CN109697299A - A kind of adaptive inertia weight Chaos particle swarm optimization algorithm - Google Patents
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Abstract
The present invention relates to field of photovoltaic power generation, and in particular to a kind of adaptive inertia weight Chaos particle swarm optimization algorithm (ACPSO).The algorithm is mapped using segmentation Logistic more higher than general Logistic mapping efficiency generates chaos sequence to initialize the position of particle, to ensure that the diversity of global search；Then particle swarm algorithm is optimized using adaptive inertia weight, improves the tracking velocity of maximum power；Finally if it is determined that algorithm falls into precocity, then disturbed extremum is carried out simultaneously to particle optimal location and global optimum position.For single disturbed extremum, the method can make algorithm jump out local optimum faster.The invention algorithm more fast and effeciently can change real-time tracking maximum power point according to sunshine, and make system work near maximum power point, meanwhile, system is reduced in the oscillatory occurences of maximum power point, improves the utilization rate of photovoltaic array.
Description
Technical field
The present invention relates to field of photovoltaic power generation, and in particular to a kind of adaptive inertia weight Chaos particle swarm optimization algorithm.
Background technique
Limitation due to traditional energy and the environmental problem that becomes increasingly conspicuous, clean reproducible energy is increasingly by the country
The concern of outer researcher.Solar energy because it has many advantages, such as that distribution is wide, pollution-free, by become the following application prospect it is best can be again
One of raw energy.In practice, blocking for the dust, Adjacent Buildings and cloud layer etc. on photovoltaic array surface can reduce its power generation effect
Rate, therefore, carrying out tracing control to the maximum power point of photovoltaic system is particularly important.Under the conditions of local shades, photovoltaic
Power vs. voltage (P-U) curve of system is in multi-peak characteristic, leads to conventional maximal power tracing (Maximum Power Point
Tracking, MPPT) algorithm such as perturbation observation method, conductance increment method etc. easily falls into local maximum, it cannot achieve maximum work
Rate point accurately tracks.
In view of the above-mentioned problems, many scholars study the photovoltaic system model under local shades, and propose one
A little multimodal MPPT algorithms.Such as: the algorithm that a kind of 1. Sliding mode variable structure controls and perturbation observation method combine is not advised to realize
Then under shade photovoltaic array maximal power tracing；2.Noguchi etc. proposes a kind of MPPT algorithm based on short circuit pulse, can
It scans P-U characteristic curve quickly to determine scale parameter, finds global extreme point；3. proposing to apply population in photovoltaic array
Optimization algorithm (Particle Swarm Optimization, PSO) realizes MPPT maximum power point tracking；4. by fuzzy control application
Into particle swarm optimization algorithm, MPPT maximum power point tracking is realized.These control strategies have respective advantage and disadvantage, tie as sliding formwork becomes
Though structure control has many advantages, such as fast response time, strong robustness, system concussion is larger after reaching steady state；Fuzzy control then needs
Experience is wanted to determine parameter, still there is certain fluctuation in stable state；Although genetic algorithm can trace into maximum power point, cannot
System is set steadily to work in maximum power point, and algorithm is complex, the parameter for needing to adjust is more；Particle swarm algorithm then phase
To relatively simple, the parameter for needing to adjust is fewer, and has preferable ability of searching optimum, but how to determine optimized parameter
It is a very complicated optimization problem.Therefore, by introducing a kind of adaptive used of Nonlinear Dynamic inertia weight coefficient composition
The particle swarm optimization algorithm (Adaptive Particle Swarm Optimization, ACPSO) of property weight can be mentioned effectively
The convergence rate and precision of high system.
Summary of the invention
The present invention is directed to more quickly track the maximum power point of photovoltaic system under partial phantom and system is made to stablize work
Make near maximum power point.
To achieve the goals above, the invention discloses a kind of adaptive inertia weight Chaos particle swarm optimization algorithms, including with
Lower step: S1: initialization inertia weight ω0, accelerated factor c1、c2, population scale N, maximum number of iterations Nm, determine that search is empty
Between [- xmax, xmax] and maximum speed vmax；S2: be randomly generated D of each component value in (0,1) range tie up to
Amount using formula (3) through N-1 grey iterative generation remaining N-1 particle, is denoted as x as the 1st particleid, i=1,2 ... N；D=
1,2 ..., D.Initialize particle rapidity；S3: each particle fitness value is calculated.By particle itself optimal location pidIt is current to be set as it
Position, global optimum position pgdIt is set as the position of optimal particle in initial population；S4: inertia weight ω is enabled to carry out more by formula (9)
Newly, according to the speed and position of formula (10) and formula (2) more new particle, p is updatedidAnd pgd.Then judge pidWith pgdWhether change
Become, if unchanged, corresponding stagnation step number t0With tgRespectively plus 1, then turn S5, if changing, turn S7；S5: judgement is stagnated
Whether step number is more than its threshold value T0Or Tg, if it does, then turning S6, otherwise turn S7；S6: by formula (12) plus disturbed extremum, office is jumped out
Portion is most worth a little, then turns S4；S7: if not up to preset maximum number of iterations Nm, then S4 is turned to, S8 is otherwise executed；
S8: output pgdAnd fbest, algorithm end of run.
Inventive algorithm is directed to the photovoltaic array under local shades state, using higher than general Logistic mapping efficiency
Segmentation Logistic mapping generate chaos sequence, to initialize the position of particle, to ensure that the diversity of global search；
Then population is optimized using adaptive inertia weight, finally if it is determined that algorithm falls into precocity, then to particle optimal location
Disturbed extremum is carried out simultaneously with global optimum position.Faster to the tracking of the maximum power point of system, search precision is more for the algorithm
Height, while reducing the fluctuation at maximum power point.
Detailed description of the invention
Fig. 1 is a kind of flow chart of adaptive inertia weight Chaos particle swarm optimization algorithm of the invention；
Fig. 2 is a set of photovoltaic array MPPT control being made of 3 × 3 photovoltaic battery panels based on ATmega16 of design of Simulation
System processed；
Fig. 3 is traditional solar-electricity pool model equivalent circuit diagram；
Fig. 4 (a) is the P-U curve under masking condition 1；
Fig. 4 (b) is the P-U curve under masking condition 2；
Fig. 5 (a) is the power tracking result of traditional PS O algorithm under masking condition 1；
Fig. 5 (b) is the power tracking result of ACPSO algorithm under masking condition 1；
Fig. 6 (a) is the power tracking result of traditional PS O algorithm under masking condition 2；
Fig. 6 (b) is the power tracking result of ACPSO algorithm under masking condition 2；
Fig. 7 (a) is the dynamic response for the traditional PS O algorithm that intensity of illumination changes front and back；
Fig. 7 (b) is the dynamic response for the ACPSO algorithm that intensity of illumination changes front and back.
Specific embodiment
A kind of adaptive inertia weight Chaos particle swarm optimization algorithm of the invention is described below in conjunction with attached drawing.
Referring to FIG. 1, a kind of adaptive inertia weight Chaos particle swarm optimization algorithm, comprising the following steps:
S1: initialization inertia weight ω0, accelerated factor c1、c2, population scale N, maximum number of iterations Nm, determine that search is empty
Between [- xmax, xmax] and maximum speed vmax；
In step sl, the speed of particle i and location updating equation are as follows:
vid(t+1)=ω vid(t)+c1r1·(pid(t)-xid(t))+c2r2·(pgd(t)-xid(t)) (1)
xid(t+1)=xid(t)+vid(t+1) (2)
Wherein, t is that particle updates the number of iterations.In t generation, " best " position that particle i is lived through in d dimension space
It is denoted asThe particle position of " best " is denoted as in populationω be particle more
New inertia weight；c1And c2For accelerated factor；r1And r2Equally distributed two independent random numbers are obeyed for section [0,1].
S2: being randomly generated D dimensional vector of each component value in (0,1) range, as the 1st particle, uses
Formula (3) is denoted as x through N-1 grey iterative generation remaining N-1 particleid, i=1,2 ... N；D=1,2 ..., D.Initialize particle speed
Degree；
In step s 2, each particle is initialized using segmentation Logistic chaotic maps:
Wherein, (0,1) y (n) ∈, initial value y (n) of the present invention take 0.355；μ is controling parameter, and when μ=4, system is completely mixed
It is ignorant.Compared with general Logistic chaotic maps, the chaos sequence that segmentation Logistic is generated is distributed in the particle between (0,1)
Symmetry is more preferable, and has good randomness and the sensibility to initial value, and algorithm is made to have better efficiency.
S3: each particle fitness value is calculated.By particle itself optimal location pidIt is set as its current location, global optimum position
pgdIt is set as the position of optimal particle in initial population；
In step s3, two evolution degree, degree of polymerization concepts are defined:
Evolution degree: assuming that objective function be F, t for when population in all particles the optimal average value of history are as follows:
Average adaptive value of the t for particles all in population are as follows:
T for when population global history optimal value are as follows:
Global optimum is obtained according to the variation of individual optimal value, in an iterative process, the global optimum of previous generation
Value is less than or equal to current global optimum and illustrates not evolve by this iteration population if equal, i.e., algorithm is whole
Only or have found optimal solution.Therefore, u is defined as the evolution degree factor, for indicating the evolution degree of population after each iteration.
Formula is as follows:
Wherein: a1、a2For relative coefficient, meet 0 < a1、a2< 1, a1+a2=1 and a1Slightly larger than a2.As can be seen that u
Value range be (0,1].Previous item in u is the optimizing degree of population.If after certain iteration, value 1 then illustrates grain
It finds optimal solution or stagnates in subgroup.Latter in u indicates the trend of overall variation in population search process, value etc.
When 1, it can be assumed that occurring stagnating or population finds optimal value.Therefore, evolution degree u can regard particle evolution degree as
Reflection.
The degree of polymerization: in an iterative process, how much the position of next-generation population particle all can be current with individual by optimal value
The influence of adaptive value, and the relationship of the two average value can also reflect the direction of motion of population particle.Therefore, the flat of the two is utilized
Mean value can generate some adjustment effects to ω.H is defined as the degree of polymerization factor, for indicating population particle in each iteration
Extent of polymerization.Formula is as follows:
Can be seen that h value range be (0,1].Degree of polymerization h reflects the diversity of particle, and value is closer to 1, grain
The identity of each particle is higher in subgroup；As h=1, the extent of polymerization of particle in space reaches maximum, and all particles are all poly-
It closes in same point.
S4: enabling inertia weight ω be updated by formula (9), according to the speed and position of formula (10) and formula (2) more new particle,
Update pidAnd pgd.Then judge pidWith pgdWhether change, if unchanged, corresponding stagnation step number toWith tgRespectively plus 1, so
After turn S5, if changing, turn S7；
In step s 4, inertia weight updates as the following formula:
According to formula (7) (8), the formula of the inertia weight ω ' after optimization is as follows:
ω '=ωo-ωuu+ωhh (9)
Wherein: ωoFor initial inertia weight, ωuFor evolution degree adjustment factor, ωhFor degree of polymerization adjustment factor.It can see
0 < u <, 1,0 < h < 1 out, so ω0-ωu< ω ' < ω0+ωh。
Inertia weight after optimization, ensure that in iterative process, when each particle position updates, be unlikely to because step-length is excessive
And global optimum is skipped, it also avoids particle and falls into local optimum.
Based on formula (9), the speed of particle more new formula becomes:
vid(t+1)=ω ' Vid(t)+c1r1·(pid(t)-xid(t))+c2r2·(pgd(t)-xid(t)) (10)
S5: judge to stagnate whether step number is more than its threshold value T0Or Tg, if it does, then turning S6, otherwise turn S7；
S6: by formula (12) plus disturbed extremum, part most value point is jumped out, S4 is then turned；
In step s 6, the optimization of particle position and inertia weight is initialized with chaos sequence, these methods all can only
It reduces algorithm and precocious probability occurs, and can not be avoided completely.When some particle in group finds an optimal location
When, other particles are close to its rapidly by its guidance, if the particle will be it is found out that a local optimum, entire group will
Search can not be re-started to fall into precocity.
When PSO algorithmic statement but not obtaining theoretical optimal solution, evolutionary process will stay cool, at this time population
The global extremum p foundgThere is Premature convergence in only locally optimal solution, algorithm.Judge whether to fall into Premature Convergence state
When the present invention using evolve stagnate step number t as trigger condition, to individual extreme value p0With global extremum pgIt is disturbed at random simultaneously
It is dynamic.Disturbed extremum operator are as follows:
Wherein: t0, tgStep number is stagnated in the evolution for respectively indicating individual and global extremum；T0, TgIndicate individual and global extremum
Stagnation step threshold when needing to add disturbance；Indicate conditional
Uniformly random function.
Therefore, the form after disturbed extremum operator is added to equation (1) are as follows:
T0And TgValue size determine the delay length that disturbed extremum operator comes into force.General value range is 3-8.
S7: if not up to preset maximum number of iterations Nm, then S4 is turned to, S8 is otherwise executed；
In the step s 7, by the position x of particle in PSO algorithmiIt is defined as duty ratio, speed viTo be sent out in the duty ratio
Raw variation size, then formula (2) can be rewritten are as follows:
From the above equation, we can see that velocity component viVariation size depend on pidAnd pg.If current duty ratio is apart from the two
It measures farther out, then speed viChange greatly, otherwise it is smaller.Therefore, for PSO algorithm, velocity component viFluctuation be according to particle
Group position and change.Therefore, possess the solution vector definition of N number of particle duty ratio:
The optimal solution of postulated particle is pi, and meeting formula (15), then piIt can indicate are as follows:
F is objective function, the i.e. power of photovoltaic system in formula.In addition, according to formula (2), piFor i-th found so far
The desired positions of son generate the duty ratio of maximum power, pgThe best duty ratio found for entire group.
S8: output pgdAnd fbest, algorithm end of run.
To make it is further understood that the present invention, will be illustrated by following example.
Traditional solar-electricity pool model equivalent circuit as shown in figure 3, the voltage-current relationship expression formula of equivalent circuit such as
Following formula:
In formula: UpvAnd IpvRespectively photovoltaic output voltage and electric current；RsAnd RpThe respectively series connection and parallel connection of photovoltaic cell
Resistance；Q is electron charge (1.6 × 10-19C)；IphFor photogenerated current；I0For diode current；A is semiconductor in photovoltaic cell
The P-N junction coefficient of device；K is Boltzmann constant (1.38 × 10-23J/K)；T is thermodynamic temperature；npAnd nsRespectively photovoltaic
The series connection number of battery and number in parallel.
Under Matlab environment, a set of photovoltaic array being made of 3 × 3 photovoltaic battery panels based on ATmega16 is constructed
MPPT Control System Imitation model is shown in that Fig. 2, voltage sensor and current sensor believe the output voltage of photovoltaic cell and electric current
It number is sent to the A/D mouth of ATmega16, then signal is handled according to algorithm, so that the on-off of pwm signal control Q1 is generated,
Achieve the purpose that MPPT is controlled.Buck-Boost DC/DC converter in figure is set as C1=470uF, C2=220 μ F, L=
2mH, f=50kHz.The output voltage of photovoltaic module is 300V.
In local shades, for convenience of explanation, 3 × 3 photovoltaic arrays are numbered, coordinate (x, y) indicates xth
The photovoltaic cell of row y column:
1) cover condition 1: coordinate is that the radiation level of the photovoltaic cell of (1,2) and (3,1) is respectively 200W/m2With
700W/m2, other modules are 1000W/m2.Shown in its P-U curve such as Fig. 4 (a).
2) cover condition 2: coordinate is (1,1), and the radiation level of the photovoltaic cell of (1,2) and (3,1) is respectively 200W/
m2, 200W/m2And 700W/m2, other modules are 1000W/m2.Shown in its P-U curve such as Fig. 4 (b).
As seen from Figure 4, two kinds of masking conditions all produce three extreme points, respectively (U1, P1), (U2, P2), (U3,
P3).The most value point of masking condition 1 is (U2, P2), maximum power P2=903W；The most value point of masking condition 2 is (U3, P3), most
High-power is P3=1020W.
In order to compare the performance of MPPT under traditional PS O and inventive algorithm, tested in two kinds of maskings.It is right
The parameter setting of PSO is as follows: N=3, c1=1.2, c2=1.6, the velocity interval of particle is [10,10]；r1And r2For in section
[0,1] equally distributed random number.To obtain traditional PS O and the MPPT curve of inventive algorithm is as shown in Figure 5, Figure 6.
From Fig. 5,6 as can be seen that under the conditions of the masking of part, traditional PS O algorithm and inventive algorithm can trace into maximum
Power points.Traditional PS O algorithm fluctuates larger in the whole process, and tracking velocity is slower, and inventive algorithm tracking is maximum
Power points fast speed, shock range is smaller, and stable state accuracy is higher.
In order to test to the case where uniform illumination, on the basis of covering condition 1, make coordinate (1,2) and (3,1)
The radiation level of photovoltaic cell be changed to 250W/m respectively2And 850W/m2, other modules are 1200W/m2.Intensity of illumination changes
The power output situation of front and back is as shown in Figure 7.Even if can be seen that in the case where weather condition mutation, which also can
It scans within a short period of time and tracks global extreme point.
As seen from Figure 7, when intensity of illumination mutates, compared with traditional PS O, what it is using inventive algorithm is
System, which reaches, stablizes that the required time is shorter, and wave distortion is smaller, has good tracking performance.
By simulation result it is found that the P-U curve of entire array is in multimodality, conventional maximum work when part is covered
Rate point control algolithm will fail, and inventive algorithm can effectively track maximum power point.The present invention is in traditional PS O algorithm
On the basis of, propose a kind of Chaos particle swarm optimization algorithm of adaptive inertia weight, and establish the light under the influence of partial phantom
Photovoltaic array circuit, is respectively adopted traditional PS O algorithm and inventive algorithm realizes MPPT control, by comparing the emulation of two kinds of algorithms
As a result it is found that inventive algorithm to the tracking velocity of maximum power point faster, search precision is higher, while reducing in maximum work
Fluctuation at rate point.
Claims (2)
1. a kind of adaptive inertia weight Chaos particle swarm optimization algorithm (ACPSO), which comprises the following steps:
S1: initialization inertia weight ω0, accelerated factor c1、c2, population scale N, maximum number of iterations Nm, determine search space [-
xmax, xmax] and maximum speed vmax；
S2: D dimensional vector of each component value in (0,1) range is randomly generated, as the 1st particle, using as follows
Formula is denoted as x through N-1 grey iterative generation remaining N-1 particleid, i=1,2 ... N；D=1,2 ..., D.Initialize particle rapidity；
In formula, y (n) ∈ (0,1), initial value y (n) of the present invention takes 0.355；μ is controling parameter, when μ=4, system Complete Chaos.
S3: each particle fitness value is calculated.By particle itself optimal location pidIt is set as its current location, global optimum position pgdIf
For the position of optimal particle in initial population；
S4: inertia weight ω is enabled to be updated as the following formula:
ω '=ω0-ωuu+ωhh
Wherein: ω0For initial inertia weight, ωuFor evolution degree adjustment factor, ωhFor degree of polymerization adjustment factor.
The speed of more new particle and position according to the following formula:
vid(t+1)=ω ' vid(t)+c1r1·(pid(t)-xid(t))+c2r2·(pgd(t)-xid(t))
xid(t+1)=xid(t)+vid(t+1)
Wherein, t is that particle updates the number of iterations.In t generation, " best " position that particle i is lived through in d dimension space is denoted asThe particle position of " best " is denoted as in populationω is what particle updated
Inertia weight；c1And c2For accelerated factor；r1And r2Equally distributed two independent random numbers are obeyed for section [0,1].
Update pidAnd pgd.Then judge pidWith pgdWhether change, if unchanged, corresponding stagnation step number t0With tgRespectively plus 1,
Then turn S5, if changing, turn S7；
S5: judge to stagnate whether step number is more than its threshold value T0Or Tg, if it does, then turning S6, otherwise turn S7；
S6: as the following formula plus disturbed extremum, part most value point is jumped out, S4 is then turned；
S7: if not up to preset maximum number of iterations Nm, then S4 is turned to, S8 is otherwise executed；
S8: output pgdAnd fbest, algorithm end of run.
2. a kind of adaptive inertia weight Chaos particle swarm optimization algorithm (ACPSO) according to claim 1, which is characterized in that
Chaos is combined with adaptive inertia weight PSO algorithm, is made up with the randomness of chaos sequence and ergodic in PSO algorithm
The defect of common random initial population.Each particle is initialized with segmentation Logistic chaotic maps, defines the evolution degree factor, polymerization
Degree, optimizes the expression formula of inertia weight and the judgement of algorithm Premature Convergence and disturbed extremum strategy.
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