CN110658487A - Meter box and system capable of achieving intelligent electric meter error online estimation - Google Patents
Meter box and system capable of achieving intelligent electric meter error online estimation Download PDFInfo
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Abstract
The invention relates to a meter box and a system capable of realizing online error estimation of an intelligent electric meter. A metering chip is embedded in the total opening of the meter box, namely a metering unit is added in the total opening to serve as a total table of all the meters of the meter box, so that the line loss can be reduced to the maximum extent. When the general table and each sub-table are located in one table box, the uncertainty of factors influencing the line loss change becomes small, and the impedance of the line therebetween is small, so that the influence of load fluctuation on the line loss can be ignored, and the line loss therebetween can be considered as a fixed loss. And fitting a regression equation by using a regression algorithm, calculating the fixed loss existing in the regression equation, and obtaining the rest of the fixed loss, namely the metering error of the electric energy meter.
Description
Technical Field
The invention belongs to the field of electric energy metering, and particularly relates to a meter box and a system capable of realizing online error estimation of an intelligent electric meter.
Background
The intelligent electric energy meter is an important component of an intelligent power grid, has the characteristics of large operation quantity, wide distribution area and severe working environment, plays an important role in aspects of trade settlement and management, power utilization information acquisition, intelligent power utilization and the like, is a terminal device directly associated with a user side, and has the advantage that the metering accuracy directly influences the benefits of users and power supply companies, so that the error verification of the intelligent electric energy meter becomes more important. At present, the national grid company carries out error verification on the intelligent electric energy meter in operation in a certain period mainly in a mode of operation spot check, but the overhaul period is long, and a user cannot use electricity during maintenance, so that the normal production and life are greatly influenced, and a large amount of manpower, material resources and financial resources are required to be arranged in the mode, so that the economy is poor. Aiming at the defects that the running spot check cannot find out the out-of-tolerance meter in time, the economy is poor and the reliability is poor, at present, national network companies propose intelligent electric energy meter error online detection, and expert scholars in many related fields have made some researches on the intelligent electric energy meter error online detection.
The existing error calculation method for the intelligent electric energy meter has a great problem in reliability, and the reason is that certain line loss exists from the general meter to each sub-meter, so that the loss is caused by a plurality of factors, such as: the line loss can change along with the reasons such as fluctuation of load size, and the line loss is difficult to express accurately by using a formula, so the error of the electric meter and the line loss are mixed together and cannot be distinguished. However, some existing algorithms try to calculate the line loss through formula derivation, but influence factors of the existing algorithms are always not considered thoroughly, so that the calculated electric meter error often contains a large part of line loss, a plurality of normal meters are also judged as over-differential meters, and the reliability of the calculation result is low. Aiming at the problem, the invention provides the intelligent meter box, wherein a metering chip is embedded in a total opening of the meter box, namely, a metering unit is added in the total opening to serve as a total table of all the meters of the meter box, so that the line loss can be reduced to the maximum extent. When the general table and each sub-table are located in one table box, the uncertainty of factors influencing the line loss change becomes small, and the impedance of the line therebetween is small, so that the influence of load fluctuation on the line loss can be ignored, and the line loss therebetween can be considered as a fixed loss. And fitting a regression equation by using a regression algorithm, calculating the fixed loss existing in the regression equation, and obtaining the rest of the fixed loss, namely the metering error of the electric energy meter.
Disclosure of Invention
The invention aims to provide a meter box and a system capable of realizing online error estimation of an intelligent electric meter, which can overcome the defect of large deviation of an error estimation value of the meter due to the fact that the line loss cannot be determined in the existing algorithm and technology; the invention also embeds a temperature and humidity sensor in the master switch for detecting the natural environment around the meter in real time, which is a function that the prior art does not have, and the collected temperature and humidity value can be used for evaluating the running state of the meter or predicting the service life of the meter, and the influence of the natural environment on the running condition of the meter is reflected therein.
In order to achieve the purpose, the technical scheme of the invention is as follows: the utility model provides a can realize table case of online estimation of smart electric meter error, be equipped with a plurality of smart electric meters in the table case, imbed a metering chip who is used for detecting the electric quantity and a temperature and humidity sensor who is used for detecting the temperature and humidity in the natural environment of smart electric meter work in the table case in the total opening of table case.
The invention also provides a system capable of realizing the online error estimation of the intelligent ammeter, which comprises the meter boxes, a concentrator connected with the metering chip, the temperature and humidity sensor and the intelligent ammeter in each meter box, and an upper computer connected with the concentrator.
In an embodiment of the invention, the upper computer comprises a power consumption information acquisition system for acquiring and storing electric quantity data of the intelligent electric meter and an intelligent electric meter error online estimation unit for realizing intelligent electric meter error online calculation.
In an embodiment of the present invention, the system implements error estimation of the smart meter in the following manner:
step S1, collecting user information of the electric meter, and corresponding the user information of the electric meter to the collected electricity utilization data;
step S2, respectively collecting user tags and power consumption data according to a preset period;
step S3, electricity utilization data are preprocessed by iForest: firstly, constructing an isolated forest containing t elements through iForest, then carrying out anomaly detection on power consumption data, and carrying out data cleaning;
step S4, establishing error calculation model
Utilizing the SVR to fit a regression model to the preprocessed data, constructing a multiple linear regression equation, and estimating the metering error of the intelligent electric meter, wherein the multiple linear regression equation has the following formula:
wherein, PΣThe electric quantity, P, collected by the metering chip for the total opening in the meter boxiFor the i-th smart meter power, alphaiIs the coefficient of the ith smart meter, PfIs a fixed loss;
let the metering error of the ith intelligent electric meter be thetaiIn fact, the θ value of each smart meter is a relative error between the measured value of the corresponding smart meter and the actual value collected during the operation process, and can be obtained by the following formula:
then
Preal＝(1+θ)P (3)
Substituting (3) into (1) to obtain:
step S5, analyzing and calculating the obtained error of the intelligent electric meter:
estimating errors of the intelligent electric meter through a multivariate linear regression equation fitted every day to obtain metering errors of the intelligent electric meter, if the continuous error out-of-tolerance days day of the intelligent electric meter is more than 5, giving a warning, analyzing the intelligent electric meter according to a historical error distribution diagram of the intelligent electric meter to determine whether the intelligent electric meter is a real out-of-tolerance intelligent electric meter or not, if the error distribution of the intelligent electric meter is very irregular and is always large or small, judging the intelligent electric meter to be an out-of-tolerance meter with a very high probability, and sending a person to a site to verify the intelligent electric meter; if the error distribution of the intelligent electric meter is regular all the time and the curve is gentle, misjudgment is possible and further analysis needs to be carried out by combining the recent electric quantity curve.
Compared with the prior art, the invention has the following beneficial effects: the invention can overcome the defect of larger deviation of the error estimation value of the meter caused by the failure to determine the line loss in the existing algorithm and technology; the invention also embeds a temperature and humidity sensor in the master switch for detecting the natural environment around the meter in real time, which is a function that the prior art does not have, and the collected temperature and humidity value can be used for evaluating the running state of the meter or predicting the service life of the meter, and the influence of the natural environment on the running condition of the meter is reflected therein.
Drawings
Fig. 1 is a schematic diagram of a meter box, namely a smart meter cluster.
FIG. 2 is a schematic diagram of a system for on-line error estimation of a smart meter according to the present invention.
FIG. 3 is a schematic diagram of an iForest anomaly detection process.
Fig. 4 is a flowchart of error estimation of the smart meter according to the present invention.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
According to the meter box capable of realizing the on-line error estimation of the intelligent electric meter, a metering chip is embedded in the total opening of the meter box, namely a metering unit is added in the total opening to serve as a total meter of each household meter of the meter box, and a temperature and humidity sensor is embedded in the total opening and used for monitoring the natural environment of the meter in work. Smart meter box and smart meter cluster sketch map as shown in FIG. 1.
The invention also provides a system capable of realizing the online error estimation of the intelligent ammeter, which comprises the meter boxes, a concentrator connected with the metering chip, the temperature and humidity sensor and the intelligent ammeter in each meter box, and an upper computer connected with the concentrator.
Data collected by the master metering chip, the temperature and humidity sensor and the intelligent electric energy meter are uploaded to the concentrator in a carrier wave or wireless communication mode, the concentrator uploads the summarized data to the user information collection system, and therefore online estimation of errors of the intelligent electric energy meter can be carried out by using electric quantity data in the user information collection system. The schematic diagram of the user information acquisition system is shown in fig. 2.
The power grid user information acquisition system generally issues an electric quantity freezing command to the bottom layer intelligent electric energy meter every day, namely, each electric energy meter or each electric quantity metering unit only acquires one data every day, namely, the electric quantity corresponding to the daily electric quantity of a user is transmitted to the acquisition system. After the electric quantity data of multiple days are collected, the metering error of the intelligent electric energy meter can be estimated at a computer terminal.
As shown in fig. 4, the electric energy meter error estimation process is as follows:
s1: and collecting the user information of the electric meter through the meter reading machine, so that the user information of the electric meter corresponds to the collected electricity utilization data.
S2: and respectively acquiring user tags and power utilization data according to a preset period.
S3: preprocessing data with iForest
Firstly, the collected electric quantity data is cleaned, and abnormal measurement data such as zero data and light load data which can influence the accuracy of the metering error result of the electric meter are eliminated. The method of 'isolated forest' is adopted to preprocess data so as to eliminate abnormal measurement data such as zero data and light load data. Isolated forest (islandform or iForest) is a typical fast anomaly detection algorithm that employs an ensemble learning strategy. The algorithm abandons large cost
The method is characterized in that the thinking of the high-precision detector is trained, and a plurality of sub-detectors are constructed and fused to obtain better detection performance, so that the complexity is relatively low. implementation of iForest includes constructing an isolated forest containing t elements and performing anomaly detection on the data.
(1) Constructing an isolated forest comprising t elements
The iForest realizes the separation of data through a random hyperplane, and the separation is circulated until the subspace has only one data point. The normal numerical value is in a high-density area, the data is required to be completely isolated and separated for multiple times, the abnormal data is in a low-density area, and the data is completely separated through few isolation. The core problem of iForest is a method for separating data, and the construction process is as follows:
1)X＝{x1,…,xnfor a given set of data (a-x),
xi＝(xi1,…,xid) Randomly extracted from X
A subset X' of X formed by sample points is placed into a root node;
2) randomly assigning a dimension q from d dimensions, and randomly generating a cut point p, min (x) in the current dataij,j＝q,xij∈X′)＜p＜max(xij,j＝q,xij∈X′)；
3) Forming a hyperplane from the cutting point, dividing the data into two subspaces, and respectively placing the data with the dimensionality smaller than P and the dimensionality larger than P on the left side and the right side of the node;
4) circularly executing 2 and 3 to form a new node when the data can not be divided continuously or the division times reach
When the division is stopped.
(2) Abnormal value detection is carried out on the detected data
Set the daily electricity data set for nearly T days as x, traverse each iTree, find the path length h (x) of x. Meanwhile, a sliding window is created for storing stream data, and whenever new daily electric quantity data flows into the window, the daily electric quantity data of the last day in the window is cleared, so that window sliding is realized, and a schematic diagram of test data traversing iTree is shown in FIG. 3.
Standardizing the depth of iTrees, given an include
A sample data set, the tree having an average path length of
Where h (i) is a harmonic number, this value can be estimated as ln (i) + ξ, ξ being the euler constant.
To give toNumber of samplesThe average of the path lengths is used to normalize the path length h (x) of the sample x.
The detected sample x anomaly score is defined as:
where E [ h (x) ] is the expectation of the path length of sample x in the collection of isolated trees. The detection conclusion is as follows:
E(h(x))→0,s→1
s (x) is more likely to be abnormal data as it approaches 1, and is more likely to be a normal point as it approaches 0. When s (x) of most data is 0.5, the data is free from abnormal values.
S4: error model building
After data is cleaned, an SVR fitting regression model is utilized to estimate the metering error of the intelligent electric energy meter, and because the general meter and each branch meter are positioned in one meter box, the uncertainty of factors influencing the line loss change becomes very small, and the impedance of a line therebetween is very small, so the influence of load fluctuation on the line loss can be ignored, the line loss therebetween can be considered as a fixed loss, the constructed multiple linear regression equation model becomes much simpler, and the formula is as follows:
wherein, PΣThe electric quantity, P, collected by the metering chip for the total opening in the meter boxiFor the i-th smart meter power, alphaiIs the coefficient of the ith smart meter, PfIs a fixed loss;
let the metering error of the ith intelligent electric meter be thetaiIn fact, the θ value of each smart meter is a relative error between the measured value of the corresponding smart meter and the actual value collected during the operation process, and can be obtained by the following formula:
then
Preal＝(1+θ)P (3)
Substituting (3) into (1) to obtain:
s5: establishing a regression model using SVR
And fitting the multiple linear regression equation through SVR to calculate the metering error of each meter, summarizing the meter errors calculated every day, and drawing an error distribution graph of each meter for subsequent analysis on whether the error distribution graph exceeds the tolerance or not.
The regression fitting based on SVR is to find an optimal classification surface in all training samples, so that the error of the sample from the selected optimal surface is minimized. By giving an insensitive loss function epsilon, training a sample by adopting a proper kernel function, calculating a penalty factor c and a variance g in the kernel function, and extracting a support vector corresponding to a parameter which is not zero, thereby establishing a regression model based on the support vector and the kernel function for a limited training sample.
1) The smaller the insensitive loss coefficient epsilon, the smaller the error of the regression function, where epsilon is set to 10-20Selecting a Gaussian Radial Basis Function (RBF) kernel
And modeling.
2) Selecting electric quantity data of a day close to T (T is more than or equal to N) as a training sample { (x)i,yi),1,2,3, …, T }, where x isiIs the input column vector of the ith training sample, which is the daily electric quantity value of each household electric meter, yiAnd metering values of the metering units for the corresponding daily total opening.
3) And calculating a penalty factor c and a variance g in the kernel function, and searching for optimal c and g parameters.
4) The linear insensitive loss function is defined as follows:
f(x)＝wTxi+b
L(f(x),y,ε)＝max{0,|y-f(x)|-ε}
assuming epsilon to be constant, the following optimal function is solved:
5) the regression function obtained after solving is:
f(x)＝wTxi+b
when the multiple linear regression equation is fitted, fitting is carried out on data collected in days close to T (T is larger than or equal to N), then recursion is carried out day by day, data in a new day are added, data in the farthest day are removed, and the fitted regression equation also has a time effect.
S6: error of meter obtained by analysis and calculation
Estimating errors of the electric energy meter through a regression equation fitted every day to obtain the metering errors of the electric energy meter, if the continuous error out-of-tolerance days day of the electric energy meter is more than 5, giving a warning, analyzing the meter according to a historical error distribution diagram of the meter to determine whether the meter is a real out-of-tolerance meter, if the error distribution of the meter is very irregular and always large or small, determining that the meter is an out-of-tolerance meter with a high probability, and sending a person to a site to verify the meter; if the error distribution of the meter is regular all the time and the curve is gentle, the error may be misjudged and further analysis needs to be performed by combining the recent electric quantity curve.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.
Claims (4)
1. The utility model provides a can realize table case of online estimation of smart electric meter error, be equipped with a plurality of smart electric meters in the table case, its characterized in that imbeds a measurement chip that is used for detecting the electric quantity and a temperature and humidity sensor who is used for detecting the temperature and humidity in the natural environment of smart electric meter work in the table case in the total opening of table case.
2. A system capable of achieving intelligent electric meter error online estimation is characterized by comprising a plurality of meter boxes according to claim 1, a concentrator connected with a metering chip, a temperature and humidity sensor and an intelligent electric meter in each meter box, and an upper computer connected with the concentrator.
3. The system for realizing the online estimation of the error of the intelligent electric meter according to claim 2, wherein the upper computer comprises an electric power consumption information acquisition system for acquiring and storing electric quantity data of the intelligent electric meter and an intelligent electric meter error online estimation unit for realizing the online calculation of the error of the intelligent electric meter.
4. The system for realizing the on-line error estimation of the intelligent electric meter according to the claim 3, is characterized in that the system realizes the error estimation of the intelligent electric meter by the following way:
step S1, collecting user information of the electric meter, and corresponding the user information of the electric meter to the collected electricity utilization data;
step S2, respectively collecting user tags and power consumption data according to a preset period;
step S3, electricity utilization data are preprocessed by iForest: firstly, constructing an isolated forest containing t elements through iForest, then carrying out anomaly detection on power consumption data, and carrying out data cleaning;
step S4, establishing error calculation model
Utilizing the SVR to fit a regression model to the preprocessed data, constructing a multiple linear regression equation, and estimating the metering error of the intelligent electric meter, wherein the multiple linear regression equation has the following formula:
wherein, PΣThe electric quantity, P, collected by the metering chip for the total opening in the meter boxiFor the i-th smart meter power, alphaiIs the coefficient of the ith smart meter, PfIs a fixed loss;
let the metering error of the ith intelligent electric meter be thetaiIn fact, the θ value of each smart meter is a relative error between the measured value of the corresponding smart meter and the actual value collected during the operation process, and can be obtained by the following formula:
then
Preal＝(1+θ)P (3)
Substituting (3) into (1) to obtain:
step S5, analyzing and calculating the obtained error of the intelligent electric meter:
estimating errors of the intelligent electric meter through a multivariate linear regression equation fitted every day to obtain metering errors of the intelligent electric meter, if the continuous error out-of-tolerance days day of the intelligent electric meter is more than 5, giving a warning, analyzing the intelligent electric meter according to a historical error distribution diagram of the intelligent electric meter to determine whether the intelligent electric meter is a real out-of-tolerance intelligent electric meter or not, if the error distribution of the intelligent electric meter is very irregular and always large or small, judging the intelligent electric meter to be an out-of-tolerance meter, and sending personnel to a site to verify the intelligent electric meter; if the error distribution rule of the intelligent electric meter is smooth, misjudgment is possible, and further analysis needs to be carried out by combining the recent electric quantity curve.
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