Electric energy meter harmonic error estimation method

Abstract

The invention discloses an electric energy meter harmonic error estimation method, which comprises the following steps of: 1, analyzing harmonic distribution of each line in a new energy grid-connected area, and carrying out harmonic analysis on the harmonic distribution condition in the line in a power grid area connected with new energy; step 2, performing grade division on harmonic waves according to a harmonic wave analysis result in the line, and then estimating Poisson intensity under each harmonic wave grade; 3, combining the Poisson intensity under each harmonic level with the metering error of the electric energy meter, and constructing the sensitivity between the error and the harmonic Poisson intensity; 4, estimating the error of the electric energy meter according to the harmonic wave distribution condition of real-time operation in the line, the Poisson intensity, the harmonic wave Poisson intensity and the error sensitivity; statistical characterization is performed on the harmonic waves after new energy grid connection and the metering error of the harmonic waves on the electric energy meter, so that necessary reference is provided for design and error compensation of the electric energy meter, and the electric energy metering precision in the field of new energy grid connection is improved.

Description

Technical Field

[0001] The present invention relates to the field of harmonic measurement technology, and in particular to a method for estimating harmonic errors of an electric energy meter. Background Art

[0002] With the development of new energy grid connection, the capacity of new energy connected to the grid is increasing. After the new energy is connected to the grid, it is very necessary to accurately measure the power generation of the new energy station. In the lines connected to the new energy power generation system, power quality problems such as harmonics and three-phase imbalance will lead to metering errors of the electric energy meter. However, in the lines connected to the new energy power generation system, harmonic analysis is relatively complicated. It is related to the form of new energy power generation, installed capacity, form of power electronic converter, grid connection form, topological structure of grid connection, etc. It is difficult to use theoretical analysis to accurately analyze the harmonics of the lines connected to the new energy station. Furthermore, it is difficult to find an exact corresponding relationship for the electric energy meter error caused by harmonics. Therefore, for the lines connected to the new energy station, how to analyze the harmonics in the lines and construct the relationship between the harmonic distribution and the electric energy meter error, so as to improve the level of electric energy metering, needs further research. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method for estimating harmonic errors of electric energy meters to solve the relationship between the measurement errors and harmonics of electric energy meters in lines connected to new energy stations, thereby improving the level of electric energy metering.

[0004] Technical solution of the present invention:

[0005] A method for estimating harmonic errors of an electric energy meter, the method comprising:

[0006] Step 1: Harmonic distribution analysis of each line in the renewable energy grid-connected area, and harmonic analysis of the harmonic distribution of the lines in the grid area connected to renewable energy;

[0007] Step 2: According to the results of harmonic analysis in the line, the harmonics are classified into grades, and then the Poisson intensity at each harmonic grade is estimated;

[0008] Step 3: Combine the Poisson intensity at each harmonic level with the metering error of the electric energy meter to construct the sensitivity between the error and the harmonic Poisson intensity;

[0009] Step 4: According to the real-time harmonic distribution in the line and the Poisson intensity, the error of the electric energy meter is estimated according to the harmonic Poisson intensity and the error sensitivity.

[0010] Step 5: When the access situation of new energy sources changes, the parameters of Poisson intensity are dynamically modified.

[0011] The harmonic analysis method includes: using voltage sensors and current sensors to sample the voltage data and current data in the line to obtain a historical data set of a certain time period Te, using a mathematical decomposition algorithm to perform harmonic analysis on these data, thereby obtaining the harmonics contained in the voltage and current in the line; further, calculating the total harmonic distortion rate THD:

[0012]

[0013] Where U h is the hth harmonic voltage; U 1 is the fundamental voltage.

[0014] The method for estimating the Poisson intensity at each harmonic level includes first classifying the THD that occurs into certain levels of THD n , the division method is:

[0015] Table 1 THD level classification table

[0016]

[0017] According to the Poisson distribution, the repeated occurrence of harmonic THD at each level in the line is counted, that is, the Poisson intensity. The Poisson distribution during statistics is as follows:

[0018]

[0019] In the formula, λ h is the Poisson intensity of the harmonic event; k is the maximum number of possible occurrences of each level of harmonic, namely, THDn events under the confidence level P(x=k).

[0020] Harmonic event Poisson intensity λ h The method for determining is:

[0021] First, the time period T e The THDn historical data at each level in the time interval t n Divide different data sets, and then determine the time period T based on these data sets e Poisson intensity value of THDn at each level:

[0022]

[0023] Where N(THDn) is the number of times each level of THDn occurs.

[0024] The confidence level P(x=k) is 95%, 93% or 90%.

[0025] The method of constructing the sensitivity between the error and the harmonic Poisson intensity includes: according to the number of each level of THD in each time interval, each corresponds to the corresponding error data of the electric energy meter, and according to these data, the sensitivity between the harmonic Poisson intensity and the metering error is constructed:

[0026]

[0027] Where Er is the measurement error of the electric energy meter; k n The sensitivity coefficient corresponding to each level of THDn.

[0028] The method for estimating the error of the electric energy meter includes: substituting the maximum number of occurrences of each level of THD under the confidence level P (x=k) into the Poisson sensitivity formula to estimate the maximum confidence value of the electric energy meter error.

[0029] The method for dynamically modifying the parameters of Poisson strength includes: a , the harmonic Poisson intensity parameters of the lines in the renewable energy grid-connected area are re-estimated and dynamically adjusted, and the re-evaluation time period T a Manual setting.

[0030] Beneficial effects of the present invention:

[0031] Based on Poisson sensitivity and dynamic parameter correction, the present invention analyzes the harmonics in the lines for historical data in the power grid area connected to new energy sources, divides the harmonic levels, estimates the harmonic Poisson intensity, and constructs a sensitivity function between the metering error of the electric energy meter and the harmonic Poisson intensity, which is used for error estimation during real-time operation of the electric energy meter, thereby providing necessary reference for the design of the electric energy meter and the error compensation of the electric energy meter, and improving the electric energy metering accuracy in the field of new energy grid connection.

[0032] The present invention statistically characterizes the harmonics after the new energy is connected to the grid and the effect of the harmonics on the metering error of the electric energy meter, thereby providing necessary references for the design of the electric energy meter and the error compensation of the electric energy meter, and improving the electric energy metering accuracy in the field of new energy connection to the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0034] A method for estimating harmonic errors of electric energy meters based on Poisson sensitivity and dynamic parameter correction comprises the following steps: harmonic distribution analysis of each line in a new energy grid-connected area, Poisson intensity calculation of harmonic events of different levels in the line, construction of error and harmonic Poisson intensity sensitivity, electric energy meter error estimation, and Poisson intensity parameter correction.

[0035] The harmonic distribution analysis of each line in the renewable energy grid-connected area is to conduct harmonic analysis on the harmonic distribution of the lines in the grid area connected to renewable energy;

[0036] The Poisson intensity calculation of harmonic events of different levels in the line is to classify the harmonics according to the results of harmonic analysis in the line, and then estimate the Poisson intensity at each harmonic level;

[0037] The error and harmonic Poisson intensity sensitivity construction is to combine the Poisson intensity at each harmonic level with the metering error of the electric energy meter to construct the sensitivity between the error and the harmonic Poisson intensity;

[0038] The error estimation of the electric energy meter is based on the real-time distribution of harmonics in the line and the Poisson intensity. The error of the electric energy meter is estimated according to the harmonic Poisson intensity and error sensitivity.

[0039] Poisson intensity parameter correction is to dynamically correct the Poisson intensity parameters when the new energy access situation changes.

[0040] The error and harmonic Poisson intensity sensitivity are constructed by combining the metering error of the electric energy meter at each harmonic level with the corresponding harmonic Poisson intensity to construct a sensitivity function between the error and the harmonic Poisson intensity. This sensitivity reflects the impact of the harmonic distribution and frequency of occurrence on the error of the electric energy meter.

[0041] The error estimation of the electric energy meter is based on the analysis of the harmonic situation of the real-time running line and the Poisson intensity estimation, and then the measurement error of the electric energy meter is estimated according to the sensitivity function between the error and the harmonic Poisson intensity.

[0042] Poisson intensity parameter correction is to re-estimate and correct the parameters of Poisson estimation when the access situation of new energy changes.

[0043] Specifically include:

[0044] The harmonic distribution analysis of each line in the new energy grid-connected area is to sample the voltage data and current data in the power grid line connected to the new energy using voltage sensors and current sensors to obtain a historical data set of a certain time period Te. Mathematical decomposition algorithms, including but not limited to Fourier series and wavelet transform algorithms, are used to perform harmonic analysis on these data to obtain the harmonics contained in the voltage and current in the line; further, the total harmonic distortion rate THD is calculated, and the calculation formula is as follows:

[0045]

[0046] Where U h is the hth harmonic voltage; U 1 is the fundamental voltage.

[0047] The THD that appears in the system is first classified into certain levels of THD. n , the division principle is shown in Table 1:

[0048] Table 1 THD level classification table

[0049]

[0050]

[0051] The THD classification principles in the above table can be flexibly set according to the characteristics of the electricity meter and the needs of management personnel.

[0052] Since the harmonics in the line are related to many factors such as the size of renewable energy power generation and the system operation mode, the THD in the line changes dynamically in each period. According to the Poisson distribution, the repeated occurrence of harmonic THD at each level in the line can be counted, that is, the Poisson intensity. The Poisson distribution required for statistics is as follows:

[0053]

[0054] In the formula, λ h is the Poisson intensity of the harmonic event; k is the maximum number of possible occurrences of each level of harmonic, namely, THDn events under the confidence level P(x=k).

[0055] In the Poisson distribution represented by formula (2), the Poisson intensity λ needs to be determined through historical data h , which can be obtained by following the steps below:

[0056] First, the time period T e The THDn historical data at each level is calculated at a certain time interval t n Divide different data sets. The above time intervals can be hourly, daily, or weekly, which can be determined based on the estimated time period. Then, based on these data sets, the time period T can be determined. e Poisson intensity value of THDn at each level:

[0057]

[0058] Where N(THDn) is the number of times each level of THDn occurs.

[0059] In the Poisson distribution formula (2), a certain evaluation confidence level P(x=k) needs to be determined. This confidence level can be determined as 95%, 93%, or 90%, depending on the evaluation needs.

[0060] The determined confidence level P(x=k) and the Poisson intensity value λ hn Substituting into formula (2), the maximum number of occurrences of each level of THD under the confidence level P (x=k) can be obtained.

[0061] Then, according to the number of each level of THD in each small time interval, the corresponding error data of the electric energy meter is corresponding, and the sensitivity of harmonic Poisson intensity and metering error is constructed based on these data:

[0062]

[0063] Where Er is the measurement error of the electric energy meter; k n The sensitivity coefficient corresponding to each level THDn can be obtained by the least squares method.

[0064] On the one hand, the sensitivity reflects the sensitivity of the metering error of the electric energy meter to each level of THDn and its order, that is, the contribution of each level of THD and its order to the error of the electric energy meter; on the other hand, the mean value of the electric energy meter error can be estimated based on the obtained harmonic Poisson intensity.

[0065] Substituting the maximum number of occurrences of each level of THD at the confidence level P (x=k) obtained in formula (2) into the Poisson sensitivity formula, the maximum confidence value of the electric energy meter error can be estimated.

[0066] Combining the above two energy meter errors, we can get a confidence estimate of the energy meter error.

[0067] The above Poisson sensitivity construction and error estimation reflect the impact of the harmonic distribution and the cumulative number of harmonic occurrences at each level on the error of the electric energy meter.

[0068] With the development of power grid and construction of new energy, the situation of new energy grid connection will change, such as the capacity of new energy access in the new energy access area, the substation of new energy access, and the line structure of new energy access. These changes in grid connection will change the harmonic distribution and harmonic Poisson intensity in the line. Therefore, every certain time period T a , then the harmonic Poisson intensity parameters of the lines in the renewable energy grid-connected area are re-estimated and dynamically adjusted. The re-evaluation period is T a To be set by relevant personnel.

Claims

1. A method for estimating harmonic errors of an electric energy meter, characterized in that: The method comprises: Step 1: Harmonic distribution analysis of each line in the renewable energy grid-connected area, and harmonic analysis of the harmonic distribution of the lines in the grid area connected to renewable energy; Step 2: According to the results of harmonic analysis in the line, the harmonics are classified into grades, and then the Poisson intensity at each harmonic grade is estimated; Step 3: Combine the Poisson intensity at each harmonic level with the metering error of the electric energy meter to construct the sensitivity between the error and the harmonic Poisson intensity; Step 4: According to the real-time harmonic distribution in the line and the Poisson intensity, the error of the electric energy meter is estimated according to the harmonic Poisson intensity and the error sensitivity.

2. The method for estimating harmonic errors of an electric energy meter according to claim 1, characterized in that: The method further comprises: Step 5: When the access situation of new energy sources changes, the parameters of Poisson intensity are dynamically modified.

3. The method for estimating harmonic errors of an electric energy meter according to claim 1, characterized in that: The harmonic analysis method includes: using voltage sensors and current sensors to sample the voltage data and current data in the line to obtain a historical data set of a certain time period Te, using a mathematical decomposition algorithm to perform harmonic analysis on these data, thereby obtaining the harmonics contained in the voltage and current in the line; further, calculating the total harmonic distortion rate THD: Where U h is the hth harmonic voltage; U1 is the fundamental voltage.

4. The method for estimating harmonic errors of an electric energy meter according to claim 1, characterized in that: The method for estimating the Poisson intensity at each harmonic level includes first classifying the THD that occurs into certain levels of THD n , the division method is: Table 1 THD level classification table According to the Poisson distribution, the repeated occurrence of harmonic THD at each level in the line is counted, that is, the Poisson intensity. The Poisson distribution during statistics is as follows: In the formula, λ h is the Poisson intensity of the harmonic event; k is the maximum number of possible occurrences of each level of harmonic, namely, THDn events under the confidence level P(x=k).

5. The method for estimating harmonic errors of an electric energy meter according to claim 4, characterized in that: Harmonic event Poisson intensity λ h The method to determine is: First, the time period T e The THDn historical data at each level in the time interval t n Divide different data sets, and then determine the time period T based on these data sets e Poisson intensity value of THDn at each level: Where N(THDn) is the number of times each level of THDn occurs.

6. The method for estimating harmonic errors of an electric energy meter according to claim 4, characterized in that: The confidence level P(x=k) is 95%, 93% or 90%.

7. The method for estimating harmonic errors of an electric energy meter according to claim 1, characterized in that: The method of constructing the sensitivity between the error and the harmonic Poisson intensity includes: according to the number of each level of THD in each time interval, the corresponding error data of the electric energy meter are corresponded, and the sensitivity between the harmonic Poisson intensity and the metering error is constructed according to these data: Where Er is the measurement error of the electric energy meter; k n The sensitivity coefficient corresponding to each level of THDn.

8. The method for estimating harmonic errors of an electric energy meter according to claim 1, characterized in that: The method for estimating the error of the electric energy meter includes: substituting the maximum number of occurrences of each level of THD under the confidence level P (x=k) into the Poisson sensitivity formula to estimate the maximum confidence value of the electric energy meter error.

9. The method for estimating harmonic errors of an electric energy meter according to claim 2, characterized in that: The method for dynamically modifying the parameters of Poisson strength includes: a , the harmonic Poisson intensity parameters of the lines in the renewable energy grid-connected area are re-estimated and dynamically adjusted, and the re-evaluation time period T a Manual setting.

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