CN106452355A - Photovoltaic power generation system maximum power tracking method based on model identification - Google Patents
Photovoltaic power generation system maximum power tracking method based on model identification Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S50/00—Monitoring or testing of PV systems, e.g. load balancing or fault identification
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
Abstract
The invention discloses a photovoltaic power generation system maximum power tracking method based on model identification. The method comprises the steps that firstly, parameter identification is conducted on a photovoltaic array reference mathematic model by adopting measurement information of a practical photovoltaic power generation system, and an identification model consistent with the practical object is obtained; secondly, a BP neural network is constructed, a series of output voltage and output current test data of the model under different illumination intensities and photovoltaic cell panel temperatures is generated by adopting the identification model, information such as output voltage and output power corresponding to the illumination intensities, the photovoltaic cell panel temperatures and the maximum power points is extracted to be used for training the BP neural network, and parameters of the BP neural network are obtained; finally, the trained BP neural network is used for maximum power tracking of the photovoltaic power generation system on line, and therefore the real-time property and the high efficiency of photovoltaic power generation system maximum power tracking are greatly improved. According to the method, the principle is easily implemented on line, and the flexibility is good.
Description
Technical field
The invention belongs to new forms of energy electric power and power supply technique field, and in particular to a kind of photovoltaic generation based on Model Distinguish
System maximum power tracking method.
Background technology
New forms of energy are particularly the application of photovoltaic, have been subjected to the attention of world community government and have fostered, have widelyd popularize photovoltaic
The popularization of industry and application.With the development of the deep and technology of application, global expert is pooled to new forms of energy electric power and is particularly
The application mode of photovoltaic generation and technique direction.
Photovoltaic generation cost and efficiency are always the crucial common problem of photovoltaic industry promotion and application, and core captures a master
It is related to two links, respectively the energy conversion efficiency of solar module optoelectronic transformation efficiency and photovoltaic generating system, front
The conversion ratio of person is generally low, for being put into actual power system, conversion efficiency highest monocrystaline silicon solar cell flat
All efficiency is only 17%, and polysilicon solar cell conversion ratio is that 16.4%, hull cell is just lower.The lifting master of this respect
If problem of materials, therefore from for the angle of the system integration, it is desirable to improve the direct current energy for sending from solaode and turn
Change the AC energy process efficiency used by user into, that is, the energy conversion efficiency in photovoltaic generating system is improved, and adjusts the sun
The working condition of energy battery so as to which it is the effective way for solving the problem that the moment is operated in maximum output power point.However,
Under different intensities of illumination and battery temperature, peak power output (MPP) point of solaode is differed, in order to obtain
Optimal energy utilization efficiency, it is necessary to take measures to make the output of battery automatically track the change of the external environment conditions such as weather conditions
Change and change, maximal power tracing technology (MPPT) is aiming at this problem and proposes.Many scholars are to photovoltaic both at home and abroad
The MPPT of electricity generation system has made in depth to study, such as constant voltage process, straight-line approximation method, perturbation observation method, total amount conductance method, mould
Some MPPT methods such as paste method and synthetic method have been implemented.
Before and after in the world 2000 being started from for the research of photovoltaic generating system MPPT, and study hotspot is increasingly becoming, phase
Close research department and North America and Europe is focused primarily upon, the research of North America mainly has Sandia National Laboratory, the main collection in Europe
In in Holland, colleges and universities and the associated mechanisms among the people of Denmark and Germany.
The external maximal power tracing research common method to photovoltaic grid-connected inverting system has：Open circuit voltage method, generally makes
With 0.76 times of open-circuit voltage as maximum power point voltage.Short circuit current method, approximate thinks to be operated in maximum power point
The output current of solaode is linear with the short circuit current of solar panel, this value about 0.85 or so,
The method dynamic response is fast, and reality is got up and uncomplicated, and which has the disadvantage that control accuracy is not high, always less than actual maximum
Power points.Nobuyoshi is drawn by analysis, under different intensities of illumination, the linear coefficient of output current and short circuit current
Difference, is less than 150W/m in brightness value2When, linear coefficient changes between 0.8 to 0.87, when brightness value is more than 150W/m2When,
Linear coefficient changes between 0.87 to 0.95.Disturbance observation method (Perturbation and observation method)
Photovoltaic system operating point is adjusted to maximum power point by regular increase and the output voltage for reducing photovoltaic cell, works as illumination
When intensity changes over little, the method tracking is simple, easily realization, less demanding to sensor accuracy.But photovoltaic battle array
It is listed in maximum power point and operation is oscillated around, causes certain power loss, and in Intensity of the sunlight and ambient temperature drastically
During change, disturbance observation method may track failure.In addition conductance increment method also widely uses in photovoltaic generating system,
Change control signal so as to track maximum power point by comparing the increment of conductance of photovoltaic array and instantaneous electric conductivity value.Control
Algorithm needs to determine by the output voltage of measurement photovoltaic array, the variable quantity of electric current.Conductance increment method control is accurate, response
Speed ratio is very fast.But the required precision that the requirement to hardware is particularly sensor is higher, thus the hardware of whole system is made
Valency is higher, while there is the defect of vibration and erroneous judgement.Scholars propose the disturbance observation method based on variable step and conductance increases
The mensuration generation restrained effectively vibration.Additionally, fuzzy theory is also applied in the middle of photovoltaic maximal power tracing.
It is a kind of with maximum work that the domestic maximal power tracing research to photovoltaic grid-connected inverting system has Zhang Xing etc. to propose
The Novel photovoltaic array sine wave combining inverter control program of rate point tracking (MPPT).Wu Libo etc. passes through 89C51 single-chip microcomputer
Using increment conductance method, tracking and accumulator Charge Management are carried out to the peak power output of photovoltaic.Zhou Dejia etc. is to photovoltaic
The characteristics such as I-V, P-V, dP/dV-I are analyzed, and are simulated photovoltaic array and are operated in maximum power point and stable work area
There is when in domain linear relationship, in the case that simulation result shows that photovoltaic array is operated in stability region, dP/dV and I is present
Linear relationship.Again the optimal control of 300kW photovoltaic parallel in system is studied with stability analyses afterwards[29], propose and real
Show with the grid-connected control system for improving maximal power tracing algorithm.Cui Yan etc. to constant voltage tracing, climbing method, climb the mountain
4 kinds of MPPT methods such as improved method and increment conductance method are compared, and by the tracking effect of each algorithm of simulation evaluation and excellent are lacked
Point.Liu Liqun etc. have studied a kind of MPPT algorithm based on diode quality factor and dark current, hereafter have also been proposed a kind of part
The fuzzy immunization MPPT control method of masking photovoltaic generating system.The domestic research with regard to maximal power tracing mainly has Huang in the recent period
Shu Yu etc. proposes a kind of Step-varied back propagation MPPT algorithm；Shu Jie etc. proposes one kind to grid-connected maximal power tracing and changes
Enter type disturbance control method；Ma Shun etc. is studied for the maximal power tracing control of energy by ocean current electricity generation system, by most
High-power tracking test obtains the static power tracking result of system；Fuzzy-adaptation PID control is incorporated into single stage type by Zheng Biwei etc.
In the MPPT control of grid-connected system, algorithm is realized on experimental prototype；Particle group optimizing is obscured by Wu Haitao etc.
Controller is applied on photovoltaic generating system maximal power tracing.2011, Zhang Xing, Cao Renxian etc. were to recent photovoltaic generation most
High-power tracking technique gives detailed introduction and analysis.The achievement in research of these scholars, is to promote photovoltaic on a large scale to send out
Electric system provides good theory and technology and supports.
The application of photovoltaic array model information all have ignored in above-mentioned overwhelming majority method.Carry out photovoltaic peak power with
There is the defect of vibration and erroneous judgement during track, cause certain power loss, and drastically become in Intensity of the sunlight and ambient temperature
During change, tracking failure is may result in.The presence of these problems have impact on the real-time of photovoltaic generating system maximal power tracing
And high efficiency.If output cannot track maximum power point and may result in serious consequence, or even photovoltaic can be caused to send out
Normally cannot running for electric system, produces the waste of huge economic loss and the energy.
Content of the invention
The purpose of the present invention is the deficiency for the existing maximum power tracking method of photovoltaic generating system, there is provided a kind of
Photovoltaic generating system maximum power tracking method based on Model Distinguish.
The purpose of the present invention is achieved through the following technical solutions：A kind of photovoltaic generating system based on Model Distinguish
Maximum power tracking method, the method comprises the steps：
(1) mathematical modeling of photovoltaic array and identification：According to the characteristic of photovoltaic array of photovoltaic generating system, photovoltaic battle array is set up
The reference mathematical model of row.At a temperature of current intensity of illumination and photovoltaic battery panel, photovoltaic array output voltage is adjusted to saw
Tooth ripple variation tendency changes from small to large, obtains output voltage and the output current metrical information (V of multigroup photovoltaic arrayi m、
), wherein Vi mFor output voltage measured value,For output current measured value.Using least square model parameter identification method, right
The unknown parameter of model is recognized.Optimizing application derivation algorithm solves the identification of Model Parameters problem so that the reference that is built
Mathematical model output voltage, output current (Vi、Ii) with the output metrical information (V of actual photovoltaic generating systemi m、) deviation is most
Little, so as to accurate parameter of the photovoltaic array with reference to mathematical model is obtained, obtain the accurate identification mould consistent with practical object
Type.
(2) photovoltaic array for being recognized based on step (1) refers to mathematical model, changes intensity of illumination from small to large with light
Volt battery plate temperature, the reference mathematical model after identification is calculated a series of different illumination intensity with photovoltaic battery panel temperature
The output voltage of degree drag and output current test data.Consistent with practical object with reference to mathematical model, these test numbers
According to the test data for approximate actual photovoltaic array.In these test datas, different illumination intensity G and photovoltaic cell is extracted
Plate temperature TjUnder maximum power point corresponding to output voltage Vi Pmax, output Pi Pmax.
(3) BP (Back Propagation) of three layers of photovoltaic array comprising input layer, hidden layer and output layer is built
Neural network model, is wherein input into as intensity of illumination, photovoltaic cell plate temperature, the output electricity being output as corresponding to maximum power point
Pressure and output.Using test data obtained by step (2) (intensity of illumination G, photovoltaic cell plate temperature Tj, maximum power point
Corresponding output voltage Vi PmaxWith output Pi Pmax), 2/3rds test data is taken as the training sample of neutral net
This, remaining 1/3rd test data is instructed to the neural network model that is built as the test sample of neutral net
Practice and test, obtain the parameter of BP neural network.The node number of adjustment hidden layer so that BP neural network training error and survey
Examination error sum minimum, so as to obtain optimum training with test result.
(4) the photovoltaic array BP neural network model that step (3) is trained is written to the maximum work of photovoltaic generating system
In rate tracking control unit, in the case of intensity of illumination with the change of photovoltaic cell plate temperature, online using above-mentioned neural network model
The output voltage predictive value being calculated corresponding to maximum power point, and using conventional maximum power tracking method to reality
Maximum power point is tracked revising, and realizes the real-time maximal power tracing to photovoltaic generating system, is greatly enhanced photovoltaic and sends out
The real-time and high efficiency of electric system maximal power tracing.
(5) by actual maximum power point information during on-line operation (including intensity of illumination G, photovoltaic cell plate temperature Tj, most
Output voltage V corresponding to high-power pointi PmaxWith output Pi Pmax) preserved, periodically offline to photovoltaic array BP nerve
Network model's parameter is modified, and obtains the parameter value with the more aligned BP neural network of reality output, realizes BP neural network
The self adaptation of parameter, so as to accelerate the process of MPPT maximum power point tracking, further improves photovoltaic generating system maximal power tracing
Real-time and high efficiency.
The invention has the beneficial effects as follows, photovoltaic generating system maximum power tracking method of the present invention based on Model Distinguish,
Metrical information initially with actual photovoltaic generating system is recognized to photovoltaic array model, is obtained consistent with practical object
Identification model；Then a kind of BP neural network is constructed, and different test sample points is produced to nerve net using the model of identification
Being trained for network, obtains the parameter of neutral net；Neural Network Online will finally be trained for photovoltaic generating system most
In high-power tracking, real-time and the high efficiency of photovoltaic generating system maximal power tracing can be greatly enhanced.Application of the present invention
Photovoltaic array model information, you can be applied to also apply be applicable on grid-connected photovoltaic system in photovoltaic off-grid electricity generation system,
Can combine from different maximum power tracking methods, method versatility is good, apply very flexible.
Description of the drawings
Fig. 1 is 50KW grid-connected photovoltaic system principle assumption diagram；
Fig. 2 is BP neural network model topology structure chart；
Fig. 3 is maximum power tracking method principle assumption diagram of the photovoltaic generating system based on Model Distinguish.
Specific embodiment
Accompanying drawing referring to the present invention is for a more detailed description to the present invention.The present invention can also be in many different forms
Implement, therefore it is not considered that it is confined to the embodiment listed by description, conversely, provide this embodiment illustrating that
The enforcement of the present invention and completely, and the specific implementation process that the present invention can be described to those skilled in the relevant art.
Fig. 1 is 50KW grid-connected photovoltaic system principle assumption diagram, and Fig. 2 is BP neural network model topology structure chart, Fig. 3
For maximum power tracking method principle assumption diagram of the photovoltaic generating system based on Model Distinguish.Come with reference to Fig. 1, Fig. 2, Fig. 3
Illustrate the specific embodiment of the present invention.A kind of photovoltaic generating system maximal power tracing side based on Model Distinguish of the present invention
Method is specifically comprising being implemented as follows step：
(1) mathematical modeling of photovoltaic array and identification：According to the characteristic of photovoltaic array of grid-connected photovoltaic system, according to base
That Hall current voltage law and the characteristic of photovoltaic battery panel, the reference mathematical model for setting up photovoltaic array is as follows：
In formula, IphPhotovoltaic cell photogenerated current, its size is relevant with intensity of illumination and temperature；I0Diode anti-
To saturation current；Q electron charge, q=1.6029 × 10-19℃；VAThe output voltage of photovoltaic array, IAPhotovoltaic array
Output current；NsSeries-connected solar cells number, NpSolar-electricity pool count in parallel；RsPhotovoltaic cell equivalent series
Resistance；The quality factor of n diode；K Botzman coefficient, k=1.3819 × 10-23J/K；Isc-refUnder standard conditions
(generally intensity of illumination GrefFor 1000W/m2, reference temperature TjrefFor 25 DEG C) short-circuit current value that is given of parameter list；
Iph-refPhotovoltaic cell photogenerated current under standard conditions；G intensity of illumination；K0Photogenerated current is corresponding with the change of temperature
Relation value；TjPhotovoltaic cell plate temperature；IorefDiode saturation current under standard conditions；Voc-refOpening under standard conditions
Road voltage；VgBand gap voltage；The slope of I-V curve during dv/dIVoc open-circuit voltage；XaEquivalent conductance.
Institute's established model corresponds to the real-time maximum power tracking method theory structure in figure of the photovoltaic generating system shown in Fig. 3
Photovoltaic array refers to mathematical model, at a temperature of current intensity of illumination and photovoltaic battery panel, adjusts photovoltaic array output voltage
Changed with sawtooth waveforms variation tendency from small to large, obtain output voltage and the output current metrical information (V of N group photovoltaic arrayim、).Using least square model parameter identification method, the unknown parameter of model is carried out recognizing as follows：
Wherein, Vi m,Vi,IiRespectively output current, the measured value of output voltage measurand and corrected value；δV,δI
It is the weight of corresponding measurand respectively；NV,NIThe measurement group number of respectively output voltage and output current；Rs, n is for recognizing
Model parameter.Using the Optimization Solution Algorithm for Solving identification of Model Parameters problem so that the reference mathematical model output that is built
Voltage, output current (Vi、Ii) with the output metrical information (V of actual photovoltaic generating systemi m、) deviation minimum, so as to obtain
Photovoltaic array acquires the accurate mathematical model consistent with practical object with reference to the accurate parameter of mathematical model.
(2) photovoltaic array for being recognized based on step (1) refers to mathematical model, changes intensity of illumination from small to large with light
Volt battery plate temperature, the reference mathematical model after identification is calculated a series of different illumination intensity with photovoltaic battery panel temperature
The output voltage of degree drag and output current test data.Because consistent with practical object with reference to mathematical model, these surveys
Examination data can approximate actual photovoltaic array test data.In these test datas, different illumination intensity G and photovoltaic electric is extracted
Pond plate temperature TjUnder maximum power point corresponding to output voltage Vi PmaxWith output Pi Pmax.
(3) BP neural network model such as Fig. 2 institute of three layers of photovoltaic array comprising input layer, hidden layer and output layer is built
Show, wherein the input layer of BP neural network is intensity of illumination, photovoltaic cell plate temperature, output node layer is maximum power point
Corresponding output voltage and output, hidden layer node is H1,H2,...,NhFor the total node number of hidden layer.Adopt
With test data (intensity of illumination G, photovoltaic cell plate temperature T obtained by step (2)j, output voltage corresponding to maximum power point
Vi PmaxWith output Pi Pmax), 2/3rds test data is taken as the training sample of neutral net, remaining three/
One test data is trained to the neural network model that is built and tests, obtain BP as the test sample of neutral net
The parameter of neutral net.Adjustment hidden layer node number Nh(such as NhTake 2～15), select to cause BP neural network training error
With hidden layer node number N corresponding to test error sum minimumh.
(4) the photovoltaic array BP neural network model that step (3) is trained is written to grid-connected photovoltaic system most
In high-power tracking control unit (dsp controller in corresponding Fig. 3), change situation in intensity of illumination with photovoltaic cell plate temperature
Under, the output voltage predictive value corresponding to maximum power point is calculated online using above-mentioned neural network model, and using normal
The disturbance observation method of rule is tracked revising to actual maximum power point, realizes the real-time maximum to grid-connected photovoltaic system
Power tracking, is greatly enhanced real-time and the high efficiency of grid-connected photovoltaic system maximal power tracing.
(5) by actual maximum power point information during on-line operation (including intensity of illumination G, photovoltaic cell plate temperature Tj, most
Output voltage V corresponding to high-power pointi PmaxWith output Pi Pmax) preserved, periodically offline to photovoltaic array BP nerve
Network model is modified (for example, every 1 month to photovoltaic array BP neural network Modifying model 1 time), obtains defeated with actual
Go out the parameter value of more consistent BP neural network, the self adaptation of BP neural network parameter is realized, so as to accelerate MPPT maximum power point tracking
Process, further improve the real-time of grid-connected photovoltaic system maximal power tracing and high efficiency.
Therefore, the present invention is directed to these problems, it is proposed that a kind of photovoltaic generating system peak power based on Model Distinguish
Tracking, the method is recognized to photovoltaic array model initially with the metrical information of actual photovoltaic generating system, is obtained
The identification model consistent with practical object；Then a kind of BP neural network is constructed, and different tests is produced using identification model
Sample point is trained to neutral net, obtains the parameter of neutral net；Finally training Neural Network Online ground is used for light
On the maximal power tracing of photovoltaic generating system, so as to be greatly enhanced real-time and the height of photovoltaic generating system maximal power tracing
Effect property.
Below according to specific embodiment, the invention will be further described.
Embodiment
Photovoltaic parallel in system in the present embodiment is that in country of Wenzhou University place joint project laboratory, generated energy is 5KW
Small photovoltaic power generation system, its principle assumption diagram is as shown in Figure 1.The environment monitoring module of the system is can achieve to intensity of illumination
Real-time detection.Photovoltaic battery panel mounting temperature sensor, the real-time inspection of achievable photovoltaic cell plate temperature in photovoltaic array
Survey.While the outfan in photovoltaic array is equipped with hall element sensor, the voltage of achievable photovoltaic array output, the reality of electric current
When accurately measure.The characteristic of photovoltaic battery panel is, it is known that including 25 DEG C of intensities of illumination 1000W/m of photovoltaic cell plate temperature2Time
The open-circuit voltage V of volt batteryocWith short circuit current Isc-ref, the corresponding output voltage V of maximum power pointPmaxWith output current IPmax.
(1) mathematical modeling of photovoltaic array and identification：According to the characteristic of photovoltaic battery panel, the ginseng of photovoltaic array is initially set up
Examine mathematical model.In 25 DEG C of intensities of illumination 1000W/m of photovoltaic cell plate temperature2Under, by the photovoltaic array output electricity of photovoltaic system
Pressure is with sawtooth waveforms variation tendency from 1% incremental variations of open-circuit voltage to open-circuit voltage Voc, each amplitude of variation is 1%Voc, obtain
Take output voltage and the output current metrical information (V of 100 groups of photovoltaic arraysi m、).Using least square identification of Model Parameters
Method, recognizes to the unknown parameter of model.Optimizing application derivation algorithm solves the identification of Model Parameters problem so that built
Reference mathematical model output voltage, output current (Vi、Ii) with the output metrical information (V of actual photovoltaic generating systemi m、)
Deviation minimum, so as to obtain accurate parameter of the photovoltaic array with reference to mathematical model, further obtains consistent with practical object
Accurate mathematical model.
(2) photovoltaic array for being recognized based on step (1) refers to mathematical model, by intensity of illumination from 50W/m2To 3000W/
m2Incremental variations, each amplitude of variation is 50W/m2.Photovoltaic cell plate temperature incremental variations from 1 DEG C to 100 DEG C, change width every time
Spend for 1 DEG C.Reference mathematical model after identification is calculated a series of (60*100 group) different illumination intensity and photovoltaic electric
The output voltage of pond plate temperature drag and output current test data.In these test datas, different illumination intensity G is extracted
With photovoltaic cell plate temperature TjUnder maximum power point corresponding to output voltage Vi PmaxWith output Pi Pmax.
(3) the BP neural network model of three layers of photovoltaic array comprising input layer, hidden layer and output layer is built.Using step
Suddenly test data (intensity of illumination G, photovoltaic cell plate temperature T obtained by (2)j, output voltage V corresponding to maximum power pointi Pmax
With output Pi Pmax), 4000 groups of test data is taken at random as the training sample of neutral net, remaining 2000 groups of tests
Data are trained to the neural network model that is built and test, obtain BP neural network as the test sample of neutral net
Parameter.Hidden layer node number is chosen as 2,3,4 ... ..., when 15, selects to cause BP neural network training error with test by mistake
Hidden layer node number corresponding to difference sum minimum.
(4) the photovoltaic array BP neural network model that step (3) is trained is written to the DSP of grid-connected photovoltaic system
In controller, in the case of intensity of illumination with the change of photovoltaic cell plate temperature, calculated using above-mentioned neural network model online
To the output voltage predictive value corresponding to maximum power point, and using conventional disturbance observation method, actual peak power is clicked through
Line trace correction, realizes the real-time maximal power tracing to photovoltaic generating system.
(5) by actual maximum power point information during on-line operation (including intensity of illumination G, photovoltaic cell plate temperature Tj, most
Output voltage V corresponding to high-power pointi PmaxWith output Pi Pmax) preserved, using the maximum power point information for preserving
Every 1 month to photovoltaic array BP neural network Modifying model 1 time, the parameter with the more consistent BP neural network of reality output is obtained
Value, realizes the self adaptation of BP neural network parameter.The method can accelerate the process of MPPT maximum power point tracking, further improve photovoltaic
The real-time and high efficiency of grid-connected system maximal power tracing.
As seen from the above-described embodiment, photovoltaic generating system maximum power tracking method principle of the present invention based on Model Distinguish
Clearly, it is convenient to realize on computers, and motility is fine, can combine from different maximum power tracking methods, both be suitable for
Photovoltaic off-grid electricity generation system is also applied in grid-connected photovoltaic system, grid-connected photovoltaic system maximum work can be greatly enhanced
The real-time and high efficiency of rate tracking.
Claims (1)
1. a kind of photovoltaic generating system maximum power tracking method based on Model Distinguish, it is characterised in that the method is specifically included
Following steps：
(1) mathematical modeling of photovoltaic array and identification：According to the characteristic of photovoltaic array of photovoltaic generating system, photovoltaic array is set up
With reference to mathematical model.At a temperature of current intensity of illumination and photovoltaic battery panel, photovoltaic array output voltage is adjusted with sawtooth waveforms
Variation tendency changes from small to large, obtains output voltage and the output current metrical information (V of multigroup photovoltaic arrayi m、), its
Middle Vi mFor output voltage measured value,For output current measured value.Using least square model parameter identification method, to model
Unknown parameter recognized.Optimizing application derivation algorithm solves the identification of Model Parameters problem so that the reference mathematics that is built
Model output voltage, output current (Vi、Ii) with the output metrical information (V of actual photovoltaic generating systemi m、) deviation minimum,
So as to accurate parameter of the photovoltaic array with reference to mathematical model is obtained, the accurate identification model consistent with practical object is obtained.
(2) photovoltaic array for being recognized based on step (1) refers to mathematical model, changes intensity of illumination from small to large with photovoltaic electric
Pond plate temperature, at a temperature of the reference mathematical model after identification is calculated a series of different illumination intensity and photovoltaic battery panel
The output voltage of model and output current test data.Consistent with practical object with reference to mathematical model, these test datas are used
Test data in approximate actual photovoltaic array.In these test datas, different illumination intensity G and photovoltaic battery panel temperature is extracted
Degree TjUnder maximum power point corresponding to output voltage Vi Pmax, output
(3) the BP neural network model of three layers of photovoltaic array comprising input layer, hidden layer and output layer is built, and wherein input is
Intensity of illumination, photovoltaic cell plate temperature, the output voltage being output as corresponding to maximum power point and output.Using step
(2) test data (intensity of illumination G, photovoltaic cell plate temperature T obtained byj, output voltage V corresponding to maximum power pointi PmaxWith
Output Pi Pmax), 2/3rds test data is taken as the training sample of neutral net, remaining 1/3rd test
Data are trained to the neural network model that is built and test, obtain BP neural network as the test sample of neutral net
Parameter.The node number of adjustment hidden layer so that BP neural network training error and test error sum minimum, so as to obtain
Optimum training and test result.
(4) the photovoltaic array BP neural network model that step (3) is trained is written to the peak power of photovoltaic generating system with
In track controller, in the case of intensity of illumination with the change of photovoltaic cell plate temperature, using above-mentioned neural network model in line computation
The output voltage predictive value corresponding to maximum power point is obtained, and using conventional maximum power tracking method to actual maximum
Power points is tracked revising, and realizes the real-time maximal power tracing to photovoltaic generating system, is greatly enhanced photovoltaic generation system
The real-time and high efficiency of system maximal power tracing.
(5) by actual maximum power point information during on-line operation (including intensity of illumination G, photovoltaic cell plate temperature Tj, maximum work
Output voltage V corresponding to rate pointi PmaxWith output Pi Pmax) preserved, periodically offline to photovoltaic array BP neural network
Model parameter is modified, and obtains the parameter value with the more aligned BP neural network of reality output, realizes BP neural network parameter
Self adaptation, so as to accelerate the process of MPPT maximum power point tracking, further improve photovoltaic generating system maximal power tracing reality
When property and high efficiency.
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