CN108170200A - The improvement population MPPT algorithm of condition is restarted based on dynamic inertia weight and multi-threshold - Google Patents
The improvement population MPPT algorithm of condition is restarted based on dynamic inertia weight and multi-threshold Download PDFInfo
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Abstract
The invention discloses a kind of improvement population MPPT algorithms that condition is restarted based on dynamic inertia weight and multi-threshold, are related to technical field of photovoltaic power generation.The present invention resolves inertia weight value using the distance of particle and optimal solution, iterations as variable dynamic, compared to fixed inertia weight or weight segmentation value method, have the advantages that fast convergence rate, oscillation amplitude are small, achieve the purpose that according to search situation dynamic adjustment iterative process；The present invention is directed to external environment complicated and changeable, it sets up multi-threshold and restarts condition, compared with conventional fixed cycle scanning method, avoid unnecessary voltage gamut scanning, and system caused by fixed cycle scanning method frequently vibrates, power attenuation increase, because the period fix caused by tracking lag the problems such as, and compared with using the single condition that changed power is judged, it can more accurately identify that external environment changes, the monitors failure caused by Rule of judgment is single is avoided, system is caused not restart normally.
Description
Technical field
The present invention relates to technical field of photovoltaic power generation, more particularly to photovoltaic maximal power tracing technical field.
Background technology
A variety of MPPT algorithms are had been presented for both at home and abroad at present, including perturbation observation method, conductance increment method etc..But these algorithms
It is only applicable to photovoltaic array and situation in single peak characteristic is exported under the conditions of uniform illumination.Due in practical application, photovoltaic battle array
Row are inevitably blocked by local shades caused by cloud movement, dunghill etc. so that the operating mode of photovoltaic module in array
There is difference, the output characteristic curve of photovoltaic array can become multi-peak from single peak.Conventional MPPT algorithm easily accidentally searches
Local Extremum so that photovoltaic array generated output reduces, and causes energy loss.To solve the above-mentioned problems, domestic and foreign scholars
Propose a variety of improved MPPT algorithms, such as the photovoltaic that the foundation adjacent peak point voltage difference that proposes of article [1] is about 0.8 times
Component open-circuit voltage carries out by stages search using conductance increment method, and there are some defects for this method：Following range selection relies on
Experience can only cause to search for error under known single environment into line trace, external environment variation.Doctor Eberhart and
Doctor Kennedy proposes particle cluster algorithm in nineteen ninety-five, and the group's sexual behaviour preyed on by simulating flock of birds is developed and suitable for non-
The algorithm model of linear global optimizing.Document [2] applies particle cluster algorithm in photovoltaic array global optimizing problem, but particle
Group's algorithm belongs to a kind of bionic model, lacks perfect mathematical theory basis, and parameter is chosen by experience, and document [2] is using solid
Determine inertia weight, photovoltaic system can be caused to vibrate, convergence speed of the algorithm and tracking are less efficient.
On the other hand, the shadow occlusion that photovoltaic array is subject to is not unalterable, when shade changes, MPPT controls
Device processed needs variation of following up, and ensures that array is operated in maximum power point.There is researcher to propose fixed cycle scanning method, i.e., every one
The section time carries out gamut scanning to P-V curves, re-starts MPPT search, to judge that maximum power point of photovoltaic array is
It is no to change.The shortcomings that this method, is scan frequency height, and voltage is changed from zero to maximum value, scanning range
Greatly, system can be caused frequently to vibrate, power attenuation increases.And due to being fixed cycle scanning, when shade changes, system
It cannot promptly respond.Therefore for changeable external environment, how photovoltaic battle array judged by variable that is simple and easily obtaining
The variation of row operating mode realizes that MPPT controller is timely and effectively restarted, becomes and carry out the important of photovoltaic maximum power real-time tracking
Problem.
The present invention designs following technical term：
MPPT：MPPT maximum power point tracking Maximum Power Point Tracking, to the output voltage of photovoltaic array
Tracing control is carried out with electric current, array operating point is made to be maintained at the position of maximum power output.
PSO algorithms：Particle cluster algorithm Particle Swarm Optimization, by following the optimal of current search
Value finds the evolution algorithm of globally optimal solution.
Invention content
Changing for condition is restarted based on dynamic inertia weight and multi-threshold in view of the deficiencies of the prior art, the present invention provides a kind of
Into population MPPT algorithm, for solving particle cluster algorithm when carrying out MPPT tracking.
To achieve the above object, the technical solution adopted by the present invention is：
Establish inertia weight w Online Integer expression formulas：wik=wmax-(wmax-wmin)(1-Lig/Lmax)k/kmax, in formula, k
Represent iterations, LigThe distance between expression particle i (i=1,2 ... n, n are total number of particles) and optimal particle, wikIt represents
The inertia weights of particle i at the kth iteration, wmaxFor maximum inertia weight, wminFor minimum inertia weight, LmaxBetween particle
Maximum distance, kmaxFor maximum iteration.Dynamic inertia weight is introduced into particle iteration speed and is carried out based on inertia weight
Weighted calculation.Early period is searched for, iterations k is smaller therefore inertia weight wikIt is larger, it can make particle that there is larger speed
Global search is carried out, finds more preferably point as far as possible；In the search later stage, iterations k is larger therefore inertia weight wikIt is smaller, have
Assemble conducive to particle to globe optimum, accelerate convergence speed of the algorithm.Introduce L simultaneouslyigParticle search domain is controlled, as particle i
Far from optimum point, inertia weight wikCorresponding increase is so as to accelerate the speed that particle is assembled to optimum point；When particle i is located at optimum point
Near, inertia weight wikIt is corresponding to reduce to prevent particle rapidity is excessive from jumping out optimum search region.
For external environment complicated and changeable, setting multi-threshold restarts condition：Δ p=| Ps-Pmax|/Pmax＞ η1With | Δ P/
Δ V | ＞ η2, η1And η2The power variation rate threshold value and P-V slope of curve threshold values respectively set, PsFor the photovoltaic monitored in real time
Array output power, PmaxThe maximum power reached when completing and search for for particle cluster algorithm.When monitoring mechanism detects that system meets
When the multi-threshold of above-mentioned setting restarts condition, controller restarts particle cluster algorithm, searches new maximum power point again.
Further, particle cluster algorithm will be improved to apply in MPPT, specific implementation step is as follows：
Step 1：Select photovoltaic array output voltage VPVAs particle, photovoltaic array output power PPVAs characterization particle
Good and bad adaptive value initializes population, particle is random and be evenly distributed in including cluster location and primary iteration speed
On photovoltaic array P-V characteristic curves；
Step 2：Each particle currently corresponding adaptive value is obtained, and is undergone in search process with particle individual optimal
Position xibestCorresponding adaptive value is compared, if current location adaptive value more preferably if update xibest, otherwise xibestIt keeps not
Become；The optimal location g undergone in search process with particle groupbestCorresponding adaptive value is compared, if current location
Adaptive value more preferably then updates gbest, otherwise gbestIt remains unchanged；
Step 3：To each particle i, itself and g are calculatedbestThe distance between Lig, by LigOneself of inertia weight w is substituted into k
Adapt to adjustment formula：wik=wmax-(wmax-wmin)(1-Lig/Lmax)k/kmax, calculate inertia power of the particle i in kth time iteration
Weight, according to iterative formula vi(k+1)=wikvi(k)+c1r1(xibest-xi(k))+c2r2(gbest-xiAnd x (k))i(k+1)=xi
(k)+vi(k+1) speed and the position of each particle are updated, v in formulai(k) and vi(k+1) represent that kth time changes for+1 time with kth respectively
For when particle i speed, xi(k) position for particle i kth time iteration, xi(k+1) it is the position of particle i+1 iteration of kth, c1
And c2For adjustment factor, r1And r2For the random number on [0,1]；
Step 4：The threshold epsilon that stopping criterion for iteration distance maximum as between particle is less than setting is set, if meeting iteration
End condition then stops iteration, and for particle buildup at globe optimum, photovoltaic array works in global maximum power point, no at this time
Then return to step 2；
Step 5：Monitoring mechanism, real-time online detection photovoltaic array output voltage and electric current are set, calculates output power, work(
Rate change rate and the P-V slopes of curve restart condition when meeting multi-threshold, and controller restarts algorithm and carries out MPPT tracking, ensures
System tracks new global maximum power point in time.
Beneficial effects of the present invention：
1st, the present invention carries out MPPT tracking using particle cluster algorithm, and the iteration parameter in algorithm is optimized, and improves and calculates
The convergence and tracking velocity of method are realized and adaptively adjust search process according to search situation, solve the lower light of local shades masking
The MPPT tracking problems of photovoltaic array, avoid being absorbed in local maximum power point；
2nd, setting multi-threshold restarts condition, by detecting photovoltaic array output voltage and electric current, calculate power variation rate and
P-V change rates judge whether to meet and restart condition, system made to timely respond to the variation of environment if meeting, evaded meaningless
Algorithm restarting process reduces energy loss, and evades the problem of restarting failure.
Description of the drawings
Fig. 1 is the circuit exemplary application map of the present invention；
Fig. 2 is based on the MPPT control flow charts for improving particle cluster algorithm.
Fig. 3 is photovoltaic array output characteristic curve.
Fig. 4 is MPPT tracking result comparison diagrams before and after particle cluster algorithm improves.
Fig. 5 is system reboot power tracking analogous diagram.
Fig. 6 is MPPT tracking test waveforms before and after particle cluster algorithm improves.
Specific embodiment
The present invention is described in detail below in conjunction with the accompanying drawings.
By taking the photovoltaic generating system based on Boost circuit as an example, system structure is as shown in Figure 1, inductance L and capacitance C, switch
Pipe S and diode D and DC load R forms Boost main circuits, Boost input termination photovoltaic array outputs.
This system application enhancements particle cluster algorithm realizes that the operation principle of MPPT tracking is as follows：Sample photovoltaic array output electricity
Flow IPVWith output voltage VPV, particle cluster algorithm is according to IPVAnd VPVSearch is iterated, and next iteration is conveyed to PWM controller
Voltage reference signal Vref, controller sends out drive signal to the switching tube S of Boost circuit, the conducting of control switching tube S and
Shutdown, changing the duty ratio of switching tube S makes photovoltaic array operating point respective change.Pass through the continuous iterative search of particle cluster algorithm
System operation can finally be made at global maximum power point.When external environment change causes output characteristics to change, lead to
It crosses multi-threshold Restart mechanisms to be monitored judgement and perform system reboot, array is made to rework at maximum power point.
Algorithm flow is as shown in Fig. 2, specific implementation step is as follows：
(1) population is initialized, including setting number of particles n, the speed v of each particleiWith position xi.It is tracked in MPPT
In, particle is photovoltaic array output voltage VPV, therefore particle position is VPV, the voltage of iteration twice before and after particle rapidity corresponds to
Difference should make particle is as uniform as possible to be distributed on P-V characteristic curves in initialization；
(2) it is iterated according to certain n particle of secondary ordered pair.For single particle, lived through with it at itself
The location updating x of adaptive value preferably (power is maximum)ibest, for particle group, the adaptive value preferably (work(that is lived through with group
Rate is maximum) location updating gbest；
(3) to each particle i (i=1,2 ... n), its corresponding inertia power at the kth iteration is calculated as follows
Weight wik：Inertia weight wik=wmax-(wmax-wmin)(1-Lig/Lmax)k/kmax；
(4) to each particle i, by iterative formula vi(k+1)=wikvi(k)+c1r1(xibest-xi(k))+c2r2(gbest-xi
And x (k))i(k+1)=xi(k)+vi(k+1) iteration speed and position are updated, obtains the corresponding adaptive value in new position, i.e., by adopting
Sample VPVAnd IPVObtain the corresponding output power P of current voltage valuePV；
(5) more current adaptive value (PPV) and xibestAnd gbestCorresponding adaptive value (PPV) size, according to adaptive value
Quality is to xibestAnd gbestIt is updated；
(6) if meeting stopping criterion for iteration Li-imax＜ ε then stop iteration, and otherwise return to step 3 carries out next round iteration；
(7) after iteration ends, monitoring mechanism passes through sampled IPVAnd VPVIt is monitored in real time, restarts item when meeting multi-threshold
During part, monitoring mechanism triggering system reboot, return to step 1 performs new round MPPT tracking.
Photovoltaic system simulation model as shown in Figure 1 is established on Matlab/Simulink emulation platforms, using 3 × 3 light
Photovoltaic array, each photovoltaic module inner parameter are as follows：Open-circuit voltage Voc=42.93V, short circuit current Isc=4.37A, maximum work
Rate point voltage Vm=33.68V, maximum power point electric current Im=3.96A.Standard intensity of illumination SrefFor 1000W/m2, temperature TrefFor
25℃.There are three kinds of illumination 1000W/m in shadow mode one2、400W/m2And 100W/m2, have two branches by the moon in array
Shadow blocks；There are two kinds of illumination 1000W/m for shadow mode two2And 400W/m2, have a branch by shadow occlusion in array.Figure
3 be output characteristics of the photovoltaic array under both shadow modes.It is of the present invention to improve particle cluster algorithm in mode 1
Power tracking result such as Fig. 4, while provide unmodified particle cluster algorithm tracking result and compare.It will become apparent from improved particle
Group's algorithm keeps track speed is improved, and iterative process is optimised, reduces the oscillation in search process, and less MPPT tracks process
Caused by energy loss.When system steady operation, shade slave pattern 1 is cut after the global maximum power point of shadow mode 1,0.02s
Pattern 2 is changed to, global maximum power point changes as shown in Figure 3, meets multi-threshold and restarts condition, and controller is restarted at this time,
MPPT search is re-started, until searching new global maximum power point.Fig. 5 is shown using fixed cycle scanning method and more thresholds
Value restarts the simulation result of method, and the multi-threshold method of restarting can be realized restarts in time, caused by avoiding the search of fixed cycle gamut
System oscillation and energy loss reduce the work frequency of circuit.
Build system experimentation Prototyping Platform as shown in Figure 1, photovoltaic array use Chroma company models for 62050 light
It lies prostrate simulator to realize, verification particle cluster algorithm improves front and rear tracking effect.Fig. 6, which is shown, uses improvement preceding and particle after improvement
Group's algorithm, the experimental waveform of photovoltaic array output voltage.Improved particle cluster algorithm can search out entirely in shorter time
Office's maximum power point, and the oscillation of search process reduces.
The above is only the preferred embodiment of the present invention, it should be pointed out that：For the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications also should
It is considered as protection scope of the present invention.
Attachment：(such as patent/article/standard)
Document 1：Patel H, Agarwal V.Maximum Power Point Tracking Scheme for PV
Systems Operating Under Partially Shaded Conditions. Control Engineering of
China, 2008,55 (4)：1689-1698.
Document 2：Phimmasone V, Endo T, Kondo Y, et al.Improvement of the Maximum
Power Point Tracker for photovoltaic generators with Particle Swarm
Optimization technique by adding repulsive force among agents[C]//
International Conference on Electrical Machines and Systems.IEEE, 2010：1-6.
Claims (3)
1. the improvement population MPPT algorithm of condition is restarted based on dynamic inertia weight and multi-threshold, it is characterised in that：It will dynamic
Inertia weight introduces population iteration speed and carries out the weighted calculation based on inertia weight, and the resolving expression formula of inertia weight is：
wik=wmax-(wmax-wmin)(1-Lig/Lmax)k/kmax, in formula, k represents iterations, LigRepresent particle i (i=1,2 ... n, n
The distance between for total number of particles) and optimal particle, wikRepresent the inertia weights of particle i at the kth iteration, wmaxAnd wminPoint
Not Wei inertia weight value bound, LmaxFor interparticle maximum distance, kmaxFor maximum iteration.
2. the improvement population MPPT algorithm of condition is restarted based on dynamic inertia weight and multi-threshold, it is characterised in that：For multiple
Miscellaneous changeable external environment, setting multi-threshold restart condition：Δ P=| Ps-Pmax|/Pmax＞ η1With | Δ P/ Δs V | ＞ η2, η1And η2
The power variation rate threshold value and P-V slope of curve threshold values respectively set, PsFor the photovoltaic array output power monitored in real time,
PmaxThe maximum power reached when completing and search for for particle cluster algorithm.When monitoring mechanism detects that system meets the more of above-mentioned setting
When threshold value restarts condition, controller restarts particle cluster algorithm, searches new maximum power point again.
3. the improvement population MPPT algorithm of condition is restarted based on dynamic inertia weight and multi-threshold, it is characterised in that：In right
It is required that on the basis of 1 and claim 2, particle cluster algorithm will be improved and applied in MPPT, specific implementation step is as follows：
Step 1：Select photovoltaic array output voltage VPVAs particle, photovoltaic array output power PPVGood and bad as characterization particle
Adaptive value initializes population, particle is random and be evenly distributed in photovoltaic battle array including cluster location and primary iteration speed
It arranges on P-V characteristic curves；
Step 2：Obtain each particle currently corresponding adaptive value, and the optimal location undergone in search process with particle individual
xibestCorresponding adaptive value is compared, if current location adaptive value more preferably if update xibest, otherwise xibestIt remains unchanged；
The optimal location g undergone in search process with particle groupbestCorresponding adaptive value is compared, if the adaptation of current location
Value more preferably then updates gbest, otherwise gbestIt remains unchanged；
Step 3：To each particle i, itself and g are calculatedbestThe distance between Lig, by LigThe adaptive of inertia weight w is substituted into k
Adjust formula：wik=wmax-(wmax-wmin)(1-Lig/Lmax)k/kmax, inertia weights of the particle i in kth time iteration is calculated,
According to iterative formula vi(k+1)=wikvi(k)+c1r1(xibest-xi(k))+c2r2(gbest-xiAnd x (k))i(k+1)=xi(k)+
vi(k+1) speed and the position of each particle are updated, v in formulai(k) and vi(k+1) when representing kth time and+1 iteration of kth respectively
The speed of particle i, xi(k) position for particle i kth time iteration, xi(k+1) it is the position of particle i+1 iteration of kth, c1And c2
For adjustment factor, r1And r2For the random number on [0,1]；
Step 4：The threshold epsilon that stopping criterion for iteration distance maximum as between particle is less than setting is set, if meeting iteration ends
Condition then stops iteration, and for particle buildup at globe optimum, photovoltaic array works in global maximum power point at this time.Otherwise it returns
Return step 2；
Step 5：Monitoring mechanism, real-time online detection photovoltaic array output voltage and electric current are set, calculates output power, power becomes
Rate and the P-V slopes of curve restart condition when meeting multi-threshold, and controller restarts algorithm and carries out MPPT tracking, ensures system
New global maximum power point is tracked in time.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109085877A (en) * | 2018-07-24 | 2018-12-25 | 上海电力学院 | QAPSO algorithm photovoltaic system MPPT control method is based under the conditions of local shades |
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104122929A (en) * | 2014-07-02 | 2014-10-29 | 珠海格力电器股份有限公司 | Maximum power point tracking control method and system |
| CN104317348A (en) * | 2014-10-28 | 2015-01-28 | 重庆理工大学 | Particle swarm algorithm based photovoltaic cell panel maximum-power tracking method and system |
| EP2993754A1 (en) * | 2014-07-15 | 2016-03-09 | Sungrow Power Supply Co., Ltd. | Centralized mppt exiting and switching method and application thereof |
| CN105590032A (en) * | 2016-02-18 | 2016-05-18 | 淮阴师范学院 | MPPT (Maximum Power Point Tracking) algorithm for photovoltaic module based on parameter identification |
| CN106408082A (en) * | 2016-09-23 | 2017-02-15 | 湖北大学 | Control method and system based on region segmentation |
| CN106444956A (en) * | 2016-10-31 | 2017-02-22 | 北京信息科技大学 | Particle swarm optimization based control method and device of photovoltaic maximum power point tracking |
| CN106527570A (en) * | 2016-12-20 | 2017-03-22 | 湘潭大学 | Photovoltaic array multi-peak maximum power cluster searching optimization tracking method |
| CN107341495A (en) * | 2017-07-17 | 2017-11-10 | 深圳森阳环保材料科技有限公司 | A kind of effective underground coal dust detecting system |
-
2018
- 2018-01-03 CN CN201810013171.6A patent/CN108170200B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104122929A (en) * | 2014-07-02 | 2014-10-29 | 珠海格力电器股份有限公司 | Maximum power point tracking control method and system |
| EP2993754A1 (en) * | 2014-07-15 | 2016-03-09 | Sungrow Power Supply Co., Ltd. | Centralized mppt exiting and switching method and application thereof |
| CN104317348A (en) * | 2014-10-28 | 2015-01-28 | 重庆理工大学 | Particle swarm algorithm based photovoltaic cell panel maximum-power tracking method and system |
| CN105590032A (en) * | 2016-02-18 | 2016-05-18 | 淮阴师范学院 | MPPT (Maximum Power Point Tracking) algorithm for photovoltaic module based on parameter identification |
| CN106408082A (en) * | 2016-09-23 | 2017-02-15 | 湖北大学 | Control method and system based on region segmentation |
| CN106444956A (en) * | 2016-10-31 | 2017-02-22 | 北京信息科技大学 | Particle swarm optimization based control method and device of photovoltaic maximum power point tracking |
| CN106527570A (en) * | 2016-12-20 | 2017-03-22 | 湘潭大学 | Photovoltaic array multi-peak maximum power cluster searching optimization tracking method |
| CN107341495A (en) * | 2017-07-17 | 2017-11-10 | 深圳森阳环保材料科技有限公司 | A kind of effective underground coal dust detecting system |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109814651A (en) * | 2019-01-21 | 2019-05-28 | 中国地质大学（武汉） | Photovoltaic cell multi-peak maximum power tracking method and system based on population |
| CN109814651B (en) * | 2019-01-21 | 2020-07-07 | 中国地质大学（武汉） | Particle swarm-based photovoltaic cell multi-peak maximum power tracking method and system |
| CN111538366B (en) * | 2020-04-03 | 2021-12-21 | 福建星云电子股份有限公司 | Maximum power point tracking method, system, device and medium |
| CN111538366A (en) * | 2020-04-03 | 2020-08-14 | 福建星云电子股份有限公司 | Maximum power point tracking method, system, device and medium |
| CN111796629A (en) * | 2020-06-11 | 2020-10-20 | 江苏大学 | Composite MPPT tracking method under photovoltaic cell local shadow condition |
| CN112596575A (en) * | 2020-12-21 | 2021-04-02 | 吉林建筑大学 | Maximum power point tracking method based on NPSO algorithm and hierarchical automatic restart |
| CN112596575B (en) * | 2020-12-21 | 2022-01-07 | 吉林建筑大学 | Maximum power point tracking method based on NPSO algorithm and hierarchical automatic restart |
| CN113240065A (en) * | 2021-04-30 | 2021-08-10 | 西安电子科技大学 | Passive radar station distribution method based on improved particle swarm optimization algorithm |
| CN114489228A (en) * | 2022-01-26 | 2022-05-13 | 四川大学 | MPPT device and method based on improved PSO algorithm |
| CN114167937A (en) * | 2022-02-12 | 2022-03-11 | 武汉理工大学 | Improved thermoelectric maximum power tracking method and system based on particle swarm optimization |
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