CN108693496A - A kind of intelligent electric energy meter error predictor method based on parameter degeneration equation - Google Patents
A kind of intelligent electric energy meter error predictor method based on parameter degeneration equation Download PDFInfo
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- CN108693496A CN108693496A CN201810425572.2A CN201810425572A CN108693496A CN 108693496 A CN108693496 A CN 108693496A CN 201810425572 A CN201810425572 A CN 201810425572A CN 108693496 A CN108693496 A CN 108693496A
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
The embodiment of the invention discloses a kind of intelligent electric energy meter error predictor methods based on parameter degeneration equation, including build parameter degeneration equation, and the model estimated based on the parameter degeneration establishing equation error;Obtain the historical data of parameter degeneration amount in parameter degeneration equation;Using the historical data, simulation several times is carried out to the model and is calculated, until result of calculation meets the constraints of error, obtains the statistical law of measurement error;According to the statistical law of the measurement error, the measurement error state of intelligent electric energy meter is estimated.The present invention is not necessarily to periodic verification and interruption maintenance, improves power supply reliability without additional setting sensor departing from standard electric energy meter, reduces O&M cost, and consider the various factors of error, improve the accuracy of estimation results.
Description
Technical field
The present invention relates to electrical equipment online supervision technical field, specifically a kind of intelligence based on parameter degeneration equation
It can electric energy meter error predictor method.
Background technology
Intelligent electric energy meter is the key equipment that electric system carries out electrical energy measurement, is the foundation of electric energy trade settlement.Intelligence
The metering accuracy of electric energy meter is directly related to the economic interests of power grid and the justice of exchange of power consumer.
The monitoring of intelligent electric energy meter accuracy is remained at present and takes periodic verification, or finds that intelligent electric energy meter misses
Difference it is overproof after interruption maintenance mode, and at present most of intelligent electric energy meter measurement error assessment modes only considered it is single
The influence of factor or by each single factors to error influence directly superposition progress measurement error assessment.
Existing detection method can not guarantee timely to find the measurement error of intelligent electric energy meter, also affect power supply
Reliability increases O&M cost.And do not consider that the coupling of a variety of error components influences, it can not accurate evaluation intelligence electricity
The measurement error of energy table.
Invention content
A kind of intelligent electric energy meter error predictor method based on parameter degeneration equation is provided in the embodiment of the present invention, with solution
Certainly in the prior art to the assessment accuracy of intelligent electric energy meter measurement error, high to the O&M cost of intelligent electric energy meter ask
Topic.
In order to solve the above-mentioned technical problem, the embodiment of the invention discloses following technical solutions:
A kind of intelligent electric energy meter error predictor method based on parameter degeneration equation, includes the following steps:
Build parameter degeneration equation, and the model estimated based on the parameter degeneration establishing equation error;
Obtain the historical data of parameter degeneration amount in parameter degeneration equation;
Using the historical data, simulation several times is carried out to the model and is calculated, until result of calculation meets the pact of error
Beam condition obtains the statistical law of measurement error;
According to the statistical law of the measurement error, the measurement error state of intelligent electric energy meter is estimated.
Further, the parameter degeneration equation is ε=A α, and ε is degeneration parameter in formula, and α is parameter degeneration amount,
Dematrix A is degeneration relational expression of the degeneration parameter under the effect of parameter degeneration amount.
Further, the parameter degeneration amount include interaction between error influence factor, error influence factor,
The measurement error of variable quantity and last moment intelligent electric energy meter of the error influence factor in each time step.
Further, the detailed process for obtaining the measurement error of the last moment intelligent electric energy meter is:
The signal acquisition channel measured in circuit is divided into two-way, respectively standard channel and tested channel, standard channel
It is upper that voltage and current signals are obtained by standard potential transformer and standard current transformer respectively, obtain standard electric flux, quilt
It surveys on channel and obtains voltage and current signals using intelligent electric energy meter to be estimated;
Intelligent electric energy meter to be estimated exports umber of pulse simultaneously, and obtains tested electric flux according to electric energy meter meter constant;
Using the tested electric flux and standard electric flux, the measurement error of intelligent electric energy meter is calculated.
Further, the constraints of the error is:
In formula, W'For the electric energy indicating value of intelligent electric energy meter to be estimated, W is mutual using standard potential transformer and normalized current
Standard electric flux obtained by sensor survey calculation, δ are allowable error setting value.
Further, the model of the predictor error is monte-Carlo model.
Further, using the historical data, the detailed process for being simulated calculating several times to the model is:
Input one or more x in parameter degeneration amountn, and generate N number of in parameter degeneration amount variation range
The variation delta x of interior equally distributed parameter degeneration amountn(i) (i=1,2 ... N);
Utilize xn'=xn+Δxn(i), simulation degeneration parameter actuating quantity x is obtainedn';
Intelligent electric energy meter is read in xn'Electric energy reading under effect, calculating ratio difference e (i), and contrast differences e (i) results carry out
Statistics.
Further, after carrying out simulation calculating several times to the model, if being unsatisfactory for the constraints of error, increase
Add number realization, and continue simulation and calculate, until result of calculation meets the constraints of error.
The effect provided in invention content is only the effect of embodiment, rather than invents all whole effects, above-mentioned
A technical solution in technical solution has the following advantages that or advantageous effect:
1, parameter degeneration equation is based on by structure, and introduces monte-Carlo model the measurement error that calculates, in model
Historical data is constantly obtained during establishing, carries out the acquisition of intelligent electric energy meter measurement data in due course, without periodic verification and is stopped
Electric-examination is repaiied, and is improved power supply reliability without additional setting sensor departing from standard electric energy meter, is reduced O&M cost.
2, different kinds of parameters degeneration amount is introduced in parameter degeneration equation, while having studied a variety of intelligent electric energy meter error shadows
Ring factor intercouple bring meter energy performance deterioration, and in the case where meeting constraints end simulation calculate, improve
The accuracy of estimation results.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, for those of ordinary skill in the art
Speech, without creative efforts, other drawings may also be obtained based on these drawings.
Fig. 1 is the invention flow chart of the present invention;
Fig. 2 is the flow diagram that the present invention establishes the monte-Carlo model;
Fig. 3 is the structural schematic diagram of online monitoring system of the present invention;
Fig. 4 is the temperature data sample graph obtained in the embodiment of the present invention;
Fig. 5 be obtained in the embodiment of the present invention make reading data sample graph;
Fig. 6 is the power consumption data sample graph obtained in the embodiment of the present invention;
Fig. 7 is the intelligent electric energy meter error information sample graph obtained in the embodiment of the present invention;
Fig. 8 is the predictor error result obtained in the embodiment of the present invention and actual motion error comparison diagram.
Specific implementation mode
In order to clarify the technical characteristics of the invention, below by specific implementation mode, and its attached drawing is combined, to this hair
It is bright to be described in detail.Following disclosure provides many different embodiments or example is used for realizing the different knots of the present invention
Structure.In order to simplify disclosure of the invention, hereinafter the component of specific examples and setting are described.In addition, the present invention can be with
Repeat reference numerals and/or letter in different examples.This repetition is that for purposes of simplicity and clarity, itself is not indicated
Relationship between various embodiments and/or setting is discussed.It should be noted that illustrated component is not necessarily to scale in the accompanying drawings
It draws.Present invention omits the descriptions to known assemblies and treatment technology and process to avoid the present invention is unnecessarily limiting.
As shown in Figure 1, the intelligent electric energy meter error predictor method based on parameter degeneration equation of the present invention includes following step
Suddenly:
S1 builds parameter degeneration equation, and the monte-Carlo model estimated based on the parameter degeneration establishing equation error;
S2 obtains the historical data of parameter degeneration amount in parameter degeneration equation;
S3 carries out simulation several times to monte-Carlo model and calculates, until result of calculation meets error using historical data
Constraints obtains the statistical law of measurement error;
S4 estimates the measurement error state of intelligent electric energy meter according to the statistical law of the measurement error.
The measured value of intelligent electric energy meter is influenced by various aspects, including the natural causes such as environment temperature, humidity, rain fall;
The electric circumstances factor such as power consumption, power quality;And supplier, hanging net operation time etc. itself attribute and decompression,
The grid events such as cutout.
In step S1, parameter degeneration equation is
X in formulanFor parameter degeneration amount n, fW(t) be t moment intelligent electric energy meter in x1,x2,x3…xnUnder collective effect
Measurement error, F be intelligent electric energy meter measurement error fW(t) in x1,x2,x3…xnTotal n parameter degeneration amount collective effect
Under degeneration equation, gn(xn) (n=1,2,3) be xnDegeneration equation,To consider xn-1,
xnThe effect that intercouples degeneration equation, Δ xn(n=1,2,3) it is xnVariation in each time step Δ t
Amount, fW(t- Δs t) is the measurement error of intelligent electric energy meter last moment.
In order to make it is following tell about clear, be further simplified error prediction model, 3 chosen in the present embodiment to intelligent electricity
The larger lasting sexual factor of energy meter amount stability influence is modeled, respectively environment temperature Temp, humidity Hum and electric energy
Consume Load.Three influence factor collective effects influence degeneration parameter in intelligent electric energy meter, by the degeneration side in above-mentioned steps S1
Journey indicates as follows:
In formula (1), fW(t) it is what t moment intelligent electric energy meter was changed over time in environment temperature, humidity and power consumption
Measurement error under collective effect, F are intelligent electric energy meter measurement error fW(t) in environment temperature, humidity and power consumption three
Degeneration equation under a parameter degeneration amount collective effect, g1(Temp), g2(Hum),g3(Load) it is respectively environment temperature
Degree variation, humidity variation and power consumption variation are to fWDegeneration equation, g4(Temp, Hum) is to consider temperature change
Change the degeneration equation with the effect that intercouples of humidity variation, g5(Temp, Load) is to consider temperature change and electricity
The degeneration equation of the consumable effect that intercouples, g6(Hum, Load) is to consider humidity variation and power consumption change
The degeneration equation for the effect that intercouples changed, Δ Temp, Δ Hum, Δ Load are respectively environment temperature, humidity and electricity
The variable quantity in each time step Δ t, f can be consumedW(t- Δs t) is the measurement error of intelligent electric energy meter last moment.
In the present embodiment, sample frequency is set as once every minute, i.e. time step Δ t=1min.
It enables:
ε=fW(t), α=(Temp, Hum, Load, Δ Temp, Δ Hum, Δ Load, fW(t-Δt)) (2)
In formula (2), ε is the degeneration parameter of the intelligent electric energy meter, and α is parameter degeneration amount;
The dematrix of formula (1) is denoted as A, then formula (1) can be write:
ε=A α (3)
Formula (3) is defined as the parameter degeneration equation of intelligent electric energy meter, wherein ε is degeneration parameter, and α is parameter degeneration
Amount, dematrix A are degeneration relational expression of the degeneration parameter under the effect of parameter degeneration amount.
The essence for solving the parameter degeneration relationship is to solve formula according to known parameter degeneration amount and degeneration parameter
(3) coefficient matrices A.Under current present Research, the element of matrix A can not parse, and cannot use general constant or simple
Elementary function describes.
As shown in Fig. 2, the present embodiment establishes monte-Carlo model, the detailed process of simulation calculating is carried out using the model
For:Three degeneration parameter actuating quantitys are inputted, such as temperature x1=25, humidity x2=55, power consumption x3=0.5, their change is set
Change measurement value ranging from:Temperature Δ x1:- 40~20;Humidity Δ x2:- 55~45;Power consumption Δ x3:- 0.5~1.5;Generate N
The variation delta x of a equally distributed parameter degeneration amount in corresponding value rangen(i) (i=1,2 ... N), this reality
It applies in example, n takes 3.Utilize xn'=xn+Δxn(i), simulation degeneration parameter actuating quantity x is obtainedn';Intelligent electric energy meter is read in xn'Make
Electric energy reading under, calculating ratio difference e (i), and contrast differences e (i) results are counted, and statistical law is analyzed.Wherein than poor e
(i) calculation formula is:
In formula (4), Wi'Error discreet value when i, W are taken for NiStandard error value when i is taken for N.
The constraints of error is in step S3
In formula (5), WN'Error discreet value after being calculated for n times stochastic simulation, WNFor corresponding standard error value, in this reality
It applies in example, δ=0.1% is set.
After carrying out n times simulation and calculating, if being unsatisfactory for the constraints of error, increase number realization, and continue
Simulation calculates, until result of calculation meets the constraints of error.
As shown in figure 3, online monitoring system includes two paths of signals Acquisition channel, upper one is obtained using the online monitoring system
The measurement error of moment intelligent electric energy meter.Monitoring mode can specifically be described as:Signal carries out comparing calculation by two paths
Error is all the way standard channel, voltage and current signals is measured respectively using standard potential transformer and standard current transformer,
Sampling unit acquires signal and carries out that standard electric flux is calculated;It is tested channel all the way, intelligent electric energy meter to be assessed measures
Voltage and current signal, the output umber of pulse proportional to electric flux, second rush collecting unit by umber of pulse and electric energy meter meter constant
In conjunction with tested electric flux is calculated;Standard electric flux and tested electric flux are input to error calculation unit, finally obtain one
W in the measurement error of moment intelligent electric energy meter, i.e. formula (4)iValue, WiCalculation formula be
Formula (in 6) Wt-1'For the electric energy indicating value of t-1 moment intelligent electric energy meter to be estimated, Wt-1Standard electric is used for the t-1 moment
Press the standard electric flux obtained by mutual inductor and standard current transformer survey calculation.
The feasibility that method is used for verification embodiment, illustrates with reference to actual application:
Intercept humiture of the morning 10 on April 12nd, 2015 when afternoon 5, power consumption and intelligent electric energy meter electric energy
Than difference data as sample, data of the Temperature and Humidity module is using the acquisition one per minute of Rotronic Instruments HP22 humiture instruments
Secondary, power consumption is indicated with electric flux that electric energy meter is shown, and record per minute is primary.Temperature, humidity, power consumption and electric energy
Datagram than difference is as shown in FIG. 4,5,6, 7;
Monte-Carlo model is established using data, by 30,000 stochastic simulations, obtains degeneration parameter actuating quantity and its variation
The statistical law of intelligent electric energy meter measurement error under the influence of amount;
According to obtained statistical law, the error shape of short time after estimating intelligent electric energy meter at afternoon 5 on the 12nd in April
State, and on-line monitoring system data acquired within the same period are analyzed, the results are shown in Figure 8, as seen from Figure 8,
Intelligent electric energy meter error estimation results based on parameter degeneration equation, substantially conform to, the short time is pre- with the actual running results trend
Error is estimated compared with actual error, and absolute error is not more than 0.1%.
The above is the preferred embodiment of the present invention, for those skilled in the art,
Without departing from the principles of the invention, several improvements and modifications can also be made, these improvements and modifications are also regarded as this hair
Bright protection domain.
Claims (8)
1. a kind of intelligent electric energy meter error predictor method based on parameter degeneration equation, it is characterized in that:Include the following steps:
Build parameter degeneration equation, and the model estimated based on the parameter degeneration establishing equation error;
Obtain the historical data of parameter degeneration amount in parameter degeneration equation;
Using the historical data, simulation several times is carried out to the model and is calculated, until result of calculation meets the constraint item of error
Part obtains the statistical law of measurement error;
According to the statistical law of the measurement error, the measurement error state of intelligent electric energy meter is estimated.
2. a kind of intelligent electric energy meter error predictor method based on parameter degeneration equation according to claim 1, feature
It is:The parameter degeneration equation is ε=A α, and ε is degeneration parameter in formula, and α is parameter degeneration amount, and dematrix A is to degenerate
Degeneration relational expression of the parameter under the effect of parameter degeneration amount.
3. a kind of intelligent electric energy meter error predictor method based on parameter degeneration equation according to claim 2, feature
It is:The parameter degeneration amount includes that interaction between error influence factor, error influence factor, error influence factor exist
The measurement error of variable quantity and last moment intelligent electric energy meter in each time step.
4. a kind of intelligent electric energy meter error predictor method based on parameter degeneration equation according to claim 3, feature
It is:The detailed process for obtaining the measurement error of the last moment intelligent electric energy meter is:
The signal acquisition channel measured in circuit is divided into two-way, respectively standard channel and tested channel, is led in standard channel
It crosses standard potential transformer and standard current transformer obtains voltage and current signals respectively, obtain standard electric flux, be tested logical
On road voltage and current signals are obtained using intelligent electric energy meter to be estimated;
Intelligent electric energy meter to be estimated exports umber of pulse simultaneously, and obtains tested electric flux according to electric energy meter meter constant;
Using the tested electric flux and standard electric flux, the measurement error of intelligent electric energy meter is calculated.
5. a kind of intelligent electric energy meter error predictor method based on parameter degeneration equation according to claim 1, feature
It is:The constraints of the error is:
In formula, W'For the electric energy indicating value of intelligent electric energy meter to be estimated, W is using standard potential transformer and standard current transformer
Standard electric flux obtained by survey calculation, δ are allowable error setting value.
6. a kind of intelligent electric energy meter error predictor method based on parameter degeneration equation according to claim 1, feature
It is:The model of the predictor error is monte-Carlo model.
7. a kind of intelligent electric energy meter error predictor method based on parameter degeneration equation according to claim 6, feature
It is:Using the historical data, the detailed process for being simulated calculating several times to the model is:
Input one or more x in parameter degeneration amountn, and generate N number of uniform in parameter degeneration amount variation range
The variation delta x of the parameter degeneration amount of distributionn(i) (i=1,2 ... N);
Utilize xn'=xn+Δxn(i), simulation degeneration parameter actuating quantity x is obtainedn';
Intelligent electric energy meter is read in xn'Electric energy reading under effect, calculating ratio difference e (i), and contrast differences e (i) results are counted.
8. a kind of intelligent electric energy meter error predictor method based on parameter degeneration equation according to claim 1, feature
It is:After carrying out simulation calculating several times to the model, if being unsatisfactory for the constraints of error, increase number realization, and
Continue simulation to calculate, until result of calculation meets the constraints of error.
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| CN115438520A (en) * | 2022-11-08 | 2022-12-06 | 云南电网有限责任公司 | Intelligent electric energy representation number simulation method based on Monte Carlo simulation method |
| CN117110977A (en) * | 2023-10-25 | 2023-11-24 | 国网浙江省电力有限公司营销服务中心 | Electric energy meter error assessment method and system |
| CN117110977B (en) * | 2023-10-25 | 2024-03-01 | 国网浙江省电力有限公司营销服务中心 | Electric energy meter error assessment method and system |
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Application publication date: 20181023 |