CN112990730B - Method for enhancing electric quantity correlation characteristics by utilizing artificial acquisition failure - Google Patents
Method for enhancing electric quantity correlation characteristics by utilizing artificial acquisition failure Download PDFInfo
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Abstract
The invention belongs to the field of power topology big data analysis, and particularly relates to a method for enhancing electric quantity correlation characteristics by utilizing artificial acquisition failure. The method is characterized by comprising the following steps of: s1: acquiring branch data, power consumption data and adjacent station area data of a station area user; s2: according to the data, establishing an electric quantity correlation analysis model based on acquisition failure optimization; s3: diagnosing the attribution relation between the user branches and the station areas by using an electric quantity correlation analysis model based on acquisition failure optimization; s4: and outputting the diagnosis result according to a given standard output format. According to the invention, the daily freezing indication number of the electricity consumption information acquisition system is processed, so that the daily electricity consumption of a certain user is combined into the daily electricity consumption, the ratio of the electricity consumption to the line loss of the transformer area and the fluctuation rate of the electricity consumption are improved, the electric quantity correlation characteristic is enhanced, and the accuracy and the detection rate of transformer area user change identification by electric quantity correlation analysis are improved.
Description
Technical Field
The invention belongs to the field of power topology big data analysis, and particularly relates to a method for enhancing electric quantity correlation characteristics by utilizing artificial acquisition failure.
Background
With the whole coverage of the electricity consumption information acquisition system, daily acquisition can be realized by taking assessment meters at the transformer side of the transformer area and sales electricity quantity data of the user electricity meter as the most important operation data. According to the law of conservation of energy, the following relations exist between the power supply and sales of the areas, namely:
the power supply quantity of the station area examination table=the sum of the sales quantity of each user sub-table+the line loss of the station area.
When the user table does not belong to the station area, the power consumption is increased by how much kWh, the station area line loss is reduced by how much kWh almost simultaneously, and the user table and the Pearson correlation coefficient of the line loss show negative correlation characteristics.
The pearson correlation coefficient is used as an index for reflecting the degree of linear correlation between 2 sequence data X and Y, and the value of the pearson correlation coefficient is between [ -1,1] and can be used for judging the degree of correlation between X and Y. When pearson coefficients are used as samples, they are denoted as R (X, Y).
Wherein: n is the number of samples, xi, yi is the i-point observation corresponding to the variable X, Y,is the average number of X samples,/-, and>is the average number of Y samples.
When the existing method for analyzing the electric quantity correlation is used for user change identification, the pearson correlation coefficient of the electric quantity of a user table and the line loss of a station area is generally calculated for a certain period of time, and if the numerical value is smaller than a threshold value of-0.8, the user and the station area are considered to have no attribution relation. As shown in fig. 2.
Because the area needing to be identified by the user is generally a public transformer area, when the user is identified by utilizing the analysis result of the correlation of the electricity consumption and the electricity consumption, if the ratio of the electricity consumption to the line loss is low and the fluctuation rate of the electricity consumption is small, the pearson correlation coefficient is relatively close to the value 0, the negative correlation characteristic is not obvious, and the user cannot be accurately judged to be not belonged to a certain area.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a method for enhancing the electric quantity correlation characteristics by utilizing artificial acquisition failure, which has the advantages of accurate identification and high detection rate.
The invention is realized in such a way that a method for enhancing the electric quantity correlation characteristic by utilizing artificial acquisition failure comprises the following steps:
s1: acquiring branch data, power consumption data and adjacent station area data of a station area user;
s2: according to the data, establishing an electric quantity correlation analysis model based on acquisition failure optimization;
s3: diagnosing the attribution relation between the user branches and the station areas by using an electric quantity correlation analysis model based on acquisition failure optimization;
s4: and outputting the diagnosis result according to a given standard output format.
According to the invention, the daily freezing indication number of the electricity consumption information acquisition system is processed, so that the daily electricity consumption of a certain user is combined into the daily electricity consumption, the ratio of the electricity consumption to the line loss of the transformer area and the fluctuation rate of the electricity consumption are improved, the electricity consumption and electricity consumption correlation characteristics are enhanced, and the accuracy and the detection rate of transformer area user change identification by electricity consumption correlation analysis are improved.
And S1, diagnosing adjacent areas by analyzing archival data before the process, wherein the adjacent area data comprises user branch data and power consumption data of area name adjacency, acquisition adjacency and/or power supply and distribution adjacency.
The electric quantity correlation analysis model based on acquisition failure optimization comprises the steps of processing a daily freezing indication number of an electricity consumption information acquisition system, classifying 1 day or more of data into one day of electricity consumption, and bringing users with original calculation results which do not reach an identification threshold into a diagnosis range.
The electric quantity correlation analysis model based on acquisition failure optimization specifically comprises the following steps:
for the station area A, the ammeter B belongs to the station area A, the electric quantity of the station area checking meter C is normally collected, and when the ammeter B freezing indication is normally collected, T is i Daily line loss delta phi of daily area i The method comprises the following steps:
ΔΦ i ＝Φ Ci -∑Φ i
wherein: phi Ci Table area checking table C daily electric quantity, Φ i Daily electricity quantity is calculated for each user;
if ammeter B is at T i The collection fails j days before the day, the electricity quantity of the electricity meter B fails to return to 0, and the collection fails T i-j Day to T i-1 Line loss delta phi k ’，k∈{T i-j ，…,T i-1 -have:
ΔΦ K ’＝ΔΦ k +Φ Bk
wherein: phi k Is the real line loss of k days, phi BK K-day electric quantity of the ammeter B;
T i daily line loss delta phi k ", there are:
ΔΦ i ”＝ΔΦ i -∑Φ Bk
wherein: phi i Is T i Real day line loss, ΣΦ BK T for ammeter B i-j To T i-1 J total daily electric quantity sum;
T i-j to T i Mutation occurs in daily line loss.
And the S3 diagnosis is to calculate positive and negative correlation coefficients of normal acquisition and failure acquisition of the user and the areas to be attributed 1 and 2 respectively. R1 Negative pole And R1 Positive direction For the user and the normal collection of the platform area 1, 2 correlation coefficients R1' Negative pole And R1' Positive direction For 2 correlation coefficients of acquisition failure of user and station area 1, R2 Negative pole And R2 Positive direction For the user and the normal collection of the station area 2, 2 correlation coefficients, R2' Negative pole And R2' Positive direction For 2 correlation coefficients of the acquisition failure of the user and the station area 1, there are:
region 1 negative correlation coefficient acquisition failure mutation difference K1 Negative pole ＝R1 Negative pole -R1’ Negative pole
Region 2 negative correlation coefficient acquisition failure mutation difference K2 Negative pole ＝R2 Negative pole -R2’ Negative pole
Region 1 positive correlation coefficient acquisition failure mutation difference K1 Positive direction ＝R1 Positive direction -R1’ Positive direction
Region 2 positive correlation coefficient acquisition failure mutation difference K2 Positive direction ＝R2 Positive direction -R2’ Positive direction
Positive and negative correlation coefficient mutation difference value K1=K1 of station area 1 acquisition failure Positive direction -K1 Negative pole
Station area 2 acquisition failure positive and negative correlation coefficient mutation difference value K2=K2 Positive direction -K2 Negative pole
The correlation coefficient acquisition failure mutation difference value delta K=K1-K2;
and judging the attribution relation of the platform area according to the correlation coefficient acquisition failure mutation difference value and the settable threshold.
The invention has the advantages and positive effects that: according to the invention, for the daily freezing indication number acquired by the electricity consumption information acquisition system, a time point set T= { T1, T2, & gt. Tn } is selected in the electricity consumption amount correlation calculation time period, the daily freezing indication value of the first 1 or j time points of Ti are replaced by the daily freezing indication number of Ti-j-1, so that the daily electricity consumption amount of the Ti time points is increased, the line loss ratio and the electricity consumption amount change fluctuation rate of the electricity consumption amount and the electricity consumption amount correlation characteristic are improved, and the accuracy and the detection rate of the electricity consumption amount correlation analysis for carrying out the household change identification of the area are improved.
Drawings
Fig. 1 is a schematic block diagram of the present invention.
FIG. 2 is a correlation identification schematic of the present invention.
Fig. 3 is a schematic diagram of the line loss mutation of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent.
Example 1:
as shown in fig. 1, the present invention is implemented by a method for enhancing a power correlation feature using a human acquisition failure, comprising: a method for enhancing power correlation characteristics using human acquisition failure, comprising the steps of:
s1: acquiring branch data, power consumption data and adjacent station area data of a station area user;
s2: according to the data, establishing an electric quantity correlation analysis model based on acquisition failure optimization;
s3: diagnosing the attribution relation between the user branches and the station areas by using an electric quantity correlation analysis model based on acquisition failure optimization;
s4: and outputting the diagnosis result according to a given standard output format.
And S1, diagnosing adjacent areas by analyzing archival data before the process, wherein the adjacent area data comprises user branch data and power consumption data of adjacent area names, adjacent acquisition and/or adjacent power supply and distribution.
The electricity quantity correlation analysis model based on acquisition failure optimization comprises the steps of processing a daily freezing indication number of an electricity consumption information acquisition system, classifying 1 day or more of data into electricity consumption of a certain day, and bringing users with original calculation results which cannot reach an identification threshold into a diagnosis range.
The electric quantity correlation analysis model based on acquisition failure optimization specifically comprises the following steps:
for the station area A, the ammeter B belongs to the station area A, the electric quantity of the station area checking meter C is normally collected, and when the ammeter B freezing indication is normally collected, T is i Daily line loss delta phi of daily area i The method comprises the following steps:
ΔΦ i ＝Φ Ci -∑Φ i
wherein: phi Ci Table area checking table C daily electric quantity, Φ i Daily electricity quantity is calculated for each user;
if ammeter B is at T i The collection fails j days before the day, the electricity quantity of the electricity meter B fails to return to 0, and the collection fails T i-j Day to T i-1 Line loss delta phi k ’，k∈{T i-j ，…,T i-1 -have:
ΔΦ K ’＝ΔΦ k +Φ Bk
wherein: phi k Is the real line loss of k days, phi BK K-day electric quantity of the ammeter B;
T i daily line loss delta phi k ", there are:
ΔΦ i ”＝ΔΦ i -∑Φ Bk
wherein: phi i Is T i Real day line loss, ΣΦ BK T for ammeter B i-j To T i-1 J total daily electric quantity sum;
T i-j to T i Mutation occurs in daily line loss. As shown in fig. 3. Y is the daily electricity consumption of the user, and DY is the daily line loss of the station area TG.
And calculating 2 correlation coefficients between the branch electric quantity of the user and the line loss of the station area. One is a negative correlation coefficient, namely, a pearson correlation coefficient of 2 time series data between the electric quantity of the users after combination under the branch and the line loss of the station area (the electric quantity of the branches to be calculated is counted into the station area) is calculated, and the users are diagnosed not to belong to a certain station area; and the other is a positive correlation coefficient, namely, calculating the combined electric quantity of the users under the branches, removing the pearson correlation coefficient of time sequence data among the line losses of the areas (the electric quantity of the branches to be calculated is not counted into the areas) after the electric quantity of the users under the branches is calculated, and diagnosing that the users belong to a certain area.
And respectively calculating positive and negative correlation coefficients of the normal acquisition and the failure acquisition of the user and the areas 1 and 2 to be attributed. R1 Negative pole And R1 Positive direction For the user and the normal collection of the platform area 1, 2 correlation coefficients R1' Negative pole And R1' Positive direction For 2 correlation coefficients of acquisition failure of user and station area 1, R2 Negative pole And R2 Positive direction For the user and the normal collection of the station area 2, 2 correlation coefficients, R2' Negative pole And R2' Positive direction For 2 correlation coefficients of the acquisition failure of the user and the station area 1, there are:
region 1 negative correlation coefficient acquisition failure mutation difference K1 Negative pole ＝R1 Negative pole -R1’ Negative pole
Region 2 negative correlation coefficient acquisition failure mutation difference K2 Negative pole ＝R2 Negative pole -R2’ Negative pole
Region 1 positive correlation coefficient acquisition failure mutation difference K1 Positive direction ＝R1 Positive direction -R1’ Positive direction
Region 2 positive correlation coefficient acquisition failure mutation difference K2 Positive direction ＝R2 Positive direction -R2’ Positive direction
Positive and negative correlation coefficient mutation difference value K1=K1 of station area 1 acquisition failure Positive direction -K1 Negative pole
Station area 2 acquisition failure positive and negative correlation coefficient mutation difference value K2=K2 Positive direction -K2 Negative pole
The correlation coefficient acquisition failure mutation difference value delta K=K1-K2;
and judging the attribution relation of the platform area according to the correlation coefficient acquisition failure mutation difference value and the settable threshold.
Specifically, the results are shown in Table 1.
Table 1 correlation coefficient acquisition failure mutation difference diagnostic table zone attribution table
The invention has the advantages that:
1. when the electricity consumption of the user is balanced and the fluctuation rate is small, the correlation coefficient value of the electricity consumption and the line loss is close to 0 value, the attribution relation of the station area is not easy to identify, and the negative correlation characteristic can be enhanced by the characteristic that the fluctuation rate of the electricity consumption is increased due to acquisition failure and artificial acquisition failure.
2. For the non-home area, the power supply quantity of the non-home area does not contain the power consumption quantity of the user, so that the power change caused by acquisition failure is not great for the non-home area, the change value of the positive correlation coefficient can be positive or negative, but the positive correlation value of the non-home area can be obviously reduced.
3. By setting the threshold value of the positive and negative correlation mutation difference value, the attribution of the user's station area can be accurately diagnosed when the threshold value is exceeded.
4. For the user branches combining the electric quantity, the electric quantity and the line loss ratio of the user branches are increased, but the fluctuation rate is reduced, so that the user branches become stable power utilization users, the correlation coefficient value characteristics are also in the condition of unobvious, and the accuracy of the home identification of the station area can be improved by artificially causing acquisition failure.
Experiments prove that
The invention aims at the users with balanced electricity consumption and small fluctuation rate, the positive and negative correlation is not outstanding enough when the electricity consumption and electricity quantity correlation analysis is carried out, the identification accuracy rate and the detection rate are limited, the collection failure is manually caused for many times in a period of time, the fluctuation rate of the electricity consumption is improved, the difference value change caused by the positive and negative correlation coefficient values of the attribution and non-attribution areas is carried out, and the threshold value quantization is carried out, so that the attribution area diagnosis is accurately carried out.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (4)
1. A method for enhancing the electric quantity correlation characteristics by utilizing artificial acquisition failure, which is characterized in that: the method comprises the following steps:
s1: acquiring branch data, power consumption data and adjacent station area data of a station area user;
s2: according to the data, establishing an electric quantity correlation analysis model based on acquisition failure optimization;
s3: diagnosing the attribution relation between the user branches and the station areas by using an electric quantity correlation analysis model based on acquisition failure optimization; the diagnosis refers to respectively calculating positive and negative correlation coefficients of the normal acquisition and the failure acquisition of the user and the areas to be attributed 1 and 2, R1 Negative pole And R1 Positive direction For the user and the normal collection of the platform area 1, 2 correlation coefficients R1' Negative pole And R1' Positive direction For 2 correlation coefficients of acquisition failure of user and station area 1, R2 Negative pole And R2 Positive direction For the user and the normal collection of the station area 2, 2 correlation coefficients, R2' Negative pole And R2' Positive direction For 2 correlation coefficients of the acquisition failure of the user and the station area 1, there are:
region 1 negative correlation coefficient acquisition failure mutation difference K1 Negative pole =R1 Negative pole -R1’ Negative pole
Region 2 negative correlation coefficient acquisition failure mutation difference K2 Negative pole =R2 Negative pole -R2’ Negative pole
Region 1 positive correlation coefficient acquisition failure mutation difference K1 Positive direction =R1 Positive direction -R1’ Positive direction
Region 2 positive correlation coefficient acquisition failure mutation difference K2 Positive direction =R2 Positive direction -R2’ Positive direction
Positive and negative correlation coefficient mutation difference value K1=K1 of station area 1 acquisition failure Positive direction -K1 Negative pole
Station area 2 acquisition failure positive and negative correlation coefficient mutation difference value K2=K2 Positive direction -K2 Negative pole
The correlation coefficient acquisition failure mutation difference value delta K=K1-K2;
judging the attribution relation of the platform area according to the correlation coefficient acquisition failure mutation difference value and the settable threshold;
s4: and outputting the diagnosis result according to a given standard output format.
2. A method for enhancing power correlation characteristics as recited in claim 1, wherein S1 is preceded by diagnosing neighboring cells by archival data analysis, said neighboring cell data comprising cell name proximity, acquisition proximity, and/or power distribution proximity cell data.
3. The method for enhancing electric quantity correlation characteristics by using artificial acquisition failure according to claim 1, wherein the electric quantity correlation analysis model based on acquisition failure optimization comprises the steps of processing a daily freezing indication of an electric energy consumption acquisition system, and classifying 1 day or more of data into one day of electric energy consumption, so that users with original calculation results which cannot reach an identification threshold value are included in a diagnosis range.
4. A method for enhancing electric quantity correlation characteristics by using artificial acquisition failure as claimed in claim 3, wherein the electric quantity correlation analysis model based on acquisition failure optimization is specifically:
for the station area A, the ammeter B belongs to the station area A, the electric quantity of the station area checking meter C is normally collected, and when the ammeter B freezing indication is normally collected, T is i Day line loss phi of day stand area i The method comprises the following steps:
∆Φ i =Φ Ci -∑Φ i
wherein: phi Ci Table area checking table C daily electric quantity, Φ i Daily electricity quantity is calculated for each user;
if ammeter B is at T i The collection fails j days before the day, the electricity quantity of the electricity meter B fails to return to 0, and the collection fails T i-j Day to T i-1 Line loss phi k ’，k∈{T i-j ，…,T i-1 -have:
∆Φ K ’=∆Φ k +Φ Bk
wherein: phi k Is the real line loss of k days, phi BK K-day electric quantity of the ammeter B;
T i line loss of day # -, phi k ", there are:
∆Φ i ”=∆Φ i -∑Φ Bk
wherein: phi i Is T i Real day line loss, ΣΦ BK T for ammeter B i-j To T i-1 J total daily electric quantity sum;
T i-j to T i Mutation occurs in daily line loss.
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