CN112070118A - Station area phase sequence identification method based on data clustering fitting analysis - Google Patents
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Abstract
The invention relates to a station area phase sequence identification method based on data cluster fitting analysis, which is characterized in that a station area three-phase total meter voltage data and a station area single-phase user meter voltage data in a marketing system are obtained through an Internet of things management terminal installed in a station area, the voltage data of all single-phase user meters are firstly subjected to cluster analysis, the voltage data are divided into three clusters, the primary identification of the station area single-phase user meter phase is carried out, then the least square fitting polynomial identification method is utilized to carry out fitting identification on each cluster of data and the three-phase voltage data of the station area total meter, so that the correctness of each cluster of data phase sequence is checked, the phase sequence relation is identified, the correctness is also judged again, the accuracy is improved, the identification is carried out on the basis of the current station area data only in the above process, the problem of high cost of equipment identification is avoided, and the method is economical and feasible.
Description
Technical Field
The invention relates to a station area household-phase-variation identification method based on data clustering fitting analysis, and belongs to the technical field of electric power low-voltage distribution networks.
Background
The accurate household-phase-change relation of the distribution transformer area is the key of lean management of the transformer area, and if the household-phase-change relation is wrong, the electric meters of other transformer areas are mistakenly considered to be in the transformer area or the electric meters of the transformer area are not in the file of the transformer area, the line loss calculation of the transformer area is inaccurate.
Knowing the 'household-phase' relationship of the transformer area, the dynamic calculation of the three-phase split-phase load and the split-phase line loss of the transformer area can be realized, and the guarantee is provided for the fine management and the line loss examination of the transformer area.
Accurate monitoring and analysis of the transformer substation line loss are achieved, the inevitable trend of power grid loss management development is achieved, loss in the power transmission process can be reduced as much as possible on the basis of guaranteeing balance and safety of transformer substation power distribution, line loss management personnel can find and solve problems in a targeted mode, and the economic operation level of the transformer substation is further improved. Therefore, how to establish the user-phase-variation relationship is a technical problem to be solved urgently in the field.
Disclosure of Invention
The invention aims to provide a platform area household-phase-change identification method based on data clustering fitting analysis, which aims to solve the problem of electric energy quality caused by three-phase unbalance adjustment of the current platform area and the problem that the split-phase load cannot be accurately adjusted at present.
In order to achieve the above object, the present invention provides a station area-phase-variation identification method based on data clustering fitting analysis, comprising the following steps:
(1) acquiring 96-point three-phase general meter voltage of a distribution area and single-phase voltage of each single-phase household meter of the distribution area at the same moment;
(2) normalizing the voltage of the three-phase general meter and the single-phase voltage of each single-phase household meter in the transformer area;
(3) respectively taking the normalized single-phase voltage of each user as a point; taking the three-phase voltage of the normalized distribution room total table voltage as three reference center points;
(4) calculating Euclidean distances between points in the same time sequence with the three reference central points and the three reference central points respectively;
(5) grouping all the points of the single-phase voltage data of the single-phase user meter into three clusters corresponding to three phase sequences by using a K-Means clustering algorithm, and obtaining new three aggregation points;
(6) and calculating Euclidean distances between each aggregation point and three reference center points, and taking the phase sequence of the center reference point with the minimum Euclidean distance value as the phase sequence of the aggregation point and the phase sequence of the points in the corresponding cluster of the aggregation point.
Further, the method also comprises the following steps:
(7) acquiring 96-point voltage data of three clusters, and performing polynomial fitting on the single-phase 96-point voltage data of the single-phase household meter of each cluster of data and the voltage data of the phase sequence of 96 points of the three-phase general meter by adopting a binomial equation fitting curve method; sequentially judging according to a deviation square sum minimum algorithm to obtain a fitting curve coefficient of a binomial equation;
comparing the coefficient of a binomial equation fitting curve of 96-point voltage data of the three-phase general table with the coefficient of a binomial fitting curve of 96-point data of each user table of each single-phase user table data, and comparing the minimum phase coefficient;
if the phase sequence of the coefficient minimum phase of the user is consistent with the phase sequence obtained by clustering in the step (6), taking the phase sequence as the phase sequence of the user; and if not, returning to the step (1) to recalculate the phase sequence of the user.
Further, deriving the coefficients of the fitted curve of the binomial equation comprises:
7.1 Single-phase 96-Point Voltage data x for Single useriFitting is carried out, and a fitting polynomial for obtaining a fitting curve is as follows:
where k is the fitting order, a0，a1，....akFitting coefficients of a binomial equation;
7.2 three-phase summary of the voltage points y of each phaseiThe sum of the distances to the fitted curve, i.e. the sum of squared deviations, is:
7.3 selection of the sum of squares of deviations σx,y 2The smallest one of the fitted curves is taken as the polynomial fitted curve of the term.
Further, the grouping of all the points into three clusters corresponding to three phase sequences using the K-Means clustering algorithm in step (5) comprises: and carrying out multiple times of sequential iteration to move the aggregation point until the aggregation point does not change according to the distance change of the transformation observation point, and the sequential iteration time N is more than or equal to 5.
Further, the voltage of the 96-point three-phase general meter in the transformer area and the single-phase voltage of each single-phase user meter in the transformer area at the same time are obtained through an internet of things management terminal installed in the transformer area in the step (1).
The technical scheme of the invention has the following beneficial technical effects:
the method comprises the steps of firstly carrying out cluster analysis on all single-phase meter voltage data, dividing the voltage data into three clusters, carrying out primary identification on the phase of the single-phase meter in a station area, then carrying out fitting identification on each cluster of data and the three-phase voltage data of a total table in the station area by using a least square fitting polynomial identification method, so as to check the correctness of the phase sequence of each cluster of data, thereby identifying the relationship between the phase and the phase, judging the correctness again, improving the accuracy, judging on the basis of the current station area data in the process, avoiding the problem of high cost of identifying by adding equipment, and being economical and feasible.
Drawings
FIG. 1 is a simulation diagram of a clustering algorithm for voltage data of a power distribution station area meter;
FIG. 2 is a diagram of a power distribution station area Internet of things terminal user voltage data access framework;
FIG. 3 is a flow chart of a power distribution station area meter voltage data clustering algorithm;
FIG. 4 is a flow chart of binomial fitting checking of the voltage data of the household electrical appliance in the distribution area;
FIG. 5 is a voltage curve of the three-phase voltage of the transformer A, B, C with the variation amplitude of + -10V, wherein the voltage variation frequency in FIG. 5(a) is 0.1Hz, the voltage variation frequency in FIG. 5(b) is 0.2Hz, and the voltage variation frequency in FIG. 5(c) is 0.4 Hz;
fig. 6 is a schematic diagram of a phase a least squares fitting polynomial of a transformer A, B, C three-phase transformer area reference table, where fig. 6(a) is phase a, fig. 6(B) is phase B, and fig. 6(C) is phase C.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
The invention provides a station area user-phase-change identification method based on data cluster fitting analysis, wherein a station area internet of things management terminal acquires voltage data of each single-phase user meter of a station area through a marketing system or on the spot, performs primary sample data elimination processing on the voltage data, performs analysis reconstruction on the voltage data of all the single-phase user meters according to a K-Means cluster analysis method, performs normalization processing on the voltage data of all the single-phase user meters, then randomly selects three central points as starting points of three cluster analysis, performs distance analysis on three random points of each single-phase user meter voltage point, takes a minimum distance point as a basis, and performs cyclic judgment. And sequentially judging the voltage data of each single-phase voltmeter and the three-phase voltage data of the table area master table according to a deviation square sum minimum algorithm, checking the accuracy of dividing each point and the three-phase voltage of the master table in each cluster of data, and basically finishing the classification of 3 clusters of single-phase voltage data when the central point is moved in a sequential iteration mode until the central point is not changed according to the change of the point distance any more and the cycle number N > is 5. With reference to fig. 3, the method comprises the following steps:
(1) the method comprises the steps that a distribution area internet-of-things management terminal directly obtains transformer user side three-phase voltage values Uaj, Ubj and Ucj (j is 1,2,.. multidot.96) of a distribution area and voltage values Uuij (i is 1,2,.. multidot.n, j is 1,2,.. multidot.96) of all n users in the distribution area within 24 hours a day at intervals of 15 minutes or 1 hour through a marketing system or on site; in connection with fig. 2.
(2) Normalizing all obtained voltage data by [0,1 ];
(3) taking a series of voltage values of each electric meter as one point in a 96-point space; taking a series of voltage values of three phase sequences of three-phase summary table data of the transformer area as three reference central points in a 96-point space;
(4) grouping all the points into three clusters (corresponding to three phase sequences) by using a K-Means clustering algorithm, obtaining three aggregation points, calculating Euclidean distances between the points of the three aggregation points under the same time sequence and the three points respectively, and comparing the distances;
(5) and calculating Euclidean distances between each aggregation point and three central reference points, and taking the phase sequence of the central reference point with the minimum Euclidean distance value as the phase sequence of the aggregation point and the phase sequence of the points in the cluster corresponding to the aggregation point.
(6) Acquiring three clusters of obtained voltage data, and performing polynomial fitting on each single-phase user meter voltage data and three-phase general meter voltage data of each cluster of data by adopting a binomial equation fitting curve method; sequentially judging according to a deviation square sum minimum algorithm to obtain a fitting curve coefficient of a binomial equation;
comparing the fitting curve coefficient of the binomial equation of the voltage of the three-phase general table with the fitting curve coefficient of the binomial equation of the data of each single-phase user table at the data of 96 points of each user table, and comparing the minimum phase coefficient; if the phase sequence of the phase with the minimum coefficient of the user is consistent with the phase sequence obtained by clustering in the step (6), taking the phase sequence as the phase sequence of the user; and if not, returning to the step (1) to recalculate the phase sequence of the user.
And according to voltage data items in the load curves of the three-phase region examination and verification meter and the single-phase intelligent electric energy meter, performing curve fitting by using a binomial equation of 96 data items, and calculating the phase sequence of each single-phase intelligent electric energy meter by taking the three-phase region examination and verification meter as a reference so as to check the phase sequence with the phase obtained in the cluster analysis.
Furthermore, in order to calculate the minimum relation of the square sum of the deviation of each single-phase household meter voltage value and each phase voltage of the transformer district total electric meter by validation, the invention also provides a calculation formula:
1) let the fitting polynomial be:
2) the sum of the distances from each point to this curve, i.e. the sum of the squared deviations, is as follows:
3) and (3) performing derivation on two sides of the equation, simplifying to obtain a simplified equation of the voltage data matrix of the transformer area:
4) that is, X ═ a ═ Y, then a ═ (X '× X) -1 ×', Y, the minimum phase coefficient matrix a is obtained, and we also obtain the fitted curve.
Wherein sigmax,yChecking the coefficient of each single-phase user meter and the general meter, x i the voltage data amplitude of each single-phase user meter
Is the effective value of each phase voltage, y, of the summary tableiFor a sequence of effective values of the voltage of each phase of the summary table, sigmaiFor the square value of the deviation of each sub-meter voltage effective value and one phase in the total meter three-phase user meter, sigmaiAnd A is a binomial fitting coefficient, and the square value of the deviation of each sub-meter voltage effective value and one phase in the total meter three-phase user meter is obtained.
Examples
The invention firstly carries out data clustering of the single-phase user table of the distribution area, and divides N data objects into 3 classes, so that the sum of squares of all the data objects in each class to the clustering center point of the class is minimum, namely, the data is divided into preset class numbers on the basis of a minimized error function.
A simulation station area is established in the MATLAB, wherein 8 users exist in the station area, the first two users are A-phase users, the third 3 and the fourth 4 users are B-phase users, and the last four users are C-phase users. The MATLAB simulation diagram is shown in fig. 1. In fig. 1, the impedance value of each transmission line between two adjacent users is 0.1 ohm, that is, each resistance value in the transmission line is 0.1 ohm; the indoor impedance value of each user can be 5.5 ohm, 11 ohm and 22 ohm, and can be changed by a timing breaker, and the corresponding rated power is 8800W, 4400W and 2200W respectively.
According to the simulation result, the number finally obtained is the group of each user, which users are classified to the same phase line by the clustering method can be known according to the group, and the phase sequence of the users can be preliminarily judged. The amplitude of the variation of the three-phase voltage of the transformer A, B, C is set to be +/-10V, and the voltage variation frequency is set to be 0.1Hz, 0.2Hz and 0.4Hz respectively (sine wave variation). The three phase voltages of the transformer, the phase voltages of the users 1-8 and the center line curve obtained by continuous calculation for 3 times are shown in FIG. 5.
Fig. 5 is a voltage curve of the variation amplitude of the three-phase voltage of the transformer A, B, C being ± 10V, wherein the voltage variation frequency in fig. 5(a) is 0.1Hz, and the corresponding output result of the clustering analysis is:
ans＝
3 3 3 3 1 1 2 2
in fig. 5(b), the voltage change frequency is 0.2Hz, and the corresponding output result of the cluster analysis is:
ans＝
3 3 3 3 1 1 2 2
in fig. 5(c), the voltage change frequency is 0.4H, and the corresponding output result of the cluster analysis is:
ans＝
2 3 3 3 1 1 2 2
it can be seen from the output result that the groups into which the user 1 is divided in the simulation result change, and the corresponding determined phase sequence changes, which indicates that the clustering result is not particularly stable, but is still very useful for clustering, so that a binomial fitting curve checking method for data is added, and the high reliability of the method is achieved.
The invention adds a least square fitting polynomial data mode to check the algorithm on the basis of cluster analysis, and analyzes through a practically obtained platform area three-phase general table and a platform area single-phase user table, wherein the example is shown in fig. 6, the invention is a least square fitting polynomial schematic diagram of an A phase of a transformer A, B, C three-phase platform area examination table, wherein fig. 6(a) is the A phase, and the A phase least square fitting polynomial data of the three-phase platform area examination table is as follows:
y＝-4E-06x6+0.0002x5-0.0026x4-0.029x3+0.7321x2-3.281x+229.3
FIG. 6(B) shows the phase B, and the least square fitting polynomial data of the phase B of the three-phase region reference table is as follows:
y＝-1E-06x6+1E-05x5+0.0036x4-0.1239x3+1.435x2-5.4308x+231.02
FIG. 6(C) shows C phase, and the three-phase region reference table C phase least square fitting polynomial data is as follows:
y＝-3E-06x6+0.0001x5-1E-05x4-0.0664x3+0.9886x2-3.8958x+228.35
the polynomial is compared with the A-phase, B-phase and C-phase polynomial coefficients of the examination table to finally obtain the conclusion of the table in the A-phase, so that the gender of the table of the transformer area is checked one by one, and the accuracy of the identification of the transformer area subscriber-phase-change is improved.
In summary, the invention relates to a zone area home-phase-change identification and check method based on data cluster fitting analysis, wherein a zone area three-phase total meter voltage data and a zone area single-phase meter voltage data in a marketing system are obtained through an Internet of things management terminal installed in a zone area, firstly, the voltage data of all single-phase meter is subjected to cluster analysis, the voltage data is divided into three clusters, the phase of the zone area single-phase meter is preliminarily identified, then, a least square fitting polynomial distinguishing method is utilized to carry out fitting distinguishing on each cluster of data and the three-phase voltage data of the zone area total meter, so that the correctness of each cluster of data phase sequence is checked, the home-phase-change relationship is identified, the correctness is also judged again, the accuracy is improved, the distinguishing process is carried out based on the current zone area data, and the problem of high cost of equipment identification is avoided, is economical and feasible.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (5)
1. A station area user-phase-change identification method based on data clustering fitting analysis is characterized by comprising the following steps:
(1) acquiring 96-point three-phase general meter voltage of a distribution area and single-phase voltage of each single-phase household meter of the distribution area at the same moment;
(2) normalizing the voltage of the three-phase general meter and the single-phase voltage of each single-phase household meter in the transformer area;
(3) respectively taking the normalized single-phase voltage of each user as a point; taking the three-phase voltage of the normalized distribution room total table voltage as three reference center points;
(4) calculating Euclidean distances between points in the same time sequence with the three reference central points and the three reference central points respectively;
(5) grouping all the points of the single-phase voltage data of the single-phase user meter into three clusters corresponding to three phase sequences by using a K-Means clustering algorithm, and obtaining new three aggregation points;
(6) and calculating Euclidean distances between each aggregation point and three reference center points, and taking the phase sequence of the center reference point with the minimum Euclidean distance value as the phase sequence of the aggregation point and the phase sequence of the points in the corresponding cluster of the aggregation point.
2. The station area phase sequence identification method based on data cluster fitting analysis according to claim 1, characterized by further comprising the following steps of checking the method:
(7) acquiring 96-point voltage data of three clusters, and performing polynomial fitting on the single-phase 96-point voltage data of the single-phase household meter of each cluster of data and the voltage data of the phase sequence of 96 points of the three-phase general meter by adopting a binomial equation fitting curve method; sequentially judging according to a deviation square sum minimum algorithm to obtain a fitting curve coefficient of a binomial equation;
comparing the coefficient of a binomial equation fitting curve of 96-point voltage data of the three-phase general table with the coefficient of a binomial fitting curve of 96-point data of each user table of each single-phase user table data, and comparing the minimum phase coefficient;
if the phase sequence of the coefficient minimum phase of the user is consistent with the phase sequence obtained by clustering in the step (6), taking the phase sequence as the phase sequence of the user; and if not, returning to the step (1) to recalculate the phase sequence of the user.
3. The method for identifying the phase sequence of the distribution room based on the data cluster fitting analysis according to claim 2, wherein the obtaining of the fitting curve coefficient of the binomial equation comprises:
7.1 Single-phase 96-Point Voltage data x for Single useriFitting is carried out, and a fitting polynomial for obtaining a fitting curve is as follows:
where k is the fitting order, a0，a1，....akFitting coefficients of a binomial equation;
7.2 three-phase summary of the voltage points y of each phaseiThe sum of the distances to the fitted curve, i.e. the sum of squared deviations, is:
7.3 selection of the sum of squares of deviations σx,y 2The smallest one of the fitted curves is taken as the polynomial fitted curve of the term.
4. The method for identifying the phase sequence of the region based on the data cluster fitting analysis according to claim 1 or 2, wherein the step (5) of grouping all the points into three clusters corresponding to three phase sequences by using a K-Means clustering algorithm comprises the following steps: and carrying out multiple times of sequential iteration to move the aggregation point until the aggregation point does not change according to the distance change of the transformation observation point, and the sequential iteration time N is more than or equal to 5.
5. The station area household-phase-change identification method based on data cluster fitting analysis according to claim 1 or 2, wherein the obtained 96-point three-phase total table voltage of the station area and the single-phase voltage of each single-phase household table of the station area at the same time in the step (1) are obtained through an internet of things management terminal installed in the station area.
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