CN108828459A - A kind of photovoltaic generation power output slip measuring device and its estimation method - Google Patents
A kind of photovoltaic generation power output slip measuring device and its estimation method Download PDFInfo
- Publication number
- CN108828459A CN108828459A CN201810938208.6A CN201810938208A CN108828459A CN 108828459 A CN108828459 A CN 108828459A CN 201810938208 A CN201810938208 A CN 201810938208A CN 108828459 A CN108828459 A CN 108828459A
- Authority
- CN
- China
- Prior art keywords
- power
- battery panel
- output
- photovoltaic
- photovoltaic battery
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 23
- 230000003287 optical effect Effects 0.000 claims abstract description 30
- 239000000443 aerosol Substances 0.000 claims abstract description 28
- 238000005286 illumination Methods 0.000 claims abstract description 28
- 238000013528 artificial neural network Methods 0.000 claims abstract description 24
- 239000000428 dust Substances 0.000 claims abstract description 23
- 238000013517 stratification Methods 0.000 claims abstract description 22
- 239000003990 capacitor Substances 0.000 claims description 16
- 210000002569 neuron Anatomy 0.000 claims description 13
- 238000012549 training Methods 0.000 claims description 11
- 229910052754 neon Inorganic materials 0.000 claims description 8
- GKAOGPIIYCISHV-UHFFFAOYSA-N neon atom Chemical compound [Ne] GKAOGPIIYCISHV-UHFFFAOYSA-N 0.000 claims description 8
- 230000001105 regulatory effect Effects 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 5
- 238000012546 transfer Methods 0.000 claims description 5
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 4
- 230000003321 amplification Effects 0.000 claims description 3
- 230000007774 longterm Effects 0.000 claims description 3
- 230000033228 biological regulation Effects 0.000 claims description 2
- 210000005036 nerve Anatomy 0.000 claims description 2
- 230000000087 stabilizing effect Effects 0.000 claims description 2
- 230000005611 electricity Effects 0.000 claims 1
- 238000010248 power generation Methods 0.000 abstract description 6
- 238000004445 quantitative analysis Methods 0.000 abstract 1
- 230000006870 function Effects 0.000 description 9
- 101100177269 Arabidopsis thaliana HCAR gene Proteins 0.000 description 6
- 238000010845 search algorithm Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 210000004027 cell Anatomy 0.000 description 3
- 230000005284 excitation Effects 0.000 description 2
- 235000008733 Citrus aurantifolia Nutrition 0.000 description 1
- 235000002918 Fraxinus excelsior Nutrition 0.000 description 1
- 235000011941 Tilia x europaea Nutrition 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 239000002956 ash Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000003292 glue Substances 0.000 description 1
- 238000011478 gradient descent method Methods 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 239000011504 laterite Substances 0.000 description 1
- 229910001710 laterite Inorganic materials 0.000 description 1
- 239000004571 lime Substances 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000001902 propagating effect Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
- 229910052724 xenon Inorganic materials 0.000 description 1
- FHNFHKCVQCLJFQ-UHFFFAOYSA-N xenon atom Chemical compound [Xe] FHNFHKCVQCLJFQ-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R21/00—Arrangements for measuring electric power or power factor
- G01R21/133—Arrangements for measuring electric power or power factor by using digital technique
Abstract
The present invention proposes the photovoltaic generation power output slip measuring device and estimation method under the conditions of a kind of dust stratification, described device is made of light line generating, photometer, photovoltaic battery panel, maximal power tracing circuit, maximal power tracing MCU, it can provide different intensities of illumination and measure the maximum power of photovoltaic battery panel, so that the photovoltaic generation power under the conditions of obtaining dust stratification exports slip；The estimation method is on the basis of constructing a large amount of aerosol optical depth aggregate-values-relative humidity aggregate-value-intensity of illumination-photovoltaic generation power output slip actual sample data, the estimation that the photovoltaic generation power under dust stratification exports slip is carried out using the prediction model of harmony neural network photovoltaic generation power output slip, and using the model.The present invention is able to solve the problem of photovoltaic battery panel dust stratification concentration is difficult to measure, the influence to photovoltaic power generation is difficult to quantitative analysis, to help to improve the precision of prediction of photovoltaic power generation.
Description
Technical field
The present invention relates to technical field of photovoltaic power generation, specifically a kind of photovoltaic generation power output slip measuring device and
Its estimation method.
Background technique
Currently, fossil energy is increasingly depleted, and environmental problem is prominent, and solar energy power generating is as a kind of inexhaustible, use
Inexhaustible clean energy resource, obtained the attention of various countries.Stabilization of the photovoltaic power generation power prediction technology for guarantee electric system
Operation is of great significance, and still faces huge challenge so far.
The dust stratification for being covered on surface of photovoltaic cell panel, which has the output power of photovoltaic battery panel, to be significantly affected.Existing research
Show that dust stratification by occlusion effect, corrosion effect, can weaken the solar radiation of photovoltaic battery panel absorption, to reduce it indirectly
Generated output.
Existing some scholars have carried out dust stratification and have worked the correlative study that photovoltaic cell capable of generating power power influences both at home and abroad, concentrate
In the dust stratification concentration of analysis photovoltaic battery panel and the relationship of photovoltaic generation power output slip.Method one is according to laterite, lime
The simulated experiments of stone and ashes can estimate that photovoltaic power output is reduced as a result, establish theoretical model according to dust stratification concentration
Rate；Method two proposes dust stratification concentration with photovoltaic power and exports the linear of slip by simulating nature dust indoors
Model of fit.In practical projects, the dust stratification concentration for measuring photovoltaic battery panel need to be connected by means of high-precision weighing equipment
Continuous property measurement is more difficult, to affect the practicability of above-mentioned model.
Since the dust stratification concentration of photovoltaic battery panel is difficult to carry out real-time measurement, more efficiently characterizing method need to be found,
With the power output slip for calculating photovoltaic power generation.The present invention proposes the photovoltaic generation power output under the conditions of a kind of dust stratification
Slip measuring device and estimation method, preferably to carry out the prediction of photovoltaic generation power.
Summary of the invention
The purpose of the present invention is to provide a kind of photovoltaic generation powers to export slip measuring device and its estimation method, with
Solve the problems mentioned above in the background art.
To achieve the above object, the present invention provides the following technical solutions：
A kind of photovoltaic generation power output slip measuring device, including light line generating, photometer, photovoltaic cell
Plate, maximal power tracing circuit and maximal power tracing MCU, the light line generating include light modulator, regulated power supply, triggering
Device, high pressure neon lamp, 220V power supply；The light modulator is connected by 220V power supply power supply, and with the regulated power supply, the pressure stabilizing
Power supply is connected by the 220V power supply power supply, anode with one end of the trigger, and cathode is negative with the high pressure neon lamp
Extremely it is connected, one end of the trigger is connected with the anode of the voltage of voltage regulation, and the other end is connected with the anode of high pressure neon lamp, institute
The anode for stating high pressure neon lamp is connected with the trigger, and cathode is connected with the regulated power supply, the photometer and the maximum
Power tracking MCU connection, the maximal power tracing circuit include voltage sensor, current sensor, inductance, capacitor, power
Pipe, driving and load, voltage sensor anode are connected with the anode of the photovoltaic battery panel, inductance, cathode and photovoltaic battery panel
Cathode, capacitor, power tube source electrode, load be connected, export the measuring signal of 0-5V into the maximal power tracing MCU；
Current sensor anode connects with inductance, capacitor, and cathode connects with the drain electrode of power tube, load, exports the measuring signal of 0-5V
Into the maximal power tracing MCU；Inductance one end is connected with the anode of the photovoltaic battery panel, voltage sensor, the other end
Be connected with the anode of current sensor, capacitor, capacitor one end is connected with the anode of inductance, current sensor, the other end with it is described
The cathode of photovoltaic battery panel, the cathode of voltage sensor, power tube source electrode, capacitor connect；Power tube drain electrode and current sense
The cathode of device, load are connected, and cathode is connected with the cathode of the photovoltaic battery panel, capacitor, load, grid and the driving phase
Even, the driving is connected with the grid of power tube, and load one end is connected with the anode of the cathode of current sensor, power tube, separately
One end connects with the source electrode of the cathode of the photovoltaic battery panel, the cathode of voltage sensor, capacitor, power tube.
As further technical solution of the present invention：The maximal power tracing MCU includes power module, MPPT algorithm mould
Block, PID controller module, attenuation rate module；The power module receive the electric signal of current sensor output 0-5V with
The electric signal of the 0-5V of voltage sensor output, and by power output to the MPPT algorithm module and the PID control
In device module；The MPPT algorithm module receives the power signal of the power module, generate power setting value signal export to
PID module finds after maximum power point Maximum Power Output value to the attenuation rate module；The PID controller module receives
To the power setting value signal of the MPPT algorithm and the power signal of the power module, operation output square width signal is arrived
In the drive module；The attenuation rate module receives the photometer and passes through the incoming intensity of illumination signal of I2C interface and institute
The maximum power value of MPPT algorithm module is stated, the attenuation rate under current light intensity is calculated.
As further technical solution of the present invention：The power module is calculated the output of photovoltaic battery panel by following algorithm
Power P：P=Ipv·kI·Vpv·kV, wherein IpvFor the current signal that the current sensor is passed to, kIFor current signal meter
Calculate amplification factor, VpvFor the voltage signal that voltage sensor is passed to, KV is that voltage signal calculates amplification factor.
As further technical solution of the present invention：The MPPT algorithm module operates in the following way：A. in k
It carves, records the output power P (k) of photovoltaic battery panel, and compared with the output P (k-1) of the photovoltaic battery panel of previous moment
Compared with；
If b. P (k) is less than P (k-1), the output voltage U (k) and previous moment photovoltaic battery panel of photovoltaic battery panel are judged
Output voltage U (k) size.If U (k) is greater than U (k-1), increase set value of the power UreIf U (k) is less than U (k-1),
Reduce output voltage setting value Ure
If c. P (k) is greater than P (k-1), the output voltage U (k) and previous moment photovoltaic battery panel of photovoltaic battery panel are judged
Output voltage U (k) size.If U (k) is greater than U (k-1), reduce set value of the power UreIf U (k) is less than U (k-1),
Increase output voltage setting value Ure
If d. P (k) stablizes after successive ignition, P (k) is exported to attenuation rate module.
As further technical solution of the present invention：A kind of photovoltaic generation power output slip estimation under the conditions of dust stratification
Method, which is characterized in that this approach includes the following steps：
A, in a series of intensity of illumination { I of the fixations in laboratoryskUnder, the output of the photovoltaic battery panel after measuring wiped clean
Power is recorded as { P0sk, wherein k=1,2 ..., N；Then, photovoltaic battery panel is placed at outdoor spaciousness, makes itself and level
The inclination angle on ground is α, nature dust stratification is carried out, and using Δ T as time interval, periodically in a series of intensity of illumination of the fixations in laboratory
{IskUnder measure its output power, while recording corresponding moment 440nm aerosol optical depth440, 1020nm aerosol optical it is thick
Spend 1020, relative humidity RH, intensity of illumination Is；I-th is measured, record photovoltaic battery panel output power is Psi, 440nm gas
Colloidal sol optical thickness is440i, 1020nm aerosol optical depth be 1020i, relative humidity RHi, intensity of illumination Isi, identical
The output power of photovoltaic battery panel under intensity of illumination after wiped clean is P0si；By long-term data record, original sample is formed
This collection { (τ440i, τ1020i, RHi, Isi, P0si, P0si), i=1,2 ..., M, M are sample total number；
B, original sample collection is handled, composing training sample set：Based on original sample collection { (τ440i, τ1020i, RHi,
Isi, Pi, P0si) and photovoltaic battery panel and the be in inclination alpha in level ground, calculating 440nm aerosol optical depth accumulated value
τ*440i, 1020nm aerosol optical depth accumulated value τ1020* i and relative humidity accumulated value RHi*, and
I-th measures corresponding photovoltaic generation power and exports slip ηi：
To obtain training sample set
C, three layers of harmony neural network prediction model are established, wherein input layer number of nodes is 4, hidden layer mind
It is 5 through first number of nodes, output layer neuron number of nodes is 1, and hidden layer neuron transfer function uses hyperbolic tangent function,
Output layer neuron transfer function uses S type function；For sample Take BP neural
First input of Network Prediction Model beSecond input beThird inputs4th input
For Isi, export as ηi；
D, the estimation that photovoltaic generation power output slip is carried out using harmony neural network prediction model, i.e., by a certain ring
The 440nm aerosol optical depth accumulated value obtained under border1020nm aerosol optical depth accumulated valueIt is relatively wet
Spend accumulated valueIntensity of illumination IsiAs the input of harmony neural network prediction model, the output of prediction model is as current
The estimated value of photovoltaic generation power output slip under environment
Compared with prior art, the beneficial effects of the invention are as follows：(1) intensity of illumination is adjusted in measuring device, and works as illumination
Maximal power tracing MCU will track the maximum power point of photovoltaic battery panel when intensity changes, and be capable of measuring the measuring device
The corresponding photovoltaic generation power of different illumination intensity exports slip under identical dust stratification concentration；
(2) photovoltaic power generation is estimated using aerosol optical depth aggregate-value, the relative humidity aggregate-value that can obtain in real time
Power output slip can carry out continuity measurement without directly measuring dust stratification concentration；
(3) the neural network estimation model of photovoltaic generation power output slip is established, and using harmony algorithm to mind
Parameter through Network Prediction Model optimizes, so as to effectively improve model accuracy.
(4) with reference to the accompanying drawing, it elaborates to preferred embodiment.It should be emphasized that following the description is only example
Property, the range and its application being not intended to be limiting of the invention.
Detailed description of the invention
Fig. 1 is that photovoltaic generation power proposed by the invention exports slip measuring device structure chart；
Fig. 2 is that photovoltaic generation power proposed by the invention exports slip prediction model structure chart；
Fig. 3 is neural network structure figure；
Fig. 4 is the flow chart of training neural network
Fig. 5 is the flow chart optimized using harmony algorithm to neural network prediction model；
Fig. 6 is estimated result of the prediction model on test samples in embodiment.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Fig. 1-6 is please referred to, the present invention is acquiring a large amount of 440nm aerosol optical depth accumulated value-1020nm aerosol light
On the basis of learning thickness accumulated value-relative humidity aggregate-value-photovoltaic generation power output slip actual sample data, utilize
Harmony neural network method establishes the prediction model of photovoltaic generation power output slip, and structure is as shown in Fig. 2, and utilize this
Model carries out photovoltaic generation power output slip estimation.The present invention utilizes the photovoltaic generation power under the conditions of dust stratification to export reduction
Rate measuring device includes that light line generating, photovoltaic battery panel, maximal power tracing circuit, maximal power tracing MCU are measured
Photovoltaic generation power exports slip.
The use of the mentioned photovoltaic generation power output slip estimation method of the present invention and measuring device includes the following steps：
A. data configuration sample set is acquired；
A series of intensity of illumination { the I of fixations in laboratoryskUnder, the output work of the photovoltaic battery panel after measuring wiped clean
Rate is recorded as { P0sk, wherein k=1,2 ..., N；Then, photovoltaic battery panel is placed at outdoor spaciousness, makes itself and level ground
Inclination angle be a, nature dust stratification is carried out, and using Δ T as time interval, periodically in a series of intensity of illumination { I of the fixations in laboratorysk}
Lower its output power of measurement, while recording corresponding moment 440nm aerosol optical depth 440,1020nm aerosol optical depth
1020, relative humidity RH, intensity of illumination Is；I-th is measured, record photovoltaic battery panel output power is Psi, 440nm gas is molten
Glue optical thickness is that 440i, 1020nm aerosol optical depth are 1020i, relative humidity RHi, intensity of illumination Isi, identical
The output power of photovoltaic battery panel under intensity of illumination after wiped clean is P0si；By long-term data record, original sample is formed
This collection { (440i, 1020i, RHi, Isi, Psi, P0si), i=1,2 ..., M, M are sample total number；
In preferred embodiment, original sample collection is collected in September, 2017 to the Baoding area between in January, 2018.?
Laboratory utilizes 1000W high pressure xenon lamp simulated solar illumination environment, and intensity of illumination is adjusted to 300W/m by light modulator2, survey
Measure clean surface, rated power 30W photovoltaic battery panel output power, be recorded as P10=18.6W, then intensity of illumination is adjusted
For 250W/m2, the output power P of record solar energy plate10=15.8W；Then, photovoltaic battery panel is placed in the room under haze weather
At outer spaciousness, make its inclination angle α=30 ° with level ground, carry out nature dust stratification and evade rainy weather, and with Δ T=24h
For time interval, its output power is periodically measured under a series of illumination condition of the fixations in laboratory, forms original sample collection
{ (440i, 1020i, RHi, Isi, Pi, P0si), i=1,2 ..., M, M=130；
B. original sample collection is handled, composing training sample set：
Based on original sample collection { (τ440i, τ1020i, RHi, Isi, Psi, P0si) and photovoltaic battery panel with level ground be in
Inclination angle a calculates 440nm aerosol optical depth accumulated value1020nm aerosol optical depth accumulated valueWith it is opposite
Humidity accumulated valueAnd i-th measures corresponding photovoltaic generation power and exports slip ηi：
To obtain training sample set
C. three layers of harmony neural network prediction model are established, model structure is as shown in Figure 3, wherein input layer section
Points are 4, and hidden layer neuron number of nodes is 5, and output layer neuron number of nodes is 1, and hidden layer neuron shifts letter
Number uses hyperbolic tangent function, and output layer neuron transfer function uses S type function；For sample First input for taking BP neural network prediction model beSecond input beThird
A input is4th input is Isi, export as ηi；
And neural network is trained, Fig. 4 is the training flow diagram of neural network.Training process by information just
It is formed to propagating with the back-propagation phase of error.In forward-propagating, each neuron of input layer receives extraneous input information, and passes
Pass hidden layer (can be multilayer) and carry out information processing, be finally transmitted to output layer, the state of each layer of neuron only under the influence of
The state of one layer of neuron.When reality output and desired output are not inconsistent, illustrate that the weight of network structure is reasonable not enough, at this moment
Into the back-propagation phase of error, i.e., error signal is calculated along the former layer-by-layer anti-pass of access, the method declined by error gradient
Each layer weight is corrected to keep error minimum.This learning process in cycles is the process that each layer weight constantly adjusts, it
It is performed until until network output error reaches desired value or preset study number.
Neural network may be fallen into local optimum during training, also need to further use harmony algorithm to nerve
Network parameter optimizes.Harmonic search algorithm (harmony search algorithm, HS) is a kind of based on music principle
First inspiration type algorithm, it is of overall importance with very strong search macro ability and optimizing.Fig. 5 is harmonic search algorithm process
Figure, algorithm steps are as follows：
1) initialization algorithm parameter：Algorithm parameter includes initialization harmony data base size (Harmony Memory
Size, HMS), harmony data base retain probability (Harmony Memory Considering Rate, HMCR), fine tuning rate of excitation
(Pitch Adjusting Rate, PAR), the number of iterations (Iteration Number, IN).The size of HMS is one of HS
Why important parameter, HS have stronger ability of searching optimum, are largely dependent upon the presence of HMS, in general,
HMS is bigger, and the ability for finding global optimum region is stronger.But since HS is that multiple spot starts, with the increase of HMS, calculation amount
It will become larger, to influence the speed of arrival optimal solution.HMCR is another key factor of harmony search, and value range is
Number between 0 to 1, it determines the mode that new explanation generates in each iterative process.In harmonic search algorithm, new explanation is every when generating
A variable all relies on HMCR, so HMCR should take biggish value.Tone fine tuning rate of excitation PAR is started to control in harmonic search algorithm
The effect of local search processed, it can make search flee from local optimum, and value generally takes between 0.1 to 0.5.In embodiment, HMS is taken
=5, HMCR=0.9, PAR=0.3, IN=50.
2) harmony memory library initialization and objective function are chosen：An initial population is randomly generated and is put into harmony data base,
The one group of weight and threshold value of the corresponding neural network of each of this group individual.The objective function that this algorithm uses is that network is defeated
Error sum of squares ERR between desired output outi, the value is lower to show that individual is more superior.
3) new explanation is generated：New explanation has a value of the probability of HMCR from HM, has the probability of 1-HMCR except HM
Any one value.If new explanation XnewFrom harmony data base HM, volume fine tuning is carried out to it, operated as follows：
Xnew=Xnew+rand*bw
Wherein：
Random number of the rand between (0,1)；
Bw is bandwidth, in embodiment, takes bw=0.01.
4) data base is updated：If new explanation replaces worst solution better than worst solution in data base, with new explanation, new memory is obtained
Library.
5) judge whether to meet termination condition, if satisfied, stopping iteration, export initial power of the optimal solution as neural network
Value and threshold value carry out the training of neural network using gradient descent method；Otherwise, it is transferred to 3).
D. the estimation that photovoltaic generation power output slip is carried out using harmony neural network prediction model, i.e., by a certain ring
The 440nm aerosol optical depth accumulated value obtained under border1020nm aerosol optical depth accumulated valueIt is relatively wet
Spend accumulated valueIntensity of illumination IsiAs the input of harmony neural network prediction model, the output of prediction model is as current
The estimated value of photovoltaic generation power output slip under environment
In embodiment, k moment 440nm aerosol optical depth 440k=0.631802,1020k=under a certain environment
0.344545, relative humidity RHk=52%, cut-off can be calculated to the corresponding 440nm aerosol optical depth accumulation of k moment
Value * 440k=10.815325,1020nm aerosol optical depth accumulated value * 440k=4.435552, relative humidity accumulated value
RH*k=873% and intensity of illumination I1k=300W/m2, as the input of harmony neural network prediction model, predict mould
The output of type is the estimated value of photovoltaic generation power output slip under current environment
In order to verify the precision of established photovoltaic battery panel power output slip prediction model, 21 groups of samples are in addition chosen
This conduct verifies sample set, verifies to estimation effect, as a result as shown in fig. 6, it is found that the average absolute in verification sample is missed
Difference is 0.551%, illustrates model estimated accuracy with higher.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
It considers the specification as a whole, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art
The other embodiments being understood that.
Claims (5)
1. a kind of photovoltaic generation power exports slip measuring device, which is characterized in that including light line generating, photometer,
Photovoltaic battery panel, maximal power tracing circuit and maximal power tracing MCU, the light line generating include light modulator, pressure stabilizing
Power supply, trigger, high pressure neon lamp and 220V power supply；The light modulator is connected by 220V power supply power supply, and with the regulated power supply
It connects, the regulated power supply is connected by the 220V power supply power supply, anode with one end of the trigger, cathode and the height
The cathode of neon lamp is pressed to be connected, one end of the trigger is connected with the anode of the voltage of voltage regulation, the other end and high pressure neon lamp
Anode is connected, and the anode of the high pressure neon lamp is connected with the trigger, and cathode is connected with the regulated power supply, the photometer
Connect with the maximal power tracing MCU, the maximal power tracing circuit include voltage sensor, current sensor, inductance,
Capacitor, power tube, driving and load, voltage sensor anode are connected with the anode of the photovoltaic battery panel, inductance, cathode and light
Lie prostrate the cathode of solar panel, capacitor, the source electrode of power tube, load are connected, export the measuring signal of 0-5V to the maximum power with
In track MCU；Current sensor anode connects with inductance, capacitor, and cathode connects with the drain electrode of power tube, load, exports 0-5V's
Measuring signal is into the maximal power tracing MCU；Anode of the inductance one end with the photovoltaic battery panel, voltage sensor phase
Even, the other end is connected with the anode of current sensor, capacitor, and capacitor one end is connected with the anode of inductance, current sensor, another
End connects with the cathode of the photovoltaic battery panel, the cathode of voltage sensor, the source electrode of power tube, capacitor；Power tube drain electrode with
The cathode of current sensor, load are connected, and cathode and the cathode of the photovoltaic battery panel, capacitor load and be connected, grid with it is described
Driving is connected, and the driving is connected with the grid of power tube, loads one end and the cathode of current sensor, the positive phase of power tube
Even, the other end connects with the source electrode of the cathode of the photovoltaic battery panel, the cathode of voltage sensor, capacitor, power tube.
2. a kind of photovoltaic generation power output slip measuring device according to claim 1 and its estimation method, special
Sign is that the maximal power tracing MCU includes power module, MPPT algorithm module, PID controller module, attenuation rate module；
The power module receives the electricity of the electric signal of the current sensor output 0-5V and the 0-5V of voltage sensor output
Signal, and by power output into the MPPT algorithm module and the PID controller module；The MPPT algorithm module receives
The power signal of the power module, generation power setting value signal, which exports, gives PID module, exports most after finding maximum power point
High-power value is to the attenuation rate module；The PID controller module receives the power setting value signal of the MPPT algorithm
With the power signal of the power module, operation exports square width signal into the drive module；The attenuation rate module
It receives the photometer and passes through the I2C interface intensity of illumination signal being passed to and the maximum power value of the MPPT algorithm module, meter
Calculate the attenuation rate under current light intensity.
3. a kind of photovoltaic generation power according to claim 2 exports slip measuring device, which is characterized in that the function
Rate module is calculated the output power P of photovoltaic battery panel by following algorithm：P=Ipv·kI·Vpv·kV, wherein IpvFor the electric current
The incoming current signal of sensor, kIAmplification factor, V are calculated for current signalpvFor the voltage signal that voltage sensor is passed to, KV
Amplification factor is calculated for voltage signal.
4. a kind of photovoltaic generation power output slip measuring device according to claim 2 and its estimation method, special
Sign is that the MPPT algorithm module operates in the following way：A. at the k moment, the output power P of photovoltaic battery panel is recorded
(k), it and with the output P (k-1) of the photovoltaic battery panel of previous moment is compared；
If b. P (k) is less than P (k-1), the defeated of the output voltage U (k) of photovoltaic battery panel and previous moment photovoltaic battery panel is judged
The size of voltage U (k) out increases set value of the power U if U (k) is greater than U (k-1)reIf U (k) is less than U (k-1), reduce
Output voltage setting value Ure；
If c. P (k) is greater than P (k-1), the defeated of the output voltage U (k) of photovoltaic battery panel and previous moment photovoltaic battery panel is judged
The size of voltage U (k) out reduces set value of the power U if U (k) is greater than U (k-1)reIf U (k) is less than U (k-1), increase
Output voltage setting value Ure；
If d. P (k) stablizes after successive ignition, P (k) is exported to attenuation rate module.
5. a kind of photovoltaic generation power under the conditions of dust stratification exports slip estimation method, which is characterized in that this method include with
Lower step：
A, in a series of intensity of illumination { I of the fixations in laboratoryskUnder, the output work of the photovoltaic battery panel after measuring wiped clean
Rate is recorded as { P0sk, wherein k=1,2 ..., N；Then, photovoltaic battery panel is placed at outdoor spaciousness, makes itself and level ground
Inclination angle be α, nature dust stratification is carried out, and using Δ T as time interval, periodically in a series of intensity of illumination { I of the fixations in laboratorysk}
Lower its output power of measurement, while recording corresponding moment 440nm aerosol optical depth440, 1020nm aerosol optical it is thick
Degree1020, relative humidity RH, intensity of illumination Is；I-th is measured, record photovoltaic battery panel output power is Psi, 440nm gas
Colloidal sol optical thickness is440i, 1020nm aerosol optical depth be1020i, relative humidity RHi, intensity of illumination Isi, same light
Output power according to the photovoltaic battery panel after wiped clean under intensity is P0si；By long-term data record, original sample is formed
Collect { (τ440i, τ1020i, RHi, Isi, P0si, P0si), i=1,2 ..., M, M are sample total number；
B, original sample collection is handled, composing training sample set：Based on original sample collection { (τ440i, τ1020i, RHi, Isi, Pi,
P0si) and photovoltaic battery panel and the be in inclination alpha in level ground, calculating 440nm aerosol optical depth accumulated value τ* 440i、
1020nm aerosol optical depth accumulated value τ1020 * iWith relative humidity accumulated value RHi *And i-th measures corresponding photovoltaic hair
Electrical power exports slip ηi：
To obtain training sample set
C, three layers of harmony neural network prediction model are established, wherein input layer number of nodes is 4, hidden layer neuron node
Number is 5, and output layer neuron number of nodes is 1, and hidden layer neuron transfer function uses hyperbolic tangent function, output layer nerve
First transfer function uses S type function；For sample Take BP neural network prediction model
First input beSecond input beThird inputs4th input is Isi, export as ηi；
D, the estimation that photovoltaic generation power output slip is carried out using harmony neural network prediction model, i.e., will be under a certain environment
The 440nm aerosol optical depth accumulated value of acquisition1020nm aerosol optical depth accumulated valueRelative humidity is tired
Product valueIntensity of illumination IsiAs the input of harmony neural network prediction model, the output of prediction model is current environment
The estimated value of lower photovoltaic generation power output slip
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810938208.6A CN108828459A (en) | 2018-08-17 | 2018-08-17 | A kind of photovoltaic generation power output slip measuring device and its estimation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810938208.6A CN108828459A (en) | 2018-08-17 | 2018-08-17 | A kind of photovoltaic generation power output slip measuring device and its estimation method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108828459A true CN108828459A (en) | 2018-11-16 |
Family
ID=64150221
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810938208.6A Pending CN108828459A (en) | 2018-08-17 | 2018-08-17 | A kind of photovoltaic generation power output slip measuring device and its estimation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108828459A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111861784A (en) * | 2020-06-01 | 2020-10-30 | 大唐东北电力试验研究院有限公司 | Photovoltaic power generation short-term prediction method based on artificial bee colony optimization neural network |
| CN111896874A (en) * | 2020-07-13 | 2020-11-06 | 江西理工大学 | Battery SOC prediction method based on evolutionary neural network |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110088748A1 (en) * | 2008-05-30 | 2011-04-21 | Kunsan National University Industry-Academy Cooper | Grid-interactive photovoltaic generation system with power quality control and energy saving |
| CN102291050A (en) * | 2011-08-17 | 2011-12-21 | 华北电力大学（保定） | Maximum power point tracking method and device for photovoltaic power generation system |
| CN106230112A (en) * | 2016-07-19 | 2016-12-14 | 吕世全 | A kind of distributed photovoltaic power generation station intelligence control operation management apparatus |
| CN106372718A (en) * | 2016-08-29 | 2017-02-01 | 华北电力大学（保定） | Photovoltaic generation power output reduction rate estimation method under haze soot formation condition |
| CN206115323U (en) * | 2016-11-04 | 2017-04-19 | 西南大学 | Photovoltaic power generation system |
| KR101842944B1 (en) * | 2017-10-25 | 2018-03-28 | 이성집 | Indication Column Structure |
-
2018
- 2018-08-17 CN CN201810938208.6A patent/CN108828459A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110088748A1 (en) * | 2008-05-30 | 2011-04-21 | Kunsan National University Industry-Academy Cooper | Grid-interactive photovoltaic generation system with power quality control and energy saving |
| CN102291050A (en) * | 2011-08-17 | 2011-12-21 | 华北电力大学（保定） | Maximum power point tracking method and device for photovoltaic power generation system |
| CN106230112A (en) * | 2016-07-19 | 2016-12-14 | 吕世全 | A kind of distributed photovoltaic power generation station intelligence control operation management apparatus |
| CN106372718A (en) * | 2016-08-29 | 2017-02-01 | 华北电力大学（保定） | Photovoltaic generation power output reduction rate estimation method under haze soot formation condition |
| CN206115323U (en) * | 2016-11-04 | 2017-04-19 | 西南大学 | Photovoltaic power generation system |
| KR101842944B1 (en) * | 2017-10-25 | 2018-03-28 | 이성집 | Indication Column Structure |
Non-Patent Citations (1)
| Title |
|---|
| 刘卫亮等: "计及雾霾影响因素的光伏发电超短期功率预测", 《中国电机工程学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111861784A (en) * | 2020-06-01 | 2020-10-30 | 大唐东北电力试验研究院有限公司 | Photovoltaic power generation short-term prediction method based on artificial bee colony optimization neural network |
| CN111896874A (en) * | 2020-07-13 | 2020-11-06 | 江西理工大学 | Battery SOC prediction method based on evolutionary neural network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104680424B (en) | The voltage power situation predictor method in large-sized photovoltaic power station | |
| CN106372718B (en) | Photovoltaic generation power under the conditions of haze dust stratification exports slip method of estimation | |
| CN104965558A (en) | Photovoltaic power generation system maximum power tracking method and apparatus considering the factor of haze | |
| Nikolaeva‐Dimitrova et al. | Seasonal variations on energy yield of a‐Si, hybrid, and crystalline Si PV modules | |
| CN102780232B (en) | Three-ring control method and three-ring control device of single-stage type photovoltaic grid-connected inverter system | |
| CN102496953B (en) | Photovoltaic power generation micro electric network system and maximum power tracking method | |
| CN107562992B (en) | Photovoltaic array maximum power tracking method based on SVM and particle swarm algorithm | |
| CN108828459A (en) | A kind of photovoltaic generation power output slip measuring device and its estimation method | |
| CN106656032B (en) | Slip method of estimation is exported for the photovoltaic generation power of haze dust stratification | |
| CN110487397B (en) | Arrangement method of photovoltaic array irradiance sensors | |
| CN109101071A (en) | A kind of photovoltaic multi-peak maximum power point tracing method based on intelligent predicting | |
| CN108536212A (en) | A kind of novel variable step photovoltaic maximum power tracking method based on power prediction | |
| CN107359860B (en) | A kind of perovskite solar cell electron lifetime test method based on EIS analysis | |
| CN103293950B (en) | The control method that a kind of maximum photovoltaic power point based on LSSVM is followed the tracks of | |
| CN103763812A (en) | DSP-based solar semiconductor illumination system and control method thereof | |
| CN104865446A (en) | On-line detection method of photovoltaic grid-connected inverter alternating-current output line impedance | |
| CN106597151B (en) | Photovoltaic power station switched inverter tracking efficiency testing method and system | |
| Hassan et al. | Investigating parasitic resistance of mesoporous-based solar cells with respect to thin-film and conventional solar cells | |
| TWI671996B (en) | Method for judging the orientation of a solar power module | |
| CN109345129A (en) | A kind of photo-voltaic power generation station health state evaluation method and system | |
| CN106546824B (en) | The system harmonic impedance estimation method of power grid containing photovoltaic returned based on variable coefficient | |
| CN108280287A (en) | A kind of method of solar cell parameter extraction | |
| CN113178896B (en) | Method and system for configuring installed capacity of fixed-output light-storage combined power station | |
| Han et al. | Electrical performance and power prediction of a roll-bond photovoltaic thermal array under dewing and frosting conditions | |
| CN108233373B (en) | Probability harmonic analysis method considering weather scene for distributed photovoltaic access power distribution network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181116 |