CN106651651B - Power grid user power consumption curve data filling method and device - Google Patents

Power grid user power consumption curve data filling method and device Download PDF

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CN106651651B
CN106651651B CN201611137865.8A CN201611137865A CN106651651B CN 106651651 B CN106651651 B CN 106651651B CN 201611137865 A CN201611137865 A CN 201611137865A CN 106651651 B CN106651651 B CN 106651651B
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power
user
loss rate
line loss
missing
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陈江琦
陈其鹏
杨訸
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State Grid Corp of China SGCC
Global Energy Interconnection Research Institute
State Grid Shanghai Electric Power Co Ltd
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State Grid Corp of China SGCC
Global Energy Interconnection Research Institute
State Grid Shanghai Electric Power Co Ltd
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Abstract

The invention provides a method and a device for filling power consumption curve data of a power grid user, wherein the method comprises the steps of estimating a line loss rate according to the state of a transformer area line T and estimating missing user power data according to the state of a user day curve; and fine-tuning the estimated line loss rate value and the estimated missing user power value to fill up the missing data. The apparatus comprises an estimation unit and an adjustment unit. The technical scheme provided by the invention fills data in a longitudinal and transverse combination mode and carries out fine adjustment, so that a better and more stable result can be obtained, and the correctness of curve data filling is improved.

Description

Power grid user power consumption curve data filling method and device
Technical Field
The invention relates to the field of data filling of big data, in particular to a method and a device for filling power consumption curve data of a power grid user.
Background
With the arrival of the big data era and the continuous improvement of the informatization degree of the power system, the big data of the power system is explosively increased and the variety of the big data is more and more. The multipurpose collected data building model in the existing power system analysis realizes the user power consumption analysis, provides help for subsequent power stealing detection, load prediction and the like, and is very important for the innovation and development of the intelligent power grid business mode.
Monitoring and acquisition of various data in the power system are the basis of scheduling operation, safety and stability analysis and equipment state and risk assessment of the power system. However, in the actual operation of the power system, the observed data is abnormal due to errors of a data acquisition channel, faults of a remote terminal unit and the like, and is inconsistent with most observed values, and the observed data possibly goes against the routine due to overhaul, load shedding and power outage of a circuit and large event impact, so that the analysis of the power system data on the smart grid system is difficult, and therefore a data supplement method is required to be adopted before data analysis to completely supplement the missing data, the accuracy of the data analysis of the power system and the availability of curve data are improved, and support is provided for subsequent analysis.
Disclosure of Invention
In order to meet the development requirement of the prior art, the invention provides a method for filling power curve data of a power grid user.
The invention provides a method for filling power curve data of power grid users, which is improved in that the method comprises the following steps:
estimating the line loss rate according to the state of the line T of the transformer area, and estimating the missing user power according to the daily curve state of the user;
and fine-tuning the estimated line loss rate and the estimated missing user power.
Further, the estimating of the line loss rate includes:
sample R is calculated as followsdmLine loss rate r at time (d, m) ∈ Gdm0：
Figure BDA0001177230960000011
Wherein, the sample Rdm＝[rdm1,rdm2,…,rdmN]，PdmT: the total power of the platform area; r isdmn: the ratio of the power of user n at time m of day d to the total power of the cell,
Figure BDA0001177230960000021
Pdmn: the power of user n; pdm0: line loss power; n: the number of users under the cell; n: the user number is an integer from 1 to N; time (d, m): record at time m on day D, D ∈ D, D: a set of recording days; m is belonged to M, M: recording a set of moments; g: and the time instants of all user power records in the station area are aggregated.
Further, the estimating of the line loss rate includes:
time of day
Figure BDA0001177230960000022
Sample RdmIf the data is not complete, the recorded data is obtained in u users by the K-nearest neighbor algorithm of' city block distance
Figure BDA0001177230960000023
The nearest k is selected from the middle section1A sample
Figure BDA0001177230960000024
Sample R is estimated as followsdmLine loss rate of
Figure BDA0001177230960000025
Figure BDA0001177230960000026
In the formula, i: days, i belongs to D; j: the time number, j belongs to M; n is1,n2,…,nu: represents u users;
Figure BDA00011772309600000214
user n at ith and jth timeuThe ratio of the power of (a) to the total power of the cell;
Figure BDA00011772309600000215
line loss rate of u users at the j time of day i.
Further, the estimating of the missing user power comprises:
let the power curve record of the user n on day d be Ldn＝[Pd1n,Pd2n,…,PdMn]At the moment of time
Figure BDA00011772309600000216
Recording power day curve of user t on ith day through K-neighbor algorithm of' correlation distance
Figure BDA0001177230960000027
K with the largest correlation of medium section2A sample
Figure BDA0001177230960000028
Estimating missing user power data as follows
Figure BDA0001177230960000029
Figure BDA00011772309600000210
In the formula, mlThe time of data acquisition;
Figure BDA00011772309600000217
t user records m on day iwPower at time point, w is 1,2,3 …, l; l: the number of samples;
further, before the fine tuning, estimating a sum of the missing powers according to the user power record and the estimated line loss rate as follows
Figure BDA00011772309600000211
Figure BDA00011772309600000212
Wherein, e: user n with recorded power1,n2,…,nu(ii) a f: user q missing power recording1,q2,…,qv；
Figure BDA00011772309600000213
With incompletely recorded time
Figure BDA0001177230960000031
While, the sample RdmThe line loss rate of (a).
Further, the fine tuning includes:
at the moment of time
Figure BDA0001177230960000032
Selection of k2An
Figure BDA0001177230960000033
As required to be padded
Figure BDA0001177230960000034
Is estimated value of
Figure BDA0001177230960000035
Respectively calculate
Figure BDA0001177230960000036
And are combined with
Figure BDA0001177230960000037
Comparing, and setting the difference value as minimum
Figure BDA0001177230960000038
Further, the line loss rate is calculated
Figure BDA0001177230960000039
Setting a threshold r1，r2And are combined with
Figure BDA00011772309600000310
And comparing and determining the final estimated line loss rate:
if it is
Figure BDA00011772309600000311
The estimated value of the line loss rate is
Figure BDA00011772309600000312
If it is
Figure BDA00011772309600000313
The estimated value of the line loss rate is
Figure BDA00011772309600000314
If it is
Figure BDA00011772309600000315
The estimated value of the line loss rate is
Figure BDA00011772309600000316
Further, based on the estimated line loss rate
Figure BDA00011772309600000317
The sum of the missing user powers is calculated as follows
Figure BDA00011772309600000318
Figure BDA00011772309600000319
For estimated user power
Figure BDA00011772309600000320
Scaling to obtain final missing power estimate
Figure BDA00011772309600000321
Figure BDA00011772309600000322
A data padding apparatus for a power grid consumer power curve, the apparatus comprising:
the estimating unit is used for estimating the line loss rate according to the state of the line T of the transformer area and estimating the missing user power data according to the state of the daily curve of the user;
and the adjusting unit is used for fine-tuning the estimation of the line loss rate and the estimation of the power of the missing user to complete the filling of the missing data.
Further, the estimation unit includes:
the first estimation subunit is used for estimating the line loss rate according to the state of the line T of the transformer area;
the second estimation subunit is used for estimating the missing user power data according to the user daily curve state;
the adjusting unit includes:
the first adjusting subunit is used for determining a final estimated value of the line loss rate according to the set threshold and the corrected line loss rate;
and the second adjusting subunit is used for calculating the final estimated value of the power value of the missing user according to the final estimated value of the line loss rate.
Compared with the closest prior art, the technical scheme provided by the invention has the following excellent effects:
(1) according to the technical scheme provided by the invention, the line loss can be estimated without classifying the station area states, and the station area time with the closest state can be quickly and conveniently found through a neighbor algorithm; the daily curves of the users do not need to be classified by electricity utilization behaviors, the most similar daily curves can be directly matched for missing power data filling, the accuracy and the effectiveness of data filling are effectively improved, and data support is provided for data analysis of the power system.
(2) According to the technical scheme provided by the invention, the state and the missing data are respectively estimated in a horizontal and vertical combination mode through two dimensional directions, and fine adjustment is comprehensively considered, so that a better and more stable result can be obtained, and the correctness of curve data filling can be effectively improved.
Drawings
FIG. 1 is a flow chart of a data padding method according to the present invention;
fig. 2 is a detailed diagram of the fine tuning of the miss rate and the line loss rate provided by the present invention.
Detailed Description
The technical solution provided by the present invention will be described in detail by way of specific embodiments in conjunction with the accompanying drawings of the specification.
The technical scheme provided by the invention is used for filling data aiming at the problem of the power utilization curve data loss of users in the power grid region. In the process of collecting curve data such as current, power and the like of a power user, a missing phenomenon usually occurs, taking the power curve data as an example, the technical scheme provided by the invention comprises the following steps:
knowing a certain phase line T of a single-phase or multi-phase power grid area, N consumers C are connected below1,C2,…,CN. Intelligent electric meter every other certain time t0Minute recording power per user, M points per day, where M x t0Data for D days, i.e., M × D recording times, are recorded continuously at 1440. For the recording at time (d, m), i.e. at time m on day d, the output power of T is PdmTAre all known; the power of N users is Pdm1,Pdm2,…,PdmNIs fully known or partially known; line loss power of Pdm0Not less than 0 and satisfies Pdm0＝PdmT-(Pdm1+Pdm2+…+PdmN) When the power of all users in the station area is known, the line loss can be obtained through calculation. The problem is that the user power data P of the missing part needs to be filled updmn。
The method for filling the power curve data loss of the power grid user is based on a K-nearest neighbor algorithm, and in the method, the total power P of the platform area is only subjected to the condition that the denominator is 0 to prevent the occurrence of the condition that the denominator is 0dmTRecording > 0 for subsequent work, for total power PdmTFor a record of 0, all the missing user powers are noted as 0 below. Firstly, estimating a line loss rate by estimating a state of a station area T, and then estimating missing user power data by estimating a state of a user daily curve, wherein a technical route diagram is shown as an attached figure 1, and the specific flow is as follows:
(1) estimating the line loss rate r according to the state of the line T in the transformer areadm0
For the power recording at the time (d, m), the ratio of the power of user n to the total power of the cell is
Figure BDA0001177230960000051
Estimating the line loss rate of the current moment by the ratio of each user power to the total power of the cell, wherein the specific details are as follows:
corresponding to the time (d, m)Is Rdm＝[rdm1,rdm2,…,rdmN]Wherein N represents the number of users using electricity, and G is a time set with complete power records of all users in the cell, that is, for the time (d, m) belonging to G, the power of all users in the cell is known, so the sample data R is the sample data R at this timedmIs complete, the line loss rate can be calculated as follows:
Figure BDA0001177230960000052
will { (R)ij,rij0) L (i, j) e } as a known (sample, line loss rate) set, where i: days, i belongs to D; j: at time j ∈ M.
For the time of day
Figure BDA0001177230960000053
The power of the user under the cell is not recorded completely, so the sample RdmIncomplete, line loss rate unknown, following for RdmAnd estimating the line loss rate. Set a moment
Figure BDA0001177230960000054
U users n are recorded1,n2,…,nuPower of (1), selecting RdmIn which there is a recorded part
Figure BDA0001177230960000055
And the same sorting operation is performed for all known samples,
Figure BDA0001177230960000056
through "Distance between city blocks"K-nearest neighbor method, on a sorted known sample
Figure BDA0001177230960000057
In search of the nearest k1Individual section sample
Figure BDA0001177230960000058
Using their corresponding original copies
Figure BDA0001177230960000059
To the sample R of the arithmetic mean of the line loss rates ofdmIs preliminarily estimated, i.e.
Figure BDA00011772309600000510
(2) Estimating missing user power data from user daily curve state
Let L be the power daily curve record of day d of user ndn＝[Pd1n,Pd2n,…,PdMn]Estimating the missing part power of the incomplete daily power curve according to the known complete daily power curve of the user, wherein the specific details are as follows:
for the moment when a record is incomplete
Figure BDA00011772309600000511
Estimating each missing user power record P separatelydmq. Due to PdmqAbsence of (2), daily curve LdqDefinitely incomplete, sorting LdqThe recorded part of the method is as follows:
Figure BDA00011772309600000512
let HdqIs LitAll of
Figure BDA00011772309600000513
And PimtAll have daily curve sets recorded and are sorted
Figure BDA00011772309600000514
Wherein m islThe time of data acquisition; pimlj: t user records m on day ilPower at a time point; l: the number of samples; get the known (sample, label) set { (L)it,Pimt)|,t)∈Hdq}. Through "Correlation Distance of sex"K-nearest neighbor method, as knownSample(s)
Figure BDA0001177230960000061
Finding the k with the largest correlation2A sample
Figure BDA0001177230960000062
Estimating P by sample total power ratiodmqK of (a)2The number of the candidate values is determined,
Figure BDA0001177230960000063
Figure BDA0001177230960000064
(3) fine-tuning correction of padding data
Set a moment
Figure BDA0001177230960000065
U users n are recorded1,n2,…,nuPower of, missing v users q1,q2…, qv. From the known user power record and the line loss rate estimated by (1), the sum of the missing powers can be estimated
Figure BDA0001177230960000066
Wherein, in the step (A),
Figure BDA0001177230960000067
missing user qfThe power of (d);
Figure BDA0001177230960000068
line loss rate average of incomplete sample data. Obtaining a plurality of estimates based on the estimate of the missing power in (2)
Figure BDA0001177230960000069
j＝1,2,…,v,u＝1,2,…,k2(ii) a Wherein the content of the first and second substances,
Figure BDA00011772309600000610
missing user qfAn estimate of (d). The line loss rate estimation is taken as a main factor, the missing power estimation and the line loss rate estimation are finely adjusted to obtain the final line loss rate and the estimation of the user power data, and a technical diagram is shown in fig. 2, and the specific details are as follows:
at the moment of time
Figure BDA00011772309600000611
For each one requiring padding
Figure BDA00011772309600000612
Can select k2An
Figure BDA00011772309600000613
One of them is used as an estimated value
Figure BDA00011772309600000614
In total k2 vAnd (6) generating an estimated value. Respectively calculate
Figure BDA00011772309600000615
Heel
Figure BDA00011772309600000616
The comparison is carried out, and the difference is set to be the minimum
Figure BDA00011772309600000617
Calculating the line loss rate estimated therefrom
Figure BDA00011772309600000618
And
Figure BDA00011772309600000619
make a comparison and set 2 thresholds r1，r2：
If it is not
Figure BDA00011772309600000620
The end of the line loss rateEstimated as
Figure BDA00011772309600000621
If it is not
Figure BDA00011772309600000622
The final estimate of the line loss rate is modified
Figure BDA00011772309600000623
If it is not
Figure BDA00011772309600000624
The final estimate of the line loss rate is modified
Figure BDA00011772309600000625
According to the finally estimated line loss rate
Figure BDA00011772309600000626
Calculate the sum of the missing user powers:
Figure BDA00011772309600000627
then to the preliminarily estimated user power
Figure BDA0001177230960000071
Scaling to obtain a final missing power estimate:
Figure BDA0001177230960000072
and finally completing the filling work of all missing data.
A device for filling power curve data of power grid users comprises:
the estimating unit is used for estimating the line loss rate according to the state of the line T of the transformer area and estimating the missing user power data according to the state of the daily curve of the user;
the estimation unit includes:
the first estimation subunit is used for estimating the line loss rate according to the state of the line T of the transformer area;
the second estimation subunit is used for estimating the missing user power data according to the user daily curve state;
and the adjusting unit is used for fine-tuning the estimation of the line loss rate and the estimation of the power of the missing user to complete the filling of the missing data.
The adjusting unit includes:
the first adjusting subunit is used for determining a final estimated value of the line loss rate according to the set threshold and the corrected line loss rate;
and the second adjusting subunit is used for calculating the final estimated value of the power value of the missing user according to the final estimated value of the line loss rate.
Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can make modifications and equivalents to the embodiments of the present invention without departing from the spirit and scope of the present invention, which is set forth in the claims of the present application.

Claims (7)

1. A method for filling user power curve data, which is characterized by comprising the following steps:
estimating the line loss rate according to the state of the line T of the transformer area, and estimating the missing user power according to the daily curve state of the user;
fine-tuning the estimated line loss rate value and the estimated missing user power value;
the estimation of the line loss rate comprises:
sample R is calculated as followsdmLine loss rate r at time (d, m) ∈ Gdm0：
Figure FDA0003116511930000011
Wherein, the sample Rdm＝[rdm1，rdm2，...，rdmN]，PdmT: the total power of the platform area; r isdmn: the ratio of the power of user n at time m of day d to the total power of the cell,
Figure FDA0003116511930000012
Pdmn: the power of user n; pdm0: line loss power; n: the number of the platform users; n: the user number is an integer from 1 to N; time (d, m): record at time m on day D, D ∈ D, D: a set of recording days; m is belonged to M, M: recording a set of moments; g: the time set of power records of all users in the distribution area is collected;
the estimation of the missing user power comprises:
let the power curve record of the user n on day d be Ldn＝[Pd1n，Pd2n，...，PdMn]At the moment of time
Figure FDA0003116511930000013
Recording power day curve of user t on ith day through K-neighbor algorithm of' correlation distance
Figure FDA0003116511930000014
K with the largest correlation of medium section2A sample
Figure FDA0003116511930000015
Estimating missing user power data as follows
Figure FDA0003116511930000016
Figure FDA0003116511930000017
In the formula (I), the compound is shown in the specification,
Figure FDA0003116511930000021
the power of user j at time m on the ith day for sample u; m islThe time of data acquisition;
Figure FDA0003116511930000022
t user records m on day iwPower at time point, w is 1,2,3 … 1; l: the number of samples;
Figure FDA0003116511930000023
sample u on day i mwRecording the power of the user j at any moment;
Figure FDA0003116511930000024
mwthe power of the missing users at the moment.
2. The method of claim 1, wherein the estimating of the line loss rate comprises:
time of day
Figure FDA0003116511930000025
Sample RdmIf the data is not complete, the recorded data is obtained in u users by the K-nearest neighbor algorithm of' city block distance
Figure FDA0003116511930000026
Figure FDA0003116511930000027
The nearest k is selected from the middle section1A sample
Figure FDA0003116511930000028
Sample R is estimated as followsdmLine loss rate of
Figure FDA0003116511930000029
Figure FDA00031165119300000210
In the formula, i: days, i belongs to D; j: the time number, j belongs to M; n is1，n2，...，nu: represents u users;
Figure FDA00031165119300000211
user n at ith and jth timeuThe ratio of the power of (a) to the total power of the cell;
Figure FDA00031165119300000212
line loss rate of u users at the j time of day i.
3. The method of claim 1, wherein prior to the fine tuning, a sum of missing powers is estimated according to a user power record and an estimated line loss rate as follows
Figure FDA00031165119300000213
Figure FDA00031165119300000214
Wherein the content of the first and second substances,
Figure FDA00031165119300000215
user n recorded at m time of dayePower of (e): user n with recorded power1，n2，...，nu(ii) a f: user q missing power recording1，q2，...，qv；
Figure FDA0003116511930000031
With incompletely recorded time
Figure FDA0003116511930000032
While, the sample RdmThe line loss rate of; pdmT: total power of the cell.
4. The method of claim 1, wherein the fine-tuning comprises:
at the moment of time
Figure FDA0003116511930000033
Selection of k2An
Figure FDA0003116511930000034
As required to be padded
Figure FDA0003116511930000035
Is estimated value of
Figure FDA0003116511930000036
Respectively calculate
Figure FDA0003116511930000037
And are combined with
Figure FDA0003116511930000038
Comparing, and setting the difference value as minimum
Figure FDA0003116511930000039
5. The method of claim 4, wherein the line loss rate is calculated
Figure FDA00031165119300000310
Setting a threshold r1，r2And are combined with
Figure FDA00031165119300000311
And comparing and determining the final estimated line loss rate:
if it is
Figure FDA00031165119300000312
The estimated value of the line loss rate is
Figure FDA00031165119300000313
If it is
Figure FDA00031165119300000314
The estimated value of the line loss rate is
Figure FDA00031165119300000315
Figure FDA00031165119300000316
If it is
Figure FDA00031165119300000317
The estimated value of the line loss rate is
Figure FDA00031165119300000318
6. The method of claim 5, wherein the line loss rate is estimated based on the estimated line loss rate
Figure FDA00031165119300000319
The sum of the missing user powers is calculated as follows
Figure FDA00031165119300000320
Figure FDA00031165119300000321
For estimated user power
Figure FDA00031165119300000322
Scaling to obtain final missing power estimate
Figure FDA00031165119300000323
Figure FDA00031165119300000324
7. An apparatus for applying the method of any of claims 1-6, the apparatus comprising:
the estimating unit is used for estimating the line loss rate according to the state of the line T of the transformer area and estimating the missing user power data according to the state of the daily curve of the user;
the adjusting unit is used for fine-tuning the estimation of the line loss rate and the estimation of the power of the missing user to complete the filling of the missing data;
the estimation unit includes:
the first estimation subunit is used for estimating the line loss rate according to the state of the line T of the transformer area;
the second estimation subunit is used for estimating the missing user power data according to the user daily curve state;
the adjusting unit includes:
the first adjusting subunit is used for determining a final estimated value of the line loss rate according to the set threshold and the corrected line loss rate;
and the second adjusting subunit is used for calculating the final estimated value of the power value of the missing user according to the final estimated value of the line loss rate.
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