CN108564174A - Using the lightning impulse voltage waveform double exponential fitting system and method for genetic algorithm - Google Patents
Using the lightning impulse voltage waveform double exponential fitting system and method for genetic algorithm Download PDFInfo
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- CN108564174A CN108564174A CN201810273725.6A CN201810273725A CN108564174A CN 108564174 A CN108564174 A CN 108564174A CN 201810273725 A CN201810273725 A CN 201810273725A CN 108564174 A CN108564174 A CN 108564174A
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
Abstract
The present invention relates to a kind of lightning impulse voltage waveform double exponential fitting system and method using genetic algorithm, which includes the first module of recording curve data processing and storage, the second module of fitting wave data processing and storage, double-exponential function third module, the 4th module of genetic algorithm parameter setting, the 5th module of iterative processing and display of lightning impulse waveform.Compared with prior art, the present invention has the advantages that simple, stable, practical and quick.
Description
Technical field
The present invention relates to lightning impulse voltage waveform fields, are rushed more particularly, to a kind of thunder and lightning using genetic algorithm optimization
Hit voltage waveform double exponential fitting system and method.
Background technology
Lightning impulse voltage all-wave can substantially fall into 5 types type：Lightning wave, the wave that i.e. smooth lightning wave, wavefront band vibrate
Top has the lightning wave of overshoot, wavefront wave crest to have the lightning wave of oscillation and wave crest to have oscillation and have the lightning wave of overshoot.According to most
New 2010 editions IEC60060-1《High-voltage test techniques, first part：General definition and test requirements document》Standard, such as Fig. 1 a and 1b
Lightning impulse waveform recording curve U that is shown, overshooting or vibrate for superpositione(t), it needs to seek its datum curve Um(t) and
Residual curve R (t), and it is filtered to being formed after residual curve R (t) progress digital filterings using the K factor filter of definition
Residual curve Rf(t), be added to Um(t) test voltage curve U is formedt(t), to carry out parameter extraction, including test voltage value
Ut, wave front time T1, half time to peak T2With opposite overshoot amplitude β '.As it can be seen that how accurately to obtain the benchmark of lightning impulse waveform
Curve Um(t) it is the key that affecting parameters extraction result, and executes the important first step of whole process.
Currently, the method that datum curve is sought has very much, but really there is vitality and adapt to the method for extensive waveform still
Biexponential model.The key that biexponential model solves is to be fitted the selection in area.Newest 2010 editions standard IEC 60060-1 provide folded
Add in the standard lightning impulse parameter calculation procedure of overshoot or oscillation and is fitted the method for seeking datum curve about biexponential model
It is as follows：
A. it seeks wavefront and is less than 0.2*Ue(UeFor recording curve Ue(t) limiting value) voltage value and wave rear be more than 0.4*Ue
Two sampled point U of voltage value0.2(the last one point) and U0.4(first point)；
B. point U is deleted0.2Preceding and point U0.4Data afterwards form matched curve Uf(t)；
C. to Uf(t) it is fitted to obtain waveform u with diexponential function modeld(t)；
D. it is based on ud(t) the datum curve U of waveform can be built according to time pointm(t)。
But biexponential model fitting algorithm is not defined in newest 2010 editions standard IEC 60060-1 or to providing
Body reference method.
Invention content
Genetic algorithm is used it is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of
Lightning impulse voltage waveform double exponential fitting system and method.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of lightning impulse voltage waveform double exponential fitting system using genetic algorithm, which includes lightning surge
The first module of recording curve data processing and storage of shape, the second module of fitting wave data processing and storage, double-exponential function
The 5th module of the 4th module, iterative processing and display is arranged in third module, genetic algorithm parameter；
The recording curve data processing of the lightning impulse waveform and the first module of storage acquire lightning impulse voltage wave
Shape stores to form recording curve, and the fitting wave data processing and the second module of storage are bent to the record of lightning impulse waveform
The recording curve of the first module of line data processing and storage is processed into fitting and waveform and is stored, the double-exponential function the
The diexponential function model that three modules set fitting wave data processing and storage the second module offer parameter is fitted,
The genetic algorithm parameter is arranged the 4th module and provides the genetic algorithm module for setting initial parameter to passing through two fingers number letter
Diexponential function model after number third resume module is iterated solution parameter, the 5th module of the iterative processing and display
Biexponential model after being solved to genetic algorithm is iterated display, finally shows recording curve, matched curve and datum curve.
Preferably, the lightning impulse waveform is the lightning impulse waveform of superposition overshoot or oscillation.
Preferably, the lightning impulse waveform includes acquiring analysis of experiments wave based on high pressure hall lightning impulse test
The lightning impulse transient-wave that shape and on-line monitoring, power transmission and transforming equipment Start-up and Adjustment obtain.
Preferably, the fitting wave data processing and the second module of storage are to the recording curve number of lightning impulse waveform
Fitting waveform is processed into according to 0.2 and 0.4 times of limiting value according to the recording curve for handling and storing the first module and is stored, have
Body includes：
Matched curve Uf(t) it obtains
It seeks wavefront and is less than 0.2*UeFirst sampled point U of voltage value0.2；
It seeks wave rear and is more than 0.4*UeSecond sampled point U of voltage value0.4；
Seek matched curve Uf(t)；
Wherein, j indicates that j-th of waveforms amplitude, o are indicated in Ue(t) o-th of waveforms amplitude point in, n indicate matched curve Uf
(t) waveform recording is counted.
Preferably, the diexponential function model is defined as：
ud(t)=A [exp (- t-D)/B-exp (- t-D)/C)]
In formula：T is the time；ud(t) it is double exponential voltage fitting functions；A, B, C and D are fitting parameters.
Preferably, the genetic algorithm basic step of the genetic algorithm module is as follows：
Step 1 determines coding strategy；
Step 2, the object function for determining solution and corresponding adaptive value；
Step 3 generates one group of initial solution group；
Step 4, according to the object function of each solution in solution group, take certain selection method, appropriate individual selected to carry out
Genetic manipulation；
Step 5 generates a new solution group by hybridizing and making a variation；
If step 6, genetic algebra reach permissible value or when other conditions of convergence have met stops, step 4 is otherwise gone to, is used
New solution group replaces initial solution group to carry out next iteration.
A method of using the lightning impulse voltage waveform double exponential fitting system using genetic algorithm, including
Following steps：
Step 1, acquisition parameter is set, analysis of experiments waveform is acquired based on high pressure hall lightning impulse test；
Step 2, the lightning impulse waveform that acquisition is obtained to superposition overshoot or oscillation stores, and forms recording curve；
Step 3, it is processed into matched curve by 0.2 and 0.4 times of limiting value；
Step 4, it is fitted waveform according to standard setting diexponential function model, solves 4 parameters to obtain datum curve；
Step 5,4 parameters of diexponential function model are solved using genetic algorithm；
Step 6,4 parameter reconstruction datum curves are utilized.
Compared with prior art, the present invention has the following advantages：
1, have simple, stable, practical the present invention provides one kind and quickly lightning impulse waveform datum curve is sought
Method, suitable for superposition overshoot or the parameter extraction of the lightning impulse waveform of oscillation.
2, the present invention provides a kind of quicker than traditional two-step least squares estimation fitting process solution diexponential function model
Acquiring method, make better use of the computing capability of current high-performance computer.
Description of the drawings
Fig. 1 a are the curve synoptic diagram in lightning impulse voltage waveform parameter extraction process of the present invention；
Fig. 1 b are the curve synoptic diagram in lightning impulse voltage waveform parameter extraction process of the present invention；
Fig. 2 is present system structure block diagram.
Fig. 3 is the system flow chart of work methods of the present invention.
Fig. 4 is the exemplary waveform diagram that the method for the present invention is handled.
Specific implementation mode
The technical scheme in the embodiments of the invention will be clearly and completely described below, it is clear that described implementation
Example is a part of the embodiment of the present invention, rather than whole embodiments.Based on the embodiments of the present invention, ordinary skill
The every other embodiment that personnel are obtained without making creative work should all belong to the model that the present invention protects
It encloses.
The present invention tries hard to from practical application, and customer service is existing to solve double-exponential function based on two-step least squares estimation fitting process
The shortcoming of model proposes a kind of lightning impulse voltage two fingers number waveform fitting method using genetic algorithm optimization.I.e. pair
In superposition overshoot or the lightning impulse waveform recording curve U of oscillatione(t), it is processed into fitting song by 0.2 and 0.4 times of limiting value
Line is sought 4 fitting parameters of diexponential function model using genetic algorithm, completes seeking for datum curve.The technology has
Simply, the advantages that stablizing, is practical and quick, the lightning impulse waveform suitable for superposition overshoot or oscillation take parameter extraction.
As seen in figure la and lb, the lightning impulse waveform recording curve U for overshooting or vibrating for superpositione(t), it needs to ask
Take its datum curve Um(t) and residual curve R (t) it, and using the K factor filter of definition to residual curve R (t) carries out digital
Filtered residual curve R is formed after filteringf(t), be added to Um(t) test voltage curve U is formedt(t), to carry out parameter
Extraction, including test voltage value Ut, wave front time T1, half time to peak T2With opposite overshoot amplitude β '.
As shown in Fig. 2, a kind of lightning impulse voltage waveform double exponential fitting system using genetic algorithm, the system include
The first module of the recording curve data processing of lightning impulse waveform and storage, the second module of fitting wave data processing and storage,
The 5th module of the 4th module, iterative processing and display is arranged in double-exponential function third module, genetic algorithm parameter.Described first
Module acquires lightning impulse voltage Waveform storage and forms recording curve, at the recording curve of second first module of module pair
It manages into fitting waveform and is stored, second module of third module pair provides the diexponential function model for setting parameter
It is fitted, the 4th module provides the genetic algorithm module for setting initial parameter to after third resume module
Diexponential function model is iterated solution parameter, and the biexponential model after the 5th module solves genetic algorithm carries out
Iteration is shown, finally shows recording curve, matched curve and datum curve.
The lightning impulse waveform is the lightning impulse waveform of superposition overshoot or oscillation.
The lightning impulse waveform include based on high pressure hall lightning impulse test acquisition analysis of experiments waveform and
The lightning impulse transient-wave of the acquisitions such as line monitoring, power transmission and transforming equipment Start-up and Adjustment.
The sample rate of high pressure hall lightning impulse test recording device need in 100MS/s or more, analog bandwidth 50M and with
On, the lightning impulse waveform recording curve U of record storagee(t) it is defined as follows：
Note：I --- i-th of waveforms amplitude；
N --- waveform recording is counted；
I-th point of Δ t (i-1) --- the waveform corresponding time (μ s, Δ t are sampling time interval)；
The recording curve of second first module of module pair is processed into fitting waveform according to 0.2 and 0.4 times of limiting value
And stored, it specifically includes：
Matched curve Uf(t) it obtains
It seeks wavefront and is less than 0.2*UeFirst sampled point U of voltage value0.2；
It seeks wave rear and is more than 0.4*UeSecond sampled point U of voltage value0.4；
Seek matched curve Uf(t)
For j=o to n
Uf(ti)=Ue(ti)
Next
Wherein, j indicates that j-th of waveforms amplitude, o are indicated in Ue(t) o-th of waveforms amplitude point in, n indicate matched curve Uf
(t) waveform recording is counted.
The diexponential function model is defined as：
ud(t)=A [exp (- t-D)/B-exp (- t-D)/C)]
In formula：T is the time；ud(t) it is double exponential voltage fitting functions；A, B, C and D are fitting parameters.
The genetic algorithm basic step of the genetic algorithm module is as follows：
Step 1 determines coding strategy；
Step 2, the object function for determining solution and corresponding adaptive value；
Step 3 generates one group of initial solution group；
Step 4, according to the object function of each solution in solution group, take certain selection method, appropriate individual selected to carry out
Genetic manipulation；
Step 5 generates a new solution group by hybridizing and making a variation；
If step 6, genetic algebra reach permissible value or when other conditions of convergence have met stops, step 4 is otherwise gone to, is used
New solution group replaces initial solution group to carry out next iteration.
As shown in figure 3, a kind of method of lightning impulse voltage waveform double exponential fitting using genetic algorithm, including it is following
Step：
Step 1, acquisition parameter is set, analysis of experiments waveform is acquired based on high pressure hall lightning impulse test；
Step 2, the lightning impulse waveform that acquisition is obtained to superposition overshoot or oscillation stores, and forms recording curve；
Step 3, it is processed into matched curve by 0.2 and 0.4 times of limiting value；
Step 4, it is fitted waveform according to standard setting diexponential function model, solves 4 parameters to obtain datum curve；
Step 5,4 parameters of diexponential function model are solved using genetic algorithm；
Step 6,4 parameter reconstruction datum curves are utilized.
The exemplary waveform diagram that the method for the present invention is handled is as shown in Figure 4.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can readily occur in various equivalent modifications or replace
It changes, these modifications or substitutions should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with right
It is required that protection domain subject to.
Claims (7)
1. a kind of lightning impulse voltage waveform double exponential fitting system using genetic algorithm, which is characterized in that the system includes
The first module of the recording curve data processing of lightning impulse waveform and storage, the second module of fitting wave data processing and storage,
The 5th module of the 4th module, iterative processing and display is arranged in double-exponential function third module, genetic algorithm parameter；
The recording curve data processing of the lightning impulse waveform and storage the first module acquisition lightning impulse voltage waveform are deposited
Storage forms the recording curve number of recording curve, the fitting wave data processing and the second module of storage to lightning impulse waveform
It is processed into fitting waveform according to the recording curve for handling and storing the first module and is stored, the double-exponential function third mould
The diexponential function model that block sets fitting wave data processing and storage the second module offer parameter is fitted, described
Genetic algorithm parameter the 4th module be set provide and set the genetic algorithm module of initial parameter to by double-exponential function the
Diexponential function model after three resume modules is iterated solution parameter, and the iterative processing and the 5th module of display are to losing
Biexponential model after propagation algorithm solves is iterated display, finally shows recording curve, matched curve and datum curve.
2. a kind of lightning impulse voltage waveform double exponential fitting system using genetic algorithm according to claim 1,
It is characterized in that：The lightning impulse waveform is the lightning impulse waveform of superposition overshoot or oscillation.
3. a kind of lightning impulse voltage waveform double exponential fitting system using genetic algorithm according to claim 1,
It is characterized in that：The lightning impulse waveform include based on high pressure hall lightning impulse test acquisition analysis of experiments waveform and
The lightning impulse transient-wave that line monitoring, power transmission and transforming equipment Start-up and Adjustment obtain.
4. a kind of lightning impulse voltage waveform double exponential fitting system using genetic algorithm according to claim 1,
It is characterized in that：Recording curve data processing of the fitting wave data processing and the second module of storage to lightning impulse waveform
Fitting waveform is processed into according to 0.2 and 0.4 times of limiting value and stored with the recording curve of the first module of storage, it is specific to wrap
It includes：
Matched curve Uf(t) it obtains
It seeks wavefront and is less than 0.2*UeFirst sampled point U of voltage value0.2；
It seeks wave rear and is more than 0.4*UeSecond sampled point U of voltage value0.4；
Seek matched curve Uf(t)；
Wherein, j indicates that j-th of waveforms amplitude, o are indicated in Ue(t) o-th of waveforms amplitude point in, n indicate matched curve Uf(t) wave
Shape record points.
5. a kind of lightning impulse voltage waveform double exponential fitting system using genetic algorithm according to claim 1,
It is characterized in that：The diexponential function model is defined as：
ud(t)=A [exp (- t-D)/B-exp (- t-D)/C)]
In formula：T is the time；ud(t) it is double exponential voltage fitting functions；A, B, C and D are fitting parameters.
6. a kind of lightning impulse voltage waveform double exponential fitting system using genetic algorithm according to claim 1,
It is characterized in that：The genetic algorithm basic step of the genetic algorithm module is as follows：
Step 1 determines coding strategy；
Step 2, the object function for determining solution and corresponding adaptive value；
Step 3 generates one group of initial solution group；
Step 4, according to the object function of each solution in solution group, take certain selection method, appropriate individual selected to carry out heredity
Operation；
Step 5 generates a new solution group by hybridizing and making a variation；
If step 6, genetic algebra reach permissible value or when other conditions of convergence have met stops, step 4 is otherwise gone to, with new
Xie Qun replaces initial solution group to carry out next iteration.
7. a kind of side using the lightning impulse voltage waveform double exponential fitting system using genetic algorithm described in claim 6
Method, it is characterised in that：Include the following steps：
Step 1, acquisition parameter is set, analysis of experiments waveform is acquired based on high pressure hall lightning impulse test；
Step 2, the lightning impulse waveform that acquisition is obtained to superposition overshoot or oscillation stores, and forms recording curve；
Step 3, it is processed into matched curve by 0.2 and 0.4 times of limiting value；
Step 4, it is fitted waveform according to standard setting diexponential function model, solves 4 parameters to obtain datum curve；
Step 5,4 parameters of diexponential function model are solved using genetic algorithm；
Step 6,4 parameter reconstruction datum curves are utilized.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110658476A (en) * | 2019-10-16 | 2020-01-07 | 北京航空航天大学 | Method for judging accelerated attenuation of lithium battery capacity under random charge and discharge conditions |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102867316A (en) * | 2012-07-19 | 2013-01-09 | 中国人民解放军第二炮兵工程大学 | Thermal wave dynamic image sequence fitting reconstruction method |
| CN103217679A (en) * | 2013-03-22 | 2013-07-24 | 北京航空航天大学 | Full-waveform laser radar echo data gaussian decomposition method based on genetic algorithm |
| CN104930993A (en) * | 2015-06-15 | 2015-09-23 | 安徽工程大学 | X-ray thickness gauge calibration method |
| CN105184070A (en) * | 2015-08-31 | 2015-12-23 | 华南理工大学 | Voltage integral method based segmentation fitting method for calculating volt-time characteristic curve |
| CN106501728A (en) * | 2016-11-23 | 2017-03-15 | 湖北大学 | A kind of battery equivalent model parameter identification method based on multi-objective genetic algorithm |
| CN106599383A (en) * | 2016-11-23 | 2017-04-26 | 国网上海市电力公司 | Time domain response-based inter-double-cable transient state temperature rise acquisition method |
-
2018
- 2018-03-29 CN CN201810273725.6A patent/CN108564174A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102867316A (en) * | 2012-07-19 | 2013-01-09 | 中国人民解放军第二炮兵工程大学 | Thermal wave dynamic image sequence fitting reconstruction method |
| CN103217679A (en) * | 2013-03-22 | 2013-07-24 | 北京航空航天大学 | Full-waveform laser radar echo data gaussian decomposition method based on genetic algorithm |
| CN104930993A (en) * | 2015-06-15 | 2015-09-23 | 安徽工程大学 | X-ray thickness gauge calibration method |
| CN105184070A (en) * | 2015-08-31 | 2015-12-23 | 华南理工大学 | Voltage integral method based segmentation fitting method for calculating volt-time characteristic curve |
| CN106501728A (en) * | 2016-11-23 | 2017-03-15 | 湖北大学 | A kind of battery equivalent model parameter identification method based on multi-objective genetic algorithm |
| CN106599383A (en) * | 2016-11-23 | 2017-04-26 | 国网上海市电力公司 | Time domain response-based inter-double-cable transient state temperature rise acquisition method |
Non-Patent Citations (3)
| Title |
|---|
| K. C. P. WONG等: "Digital measurement of lightning impulse parameters using curving fitting algorithms", 《ELEVENTH INTERNATIONAL SYMPOSIUM ON HIGH-VOLTAGE ENGINEERING 1999》 * |
| 司文荣等: "雷电冲击电压波形K因子零相位数字滤波器设计", 《电工电能新技术》 * |
| 陈华等: "差分进化算法在双指数拟合中的应用", 《计算机工程与应用》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110658476A (en) * | 2019-10-16 | 2020-01-07 | 北京航空航天大学 | Method for judging accelerated attenuation of lithium battery capacity under random charge and discharge conditions |
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Application publication date: 20180921 |