CN113189397A - Harmonic responsibility division method and system based on shape context matching - Google Patents
Harmonic responsibility division method and system based on shape context matching Download PDFInfo
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Abstract
The invention relates to a harmonic responsibility division method and a harmonic responsibility division system based on shape context matching, wherein the method comprises the following steps: the method comprises the steps of searching the position of a harmonic data change point by adopting a change point detection CPM technology based on a screening sorting algorithm SARA, positioning a large fluctuation area with rich information, and partitioning data; calculating the correlation between the whole user side of the fluctuation region and the harmonic of the PCC point by using a typical correlation analysis CCA, and obtaining and separating background harmonic responsibility; and calculating the relevance between each user and the PCC point based on a dynamic adjustment shape context matching algorithm DASC to obtain the relevance score of each user as the harmonic responsibility of the user. The method and the system are beneficial to improving the accuracy of harmonic responsibility division.
Description
Technical Field
The invention belongs to the technical field of electric power, and particularly relates to a harmonic responsibility division method and system based on shape context matching.
Background
With the use of a large number of power electronic devices and a distributed power supply access system, the harmonic content in a power grid is increasing, and the harmonic problem is becoming more serious. In order to measure the influence of the harmonic wave emitted by each user on the harmonic voltage of the public coupling Point (PCC) of the power grid, the harmonic responsibility of each user needs to be calculated.
According to the traditional harmonic responsibility division, harmonic voltage at the PCC and the harmonic current effective value of each feeder line are measured, harmonic impedance is solved based on a local linearization method according to a Noton equivalent circuit of a certain harmonic, harmonic voltage of each feeder line acting on the PCC independently is calculated, and a single-bus harmonic responsibility division index is defined based on the proportion of the harmonic voltage projected on the PCC in the total harmonic voltage direction. The existing harmonic responsibility division methods have the problems that instantaneous data containing harmonic phase information is needed, the influence of background harmonic voltage fluctuation cannot be thoroughly overcome, and the like. In practical situations, the number of enough special harmonic monitoring devices installed on the public connection point is small, and due to the display of a transmission channel and storage capacity, the existing power quality monitoring system is not specified in the industry standard to store harmonic phase information, and the derived data are long-time statistical values, so that the traditional responsibility division method is lack of basic data. Harmonic waves are steady-state quantity existing for a long time, harmonic wave tracing and responsibility division are not accurate enough through short-time instantaneous data measurement, and multiple parties are difficult to accept and accept.
Disclosure of Invention
The invention aims to provide a harmonic responsibility division method and a harmonic responsibility division system based on shape context matching, and the method and the system are favorable for improving the accuracy of harmonic responsibility division.
In order to achieve the purpose, the invention adopts the technical scheme that: a harmonic responsibility division method based on shape context matching comprises the following steps:
the method comprises the steps of searching the position of a harmonic data change point by adopting a change point detection CPM technology based on a screening sorting algorithm SARA, positioning a large fluctuation area with rich information, and partitioning data;
calculating the correlation between the whole user side of the fluctuation region and the harmonic of the PCC point by using a typical correlation analysis CCA, and obtaining and separating background harmonic responsibility;
and calculating the relevance between each user and the PCC point based on a dynamic adjustment shape context matching algorithm DASC to obtain the relevance score of each user as the harmonic responsibility of the user.
Further, detecting a change point in the harmonic voltage mean value model through a screening sorting algorithm to divide different fluctuation areas of harmonic data;
the harmonic voltage mean model is:
Upcc,i＝μi+εi,εi～N(0,δ2) (1)
for location i, consider its local statistics:
wherein the content of the first and second substances,
in order to test the local statistic of the front and back variation amplitude in the taken bandwidth, k is the midpoint of the taken bandwidth, h is a fixed bandwidth and h & lt n, and n is the total data amount;
first, D of the statistical data at each point is calculatedh(. -) and finding the maximum value of the calculation result of each point in each bandwidth;
then setting a threshold Dh(. lambda band width calculation result DhAnd (c) screening, detecting a change point by using an SARA technology, and taking a data block with the bandwidth length of h by taking the change point as a center.
Further, to calculate the harmonic voltage U representing the harmonic statepccAnd an average active power P ═ P (P) representing the power consumption of the user1,P2,…,Pk) The relation between the two groups of indexes is reflected by analyzing the correlation of the pair of comprehensive variables through a CCA method;
are respectively driven from UpccAnd P ═ P (P)1,P2,…,PK) Two representative composite variables U are extracted from the two groups of indexes1And V1I.e. linearity of two sets of variablesCombining and calculating U1And V1The correlation coefficient of the two groups of indexes is measured by CCA;
two sets of indexes U are givenpcc＝(Upcc,1,Upcc,2,…,Upcc,n)，P＝(P1,P2,…,Pk) Then U is1And V1Is defined as:
U1＝αTUpcc (3)
V1＝βTP (4)
wherein α ═ a1,a2,…,am)TAnd β ═ b1,b2,…,bn)TIs a vector of combined coefficients; actual representative combination variables are obtained by searching for optimal combination coefficients, so that optimal correlation measurement of the two indexes is obtained, and harmonic responsibility on the system side is separated from harmonic responsibility on the user side;
the results of CCA are as follows:
the above formula represents the overall harmonic responsibility on the user side, and the rest is the harmonic responsibility on the system side:
Hutility＝1-ρu,c (6)
thereby completing the separation of background harmonic responsibilities.
Further, performing data association calculation based on the DASC algorithm specifically includes the following steps:
1) inputting a time sequence Upcc＝(Upcc,1,Upcc,2,…,Upcc,n) And Pk＝(Pk,1,Pk,2,…,Pk,m)；
2) Area of [0,1 ]]Are divided into equal Pk,numIn part, for point Upcc,iArea of the surface
Are divided into equal Upcc,numA moiety; combining the division of the x axis and the y axis to obtain B block data;
3) calculating the number of points on each block to obtain a two-dimensional histogram
i ∈ (1, n), called point Upcc,iAn adjusted shape context;
4) for sequence PkThe same operation is carried out, at point Pk,jObtaining a two-dimensional histogram
j ∈ (1, m); repeating the above steps to generate two new sequences UpccSAnd PkSWherein each point is represented by a two-dimensional histogram;
5) calculation of U by Babbitt coefficientpccSAnd PkSAt Upcc,iAnd Pk,jSimilarity of points, will UpccSAnd PkSConstructing an overall dynamic curve similarity index by the measured value of each point in the test chart:
6) the total number of sequences representing the similarity of the two time sequences is output.
Further, performing data correlation calculation based on a DASC algorithm on the harmonic voltage data subjected to background harmonic separation and each user load data, calculating the similarity of the harmonic voltage data and each user load data, and normalizing the calculation result;
and (4) taking the similarity score as a relevance score, comparing the relevance scores of different user loads, and considering that the user with stronger relevance has greater responsibility for harmonic waves.
The invention also provides a harmonic responsibility division system based on shape context matching, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein when the computer program is run by the processor, the steps of the method are realized.
Compared with the prior art, the invention has the following beneficial effects: a harmonic responsibility division method and system based on shape context matching are provided, and the method and system utilize CCA and DASC to realize CPM under background harmonic voltage fluctuation to determine harmonic contribution. The method completes accurate evaluation and evaluation of harmonic contribution, can effectively adapt to the fluctuation of background harmonic voltage, has good robustness to long-time scale time series data, has high user acceptance, only needs harmonic voltage monitoring data and user active data, does not need additional monitoring equipment investment, and has strong practicability and wide application space.
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FIG. 1 is a flow chart of a method implementation of an embodiment of the present invention.
FIG. 2 is a schematic diagram of the location of the change point of the time-series data in the embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the embodiments.
As shown in fig. 1, the present embodiment provides a harmonic responsibility division method based on shape context matching, including the following steps:
the method comprises the steps of searching the position of a harmonic data change point by adopting a change point detection CPM technology based on a screening sorting algorithm SARA, positioning a large fluctuation area with rich information, and carrying out data blocking, thereby providing a foundation for more detailed and accurate measurement of harmonic responsibility division;
calculating the correlation between the whole user side of the fluctuation region and the harmonic of the PCC point by using a typical correlation analysis CCA, and obtaining and separating background harmonic responsibility;
and calculating the relevance between each user and the PCC point based on a dynamic adjustment shape context matching algorithm DASC to obtain the relevance score of each user as the harmonic responsibility of the user.
1. Harmonic change point detection by sequencing screening method
The method comprises the steps of detecting a change point in a mean value model through a screening and sorting algorithm (SARA) to divide different fluctuation areas of harmonic data;
the harmonic voltage mean model is:
Upcc,i＝μi+εi,εi～N(0,δ2) (1)
the harmonic voltage mean model comprises a mean part and a variance part, and the invention mainly focuses on the change points generated in the mean change process;
for location i, consider its local statistics:
wherein the content of the first and second substances,
in order to test the local statistic of the front and back variation amplitude in the taken bandwidth, k is the midpoint of the taken bandwidth, h is a fixed bandwidth and h & lt n, and n is the total data amount;
first, D of the statistical data at each point is calculatedh(. -) and finding the maximum value of the calculation result of each point in each bandwidth;
then setting a threshold Dh(. lambda band width calculation result DhAnd (c) screening, detecting a change point by using an SARA technology, and taking a data block with the bandwidth length of h by taking the change point as a center. The positions of the time-series data change points are shown in fig. 2.
2. Harmonic responsibility division method based on Canonical Correlation Analysis (CCA)
For calculating harmonic voltage U representing harmonic statepccAnd an average active power P ═ P (P) representing the power consumption of the user1,P2,…,Pk) The relation between the two groups of indexes is reflected by analyzing the correlation of the pair of comprehensive variables through a CCA method;
are respectively driven from UpccAnd P ═ P (P)1,P2,…,PK) Two representative composite variables U are extracted from the two groups of indexes1And V1I.e. linear combination of two sets of variables, calculate U1And V1The correlation coefficient of the two groups of indexes is measured by CCA;
two sets of indexes U are givenpcc＝(Upcc,1,Upcc,2,…,Upcc,n)，P＝(P1,P2,…,Pk) Then U is1And V1Is defined as:
U1＝αTUpcc (3)
V1＝βTP (4)
wherein α ═ a1,a2,…,am)TAnd β ═ b1,b2,…,bn)TIs a vector of combined coefficients; actual representative combination variables are obtained by searching for optimal combination coefficients, so that optimal correlation measurement of the two indexes is obtained, and harmonic responsibility on the system side is separated from harmonic responsibility on the user side;
the results of CCA are as follows:
the above formula represents the overall harmonic responsibility on the user side, and the rest is the harmonic responsibility on the system side:
Hutility＝1-ρu,c (6)
thereby completing the separation of background harmonic responsibilities.
3. Harmonic responsibility division by dynamically adjusting shape context matching (DASC) algorithm
After removing the background harmonic influence in the harmonic voltage data by the above typical correlation analysis, the present invention performs data correlation calculation by an adjusted DASC algorithm. DASC captures some variations of data near the point of change, and evaluates the similarity of such variations of the two data as the degree of influence of the user on the harmonics.
Performing data association calculation based on a DASC algorithm, specifically comprising the following steps:
1) inputting a time sequence Upcc＝(Upcc,1,Upcc,2,…,Upcc,n) And Pk＝(Pk,1,Pk,2,…,Pk,m)；
2) Area of [0,1 ]]Are divided into equal Pk,numIn part, for point Upcc,iArea of the surface
Are divided into equal Upcc,numA moiety; combining the division of the x axis and the y axis to obtain B block data;
3) calculating the number of points on each block to obtain a two-dimensional histogram
i ∈ (1, n), called point Upcc,iAn adjusted shape context;
4) for sequence PkThe same operation is carried out, at point Pk,jObtaining a two-dimensional histogram
j ∈ (1, m); repeating the above steps to generate two new sequences UpccSAnd PkSWherein each point is represented by a two-dimensional histogram;
5) calculation of U by Babbitt coefficientpccSAnd PkSAt Upcc,iAnd Pk,jSimilarity of points, will UpccSAnd PkSConstructing an overall dynamic curve similarity index by the measured value of each point in the test chart:
6) the total number of sequences representing the similarity of the two time sequences is output.
And performing data relevance calculation based on a DASC algorithm on the harmonic voltage data subjected to background harmonic separation and each user load data, calculating the similarity of the harmonic voltage data and each user load data, and normalizing the calculation result to enable the result to be displayed more visually.
And (4) taking the similarity score as a relevance score, comparing the relevance scores of different user loads, and considering that the user with stronger relevance has greater responsibility for harmonic waves.
The embodiment also provides a harmonic responsibility division system based on shape context matching, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein when the computer program is run by the processor, the steps of the method are realized.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.
Claims (6)
1. A harmonic responsibility division method based on shape context matching is characterized by comprising the following steps:
the method comprises the steps of searching the position of a harmonic data change point by adopting a change point detection CPM technology based on a screening sorting algorithm SARA, positioning a large fluctuation area with rich information, and partitioning data;
calculating the correlation between the whole user side of the fluctuation region and the harmonic of the PCC point by using a typical correlation analysis CCA, and obtaining and separating background harmonic responsibility;
and calculating the relevance between each user and the PCC point based on a dynamic adjustment shape context matching algorithm DASC to obtain the relevance score of each user as the harmonic responsibility of the user.
2. The harmonic responsibility division method based on the shape context matching is characterized in that change points in a harmonic voltage mean value model are detected through a screening sorting algorithm to divide different fluctuation areas of harmonic data;
the harmonic voltage mean model is:
Upcc,i＝μi+εi,εi～N(0,δ2) (1)
for location i, consider its local statistics:
wherein the content of the first and second substances,
in order to test the local statistic of the front and back variation amplitude in the taken bandwidth, k is the midpoint of the taken bandwidth, h is a fixed bandwidth and h & lt n, and n is the total data amount;
first, D of the statistical data at each point is calculatedh(. -) and finding the maximum value of the calculation result of each point in each bandwidth;
then setting a threshold Dh(. lambda band width calculation result DhAnd (c) screening, detecting a change point by using an SARA technology, and taking a data block with the bandwidth length of h by taking the change point as a center.
3. The harmonic responsibility division method based on shape context matching according to claim 2, wherein the harmonic voltage U representing the harmonic state is calculatedpccAnd an average active power P ═ P (P) representing the power consumption of the user1,P2,…,Pk) The relation between the two groups of indexes is reflected by analyzing the correlation of the pair of comprehensive variables through a CCA method;
are respectively driven from UpccAnd P ═ P (P)1,P2,…,PK) Two representative composite variables U are extracted from the two groups of indexes1And V1I.e. linear combination of two sets of variables, calculate U1And V1The correlation coefficient of the two groups of indexes is measured by CCA;
two sets of indexes U are givenpcc＝(Upcc,1,Upcc,2,…,Upcc,n)，P＝(P1,P2,…,Pk) Then U is1And V1Is defined as:
U1＝αTUpcc (3)
V1＝βTP (4)
wherein α ═ a1,a2,…,am)TAnd β ═ b1,b2,…,bn)TIs a vector of combined coefficients; actual representative combination variables are obtained by searching for optimal combination coefficients, so that optimal correlation measurement of the two indexes is obtained, and harmonic responsibility on the system side is separated from harmonic responsibility on the user side;
the results of CCA are as follows:
the above formula represents the overall harmonic responsibility on the user side, and the rest is the harmonic responsibility on the system side:
Hutility＝1-ρu,c (6)
thereby completing the separation of background harmonic responsibilities.
4. The harmonic responsibility division method based on the shape context matching according to claim 3, wherein the data association calculation is performed based on a DASC algorithm, and specifically comprises the following steps:
1) inputting a time sequence Upcc＝(Upcc,1,Upcc,2,…,Upcc,n) And Pk＝(Pk,1,Pk,2,…,Pk,m)；
2) Area of [0,1 ]]Are divided into equal Pk,numIn part, for point Upcc,iArea of the surface
Are divided into equal Upcc,numA moiety; combining the division of the x axis and the y axis to obtain B block data;
3) calculating the number of points on each block to obtain a two-dimensional histogram
Called point Upcc,iAn adjusted shape context;
4) for sequence PkThe same operation is carried out, at point Pk,jObtaining a two-dimensional histogram
Repeating the above steps to generate two new sequences UpccSAnd PkSWherein each point is represented by a two-dimensional histogram;
5) calculation of U by Babbitt coefficientpccSAnd PkSAt Upcc,iAnd Pk,jSimilarity of points, will UpccSAnd PkSConstructing an overall dynamic curve similarity index by the measured value of each point in the test chart:
6) the total number of sequences representing the similarity of the two time sequences is output.
5. The harmonic responsibility division method based on shape context matching according to claim 4, wherein the harmonic voltage data after background harmonic separation and each user load data are subjected to data association calculation based on DASC algorithm, the similarity of the harmonic voltage data and each user load data is calculated, and the calculation result is normalized;
and (4) taking the similarity score as a relevance score, comparing the relevance scores of different user loads, and considering that the user with stronger relevance has greater responsibility for harmonic waves.
6. A harmonic responsibility division system based on shape context matching, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the method steps according to any of claims 1-5 are performed when the computer program is executed by the processor.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2000010017A1 (en) * | 1998-08-11 | 2000-02-24 | The Governor Of The University Of Alberta | Method and apparatus for differentiating contributions of harmonic pollution sources |
| CN104502704A (en) * | 2015-01-20 | 2015-04-08 | 西南交通大学 | Harmonic contribution calculating method applicable to background harmonic voltage change |
| CN104851095A (en) * | 2015-05-14 | 2015-08-19 | 江南大学 | Workpiece image sparse stereo matching method based on improved-type shape context |
| CN106019026A (en) * | 2016-07-20 | 2016-10-12 | 东南大学 | Distribution method based on waveform matching for harmonic voltage responsibility |
| CN110244185A (en) * | 2019-07-08 | 2019-09-17 | 厦门理工学院 | A kind of multi-source harmonic contributions division methods, terminal device and storage medium |
| CN110850167A (en) * | 2019-12-27 | 2020-02-28 | 福州大学 | Multi-harmonic source responsibility division method |
-
2021
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2000010017A1 (en) * | 1998-08-11 | 2000-02-24 | The Governor Of The University Of Alberta | Method and apparatus for differentiating contributions of harmonic pollution sources |
| CN104502704A (en) * | 2015-01-20 | 2015-04-08 | 西南交通大学 | Harmonic contribution calculating method applicable to background harmonic voltage change |
| CN104851095A (en) * | 2015-05-14 | 2015-08-19 | 江南大学 | Workpiece image sparse stereo matching method based on improved-type shape context |
| CN106019026A (en) * | 2016-07-20 | 2016-10-12 | 东南大学 | Distribution method based on waveform matching for harmonic voltage responsibility |
| CN110244185A (en) * | 2019-07-08 | 2019-09-17 | 厦门理工学院 | A kind of multi-source harmonic contributions division methods, terminal device and storage medium |
| CN110850167A (en) * | 2019-12-27 | 2020-02-28 | 福州大学 | Multi-harmonic source responsibility division method |
Non-Patent Citations (2)
| Title |
|---|
| 孟思雨 等: "基于有效数据段选取的多谐波源责任划分方法", 《电网技术》, vol. 41, no. 6, pages 2006 - 2011 * |
| 张逸 等: "基于监测数据相关性分析的用户谐波责任划分方法", 《电力系统自动化》, vol. 44, no. 2, pages 189 - 197 * |
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