CN111950165A - Electric energy meter reliability analysis method under typical environment - Google Patents
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Abstract
The invention discloses an electric energy meter reliability analysis method in a typical environment, which is used for carrying out tests such as a basic error test, a daily timing error test, a voltage fluctuation test and the like on an intelligent electric energy meter in the typical environment, and acquiring test data, environment variable data and electric energy meter state information; and then, establishing a regression model by using a partial least square method to obtain the specific coefficient values of the influence errors of a single manufacturer, a single batch or a single electric energy meter and all the electric energy meters in a typical environment, and then comparing the influence coefficient values of the single manufacturer, the single batch or the single electric energy meter in different influences of different environments with the overall coefficients of all the electric energy meters to finish the reliability evaluation of the electric energy meters. The invention can improve the overall quality of the bidding electric energy meter, reduce the work of assembling, disassembling and replacing the electric energy meter and processing faults, improve the power supply quality, ensure the fairness and justice of electric energy metering and create huge economic benefit and social benefit through the reliability analysis of the electric energy meter under the typical environment.
Description
Technical Field
The invention belongs to the technical field of intelligent electric energy meters, mainly relates to a detection technology of an electric energy meter, and particularly relates to an electric energy meter reliability analysis method in a typical environment.
Background
The electric energy meter is the most basic metering equipment in the power industry, is used under various severe natural environment conditions, and has important influence on the production and operation of power enterprises under the quality condition. However, various tests of the electric energy meter developed according to domestic technical standards hardly find potential quality defects in typical environments, such as areas of Heilongjiang, Tibet, Fujian, Guangdong, Xinjiang Turpan and the like: the temperature is generally between-30 ℃ and-40 ℃ in the Heilongjiang region, and the lowest temperature can reach-52 ℃ under extreme conditions; the average altitude of the Tibet area is more than 4000 meters, the air density is small, the solar radiation is strong, the annual average sunshine duration is more than 3000 hours, and the annual radiation quantity is 6000-8000 megajoules per square meter: fujian and Guangdong belong to subtropical marine monsoon climate, have the characteristic of "high temperature and humidity", coastal area has very strong salt fog, corrode the internal spare part of the electric energy meter easily; the Turpan area in Xinjiang is a typical 'high dry heat' climate condition, the annual sunshine duration is about 3000-3200 hours, the annual average precipitation is only 16.4 mm, and the evaporation is as high as more than 3000 mm. Under the typical environments, temperature, humidity, illumination, salt fog and the like are main factors influencing the reliability of the intelligent electric energy meter, various faults of key components of the electric energy meter are easily caused, the accurate and reliable operation of the electric energy meter is directly influenced, and the vital economic benefits of a supply side and a demand side are damaged.
Disclosure of Invention
The purpose of the invention is: the method for analyzing the reliability of the electric energy meter in the typical environment is provided, and weak links of the intelligent electric energy meter in the typical environment are exposed, so that manufacturers of the intelligent electric energy meter improve production and manufacturing processes, and the operation reliability of the intelligent electric energy meter is improved.
The technical scheme of the invention is as follows: a method for analyzing the reliability of an electric energy meter in a typical environment comprises the following steps:
firstly, under a typical environment, carrying out a basic error test, a daily timing error test and a voltage fluctuation test aiming at an intelligent electric energy meter, and acquiring test result data, environment variable data and electric energy meter state data information;
secondly, a regression model is constructed by using a partial least square method:
a. setting original characteristic data independent variable XiIs a matrix of dimensions N x m, dependent variable YiFor a matrix of dimensions N X N, having a total of N pairs of samples, X0The middle sample is characterized by m dimension, Y0The middle sample is characterized by n dimensions;
b. mixing XiAnd YiPerforming standardization process, and setting w1，c1Is XiAnd YiThe first principal component axis vector, then w1，c1Then X can be expressediAnd YiA first pair of principal components t1，u1Wherein t is1＝Xi*w1，u1＝Yi*c1；
c. Further solving for w by1,c1：
d. Obtaining t from steps b and c1，u1：
t1＝Xiw1，u1＝Yic1；
e. According to the principle of regressionTo move Xi、XiYi、YiRespectively for their principal components t1，u1Regression modeling was performed as follows:
Xi＝t1pT 1+E Yi＝u1qT 1+G
wherein p is1,q1Is an axis vector, E, G is a residual error matrix;
f. using t1And u1Correlation between Y and YiTo XiMain component t of (2)1Performing regression modeling, and obtaining the following through derivation and solution:
performing regression according to the steps, and performing cyclic reciprocating until the residual F meets the precision requirement or the initial X of the number of the principal componentsiHas reached the upper limit;
g. finally, the original X can be converted intoi,XiYi,YiExpressed as:
h. binding wT itj＝1(i＝j),wT itjThe relationship of 1(i ≠ j) converts the above equation into a matrix form:
X＝TPT+E
Y＝TRT+F＝XWRT+F＝XA+F
namely Xi→YiWherein a ═ WRT；
i. The calculated values of W, RW, R are collected during the algorithm and can be predicted by PLS, i.e.For a newly input piece of data xx, each principal component, i.e., t, is first calculated using WW1＝xTw1,t2＝xTw2，...，tk＝xTwkThen substituting into formula to obtain the predicted value of vector y, or directly substituting into yT＝xTAyT＝xTA, solving;
thirdly, aiming at all the electric energy meters in the typical environment, obtaining the influence coefficient values of the electric energy meters under different influence factors of different environments by using the regression model, and then taking 80% of the whole range as a standard coefficient range;
and fourthly, obtaining the influence coefficient values of the single manufacturer, the single batch or the single electric energy meter under different influence factors of different environments by using the regression model from the dimensionality of the single manufacturer, the single batch or the single electric energy meter, and judging that the anti-interference capability of the single manufacturer, the single batch or the single electric energy meter is weaker and the reliability is lower if the influence coefficient value of the single manufacturer, the single batch or the single electric energy meter exceeds the standard coefficient range.
In the process of constructing the model, linear output of the model and stability of the model are considered, when a partial least square method is used for modeling, selected input variables only comprise environment factor data and time factor data, the type of the electric energy meter selects a single-term intelligent electric energy meter, current selects standard current 'I b', and power factor selects '1'.
And if the acquired test result data, the environmental variable data and the electric energy meter state data information have data abnormality and data loss, deleting or interpolating the loss value by combining the test sample data, the data quantity and the data characteristics, and directly removing the abnormal value.
The processing rule of the data exception and data missing condition is as follows: if the environment variable data is missing, a linear model of the last hours and an environment value of a predicted time point are required to be taken for missing value filling; if the error value is missing, the error value of the same sample close time point in the last few days needs to be filled; and (4) directly removing abnormal temperature data and error value data in order to avoid the abnormal values from influencing the model effect.
The invention has the beneficial effects that: according to the method, based on test data, environment variable data and electric energy meter state data information in typical environments such as high severe cold, high dry heat, high altitude, high salt spray, high damp heat and the like, a regression model is constructed by using a partial least square method to analyze the reliability of the electric energy meter, the analysis effect is good, and the evaluation result is scientific, accurate, fair and fair; aiming at the analysis and evaluation of a single manufacturer, a single batch or a single electric energy meter, the invention can expose the weak link of the electric energy meter in the typical environment, support the manufacturer to improve the manufacturing process and improve the operation reliability of the electric energy meter; the method analyzes and ranks the operation reliability of the electric energy meter of a manufacturer in a typical environment, provides technical support for model selection, bidding and technical updating of the electric energy meter in the future, improves the overall quality of the bidding electric energy meter, reduces the work of assembling, disassembling and replacing the electric energy meter and fault treatment, prolongs the cycle and reduces the system operation cost; the power failure times of users are reduced, and the power supply quality is improved; the electric energy metering is ensured to be fair and fair, and great economic benefits and social benefits are created.
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Fig. 1 is a schematic flow chart of a method for analyzing reliability of an electric energy meter in a typical environment of the present invention.
Detailed Description
The method for analyzing the reliability of the electric energy meter in the typical environment firstly selects model variables: model variable selection is the construction of a test influence factor model, and mainly considers the environmental factors of the operation of the electric energy meter, the operation state of the electric energy meter, the historical reason of the operation and the influence of the electric energy meter on the error value. The construction variable is that the temperature and the humidity are processed in a segmented mode so as to further analyze the influence of high-temperature and high-humidity factors on the operation of the electric energy meter. Specific variable information is shown in table 1:
table 1: variable information
In a typical environment, carrying out a basic error test, a daily timing error test and a voltage fluctuation test aiming at the intelligent electric energy meter, and acquiring test result data, environment variable data and electric energy meter state data information; a regression model is then constructed using partial least squares.
Partial Least Squares (PLS) is a novel multivariate statistical data analysis method which was first proposed in 1983 by Wood and Abanol et al. It has developed rapidly in theory, methodology and application over the last decade. The second generation regression analysis method is called partial least squares regression by professor fonner of the university of michigan. Partial least squares regression is a regression modeling method of multiple dependent variables to multiple independent variables; can better solve the problems which can not be solved by the common multiple regression in the past. However, the partial least square regression coefficient is difficult to explain, the common least square regression coefficient is generally explained as the average variation of the dependent variable caused by one unit of independent variable variation, and the partial least square regression coefficient is difficult to find out a reasonable explanation; when the number of independent variables is small, the independent variables are not suitable for selection.
When the reliability of the electric energy meter is analyzed in a typical environment, the method adopts a partial least square method to construct a regression model, and comprises the following specific steps:
a. setting original characteristic data independent variable XiIs a matrix of dimensions N x m, dependent variable YiFor a matrix of dimensions N X N, having a total of N pairs of samples, X0The middle sample is characterized by m dimension, Y0The middle sample is characterized by n dimensions;
b. mixing XiAnd YiPerforming normalization such as subtracting mean value and standard deviation, and setting w1，c1Is XiAnd YiThe first principal component axis vector, then w1，c1Then X can be expressediAnd YiA first pair of principal components t1，u1Wherein t is1＝Xi*w1，u1＝Yi*c1；
c. Further solving for w by1,c1：
d. Obtaining t from steps b and c1，u1：
t1＝Xiw1，u1＝Yic1、
e. X can be regressed according to principle component regression ideai、XiYi、YiRespectively for their principal components t1，u1Regression modeling was performed as follows:
Xi＝t1pT 1+E Yi＝u1qT 1+G
wherein p is1,q1Is an axis vector, E, G is a residual error matrix;
f. using t1And u1Correlation between Y and YiTo XiMain component t of (2)1Performing regression modeling, and obtaining the following through derivation and solution:
performing regression according to the steps, and performing cyclic reciprocating until the residual F meets the precision requirement or the initial X of the number of the principal componentsiUntil the rank of (2) has reached the upper limit, the algorithm ends;
finally, the original X can be converted intoi,XiYi,YiExpressed as:
binding wT itj＝1(i＝j),wT itjThe relationship of 1(i ≠ j) converts the above equation into a matrixForm (a):
Xi＝TPT+E
Yi＝TRT+F＝XWRT+F＝XA+F
namely Xi→YiWherein a ═ WRT，
The calculated values of W, RW, R are collected during the algorithm and can be predicted by PLS, i.e. for a new input piece of data xx, the principal components, i.e. t, are first calculated by WW1＝xTw1,t2＝xTw2，...，tk＝xTwkThen substituting into formula to obtain the predicted value of vector y, or directly substituting into yT＝xTAyT＝xTAnd A is solved.
Goodness of fit (R-square) refers to the degree of fit of the regression line to the observed value. The statistic for measuring goodness of fit is the coefficient of likelihood (also known as the deterministic coefficient) R2. R2 has a maximum value of 1. The closer the value of R2 is to 1, the better the fitting degree of the regression straight line to the observed value is; conversely, a smaller value of R2 indicates a poorer fit of the regression line to the observed value. The regression model constructed by the partial least square method is evaluated by using the goodness-of-fit method, the final R2 value of the regression model constructed by the method is 0.88, and the model effect is good.
Aiming at all electric energy meters under typical environment, the regression model is used for obtaining the influence coefficient values of the electric energy meters under different influence factors of different environments, then 80% of the whole range is taken as a standard coefficient range, the influence coefficient values of the single factory, the single batch or the single electric energy meter under different influence factors of different environments are obtained by using the regression model from the dimensionality of the single factory, the single batch or the single electric energy meter, the influence coefficient of the single factory, the single batch or the single electric energy meter obtained by the regression model is compared with the whole standard coefficient range, the reliability of the electric energy meter is evaluated, if the influence coefficient value of the single factory, the single batch or the single electric energy meter exceeds the standard coefficient range, the capacity of the single factory, the single batch or the single electric energy meter is judged to be weak in anti-interference and low in reliability.
In the process of constructing the model, linear output of the model and stability of the model are considered, when a partial least square method is used for modeling, selected input variables only comprise environment factor data and time factor data, the type of the electric energy meter selects a single-term intelligent electric energy meter, current selects standard current 'I b', and power factor selects '1'.
The following describes in detail the method for analyzing reliability of an electric energy meter in a typical environment according to the present invention with specific examples:
firstly, modeling is carried out by a partial least square method, and the coefficient ranges of basic errors of all electric energy meters in typical environments such as severe cold, high dry heat, high altitude, high salt spray, high damp heat and the like to different influence factors are obtained as shown in table 2:
table 2: basic error influence factor coefficient table
Taking the electric energy meter produced by the manufacturer with the code of 016 in the typical environment of high humidity and heat in Xinjiang as an example, the number of the single intelligent electric energy meters operated by the manufacturer in the typical environment of high humidity and heat in Xinjiang is 12, and the correlation coefficient of factors influencing the basic error of the single intelligent electric energy meter of the manufacturer 016 is obtained by adopting a partial least square method and is shown in table 3:
table 3: 016 factory basic error environment variable coefficient table
As can be seen from Table 3, the single intelligent electric energy meter manufactured by the manufacturer with the code of 016 has the largest influence coefficient range of temperature on errors, and the smallest influence coefficient range of humidity, air pressure, air speed and illumination among environmental factors in the typical environment of high dryness and heat in Xinjiang. Comparing the basic error environment variable coefficient of the electric energy meter of the manufacturer with that of the electric energy meter of the manufacturer in the table 2, the following conclusion can be obtained:
firstly, under the condition of comprehensive environment operation, compared with all single intelligent electric energy meters in the typical environment of high dry heat in Xinjiang, the coefficient ranges of temperature, humidity, air pressure and operation duration of a manufacturer 016 single intelligent electric energy meter operating in the typical environment of high dry heat in Xinjiang exceed the standard ranges, and the Sinkiang extreme environment interference resistance of the manufacturer is poor;
secondly, under the comprehensive environment operation condition, compared with all single intelligent electric energy meters in the typical environment of high dry heat in Xinjiang, the single intelligent electric energy meter operated by the manufacturer 016 in the typical environment of high dry heat in Xinjiang has the wind speed and illumination influence coefficient values in a normal operation range, and during the test operation period, the operation basic error of the electric energy meter is not increased along with the change of the wind speed, so that the illumination influence resistance is strong.
According to the method, based on test data, environment data and electric energy meter state information in typical environments such as high severe cold, high dry heat, high altitude, high salt spray, high damp heat and the like, a partial least square method is used for constructing and constructing a regression model to analyze the reliability of the electric energy meter, and the evaluation result is scientific, accurate, fair and fair; through the reliability analysis of a single manufacturer, a single batch or a single electric energy meter in typical environments such as high severe cold, high dry heat, high altitude, high salt spray, high damp heat and the like, the weak link of the electric energy meter in the typical environment can be exposed, the manufacturer is supported to improve the manufacturing process, the overall quality and the operation reliability of the electric energy meter are improved, the assembling, disassembling and replacing and fault processing work of the electric energy meter are reduced, the rotation period is prolonged, and the system operation cost is reduced; the power failure times of users are reduced, and the power supply quality is improved; the electric energy metering is ensured to be fair and fair, and great economic benefits and social benefits are created.
Claims (5)
1. The method for analyzing the reliability of the electric energy meter in the typical environment is characterized by comprising the following steps of:
firstly, under a typical environment, carrying out a basic error test, a daily timing error test and a voltage fluctuation test aiming at an intelligent electric energy meter, and acquiring test result data, environment variable data and electric energy meter state data information;
secondly, a regression model is constructed by using a partial least square method:
a. setting original characteristic data independent variable XiIs a matrix of dimensions N x m, dependent variable YiFor a matrix of dimensions N X N, having a total of N pairs of samples, X0The middle sample is characterized by m dimension, Y0The middle sample is characterized by n dimensions;
b. mixing XiAnd YiPerforming standardization process, and setting w1，c1Is XiAnd YiThe first principal component axis vector, then w1，c1Then X can be expressediAnd YiA first pair of principal components t1，u1Wherein t is1＝Xi*w1，u1＝Yi*c1；
c. Further solving for w by1,c1：
d. Obtaining t from steps b and c1，u1：
t1＝Xiw1，u1＝Yic1、
e. X can be regressed according to principle component regression ideai、XiYi、YiRespectively for their principal components t1，u1Regression modeling was performed as follows:
Xi＝t1pT 1+E Yi＝u1qT 1+G
wherein p is1,q1Is an axis vector, E, G is a residual error matrix;
f. using t1And u1Correlation between Y and YiTo XiMain component t of (2)1Performing regression modeling, and obtaining the following through derivation and solution:
performing regression according to the steps, and performing cyclic reciprocating until the residual F meets the precision requirement or the initial X of the number of the principal componentsiHas reached the upper limit;
g. finally, the original X can be converted intoi,XiYi,YiExpressed as:
h. binding wT itj＝1(i＝j),wT itjThe relationship of 1(i ≠ j) converts the above equation into a matrix form:
X＝TPT+E
Y＝TRT+F＝XWRT+F＝XA+F
namely Xi→YiWherein a ═ WRT；
i. The calculated values of W, RW, R are collected during the algorithm and can be predicted by PLS, i.e. for a new input piece of data xx, the principal components, i.e. t, are first calculated by WW1＝xTw1,t2＝xTw2，...，tk＝xTwkThen substituting into formula to obtain the predicted value of vector y, or directly substituting into yT＝xTAyT＝xTA, solving;
thirdly, aiming at all the electric energy meters in the typical environment, obtaining the influence coefficient values of the electric energy meters under different influence factors of different environments by using the regression model, and then taking 80% of the whole range as a standard coefficient range;
and fourthly, obtaining the influence coefficient values of the single manufacturer, the single batch or the single electric energy meter under different influence factors of different environments by using the regression model from the dimensionality of the single manufacturer, the single batch or the single electric energy meter, and judging that the anti-interference capability of the single manufacturer, the single batch or the single electric energy meter is weaker and the reliability is lower if the influence coefficient value of the single manufacturer, the single batch or the single electric energy meter exceeds the standard coefficient range.
2. The method according to claim 1, wherein X is a function of the reliability of the electric energy meter under typical circumstancesiAnd YiThe normalization process includes subtracting the mean value and dividing the standard deviation.
3. The method for analyzing the reliability of the electric energy meter under the typical environment according to claim 1, wherein in the process of constructing the model, in consideration of the linear output of the model and the stability of the model, when the modeling is performed by using a partial least squares method, the selected input variables only include environmental factor data and time factor data, the type of the electric energy meter selects a single-item intelligent electric energy meter, the current selects standard current "Ib", and the power factor selects "1".
4. The method for analyzing the reliability of the electric energy meter in the typical environment according to claim 1, wherein if data abnormality and data loss exist in the acquired test result data, the acquired environment variable data and the acquired state data information of the electric energy meter, the loss value is deleted or interpolated by combining the data, the data quantity and the data characteristics of the test sample, and the abnormal value is directly removed.
5. The method for analyzing the reliability of the electric energy meter under the typical environment according to claim 4, wherein the processing rules of the data exception and data missing condition are as follows: if the environment variable data is missing, a linear model of the last hours and an environment value of a predicted time point are required to be taken for missing value filling; if the error value is missing, the error value of the same sample close time point in the last few days needs to be filled; and (4) directly removing abnormal temperature data and error value data in order to avoid the abnormal values from influencing the model effect.
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| CN116708514A (en) * | 2023-08-02 | 2023-09-05 | 深圳龙电华鑫控股集团股份有限公司 | Electric energy meter data acquisition method and system based on Internet of things |
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