CN112129996B - Electric energy meter phase identification method based on Bayesian method - Google Patents
Electric energy meter phase identification method based on Bayesian method Download PDFInfo
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- CN112129996B CN112129996B CN202010906690.2A CN202010906690A CN112129996B CN 112129996 B CN112129996 B CN 112129996B CN 202010906690 A CN202010906690 A CN 202010906690A CN 112129996 B CN112129996 B CN 112129996B
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- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000013398 bayesian method Methods 0.000 title claims abstract description 17
- 238000005070 sampling Methods 0.000 claims abstract description 5
- 238000004891 communication Methods 0.000 claims abstract description 3
- 238000004364 calculation method Methods 0.000 claims description 4
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R25/00—Arrangements for measuring phase angle between a voltage and a current or between voltages or currents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R35/00—Testing or calibrating of apparatus covered by the other groups of this subclass
- G01R35/04—Testing or calibrating of apparatus covered by the other groups of this subclass of instruments for measuring time integral of power or current
Abstract
The invention belongs to the technical field of phase identification, and particularly relates to a Bayesian method-based electric energy meter phase identification method, which comprises the following steps: step one: the terminal sensing terminal collects voltage data of the electric energy meter through RS485 and performs voltage sampling through the built-in metering chip; step two: the terminal sensing terminal obtains the metering voltage of the electric energy meter by means of RS485 communication; step three: the statistical Pearson correlation is used for calculating the similarity between the voltage change curve of the electric energy meter and the fluctuation of ABC three-phase voltage, and the phase of the electric energy meter can be determined according to the similarity degree.
Description
Technical Field
The invention relates to the technical field of phase identification, in particular to an electric energy meter phase identification method based on a Bayesian method.
Background
The electric meter phase identification through RS485 data acquisition is one of the key attack technologies of the electric power Internet of things of the national power grid company. Most of the existing phase identification technologies are based on the pearson correlation coefficient for judgment, but the algorithm cannot make effective judgment when the correlation coefficients of the ABC phases are very close.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above and/or problems occurring in the prior art of phase identification.
Therefore, the invention aims to provide a Bayesian method-based electric energy meter phase identification method, which can be used for identifying the electric energy meter phase.
In order to solve the technical problems, according to one aspect of the present invention, the following technical solutions are provided:
a Bayesian method-based electric energy meter phase identification method comprises the following steps:
step one: the terminal sensing terminal collects voltage data of the electric energy meter through RS485 and performs voltage sampling through the built-in metering chip;
step two: the terminal sensing terminal obtains the metering voltage of the electric energy meter by means of RS485 communication;
step three: the statistical Pearson correlation is used for calculating the similarity between the voltage change curve of the electric energy meter and the fluctuation of ABC three-phase voltage, and the phase of the electric energy meter can be determined through the similarity degree.
As a preferable scheme of the electric energy meter phase identification method based on the Bayesian method, the invention comprises the following steps: the specific steps of the sampling in the step one are as follows:
the ABC electric loads of each phase in each distribution area are unbalanced, each phase of voltage can slightly fluctuate, the fluctuation amplitude is usually between 0.2V and 0.5V, the fluctuation characteristics of each phase of voltage are inconsistent, and certain randomness is shown.
As a preferable scheme of the electric energy meter phase identification method based on the Bayesian method, the invention comprises the following steps: in the second step, the direct comparison result of the ammeter voltage value and the ABC reference voltage cannot be adopted in operation.
As a preferable scheme of the electric energy meter phase identification method based on the Bayesian method, the invention comprises the following steps: in the third step, the operation is performed based on a Bayesian algorithm, and the method specifically comprises the following steps:
p (A) represents all meter probabilities belonging to the A phase of the electric meter box, P (B) represents all meter probabilities belonging to the B phase of the electric meter box, P (C) represents all meter probabilities belonging to the C phase of the electric meter box, P (X/A) represents the probability that meter X is likely to be connected in the A phase of the electric meter box, P (X/B) represents the probability that meter X is likely to be connected in the B phase of the electric meter box, P (X/C) represents the probability that meter X is likely to be connected in the C phase of the electric meter box, and for convenience of description, A, B, C three-phase data sets are expressed as an omega set of events, wherein three events { omega 1, omega 2 and omega 3} in the omega set correspond to A, B, C three events respectively.
As a preferable scheme of the electric energy meter phase identification method based on the Bayesian method, the invention comprises the following steps: the probability calculation formula of the X meter X in the third step belonging to three terms is as follows:
p (A/X) represents the probability that meter X belongs to the A phase, P (B/X) represents the probability that meter X belongs to the B phase, and P (C/X) represents the probability that meter X belongs to the C phase.
As a preferable scheme of the electric energy meter phase identification method based on the Bayesian method, the invention comprises the following steps: the probability calculation formula is specifically as follows:
still further, using the minimum false positive probability criterion, we consider the log likelihood ratio:
the error probability of misjudging the classification belonging to the omega i class as belonging to the omega j class is as follows:
the error probability of misjudging the classification belonging to the omega j class as belonging to the omega i class is as follows:
in the middle of
Thus, the total false positive probability is:
compared with the prior art: most of the existing phase identification technologies are based on Pearson correlation coefficients, but the algorithm cannot make effective judgment when the correlation coefficients of all phases of the belonging ABC are very close, and in the application document, the Bayesian method is creatively utilized to judge the phase of the electric meter, so that the problem that the Pearson coefficients are very close and cannot be effectively and rapidly judged is effectively solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following detailed description of the embodiments of the present invention will be given with reference to the accompanying drawings, which are to be understood as merely some embodiments of the present invention, and from which other drawings can be obtained by those skilled in the art without inventive faculty. Wherein:
fig. 1 is a schematic structural diagram of a bayesian method-based electric energy meter phase identification method.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Next, the present invention will be described in detail with reference to the drawings, wherein the sectional view of the device structure is not partially enlarged to general scale for the convenience of description, and the drawings are only examples, which should not limit the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.
Example 1
11 ammeter in a certain meter box, the Pearson correlation coefficient is as follows:
taking the ammeter 176 as an example, the correlation coefficient is not obvious in significance, and the following is calculated by adding Bayes:
p (X/a) =0.394, P (X/B) =0.354, and P (X/C) =0.154, the following equations (1), (2), and (3) can be calculated: p (a/X) =0.663, P (B/X) =0.596, P (C/X) =0.259.
Therefore, P (A/X) > P (B/X) > P (C/X), so the meter 176 should belong to phase A.
Further, the total misjudgment probability is calculated, and the following can be obtained:
P(e)＝0.00346。
that is, the judgment table 176 has very small probability of misjudgment of the A phase, and the judgment is reliable.
The conclusion is correct through field verification.
Compared with the Pearson coefficient judging method, the Bayesian judgment and minimum misjudgment probability criterion are further adopted, so that the judging efficiency is improved, and the original 60% judging accuracy is improved to 100%.
Although the invention has been described hereinabove with reference to embodiments, various modifications thereof may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the features of the disclosed embodiments may be combined with each other in any manner as long as there is no structural conflict, and the exhaustive description of these combinations is not given in this specification merely for the sake of omitting the descriptions and saving resources. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (4)
1. A Bayesian method-based electric energy meter phase identification method is characterized in that: the identification method comprises the following steps:
step one: the terminal sensing terminal collects voltage data of the electric energy meter through RS485 and performs voltage sampling through the built-in metering chip;
step two: the terminal sensing terminal obtains the metering voltage of the electric energy meter by means of RS485 communication;
step three: calculating the similarity between the voltage change curve of the electric energy meter and the fluctuation of ABC three-phase voltage by using the statistical Pearson correlation, and determining the phase of the electric energy meter according to the degree of similarity;
the probability calculation formula of the X meter X in the third step belonging to three terms is as follows:
p (A/X) represents the probability that meter X belongs to phase A, P (B/X) represents the probability that meter X belongs to phase B, and P (C/X) represents the probability that meter X belongs to phase C;
the probability calculation formula is specifically as follows:
still further, a minimum false positive probability criterion is employed to calculate a log likelihood ratio:
the error probability of misjudging the classification belonging to the omega i class as belonging to the omega j class is as follows:
the error probability of misjudging the classification belonging to the omega j class as belonging to the omega i class is as follows:
in the middle ofThus, the total false positive probability is:
2. the electric energy meter phase identification method based on the Bayesian method according to claim 1, wherein the electric energy meter phase identification method is characterized in that: the specific steps of the sampling in the step one are as follows:
the ABC electric loads of all phases in each distribution area are unbalanced, each phase of voltage can slightly fluctuate, the fluctuation amplitude is between 0.2V and 0.5V, and the fluctuation characteristics of each phase of voltage are inconsistent, so that randomness is shown.
3. The electric energy meter phase identification method based on the Bayesian method according to claim 1, wherein the electric energy meter phase identification method is characterized in that: in the second step, the direct comparison result of the ammeter voltage value and the ABC reference voltage cannot be adopted in operation.
4. The electric energy meter phase identification method based on the Bayesian method according to claim 1, wherein the electric energy meter phase identification method is characterized in that: in the third step, the operation is performed based on a Bayesian algorithm, and the method specifically comprises the following steps:
p (A) represents all meter probabilities belonging to the A phase of the electric meter box, P (B) represents all meter probabilities belonging to the B phase of the electric meter box, P (C) represents all meter probabilities belonging to the C phase of the electric meter box, P (X/A) represents the probability of meter X being connected in the A phase of the electric meter box, P (X/B) represents the probability of meter X being connected in the B phase of the electric meter box, P (X/C) represents the probability of meter X being connected in the C phase of the electric meter box, and for convenience of description, a A, B, C three-phase data set is expressed as an event omega set, wherein three events { omega 1, omega 2 and omega 3} in the omega set correspond to A, B, C three events respectively.
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| US8682604B2 (en) * | 2011-08-03 | 2014-03-25 | General Electric Company | Method and system of phase identification |
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