CN105842535B - A kind of main syndrome screening technique of harmonic wave based on similar features fusion - Google Patents
A kind of main syndrome screening technique of harmonic wave based on similar features fusion Download PDFInfo
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- CN105842535B CN105842535B CN201610162494.2A CN201610162494A CN105842535B CN 105842535 B CN105842535 B CN 105842535B CN 201610162494 A CN201610162494 A CN 201610162494A CN 105842535 B CN105842535 B CN 105842535B
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- 208000011580 syndromic disease Diseases 0.000 title claims abstract description 18
- 238000000034 method Methods 0.000 title claims abstract description 12
- 238000012216 screening Methods 0.000 title claims abstract description 10
- 230000004927 fusion Effects 0.000 title claims abstract description 8
- 239000011159 matrix material Substances 0.000 claims description 14
- 238000005259 measurement Methods 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 abstract description 11
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
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- 238000011156 evaluation Methods 0.000 description 1
- 238000012880 independent component analysis Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/16—Spectrum analysis; Fourier analysis
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Abstract
The present invention relates to a kind of main syndrome screening techniques of harmonic wave based on similar features fusion, the decentralization of harmonic current class monitor sample is carried out first, obtain the numerical value of decentralization, then the index of similarity for calculating harmonic current class monitor sample, finally screens main syndrome based on index of similarity distribution character.The present invention can quickly determine the major harmonic pollution number of monitoring point.
Description
Technical field
The present invention relates to Harmonious Waves in Power Systems to pollute field, especially a kind of main feature of harmonic wave based on similar features fusion
Group's screening technique.
Background technique
With a large amount of power electronic equipments be incorporated into the power networks and the increase of other nonlinear-load quantity, in electric system
Harmonic pollution is increasingly severe.Currently, having had been built up more complete power quality on-line monitoring network, power grid electricity can be monitored
Press total harmonic distortion factor, each harmonic voltage containing ratio and phase angle, current total harmonic distortion rate, individual harmonic current containing ratio,
The harmonic informations such as virtual value and phase angle.Facilitate harmonic pollution user modeling in a large amount of on-line monitoring information, obtains user's fortune
Capable master data.But complete harmonic-model includes whole harmonic voltages, harmonic current monitoring index, and mutual shadow between index
It rings, so that model is extremely complex and cannot achieve parameter identification.In engineering practice, which Detecting Power Harmonicies index needed to pick out
It should include in a model which variable should be rejected from model, that is, need to determine from a large amount of Historical Monitoring data
The main syndrome of harmonic wave, to establish Practical model for the main syndrome of harmonic wave.
User's voltage and current usually is monitored at points of common connection (Point of Common Coupling, PCC) at present,
The harmonic components for calculating voltage and current, using maximum value, average value or the 95% big value evaluation user couple in detection time section
The influence of grid power quality, and in this, as the foundation of user's development harmonic wave control.This way is exactly using harmonic wave in fact
Current source model characterizes user's pollution, and assigns monitoring data as user model parameter, without the different overtone orders of consideration it
Between influence each other, be not to user's harmonic pollution characteristic essence reflection.
Since the principle that harmonic source generates harmonic wave is complicated, it tends to be difficult to establish general mathematical model.Harmonic source can at present
To use the models such as equivalent source, crossover frequency admittance matrix, using independent component analysis, least square approximation and neural network
The methods of from monitor sample data identification model parameter.Wherein, the Harmonic source model based on crossover frequency admittance matrix considers
Influence of the harmonic voltage to harmonic current, but model parameter is recalculated under different operating conditions.Based on least square approximation
Harmonic current is expressed as fundamental wave, each harmonic component of voltage and not by constant point of the electric current of voltage variations affect by Harmonic source model
The expression formula of amount seeks model parameter using least square approximation, and accuracy is higher, but there are model parameters to seek difficulty etc.
Problem.Harmonic Source Modeling neural network based is not required to the internal structure it is to be understood that harmonic-producing load, but model accuracy is trained
Sample number restricts.
If operating condition to be analyzed is close with the sample operating condition of parameter identification, error is calculated mainly by parameter identification precision
It determines, and it is substantially unrelated with the types of models of selection.When operating condition to be analyzed and sample operating condition differ greatly, different harmonic sources
Model is affected to error is calculated.
The thinking of Harmonic Source Modeling is to simplify to model structure, thus error is larger at present.If can be from history
The main feature number of harmonic wave is determined in monitoring data, establishes detailed model for main feature number, so that it may in reserving model precision
While largely reduce parameter identification difficulty.
Summary of the invention
In view of this, the purpose of the present invention is to propose to a kind of main syndrome screening sides of harmonic wave based on similar features fusion
Method can quickly determine the major harmonic pollution number of monitoring point.
The present invention is realized using following scheme: a kind of main syndrome screening technique of harmonic wave based on similar features fusion, tool
Body the following steps are included:
Step S1: individual harmonic current benchmark limit value C is subtracted from harmonic current class monitor sample0, harmonic current class is supervised
This decentralization of test sample obtains the numerical value C of decentralization*；
Step S2: the index of similarity of harmonic current class monitor sample is calculated: note S24*24For C*Between middle each harmonic
Index of similarity matrix, S (i, j) indicate the similarity between i-th harmonic current and jth subharmonic current monitor sample,
In, S (i, j) is calculated as follows in 1≤i, j≤25, as a result between 1 and -1:
Step S3: main syndrome is screened based on index of similarity distribution character.
Further, the step S1 specifically includes the following steps:
Step S11: measuring point harmonic current measurement value matrix is set as Cm*25, wherein Cm*25For m row, the matrix of 25 column；Wherein
J column represent j subharmonic, 1≤j≤25, and the i-th row represents ith measurement value, 1≤i≤m；
Step S12: according to measuring point voltage class, it is specified that individual harmonic current benchmark limit value is C0, C0Be 1*25 row to
Amount, unit A；
Step S13: to Cm*25Every a line, subtract harmonic current benchmark limit value C0, obtain the numerical value C of decentralization*, C*
For m row, the matrix of 25 column, unit A.Wherein, C0It is provided according to national standard GB/T 14549-93.For example, following table is exactly to inject
The allowable harmonic current of 10kV points of common connection.
Further, the step S3 specifically includes the following steps:
Step S31: being divided into 10 minizones for [- 1,1], according to S (i, j) (1 j≤25 < i≤25, i <) value and incites somebody to action
It adheres to each section separately, obtains 10 initial populations；
Step S32: the center c of each initial population is calculatedk, the ckIt is the average value of S (i, j) in each group；
Step S33: calculate S (i, j) (1 j≤25 < i≤25, i <) and each group center's distance l (i, j)=| S (i,
j)-ck|；
Step S34: S (i, j) is belonged to that group nearest with its distance；
Step S35: removing members are empty group, obtain new group and its member；
Step S36: returning to step S32 and restart a point group, until the grouping result between iteration twice no longer changes, or
Until person's the number of iterations is greater than 100；
Step S37: listing S (i, j) subscript in each group, then every group of subscript represents the main syndrome of harmonic wave.
Compared with prior art, the invention has the following beneficial effects:
1, invention defines the index of similarity of harmonic current class monitor sample；It proposes a kind of based on harmonic current class prison
The main syndrome screening technique for surveying index similarity, can quickly determine the major harmonic pollution number of monitoring point.
2, the present invention carries out statistics calculating using a large amount of online monitoring datas, obtains monitoring point harmonic pollution feature number
Statistical information, rather than just the calculated result under certain exceptional operating conditions, conclusion is more comprehensively more rationally.
Detailed description of the invention
Fig. 1 is method flow schematic diagram of the invention.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and embodiments.
As shown in Figure 1, present embodiments providing a kind of main syndrome screening technique of harmonic wave based on similar features fusion, tool
Body the following steps are included:
Step S1: individual harmonic current benchmark limit value C is subtracted from harmonic current class monitor sample0, harmonic current class is supervised
This decentralization of test sample obtains the numerical value C of decentralization*；
Step S2: the index of similarity of harmonic current class monitor sample is calculated: note S24*24For C*Between middle each harmonic
Index of similarity matrix, S (i, j) indicate the similarity between i-th harmonic current and jth subharmonic current monitor sample,
In, S (i, j) is calculated as follows in 1≤i, j≤25, as a result between 1 and -1:
Step S3: main syndrome is screened based on index of similarity distribution character.
In the present embodiment, the step S1 specifically includes the following steps:
Step S11: measuring point harmonic current measurement value matrix is set as Cm*25, wherein Cm*25For m row, the matrix of 25 column；Wherein
J column represent j subharmonic, 1≤j≤25, and the i-th row represents ith measurement value, 1≤i≤m；
Step S12: according to measuring point voltage class, it is specified that individual harmonic current benchmark limit value is C0, C0Be 1*25 row to
Amount, unit A；
Step S13: to Cm*25Every a line, subtract harmonic current benchmark limit value C0, obtain the numerical value C of decentralization*, C*
For m row, the matrix of 25 column, unit A.Wherein, C0It is provided according to national standard GB/T 14549-93.For example, following table is exactly to inject
The allowable harmonic current of 10kV points of common connection.
| Overtone order (h) | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| Allowable harmonic current (A) | 26.0 | 20.0 | 13.0 | 20.0 | 8.5 | 15.0 | 6.4 | 6.8 |
| Overtone order (h) | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 |
| Allowable harmonic current (A) | 5.1 | 9.3 | 4.3 | 7.9 | 3.7 | 4.1 | 3.2 | 6.0 |
| Overtone order (h) | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 |
| Allowable harmonic current (A) | 2.8 | 5.4 | 2.6 | 2.9 | 2.3 | 4.5 | 2.1 | 4.1 |
In the present embodiment, the step S3 specifically includes the following steps:
Step S31: being divided into 10 minizones for [- 1,1], according to S (i, j) (1 j≤25 < i≤25, i <) value and incites somebody to action
It adheres to each section separately, obtains 10 initial populations；
Step S32: the center c of each initial population is calculatedk, the ckIt is the average value of S (i, j) in each group；
Step S33: calculate S (i, j) (1 j≤25 < i≤25, i <) and each group center's distance l (i, j)=| S (i,
j)-ck|；
Step S34: S (i, j) is belonged to that group nearest with its distance；
Step S35: removing members are empty group, obtain new group and its member；
Step S36: returning to step S32 and restart a point group, until the grouping result between iteration twice no longer changes, or
Until person's the number of iterations is greater than 100；
Step S37: listing S (i, j) subscript in each group, then every group of subscript represents the main syndrome of harmonic wave.
The foregoing is merely presently preferred embodiments of the present invention, all equivalent changes done according to scope of the present invention patent with
Modification, is all covered by the present invention.
Claims (2)
1. a kind of main syndrome screening technique of harmonic wave based on similar features fusion, it is characterised in that the following steps are included:
Step S1: individual harmonic current benchmark limit value C is subtracted from harmonic current class monitor sample0, to harmonic current class monitor sample
Decentralization obtains the numerical value C of decentralization*；
Step S2: the index of similarity of harmonic current class monitor sample is calculated: note S24*24For C*It is similar between middle each harmonic
Index matrix is spent, S (i, j) indicates the similarity between i-th harmonic current and jth subharmonic current monitor sample, wherein 1
S (i, j) is calculated as follows in≤i, j≤25, as a result between 1 and -1:
Step S3: main syndrome is screened based on index of similarity distribution character；
Wherein, the step S3 specifically includes the following steps:
Step S31: being divided into 10 minizones for [- 1,1], according to S (i, j) (1 j≤25 < i≤25, i <) value and by its point
Belong to each section, obtains 10 initial populations；
Step S32: the center c of each initial population is calculatedk, the ckIt is the average value of S (i, j) in each group；
Step S33: calculate S (i, j) (1 j≤25 < i≤25, i <) and each group center's distance l (i, j)=| S (i, j)-ck
|；
Step S34: S (i, j) is belonged to that group nearest with its distance；
Step S35: removing members are empty group, obtain new group and its member；
Step S36: returning to step S32 and restart a point group, until the grouping result between iteration twice no longer changes, or repeatedly
Until generation number is greater than 100；
Step S37: listing S (i, j) subscript in each group, then every group of subscript represents the main syndrome of harmonic wave.
2. a kind of main syndrome screening technique of harmonic wave based on similar features fusion according to claim 1, feature exist
In: the step S1 specifically includes the following steps:
Step S11: measuring point harmonic current measurement value matrix is set as Cm*25, wherein Cm*25For m row, the matrix of 25 column；Wherein jth arranges
J subharmonic, 1≤j≤25 are represented, the i-th row represents ith measurement value, 1≤i≤m；
Step S12: according to measuring point voltage class, it is specified that individual harmonic current benchmark limit value is C0, C0It is the row vector of 1*25, it is single
Position is A；
Step S13: to Cm*25Every a line, subtract harmonic current benchmark limit value C0, obtain the numerical value C of decentralization*, C*For m row,
The matrix of 25 column, unit A.
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| CN105868918A (en) * | 2015-12-23 | 2016-08-17 | 国网福建省电力有限公司 | Similarity index computing method of harmonic current type monitoring sample |
| CN107367647A (en) * | 2017-06-22 | 2017-11-21 | 上海理工大学 | The detection of mains by harmonics source and localization method based on EEMD SOM |
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| US6215316B1 (en) * | 1998-08-11 | 2001-04-10 | The Governor Of The University Of Alberta | Method and apparatus for measuring harmonic current sources in electric power distribution systems |
| CN102680825A (en) * | 2012-05-17 | 2012-09-19 | 西安电子科技大学 | Interference source identification method in system-grade electromagnetic compatibility fault diagnosis |
| CN103743949A (en) * | 2014-01-06 | 2014-04-23 | 国家电网公司 | Detection method of harmonic and inter-harmonic based on single-channel FastICA (Fast Independent Component Analysis) |
| CN105021888A (en) * | 2015-07-06 | 2015-11-04 | 广州供电局有限公司 | Harmonic wave data monitoring method based on data clustering |
| CN105137177A (en) * | 2015-08-13 | 2015-12-09 | 广东电网有限责任公司东莞供电局 | Harmonic voltage responsibility calculation alarm method for single-point monitoring of power distribution network |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6215316B1 (en) * | 1998-08-11 | 2001-04-10 | The Governor Of The University Of Alberta | Method and apparatus for measuring harmonic current sources in electric power distribution systems |
| CN102680825A (en) * | 2012-05-17 | 2012-09-19 | 西安电子科技大学 | Interference source identification method in system-grade electromagnetic compatibility fault diagnosis |
| CN103743949A (en) * | 2014-01-06 | 2014-04-23 | 国家电网公司 | Detection method of harmonic and inter-harmonic based on single-channel FastICA (Fast Independent Component Analysis) |
| CN105021888A (en) * | 2015-07-06 | 2015-11-04 | 广州供电局有限公司 | Harmonic wave data monitoring method based on data clustering |
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