Charging gun metering error monitoring method based on energy router

Abstract

The invention relates to the technical field of electric energy metering, and discloses a charging gun metering error monitoring method based on an energy router, and the method comprises the following steps: obtaining the data of a charging gun and an energy router; and calculating the line loss and the line loss rate of the charging station. And identifying line loss rate abnormal points and eliminating corresponding data. Windowing is carried out, weighted accumulation is carried out on data in a window, and sample data are traversed when one sample point is slid every time; and establishing a linear model to solve a charging gun error coefficient, and monitoring a charging gun metering error. According to the method, the line loss rate is calculated through the data of the energy router and the charging gun based on the node sampling data at the interval of 15 minutes, after abnormal value processing is performed on the line loss rate through the 3 sigma principle, accumulation summation is performed on the original electric quantity data in a sliding window mode, and meanwhile, the problem that the importance of sample data after each group of sliding windows is different in an actual application scene is considered. Weights are given to the sampling data after window sliding, the solving precision of the error coefficient of the charging gun is further improved, and accurate detection of the abnormal charging gun is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy metering, and in particular to a charging gun metering error monitoring method based on an energy router. Background Art

[0002] With the development of technology, the application scenarios of charging guns are constantly expanding, and their metering function is becoming more and more critical. Accurate metering of charging guns helps collect electricity bills and ensures fairness between power companies and users. In the new energy industry, charging guns need to be checked frequently to ensure their accurate and stable operation.

[0003] Sampling data with a time interval of 15 minutes can monitor grid current, voltage, power and other information at a smaller time granularity, which is helpful to use big data analysis to troubleshoot abnormal power consumption of users. However, the 15-minute interval sampling data has a small value and contains many zero values, which will interfere with the accurate solution of the charging gun error coefficient. To address this, a sliding window algorithm is used to accumulate and sum the 15-minute interval power data to improve data quality. Taking into account the actual application scenarios, the importance of each group of sliding window summed sample data is different. The existing method of ignoring the confidence of the sample data and directly averaging it is difficult to obtain an accurate charging gun error coefficient.

[0004] In this regard, patent application document CN118818141A discloses a charging gun metering error monitoring method based on an energy router. Based on node sampling data at 15-minute intervals, the line loss rate is calculated through energy routers and charging gun data. After the line loss rate is processed for abnormal values ​​using the 3σ principle, the original power data is accumulated and summed using an adaptive cumulative sliding window method. At the same time, considering the different importance of sample data after each group of sliding windows in actual application scenarios, the sample data after the sliding window is given a weight to improve the accuracy of solving the charging gun error coefficient. However, the idea of ​​determining the cumulative window width based on the proportion of low load in the initial window does not meet the original intention: if the low-load power is in front of the initial window, the accumulated power may still be very small and cannot achieve the effect of sliding window accumulation. Summary of the invention

[0005] In view of the shortcomings and defects of the prior art, the present invention provides a charging gun metering error monitoring method based on an energy router, in which the window width is determined based on the accumulation method on the basis of the initial window, so that the accumulated power will not have a small value from the source, and each accumulated power value is random (the accumulated power of the initial window at different positions pos is different).

[0006] The purpose of the present invention can be achieved through the following technical solutions.

[0007] A charging gun metering error monitoring method based on an energy router comprises the following steps:

[0008] Step 1: Obtain the power data of the charging gun and energy router according to the charging station file information;

[0009] Step 2: Calculate the line loss and line loss rate of the charging station based on the power data of the charging gun and the energy router;

[0010] Step 3: Identify abnormal points in line loss rate through the 3σ principle and remove the corresponding charging gun and energy router power data;

[0011] Step 4: perform windowing on the processed power data and perform weighted processing on the windowed data:

[0012] Step 4-1, initialize the maximum window width window max , minimum target power Tmin, window position pos = 1, sliding step step = 1, target window width window new =1;

[0013] Step 4-2: Take the first energy router data as the sliding window starting point and intercept the window max Sample points are used as observation areas, and the range of the observation area is: [pos,pos+window max -1];

[0014] Step 4-3, determine the target power CUMpos when the window position is pos:

[0015]

[0016] where x i-all are all the charging guns in the i-th sample;

[0017] Step 4-4, if CUMpos does not exceed Tmin, then window max ×c, until CUMpos is greater than Tmin;

[0018] Where c is the window width expansion coefficient;

[0019] Step 4-5, determine the target window width:

[0020] Will satisfy for the first time Window width new1 The value is used as the target window width window new ;

[0021] Where η represents the target power coefficient; window new1 Increment by adding 1;

[0022] Step 4-6, add the data of the charging gun and energy router in the window:

[0023]

[0024] Among them, x i-all is all the charging guns in the i-th sample, y i is the power data of the i-th sample energy router, x′ k is the charging gun data after addition, y′ k is the energy router data after addition;

[0025] Step 4-7, update the sliding window position, the position update formula is as follows:

[0026] pos=pos+step;

[0027] Step 4-8, when the sliding window moves to the last window max Position, pos+window max -1=m1, where m1 is the number of charging station samples after removing abnormal points;

[0028] Directly perform cumulative calculation on Window_max points;

[0029] Step 4-9, update the number of charging station samples to m2;

[0030] Where m2 = m1-Window max +1;

[0031] Step 4-10, calculate the weight ω of the power data after sliding window k ;

[0032]

[0033] Where k represents the sequence number of the accumulated sample points, 1≤k≤m2;

[0034] Step 4-11, assign weight ω k The formula for assigning the power data after sliding the window is as follows;

[0035] ″′

[0036] x k =w k ·x k ;

[0037] ″′

[0038] y k =w k ·y k ;

[0039] Among them, xk ″、y k ″ is the power data of the charging gun and energy router after weighting;

[0040] Step 5: Based on the constructed weighted data, a linear model is established to solve the charging gun error coefficient and monitor the charging gun metering error.

[0041] Preferably, the calculation formula of the charging station line loss rate in step 2 is as follows:

[0042]

[0043] Among them, y i is the power data of the i-th sample energy router, 1≤i≤m0, m0 is the number of charging station samples, x ij is the power data of the jth charging gun in the i-th sample, lr i is the line loss rate corresponding to the i-th sample, and n0 is the number of charging guns.

[0044] Preferably, the specific process of step 3 is as follows:

[0045] A1, calculate the mean μ of the overall distribution of line loss rate;

[0046]

[0047] Among them, lr i is the line loss rate corresponding to the i-th sample, m0 is the number of charging station samples;

[0048] A2, calculate the variance σ of the overall distribution of line loss rate;

[0049]

[0050] A3, calculate the upper limit of line loss rate lr according to the 3σ principle up ;

[0051] 1 up =μ+3σ;

[0052] A4, calculate the lower limit of line loss rate lr according to the 3σ principle down ;

[0053] 1 down =μ-3σ;

[0054] A5, according to the upper and lower limits of the line loss rate, abnormal point identification is performed. The abnormal point identification formula is as follows;

[0055]

[0056] Among them, when sgn=1, it is identified as an abnormal point;

[0057] A6, based on the identified abnormal points, eliminate the corresponding charging gun and energy router power data.

[0058] Preferably, the process of establishing a linear model to solve the error coefficient of the charging gun in step 5 is as follows;

[0059] C1, calculate line loss;

[0060]

[0061] in, is the line loss of the kth sample; x kj ″ is the power data of the jth charging gun in the kth sample after weighting;

[0062] C2, build a linear model;

[0063]

[0064] Where β is the error coefficient of the charging gun, the subscript is the charging gun number, ranging from 1 to n; δ is other losses in the line; α is the measurement error coefficient of the energy router;

[0065] C3, solve the charging gun error coefficient β;

[0066] β=(X T X+λE) -1 X T Error;

[0067] Among them, the matrix X = [x1″, x2″, ..., x n ″,y″,1]; λ is the regularization parameter; y” is the accumulated power data vector of m2 energy routers.

[0068] Preferably, λ is 1.

[0069] Preferably, window max =48.

[0070] Preferably, η is 0.8.

[0071] Preferably, Tmin is 10 kWh.

[0072] Preferably, c is 2, 3 or 4.

[0073] The beneficial technical effect of the present invention is as follows: the window width is determined based on the accumulation method on the basis of the initial window, so that the accumulated power will not have small values ​​from the source, and each accumulated power value is random (the accumulated power of the initial window at different positions pos is different), which truly guarantees the accuracy of solving the error coefficient of the charging gun, and further ensures the accurate detection of abnormal charging guns. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is the overall flow chart of the present invention.

[0075] Figure 2 This is a line loss rate abnormal point detection diagram in an embodiment of the present invention.

[0076] Figure 3 This is a comparison chart of the solution results of the charging gun error coefficient of the method in the embodiment of the present invention and the patent described in the background technology. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not limit the present invention.

[0078] Example: Figure 1 As shown, a charging gun metering error monitoring method based on an energy router includes the following steps.

[0079] Step 1: Obtain 10 days of charging gun and energy router power data based on the charging station archive information.

[0080] Step 2: Calculate the charging station line loss and line loss rate based on the power data of the charging gun and energy router.

[0081]

[0082] Among them, y i is the power data of the i-th sample energy router, 1≤i≤m0, m0 is the number of charging station samples, which is 960 in the embodiment, x ij is the power data of the jth charging gun in the i-th sample, lr i is the line loss rate corresponding to the i-th sample, and n0 is the number of charging guns, which is 8 in the embodiment.

[0083] Step 3: Identify abnormal points in line loss rate through the 3σ principle and eliminate the corresponding charging gun and energy router power data.

[0084] A1, calculate the mean μ of the overall distribution of line loss rate.

[0085]

[0086] Among them, lri is the line loss rate corresponding to the i-th sample, and m0 is the number of charging station samples.

[0087] A2, calculate the variance σ of the overall distribution of line loss rate.

[0088]

[0089] A3, calculate the upper limit of line loss rate lr according to the 3σ principle up .

[0090] 1 up =μ+3σ.

[0091] A4, calculate the lower limit of line loss rate lr according to the 3σ principle down .

[0092] 1 down =μ-3σ.

[0093] A5, according to the upper and lower limits of the line loss rate, abnormal points are identified. The identification formula is as follows.

[0094]

[0095] Among them, when sgn=1, it is identified as an abnormal point.

[0096] A6, based on the identified abnormal points, remove the corresponding charging gun and energy router power data, such as Figure 2 The position indicated by the black *.

[0097] Step 4: perform windowing on the processed power data and perform weighted processing on the windowed data:

[0098] Step 4-1, initialize the maximum window width window max =48, minimum target power Tmin=10kWh, window position pos=1, sliding step step=1, target window width window new =1;

[0099] Step 4-2: Take the first energy router data as the sliding window starting point and intercept the window max Sample points are used as observation areas, and the range of the observation area is: [pos,pos+window max -1];

[0100] Step 4-3, determine the target power CUMpos when the window position is pos:

[0101]

[0102] where x i-all are all the charging guns in the i-th sample;

[0103] Step 4-4, if CUMpos does not exceed Tmin, then window max ×c, until CUMpos is greater than Tmin;

[0104] Wherein c is the window width expansion coefficient, and in the embodiment, a maximum value of 2 is generally sufficient;

[0105] Step 4-5, determine the target window width:

[0106] Will satisfy for the first time Window width new1 The value is used as the target window width window new ;

[0107] Where η represents the target power coefficient, which is set to 0.8; window new1 Increment by adding 1;

[0108] Step 4-6, add the data of the charging gun and energy router in the window:

[0109]

[0110] Among them, x i-all is all the charging guns in the i-th sample, y i is the power data of the i-th sample energy router, x′ k is the charging gun data after addition, y′ k is the energy router data after addition;

[0111] Step 4-7, update the sliding window position, the position update formula is as follows:

[0112] pos=pos+step;

[0113] Step 4-8, when the sliding window moves to the last window max Position, pos+window max -1=m1, where m1 is the number of charging station samples after removing abnormal points, which is 921;

[0114] Directly perform cumulative calculation on Window_max points;

[0115] Step 4-9, update the number of charging station samples to m2=904;

[0116] Step 4-10, calculate the weight ω of the power data after sliding window k ;

[0117]

[0118] Where k represents the sequence number of the accumulated sample points, 1≤k≤m2;

[0119] Step 4-11, assign weight ω k The formula for assigning the power data after sliding the window is as follows;

[0120] ″′

[0121] x k =w k ·x k ;

[0122] ″′

[0123] y k =w k ·y k ;

[0124] Among them, x k ″、y k ″ is the power data of the charging gun and energy router after weighting.

[0125] Step 5: Based on the constructed weighted cumulative data, a linear model is established to solve the charging gun error coefficient and monitor the charging gun metering error.

[0126] The process of establishing a linear model to solve the error coefficient of the charging gun is as follows.

[0127] C1, calculate line loss.

[0128]

[0129] in, is the line loss of the kth sample; x kj ″ is the power data of the jth charging gun in the kth sample after weighting.

[0130] C2, build a linear model.

[0131]

[0132] Where β is the error coefficient of the charging gun, the subscript is the charging gun number, ranging from 1 to n; δ is other losses in the line; α is the measurement error coefficient of the energy router.

[0133] C3, solve the charging gun error coefficient β.

[0134] β=(X T X+λE) -1 X T Error.

[0135] Among them, the matrix X = [x1″, x2″, ..., x n ″,y″,1]; λ is a regularization parameter, which is set to 1 in the embodiment; y" is the accumulated energy router power data vector.

[0136] The tolerance value of the charging gun with the label 6 is set to -7%. The measurement error of other charging guns is within the range of ±0.6% after calibration by professional equipment. Figure 3 It can be seen that for the modeling and solution of the power data before and after the improved sliding window, when the charging gun has a small excess value, the error coefficient of the original sliding window solution is deviated from the normal range of ±0.02, and the charging gun excess value solution is not good; while the error coefficient of the improved sliding window solution is distributed within the normal range of ±0.02 and the charging gun excess value solution is closer to the set value. Therefore, by effectively accumulating the 15-minute interval sampling data of the node after the sliding window, the solution accuracy of the charging gun error coefficient is further improved, ensuring the accurate detection of abnormal nodes.

[0137] The above embodiments are descriptions of specific implementation methods of the present invention rather than limitations of the present invention. Technical personnel in the relevant technical field may make various changes and modifications to obtain corresponding equivalent technical solutions without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions should be included in the patent protection scope of the present invention.

Claims

1. A charging gun metering error monitoring method based on an energy router, characterized in that: The following steps are involved: Step 1: Obtain the power data of the charging gun and energy router according to the charging station file information; Step 2: Calculate the line loss and line loss rate of the charging station based on the power data of the charging gun and the energy router; Step 3: Identify abnormal points in line loss rate through the 3σ principle and remove the corresponding charging gun and energy router power data; Step 4: perform windowing on the processed power data and perform weighted processing on the windowed data: Step 4-1, initialize the maximum window width window max , minimum target power Tmin, window position pos=1, sliding step step=1; Step 4-2: Take the first energy router data as the sliding window starting point and intercept the window max Sample points are used as observation areas, and the range of the observation area is: [pos,pos+window max -1]; Step 4-3, determine the target power CUMpos when the window position is pos: where x i-all are all the charging guns in the i-th sample; Step 4-4, if CUMpos does not exceed Tmin, then window max ×c, until CUMpos is greater than Tmin; Where c is the window width expansion coefficient; Step 4-5, determine the target window width: Will satisfy for the first time Window width new1 The value is used as the target window width window new ; in η represents the target power coefficient; window new1 Increment by adding 1; Step 4-6, add the data of the charging gun and energy router in the window: Among them, x i-all is all the charging guns in the i-th sample, y i is the power data of the i-th sample energy router, x′ k is the charging gun data after addition, y′ k is the energy router data after addition; Step 4-7, update the sliding window position, the position update formula is as follows: pos=pos+step; Step 4-8, when the sliding window moves to the last window max Position, pos+window max -1=m1, where m1 is the number of charging station samples after removing abnormal points; Directly perform cumulative calculation on Window_max points; Step 4-9, update the number of charging station samples to m2; Where m2 = m1-Window max +1; Step 4-10, calculate the weight ω of the power data after sliding window k ; Where k represents the sequence number of the accumulated sample points, 1≤k≤m2; Step 4-11, assign weight ω k The formula for assigning the power data after sliding the window is as follows; ″′ x k =w k ·x k ; ″′ y k =w k ·y k ; Among them, x k ″、y k ″ is the power data of the charging gun and energy router after weighting; Step 5: Based on the constructed weighted data, a linear model is established to solve the charging gun error coefficient and monitor the charging gun metering error.

2. According to claim 1, a charging gun metering error monitoring method based on an energy router is characterized in that: The calculation formula of the charging station line loss rate in step 2 is as follows: Among them, y i is the power data of the i-th sample energy router, 1≤i≤m0, m0 is the number of charging station samples, x ij is the power data of the jth charging gun in the i-th sample, lr i is the line loss rate corresponding to the i-th sample, and n0 is the number of charging guns.

3. According to claim 1, a charging gun metering error monitoring method based on an energy router is characterized in that: The specific process of step 3 is as follows: A1, calculate the mean μ of the overall distribution of line loss rate; Among them, lr i is the line loss rate corresponding to the i-th sample, m0 is the number of charging station samples; A2, calculate the variance σ of the overall distribution of line loss rate; A3, calculate the upper limit of line loss rate lr according to the 3σ principle up ; lr up =μ+3σ; A4, calculate the lower limit of line loss rate lr according to the 3σ principle down ; lr down =μ-3σ; A5, according to the upper and lower limits of the line loss rate, abnormal point identification is performed. The abnormal point identification formula is as follows; Among them, when sgn=1, it is identified as an abnormal point; A6, based on the identified abnormal points, eliminate the corresponding charging gun and energy router power data.

4. According to claim 1, a charging gun metering error monitoring method based on an energy router is characterized in that: The process of establishing a linear model to solve the error coefficient of the charging gun in step 5 is as follows; C1, calculate line loss; in, is the line loss of the kth sample; x″ kj is the power data of the jth charging gun in the kth sample after weighting; C2, build a linear model; Where β is the error coefficient of the charging gun, the subscript is the charging gun number, ranging from 1 to n; δ is other losses in the line; α is the measurement error coefficient of the energy router; C3, solve the charging gun error coefficient β; β=(X T X+λE) -1 X T Error; Among them, the matrix X = [x1″, x2″, ..., x n ″,y″,1]; λ is the regularization parameter; y” is the accumulated power data vector of m2 energy routers.

5. According to claim 4, a charging gun metering error monitoring method based on an energy router is characterized in that: λ is 1.

6. According to a method for monitoring charging gun metering error based on an energy router according to claim 1, it is characterized in that: window max =48。 7. According to claim 1, a charging gun metering error monitoring method based on an energy router is characterized in that: η is 0.

8.

8. The method for monitoring charging gun metering error based on energy router according to claim 1 is characterized in that: Tmin is 10kWh.

9. The method for monitoring charging gun metering error based on energy router according to claim 1 is characterized in that: c is 2, 3 or 4.

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