CN108398982A - 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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- 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
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- G05F1/67—Regulating electric power to the maximum power available from a generator, e.g. from solar cell
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Abstract
The present invention relates to a kind of maximum power tracking methods of photovoltaic array under local shadow, include 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 algorithms based on Bloch spherical surfaces 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 stage based on the QPSO innovatory algorithms of Bloch spherical surfaces, 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 behaviour.
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 technology
The energy is creating new opportunities and the aspect that boosts economic growth plays extremely important role, 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：When
Energy resource structure is based on coal；Second is that Oil Safety problem increasingly significant；Third, 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
Input a huge sum of money competitively researchs and develops, and actively pushes forward industrialization process, application of exploiting market energetically.But photovoltaic generation
Industry also encounters many problems in development：Photovoltaic cell is with high costs, electricity conversion is relatively low, 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 ratio 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 susceptible to during " erroneous judgement " of method, " erroneous judgement " increases the tracking time, reduces the delivery efficiency of photovoltaic array,
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 passing
Sensor has higher requirement, while the selection of step-length will also influence the performance of algorithm, in the faster feelings of extraneous changes in environmental conditions
Also it will appear under condition " erroneous judgement ".
In recent years, with the constantly improve of intelligent algorithm, particle cluster 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, the high disadvantage of requirement to 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 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 being tracked to multimodal valve system, tracking can be caused to fail.Therefore, one is studied
Maximum power tracking method of the kind with global optimizing characteristic is 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,
Invention content
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind being based on Bloch spherical surfaces
QPSO innovatory algorithms, improve system stability photovoltaic array under local shadow maximum power tracking method.
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, includes 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 algorithms (IBQPSO algorithms) based on Bloch spherical surfaces
Take output power；
(3) using the output power of acquisition as fitness function, by iterative search, the maximum work of photovoltaic array is realized
Rate point tracks.
In the step (two), the QPSO innovatory algorithms based on Bloch spherical surfaces specifically include 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 coordinates, point can by two angle, θs andIt determines, quantum bit Bloch spherical surfaces
Coordinate representation is：
102) the Bloch spherical coordinates of quantum bit are used to encode, then i-th of particle P in populationiBloch spherical coordinates
For：
In formula,θij=π × rand, rand are the random number in [0,1] section；I=1,2 Λ, m, m are
Population scale；N is the number of optimized variable；In IBQPSO algorithms, each particle takes up space three positions simultaneously, i.e., simultaneously
Represent three optimization solutions, respectively X solutions, Y solutions, Z solutions：
Piz=(cos θi1,cosθi2,Λ,cosθin)；
103) remember i-th of particle PiOn j-th of quantum bit Bloch coordinates be [xij,yij,zij]T, j=1,2, Λ n, n
For the number of optimized variable；Optimization problem solution space jth dimension value range beThen by unit space In=[- 1,
1]nThe transformation for mula for being mapped to optimization problem solution space is：
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 iterationiDefinition be：
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 iterations；Maxgen is the maximum iteration 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 more 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 upper level highest fitness value particle less than the highest fitness value of upper level, then global optimum's particle, and phase is complete
Office's optimum angle.
6) it preserves individual optimum angle and judges whether to reach maximum iteration, if not up to, step 7) is gone to, if reaching
It arrives, goes to step 8)；
7) with mutation probability pa selection variation particles, two phases of quantum bit are adjusted using adaptive Quantum rotating gate
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 iterationiDefinition be：
To each particle according to mutation probability, two phase parameter θ of quantum bit are adjusted using adaptive Quantum rotating gate
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 exports photovoltaic array power by photovoltaic array and is used as the fitness function of particle, passes through constantly iteration
Search, searches out photovoltaic array maximum power, reaches optimizing purpose.
Compared with prior art, the present invention has the following advantages：
1, the method for the present invention is based on Bloch spherical surfaces, is improved QPSO, applied to maximum power point of photovoltaic array
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 behaviour of system；
2, IBQPSO algorithms carry out particle coding using Bloch spherical surfaces, 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, to after iteration
Phase still keeps the diversity of particle, can improve 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 being absorbed in part most
While excellent, stable steady state power output is realized, under the variation for environment, including local shades and shade catastrophe
Maximum power point can be found, the ability of tracking of system is enhanced.
Description of the drawings
Fig. 1 is the flow chart of the method for the present invention；
Fig. 2 is the flow chart of the QPSO innovatory algorithms based on Bloch spherical surfaces；
Fig. 3 is the photovoltaic array P-U performance diagrams under the present embodiment different illumination conditions, wherein Fig. 3 (a) is standard
The P-U characteristic curves of photovoltaic array under illumination, Fig. 3 (b) are the P-U characteristic curves of the photovoltaic array under shadow condition 1,2,3,
Fig. 3 (c) is the P-U characteristic curves of the photovoltaic array under shadow condition 4,5,6；
Fig. 4 is that photovoltaic array applies IBQPSO, PSO, QPSO and BQPSO under standard illumination condition in the embodiment of the present invention
The P-T curve simulation result diagrams of algorithm；
Fig. 5 is that photovoltaic array applies IBQPSO, PSO, QPSO and BQPSO under six kinds of shadowed conditions in the embodiment of the present invention
The P-T Dependence Results figures of algorithm, wherein Fig. 5 (a) is the P-T curves of the photovoltaic array under shadow condition 1, and Fig. 5 (b) is shade
The P-T curves of photovoltaic array in situation 2, Fig. 5 (c) are the P-T curves of the photovoltaic array under shadow condition 3, and Fig. 5 (d) is the moon
The P-T curves of photovoltaic array in shadow situation 4, Fig. 5 (e) are the P-T curves of the photovoltaic array under shadow condition 5, and Fig. 5 (f) is
The P-T curves of photovoltaic array under shadow condition 6；
Fig. 6 is that photovoltaic array applies IBQPSO, PSO, QPSO and BQPSO under shade catastrophe in the embodiment of the present invention
The P-T curve simulation result diagrams of algorithm, wherein Fig. 6 (a) is the P-T curves of the photovoltaic array in situation 1, and Fig. 6 (b) is situation 2
Under photovoltaic array P-T curves, Fig. 6 (c) is the P-T curves of the lower photovoltaic array of situation 3, and Fig. 6 (d) is the light in situation 4
The P-T curves of photovoltaic array.
Specific implementation mode
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 algorithms (IBQPSO algorithms) based on Bloch spherical surfaces
Take output power；
(3) using the output power of acquisition as fitness function, by iterative search, the maximum work of photovoltaic array is realized
Rate point tracks.
As shown in Fig. 2, the QPSO innovatory algorithms based on Bloch spherical surfaces specifically include following steps in the present invention：
1) parameter of the QPSO innovatory algorithms based on Bloch spherical surfaces 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 preserves individual optimum angle and judges whether to reach maximum iteration, 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, and adaptive grain is carried out 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) is：
A point under Bloch spherical coordinates can by two angle, θs andIt determines, quantum bit is sat with Bloch spherical surfaces
Mark is expressed as：
It is encoded using the Bloch spherical coordinates of quantum bit in BQPSO algorithms, IBQPSO algorithms of the invention are using identical
Coding mode, i.e.,：
In formula, PiFor the Bloch spherical coordinates of i-th of particle in population；θij=π × rand,
Rand is the random number in [0,1] section；I=1,2 Λ, m, m are population scale；N is the number of optimized variable；In IBQPSO algorithms
In, each particle takes up space three positions simultaneously, i.e., represents three optimization solutions, respectively X solutions, Y solutions, Z solutions simultaneously：
Piz=(cos θi1,cosθi2,Λ,cosθin)
Remember i-th of particle PiOn j-th of quantum bit Bloch coordinates be [xij,yij,zij]T, j=1,2, Λ 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
The transformation for mula for being mapped to optimization problem solution space is：
Terminate initialization, exports primary information.
The particular content of step 4) is：
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 iterationiDefinition be：
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 iterations；Maxgen is the maximum iteration of algorithm setting.
The particular content of step 5) is：
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 more 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 upper level highest fitness value particle less than the highest fitness value of upper level, then global optimum's 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 algorithms
Under the conditions of emulation, and carried out the comparison of simulation result with PSO, QPSO and BQPSO algorithm.The present embodiment devises six kinds of the moon
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 curves such as Fig. 3 (a).In actual 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 curves of photovoltaic array under local shadow：
1) shadow condition 1：Shade is distributed as [3:2:1], wherein 1B, 1C, 1D irradiation level are 800W/m2, 2C, 2D irradiation level
For 600W/m2, 3D irradiation level is 200W/m2。
2) shadow condition 2：Shade is distributed as [2:1:0], 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], wherein 1C, 1D irradiation level are 800W/m2, shadow condition 1,2 and 3
Under P-U characteristic curves such as 3 figures (b) shown in:
Analysis is carried out to Fig. 3 (b) to obtain：When intensity of illumination in photovoltaic array residing for photovoltaic cell differs, photovoltaic
Multi-peak is presented in the P-U characteristic curves of array.Four peak values, three peaks are presented in P-U characteristic curves under shadow condition 1,2 and 3 respectively
Value and bimodal.
4) shadow condition 4：Shade is distributed as [3:2:1], 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], 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], wherein 1C, 1D temperature are 50 DEG C, under shadow condition 4,5 and 6
Shown in P-U characteristic curves such as Fig. 3 (c)：
Analysis is carried out to Fig. 3 (c) to obtain：When temperature in photovoltaic array residing for photovoltaic cell differs, photovoltaic array
P-U characteristic curves also will present multi-peak.To obtain, no matter by intensity of illumination difference or caused by temperature difference
It is required for carrying 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 realize the steady state power output of fast and stable, significantly increase photovoltaic efficiency.
Fig. 5 is simulation result diagram of IBQPSO, PSO, QPSO and BQPSO algorithm under six kinds of shadowed conditions of photovoltaic array,
In actual 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 degree and temperature condition, multi-peak characteristic is presented in the P-U curves 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
It is slow with BQPSO algorithm the convergence speed and can be absorbed in local optimum in multimodal situation.IBQPSO algorithm the convergence speed is fast, Neng Goushi
Now stable steady state power output, significantly increases photovoltaic efficiency.
It is 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 algorithms 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 absorbed in, make photovoltaic efficiency that cannot improve.QPSO and BQPSO is calculated
Although method convergence rate is faster than PSO algorithms, the oscillation at mutation is less than PSO algorithms, but still may be absorbed in local optimum, makes
Photovoltaic efficiency cannot improve.IBQPSO algorithm the convergence speed is most fast and there is no oscillations near maximum power point, real
Show the steady state power output stablized, considerably improves photovoltaic efficiency.
In conclusion no matter IBQPSO algorithms 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 curves.
It is obtained by emulation：No matter temperature is unexpected increases or also can quickly and efficiently be converged in the case of reducing global maximum
Power points.Therefore IBQPSO algorithms can quickly and efficiently realize the tracking of global maximum power point under complicated obstruction conditions, show
It writes ground 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 domain subject to.
Claims (8)
1. a kind of maximum power tracking method of photovoltaic array under local shadow, which is characterized in that include 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 algorithms based on Bloch spherical surfaces to photovoltaic array model solution, output power is obtained；
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
In the step S2, the QPSO innovatory algorithms based on Bloch spherical surfaces include the following steps：
1) parameter of the QPSO innovatory algorithms based on Bloch spherical surfaces 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
Go out global optimum's phase；
6) it preserves individual optimum angle and judges whether to reach maximum iteration, 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.
3. a kind of maximum power tracking method of photovoltaic array under local shadow according to claim 2, which is characterized in that
In the step 4) and step 7), phase parameter include two phase parameter θ and
4. a kind of maximum power tracking method of photovoltaic array under local shadow according to claim 3, which is characterized in that
The particular content of the step 1) is：
101) by phase parameter θ andPoint of two angle-determinings under Bloch spherical coordinates, quantum bit Bloch spherical coordinates
It is expressed as：
102) the Bloch spherical coordinates of quantum bit are used to encode, then i-th of particle P in populationiBloch spherical coordinates be：
In formula,θij=π × rand, rand are the random number in [0,1] section；I=1,2 Λ, m, m are population
Scale；N is the number of optimized variable；In IBQPSO algorithms, each particle takes up space three positions simultaneously, i.e., represents simultaneously
Three optimization solutions, respectively X solutions, Y solutions, Z solutions：
Piz=(cos θi1,cosθi2,Λ,cosθin)
103) remember i-th of particle PiOn j-th of quantum bit Bloch coordinates be [xij,yij,zij]T, j=1,2, Λ n, n are excellent
Change the number of variable；The value range of the jth dimension of optimization problem solution space is [aj,bj], then by unit space In=[- 1,1]n
The transformation for mula for being mapped to optimization problem solution space is：
104) terminate initialization, export primary information.
5. a kind of maximum power tracking method of photovoltaic array under local shadow according to claim 4, which is characterized in that
The particular content of the step 4) is：
Using adaptive Quantum rotating gate, two phase parameter θ to quantum bit andIt is adjusted, realizes the position of particle
Update, and map 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.
6. a kind of maximum power tracking method of photovoltaic array under local shadow according to claim 5, which is characterized in that
The corresponding rotation angle α of the current iterationiDefinition be：
In formula, αminIt is minimum rotation angle；αmaxIt is maximum rotation angle；fiIt refer 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 iterations；Maxgen is to calculate
The maximum iteration of method setting.
7. a kind of maximum power tracking method of photovoltaic array under local shadow according to claim 6, which is characterized in that
The particular content of the step 5) is：
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 more 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.
8. a kind of maximum power tracking method of photovoltaic array under local shadow according to claim 6, 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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