A method for estimating harmonic errors in electricity meters

Abstract

This invention discloses a method for estimating harmonic errors in electricity meters, comprising: Step 1, harmonic distribution analysis of each line in the new energy grid-connected area, performing harmonic analysis on the harmonic distribution in the lines within the grid area connected to new energy; Step 2, classifying harmonics into levels based on the results of the harmonic analysis, and then estimating the Poisson intensity at each harmonic level; Step 3, combining the Poisson intensity at each harmonic level with the metering error of the electricity meter to construct a sensitivity between the error and the harmonic Poisson intensity; Step 4, estimating the error of the electricity meter based on the real-time harmonic distribution and Poisson intensity in the lines, according to the harmonic Poisson intensity and the error sensitivity; This invention statistically characterizes the harmonics after new energy grid connection and the harmonics' impact on the metering error, thereby providing necessary reference for electricity meter design and error compensation, and improving the accuracy of electricity metering in the field of new energy grid connection.

Description

Technical Field

[0001] This invention relates to the field of harmonic measurement technology, and particularly to a method for estimating harmonic errors in electricity meters. Background Technology

[0002] With the development of new energy grid connection, the capacity of new energy sources connected to the grid is increasing. After new energy is connected to the grid, accurate metering of the power generation from new energy power plants is essential. In the lines connecting to new energy power generation systems, power quality issues such as harmonics and three-phase imbalance can lead to metering errors. However, harmonic analysis in lines connecting to new energy power generation systems is complex, as it is related to the type of new energy generation, installed capacity, type of power electronic converter, grid connection method, and grid topology, making it difficult to conduct accurate harmonic analysis of lines connecting to new energy power plants using theoretical methods. Furthermore, it is difficult to find a precise correlation between harmonic-induced metering errors and the actual harmonics. Therefore, how to analyze harmonics in lines connecting to new energy power plants and establish a relationship between harmonic distribution and metering errors to improve electricity metering accuracy requires further research. Summary of the Invention

[0003] The technical problem to be solved by this invention is to provide a method for estimating harmonic errors in electricity meters, so as to solve the relationship between metering errors and harmonics in lines connected to new energy power plants, thereby improving the level of electricity metering.

[0004] Technical solution of the present invention:

[0005] A method for estimating harmonic errors in an electricity meter, the method comprising:

[0006] Step 1: Harmonic distribution analysis of each line in the new energy grid connection area. Harmonic analysis is performed on the harmonic distribution of the lines in the grid area where new energy is connected.

[0007] Step 2: Based on the results of harmonic analysis in the line, classify the harmonics into levels, and then estimate the Poisson intensity for each harmonic level;

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

[0009] Step 4: Based on the real-time harmonic distribution and Poisson intensity in the line, estimate the error of the electricity meter according to the harmonic Poisson intensity and error sensitivity.

[0010] Step 5: When the situation of new energy access changes, dynamically adjust the parameters of Poisson strength.

[0011] Harmonic analysis methods include: sampling voltage and current data in the line using voltage and current sensors to obtain historical datasets for a certain time period Te; performing harmonic analysis on these datasets using mathematical decomposition algorithms to obtain the various harmonics contained in the voltage and current of the line; and further calculating the total harmonic distortion (THD).

[0012]

[0013] In the formula, U h U1 is the voltage of the h-th harmonic; U2 is the fundamental voltage.

[0014] Methods for estimating Poisson intensity at each harmonic level include: firstly, classifying the occurring THDs into certain levels. n The method of division is as follows:

[0015] Table 1. THD Classification Table

[0016]

[0017] Based on the Poisson distribution, the recurrence of harmonic THD at each level in the line was statistically analyzed, i.e., the Poisson intensity. The Poisson distribution used in the statistical analysis is as follows:

[0018]

[0019] In the formula, λ h Let be the Poisson intensity of the harmonic event; k is the maximum possible number of times each harmonic event, i.e., the THDn event, can occur at a confidence level P(x=k).

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

[0021] First, let's define the time period T. e Historical THDn data for each level within the range are arranged according to time interval t. n Divide the dataset into different parts, and then determine the time period T based on these datasets. e Poisson intensity values ​​of THDn at each level:

[0022]

[0023] In the formula, 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] Methods for constructing the sensitivity between error and harmonic Poisson intensity include: based on the number of THD levels at each time interval, corresponding to the corresponding error data of the energy meter, and constructing the sensitivity between harmonic Poisson intensity and metering error based on these data.

[0026]

[0027] In the formula, Er represents the metering error of the electricity meter; k n The sensitivity coefficient corresponding to each THDn level.

[0028] Methods for estimating the error of electricity meters include substituting the maximum number of occurrences of each level of THD at confidence level P (x = k) into the Poisson sensitivity formula to estimate the maximum confidence value of the electricity meter error.

[0029] Methods for dynamically correcting the parameters of Poisson strength include: adjusting the parameters at regular time intervals T. a The harmonic Poisson intensity parameters of power lines in the new energy grid-connected area are re-estimated and dynamically adjusted, with a re-evaluation time period T. a It is set manually.

[0030] The beneficial effects of this invention are:

[0031] This invention, based on Poisson sensitivity and dynamic parameter correction, performs harmonic analysis on historical data of power lines in grid areas connected to new energy sources. It classifies harmonic levels, estimates harmonic Poisson intensities, and constructs a sensitivity function between the metering error and harmonic Poisson intensities. This function is used for error estimation during real-time operation of the meter, thus providing necessary reference for meter design and error compensation, and improving the metering accuracy in the field of new energy grid connection.

[0032] This invention statistically characterizes the harmonics after grid connection of new energy sources and their impact on the metering error of electricity meters, thereby providing necessary reference for the design of electricity meters and the compensation of electricity meter errors, and improving the metering accuracy of electricity in the field of new energy grid connection. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

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

[0035] Harmonic distribution analysis of lines in the new energy grid connection area is a harmonic analysis of the harmonic distribution in the lines in the power grid area where new energy is connected.

[0036] The calculation of Poisson intensity for different levels of harmonic events in the line is based on the results of harmonic analysis in the line, which classifies the harmonics into levels and then estimates the Poisson intensity for each harmonic level.

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

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

[0039] Poisson strength parameter correction is a dynamic adjustment of the Poisson strength parameter when the renewable energy access situation changes.

[0040] The sensitivity construction between error and harmonic Poisson intensity is achieved by combining the metering error of the energy meter at each harmonic level with the corresponding harmonic Poisson intensity to construct a sensitivity function between error and harmonic Poisson intensity. This sensitivity reflects the magnitude of the influence of the harmonic distribution and frequency of occurrence on the energy meter error.

[0041] The error estimation of electricity meters is based on the analysis of harmonic conditions and Poisson intensity estimation of the circuit in real time. Then, the metering error of the electricity meter is estimated according to the sensitivity function between the error and the harmonic Poisson intensity.

[0042] Poisson strength parameter correction is a process of re-estimating and correcting the parameters estimated by Poisson when the situation of new energy access changes.

[0043] Specifically, it includes:

[0044] The harmonic distribution analysis of power lines within the renewable energy grid connection area involves sampling voltage and current data from the grid lines connected to renewable energy sources using voltage and current sensors to obtain historical datasets (Te) over a specific time period. Mathematical decomposition algorithms, including but not limited to Fourier series and wavelet transform algorithms, are then used to perform harmonic analysis on this data, thereby obtaining the various harmonics contained in the voltage and current of the lines. Furthermore, the total harmonic distortion (THD) is calculated using the formula shown below:

[0045]

[0046] In the formula, U h U1 is the voltage of the h-th harmonic; U2 is the fundamental voltage.

[0047] The THDs appearing in the system are first classified into certain levels. n The principles for the division are shown in Table 1:

[0048] Table 1. THD Classification Table

[0049]

[0050]

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

[0052] Because harmonics in a power line are related to various factors such as the amount of renewable energy generation and the system's operating mode, the total harmonic density (THD) in the line changes dynamically over different time periods. Based on the Poisson distribution, the recurrence of harmonic THD at different levels in the line can be statistically analyzed, i.e., the Poisson intensity. The Poisson distribution required for this statistical analysis is as follows:

[0053]

[0054] In the formula, λ h Let be the Poisson intensity of the harmonic event; k is the maximum possible number of times each harmonic event, i.e., the THDn event, can occur at a confidence level P(x=k).

[0055] In the Poisson distribution characterized by formula (2), the Poisson intensity λ needs to be determined through historical data. h This can be achieved through the following steps:

[0056] First, let's define the time period T. e Historical THDn data for each level within the range are arranged at certain time intervals t. n The dataset is divided into different segments. The aforementioned time intervals can be hourly, daily, or weekly, depending on the estimated required time period. Then, based on these datasets, the time period T can be determined. e Poisson intensity values ​​of THDn at each level:

[0057]

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

[0059] In the formula (2) of the Poisson distribution, a certain evaluation confidence level P (x=k) also 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) is compared with the Poisson intensity value λ.hn Substituting into formula (2), we can obtain the maximum number of occurrences of each level of THD at confidence level P (x = k).

[0061] Then, based on the number of THDs at each level within each small time interval, corresponding to the relevant error data of the electricity meter, a sensitivity of harmonic Poisson intensity and metering error is constructed using these data:

[0062]

[0063] In the formula, Er represents the metering error of the electricity meter; k n The sensitivity coefficient for each THDn level can be obtained using the least squares method.

[0064] This sensitivity reflects, on the one hand, the degree to which the metering error is sensitive to the various levels of THDn and their frequencies, i.e., the contribution of each level of THD and its frequencies to the meter error; on the other hand, based on the obtained harmonic Poisson intensities, the mean value of the meter error can be estimated.

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

[0066] By combining the two electricity meter errors mentioned above, a confidence estimate of the electricity meter error can be obtained.

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

[0068] With the development of power grids and the construction of new energy sources, the grid connection of new energy sources will change to some extent. For example, the capacity of new energy sources within the grid connection area will change, as will the substations connecting to the new energy sources, and the line structure connecting to the new energy sources. These changes in grid connection will alter the harmonic distribution and harmonic Poisson intensity in the lines. Therefore, at regular time intervals T... a Therefore, the harmonic Poisson intensity parameters of the power lines in the new energy grid-connected area are re-estimated and dynamically adjusted. The re-evaluation time period T a It is set by relevant personnel.

Claims

1. A method for estimating harmonic error of an electric energy meter, characterized in that: The method comprises: Step 1, harmonic distribution analysis of each line in the new energy grid-connected area, the harmonic distribution in the line in the power grid area connected with new energy is analyzed; Step 2, according to the result of harmonic analysis in the line, the harmonics are classified, and then the poisson intensity under each harmonic level is estimated; Step 3, combine the poisson intensity under each harmonic level with the metering error of the electric energy meter, and construct the sensitivity between the error and the harmonic poisson intensity; Step 4, according to the real-time running 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; The method for estimating the Poisson intensity of each harmonic level comprises: firstly classifying the THD into certain levels according to the occurrence of the THD n According to the Poisson distribution, the repeated occurrence of the harmonic THD in each level of the line, i.e. the Poisson intensity, is counted. The Poisson distribution in the counting is as follows: （2）； where λ h is the harmonic event Poisson intensity; k is the harmonic, i.e. THD, of each order n is the maximum number of occurrences of an event at a confidence level P(x=k). Harmonic event Poisson intensity λ h The determination method is as follows: First, the time period T e is divided into a number of levels, each level having a THD n value. The historical data is then divided into different data sets according to time intervals t n , and the Poisson intensity value of THD e for each level within the time period T n is determined from these data sets. （3）； where N(THD n ) is the number of times THD n occurs for each level. The confidence P(x=k) is 95%, 93% or 90%; The method for constructing the sensitivity between the error and the harmonic poisson intensity comprises: according to the number of each level THD in each time interval, the corresponding error data of the electric energy meter is obtained, and the sensitivity between the harmonic poisson intensity and the metering error is constructed according to the data: （4）； where Er is the metering error of the electric energy meter; k n for each level THD n corresponding sensitivity coefficient.

2. The method of claim 1, wherein: The method further comprises: Step 5, when the new energy access condition changes, the parameters of the poisson intensity are dynamically corrected.

3. The method of claim 1, wherein: The harmonic analysis method comprises: voltage and current data in the line are sampled by using voltage and current sensors to obtain a historical data set in a certain time period Te, mathematical decomposition algorithm is used to analyze the data to obtain each harmonic contained in the voltage and current in the line; further, the total harmonic distortion THD is calculated: （1）； In the formula, U h is the hth harmonic voltage; U1is the fundamental voltage.

4. The method of claim 1, wherein: The method for estimating the error of the electric energy meter comprises: the maximum number of each level THD under the confidence P(x=k) is substituted into the poisson sensitivity formula to estimate the confidence maximum value of the electric energy meter error.

5. The method of claim 2, wherein: The method for dynamically correcting the parameters of the Poisson intensity includes: correcting the parameters of the Poisson intensity every certain time period T a The method for dynamically correcting the parameters of the Poisson intensity includes: correcting the parameters of the Poisson intensity every certain time period T a The method for dynamically correcting the parameters of the Poisson intensity includes: correcting the parameters of the Poisson intensity every certain time period T

Images