Electric energy meter error analysis method based on multiple multi-factor variance algorithm

Abstract

The invention discloses an electric energy meter error analysis method based on a multiple multi-factor variance algorithm, and the method comprises the steps: carrying out the first multi-factor variance analysis of the metering error of an electric energy meter and each interference source, and carrying out the significance verification of the relation between the metering error of the electric energy meter and each interference source; performing second multi-factor variance analysis on the metering error of the electric energy meter and each interference source index, and measuring the relationship between the metering error of the electric energy meter and each interference source subdivision index; a multi-factor variance analysis algorithm is adopted to analyze the influence significance degree of the power generation factor of the new energy station in the system on each index of the interference source; the problems that in the prior art, specific relations between electric energy meter metering errors and interference sources and between indexes for interference source subdivision and relations between interference source system factors are not analyzed, and the contribution degree of all interference sources to the electric energy meter errors cannot be clarified, and the interference source subdivision indexes cannot be analyzed are solved. And beneficial support is provided for error responsibility distinguishing and error evaluation and correction in the error research of the electric energy meter.

Description

Technical Field

[0001] The invention belongs to the technical field of electric energy meter error analysis, and in particular relates to an electric energy meter error analysis method based on a multiple multi-factor variance algorithm. Background Art

[0002] In recent years, the energy structure has changed greatly. New energy power generation systems have received more attention and development. More and more new energy sites have been incorporated into the power system, including a large number of photovoltaic power stations and wind farms. The power generation of these new energy sites is generally greatly affected by weather factors, so the output power has certain volatility and intermittency. The fluctuating and intermittent power injected into the power system will cause fluctuations in the line voltage and changes in frequency in the power system. In addition, new energy sites generally contain a large number of power electronic devices, which also cause certain harmonic effects on the system. The above-mentioned factors will affect the metering error of the energy meter, which is called the interference source affecting the metering error of the energy meter. Although the sources and effects of various interference sources in the system have been studied to a certain extent, the specific relationship between the metering error of the energy meter and the interference source, and the specific relationship between the indicators of the interference source subdivision, as well as the relationship between the interference source system factors, still needs further study. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an electric energy meter error analysis method based on a multiple multi-factor variance algorithm to solve the technical problems that the prior art does not analyze the specific relationship between the electric energy meter measurement error and the interference source, and between the indicators of the interference source subdivision, as well as the relationship between the interference source system factors, and cannot clarify the contribution of each interference source to the electric energy meter error, and provide useful support for error responsibility distinction, error evaluation and correction in electric energy meter error research.

[0004] Technical solution of the present invention:

[0005] A method for analyzing an error of an electric energy meter based on a multiple multi-factor variance algorithm, the method comprising: a first multi-factor variance analysis of the measurement error of the electric energy meter and each interference source, to verify the significance of the relationship between the measurement error of the electric energy meter and each interference source; a second multi-factor variance analysis of the measurement error of the electric energy meter and each interference source index, to measure the relationship between the measurement error of the electric energy meter and the subdivided index of each interference source; and a multi-factor variance analysis algorithm is used to analyze the significance of the influence of the power generation factor of a new energy station in the system on each index of the interference source.

[0006] The interference sources include: harmonics in the line, voltage deviation, frequency deviation, three-phase imbalance and other factors; the other factors include: voltage fluctuation and voltage flicker factors.

[0007] Interference source index refers to the subdivided index of each interference source. The harmonic index is divided into high-frequency, medium-frequency and low-frequency types; the voltage deviation is divided into high-order fluctuation type, medium-order fluctuation type and low-order fluctuation type; the frequency deviation is divided into high-order fluctuation type, medium-order fluctuation type and low-order fluctuation type; the three-phase imbalance is divided into high-order fluctuation type, medium-order fluctuation type and low-order fluctuation type; other factors are also divided into three levels in analogy with the above classification method.

[0008] The first multi-factor variance analysis method for the energy meter measurement error and each interference source includes:

[0009] The measurement error of the electric energy meter caused by each interference source is expressed as:

[0010] E m =E mh +E mu +E mv +E mf +E mt (1)

[0011] In the formula, E m is the measurement error of the electric energy meter, E mh 、E mu 、E mv 、E mf and E mt They are the errors caused by various interference sources, namely harmonic interference error, three-phase unbalanced interference error, voltage deviation interference error, frequency deviation interference error and other factors interference error;

[0012] Harmonics are measured using THD, and the calculation formula is as follows:

[0013]

[0014] Where U eh is the hth harmonic voltage in the voltage of the line to be evaluated; U e1 is the corresponding fundamental voltage, M is the total harmonic order;

[0015] For three-phase imbalance, the three-phase imbalance degree is used as the measurement, as follows:

[0016]

[0017] The expression of γ is:

[0018]

[0019] Where U ea , U eb , U eb is the three-phase voltage value;

[0020] For voltage deviation and frequency deviation, the voltage deviation rate and frequency deviation rate are used for measurement, as follows:

[0021]

[0022] δ f =f e -f N (6)

[0024] Where U e 、f e is the actual voltage amplitude and frequency of the system; U N 、f N is the amplitude and frequency of the rated voltage; multi-factor variance analysis is performed on the above factors one by one, and two factors are checked each time.

[0025] The multi-factor variance analysis methods for harmonic factors and voltage deviation factors include:

[0026] Calculate the sum of squares of the harmonic factor and voltage deviation factor, S h , S v , memory random error sum of squares S hve , the calculation formula is as follows:

[0027]

[0028] In the formula, k and n are the data dimensions of harmonic factors and voltage deviation factors respectively; the calculation formula of each mean is as follows:

[0029]

[0030] In the formula, E hvij is the measurement error data under harmonic factors and voltage deviation factors; then calculate the F value of significance verification as follows:

[0031]

[0032]

[0033] The test threshold F of the two factors of harmonics and voltage deviation is obtained at a given significance level α ch 、F cv , by comparing F h With F ch 、F v With F cv Determine the significance between the two factors of measurement error and harmonics and voltage deviation;

[0034] When F h >F chWhen F v >F cv When , the voltage deviation factor passes the significance test, that is, the measurement error is related to the voltage deviation factor; otherwise, the measurement error is not related to the corresponding factor.

[0035] Comparison F h / F ch With F v / F cv The size of these two ratios reflects the correlation between the measurement error and the harmonic factors and voltage deviation factors. The larger the ratio, the stronger the correlation.

[0036] According to the above calculation process, each interference source factor is compared one by one, and the corresponding F value is compared with the test threshold F. c The comparison results of the measurement error are used to determine whether the measurement error is significantly related to the corresponding interference source factors; according to the corresponding F value and the test threshold F c The comparison of the ratios is used to determine the degree of correlation between the measurement error and the corresponding interference source factors.

[0037] The second level variance analysis method for the subdivision indexes of the energy meter measurement error and harmonics includes:

[0038] The calculation formulas for the sum of squares of deviations and the sum of squares of errors are as follows:

[0039]

[0040] In the formula, k THD 、n f are the data dimensions under the harmonic subdivision index respectively. The calculation formulas for the means of each item in the formula are as follows:

[0041]

[0042] In the formula, E THDfij It is the measurement error data under harmonic factors and voltage deviation factors;

[0043] Then calculate the F value of the significance test as follows:

[0044]

[0045] At a given significance level α, by comparing F f and the test threshold F fc The size of the error can be used to determine whether the metering error of the electric energy meter is significantly related to the subdivision index of the harmonics, namely the distribution of the harmonics: high-frequency-dominated type, medium-frequency-dominated type and low-frequency-dominated type.

[0046] When using the multi-factor variance analysis algorithm to analyze the significant degree of influence of the power generation factors of the new energy station in the system on various indicators of the interference source, the analysis method of the significant degree of influence of harmonics includes:

[0047] The photovoltaic power stations and wind farms in the area where the energy meter is located are classified into two categories. The relationship between the harmonics of the line where the energy meter is located and the power generation of the photovoltaic power stations and wind farms connected to the area is analyzed. e The harmonic THD data is taken together with the real-time power generation data of the photovoltaic power station and the wind farm power generation data, and the power generation is divided by the capacity of the power station to obtain the corresponding power generation state S tPV With S tW , that is:

[0048] S tPV =S PVp / S PVN (twenty four)

[0049] S tW =S Wp / S WN (25)

[0050] In the formula, S PVP With S PVN is the actual power generation and rated installed capacity of the photovoltaic power station; S WP With S WN is the actual power generation and rated installed capacity of the wind farm;

[0051] According to a certain threshold, it is divided into high-generation state, medium-generation state and low-generation state. Taking photovoltaic power generation as an example, the threshold of division is defined as S tPVm , S tPVh , so it can be divided as follows:

[0052]

[0053] The power generation data of the wind farm is also divided according to the above division method;

[0054] The corresponding harmonic THD, using the actual value or divided into a certain range of THD segments, forms a data set with the above power generation status, and then conducts multi-factor variance analysis:

[0055]

[0056] In the formula, k PV 、n W are the data dimensions of the photovoltaic power station and wind farm power generation data sets; E PVWij It is the measurement error data under the power generation factor of photovoltaic power station and wind farm;

[0057] The calculation formula for the mean of each item in the formula is as follows:

[0058]

[0059] Then calculate the F value of the significance test as follows:

[0060]

[0061] At a given significance level α, by comparing the above F PV 、F W With its test threshold F PVc 、F Wc The size of the harmonics in the line can be used to determine whether they are significantly related to the high-generation state, medium-generation state, and low-generation state of the photovoltaic power station and wind farm.

[0062] A calculation process similar to that of harmonics is used to measure the significant relationship between the voltage deviation, frequency deviation, three-phase imbalance and other factors in the line and the high-generation state, medium-generation state and low-generation state of photovoltaic power stations and wind farms.

[0063] Beneficial effects of the present invention:

[0064] The present invention adopts a triple multi-factor variance algorithm to analyze the relationship between the metering error of the electric energy meter and the interference source of the line, and the relationship between the metering error and the indicators of each interference source, and further analyzes the relationship between the line interference source and the system's new energy power generation system factors. Through the above analysis, the factors affecting the metering error of the electric energy meter and the contribution of each factor are clarified, that is, the metering error of the electric energy meter is traced, and then the responsibility of each interference source for the error of the electric energy meter is clarified, which provides useful support for the error responsibility distinction, error evaluation and correction in the study of electric energy meter errors.

[0065] The invention solves the technical problems that the prior art does not analyze the specific relationship between the metering error of the electric energy meter and the interference source, and between the indicators of the interference source subdivision, as well as the relationship between the interference source system factors, and cannot clarify the contribution of each interference source to the error of the electric energy meter, and provides useful support for error responsibility distinction, error evaluation and correction in the study of electric energy meter errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0067] A method for analyzing an electric energy meter error based on a multiple multi-factor variance algorithm comprises the following steps: a first multi-factor variance analysis of the electric energy meter measurement error and various interference sources, a second multi-factor variance analysis of the electric energy meter measurement error and interference source indicators, and a third multi-factor variance analysis of line interference sources and system factors.

[0068] The energy meter measurement error refers to the energy meter connected to each node in the new energy power system for energy measurement. During the energy measurement process, the energy meter is interfered by various interference sources in the metering line, which causes the measurement error. The interference sources that affect the energy meter measurement error refer to harmonics, voltage deviation, frequency deviation, three-phase imbalance and other factors in the line.

[0069] The other factors refer to other factors that may cause metering errors of electric energy meters in addition to the above-mentioned harmonics, voltage deviation, frequency deviation, and three-phase imbalance. The other factors include voltage fluctuations, voltage flicker, and other factors. Because the power quality factors that cause voltage meter errors may be different in different occasions, technicians can choose different factors according to actual occasions. The method of the present invention is generally applicable to these factors, and is not limited to the four factors of harmonics, voltage deviation, frequency deviation, and three-phase imbalance.

[0070] Interference source index refers to the subdivided index of each interference source. For harmonics, the index is divided into high-frequency type, medium-frequency type and low-frequency type; for voltage deviation, it is divided into high-order fluctuation type, medium-order fluctuation type and low-order fluctuation type; for frequency deviation, it is divided into high-order fluctuation type, medium-order fluctuation type and low-order fluctuation type; for three-phase imbalance, it is divided into high-order fluctuation type, medium-order fluctuation type and low-order fluctuation type.

[0071] Other factors are also classified into three levels by analogy with the above classification method. It should be noted that the present invention does not impose any special restrictions on the number of levels. The above three levels are applicable to the level division in general situations. The technicians can divide the specific level into multiple levels according to the specific occasions and technical requirements.

[0072] The measurement error of the electric energy meter caused by a certain interference source can be specifically expressed by the following formula:

[0073] E m =E mh +E mu +E mv +E mf +E mt (1)

[0074] In the formula, E m is the measurement error of the electric energy meter, which is caused by interference sources in multiple systems; E mh 、E mu 、E mv 、E mf 、E mt They are the errors caused by various interference sources, namely harmonic interference error, three-phase unbalanced interference error, voltage deviation interference error, frequency deviation interference error and interference errors from other factors.

[0075] In the first multi-factor variance analysis of the energy meter measurement error and multiple interference sources, a period of time T e The measurement error of the electric energy meter within the range and the specific indicators of each interference source are analyzed. e It refers to a certain time period for error assessment, and the length of this time period can be set by technical personnel.

[0076] Harmonics are measured using THD (total harmonic distortion), and the specific calculation formula is as follows:

[0077]

[0078] Where U eh is the hth harmonic voltage in the voltage of the line to be evaluated; U e1 is the corresponding fundamental voltage, and M is the total harmonic order.

[0079] For three-phase imbalance, the three-phase imbalance degree is used as the measurement, as follows:

[0080]

[0081] The expression of γ is:

[0082]

[0083] Where U ea , U eb , U eb is the three-phase voltage value.

[0084] For voltage deviation and frequency deviation, the voltage deviation rate and frequency deviation rate are used for measurement, as follows:

[0085]

[0086] δ f =f e -f N (6)

[0088] Where U e 、f e is the actual voltage amplitude and frequency of the system; U N 、f N is the amplitude and frequency of the rated voltage.

[0089] Perform multi-factor variance analysis on the above factors one by one, and check two factors each time. The check example process is as follows:

[0090] First, calculate the sum of squares of the harmonic factor and voltage deviation factor, which are S h , Sv , and the sum of squares of random errors S hve , the calculation formula is as follows:

[0091]

[0092] In the formula, k and n are the data dimensions of harmonic factors and voltage deviation factors respectively; the calculation formula of the mean value of each item in the formula is as follows:

[0093]

[0094] In the formula, E hvij is the measurement error data under harmonic factors and voltage deviation factors; then calculate the F value of significance verification, that is, the F value of harmonic factors h Value and voltage deviation factor F v The values ​​are as follows:

[0095]

[0096]

[0097] At a given significance level α, the test threshold F of the two factors of harmonics and voltage deviation can be obtained. ch 、F cv By comparing F h With F ch 、F v With F cv The size of the measurement error can be used to determine whether the measurement error is significant to the two factors of harmonics and voltage deviation.

[0098] In F h >F ch When , the harmonic factor passes the significance test, that is, the measurement error is related to the harmonic factor; similarly, in F v >F cv When , the voltage deviation factor passes the significance test, that is, the measurement error is related to the voltage deviation factor. Otherwise, the measurement error is not related to the corresponding factor.

[0099] Compare F h / F ch With F v / F cv The size of these two ratios also reflects the degree of correlation between the measurement error and the harmonic factors and voltage deviation factors, that is, the larger the ratio, the stronger the correlation.

[0100] Similarly, according to the above calculation process, the above multiple factors can be compared one by one, and the corresponding F value and the test threshold F cThe comparison results of the above data can be used to determine whether the measurement error is significantly related to the corresponding factors; according to the corresponding F value and the test threshold F c By comparing the ratios, we can judge the degree of correlation between the measurement error and the corresponding factors.

[0101] The first-level multi-factor variance analysis above has verified the significance of the relationship between the metering error of the energy meter and multiple interference source factors, and can calculate the degree of influence. However, in some interference sources, some specific indicator factors will also have different effects on the metering error of the energy meter. For example, in harmonics, even if the THD of the harmonics is in a similar range, the specific harmonic distribution is different, and the impact on the metering error of the energy meter will be different; in voltage deviation, frequency deviation, and three-phase imbalance, the number of fluctuations of these factors within a certain range will also have different effects on the metering error of the energy meter.

[0102] Therefore, it is necessary to carry out the second level of multi-factor variance analysis to measure the relationship between the metering error of the electric energy meter and the specific subdivision indicators in the interference source factors. For the harmonic factors, the subdivision indicators are high-frequency-dominated type, medium-frequency-dominated type, and low-frequency-dominated type. That is, under certain THD conditions, when the harmonic distribution is mainly high-frequency harmonics, it is high-frequency-dominated type; when the harmonic distribution is mainly medium-frequency harmonics, it is medium-frequency-dominated type; when the harmonic distribution is mainly low-frequency harmonics, it is low-frequency-dominated type; the segmentation of high-frequency harmonics, medium-frequency harmonics, and low-frequency harmonics can be determined by technical personnel according to specific occasions.

[0103] For the three factors of voltage deviation, frequency deviation and three-phase imbalance, there are specific values ​​of these factors at a certain time T e The number of fluctuations around the average value within a certain numerical range is divided into high-order fluctuation type, medium-order fluctuation type, and low-order fluctuation type. Taking voltage deviation as an example, within the range of voltage deviation rate [0.5% 0.8%], its average value is 0.65%. Within a certain time T e The number of times the voltage deviation data fluctuates around the average value is n. v ; At the same time, the thresholds for dividing high-order fluctuations, medium-order fluctuations, and low-order fluctuations are set to n respectively vh 、n vm , then it can be judged according to the following formula:

[0104]

[0105] Similarly, frequency deviation and three-phase imbalance can also be classified into high-order fluctuation type, medium-order fluctuation type and low-order fluctuation type using the above method.

[0106] When performing the second level variance analysis on the harmonic subdivision indicators, the specific sum of squares of the deviations is the sum of squares of the harmonic subdivision indicators S THD , the sum of squares of the frequency deviation sub-indicators S f and the sum of squares of random errors S THDfe The calculation formula is as follows:

[0107]

[0108] In the formula, k THD 、n f are the data dimensions under the harmonic subdivision index respectively; the calculation formulas for the means of each item in the formula are as follows:

[0109]

[0110] In the formula, E THDfij It is the measurement error data under the harmonic subdivision index factor and the frequency deviation subdivision factor;

[0111] Then calculate the F value of the significance test as follows:

[0112]

[0113] In the formula, F THD F is the F value of the harmonic subdivision index factor; f is the F value of the frequency deviation subdivision factor. Similar to the first-level ANOVA, at a given significance level α, by comparing the F value of the above harmonic subdivision index factor, that is, F THD With its test threshold F THDc The size of the error can be used to determine whether the metering error of the electric energy meter is significantly related to the subdivision index of the harmonics, namely the distribution of the harmonics: high-frequency-dominated type, medium-frequency-dominated type, and low-frequency-dominated type.

[0114] Similarly, by using a similar calculation process, it is possible to determine whether the metering error of the electric energy meter has a significant relationship with the high-order fluctuation type, medium-order fluctuation type, and low-order fluctuation type of the three factors of voltage deviation, frequency deviation, and three-phase imbalance. The specific calculation formula can refer to the similar formula mentioned above, and will not be listed again in the present invention.

[0115] The second multi-factor variance analysis proposed in the present invention quantitatively analyzes the relationship between the measurement error of the electric energy meter and the specific detailed indicators of the relevant interference source factors, indicating the specific factors affecting the measurement error of the electric energy meter.

[0116] On this basis, the present invention adopts the third level of multi-factor variance analysis to characterize the influence of the interference source in the line where the electric energy meter is located and the operating factors of the new energy station in the line, that is, the multi-factor variance analysis algorithm is used to analyze the significance of the influence of the power generation factors of the new energy station in the system on each indicator of the interference source.

[0117] Still taking harmonics as an example, the photovoltaic power stations and wind farms in the area where the energy meter is located are classified into two categories, and the relationship between the harmonics of the line where the energy meter is located and the power generation of the photovoltaic power station and wind farm connected to the area is analyzed. e The harmonic THD data is taken together with the real-time power generation data of the photovoltaic power station and the wind farm power generation data, and the power generation is divided by the capacity of the power station to obtain the corresponding power generation status S tPV With S tW , that is:

[0118] S tPV =S PVp / S PVN (twenty four)

[0119] S tW =S Wp / S WN (25)

[0120] In the formula, S PVP With S PVN is the actual power generation and rated installed capacity of the photovoltaic power station; S WP With S WN is the actual power generation and rated installed capacity of the wind farm;

[0121] According to the actual application scenario, the above state data can be divided into high-generation state, medium-generation state and low-generation state according to certain thresholds. Taking photovoltaic power generation as an example, the threshold for division is defined as S tPVm , S tPVh , so it can be divided as follows:

[0122]

[0123] The power generation data of the wind farm can also be divided according to the above division method.

[0124] The corresponding harmonic THD can be either an actual value or divided into a certain range of THD segments according to certain standards, forming a data set with the above power generation status. Then, a multi-factor variance analysis is performed, and the square sum of the deviations of the photovoltaic power station power generation factors S PV , the sum of squares of wind farm power generation factors S W and the sum of squares of random errors S PVWe , can be calculated as follows:

[0125]

[0126] In the formula, k PV 、n W are the data dimensions of the photovoltaic power station and wind farm power generation data sets; E PVWij It is the measurement error data under the power generation factor of photovoltaic power station and wind farm;

[0127] The calculation formula for the mean of each item in the formula is as follows:

[0128]

[0129] Then calculate the F value of the significance check, that is, the F value of the photovoltaic power station power generation factor F PV , F value of wind farm power generation factor W ,as follows:

[0130]

[0131] Similar to the above two-way ANOVA, at a given significance level α, by comparing the above F PV 、F W With its test threshold F PVc 、F Wc The size of the harmonics in the line can be used to determine whether they are significantly related to the high-generation state, medium-generation state, and low-generation state of the photovoltaic power station and wind farm.

[0132] Similarly, by using a similar calculation process, other interference source factors in the line, such as voltage deviation, frequency deviation, three-phase imbalance, and other factors, can be measured with respect to the significant relationship between the high-generation state, medium-generation state, and low-generation state of the photovoltaic power station and the wind farm. The specific calculation formula can refer to the similar formula mentioned above, and will not be listed again in the present invention.

[0133] It should be noted that in the example of the present invention, in the third multi-factor variance analysis, the system factors discussed are the power generation status of the photovoltaic power station and the wind farm. The technicians can also analyze other factors that may affect the line interference source in the system according to the actual analysis needs.

[0134] It should be noted that in the above three-way multi-factor analysis of variance, the significance level α can be set by the technicians or according to the actual analysis needs, and is generally set to 0.05.

Claims

1. A method for analyzing an electric energy meter error based on a multiple multi-factor variance algorithm, characterized in that: The method comprises: a first multi-factor variance analysis between the metering error of the electric energy meter and each interference source, to verify the significance of the relationship between the metering error of the electric energy meter and each interference source; a second multi-factor variance analysis between the metering error of the electric energy meter and each interference source index, to measure the relationship between the metering error of the electric energy meter and the subdivided index of each interference source; and a multi-factor variance analysis algorithm is used to analyze the significance of the influence of the power generation factors of the new energy station in the system on each index of the interference source.

2. The method for analyzing the error of an electric energy meter based on a multiple multi-factor variance algorithm according to claim 1, characterized in that: The interference sources include: harmonics in the line, voltage deviation, frequency deviation, three-phase imbalance and other factors; the other factors include: voltage fluctuation and voltage flicker factors.

3. The method for analyzing the error of an electric energy meter based on a multiple multi-factor variance algorithm according to claim 1 is characterized in that: Interference source index refers to the subdivided index of each interference source. The harmonic index is divided into high-frequency, medium-frequency and low-frequency types; the voltage deviation is divided into high-order fluctuation type, medium-order fluctuation type and low-order fluctuation type; the frequency deviation is divided into high-order fluctuation type, medium-order fluctuation type and low-order fluctuation type; the three-phase imbalance is divided into high-order fluctuation type, medium-order fluctuation type and low-order fluctuation type; other factors are also divided into three levels in analogy with the above classification method.

4. The method for analyzing the error of an electric energy meter based on a multiple multi-factor variance algorithm according to claim 1 is characterized in that: The first multi-factor variance analysis method for the energy meter measurement error and each interference source includes: The measurement error of the electric energy meter caused by each interference source is expressed as: AND m =And mh +E mu +E mv +E mf +E mt (1) In the formula, E m is the measurement error of the electric energy meter, E mh 、E mu 、E mv 、E mf and E mt They are the errors caused by various interference sources, namely harmonic interference error, three-phase unbalanced interference error, voltage deviation interference error, frequency deviation interference error and other factors interference error; Harmonics are measured using THD, and the calculation formula is as follows: Where U eh is the hth harmonic voltage in the voltage of the line to be evaluated; U e1 is the corresponding fundamental voltage, M is the total harmonic order; For three-phase imbalance, the three-phase imbalance degree is used as the measurement, as follows: The expression of γ is: Where U ea , U eb , U eb is the three-phase voltage value; For voltage deviation and frequency deviation, the voltage deviation rate and frequency deviation rate are used for measurement, as follows: d f =f e -f N (6) Where U e 、f e is the actual voltage amplitude and frequency of the system; U N 、f N is the amplitude and frequency of the rated voltage; multi-factor variance analysis is performed on the above factors one by one, and two factors are checked each time.

5. The method for analyzing the error of an electric energy meter based on a multiple multi-factor variance algorithm according to claim 4 is characterized in that: The multi-factor variance analysis methods for harmonic factors and voltage deviation factors include: Calculate the sum of squares of the harmonic factor and voltage deviation factor, S h , S v , memory random error sum of squares S hve , the calculation formula is as follows: In the formula, k and n are the data dimensions of harmonic factors and voltage deviation factors respectively; the calculation formula of each mean is as follows: In the formula, E hvij is the measurement error data under harmonic factors and voltage deviation factors; then calculate the F value of significance verification as follows: The test threshold F of the two factors of harmonics and voltage deviation is obtained at a given significance level α ch 、F cv , by comparing F h With F ch 、F v With F cv Determine the significance between the two factors of measurement error and harmonics and voltage deviation; When F h >F ch When F v >F cv When , the voltage deviation factor passes the significance test, that is, the measurement error is related to the voltage deviation factor; otherwise, the measurement error is not related to the corresponding factor.

6. The method for analyzing the error of an electric energy meter based on a multiple multi-factor variance algorithm according to claim 5, characterized in that: Comparison F h / F ch With F v / F cv The size of these two ratios reflects the correlation between the measurement error and the harmonic factors and voltage deviation factors. The larger the ratio, the stronger the correlation.

7. The method for analyzing the error of an electric energy meter based on a multiple multi-factor variance algorithm according to claim 6, characterized in that: According to the above calculation process, each interference source factor is compared one by one, and the corresponding F value is compared with the test threshold F. c The comparison results of the measurement error are used to determine whether the measurement error is significantly related to the corresponding interference source factors; according to the corresponding F value and the test threshold F c The comparison of the ratios is used to determine the degree of correlation between the measurement error and the corresponding interference source factors.

8. The method for analyzing the error of an electric energy meter based on a multiple multi-factor variance algorithm according to claim 3 is characterized in that: The second level variance analysis method for the subdivision indexes of the energy meter measurement error and harmonics includes: The calculation formulas for the sum of squares of deviations and the sum of squares of errors are as follows: In the formula, k THD 、n f are the data dimensions under the harmonic subdivision index respectively. The calculation formulas for the means of each item in the formula are as follows: In the formula, E THDfij It is the measurement error data under harmonic factors and voltage deviation factors; Then calculate the F value of the significance test as follows: At a given significance level α, by comparing F f and the test threshold F fc The size of the error can be used to determine whether the metering error of the electric energy meter is significantly related to the subdivision index of the harmonics, namely the distribution of the harmonics: high-frequency-dominated type, medium-frequency-dominated type and low-frequency-dominated type.

9. The method for analyzing the error of an electric energy meter based on a multiple multi-factor variance algorithm according to claim 1, characterized in that: When using the multi-factor variance analysis algorithm to analyze the significant degree of influence of the power generation factors of the new energy station in the system on various indicators of the interference source, the analysis method of the significant degree of influence of harmonics includes: The photovoltaic power stations and wind farms in the area where the energy meter is located are classified into two categories. The relationship between the harmonics of the line where the energy meter is located and the power generation of the photovoltaic power stations and wind farms connected to the area is analyzed. e The harmonic THD data is taken together with the real-time power generation data of the photovoltaic power station and the wind farm power generation data, and the power generation is divided by the capacity of the power station to obtain the corresponding power generation state S tPV With S tW , that is: In the formula, S PVP With S PVN is the actual power generation and rated installed capacity of the photovoltaic power station; S WP With S WN is the actual power generation and rated installed capacity of the wind farm; According to a certain threshold, it is divided into high-generation state, medium-generation state and low-generation state. Taking photovoltaic power generation as an example, the threshold of division is defined as S tPVm , S tPVh , so it can be divided as follows: The power generation data of the wind farm is also divided according to the above division method; The corresponding harmonic THD, using the actual value or divided into a certain range of THD segments, forms a data set with the above power generation status, and then conducts multi-factor variance analysis: In the formula, k PV 、n W are the data dimensions of the photovoltaic power station and wind farm power generation data sets; E PVWij It is the measurement error data under the power generation factor of photovoltaic power station and wind farm; The calculation formula for the mean of each item in the formula is as follows: Then calculate the F value of the significance test as follows: At a given significance level α, by comparing the above F PV 、F W With its test threshold F PVc 、F Wc The size of the harmonics in the line can be used to determine whether they are significantly related to the high-generation state, medium-generation state, and low-generation state of the photovoltaic power station and wind farm.

10. The method for analyzing the error of an electric energy meter based on a multiple multi-factor variance algorithm according to claim 9, characterized in that: A calculation process similar to that of harmonics is used to measure the significant relationship between the voltage deviation, frequency deviation, three-phase imbalance and other factors in the line and the high-generation state, medium-generation state and low-generation state of photovoltaic power stations and wind farms.

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