive value in particle；fmaxIt is the maximum adaptation value in contemporary particle；Gen is current the number of iterations；Maxgen is
The maximum number of iterations of algorithm setting；
S3: using the output power of acquisition as fitness function, by iterative search, realize the maximum power point of photovoltaic array with
Track.
2. a kind of maximum power tracking method of photovoltaic array under local shadow according to claim 1, which is characterized in that
The particular content of the step 5) are as follows:
According to the fitness value of particle, the initial position of particle is judged, and by the position of the position of this particle and other particles
It is compared；The highest particle of fitness value is individual optimal particle, and phase is individual optimum angle；By individual optimal particle
It is compared with the fitness value of four particles of upper level, it, should if fitness value is greater than the highest fitness value of upper level
Individual optimal particle is global optimum's particle, and phase is global optimum's phase；If the highest that fitness value is less than upper level is suitable
Angle value is answered, then global optimum's particle is upper level highest fitness value particle, and phase is global optimum's phase.
3. a kind of maximum power tracking method of photovoltaic array under local shadow according to claim 1, which is characterized in that
The step 7) is identical as the corresponding rotation angle of current iteration in the adaptive Quantum rotating gate that step 4) uses.
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