CN110348106A - A kind of wireless power transmission EFFICIENCY PREDICTION method based on Gaussian process - Google Patents
A kind of wireless power transmission EFFICIENCY PREDICTION method based on Gaussian process Download PDFInfo
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Abstract
The wireless power transmission EFFICIENCY PREDICTION method based on Gaussian process that the invention discloses a kind of.The method that the present invention substitutes HFSS software phantom using Gaussian process model, by the radius of spin of wireless power transmission antenna, transmitting antenna between receiving antenna at a distance from, and the relevant parameters such as matching capacitance and Frequency Structure Simulator software HFSS simulate the S parameter i.e. scattering parameter come as training sample, construct corresponding Gaussian process model, the Gaussian process model can be used to predict the S parameter of other wireless power transmission antennas, and wireless power transmission efficiency can be calculated by this S parameter.By the Gaussian process model of foundation compared with other modeling methods from the point of view of, the present invention can solve using the problem of HFSS software emulation overlong time and the S parameter error that predicts is smaller meets professional standard.
Description
Technical field
The wireless power transmission EFFICIENCY PREDICTION method based on Gaussian process that the present invention relates to a kind of, belongs to near-field coupling class day
Line technology field.
Background technique
The transmission of magnet coupled resonant type wireless electric energy has many merits and is widely applied, wherein utilizing identical resonance
Coupling occurs between frequency antenna to transmit energy, the efficiency of transmission between antenna then becomes most important one during the Antenna Design
A parameter directly determines the success or failure of Antenna Design.In general people utilize analytic method and numerical method and electromagnetic simulation software phase
Overlong time and the larger problem of Prediction Parameters error are calculated in conjunction with being will lead in this way come the design of Simulation that carries out antenna.By grain
Subgroup optimization algorithm is easily realized and is applied in antenna optimization design the characteristics of fast convergence rate, can be very good to solve above-mentioned ask
Topic.
It is a kind of coil antenna used in wireless power transmission.In order to overcome previous methods for EFFICIENCY PREDICTION between antenna
In the presence of defect, so having studied a kind of wireless power transmission model based on Gaussian process to predict the transmission between antenna
Efficiency.The Gaussian process model of foundation can be in coil antenna related parameters (including the radius of spin, transmitting antenna and receiving antenna
Between distance, matching capacitance etc.) and electromagnetic simulation software HFSS (High Frequency Structure Simulator) in obtain
Mapping relations are established between the antenna S parameter (scattering parameter) obtained, to predict the S parameter of coil antenna, this method is direct
It avoids and HFSS software emulation is called to go out antenna S parameter the time it takes.In addition Gaussian process modeling method is predicted
Antenna S parameter is compared with the antenna S parameter for directly HFSS software emulation being used to go out, it can be seen that Gaussian process modeling method institute
The less and error precision that takes time meets industry requirement.
Summary of the invention
The wireless power transmission EFFICIENCY PREDICTION method based on Gaussian process that the purpose of the present invention is to provide a kind of, utilizes height
This process model and particle swarm algorithm combine, and apply in the EFFICIENCY PREDICTION problem of wireless power transmission, are called with reducing
Plenty of time required for HFSS software emulation, while also going out the efficiency that Gaussian process model prediction goes out with HFSS software emulation
Efficiency make error analysis, to confirm the efficiently and accurately of Gaussian process model prediction.
The purpose of the present invention is achieved by the following technical programs:
A kind of wireless power transmission EFFICIENCY PREDICTION method based on Gaussian process comprising the steps of:
1) coupled mode theory establishes wireless power transmission model
It is that there is no energy to pass in the case that system is between uniform passive states and each unit and is mutually orthogonal
It passs；Only when system enters couple state, that is antennas when two with identical resonance frequency are intercoupling
In the case of the phenomenon that just generating energy transmission.The correlation formula of coupled mode theory are as follows:
In formula, am(t) is defined as: | am(t)|2The energy for including for coil antenna m；an(t) is defined as: | an(t)|2For line
The energy that circle antenna n includes；T indicates the time；ωmIndicate the resonance frequency of coil antenna m；J is complex unit；kmn(m ≠ n) table
Show the coefficient of coup of coil antenna m and n；ΓmIt is the loss factor of coil antenna m；Sm(t) it is then used to indicate that in coil antenna m
Driving source.
Magnet coupled resonant type wireless electric energy transmission system by high frequency electric source, impedance matching box, transmitting antenna, receiving antenna,
Driving circuit and load are constituted.In system work, high frequency electric source is exported at high-frequency alternating current to transmitting antenna, in impedance
Under the action of adaptation, receiving antenna realizes the transmission process of radio energy with transmitting antenna generation coupled resonance, this
The electric energy received afterwards can direct powering load after overdrive circuit carries out rectifying and wave-filtering.It is managed according to equivalent circuit
By the equivalent circuit for the magnet coupled resonant type wireless electric energy transmission system being created as.Equivalent circuit emitter part is by coil
Antenna L1, capacitor C1, resistance R1 and current source S are connected in series；Reception device part is by receiving antenna L2, capacitor C2, resistance
R2 and load RL composition.
2) acquisition of training sample
By the radius of spin, transmitting antenna and the receiving antenna of magnet coupled resonant type wireless electric energy transmission system coil antenna
Between distance, these three parameters of matching capacitance as training sample input, the efficiency of transmission obtained by HFSS software emulation is as sample
This training output；
3) foundation of Gaussian process model
Gaussian process model can establish training set input X and export the mapping relations between y, and according to this mapping relations
Provide the corresponding predicted value of test sample x'.Gaussian process describes a kind of function distribution, it is an infinite number of stochastic variable
The set that any subset all meets Joint Gaussian distribution is formed, property can be determined by mean function and covariance function,
Value is defined as:
μ (x)=E [Y (x)]
Wherein, E [x] is expressed as the mathematic expectaion i.e. mean value of input x, and Y (x) is expressed as being distributed about the function of x；
Covariance function is defined as:
C (x, x')=E [(Y (x)-μ (x)) (Y (x')-μ (x'))]
Wherein x, x' ∈ RdFor any d n dimensional vector n, μ (x) and C (x, x') respectively indicate mean function and covariance function, Y
(x') it is distributed for the function of test sample x'；
Therefore Gaussian process may be defined as:
F (x)~GP (μ (x), C (x, x'))
Wherein, f (x) is expressed as the mapping relations about mean function μ (x) and covariance function C (x, x'), i.e. Gauss mistake
Journey (GP) model；
4) optimization design
After Gaussian process model foundation has been got well, optimizing is optimized to model using particle swarm algorithm；Population is set
Initial value, i.e. Studying factors c1, aceleration pulse c2, the number of iterations k, particle number i and particle maximum speed Vmax, choose in addition
9 groups of data carry out the prediction of efficiency of transmission using Gaussian process model substitution particle swarm algorithm as test sample to particle, when
The error amount that Gaussian process model prediction goes out terminates process when reaching preset requirement, and error amount will ask for an interview to step 5)；
5) reliability of Gaussian process model is detected
4) prediction in is compared with the result that HFSS simulation software obtains, if error is less than required precision, then it is assumed that
Obtain accurate Gaussian process model；If error is greater than required precision, by best particle (position of particle and maximum speed)
And accurate solution is added in original training sample, more new database, so that Gaussian process model is had updated, until error reaches precision
It is required that (required precision: mean absolute error is less than 0.05, and mean square error is less than 0.01, and mean percent ratio error is less than 0.05)；
6) it predicts
Efficiency of transmission is predicted using Gaussian process model, and the efficiency of transmission obtained with HFSS software emulation carries out
Compare, calculate its mean absolute error (MAE), mean square error (MSE) and be averaged percentage error (APE), correlation formula is as follows:
In formula,For the predicted value of i-th of sample,For the test value of i-th of sample, n is number of samples.
The purpose of the present invention can also be further realized by following technical measures:
A kind of aforementioned wireless power transmission EFFICIENCY PREDICTION method based on Gaussian process, the acquisition of training sample in step 2)
Simulation analysis is carried out to magnet coupled resonant type wireless power transmitting antenna using HFSS software, result is defeated as the training of sample
Out, the value range of the radius of spin r of the selected antenna of the simulation analysis is 50≤r≤100 (mm), transmitting antenna and reception
The value range of antenna distance d is 100≤d≤300 (mm), and the value range of matching capacitance c is 10≤c≤100 (pF).
A kind of aforementioned wireless power transmission EFFICIENCY PREDICTION method based on Gaussian process, in the particle swarm algorithm, speed
With location update formula are as follows:
In formula, c1And c2For Studying factors and aceleration pulse；Rand () is the random number between (0,1)；WithPoint
It Wei not the particle i speed that d is tieed up in k iteration and position；It is particle i in the position of the d individual extreme value tieed up；For
Group is in the position of the d global extremum tieed up.
A kind of aforementioned wireless power transmission EFFICIENCY PREDICTION method based on Gaussian process, the greatest iteration time of particle swarm algorithm
Number k is 1000, and particle number chooses 25, particle maximum speed Vmax=(11).
A kind of aforementioned wireless power transmission EFFICIENCY PREDICTION method based on Gaussian process, wherein c1=c2=2；
Compared with prior art, the beneficial effects of the present invention are:
(1) by prediction of the Gaussian process models coupling particle swarm algorithm to wireless power transmission efficiency, reduce calling
The plenty of time loss that electromagnetic simulation software calculates；(2) by constantly comparing between efficiency of transmission and HFSS software emulation value
Error may finally obtain accurate Gaussian process model to constantly update Gaussian process model；(3) pass through Gaussian process mould
Type directly predicts wireless power transmission efficiency, more convenient simple, and not needing to repeat to establish model in HFSS software can
To obtain.
Detailed description of the invention
Fig. 1 is a kind of wireless power transmission EFFICIENCY PREDICTION method flow diagram based on Gaussian process of the present invention；
Fig. 2 is magnet coupled resonant type wireless electric energy transmission principle block diagram；
Fig. 3 is the equivalent circuit diagram of magnet coupled resonant type wireless electric energy transmission system；
Fig. 4 is a kind of structural schematic diagram of wireless power transmission antenna of the present invention；
Fig. 5 is the training sample of Gaussian process model；
Fig. 6 is the test sample of Gaussian process model；
Fig. 7 is the comparison diagram of the 1st group of model prediction of test sample Gaussian process and HFSS software emulation result；
Fig. 8 is the comparison diagram of the 2nd group of model prediction of test sample Gaussian process and HFSS software emulation result；
Fig. 9 is the comparison diagram of the 3rd group of model prediction of test sample Gaussian process and HFSS software emulation result；
Figure 10 is the comparison diagram of the 4th group of model prediction of test sample Gaussian process and HFSS software emulation result；
Figure 11 is the comparison diagram of the 5th group of model prediction of test sample Gaussian process and HFSS software emulation result；
Figure 12 is the comparison diagram of the 6th group of model prediction of test sample Gaussian process and HFSS software emulation result；
Figure 13 is the comparison diagram of the 7th group of model prediction of test sample Gaussian process and HFSS software emulation result；
Figure 14 is the comparison diagram of the 8th group of model prediction of test sample Gaussian process and HFSS software emulation result；
Figure 15 is the comparison diagram of the 9th group of model prediction of test sample Gaussian process and HFSS software emulation result；
Figure 16 is the mean absolute error figure for choosing 9 groups of test sample Gaussian process model predictions, mean square error figure, percentage
Ratio error figure；
Figure 17 is the three-dimensional figure of wireless power transmission antenna parameter.
Specific embodiment
Before in the solution of antenna transmission efficiency, generally will use analytic method and numerical method etc. in wireless power transmission
Method combines to realize with electromagnetic simulation software, these methods cause to calculate overlong time.The present invention uses particle swarm algorithm
The prediction for carrying out efficiency to wireless power transmission is combined with Gaussian process model, and obtains radio using Gaussian process model
The three-dimensional figure of energy transmission antenna parameter.
The method that the present invention combines Gaussian process particle swarm optimization algorithm with electromagnetic simulation software utilizes HFSS software
The training sample of model is obtained, to set up Gaussian process model.After model foundation is good, model can use to radio
Energy transmission antenna efficiency is predicted.
It is as shown in Figure 1 a kind of wireless power transmission EFFICIENCY PREDICTION method flow diagram based on Gaussian process of the present invention, this
Summary of the invention is broadly divided into six parts, and specific with reference to the accompanying drawing the invention will be further described.
(1) coupled mode theory establishes wireless power transmission model
In general, being not deposit in the case that system is between uniform passive states and each unit and is mutually orthogonal
In energy transmission；Only when system enters couple state that is when two antennas with identical resonance frequency are in phase
The phenomenon that energy transmission can be just generated in the case where mutual coupling.The correlation formula of coupled mode theory are as follows:
In formula, am(t) is defined as: | am(t)|2The energy for including for coil antenna m；T indicates the time；an(t) is defined as: |
an(t)|2The energy for including for coil antenna n；ωmIndicate the resonance frequency of coil antenna m；J is complex unit；kmn(m ≠ n) table
Show the coefficient of coup of coil antenna m and n；ΓmIt is the loss factor of coil antenna m；Sm(t) it is then used to indicate that in coil antenna m
Driving source.
Magnet coupled resonant type wireless electric energy transmission system is generally by high frequency electric source, impedance matching box, transmitting antenna, reception day
Line, driving circuit and load are constituted, and functional block diagram is as shown in Figure 2.In system work, high frequency electric source exports high frequency and hands over
At galvanic electricity to transmitting antenna, under the action of impedance matching box, coupled resonance occurs for receiving antenna to realize with transmitting antenna
The transmission process of radio energy, the electric energy that hereafter receives can be directly to negative after overdrive circuit carries out rectifying and wave-filtering
Carry power supply.According to equivalent circuit theory, the equivalent circuit diagram for the magnet coupled resonant type wireless electric energy transmission system being created as such as Fig. 3
It is shown.Equivalent circuit diagram emitter part is connected in series by coil antenna L1, capacitor C1, resistance R1 and current source S；It receives
Device part is by receiving antenna L2, capacitor C2, resistance R2 and load RL composition.
The coupled mode theory of the system are as follows:
Due to the presence of driving source, its adjustable output power is equal with the power of system consumption, and such system is wrapped
The gross energy contained is constant, formula are as follows:
Energy gradient formula in transmitting antenna are as follows:
Energy gradient formula in receiving antenna are as follows:
Work as ω1=ω2When, jk12(a2+a1--a1+a2-) it is equal to 0, s (a++a-) it is the function that driving source injects in transmitting antenna
Rate, 2 Γ1|a1|2, 2 Γ2|a2|2With 2 ΓL|a2| respectively indicate transmitting antenna, receiving antenna and the loss power for loading RL.Setting
S is P to the input power of system, in conjunction with three formula formulas above it can be concluded that formula are as follows:
P=s (a1++a1The Γ of)=21|a1±|2+2(Γ2+ΓL)|a2±|2=2 Γ1W1+2(Γ2+ΓL)W2
Assuming that load RL power is PL, then available formula are as follows:
PL=2 ΓLW2
Therefore, the efficiency eta formula of system are as follows:
It further indicates that are as follows:
In formula, a1For the coupled mode amplitude of transmitting antenna；a2For the coupled mode amplitude of receiving antenna；ω1For transmitting antenna
Natural angular frequency, ω1=2 π f1；ω2For the natural angular frequency of receiving antenna, ω2=2 π f2；Γ1For for indicating transmitting antenna
Loss factor；Γ2For the loss factor for indicating receiving antenna；k12And k21Coupled systemes between transmitting and receiving antenna
Number, if two antennas are identical, there is k12=k21=k, s are driving item.
(2) acquisition of training sample
Training sample is made of training sample input and training sample output, and magnet coupled resonant type wireless electric energy is transmitted system
Distance between the radius of spin of coil antenna, transmitting antenna and receiving antenna, these three relevant parameters of matching capacitance unite as instructing
Practice sample input, the efficiency of transmission obtained by HFSS software emulation is exported as the training of sample.
It is illustrated in figure 4 a kind of structural schematic diagram of wireless power transmission antenna of the present invention, upper figure is top view, and the following figure is
Front view.Wherein lower section is transmitting antenna, and top is receiving antenna.The radius of spin of antenna is between 50mm to 100mm, line footpath
For 2.01mm, coil number is 10 circles, and rotation mode is right rotation.Suitable capacitor is chosen according to the size of antenna for it
Value.When receiving antenna and transmitting antenna are apart less than 100mm, overcoupling phenomenon occurs for antenna, and efficiency of transmission is affected.Cause
This primary study transmitting antenna and receiving antenna are at a distance of 100 to the antenna transmission efficiency between 300mm.The end of transmitting antenna is set
Mouth is 1, and the port of receiving antenna is 2, the S of the antenna simulated21When parameter, that is, port 2 matches, the forward direction of port 2 is arrived in port 1
Transmission coefficient.
It is illustrated in figure 5 a kind of training sample of the wireless power transmission EFFICIENCY PREDICTION method based on Gaussian process of the present invention
This, totally 45 groups of training sample data.Training sample first uses HFSS software emulation to obtain in the present invention, then result is passed back
MATLAB is handled.Efficiency of transmission can be directly obtained by only needing to modify parameter for wireless power transmission model.
(3) foundation of Gaussian process model
Whole statistical natures of Gaussian process determine by its mean value and covariance function completely, mean value is defined as:
μ (x)=E [Y (x)]
Wherein, E [x] is expressed as the mathematic expectaion i.e. mean value of x, and Y (x) is expressed as being distributed about the function of x.
Covariance function is defined as:
C (x, x')=E [(Y (x)-μ (x)) (Y (x')-μ (x'))]
Wherein x, x' ∈ RdFor any d n dimensional vector n, μ (x) and C (x, x') respectively indicate mean function and covariance function, Y
(x') it is distributed for the function of test sample x'.
Therefore Gaussian process may be defined as:
F (x)~GP (μ (x), C (x, x'))
Wherein, f (x) is expressed as the mapping relations about mean function μ (x) and covariance function C (x, x'), i.e. Gauss mistake
Journey (GP) model.
(4) optimization design
After Gaussian process model foundation is good, model is optimized using particle swarm algorithm and finds optimal result；If
Set the initial parameter of particle swarm algorithm, i.e. Studying factors c1, aceleration pulse c2, the number of iterations k, the maximum speed of particle number i and particle
Vmax is spent, other 9 groups of data is chosen and is used as test sample, a kind of wireless power transmission model based on Gaussian process of the invention
Test sample is as shown in fig. 6, use approximate Gaussian process model to replace particle swarm optimization algorithm really to fit as fitness function
Response function carries out efficiency of transmission prediction to particle, constantly updates particle, when iteration reaches maximum times or error less than pre-
If stopping updating when value.
The more new formula of speed and position in particle swarm algorithm are as follows:
In formula, parameter i is the position of particle x, that is, indicates i-th of particle；Parameter d is the dimension of particle, indicates particle
Complexity；Parameter k is the number of iterations of particle swarm algorithm, indicates kth for particle；c1And c2Referred to as Studying factors and acceleration are normal
Number, takes c in the present invention1=c2=2；Rand () is the random number between (0,1)；WithRespectively particle i is at k times
The speed and position that d is tieed up in iteration；It is particle i in the position of the d individual extreme value tieed up；It is tieed up for group in d
The position of global extremum.In the training process, the speed of particle and position need continuous update, and the maximum of particle swarm algorithm changes
Generation number is 1000, and particle number chooses 25, particle maximum speed Vmax=(11).
(5) reliability of Gaussian process model is detected
Predicted value in (4) is compared with the result of HFSS software emulation, if error is less than required precision, then it is assumed that
Obtain accurate Gaussian process model；If error is greater than required precision, best particle and accurate solution are added to original instruction
Practice in sample, experience knowledge base has been sought in update, so that Gaussian process model is updated, until obtaining accurate model.(precision is wanted
Absolute error is averaging less than 0.05, mean square error is less than 0.01, and mean percent ratio error is less than 0.05.)
(6) it predicts
Efficiency of transmission is predicted using Gaussian process model, and the efficiency of transmission obtained with HFSS software emulation carries out
Compare, calculate its mean absolute error (MAE), mean square error (MSE) and be averaged percentage error (APE), verifies whether to meet
Design requirement.
It is illustrated in figure 6 a kind of wireless power transmission EFFICIENCY PREDICTION method testing sample based on Gaussian process of the present invention
This, shares 9 groups of training sample data.
As Fig. 7-15 show the comparison of 9 groups of model predictions of test sample Gaussian process and HFSS software emulation result of selection
Figure, the antenna S that as can be seen from the figure Gaussian process model prediction goes out21The antenna S that parameter and HFSS software emulation go out21Parameter
Identical is fine, illustrates that the Gaussian process model accuracy established herein is higher.But the S for also thering is Gaussian process to predict21Parameter
It will appear wave phenomenon, it is contemplated that the problem of the optimum transport efficiency of antenna, therefore the only S at selective resonance Frequency point21Parameter
To show the error of the two.
It is as shown in figure 16 the mean absolute error figure for choosing 9 groups of test sample Gaussian process model predictions, mean square error
Figure, percentage error figure, from S from 9 sample antenna resonance frequencies can be calculated in figure21The mean percent ratio error of parameter is about
It is 4.86%, mean square error is about 0.00080, and mean absolute error is about 0.021, and error is smaller, reaches requirement.
(7) wireless power transmission parametric relationship
The data that the data and Gaussian process go out to HFSS software emulation predict are handled, to the different radius of spin
The suitable capacitance of antenna match, in order to more intuitively show distance between the antenna radius of spin, transmitting antenna and receiving antenna
And the relationship between antenna transmission efficiency three, above data is depicted as three-dimensional figure.
As shown in figure 17 is the three-dimensional figure of wireless power transmission antenna parameter, as can be seen from the figure transmitting and receiving antenna
Between distance in 100mm, the efficiency of transmission highest of antenna, about 90%；Distance is arrived 100 between transmitting and receiving antenna
When within the scope of 250mm, the radius of spin of antenna is bigger, and the efficiency of transmission of antenna is higher；Distance is between transmitting and receiving antenna
When 300mm, the efficiency of transmission of antenna is very low, and almost 0.
In addition to the implementation, the present invention can also have other embodiments, all to use equivalent substitution or equivalent transformation shape
At technical solution, be all fallen within the protection domain of application claims.
Claims (5)
1. a kind of wireless power transmission EFFICIENCY PREDICTION method based on Gaussian process, which comprises the following steps:
1) coupled mode theory establishes wireless power transmission model
It is that there is no energy transmissions in the case that system is between uniform passive states and each unit and is mutually orthogonal；
Only when system enters couple state that is when two have the antenna of identical resonance frequency the case where intercoupling
Lower the phenomenon that just generating energy transmission, the formula of coupled mode theory are as follows:
In formula, am(t) is defined as: | am(t)|2The energy for including for coil antenna m；an(t) is defined as: | an(t)|2For coil day
The energy that line n includes；T indicates the time；ωmIndicate the resonance frequency of coil antenna m；J is complex unit；kmn(m ≠ n) indicates line
Enclose the coefficient of coup of antenna m and n；ΓmIt is the loss factor of coil antenna m；Sm(t) then it is used to indicate that swashing in coil antenna m
Encourage source；
Magnet coupled resonant type wireless electric energy transmission system is by high frequency electric source, impedance matching box, transmitting antenna, receiving antenna, driving
Circuit and load are constituted, and in system work, high frequency electric source is exported at high-frequency alternating current to transmitting antenna, in impedance matching
Under the action of device, coupled resonance occurs for receiving antenna and transmitting antenna to realize the transmission process of radio energy, this is followed by
The electric energy received can direct powering load after overdrive circuit carries out rectifying and wave-filtering；According to equivalent circuit theory, build
The equivalent circuit of the magnet coupled resonant type wireless electric energy transmission system stood, equivalent circuit emitter part is by coil antenna
L1, capacitor C1, resistance R1 and current source S are connected in series；Reception device part by receiving antenna L2, capacitor C2, resistance R2 with
And load RL composition；
2) acquisition of training sample
By the radius of spin, transmitting antenna and receiving antenna spacing of magnet coupled resonant type wireless electric energy transmission system coil antenna
It is inputted from, these three parameters of matching capacitance as training sample, the efficiency of transmission obtained by HFSS software emulation is as sample
Training output；
3) foundation of Gaussian process model
Gaussian process model can establish training set input X and export the mapping relations between y, and be provided according to this mapping relations
The corresponding predicted value of test sample x', Gaussian process describe a kind of function distribution, it is an infinite number of stochastic variable composition
Any subset all meets the set of Joint Gaussian distribution, and property can be determined by mean function and covariance function, and mean value is fixed
Justice are as follows:
μ (x)=E [Y (x)]
Wherein, E [x] is expressed as the mathematic expectaion i.e. mean value of input x, and Y (x) is expressed as being distributed about the function of x；
Covariance function is defined as:
C (x, x')=E [(Y (x)-μ (x)) (Y (x')-μ (x'))]
Wherein x, x' ∈ RdFor any d n dimensional vector n, μ (x) and C (x, x') respectively indicate mean function and covariance function, Y (x')
It is distributed for the function of test sample x'；
Therefore Gaussian process may be defined as:
F (x)~GP (μ (x), C (x, x'))
Wherein, f (x) is expressed as the mapping relations about mean function μ (x) and covariance function C (x, x'), i.e. Gaussian process
(GP) model；
4) optimization design
After Gaussian process model foundation has been got well, optimizing is optimized to model using particle swarm algorithm；The first of population is set
Initial value, i.e. Studying factors c1, aceleration pulse c2, the number of iterations k, particle number i and particle maximum speed Vmax, choose other 9 groups
Data are carried out the prediction of efficiency of transmission to particle using Gaussian process model substitution particle swarm algorithm, work as height as test sample
The error amount that this process model predicts terminates process when reaching preset requirement, error amount will ask for an interview to step 5)；
5) reliability of Gaussian process model is detected
Prediction result in step 4) is compared with the result that HFSS simulation software obtains, if error is less than required precision,
Obtain accurate Gaussian process model；If error is greater than required precision, original trained sample is added in best particle and accurate solution
In this, more new database, so that Gaussian process model is had updated, until error reaches required precision, required precision are as follows: average exhausted
To error less than 0.05, mean square error is less than 0.01, and mean percent ratio error is less than 0.05；
6) it predicts
Efficiency of transmission is predicted using Gaussian process model, and the efficiency of transmission obtained with HFSS software emulation is compared
Compared with calculating its mean absolute error MAE, mean square error MSE and average percentage error APE, correlation formula is as follows:
In formula,For the predicted value of i-th of sample,For the test value of i-th of sample, n is number of samples.
2. a kind of wireless power transmission EFFICIENCY PREDICTION method based on Gaussian process as described in claim 1, which is characterized in that
Step 2) carries out simulation analysis, simulation analysis to magnet coupled resonant type wireless power transmitting antenna using electromagnetism technology of numerical simulation
The value range of the radius of spin r of selected antenna is 50≤r≤100mm, the value of distance d between transmitting antenna and receiving antenna
Range is 100≤d≤300mm, and the value range of matching capacitance c is 10≤c≤100pF.
3. a kind of wireless power transmission EFFICIENCY PREDICTION method based on Gaussian process as described in claim 1, which is characterized in that
In the step 3) in particle swarm algorithm, the more new formula of speed and position are as follows:
In formula, c1And c2Referred to as Studying factors and aceleration pulse；Rand () is the random number between (0,1)；WithPoint
It Wei not the particle i speed that d is tieed up in k iteration and position；It is particle i in the position of the d individual extreme value tieed up；
It is group in the position of the d global extremum tieed up.
4. a kind of wireless power transmission EFFICIENCY PREDICTION method based on Gaussian process as described in claim 1, which is characterized in that
The maximum number of iterations k of the step 3) particle swarm algorithm is 1000, and particle number chooses 25, particle maximum speed Vmax=
(11)。
5. a kind of wireless power transmission EFFICIENCY PREDICTION method based on Gaussian process as claimed in claim 3, which is characterized in that
Wherein Studying factors and aceleration pulse c1=c2=2.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112257373A (en) * | 2020-11-13 | 2021-01-22 | 江苏科技大学 | Snake-shaped PCB antenna return loss prediction method based on three-body training algorithm |
| CN113933637A (en) * | 2021-10-28 | 2022-01-14 | 中国航空工业集团公司洛阳电光设备研究所 | Power line conduction emission semi-physical simulation method using vector network analyzer |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105575235A (en) * | 2016-02-18 | 2016-05-11 | 云南电网有限责任公司电力科学研究院 | Magnetic coupling wireless energy transmission simulation experiment method and device |
| CN106897511A (en) * | 2017-02-17 | 2017-06-27 | 江苏科技大学 | Annulus tie Microstrip Antenna Forecasting Methodology |
-
2019
- 2019-07-08 CN CN201910608822.0A patent/CN110348106A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105575235A (en) * | 2016-02-18 | 2016-05-11 | 云南电网有限责任公司电力科学研究院 | Magnetic coupling wireless energy transmission simulation experiment method and device |
| CN106897511A (en) * | 2017-02-17 | 2017-06-27 | 江苏科技大学 | Annulus tie Microstrip Antenna Forecasting Methodology |
Non-Patent Citations (1)
| Title |
|---|
| 夏俊等: "磁耦合谐振式无线电能传输天线仿真设计", 《舰船科学技术》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112257373A (en) * | 2020-11-13 | 2021-01-22 | 江苏科技大学 | Snake-shaped PCB antenna return loss prediction method based on three-body training algorithm |
| CN112257373B (en) * | 2020-11-13 | 2022-05-17 | 江苏科技大学 | Snake-shaped PCB antenna return loss prediction method based on three-body training algorithm |
| CN113933637A (en) * | 2021-10-28 | 2022-01-14 | 中国航空工业集团公司洛阳电光设备研究所 | Power line conduction emission semi-physical simulation method using vector network analyzer |
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