CN112464550A - Intelligent ammeter error estimation method based on dimension reduction estimation and damped least square method - Google Patents
Intelligent ammeter error estimation method based on dimension reduction estimation and damped least square method Download PDFInfo
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Abstract
The invention relates to an intelligent electric meter error estimation method based on dimension reduction estimation and a damped least square method, which comprises the following steps: the method comprises the steps of obtaining intelligent electric meter error remote estimation data, initializing an intelligent electric meter error remote estimation method, constructing a dimension reduction estimation model and solving by a damping least square method.
Description
Technical Field
The invention relates to the field of intelligent electric meter error remote estimation, in particular to an intelligent electric meter error remote estimation method.
Background
Along with the comprehensive construction of the electricity consumption information acquisition system, the traditional manual meter reading mode is replaced by automatic acquisition, the workload of manual on-site meter reading is greatly reduced, and meanwhile, the work of on-site patrol of the operation working condition of electricity customers, especially residential electricity customer metering devices in low-voltage transformer areas, is also greatly reduced. The intelligent electric meter is used as a metering tool for consuming electric energy by a user, and the operation reliability of the intelligent electric meter not only influences the operation income of a power grid company, but also directly relates to the actual benefits of thousands of households. In order to strengthen the dynamic management of a transformer area and improve the service level of a power grid, an efficient and accurate remote diagnosis method for the operation error of the intelligent electric energy meter is imperative to be found. The state monitoring of the traditional intelligent electric energy meter firstly realizes the online error monitoring and alarming of a monitoring target by means of adding online monitoring equipment and online detecting data such as voltage, current, power, load, electric quantity and the like of a metering device and a secondary circuit. Although the method improves the management work efficiency, the method brings about the increase of equipment purchase and operation maintenance cost.
Under the above background, the big data analysis technology can be utilized to collect the situation from the station based on the data of the station user power consumption, the station total table power consumption, the network loss, the user file, the user table relationship, etc. in the power consumption information collection system, the method comprises the steps of researching statistical rules of total meter electric quantity and sub meter electric quantity in the same power station area through multiple dimensions such as household variation relation, power consumption, network loss and the like, establishing an intelligent meter operation error calculation model and an analysis model, calculating operation errors of intelligent meters in the power station area, obtaining operation health conditions of all the intelligent meters in the power station area, realizing remote diagnosis and evaluation of the operation errors of the intelligent meters, providing an effective technical means for power utilization inspection work, overcoming the bottleneck that manual inspection work is large and specific at present, finding suspected metering points of abnormal operation in time, realizing efficient and accurate field inspection, and developing the field inspection and abnormal inspection work of the intelligent meters in a specific manner.
Currently, some efforts have been made by power workers to perform remote error estimation on smart meters. Most of the methods are based on a generalized flow conservation model, a high-dimensional linear equation set is solved by adopting a triangular decomposition method, a least square method and the like, and the similar method is like a meter box and a system which can realize the online error estimation of the intelligent electric meter in the prior patent (the patent number is CN 201910992342.9). However, such methods have the following problems: (1) the remote estimation precision of the electric meter errors is seriously influenced by the network loss of the cell, and the precise value of the real-time network loss is difficult to obtain; (2) if the influence of network loss is to be reduced, extra monitoring equipment is often required to be installed for processing; (3) the current remote error estimation method for the intelligent ammeter cannot well cope with the condition of network loss change of a distribution room, and when the network loss change is frequent and severe, the stability and robustness of estimation are greatly reduced;
for the above problems, there is a need for a technology that can be implemented under the existing metering conditions, can estimate network loss and user sub-meter errors in real time without independently calculating the network loss of the distribution room in advance, can effectively overcome the problem of reduction in estimation stability and robustness caused by network mutation, is helpful for realizing the mode change of the intelligent electric energy meter from periodic replacement to state replacement, can discover suspected abnormal metering points in time, overcomes the bottleneck that the workload of manual troubleshooting is large and the pertinence is lacking at present, and provides support for efficient power utilization polling.
Disclosure of Invention
The invention provides an intelligent electric meter error remote estimation method based on a dimension reduction estimation model and a damping least square method, which can realize real-time estimation of the network loss of a transformer area and the user sub-meter error under the existing metering condition and without independently calculating the network loss of the transformer area in advance, and can overcome the problems of estimation stability and robustness reduction caused by network loss mutation, and the technical scheme is as follows:
a smart meter error estimation method based on dimension reduction estimation and a damped least square method comprises the following steps: obtaining remote error estimation data of the intelligent electric meter, initializing the remote error estimation method of the intelligent electric meter, constructing a dimension reduction estimation model, and solving by a damped least square method, wherein,
step 1: the method for acquiring the error estimation data of the intelligent ammeter comprises the following steps:
step 1.1: obtaining static data, namely obtaining (1) intelligent electric meter information: the method comprises the steps that the total amount m of the intelligent electric meters of users in a transformer area is included, and the number n of the intelligent electric meters is 1,2, … and m; (2) main electrical characteristic parameters related to the topology architecture of the transformer area network comprise power supply radius and total length of a low-voltage line; (3) the main electrical characteristic parameters related to the load include load rate, power usage properties and proportion.
Step 1.2: acquiring dynamic information, namely acquiring electric energy metering data with time scales and sorted according to time, wherein the electric energy metering data comprises (1) the sum y (t) of three-phase active electric quantity of a total electric energy meter of a transformer area and the sum of three-phase active current, namely the total meter current i (t); (2) active electric quantity r of all user sub-meters in the regionn(t)。
Step 2: the intelligent electric meter error estimation method is initialized and comprises the following steps:
step 2.1: setting parameters including the setting of the number of clusters s, the setting of a forgetting factor rho and a damping factor mu0Setting;
step 2.2: reading historical data, and selecting applicable augmentation measurement items and augmentation estimation items according to the metering data: (1) if the metering data comprises a summary table Current i (t), selecting a Current-Resistance (CR) scheme: augmented measurement term rex(t)＝i2(t), augmented estimation term
Wherein
Is an estimate of the product of the measurement interval time and the plateau equivalent resistance; (2) if the summary current is not included in the metering data, a Power-Loss (PL) scheme is selected:
wherein
Is the average value of the active power of the summary, y*(t) is the per unit value of the total table active power,
is an estimated value of the network loss of the distribution area;
step 2.3: according to the augmented measurement term rex(t) clustering out a cluster center
Arranging and writing the 1 st to t th augmented measurement items into a matrix form Rex(t)(t×1)The dimension is t × 1;
step 2.4: estimator matrix
Performing initialization including the initial values of the first calculation and the second calculation
And
step 2.5: covariance matrix Pμ(t) initialization, i.e. Pμ(1)＝(0.001×I(m+s)×(m+s))-1Wherein I is an identity matrix with dimensions (m + s) × (m + s);
and step 3: the dimension reduction estimation model is constructed, and the process comprises the following steps:
step 3.1: reading the latest dynamic data including the total electric quantity y (t) of the distribution room and the electric quantity r of the user sub-metern(t), n is 1,2, …, m, and an augmentation measure r determined after initializationex(t)；
Step 3.2: get
Determining a membership sequence M (t) of the metrology data;
step 3.3: the measurement data matrix of this time is established according to the following formulas:
and 4, step 4: and (5) solving by a damped least square method.
Further, the solution of the damped least squares in step 4 includes the following steps:
step 4.1: reading the latest measurement data matrix, including Δ y (t),
step 4.2: reading the covariance matrix P of the last timeμ(t-1)；
Step 4.3: matrix of estimators of the first two readings
Step 4.4: updating the damping gain according to:
wherein mean refers to the average of the matrix;
step 4.5: the covariance matrix is updated according to:
step 4.6: the estimator matrix is updated according to:
step 4.7: reading the estimated value of the latest user sub-table error from the estimator matrix:
step 4.8: and reading and calculating the latest primary network loss rate estimated value from the estimator matrix:
step 4.9: ending or waiting for a new dynamic data entry.
The technical scheme provided by the invention has the beneficial effects that:
(1) the method and the device can realize remote error estimation of the intelligent electric meter and greatly reduce the operation and maintenance cost of the intelligent electric meter. The existing research and patent aiming at the remote error estimation of the intelligent electric meter mainly have two aspects: one is to install additional on-line monitoring equipment, and the other is to directly analyze by adopting a big data method. The installation of the additional monitoring equipment can accurately monitor the health condition of the smart meter in real time, but the investment and the operation and maintenance cost are increased. The technical scheme provided by the invention is a big data-based intelligent electric meter error remote estimation method, and the method only needs to perform calculator modeling and solving after reading the measurement data from a measurement data management system and an automatic data collection system of a main station, and basically does not need additional investment and operation and maintenance. In the regular verification of the large-scale intelligent electric meter, the method can be used for positioning suspicious metering points in advance and then sending personnel for transportation and inspection in a targeted manner, so that the cost of the verification of the intelligent electric meter can be greatly reduced.
(2) The invention adopts a dimension reduction estimation model, and can realize real-time estimation of network loss and ammeter error under the condition of not calculating network loss independently in advance. Most of the existing methods for remotely estimating the error of the smart meter based on big data require to obtain a high-precision network loss value of a cell in advance, and the requirement is very strict. The problems of unclear topology, unclear parameters and low data return rate generally exist in the conventional power distribution network, particularly in a low-voltage distribution area network, so that accurate distribution area network loss is difficult to obtain, and the application range of most intelligent electric meter error remote estimation methods based on big data is limited. The method provided by the invention adopts the dimension reduction estimation model to carry out linear approximation on the nonlinear model, so that the real-time estimation of the network loss and the ammeter error can be realized under the condition of not calculating the network loss independently in advance, and the method has wider application range.
(3) The method adopts the damping recursion least square algorithm to solve the estimation model, ensures the utilization rate of new data, and simultaneously reduces the variation range of the estimation quantity, so that the error estimation has better stability and robustness. The existing intelligent electric meter error remote estimation solving method is generally influenced by network loss change. Frequent and drastic changes in network loss often lead to instability of the algorithm and inaccuracy of the estimation result. The recursive damping least square method adopted by the invention, by adding the damping item, reduces the variation range of the estimation quantity under the condition of ensuring the utilization rate of new data, improves the resistance of the estimation process to network loss variation and other interferences, and further improves the stability and robustness of the error estimation of the intelligent ammeter.
Drawings
FIG. 1 is a schematic diagram of a typical topology and quantity measurement of a suitable cell for use with the present invention;
FIG. 2 is a schematic diagram of membership sequence acquisition according to the present invention;
FIG. 3 is a diagram of the augmented estimation term r of the present inventionex(t) schematic representation of the result after multiplication with the membership sequence m (t);
FIG. 4 shows the result of network loss and error estimation for 112 smart meters in an actual distribution area using the PL scheme;
FIG. 5 shows the network loss and error estimation results of 112 smart meters in an actual distribution area using the CR model according to the present invention;
FIG. 6 is a flow chart of an error estimation method of the present invention.
Table 1 shows the analysis table of the overdetection rate and the missed detection rate of the estimation result of the actual distribution area of the Power Loss (PL) scheme and the Current Resistance (CR) scheme according to the present invention, where the overdetection refers to the discrimination of the error-free electric meter as the over-error electric meter, and the missed detection refers to the discrimination of the error-free electric meter as the error-free electric meter.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below.
In order to meet the existing measurement conditions and operation systems and estimate the error and the network loss of the electric energy meter of the area user in real time under the condition of not calculating the network loss of the area independently in advance, the embodiment of the invention provides an intelligent electric meter error remote estimation method based on a dimension reduction estimation model and a damping least square method, which comprises the following steps: the method comprises four steps of obtaining intelligent electric meter error remote estimation data, initializing an intelligent electric meter error remote estimation method based on a dimension reduction estimation model and a damping least square method, constructing the dimension reduction estimation model, and solving the model by the damping least square method. See the description below for details:
step 1: the method for acquiring the error remote estimation data of the intelligent electric meter comprises the following steps:
step 1.1: obtaining static data, namely obtaining (1) intelligent electric meter information: the total amount m of the district user intelligent electric meters, the number n of the intelligent electric meters is 1,2, …, m and the like; (2) main electrical characteristic parameters related to the topology of the distribution network, such as power supply radius, total length of low-voltage lines, etc.; (3) the main electrical characteristic parameters related to the load, such as load rate, electrical property and proportion, etc.
Step 1.2: and acquiring dynamic information, namely acquiring the electric energy metering data which is provided with relatively precise time scales and is sequenced in time. The method comprises the following steps of (1) the sum y (t) of three-phase active electric quantity of a total electric energy meter of a transformer area and the sum i (t) of three-phase active current; (2) active electric quantity r of all user sub-meters in the regionn(t), etc., as shown in FIG. 1. In the figure, Δ w represents the network loss, ξ, of the station arean(n is 1,2, …, m) is a correction coefficient of the nth user sub-table, and the relation of the correction coefficient and the electric meter error is as follows: xin＝1+Xn. A main station of the applied transformer area needs to be provided with a measurement data management system and an automatic data acquisition system so as to repair and clean data; time mark dislocation, and the artificially filled data should be eliminated; y (t), i (t) and rn(t) the data freezing time is recommended to be once freezing for 15 minutes, or once freezing for 1 hour, and once freezing for 1 day is not recommended; too short a freezing time increases communication load, and too long a freezing time lengthens estimation time, which reduces real-time performance.
Step 2: the intelligent electric meter error remote estimation method based on the dimension reduction estimation model and the damping least square method is initialized, and the process comprises the following steps:
step 2.1:and setting parameters of the model and the algorithm. The method comprises the setting of a cluster number s, the setting of a forgetting factor rho and a damping factor mu0Setting; the cluster number s reflects the approximation degree of the network loss of the distribution area, the more the cluster number is, the more accurate the estimation of the network loss of the distribution area is, but the larger the dimension of the unknown quantity is, the larger the calculated quantity is, and the longer the estimation time is, and experimental research shows that when s is 20, the estimation effect is relatively good; the forgetting factor rho reflects the influence degree of old data on new data, the larger the forgetting factor is, the larger the estimation result of the past data on the current is, the smaller the forgetting factor is, the utilization rate of the new data can be improved, and as we pay attention to the current condition of the electric meter, the smaller forgetting factor is set, but the smaller forgetting factor can cause the reduction of the estimated anti-interference capability, so that the compromise setting is needed, and experimental research finds that when the rho is set to be 0.5, the estimation result is the best; when the parameter value is close to the following values, the estimation effect is best: damping factor mu0The method is the core of damping minimum quadratic multiplication, which embodies the size of the variation range of the estimator, and is found through research that the larger the variation amplitude of network loss is, the larger the variation of the error estimation value of the electric meter is, the smaller the damping factor is, the larger the variation range of the estimation value is, the more sensitive the estimation value is to disturbance and variation, the smaller the damping factor is, the smaller the variation range of the estimation value is, the higher the stability and robustness of estimation is, but the sensitivity of estimation is reduced, and through experimental research, when the variation range of mu is smaller, the method can be used for estimating the power of the electric meter, and the power consumption0When 0.01, the effect is estimated to be optimal.
Step 2.2: reading the measurement history data. Selecting applicable augmented measurement items and augmented estimation items according to metering data
(1) If the metering data comprises a summary Current i (t), selecting a Current-Resistance scheme: r isex(t)＝i2(t)， 
Wherein
Is an estimate of the product of the measurement interval time and the equivalent resistance of the mesa (see reference 7). (2) If the summary current is not included in the metering data, a Power-Loss (Power-Loss) scheme is selected:
step 2.3: according to the augmented measurement term rex(t) clustering out a cluster center
The 1 st to t th augmented measurement items are written into a matrix form Rex(t)(t×1)The dimension is t × 1; k-means clustering program pair R adopting MATLAB self-carryingex(t)(t×1)Is processed to directly obtain
Step 2.4: estimator matrix
Carry out initialization, including
And
namely, it is
Wherein
For MATLAB language, i.e. to
The t-th column of (1);
step 2.5: covariance matrix Pμ(t) initialization, i.e. Pμ(1)＝(0.001×I(m+s)×(m+s))-1Wherein I is an identity matrix with dimensions (m + s) × (m + s);
and step 3: the method for constructing the dimension reduction estimation model comprises the following steps:
step 3.1: reading the latest dynamic data including the total electric quantity y (t) of the distribution room and the electric quantity r of the user sub-metern(t) (n ═ 1,2, …, m) and the augmentation measure r determined by Step 2, substep 2)ex(t); note that the freeze interval for the new dynamic data must be consistent with the historical data;
step 3.2: get
Determining the membership sequence M (t) of the metrology data, i.e., the membership sequence m (t) and rex(t) nearest centers to determine membership to the measurement; the specific process schematic is shown in fig. 2. The dots on the solid line above the graph represent the respective augmented measurement term rex(t), each triangle on the dotted line represents each cluster center
In the figure, data are divided into 3 types, that is, if s is set to be 3, k is set to be 1,2, 3; the membership sequence M (t) composed of 1 and 0 at the lower part of the graph represents the membership condition of each measurement relative to the clustering center;
step 3.3: the measurement data matrix of this time is established according to the following formulas:
r(t)1×m＝[r1,r2,…,rm]，
wherein, the delta y (t) is a total loss matrix which represents the sum of the network loss of the transformer area and the loss caused by the error of the user electric meter;
the dimension is 1 × s for the augmented measurement item estimation value; r (t)1×mA reading matrix of the user sub-meter with the dimension of 1 × m;
is an approximate augmented reading matrix with dimensions of t x (m + s); from rex(t) conversion to
The diagram is shown in fig. 3, in the diagram, the number of the augmentation unknowns χ (τ) (τ is 1,2, …, t) without being processed by the membership sequence is t, after being processed by the membership sequence, the infinite augmentation unknowns are approximated by the finite estimation values, and the number of the unknowns in the diagram is reduced to 3, so that the model solution is possible. This is a process of dimension reduction of unknown quantities, so the model is called a dimension reduction estimation model;
and 4, step 4: the method for solving the model by the damping least square method comprises the following steps:
step 4.1: reading the latest measurement data matrix, including Δ y (t),
step 4.2: reading the covariance matrix P of the last timeμ(t-1)；
Step 4.3: matrix of estimators of the first two readings
Step 4.4: updating the damping gain according to:
mean is the average value of the matrix, namely the sum of all elements of the matrix is divided by the number of the elements contained in the matrix, and the function can be directly called by MATLAB;
step 4.5: the covariance matrix is updated according to:
wherein I is an identity matrix having dimensions (m + s) × (m + s);
step 4.6: the estimator matrix is updated according to:
step 4.7: reading the estimated value of the latest user sub-table error from the estimator matrix:
wherein
Is MATLAB language, i.e. the t column from the first row to the m row;
step 4.8: and reading and calculating the latest primary network loss rate estimated value from the estimator matrix:
the t column in MATLAB language, i.e., lines m +1 through s;
step 4.9: waiting for a new dynamic data input; and when new dynamic data are input, returning to the step 3.1.
Description of the drawings: all the steps can be implemented on Visual Studio by adopting C language programming. To make the implementation more convenient and the calculation more efficient, a MATLAB implementation is proposed.
Examples and analysis
The application analysis is performed by taking 112 intelligent electric meters in a certain region of Tianjin as an example. The data freezing time is 15 minutes once, and 2880 times of measurement are performed in total, and the measurement type is three-phase of a distribution room general tableThe sum y (t) of the power and electric quantity, the sum i (t) of three-phase currents of the general meter, and the power and electric quantity r of 112 intelligent electric metersn(t) (n ═ 1,2, …,112), the parameters are set as follows: s is 20; ρ is 0.5; mu.s00.01. The results of the experiments using the PL and CR protocols are shown in FIGS. 4 and 5, respectively. It can be seen that if the electric meter whose absolute value of the electric meter estimation value exceeds 2% is considered as an out-of-tolerance electric meter, the out-of-tolerance electric meter numbers are 59 and 82, and the average value of all the estimation results of each electric meter is taken as the final estimation result, the respective errors are about + 5.00%, + 9.10%. The 14 th, 59 th and 82 th electric meters have errors of-1.84%, + 4.61%, + 9.22% respectively through field calibration based on standard meters. Since the 14 th meter has a small error and cannot be distinguished, in order to avoid missing detection, it is suggested that the meters with error estimation values exceeding 1% in absolute value are judged as out-of-tolerance meters, and the situations of over detection and missing detection are shown in table 1. It can be seen that, because the threshold value for judging the out-of-tolerance is low, the two schemes do not have missed detection; because the PL scheme is approximate to the CR scheme, the estimation accuracy of the PL scheme is lower than that of the CR scheme, so the overdetection rate of the PL scheme is higher than that of the CR scheme, but the work load of inspection personnel can be greatly reduced by 12.5 percent of overdetection rate, and therefore, the method provided by the invention is still suitable for the existing measurement conditions and marketing systems.
TABLE 1 overdetection and missed detection rates for PL and CR protocols
Reference to the literature
[1] And (3) carrying out Lu forward, and realizing [ D ] sand growing by using frequency tracking and FPGA based on recursive damping least square method: university of sand finishing, 2015.
[2] Gujing, an intelligent power grid AMI oriented network metering key technology and user electricity consumption data mining research [ D ]. Tianjin, Tianjin university, 2012.
[3]Xiangyu Kong,Yuying Ma,Xin Zhao,Ye Li,Yongxing Teng.A Recursive Least Squares Method with Double-Parameter for Online Estimation of Electric Meter Errors.[J].ENERGIES,2019,12(805).
[4]Korhonen,A.Verification of Energy Meters Using Automatic Meter Reading Data.Master’s Thesis,AALTO University,Espoo,Finland,2012.
[5] The research on the line loss analysis and the loss reduction measures of the low-voltage transformer area [ D ]. southeast university, 2017.
[6] Shenli, DLT 1507-.
[7] DL/T686 and 2018 electric power network electric energy loss calculation guide rule [ S ], national energy agency, 2018.
Those skilled in the art will appreciate that the drawings are only schematic illustrations of preferred embodiments, and the above-described embodiments of the present invention are merely provided for description and do not represent the merits of the embodiments.
The above description is only exemplary of the present invention and should not be taken as limiting the invention, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. A smart meter error estimation method based on dimension reduction estimation and a damped least square method comprises the following steps: obtaining remote error estimation data of the intelligent electric meter, initializing the remote error estimation method of the intelligent electric meter, constructing a dimension reduction estimation model, and solving by a damped least square method, wherein,
step 1: the method for acquiring the error estimation data of the intelligent ammeter comprises the following steps:
step 1.1: obtaining static data, namely obtaining (1) intelligent electric meter information: the method comprises the steps that the total amount m of the intelligent electric meters of users in a transformer area is included, and the number n of the intelligent electric meters is 1,2, … and m; (2) main electrical characteristic parameters related to the topology architecture of the transformer area network comprise power supply radius and total length of a low-voltage line; (3) main electrical characteristic parameters related to the load, including load rate, power utilization properties and proportion;
step 1.2: obtaining dynamic information, namely obtaining electric energy metering data with time scales and sorted according to time, wherein the electric energy metering data comprises (1) the sum y (t) of three-phase active electric quantity and the sum of three-phase active current of a total electric energy meter of a transformer areaThe summary current i (t); (2) active electric quantity r of all user sub-meters in the regionn(t)。
Step 2: the intelligent electric meter error estimation method is initialized and comprises the following steps:
step 2.1: setting parameters including the setting of the number of clusters s, the setting of a forgetting factor rho and a damping factor mu0Setting;
step 2.2: reading historical data, and selecting applicable augmentation measurement items and augmentation estimation items according to the metering data: (1) if the metering data comprises a summary Current i (t), selecting a Current-Resistance (CR) scheme: augmented measurement term rex(t)＝i2(t), augmented estimation term
Wherein
s is an estimated value of the product of the measurement interval time and the equivalent resistance of the platform area; (2) if the summary current is not included in the metering data, a Power-Loss (PL) scheme is selected:
wherein
Is the average value of the active power of the summary, y*(t) is the per unit value of the total table active power,
s is an estimated value of the network loss of the transformer area;
step 2.3: according to the augmented measurement term rex(t) clustering out a cluster center
Arranging and writing the 1 st to t th augmented measurement items into a matrix form Rex(t)(t×1)The dimension is t × 1;
step 2.4: estimator matrix
Performing initialization including the initial values of the first calculation and the second calculation
And
step 2.5: covariance matrix Pμ(t) initialization, i.e. Pμ(1)＝(0.001×I(m+s)×(m+s))-1Wherein I is an identity matrix with dimensions (m + s) × (m + s);
and step 3: the dimension reduction estimation model is constructed, and the process comprises the following steps:
step 3.1: reading the latest dynamic data including the total electric quantity y (t) of the distribution room and the electric quantity r of the user sub-metern(t), n is 1,2, …, m, and an augmentation measure r determined after initializationex(t)；
Step 3.2: get
Determining a membership sequence M (t) of the metrology data;
step 3.3: the measurement data matrix of this time is established according to the following formulas:
and 4, step 4: and (5) solving by a damped least square method.
2. The method of claim 1, wherein s-20; ρ is 0.5; mu.s0＝0.01。
3. The method of claim 1, wherein the damped least squares solution of step 4 comprises the steps of:
step 4.1: reading the latest measurement data matrix, including Δ y (t),
step 4.2: reading the covariance matrix P of the last timeμ(t-1)；
Step 4.3: matrix of estimators of the first two readings
Step 4.4: updating the damping gain according to:
wherein mean refers to the average of the matrix;
step 4.5: the covariance matrix is updated according to:
step 4.6: the estimator matrix is updated according to:
step 4.7: reading the estimated value of the latest user sub-table error from the estimator matrix:
step 4.8: and reading and calculating the latest primary network loss rate estimated value from the estimator matrix:
step 4.9: ending or waiting for a new dynamic data entry.
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