Charging pile group error characteristic analysis method and device, equipment and medium

Abstract

The invention provides a charging pile group error characteristic analysis method and device, equipment and a medium. The method comprises the following steps: constructing an initial state variable set; calculating an error coefficient state variable estimation value set of the charging pile group at the moment k; calculating an output variable estimation value set at the moment k; calculating a state variable correction value set at the moment k and a covariance thereof; and outputting the corrected charging pile group error coefficient vector estimation value at the moment k and the uncertainty thereof. According to the invention, based on the thought of the ensemble Kalman filtering algorithm and the energy conservation law of each pile in the charging station, the randomness characteristic of the error coefficient of the charging pile is fully considered, the error coefficient is calculated and corrected in multiple measurement processes, and the uncertainty of each pile is obtained, so that the out-of-tolerance charging pile can be effectively identified and judged from the charging pile group; therefore, the verification efficiency of the charging pile is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging pile verification, and in particular to a method, device, equipment and medium for analyzing error characteristics of a charging pile group. Background Art

[0002] Charging piles are important facilities for providing electric energy for electric vehicles. With the popularization of new energy vehicles, large-scale vehicle charging piles have been put into use. Charging piles are mainly divided into two categories: AC charging piles and DC charging piles. AC charging piles have a relatively slow charging speed, but the installation cost is low and it is easy to use; DC charging piles have a fast charging speed and can replenish a large amount of electricity for electric vehicles in a short time, but the cost is relatively high. In the future, with the continuous advancement of technology and the improvement of infrastructure, charging piles will become more popular, further promoting the development of the electric vehicle industry and making important contributions to green travel and energy conservation and emission reduction.

[0003] However, during the charging process, various internal or external factors will affect the accuracy of the electric energy metering of the charging pile, which is directly related to the interests of users and service providers. Since charging piles involve the trade settlement of electric energy, they are required to be mandatory working measuring instruments. The traditional on-site manual identification method is gradually unable to adapt to the existing requirements due to its high cost and low efficiency. Most of the existing studies use big data to verify the charging piles. According to different principles, they can be divided into particle swarm optimization algorithm, solution of multidimensional equations, Kalman filtering, etc. Among them, the particle swarm algorithm is an optimization algorithm based on swarm intelligence, which has strong global search ability and is suitable for the analysis of the error characteristics of the charging pile group. However, its parameters are usually set before operation, and it is impossible to dynamically adjust the parameters according to the actual situation during the search process, and there is a lack of dynamic adjustment mechanism; while the method of using electric energy data and error coefficients to establish a multidimensional equation group for solution is relatively simple and easy to implement, but it ignores the random characteristics of the error coefficient. Kalman filtering adopts the idea of ​​recursive algorithm, combined with the error analysis model of the charging pile group, it can realize the estimation of the error coefficient. However, Kalman filtering is only applied to linear systems, and the estimation accuracy of nonlinear systems such as charging pile groups is poor.

[0004] The present invention proposes a method and system for analyzing the error characteristics of a charging pile group based on an ensemble Kalman filter, which overcomes the weakness of the Kalman filter that it is limited to processing linear problems, fully considers the random characteristics of the error coefficient of the charging pile, calculates and dynamically corrects the error coefficient during multiple measurements, and obtains the uncertainty of each pile at the same time. It can effectively identify and judge out-of-tolerance charging piles from a charging pile group, thereby greatly improving the calibration efficiency of the charging piles. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method, device, equipment and medium for analyzing the error characteristics of a charging pile group. Based on the ensemble Kalman filter algorithm and the idea of ​​the law of conservation of energy of each pile in a charging station, the random characteristics of the error coefficient of the charging pile are fully considered, the error coefficient is calculated and corrected during multiple measurements, and the uncertainty of each pile is obtained at the same time. It can effectively identify and judge the out-of-tolerance charging piles from the charging pile group, thereby greatly improving the calibration efficiency of the charging piles.

[0006] In a first aspect, the present invention provides a method for analyzing error characteristics of a charging pile group, comprising:

[0007] According to the measured value of the electric energy consumed by the charging pile and the electric energy value consumed by the charging pile group obtained from the reading of the total electric energy meter on the high-voltage side of the charging station distribution transformer, the initial mean value and covariance of the state variable of the error coefficient of the charging pile group at time k-1 are determined, and the initial state variable set is formed by sampling;

[0008] Calculate the estimated value set of state variables of the error coefficient of the charging pile group at time k based on the initial state variable set;

[0009] Calculate the output variable estimation value set at time k based on the charging pile group error coefficient state variable estimation value set at time k and the electric energy measurement value consumed by the charging piles;

[0010] Calculate the set of state variable correction values ​​at time k and their covariance based on the estimated value set of the output variables at time k, the electric energy consumed by the charging pile group obtained from the reading of the total electric energy meter on the high-voltage side of the charging station distribution transformer, and the filter gain of the state variables;

[0011] Based on the k-time state variable correction value set and its covariance, an estimated value of the charging pile group error coefficient vector corrected at the k-time and its uncertainty are calculated.

[0012] In a second aspect, the present invention provides a charging pile group error characteristic analysis system, comprising:

[0013] A construction module is used to determine the initial mean value and covariance of the state variable of the error coefficient of the charging pile group at time k-1 according to the measured value of the electric energy consumed by the charging pile and the electric energy value consumed by the charging pile group obtained from the reading of the total electric energy meter on the high-voltage side of the charging station distribution transformer, and to form an initial state variable set by sampling;

[0014] A state variable estimated value calculation module, used to calculate a set of state variable estimated values ​​of error coefficients of a charging pile group at time k based on the initial state variable set;

[0015] An output variable estimated value calculation module, used to calculate the output variable estimated value set at time k based on the charging pile group error coefficient state variable estimated value set at time k and the electric energy measurement value consumed by the charging piles;

[0016] A state variable correction value calculation module, used to calculate the state variable correction value set at time k and its covariance based on the output variable estimation value set at time k, the electric energy consumed by the charging pile group obtained from the electric energy total meter reading on the high-voltage side of the charging station distribution transformer, and the filter gain of the state variable;

[0017] The output module is used to calculate the estimated value of the error coefficient vector of the charging pile group corrected at time k and its uncertainty based on the set of state variable correction values ​​at time k and their covariance.

[0018] Furthermore, the calculation formula of the initial state variable set is:

[0019] X k-1 ={x k-1.i}(k=1;i=1,2,...,M);

[0020] In the formula, X k-1 is the initial state variable set at time k-1, x k-1.i =[α k-1.i.n ] T (k=1;n=1,2,...,N;i=1,2,...,M), is the M sampling of the initial value of the state variable of the error coefficient of the charging pile group and its covariance, N is the number of charging piles in the charging pile group, α k-1.i.n is the error coefficient of the nth charging pile in the i-th state variable at time k-1.

[0021] The initial state variable set X k-1 The specific process is as follows:

[0022] Obtain charging data of a charging pile group of N charging piles and perform preprocessing;

[0023] Determine the initial value of the state variable of the error coefficient of the charging pile group according to the charging data of the charging pile group of N charging piles Construct the mean of the initial state variables

[0024] According to the prior knowledge, the covariance matrix P0 of the initial value of the state variable of the error coefficient of the charging pile group is determined to form a distribution

[0025] From the distribution Sampling M variables

[0026] x k-1.i =[α k-1.i.n ] T (k=1;n=1,2,...,N;i=1,2,...,M) constitutes the initial state variable set X k-1 ={x k-1.i}(k=1;i=1,2,...,M), where is the error coefficient of the nth charging pile in the i-th initial state variable at time k-1.

[0027] The calculation formula of the estimated value set of the error coefficient state variable of the charging pile group at time k is as follows:

[0028]

[0029] In the formula, is the estimated value set of state variables at time k, is the estimated value of the i-th state variable at time k, is the error coefficient of the nth charging pile in the i-th state variable at time k;

[0030] X k-1 ={x (k-1 ) .i}(i=1,2,...,M) is the set of state variable correction values ​​at time k-1;

[0031] W k-1 ={w (k-1 ) .i}(i=1,2,...,M) is the process noise set at time k-1;

[0032] w (k-1).i =[w (k-1).i.n ](n=1,2,...,N) is the process noise set of the i-th state variable in the process noise set at time k-1, which obeys N(0,Q) distribution, and Q is the process noise set w (k-1).i The covariance matrix, w (k-1).i.n is the process noise of the nth charging pile in the i-th state variable at time k-1;

[0033] The calculation formula for the estimated value set of the output variable at time k is:

[0034]

[0035] In the formula, Output the estimated value set of variables for time k;

[0036] e k.n is the measured value of the electric energy consumed by the nth charging pile at time k;

[0037] in is the estimated value set of state variables at time k Elements in With matrix 1 N×1 The matrix formed by taking the inverse of each element of the matrix obtained by adding, 11×N is a 1×N all-one matrix;

[0038] Output variable estimated value set for k time, is the estimated value of the i-th output variable at time k, is the estimated value of the electric energy of the nth charging pile;

[0039] V k = {v k.i}(i=1,2,...,M) is the measurement noise set at time k, v k.i It obeys N(0,R) distribution, is the i-th measurement noise amplitude at time k; R is the covariance matrix of the measurement noise set at time k;

[0040] The k-time state variable correction value set X k and its covariance P xxk The calculation formula is:

[0041]

[0042] In the formula, X k is the set of modified values ​​of state variables at time k;

[0043] P xxk is the covariance of the set of state variable correction values ​​at time k;

[0044] E k The electric energy consumed by the charging pile group is obtained from the total electric energy meter reading on the high-voltage side of the charging station distribution transformer at time k;

[0045] is a 1×M all-one matrix;

[0046] K is the filter gain of the state variable.

[0047] The calculation formula of the filter gain K of the state variable is:

[0048]

[0049] in, is the average of the estimated values ​​of the state variables at time k;

[0050] The average of the estimated values ​​of the output variables at time k; is the estimated value of the i-th output variable at time k.

[0051] The calculation formula of the estimated value of the charging pile group error coefficient vector and its uncertainty corrected at time k is:

[0052]

[0053]

[0054] In the formula, is the estimated value of the error coefficient vector of the charging pile group corrected at time k, and is the final estimated value of the error coefficient of the nth charging pile at time k;

[0055] U k The uncertainty of the estimated value of the error coefficient vector of the charging pile group corrected at time k;

[0056] x k.i is the set of corrected values ​​of state variables at time k X k The i-th element in ;

[0057] P xxk 'For P xxk The diagonal matrix obtained after eigenvalue decomposition, P xxk ' and P xxk The relationship between the two is: xxk =QP xxk 'Q T , where Q is the matrix P xxk is the matrix composed of the eigenvectors of .

[0058] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the program.

[0059] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0060] One or more technical solutions provided by the present invention have at least the following technical effects or advantages: the present invention is based on the ensemble Kalman filter algorithm and combines the idea of ​​the law of conservation of energy of each pile in the charging station, fully considers the random characteristics of the error coefficient of the charging pile, calculates and corrects the error coefficient during multiple measurements, and obtains the uncertainty of each pile at the same time. It can effectively identify and judge out-of-tolerance charging piles from a group of charging piles, thereby greatly improving the calibration efficiency of the charging piles.

[0061] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The present invention will be further described below in conjunction with embodiments with reference to the accompanying drawings.

[0063] Figure 1 Flow chart of the method in Embodiment 1 of the present invention.

[0064] Figure 2 A simplified model for establishing the law of conservation of energy in the present invention.

[0065] Figure 3 This is a structural block diagram of the system in Embodiment 2 of the present invention.

[0066] Figure 4 It is a schematic diagram of the structure of an electronic device in Embodiment 3 of the present invention.

[0067] Figure 5 Schematic diagram of the structure of the medium in the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0068] The embodiment of the present application provides a method and system for analyzing the error characteristics of a charging pile group. Based on the ensemble Kalman filter algorithm and the law of conservation of energy of each pile in a charging station, the random characteristics of the error coefficient of the charging pile are fully considered, the error coefficient is calculated and corrected during multiple measurements, and the uncertainty of each pile is obtained at the same time. It can effectively identify and judge out-of-tolerance charging piles from a charging pile group, thereby greatly improving the calibration efficiency of the charging piles.

[0069] The technical solution in the embodiment of the present application has the following general idea: the present invention establishes a structural model of the charging pile group, determines the state equation, measurement equation and state variables, output variables and their initial values; samples the initial state variable set according to the initial value and covariance of the state variable of the charging pile group error coefficient; estimates the state variable set at the current moment according to the state variable set at the previous moment using the state equation; calculates the output variable estimated value set at the current moment using the measurement equation and the state variable set estimated at the current moment; calculates the state variable set corrected at the current moment according to the deviation between the reading of the total electric energy meter on the high-voltage side of the charging station distribution transformer at the current moment and the output variable estimated value set, and calculates its covariance; finally, outputs the estimated value of the error coefficient vector of the charging pile group corrected at the current moment and its uncertainty according to the state variable set corrected at the current moment. The present invention is based on the idea of ​​the collective Kalman filter algorithm and the law of conservation of energy of each pile in the charging station, fully considers the random characteristics of the error coefficient of the charging pile, calculates and corrects the error coefficient in multiple measurement processes, and obtains the uncertainty of each pile at the same time, which can effectively identify and judge the charging piles with excessive tolerance from the charging pile group, thereby greatly improving the calibration efficiency of the charging piles.

[0070] Embodiment 1

[0071] like Figure 1 and Figure 2As shown, the charging pile group error characteristic analysis method based on ensemble Kalman filtering provided in this embodiment mainly includes the following steps:

[0072] S101. According to the measured value of the electric energy consumed by the charging pile and the electric energy value consumed by the charging pile group obtained from the reading of the electric energy total meter on the high-voltage side of the charging station distribution transformer, determine the initial mean value and covariance of the error coefficient state variable of the charging pile group at time k-1, and construct the initial state variable set by sampling:

[0073] The calculation formula of the initial state variable set is:

[0074] X k-1 ={x k-1.i}(k=1;i=1,2,...,M);

[0075] In the formula, X k-1 is the set of initial state variables at time k-1;

[0076] x k-1.i is the M samples of the initial value of the state variable of the error coefficient of the charging pile group and its covariance;

[0077] x k-1.i =[α k-1.i.n ] T (k=1;n=1,2,...,N;i=1,2,...,M), N is the number of charging piles in the charging pile group, α k-1.i.n is the error coefficient of the nth charging pile in the i-th state variable at time k-1.

[0078] The initial state variable set X k-1 The specific process is as follows:

[0079] Obtain the charging data of a group of N charging piles and perform preprocessing; the charging data includes: the number of charging piles; sampling time; charging pile efficiency; total electric energy meter reading on the high-voltage side of the charging station distribution transformer; charging power; three-phase voltage and current; electric energy meter reading, temperature, power, voltage and current of each charging gun; the data processing required includes: eliminating unavailable data; calculating the amount of electric energy in a specified time interval; interpolating or predicting at sampling points where data is missing; appropriately correcting sampling points with abnormal values; standardizing the data, including units and types; aligning various types of data according to time.

[0080] Determine the initial value of the state variable of the error coefficient of the charging pile group according to the charging data of the charging pile group of N charging piles Construct the mean of the initial state variables

[0081] According to the prior knowledge, the covariance matrix P0 of the initial value of the state variable of the error coefficient of the charging pile group is determined to form a distribution

[0082] From the distribution Sampling M variables x k-1.i =[α k-1.i.n ] T (k=1;n=1,2,...,N;i=1,2,...,M) constitutes the initial state variable set X k-1 ={x k-1.i}(k=1;i=1,2,...,M), where is the error coefficient of the nth charging pile in the i-th initial state variable at time k-1.

[0083] S102, based on the initial state variable set X k-1 Calculate the estimated value set of state variables of the error coefficient of the charging pile group at time k

[0084]

[0085] In the formula, is the estimated value set of state variables at time k, is the estimated value of the i-th state variable at time k, is the error coefficient of the nth charging pile in the i-th state variable at time k;

[0086] X k-1 ={x (k-1).i}(i=1,2,...,M) is the set of state variable correction values ​​at time k-1;

[0087] W k-1 ={w (k-1).i}(i=1,2,...,M) is the process noise set at time k;

[0088] w (k-1).i =[w (k-1).i.n ](n=1,2,...,N) is the process noise set of the i-th state variable in the process noise set at time k-1, which obeys N(0,Q) distribution, and Q is the process noise set w (k-1).i The covariance matrix, w (k-1).i.n is the process noise of the nth charging pile in the i-th state variable at time k-1;

[0089] S103, calculating a set of estimated output variable values ​​at time k based on the set of estimated state variables of the charging pile group error coefficient at time k and the measured values ​​of electric energy consumed by the charging piles;

[0090] The calculation formula for the estimated value set of the output variable at time k is:

[0091]

[0092] In the formula, Output the estimated value set of variables for time k;

[0093] e k.n is the measured value of the electric energy consumed by the nth charging pile at time k;

[0094] in is the estimated value set of state variables at time k Elements in With matrix 1 N×1 The matrix formed by taking the inverse of each element of the matrix obtained by adding, 1 1×N is a 1×N all-one matrix;

[0095] Output variable estimated value set for k time, is the estimated value of the i-th output variable at time k, is the estimated value of the electric energy of the nth charging pile;

[0096] V k = {v k.i}(i=1,2,...,M) is the measurement noise set at time k, v k.i It obeys N(0,R) distribution and is the i-th measurement noise amplitude at time k; R is the covariance matrix of the measurement noise set at time k.

[0097] S104, calculating a set of state variable correction values ​​at time k and their covariance based on the set of estimated values ​​of the output variables at time k, the electric energy consumed by the charging pile group obtained from the reading of the total electric energy meter on the high-voltage side of the charging station distribution transformer, and the filter gain of the state variables;

[0098] The k-time state variable correction value set X k and its covariance P xxk The calculation formula is:

[0099]

[0100] In the formula, X k is the set of modified values ​​of state variables at time k;

[0101] P xxk is the covariance of the set of state variable correction values ​​at time k;

[0102] E k The electric energy consumed by the charging pile group is obtained from the total electric energy meter reading on the high-voltage side of the charging station distribution transformer at time k;

[0103] is a 1×M all-one matrix;

[0104] K is the filter gain of the state variable, and the calculation formula of K is:

[0105]

[0106] in, is the average of the estimated values ​​of the state variables at time k;

[0107] The average of the estimated values ​​of the output variables at time k; is the estimated value of the i-th output variable at time k.

[0108] S105, calculating the estimated value of the charging pile group error coefficient vector corrected at time k and its uncertainty based on the set of state variable correction values ​​at time k and their covariance;

[0109] The calculation formula of the estimated value of the charging pile group error coefficient vector and its uncertainty corrected at time k is:

[0110]

[0111] In the formula, is the estimated value of the error coefficient vector of the charging pile group corrected at time k, and is the final estimated value of the error coefficient of the nth charging pile at time k;

[0112] U k The uncertainty of the estimated value of the error coefficient vector of the charging pile group corrected at time k;

[0113] x k.i is the set of corrected values ​​of state variables at time k X k The i-th element in ;

[0114] P xxk 'For P xxk The diagonal matrix obtained after eigenvalue decomposition, P xxk ' and P xxk The relationship between the two is: xxk =QP xxk 'Q T , where Q is the matrix P xxk is the matrix composed of the eigenvectors of .

[0115] When it is necessary to update the estimated value of the error coefficient vector of the charging pile group and its uncertainty, a new set of measured values ​​of the electric energy consumed by the charging piles and the electric energy consumed by the charging pile group obtained from the reading of the total electric energy meter on the high-voltage side of the charging station distribution transformer and the filter gain of the state variable are obtained, and the set of estimated values ​​of the error coefficient state variables of the charging pile group at time k is used as the initial state variable set, and the set of estimated values ​​of the error coefficient state variables of the charging pile group at time k+1 is calculated.

[0116] Embodiment 2

[0117] Based on the same inventive concept, the present application also provides a device corresponding to the method in Example 1.

[0118] like Figure 3 As shown, a charging pile group error characteristic analysis device provided in this embodiment includes:

[0119] A construction module is used to determine the initial mean value and covariance of the state variable of the error coefficient of the charging pile group at time k-1 according to the measured value of the electric energy consumed by the charging pile and the electric energy value consumed by the charging pile group obtained from the reading of the total electric energy meter on the high-voltage side of the charging station distribution transformer, and to form an initial state variable set by sampling;

[0120] The calculation formula of the initial state variable set is:

[0121] X k-1 ={x k-1.i}(k=1;i=1,2,...,M);

[0122] In the formula, X k-1 is the initial state variable set at time k-1, x k-1.i =[α k-1.i.n ] T (k=1;n=1,2,...,N;i=1,2,...,M), is the M sampling of the initial value of the state variable of the error coefficient of the charging pile group and its covariance, N is the number of charging piles in the charging pile group, α k-1.i.n is the error coefficient of the nth charging pile in the i-th state variable at time k-1.

[0123] The initial state variable set X k-1 The specific process is as follows:

[0124] Obtain charging data of a charging pile group of N charging piles and perform preprocessing;

[0125] Determine the initial value of the state variable of the error coefficient of the charging pile group according to the charging data of the charging pile group of N charging piles Construct the mean of the initial state variables

[0126] According to the prior knowledge, the covariance matrix P0 of the initial value of the state variable of the error coefficient of the charging pile group is determined to form a distribution

[0127] From the distribution Sampling M variables x k-1.i =[α k-1.i.n ] T(k=1;n=1,2,...,N;i=1,2,...,M) constitutes the initial state variable set X k-1 ={x k-1.i}(k=1;i=1,2,...,M), where is the error coefficient of the nth charging pile in the i-th initial state variable at time k-1.

[0128] A state variable estimated value calculation module is used to calculate the state variable estimated value set of the charging pile group error coefficient at time k based on the initial state variable set.

[0129] The calculation formula of the estimated value set of the error coefficient state variable of the charging pile group at time k is as follows:

[0130]

[0131] In the formula, is the estimated value set of state variables at time k, is the estimated value of the i-th state variable at time k, is the error coefficient of the nth charging pile in the i-th state variable at time k;

[0132] X k-1 ={x (k-1).i}(i=1,2,...,M) is the set of state variable correction values ​​at time k-1;

[0133] W k-1 ={w (k-1).i}(i=1,2,...,M) is the process noise set at time k-1;

[0134] w (k-1).i =[w (k-1).i.n ](n=1,2,...,N) is the process noise set of the i-th state variable in the process noise set at time k-1, which obeys N(0,Q) distribution, and Q is the process noise set w (k-1).i The covariance matrix, w (k-1).i.n is the process noise of the nth charging pile in the ith state variable at time k-1.

[0135] The output variable estimated value calculation module is used to calculate the output variable estimated value set at time k based on the charging pile group error coefficient state variable estimated value set at time k and the electric energy measurement value consumed by the charging pile; the calculation formula of the output variable estimated value set at time k is:

[0136]

[0137] In the formula, Output the estimated value set of variables for time k;

[0138] e k.n is the measured value of the electric energy consumed by the nth charging pile at time k;

[0139] in For collection Elements in With matrix 1 N×1 The matrix formed by taking the inverse of each element of the matrix obtained by adding, 1 1×N is a 1×N all-one matrix;

[0140] Output variable estimated value set for k time, is the estimated value of the i-th output variable at time k, is the estimated value of the electric energy of the nth charging pile;

[0141] V k = {v k.i}(i=1,2,...,M) is the measurement noise set at time k, v k.i It obeys N(0,R) distribution, is the i-th measurement noise amplitude at time k; R is the covariance matrix of the measurement noise set at time k;

[0142] A state variable correction value calculation module calculates a state variable correction value set at time k and its covariance based on the output variable estimation value set at time k, the electric energy consumed by the charging pile group obtained from the electric energy total meter reading on the high-voltage side of the charging station distribution transformer, and the filter gain of the state variable;

[0143] The k-time state variable correction value set X k and its covariance P xxk The calculation formula is:

[0144]

[0145] In the formula, X k is the set of modified values ​​of state variables at time k;

[0146] P xxk is the covariance of the set of state variable correction values ​​at time k;

[0147] E k The electric energy consumed by the charging pile group is obtained from the total electric energy meter reading on the high-voltage side of the charging station distribution transformer at time k; is a 1×M all-one matrix;

[0148] K is the filter gain of the state variable; the calculation formula of K is:

[0149]

[0150] in, is the average of the estimated values ​​of the state variables at time k; The average of the estimated values ​​of the output variables at time k; is the estimated value of the i-th output variable at time k.

[0151] Output module, used to output the estimated value of the error coefficient vector of the charging pile group corrected at time k and its uncertainty U k :

[0152]

[0153] In the formula, is the final estimated value of the error coefficient of the nth charging pile at time k;

[0154] x k.i is the set of corrected values ​​of state variables at time k X k The i-th element in ;

[0155] P xxk 'For P xxk The diagonal matrix obtained after eigenvalue decomposition, P xxk ' and P xxk The relationship between the two is: xxk =QP xxk 'Q T , where Q is the matrix P xxk is the matrix composed of the eigenvectors of .

[0156] In addition, the present embodiment may also include a data acquisition module for acquiring charging data of a charging pile group having N charging piles and performing preprocessing; wherein the acquired charging data includes: the number of charging piles; sampling time; charging pile efficiency; total electric energy meter reading on the high-voltage side of the charging station distribution transformer; charging power; three-phase voltage and current; electric energy meter reading, temperature, power, voltage and current of each charging gun; the data processing required includes: eliminating unavailable data; calculating the amount of electric energy in a specified time interval; interpolating at sampling points where data is missing or using a model for prediction; appropriately correcting sampling points with abnormal values; standardizing the data, including units and types, etc.; aligning various types of data according to time.

[0157] When it is necessary to update the estimated value of the error coefficient vector of the charging pile group and its uncertainty, a new set of measured values ​​of the electric energy consumed by the charging piles and the electric energy consumed by the charging pile group obtained from the reading of the total electric energy meter on the high-voltage side of the charging station distribution transformer and the filter gain of the state variable are obtained, and the set of estimated values ​​of the error coefficient state variables of the charging pile group at time k is used as the initial state variable set, and the set of estimated values ​​of the error coefficient state variables of the charging pile group at time k+1 is calculated.

[0158] Since the device introduced in the second embodiment of the present invention is a device used to implement the method of the first embodiment of the present invention, those skilled in the art can understand the specific structure and deformation of the device based on the method introduced in the first embodiment of the present invention, so it is not described here in detail. All devices used in the method of the first embodiment of the present invention belong to the scope of protection of the present invention.

[0159] Embodiment 3

[0160] Based on the same inventive concept, the present application provides an electronic device embodiment corresponding to the first embodiment, see the third embodiment for details. This embodiment provides an electronic device, such as Figure 4 As shown, it includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any implementation method in the first embodiment can be implemented.

[0161] Since the electronic device introduced in this embodiment is a device used to implement the method in the first embodiment of the present application, based on the method introduced in the first embodiment of the present application, a person skilled in the art can understand the specific implementation of the electronic device of the present embodiment and its various variations, so how the electronic device implements the method in the embodiment of the present application is not described in detail here. As long as a person skilled in the art implements the device used by the method in the embodiment of the present application, it belongs to the scope of protection of the present application.

[0162] Embodiment 4

[0163] Based on the same inventive concept, this application provides a storage medium corresponding to Embodiment 1, see Embodiment 4 for details. This embodiment provides a computer-readable storage medium, such as Figure 5 As shown, a computer program is stored thereon, and when the computer program is executed by a processor, any implementation method in Example 1 can be implemented.

[0164] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0165] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0166] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0167] Computer program code for performing the operation of the present invention may be written in one or more programming languages ​​or combinations thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0168] Although the specific implementation modes of the present invention are described above, those skilled in the art should understand that the specific implementation modes described are only illustrative and are not intended to limit the scope of the present invention. Equivalent modifications and changes made by those skilled in the art in accordance with the spirit of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A method for analyzing the error characteristics of a charging pile group, characterized by: include: According to the measured value of the electric energy consumed by the charging pile and the electric energy value consumed by the charging pile group obtained from the reading of the total electric energy meter on the high-voltage side of the charging station distribution transformer, the initial mean value and covariance of the state variable of the error coefficient of the charging pile group at time k-1 are determined, and the initial state variable set is formed by sampling; Calculate the estimated value set of state variables of the error coefficient of the charging pile group at time k based on the initial state variable set; Calculate the output variable estimation value set at time k based on the charging pile group error coefficient state variable estimation value set at time k and the electric energy measurement value consumed by the charging piles; Calculate the set of state variable correction values ​​at time k and their covariance based on the estimated value set of the output variables at time k, the electric energy consumed by the charging pile group obtained from the reading of the total electric energy meter on the high-voltage side of the charging station distribution transformer, and the filter gain of the state variables; Based on the k-time state variable correction value set and its covariance, an estimated value of the charging pile group error coefficient vector corrected at the k-time and its uncertainty are calculated.

2. The method for analyzing the error characteristics of a charging pile group according to claim 1, characterized in that: The calculation formula of the initial state variable set is: X k-1 ={x k-1.i }(k=1;i=1,2,...,M); Where, X k-1 is the initial state variable set at time k-1, x k-1.i =[α k-1.i.n ] T (k=1;n=1,2,...,N;i=1,2,...,M), is the M sampling of the initial value of the state variable of the error coefficient of the charging pile group and its covariance, N is the number of charging piles in the charging pile group, α k-1.i.n is the error coefficient of the nth charging pile in the i-th state variable at time k-1.

3. The method for analyzing the error characteristics of a charging pile group according to claim 2, characterized in that: The initial state variable set X k-1 The specific process is as follows: Obtain charging data of a charging pile group of N charging piles and perform preprocessing; Determine the initial value of the state variable of the error coefficient of the charging pile group according to the charging data of the charging pile group of N charging piles Construct the mean of the initial state variables According to the prior knowledge, the covariance matrix P0 of the initial value of the state variable of the error coefficient of the charging pile group is determined to form a distribution From the distribution Sampling M variables x k-1.i =[α k-1.i.n ] T (k=1;n=1,2,...,N;i=1,2,...,M) constitutes the initial state variable set X k-1 ={x k-1.i }(k=1;i=1,2,...,M), where is the error coefficient of the nth charging pile in the i-th initial state variable at time k-1.

4. A method for analyzing the error characteristics of a charging pile group as claimed in claim 2 or 3, characterized in that: The calculation formula of the estimated value set of the error coefficient state variable of the charging pile group at time k is as follows: In the formula, is the estimated value set of state variables at time k, is the estimated value of the i-th state variable at time k, is the error coefficient of the nth charging pile in the i-th state variable at time k; X k-1 ={x (k-1 ) .i }(i=1,2,...,M) is the set of state variable correction values ​​at time k-1; W k-1 ={w (k-1 ) .i }(i=1,2,...,M) is the process noise set at time k-1; w (k-1).i =[w (k-1).i.n ](n=1,2,...,N) is the process noise set of the i-th state variable in the process noise set at time k-1, which obeys N(0,Q) distribution, and Q is the process noise set w (k-1).i The covariance matrix, w (k-1).i.n is the process noise of the nth charging pile in the ith state variable at time k-1.

5. The method for analyzing the error characteristics of a charging pile group according to claim 4, characterized in that: The calculation formula for the estimated value set of the output variable at time k is: In the formula, Output the estimated value set of variables for time k; e k.n is the measured value of the electric energy consumed by the nth charging pile at time k; in is the estimated value set of state variables at time k Elements in With matrix 1 N×1 The matrix formed by taking the inverse of each element of the matrix obtained by adding, 1 1×N is a 1×N all-one matrix; Output variable estimated value set for k time, is the estimated value of the i-th output variable at time k, is the estimated value of the electric energy of the nth charging pile; V k = {v k.i }(i=1,2,...,M) is the measurement noise set at time k, v k.i It obeys N(0,R) distribution and is the i-th measurement noise amplitude at time k; R is the covariance matrix of the measurement noise set at time k.

6. The method for analyzing the error characteristics of a charging pile group according to claim 5, characterized in that: The k-time state variable correction value set X k and its covariance P xxk The calculation formula is: Where, X k is the set of corrected values ​​of state variables at time k; P xxk is the covariance of the set of state variable correction values ​​at time k; E k The electric energy consumed by the charging pile group is obtained from the total electric energy meter reading on the high-voltage side of the charging station distribution transformer at time k; is a 1×M all-one matrix; K is the filter gain of the state variable.

7. The method for analyzing the error characteristics of a charging pile group according to claim 6, characterized in that: The calculation formula of the filter gain K of the state variable is: in, is the average of the estimated values ​​of the state variables at time k; The average of the estimated values ​​of the output variables at time k; is the estimated value of the i-th output variable at time k.

8. A method for analyzing error characteristics of a charging pile group as claimed in claim 6 or 7, characterized in that: The calculation formula of the estimated value of the error coefficient vector of the charging pile group corrected at time k and its uncertainty is: In the formula, is the estimated value of the error coefficient vector of the charging pile group corrected at time k, and is the final estimated value of the error coefficient of the nth charging pile at time k; U k The uncertainty of the estimated value of the error coefficient vector of the charging pile group corrected at time k; x k.i is the set of corrected values ​​of state variables at time k X k The i-th element in ; P xxk 'For P xxk The diagonal matrix obtained after eigenvalue decomposition, P xxk ' and P xxk The relationship between the two is: xxk =QP xxk 'Q T , where Q is the matrix P xxk is the matrix composed of the eigenvectors of .

9. A device for analyzing the error characteristics of a charging pile group, characterized in that: include: A construction module is used to determine the initial mean value and covariance of the state variable of the error coefficient of the charging pile group at time k-1 according to the measured value of the electric energy consumed by the charging pile and the electric energy value consumed by the charging pile group obtained from the reading of the total electric energy meter on the high-voltage side of the charging station distribution transformer, and to form an initial state variable set by sampling; A state variable estimated value calculation module, used to calculate a set of state variable estimated values ​​of error coefficients of a charging pile group at time k based on the initial state variable set; An output variable estimated value calculation module, used to calculate the output variable estimated value set at time k based on the charging pile group error coefficient state variable estimated value set at time k and the electric energy measurement value consumed by the charging piles; A state variable correction value calculation module, used to calculate the state variable correction value set at time k and its covariance based on the output variable estimation value set at time k, the electric energy consumed by the charging pile group obtained from the electric energy total meter reading on the high-voltage side of the charging station distribution transformer, and the filter gain of the state variable; The output module is used to calculate the estimated value of the error coefficient vector of the charging pile group corrected at time k and its uncertainty based on the set of state variable correction values ​​at time k and their covariance.

10. The device for analyzing the error characteristics of a charging pile group according to claim 9, characterized in that: The calculation formula of the initial state variable set is: X k-1 ={x k-1.i }(k=1;i=1,2,...,M); Where, X k-1 is the initial state variable set at time k-1, x k-1.i =[α k-1.i.n ] T (k=1;n=1,2,...,N;i=1,2,...,M), is the M sampling of the initial value of the state variable of the error coefficient of the charging pile group and its covariance, N is the number of charging piles in the charging pile group, α k-1.i.n is the error coefficient of the nth charging pile in the i-th state variable at time k-1.

11. The device for analyzing the error characteristics of a charging pile group according to claim 10, characterized in that: The initial state variable set X k-1 The specific process is as follows: Obtain charging data of a charging pile group of N charging piles and perform preprocessing; Determine the initial value of the state variable of the error coefficient of the charging pile group according to the charging data of the charging pile group of N charging piles Construct the mean of the initial state variables According to the prior knowledge, the covariance matrix P0 of the initial value of the state variable of the error coefficient of the charging pile group is determined to form a distribution From the distribution Sampling M variables x k-1.i =[α k-1.i.n ] T (k=1;n=1,2,...,N;i=1,2,...,M) constitutes the initial state variable set X k-1 ={x k-1.i }(k=1;i=1,2,...,M), where is the error coefficient of the nth charging pile in the i-th initial state variable at time k-1.

12. A charging pile group error characteristic analysis device as claimed in claim 10 or 11, characterized in that: The calculation formula of the estimated value set of the error coefficient state variable of the charging pile group at time k is as follows: In the formula, is the estimated value set of state variables at time k, is the estimated value of the i-th state variable at time k, is the error coefficient of the nth charging pile in the i-th state variable at time k; X k-1 ={x (k-1 ) .i }(i=1,2,...,M) is the set of state variable correction values ​​at time k-1; W k-1 ={w (k-1 ) .i }(i=1,2,...,M) is the process noise set at time k-1; w (k-1).i =[w (k-1).i.n ](n=1,2,...,N) is the process noise set of the i-th state variable in the process noise set at time k-1, which obeys N(0,Q) distribution, and Q is the process noise set w (k-1).i The covariance matrix, w (k-1).i.n is the process noise of the nth charging pile in the ith state variable at time k-1.

13. The device for analyzing the error characteristics of a charging pile group according to claim 12, characterized in that: The calculation formula for the estimated value set of the output variable at time k is: In the formula, Output the estimated value set of variables for time k; e k.n is the measured value of the electric energy consumed by the nth charging pile at time k; in is the estimated value set of state variables at time k Elements in With matrix 1 N×1 The matrix formed by taking the inverse of each element of the matrix obtained by adding, 1 1×N is a 1×N all-one matrix; Output variable estimated value set for k time, is the estimated value of the i-th output variable at time k, is the estimated value of the electric energy of the nth charging pile; V k = {v k.i }(i=1,2,...,M) is the measurement noise set at time k, v k.i It obeys N(0,R) distribution and is the i-th measurement noise amplitude at time k; R is the covariance matrix of the measurement noise set at time k.

14. The device for analyzing the error characteristics of a charging pile group according to claim 13, characterized in that: The k-time state variable correction value set X k and its covariance P xxk The calculation formula is: Where, X k is the set of corrected values ​​of state variables at time k; P xxk is the covariance of the set of state variable correction values ​​at time k; E k The electric energy consumed by the charging pile group is obtained from the total electric energy meter reading on the high-voltage side of the charging station distribution transformer at time k; is a 1×M all-one matrix; K is the filter gain of the state variable.

15. The device for analyzing the error characteristics of a charging pile group according to claim 14, characterized in that: The calculation formula of K is: in, is the average of the estimated values ​​of the state variables at time k; The average of the estimated values ​​of the output variables at time k; is the estimated value of the i-th output variable at time k.

16. A charging pile group error characteristic analysis device as claimed in claim 14 or 15, characterized in that: The calculation formula of the estimated value of the error coefficient vector of the charging pile group corrected at time k and its uncertainty is: In the formula, is the estimated value of the error coefficient vector of the charging pile group corrected at time k, and is the final estimated value of the error coefficient of the nth charging pile at time k; U k The uncertainty of the estimated value of the error coefficient vector of the charging pile group corrected at time k; x k.i is the set of corrected values ​​of state variables at time k X k The i-th element in ; P xxk 'For P xxk The diagonal matrix obtained after eigenvalue decomposition, P xxk ' and P xxk The relationship between the two is: xxk =QP xxk 'Q T , where Q is the matrix P xxk is the matrix composed of the eigenvectors of .

17. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

18. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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