CN113466548B - Intelligent ammeter area identification method based on phasor measurement technology - Google Patents
Intelligent ammeter area identification method based on phasor measurement technology Download PDFInfo
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Abstract
The invention discloses a smart electric meter zone identification method based on a phasor measurement technology, which comprises the following steps: acquiring a phase angle acquired by the intelligent ammeter in non-meter reading time; calculating the similarity of any two intelligent electric meters under the same phase angle according to the phase angle and the eigenvalue analysis method; and when the similarity is greater than a similarity threshold, the two intelligent electric meters are located in the same area. The invention aims to provide a smart electric meter zone identification method based on a phasor measurement technology, which can obtain phase information with high precision under the conditions of high noise, harmonic interference and fundamental wave dynamic change by measuring electric power signal phasor information, and effectively improve the phase measurement precision and the zone identification accuracy.
Description
Technical Field
The invention relates to the technical field of power system detection, in particular to a smart meter area identification method based on a phasor measurement technology.
Background
In recent years, with the development of power line carrier communication (power line communication, PLC) technology, high-speed power line communication (HPLC) power line carrier communication has been widely used as a step-by-step replacement for conventional low-speed narrowband power line carrier technology. The HPLC technology has large bandwidth and high transmission rate, can meet the higher demand of power line carrier communication of the voltage, and provides a foundation for the deepening application of the power consumption information acquisition system. The technical aim of the platform region identification is to solve the problem of the membership of the intelligent ammeter and the platform region transformer, and the intelligent ammeter belongs to the platform region by comparing and analyzing the common characteristics of all metering points in the platform region according to the similarity of the power signal characteristics of the same platform region and setting a similarity threshold value.
The area identification currently mainly comprises two methods, namely a characteristic value data analysis method and an injection special signal analysis method. Due to the technical difficulty of injection-specific signal analysis, eigenvalue data analysis is often employed. At present, a signal-to-noise ratio-based zero crossing characteristic value comprehensive judgment method is mostly adopted in the characteristic value data analysis method, and the phase characteristic is utilized to identify the affiliated station area of the intelligent ammeter. With the wide use of nonlinear loads such as electric automobiles and variable frequency household appliances, the power distribution network environment becomes increasingly complicated, the characteristics such as high noise, harmonic interference, fundamental wave dynamic change and the like are presented, the measurement accuracy of phase information obtained by zero crossing point detection is seriously affected, and the identification of a station area under the complex power distribution network environment has no good identification result.
Disclosure of Invention
The invention aims to provide a smart electric meter zone identification method based on a phasor measurement technology, which can obtain phase information with high precision under the conditions of high noise, harmonic interference and fundamental wave dynamic change by measuring electric power signal phasor information, and effectively improve the phase measurement precision and the zone identification accuracy.
The invention is realized by the following technical scheme:
A smart meter area identification method based on a phasor measurement technology comprises the following steps:
s1: acquiring a phase angle acquired by the intelligent ammeter in non-meter reading time;
S2: calculating the similarity of any two intelligent electric meters under the same phase angle according to the phase angle and the eigenvalue analysis method;
s3: and when the similarity is greater than a similarity threshold, the two intelligent electric meters are located in the same area.
Preferably, the step S1 comprises the following substeps:
S11: sampling the power signal of the intelligent ammeter at a fixed sampling frequency to obtain a discrete power signal;
s12: performing Fourier transform on the power signal to obtain a vector measurement value ；
S13: and solving the vector measurement value based on a Taylor expansion coefficient model to obtain the phase angle.
Preferably, the step S13 includes the following substeps:
s131: generating an offline matrix Q and an offline matrix W based on the fixed frequency;
Q(ω)＝[E(0,ω),E(1,ω),E(2,ω)]
W(ω)＝[E(0,ω+2ω0),E(1,ω+2ω0),E(2,ω+2ω0)]；
Wherein E (k, ω) is an intermediate calculation formula formed by integrating the twiddle factor E -jnω and the data window h (N), k is the order of the Taylor expansion coefficient, and N represents the data window sequence length;
S132: separating off-line matrix Q, off-line matrix W and the vector measurement values The imaginary and real parts of (2):
Q＝QR+jQI；
W＝WR+jWI；
s133: constructing a matrix equation as follows;
Wherein A R is the imaginary part matrix of each order Taylor coefficient, and A I is the real part matrix of each order Taylor coefficient;
And abbreviated as Wherein/>
S134: solving the phasor matrix A by adopting a least square method, namely:
s135: summing the taylor coefficients of each order in the phasor matrix A;
Wherein, X represents the summation value of taylor coefficients of each order in the phasor matrix a, p represents taylor orders, and α (p) represents corresponding taylor coefficients of each order for obtaining a final phasor measurement value;
S136: obtaining the phase angle according to the phasor matrix X;
θ＝arctan(X)；
θ represents the phase angle.
Preferably, the similarity is obtained by:
X＝[θX1,θX2,…,θXn]；
Y＝[θY1,θY2,…,θYn]；
Wherein c X,Y represents similarity, X and Y represent power frequency phase synchronization sequences with the length of n respectively collected by two intelligent electric meters, cov (X, Y) is covariance of X and Y, var [ X ] is variance of X, var [ Y ] is variance of Y.
Preferably, the smart meter collects the phase angle at the non-meter reading time after the smart meter performs the time synchronization and the unified time scale.
Preferably, the step S3 includes the following substeps:
S31: calculating an average value of the similarity;
Wherein S represents the average value of the similarity, m represents the total number of the similarity, and c (k) X,Y represents the kth similarity;
S32: and when the average value is larger than the similarity threshold value, the two intelligent electric meters are located in the same area.
Preferably, m.gtoreq.15.
Compared with the prior art, the invention has the following advantages and beneficial effects:
The home relation of the areas of a large number of electric meters can be automatically identified remotely. Because the distribution network has complex environment and serious interference, the problem of low accuracy of identification of the district under the interference conditions of high noise, harmonic waves, fundamental wave dynamic changes and the like of the distribution network is solved through collecting and analyzing the phasor data of the same time point acquired by the intelligent electric meter, and the accuracy of judgment of the change membership of the district can be obviously improved; the method has the advantages of convenient operation, easy realization, strong economy, short recognition period, high recognition accuracy, low equipment technical requirement and the like, solves the problems of long period, low accuracy and complex process in the existing platform region recognition technology, is beneficial to the management of the platform region line loss and improves the economic operation level of the power grid.
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The accompanying drawings, which are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:
FIG. 1 is a schematic diagram of a cell identification network according to the present invention;
FIG. 2 is a schematic flow chart of the present invention;
FIG. 3 is a schematic diagram of the physical meaning of the phasor measurement according to the present invention;
FIG. 4 is a flow chart of a phasor measurement algorithm based on a Taylor coefficient model according to the present invention;
Fig. 5 shows the fundamental wave dynamic change phasor measurement phase angle error of the present invention.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
Examples
A smart meter area identification method based on a phasor measurement technology comprises the following steps:
S1: acquiring a phase angle acquired by the intelligent ammeter in non-meter reading time; specifically, as shown in fig. 1 to 5, it includes:
The master station control terminal sends a district identification instruction to the district concentrator CCO in a 4G communication mode, initiates a district identification process once, and collects phase angle information of intelligent electric meters of the whole network at the same time; after receiving the platform identification instruction, the platform concentrator CCO transmits the platform identification instruction to the intelligent ammeter in non-meter reading time according to the requirement of the platform identification instruction; the intelligent ammeter monitoring units participating in the platform area identification calculate phasors through the phasor measuring units and calculate phase angle information; after the intelligent ammeter in the transformer area completes the collection of the phasor information data based on the phasor measurement unit, the broadband carrier communication module is utilized to report the collected phasor information to the transformer area concentrator CCO in the non-meter reading time data. Preferably, in order to avoid errors in measurement results caused by non-uniform time of each intelligent ammeter, after the district concentrator CCO receives the district identification instruction, the district concentrator CCO and the intelligent ammeter monitoring unit are firstly subjected to time synchronization and uniform time marks, and then the district identification instruction is transmitted to the intelligent ammeter.
Specifically, in the present embodiment:
The phasor measurement unit collects power signals at a sampling rate of fixed frequency f s =2400 Hz to obtain discrete power signals x (n);
Discrete power signal x (n) is fourier transformed to obtain phasor measurement
Where ω 0 represents a fourier transform filter frequency, ω 0 =2pi/N; h (n) represents a data window and is a rectangular window; n=48 is 48 consecutive sampling points at power frequency.
Can be obtained by the same wayAnd/>Where ω=2pi/f s is the frequency bin offset.
Solving phasors based on a taylor expansion coefficient model:
An offline matrix Q and an offline matrix W are generated based on a 50Hz frequency.
Q(ω)＝[E(0,ω),E(1,ω),E(2,ω)]
W(ω)＝[E(0,ω+2ω0),E(1,ω+2ω0),E(2,ω+2ω0)]
Wherein: k is the order of the taylor expansion coefficient.
Separating the offline matrix Q, the offline matrix W, and the imaginary and real parts of the vector measurements:
Q＝QR+jQI；
W＝WR+jWI；
constructing a matrix equation, as follows:
Wherein A R is the imaginary part matrix of each order Taylor coefficient, and A I is the real part matrix of each order Taylor coefficient;
And abbreviated as Wherein/>
And solving a matrix A by adopting a least square method, namely:
summing the taylor coefficients of each order in matrix a:
Wherein, X represents the summation value of each order Taylor coefficient in the matrix A, namely, the obtained final phasor measurement value, p represents the Taylor order, and alpha (p) represents the corresponding each order Taylor coefficient;
obtaining a phase angle according to the phasor matrix X;
θ＝arctan(X)；
θ represents the phase angle.
S2: calculating the similarity of any two intelligent electric meters under the same phase angle according to the phase angle and the eigenvalue analysis method;
In the embodiment, the analysis of the correlation is that any two intelligent electric meters respectively collect a section of power frequency phase synchronization sequence with the length of n, the power frequency phase synchronization sequence is recorded as a sequence of X= [ theta X1,θX2,…,θXn ] and Y= [ theta Y1,θY2,…,θYn ], and the absolute value of a correlation coefficient c X,Y,cX,Y of the two is calculated to be larger, so that the higher the correlation of the sequence of X and the sequence of Y is indicated; the smaller the absolute value of c X,Y, the lower the correlation of the series X with the series Y, the following formula is calculated:
Where c X,Y denotes similarity, cov (X, Y) is covariance of the series X and the series Y, var [ X ] is variance of the series X, var [ Y ] is variance of the series Y.
Preferably, in order to make the identification result of the area more accurate, the identification error caused by single measurement is avoided, in this embodiment, when the similarity evaluation is performed, an average value of multiple identification results is taken as a judgment basis, and specifically:
Since the criteria for evaluating the correlation coefficient are as follows:
therefore, in this embodiment, the area membership is determined comprehensively by summing and averaging the area identification results each time, where the following formula exists:
In this embodiment, the number of running times m of the station identification process is at least 15, that is: and m is more than or equal to 15, the district concentrator CCO comprehensively analyzes the attribution relation between each intelligent ammeter and the district CCO based on the obtained similarity data, classifies all intelligent ammeter identification results with S more than 0.8 into the same district, and classifies intelligent ammeters with S less than 0.8 into other districts.
It should be noted that, when the district concentrator CCO collects phasor data, if the power distribution network adopts a three-phase power supply mode, the district concentrator CCO needs to collect fundamental phasor measurement data on the a phase, the B phase and the C phase at the same time, and if the power distribution network adopts a single-phase power supply mode, the district concentrator CCO collects only fundamental phasor measurement data on a single-phase power line. When the intelligent ammeter collects fundamental wave phasor measurement data of the intelligent ammeter, only fundamental wave phasor measurement data on a single-phase electric wire is collected.
Further, at different time points of a day, at least 15 minutes are needed each time, the station area concentrator CCO initiates a station area identification process for a plurality of times through non-meter reading time selection of the power line broadband power line carrier communication network, and the station area identification is carried out by calculating a similarity coefficient average value after the required times are reached. After receiving the platform region identification command, issuing a command to complete phasor acquisition in non-meter reading time when the power line broadband power carrier communication network does not execute tasks; similarly, smart meters also need to upload phasor measurement data at non-meter-reading times.
S3: when the similarity is larger than the similarity threshold, the two intelligent electric meters are located in the same area.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (5)
1. The intelligent ammeter area identification method based on the phasor measurement technology is characterized by comprising the following steps of:
s1: acquiring a phase angle acquired by the intelligent ammeter in non-meter reading time;
S2: calculating the similarity of any two intelligent electric meters under the same phase angle according to the phase angle and the eigenvalue analysis method;
s3: when the similarity is larger than a similarity threshold, the two intelligent electric meters are located in the same area;
wherein, the step S1 comprises the following substeps:
S11: sampling the power signal of the intelligent ammeter at a fixed sampling frequency to obtain a discrete power signal;
s12: performing Fourier transform on the power signal to obtain a phasor measurement value
S13: solving the phasor measurement value based on a taylor expansion coefficient model to obtain the phase angle;
The step S13 includes the sub-steps of:
s131: generating an offline matrix Q and an offline matrix W based on the fixed frequency;
Q(ω)＝[E(0,ω),E(1,ω),E(2,ω)]
W(ω)＝[E(0,ω+2ω0),E(1,ω+2ω0),E(2,ω+2ω0)]；
Wherein E (k, ω) is an intermediate calculation formula formed by integrating the twiddle factor E -jnω and the data window h (N), k is an order of a Taylor expansion coefficient, N represents a data window sequence length, ω represents a frequency point deviation, ω 0 represents a Fourier transform filter frequency;
s132: separating off-line matrix Q, off-line matrix W and phasor measurements The imaginary and real parts of (2):
Q＝QR+jQI；
W＝WR+jWI；
s133: constructing a matrix equation as follows;
Wherein A R is the imaginary part matrix of each order Taylor coefficient, and A I is the real part matrix of each order Taylor coefficient;
And abbreviated as Wherein/>
S134: and solving a phasor matrix A by adopting a least square method, namely:
S135: summing the Taylor coefficients of each order in the phasor matrix A;
Wherein, X represents the summation value of taylor coefficients of each order in the phasor matrix a, p represents taylor orders, and α (p) represents corresponding taylor coefficients of each order for obtaining a final phasor measurement value;
S136: obtaining the phase angle according to the phasor matrix X;
θ＝arctan(X)；
θ represents the phase angle.
2. The smart meter zone identification method based on the phasor measurement technique of claim 1, wherein the similarity is obtained by:
X＝[θX1,θX2,…,θXn]；
Y＝[θY1,θY2,…,θYn]；
Wherein c X,Y represents similarity, X and Y represent power frequency phase synchronization sequences with the length of n respectively collected by two intelligent electric meters, cov (X, Y) is covariance of X and Y, var [ X ] is variance of X, var [ Y ] is variance of Y.
3. The method for identifying a smart meter area based on a phasor measurement technique according to any one of claims 1-2, wherein the smart meter collects a phase angle at a non-meter reading time after the smart meter performs a time-alignment and uniform time scale.
4. The smart meter zone identification method based on the phasor measurement technique of claim 1, wherein S3 comprises the sub-steps of:
S31: calculating an average value of the similarity;
Wherein S represents the average value of the similarity, m represents the total number of the similarity, and c (k) X,Y represents the kth similarity;
S32: and when the average value is larger than the similarity threshold value, the two intelligent electric meters are located in the same area.
5. The intelligent ammeter area identification method based on the phasor measurement technology according to claim 4, wherein m is larger than or equal to 15.
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