CN114217128A - Harmonic responsibility division method considering harmonic variation trend - Google Patents
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Abstract
The invention relates to a harmonic responsibility division method considering harmonic variation trend, which comprises the following steps: step S1, acquiring a PCC harmonic voltage data sequence and a harmonic current data sequence of each line connected with the PCC; step S2, obtaining responsibility indexes of each harmonic source by adopting multi-harmonic responsibility division, and correcting interference data; step S3, carrying out piecewise linearization processing on the harmonic data, and calculating the trend weight based on correlation analysis; and step S4, obtaining the final harmonic responsibility division index according to the weight calculation result. The method effectively improves the reliability of harmonic responsibility division and further improves the regional harmonic treatment efficiency of the power grid.
Description
Technical Field
The invention relates to the field, in particular to a harmonic responsibility division method considering harmonic variation trend.
Background
The harmonic pollution problem of the power system becomes more serious and the pollution condition becomes more complex due to the increasing number of nonlinear devices. The Point of Common Coupling (PCC) harmonic voltages of the grid are generated by the joint action of all harmonic source harmonic voltages connected, including the user harmonic sources as well as the system harmonic sources. And the harmonic responsibility division is realized by measuring the PCC harmonic voltage and the branch harmonic current, solving the equivalent harmonic impedance of each user and the background harmonic voltage on the system side according to the PCC harmonic equation and various harmonic responsibility division methods, and further calculating the projection ratio of the harmonic voltage of each user in the PCC total harmonic voltage direction as the harmonic responsibility index of the user. In order to quantify the influence of the harmonic waves emitted by each user on the PCC harmonic wave voltage, the problem of dividing responsibility of multiple harmonic wave sources is a key and is a premise for accurate harmonic wave treatment.
Disclosure of Invention
In view of this, the invention aims to provide a harmonic responsibility division method considering a harmonic variation trend, so that the reliability of harmonic responsibility division is effectively improved, and the regional harmonic management efficiency of a power grid is further improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
a harmonic responsibility division method considering a harmonic variation tendency, comprising the steps of:
step S1, acquiring a PCC harmonic voltage data sequence and a harmonic current data sequence of each line connected with the PCC;
step S2, obtaining responsibility indexes of each harmonic source by adopting multi-harmonic responsibility division, and correcting interference data;
step S3, carrying out piecewise linearization processing on the harmonic data, and calculating the trend weight based on correlation analysis;
and step S4, obtaining the final harmonic responsibility division index according to the weight calculation result.
Further, the step S2 is specifically:
step S21, under the condition of multi-harmonic source access, constructing an equivalent circuit diagram and setting
Respectively a system side h-order harmonic voltage and a system side harmonic impedance,
is the h-th harmonic voltage of the PCC point,
respectively representing the ith user harmonic impedance and the h harmonic current;
the circuit of FIG. 1 is calculated using the superposition theorem to yield equation (1)
Wherein
In order to be a background harmonic voltage,
for the harmonic voltages of the harmonic users a,
for the harmonic voltages of the harmonic users B,
is the harmonic voltage of harmonic user C;
the harmonic responsibility of the user i at the h-th harmonic frequency is determined by equation (2)
In the formula, phici-φpccIs composed of
And
the included angle of (A);
and step S22, correcting the harmonic responsibility index of the period corresponding to the negative harmonic responsibility index to be zero, and normalizing the harmonic sources of other harmonic responsibility indexes which are not negative to obtain the corrected harmonic responsibility index of each harmonic source.
Further, the piecewise linearization process specifically includes:
(1): setting a time sequence: x ═ X1,…,xi,…,xn]Wherein x isi＝(ti,vi),t i1,2,3, …, n denotes the i-th acquisition instant, viIs tiThe data value of time. And (3) forming a new sequence after piecewise linearization by using main characteristic points in X: x '═ X'1,…,x'i,…,x'n'](ii) a Wherein, x'i＝(ti,v'i) The characteristic points in the original sequence;
the compression ratio R is expressed as
R＝(1-n′/n)×100％ (3)
N and n' are time sequence data quantity before and after piecewise linearity respectively;
(2): reading harmonic data and setting target compression ratio R0；
(3): and (4) calculating the vertical distance from the middle point to the connecting line of the initial point and the final point of the sequence, selecting a key point and dividing the sequence into two sections.
(4): and when the number of the sequence segments is more than 5, selecting the sub-segment with the shortest length from the first 5 segments with the largest fitting error to preferentially search the characteristic points in the sub-segment.
(5): calculating a segment compression ratio R when Ri≥R0Then, outputting a new sequence after preprocessing; otherwise, repeating the steps (3) to (4).
Further, the trend weight calculation based on the correlation analysis specifically includes:
taking the new sequence after the PLR processing as an input, calculating a harmonic voltage data sequence X which represents a harmonic state (X ═ X)1,X2,…,Xm) And the harmonic current data sequence Y ═ Y (Y)1,Y2,…,Yn) The correlation relationship between them. The correlation coefficient ρ (X, Y) between the two arrays is defined as follows:
wherein cov (X, Y) is the covariance of X and Y, and D (X), D (Y) are the variances of X and Y, respectively; when the correlation coefficient rho (X, Y) > 0 represents that the two arrays X and Y are in positive correlation; when the correlation coefficient rho (X, Y) < 0 indicates that the two arrays X and Y are in negative correlation;
windowing is carried out on the input data sequence data, and the front and the back of the data sequence are filled up through the average value of the periodic trend data to finally obtain m groups of trend sequence segments;
carrying out correlation analysis on the ith line harmonic current trend sequence data segment one by one to obtain a series of trend correlation coefficient indexes rho between the ith line harmonic current and the harmonic voltagei＝[ρi1,ρi2,…ρim]
Wherein (i belongs to 1,2, …, N), N is the total number of circuits connected by the bus;
respectively normalizing the correlation coefficients calculated by the N lines according to time sequence to obtain a trend correlation coefficient index rhoi＝[ρi1,ρi2,…ρim]Wherein
Further, in step S4, specifically, the step includes:
for data in different periods, firstly calculating a harmonic responsibility division result of the period based on phasor projection, and then calculating the harmonic electricity of the i users in different periods by an analytic hierarchy processWeight rho corresponding to pressure amplitudeikObtaining harmonic wave responsibility H 'of the user in a k period'ci,kComprises the following steps:
H′ci,k＝ρikHci,k (6)
and (3) summing the harmonic responsibility of each time period to obtain a final harmonic responsibility division index:
where n is the total number of data, nkThe number of data in the kth time period.
A harmonic responsibility division system considering harmonic variation tendency, comprising a processor, a memory and a computer program stored on the memory, the processor, when executing the computer program, specifically performing the steps of the harmonic responsibility division method as described above.
Compared with the prior art, the invention has the following beneficial effects:
the method effectively improves the reliability of harmonic responsibility division and further improves the regional harmonic treatment efficiency of the power grid.
Drawings
FIG. 1 is a multiple harmonic source access equivalent circuit diagram in a one embodiment of the present invention;
FIG. 2 is a harmonic voltage projection diagram in one embodiment of the invention;
FIG. 3 is periodic harmonic timing data in one embodiment of the invention;
FIG. 4 is a schematic diagram of important point selection in one embodiment of the present invention;
FIG. 5 is a data sequence at different target compression ratios in an embodiment of the present invention;
FIG. 6 is a diagram illustrating a sliding window data segment capture method according to an embodiment of the present invention;
FIG. 7 is a general flow chart of the invention for responsibility division based on harmonic variation trends.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
In the present embodiment, referring to fig. 7, there is provided a harmonic responsibility division method considering a harmonic variation tendency, including the steps of:
step S1, acquiring a PCC harmonic voltage data sequence and a harmonic current data sequence of each line connected with the PCC;
step S2, obtaining responsibility indexes of each harmonic source by adopting multi-harmonic responsibility division, and correcting interference data;
step S3, carrying out piecewise linearization processing on the harmonic data, and calculating the trend weight based on correlation analysis;
and step S4, obtaining the final harmonic responsibility division index according to the weight calculation result.
In the present embodiment, in the case of multiple harmonic source access, an equivalent circuit diagram as in fig. 1 is established, wherein
Respectively a system side h-order harmonic voltage and a system side harmonic impedance,
is the h-th harmonic voltage of the PCC point,
respectively representing the i-th user harmonic impedance and h-th harmonic current (assuming that there are N users in total). The circuit of fig. 1 is calculated using the superposition theorem, and equation (1) can be derived. And calculating the projection of the harmonic voltage contributed by each harmonic user on the PCC, namely the harmonic contribution, on the harmonic voltage of the PCC point, as shown in FIG. 2 and equation (2).
Wherein
In order to be a background harmonic voltage,
for the harmonic voltages of the harmonic users a,
for the harmonic voltages of the harmonic users B,
is the harmonic voltage of harmonic user C. By the linear regression method, the following equation (1) can be obtained
Then, the harmonic responsibility of the user i under the h harmonic frequency is obtained through the formula (2)
In the formula, phici-φpccIs composed of
And
the included angle of (a).
Since the harmonic vector is a time-varying vector, there may be h-order harmonic voltage and voltage generated on the PCC by a certain harmonic source
The phase angle difference is obtuse angle, and the h-th harmonic voltage of the harmonic source is at
The upper projection is negative and the calculated harmonic responsibility will also be negative, i.e. the harmonic source acts as an absorption harmonic for the PCC. It is considered that harmonic sources do not actively absorb harmonics and that the "compensation" action is short-time, time-varying, and it is not reasonable to reward harmonics for their responsibility. Therefore, the harmonic responsibility index of the period with negative harmonic responsibility index is corrected to be zero, and the normalization is carried out on the harmonic sources with non-negative harmonic responsibility indexesAnd obtaining the corrected harmonic responsibility indexes of each harmonic source.
In this embodiment, due to the power consumption period rule, the characteristics of the interference source, and the like, under the condition that the system operation mode is relatively unchanged, the harmonic indexes of some monitoring points also present a certain regular change condition in time sequence. Fig. 3 shows statistical data of a phase a harmonic 95% probability value monitored and collected at a certain substation bus monitoring point within 15 days, and it can be found that the time sequence trend of each day shows a certain regular change, and the data with the same value and different ascending/descending trends have different importance considering that the data may be caused by load power consumption change, interference source characteristic change or operation mode adjustment at the power grid side.
In this embodiment, the piecewise linearization process specifically includes:
setting a time sequence: x ═ X1,…,xi,…,xn]Wherein x isi＝(ti,vi),t i1,2,3, …, n denotes the i-th acquisition instant, viIs tiThe data value of time. And (3) forming a new sequence after piecewise linearization by using main characteristic points in X: x '═ X'1,…,x'i,…,x'n'](ii) a Wherein, x'i＝(ti,v'i) The characteristic points in the original sequence;
the compression ratio R is expressed as
R＝(1-n′/n)×100％ (3)
N and n' are time sequence data quantity before and after piecewise linearity respectively;
in this embodiment, preferably, a method for improving selection of the important point is used to search for the feature point, and the important point is selected according to the vertical distance from the data to the connecting line between the two points. The important point selection criterion is as shown in fig. 4, the original sequence has 6 data points, and if the original sequence is compressed to 50%, one important point needs to be selected from the points except the end points, and the point x4 with the longest vertical distance from the head-end line x1x6 is used as the important point to complete linear segmentation. The PLR process is to repeatedly calculate and segment important points according to a certain target compression ratio R0 until the requirements are met.
Preferably, the step of piecewise linearization based on the points of importance of the improvement:
step 1: reading harmonic data and setting target compression ratio R0；
Step 2: and (4) calculating the vertical distance from the middle point to the connecting line of the initial point and the final point of the sequence, selecting a key point and dividing the sequence into two sections.
And step 3: and when the number of the sequence segments is more than 5, selecting the sub-segment with the shortest length from the first 5 segments with the largest fitting error to preferentially search the characteristic points in the sub-segment.
And 4, step 4: calculating a segment compression ratio R when Ri≥R0Then, outputting a new sequence after preprocessing; otherwise, repeating the step 2-3.
When the PLR processing is performed on the actual monitoring statistical data, the selection of the compression rate needs to consider the analysis time scale and the monitoring data acquisition interval. In the current domestic PQ monitoring system, the data statistics period of various indexes comprises types of 1, 3, 5, 10min and the like. Taking harmonic wave monitorable data with a statistical period of 3min as an example, 480 data points are acquired every day, and fig. 5 shows PLR results of 95% probability value data of the total harmonic wave voltage distortion rate acquired within 1 day of a certain 110kV monitoring point under different target compression rates. It can be seen that, as the compression degree increases, the overall trend characteristics of the sequence are better embodied, and when the compression degree is too small, a lot of noise and small fluctuation are reserved, and the large trend is covered. Because the overall major trend of the data is concerned, after repeated tests, a target compression ratio is selected according to different working conditions in the actual data and by adopting a criterion of reserving 1-3 important characteristic points per hour on average, a good major trend extraction effect can be obtained, and meanwhile, a compression ratio selection principle in the actual engineering application of the graph is obtained by referring to a selected value in the existing research: for common 1min and 3min statistical interval data, a compression ratio of 90% or more is selected, so that the overall trend characteristics are highlighted (as shown in fig. 5), and for data with a period of 5min or 10min, a target compression ratio of 80% can be set.
In this embodiment, the trend sequence weight calculation based on the correlation analysis specifically includes the following steps:
taking the new sequence after PLR processing as input, calculating the tableHarmonic voltage data sequence X ═ X (X) showing harmonic state1,X2,…,Xm) And the harmonic current data sequence Y ═ Y (Y)1,Y2,…,Yn) The correlation relationship between them. The correlation coefficient ρ (X, Y) between the two arrays is defined as follows:
where cov (X, Y) is the covariance of X and Y, and D (X), D (Y) are the variances of X and Y, respectively.
When the correlation coefficient rho (X, Y) > 0 represents that the two arrays X and Y are in positive correlation; when the correlation coefficient ρ (X, Y) < 0 indicates that the two arrays X and Y are negatively correlated. When | ρ (X, Y) | is closer to 1, the degree of correlation between the two arrays X and Y is higher; as | ρ (X, Y) | is closer to 0, the degree of correlation of the two arrays X and Y is lower.
In the present embodiment, the target compression ratio R0And taking 75%, and compressing harmonic current data and harmonic voltage data of each harmonic source by the PLR method in the previous section to obtain integral trend sequence data. And then, as shown in fig. 6, windowing is carried out on the input data sequence data, and the front and the back of the data sequence are filled up through the average value of the periodic trend data to finally obtain m groups of trend sequence segments. Carrying out correlation analysis on the ith line harmonic current trend sequence data segment one by one to obtain a series of trend correlation coefficient indexes rho between the ith line harmonic current and the harmonic voltagei＝[ρi1,ρi2,…ρim]. Where (i ∈ 1,2, …, N), N is the total number of lines connected by the bus.
Respectively normalizing the correlation coefficients calculated by the N lines according to time sequence to obtain a trend correlation coefficient index rhoi＝[ρi1,ρi2,…ρim]Wherein
In this embodiment, for different periods of data, the first calculation is thatThe period is based on the harmonic responsibility division result of phasor projection, and then the weight rho corresponding to the harmonic voltage amplitude of the user in different periods i is calculated by an analytic hierarchy processikObtaining harmonic wave responsibility H 'of the user in a k period'ci,kComprises the following steps:
H′ci,k＝ρikHci,k (6)
and (3) summing the harmonic responsibility of each time period to obtain a final harmonic responsibility division index:
where n is the total number of data, nkThe number of data in the kth time period.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.
Claims (6)
1. A harmonic responsibility division method considering a harmonic variation tendency, comprising the steps of:
step S1, acquiring a PCC harmonic voltage data sequence and a harmonic current data sequence of each line connected with the PCC;
step S2, obtaining responsibility indexes of each harmonic source by adopting multi-harmonic responsibility division, and correcting interference data;
step S3, carrying out piecewise linearization processing on the harmonic data, and calculating the trend weight based on correlation analysis;
and step S4, obtaining the final harmonic responsibility division index according to the weight calculation result.
2. The harmonic responsibility division method considering the harmonic variation tendency according to claim 1, wherein the step S2 is specifically as follows:
step S21, under the condition of multi-harmonic source access, constructing an equivalent circuit diagram and setting
Respectively a system side h-order harmonic voltage and a system side harmonic impedance,
is the h-th harmonic voltage of the PCC point,
respectively representing the ith user harmonic impedance and the h harmonic current;
the circuit of FIG. 1 is calculated using the superposition theorem to yield equation (1)
Wherein
In order to be a background harmonic voltage,
for the harmonic voltages of the harmonic users a,
for the harmonic voltages of the harmonic users B,
is the harmonic voltage of harmonic user C;
the harmonic responsibility of the user i at the h-th harmonic frequency is determined by equation (2)
In the formula, phici-φpccIs composed of
And
the included angle of (A);
and step S22, correcting the harmonic responsibility index of the period corresponding to the negative harmonic responsibility index to be zero, and normalizing the harmonic sources of other harmonic responsibility indexes which are not negative to obtain the corrected harmonic responsibility index of each harmonic source.
3. The harmonic responsibility division method considering the harmonic variation trend according to claim 1, wherein the piecewise linearization process specifically comprises the following steps:
(1): setting a time sequence: x ═ X1,…,xi,…,xn]Wherein x isi＝(ti,vi),ti1,2,3, …, n denotes the i-th acquisition instant, viIs tiThe data value of time. And (3) forming a new sequence after piecewise linearization by using main characteristic points in X: x '═ X'1,…,x'i,…,x'n'](ii) a Wherein, x'i＝(ti,v'i) The characteristic points in the original sequence;
the compression ratio R is expressed as
R＝(1-n′/n)×100％ (3)
N and n' are time sequence data quantity before and after piecewise linearity respectively;
(2): reading harmonic data and setting target compression ratio R0；
(3): and (4) calculating the vertical distance from the middle point to the connecting line of the initial point and the final point of the sequence, selecting a key point and dividing the sequence into two sections.
(4): and when the number of the sequence segments is more than 5, selecting the sub-segment with the shortest length from the first 5 segments with the largest fitting error to preferentially search the characteristic points in the sub-segment.
(5): calculating a segment compression ratio R when Ri≥R0Then, outputting a new sequence after preprocessing; otherwise, repeating the steps (3) to (4).
4. The harmonic responsibility division method considering the harmonic variation trend according to claim 1, wherein the trend weight calculation based on the correlation analysis is specifically as follows:
taking the new sequence after the PLR processing as an input, calculating a harmonic voltage data sequence X which represents a harmonic state (X ═ X)1,X2,…,Xm) And the harmonic current data sequence Y ═ Y (Y)1,Y2,…,Yn) The correlation relationship between them. The correlation coefficient ρ (X, Y) between the two arrays is defined as follows:
wherein cov (X, Y) is the covariance of X and Y, and D (X), D (Y) are the variances of X and Y, respectively; when the correlation coefficient rho (X, Y) > 0 represents that the two arrays X and Y are in positive correlation; when the correlation coefficient rho (X, Y) < 0 indicates that the two arrays X and Y are in negative correlation;
windowing is carried out on the input data sequence data, and the front and the back of the data sequence are filled up through the average value of the periodic trend data to finally obtain m groups of trend sequence segments;
carrying out correlation analysis on the ith line harmonic current trend sequence data segment one by one to obtain a series of trend correlation coefficient indexes rho between the ith line harmonic current and the harmonic voltagei＝[ρi1,ρi2,···ρim]
Wherein (i belongs to 1,2, N), N is the total number of lines connected by the bus;
respectively normalizing the correlation coefficients calculated by the N lines according to time sequence to obtain a trend correlation coefficient index rhoi＝[ρi1,ρi2,···ρim]Wherein
5. The harmonic responsibility division method considering the harmonic variation tendency according to claim 1, wherein the step S4 specifically comprises:
for data in different periods, firstly calculating a harmonic responsibility division result of the period based on phasor projection, and then calculating weights rho corresponding to harmonic voltage amplitudes of users in different periods i by an analytic hierarchy processikObtaining harmonic wave responsibility H 'of the user in a k period'ci,kComprises the following steps:
H′ci,k＝ρikHci,k (6)
and (3) summing the harmonic responsibility of each time period to obtain a final harmonic responsibility division index:
where n is the total number of data, nkThe number of data in the kth time period.
6. Harmonic responsibility division system taking into account a harmonic variation trend, comprising a processor, a memory and a computer program stored on the memory, the processor when executing the computer program specifically performing the steps in the harmonic responsibility division method according to any of the claims 1-5.
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|---|---|---|---|---|
| CN103278685A (en) * | 2013-05-17 | 2013-09-04 | 国家电网公司 | Harmonic duty quantitative allocation method based on statistical data correlation analysis |
| CN109839538A (en) * | 2019-03-29 | 2019-06-04 | 云南电网有限责任公司电力科学研究院 | A kind of harmonic source identification method and system based on correlation analysis |
| CN110850167A (en) * | 2019-12-27 | 2020-02-28 | 福州大学 | Multi-harmonic source responsibility division method |
| CN111693773A (en) * | 2020-04-29 | 2020-09-22 | 国网江苏省电力有限公司电力科学研究院 | Harmonic source responsibility division method based on mutual approximation entropy data screening |
| CN113283061A (en) * | 2021-05-07 | 2021-08-20 | 福建亿力优能电力科技有限公司 | Harmonic responsibility division method considering PCC harmonic voltage severity |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103278685A (en) * | 2013-05-17 | 2013-09-04 | 国家电网公司 | Harmonic duty quantitative allocation method based on statistical data correlation analysis |
| CN109839538A (en) * | 2019-03-29 | 2019-06-04 | 云南电网有限责任公司电力科学研究院 | A kind of harmonic source identification method and system based on correlation analysis |
| CN110850167A (en) * | 2019-12-27 | 2020-02-28 | 福州大学 | Multi-harmonic source responsibility division method |
| CN111693773A (en) * | 2020-04-29 | 2020-09-22 | 国网江苏省电力有限公司电力科学研究院 | Harmonic source responsibility division method based on mutual approximation entropy data screening |
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