CN111950165B - Electric energy meter reliability analysis method under typical environment - Google Patents
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Abstract
The invention discloses a reliability analysis method of an electric energy meter under 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 aiming at an intelligent electric energy meter under the typical environment, and collecting test data, environment variable data and electric energy meter state information; and then constructing a regression model by using a partial least square method to obtain specific coefficient values of influence errors of single manufacturer, single batch or single electric energy meter and all electric energy meters under typical environments, and comparing the influence coefficient values of the single manufacturer, single batch or single electric energy meter under different influence factors of different environments with the integral coefficient of all electric energy meters to finish reliability evaluation of the electric energy meters. According to the invention, through the reliability analysis of the electric energy meter in a typical environment, the overall quality of the electric energy meter for bidding can be improved, the assembly, disassembly and fault handling work of the electric energy meter are reduced, the power supply quality is improved, the fairness and fairness of electric energy metering are ensured, and huge economic and social benefits are created.
Description
Technical Field
The invention belongs to the technical field of intelligent electric energy meters, and mainly relates to an electric energy meter detection technology, in particular 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 in terms of quality. However, each test of the electric energy meter according to the domestic technical standard is difficult to find potential quality defects in typical environments, such as regions of Heilongjiang, tibet, fujian, guangdong, xinjiang Turpan and the like: the winter in Heilongjiang area is extremely cold, the temperature is generally between-30 degrees and-40 degrees, and the lowest temperature can reach-52 ℃ under extreme conditions; the average altitude of the Tibet area is more than 4000 m, the air density is small, the solar radiation is strong, the annual average sunlight time is more than 3000 hours, and the annual radiation quantity is 6000-8000 megajoules per square meter: fujian, guangdong, which belongs to subtropical marine monsoon climate, has the characteristic of high temperature and humidity, has strong salt fog in coastal areas, and is easy to corrode parts in the electric energy meter; the region of Xinjiang Turpan is a typical "high dry heat" climate condition, the number of sunshine hours in the whole year is about 3000-3200 hours, the annual average precipitation is only 16.4 mm, and the evaporation capacity is up to more than 3000 mm. Under these typical environments, temperature, humidity, illumination, salt fog and the like are main factors influencing the reliability of the intelligent electric energy meter, so that various faults of key components of the electric energy meter are very easy to occur, the accurate and reliable operation of the electric energy meter is directly influenced, and the tangential economic benefits of a supply side and a demand side are damaged.
Disclosure of Invention
The purpose of the invention is that: the reliability analysis method for 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 the production and manufacturing process, and the operation reliability of the intelligent electric energy meter is improved.
The technical scheme of the invention is as follows: the method for analyzing the reliability of the electric energy meter in the typical environment comprises the following steps:
(1) 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 collecting test result data, environment variable data and electric energy meter state data information;
(2) constructing a regression model by using a partial least square method:
a. set up original characteristic data argument X i Is an N-m dimensional matrix, and the dependent variable Y i Is an N-N-dimensional matrix, and has N sample pairs and X 0 The middle sample is characterized by m dimension, Y 0 The middle sample is characterized by n dimensions;
b. x is to be i And Y i Performing standardization processing, and setting w 1 ，c 1 Is X i And Y i The first principal component axial quantity, w 1 ，c 1 Then X can be expressed i And Y i A first pair of principal components t 1 ，u 1 Wherein t is 1 ＝X i *w 1 ，u 1 ＝Y i *c 1 ；
c. Further solving for w by 1 ,c 1 ：
d. From steps b, c, t is determined 1 ，u 1 ：
t 1 ＝X i w 1 ，u 1 ＝Y i c 1 ；
e. X can be calculated according to principal component regression concept i 、X i Y i 、Y i For their main components t respectively 1 ，u 1 Regression modeling was performed as follows:
X i ＝t 1 p T 1 +E Y i ＝u 1 q T 1 +G
wherein p is 1 ,q 1 Is an axial vector, E, G is a residual matrix;
f. by t 1 And u 1 Correlation construction between Y i For X i Is the main component t of (2) 1 Regression modeling is carried out, and the regression model is obtained through deduction and solving:
regression is carried out according to the steps, and the cycle is repeated until the residual error F reaches the precision requirement or the number of the main components is initially X i The rank of (2) has reached an upper limit;
g. finally, the original X can be obtained i ,X i Y i ,Y i Expressed as:
h. binding w T i t j ＝1(i＝j),w T i t j The relation =1 (i+.j) converts the above formula into a matrix form:
X＝TPT+E
Y＝TR T +F＝XWR T +F＝XA+F
i.e. X i →Y i Regression equation of (2), wherein a=wr T ；
i. The values of W, RW, R calculated in the algorithm process can be predicted by PLS, i.e. for a new input piece of data xx, the WW is used to calculate the principal components, i.e. t 1 ＝x T w 1 ,t 2 ＝x T w 2 ，...，t k ＝x T w k Then substituting the predicted value into a formula to obtain a predicted value of the vector y, or directly substituting the predicted value into y T ＝x T Ay T ＝x T A, solving;
(3) aiming at all electric energy meters in a typical environment, obtaining 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;
(4) and 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 dimension of the single manufacturer, the single batch or the single electric energy meter, if the influence coefficient values of the single manufacturer, the single batch or the single electric energy meter exceed the standard coefficient range, judging that the single manufacturer, the single batch or the single electric energy meter has weaker anti-interference capability and lower reliability.
In the process of constructing the model, considering the linear output of the model and the stability of the model, when modeling is carried out by using a partial least square method, the selected input variables only comprise environmental factor data and time factor data, the type of the electric energy meter selects a single intelligent electric energy meter, the current selects standard current 'I b', and the power factor selects '1'.
If the collected test result data, environment variable data and electric energy meter state data information have data abnormality and data missing conditions, deleting or interpolating missing values by combining test sample data, data quantity and data characteristics, and directly eliminating abnormal values.
The processing rules of the data exception and data missing condition are as follows: if the environment variable data is missing, the environment values of the linear model and the predicted time point of the last several hours are needed to be taken for missing value filling; if the error value is missing, the error value of the sample at the similar time point in the last few days is needed to be filled; for abnormal temperature data and error value data, the abnormal values are directly removed to avoid affecting the model effect.
The beneficial effects of the invention are as follows: the method is based on test data, environment variable data and electric energy meter state data information in typical environments such as severe cold, high dry heat, high altitude, high salt fog, high wet heat and the like, and a regression model is constructed by using a partial least square method to analyze the reliability of the electric energy meter, so that the analysis effect is good, and the evaluation result is scientific, accurate, fair and fair; aiming at analysis and evaluation of single manufacturer, single batch or single electric energy meter, the invention can expose weak links of the electric energy meter in typical environment, support manufacturer to improve manufacturing process and improve operation reliability of the electric energy meter; according to the invention, the operation reliability of the factory electric energy meter in a typical environment is analyzed and ranked, technical support is provided for selection, bidding and technical updating of the electric energy meter in the future, the overall quality of the bidding electric energy meter is improved, the assembly and disassembly of the electric energy meter and fault handling work 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; ensuring the fairness and fairness of electric energy metering and creating great economic and social benefits.
Drawings
Fig. 1 is a schematic flow chart of a method for analyzing reliability of an electric energy meter in a typical environment of the invention.
Detailed Description
The method for analyzing the reliability of the electric energy meter under the typical environment comprises the following steps of firstly, selecting model variables: the model variable selection, namely the construction of a test influence factor model, mainly considers the running environment factors of the electric energy meter, the running state of the electric energy meter, the running history reasons and the influence of the electric energy meter on an error value. The construction variables are to conduct sectional treatment on the temperature and the humidity so as to further analyze the influence of high-temperature and high-humidity factors on the operation of the electric energy meter. The specific variable information is shown in table 1:
table 1: variable information
Under a typical environment, a basic error test, a daily timing error test and a voltage fluctuation test are carried out aiming at an intelligent electric energy meter, and test result data, environment variable data and electric energy meter state data information are collected; and then constructing a regression model by using a partial least square method.
The partial least squares method (PLS) is a novel multivariate statistical data analysis method, which was first proposed by wood and Abanol et al in 1983. In the last decade, it has been rapidly developed in theory, methods and applications. The fei-ni professor of university of misshapen called partial least squares regression as the second generation regression analysis method. Partial least squares regression is a regression modeling method of multiple dependent variables to multiple independent variables; can better solve a plurality of problems which can not be solved by 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 interpreted as the average variable quantity of the dependent variable caused by one unit of independent variable change, and the partial least square regression coefficient is difficult to find reasonable interpretation; when the number of independent variables is small, the independent variables are not preferable.
When the reliability of the electric energy meter is analyzed in a typical environment, the specific steps of constructing a regression model by adopting a partial least square method are as follows:
a. set up original characteristic data argument X i Is an N-m dimensional matrix, and the dependent variable Y i Is an N-N-dimensional matrix, and has N sample pairs and X 0 The middle sample is characterized by m dimension, Y 0 The middle sample is characterized by n dimensions;
b. x is to be i And Y i Carrying out standardization processing such as mean reduction, standard deviation division and the like, and setting w 1 ，c 1 Is X i And Y i The first principal component axial quantity, w 1 ，c 1 Then X can be expressed i And Y i A first pair of principal components t 1 ，u 1 Wherein t is 1 ＝X i *w 1 ，u 1 ＝Y i *c 1 ；
c. Further solving for w by 1 ,c 1 ：
d. From steps b, c, t is determined 1 ，u 1 ：
t 1 ＝X i w 1 ，u 1 ＝Y i c 1 、
e. X can be calculated according to principal component regression concept i 、X i Y i 、Y i For their main components t respectively 1 ，u 1 Regression modeling was performed as follows:
X i ＝t 1 p T 1 +E Y i ＝u 1 q T 1 +G
wherein p is 1 ,q 1 Is an axial vector, E, G is a residual matrix;
f. by t 1 And u 1 Correlation construction between Y i For X i Is the main component t of (2) 1 Regression modeling is carried out, and the regression model is obtained through deduction and solving:
regression is carried out according to the steps, and the cycle is repeated until the residual error F reaches the precision requirement or the number of the main components is initially X i Until the rank of (2) has reached an upper limit, the algorithm ends;
finally, the original X can be obtained i ,X i Y i ,Y i Expressed as:
binding w T i t j ＝1(i＝j),w T i t j The relation =1 (i+.j) converts the above formula into a matrix form:
X i ＝TP T +E
Y i ＝TR T +F＝XWR T +F＝XA+F
i.e. X i →Y i Regression equation of (2), wherein a=wr T ，
The values of W, RW, R calculated in the algorithm process can be predicted by PLS, i.e. for a new input piece of data xx, the WW is used to calculate the principal components, i.e. t 1 ＝x T w 1 ,t 2 ＝x T w 2 ，...，t k ＝x T w k Then substituting the predicted value into a formula to obtain a predicted value of the vector y, or directly substituting the predicted value into y T ＝x T Ay T ＝x T And A, solving.
The goodness of fit (R-square) refers to the degree of fit of the regression line to the observed value. The statistic that measures the goodness of fit is the determinable coefficient (also known as the deterministic coefficient) R2. The maximum value of R2 is 1. The closer the value of R2 is to 1, the better the fitting degree of the regression line to the observed value is; conversely, the smaller the value of R2, the worse the fitting degree of the regression line to the observed value. And the regression model constructed by the partial least square method is evaluated by using a fitting goodness method, the final R2 value of the regression model constructed by the method is 0.88, and the model effect is good.
For all electric energy meters in a 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 manufacturer, 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 dimension of the single manufacturer, the single batch or the single electric energy meter, the influence coefficient values of the single manufacturer, the single batch or the single electric energy meter are obtained by comparing the influence coefficient values of the single manufacturer, the single batch or the single electric energy meter with the whole standard coefficient range, the reliability of the electric energy meter is evaluated, and if the influence coefficient values of the single manufacturer, the single batch or the single electric energy meter exceed the standard coefficient range, the anti-interference capability of the single manufacturer, the single batch or the single electric energy meter is judged to be weaker, and the reliability is lower.
In the process of constructing the model, considering the linear output of the model and the stability of the model, when modeling is carried out by using a partial least square method, the selected input variables only comprise environmental factor data and time factor data, the type of the electric energy meter selects a single intelligent electric energy meter, the current selects standard current 'I b', and the power factor selects '1'.
The following describes in detail the method for analyzing reliability of the electric energy meter in the typical environment of the present invention by using specific examples:
firstly, modeling is carried out through a partial least square method to obtain coefficient ranges of different influence factors caused by basic errors of all electric energy meters in typical environments such as severe cold, high dry heat, high altitude, high salt fog, high wet heat and the like, wherein the coefficient ranges are shown in table 2:
table 2: basic error influencing factor coefficient table
Taking an electric energy meter produced by a manufacturer with the code 016 in a Xinjiang high-humidity and high-heat typical environment as an example, the total number of single intelligent electric energy meters operated by the manufacturer in the Xinjiang high-humidity and high-heat typical environment is 12, and a factor correlation coefficient affecting the basic error of the single intelligent electric energy meter of the manufacturer 016 is obtained by adopting a partial least square method is shown in a table 3:
table 3: basic error environment variable coefficient table of 016 manufacturer
As can be seen from Table 3, in the typical environment of Xinjiang high dry heat, the single intelligent electric energy meter produced by manufacturer with code number 016 has the largest influence coefficient range of temperature on errors, and the smallest influence coefficient range of humidity and air pressure, wind speed and illumination. Comparing the basic error environment variable coefficient of the electric energy meter of the manufacturer with the basic error environment variable coefficient of the electric energy meter of the manufacturer in Table 2, the following conclusion can be obtained:
(1) the manufacturer 016 operates the single intelligent electric energy meter in the Xinjiang high dry heat typical environment, under the condition of comprehensive environment operation, compared with all single intelligent electric energy meters in the Xinjiang high dry heat typical environment, the coefficient ranges of temperature, humidity, air pressure and operation duration are over the standard range, and the capability of resisting the interference of the Xinjiang extreme environment is poor;
(2) the manufacturer 016 operates the single intelligent electric energy meter in the Xinjiang high dry heat typical environment, under the comprehensive environment operation condition, compared with all single intelligent electric energy meters in the Xinjiang high dry heat typical environment, the wind speed and the illumination influence coefficient value of the single intelligent electric energy meter are in a normal operation interval, and during the test operation period, the basic error of the electric energy meter operation is not increased along with the change of the wind speed, and the illumination influence resistance capability is strong.
The method is based on test data, environment data and electric energy meter state information in typical environments such as severe cold, high dry heat, high altitude, high salt fog, high wet heat and the like, and a regression model is constructed by using a partial least square method to analyze the reliability of the electric energy meter, so that the evaluation result is scientific, accurate, fair and fair; the weak links of the electric energy meter in the typical environment can be exposed through the reliability analysis of single manufacturer, single batch or single electric energy meter in the typical environment such as severe cold, high dry heat, high altitude, high salt fog, high wet heat and the like, the manufacturing process is improved by a supporting manufacturer, the overall quality and the operation reliability of the electric energy meter are improved, the mounting and dismounting and fault treatment work of the electric energy meter is reduced, the rotation period is prolonged, and the operation cost of the system is reduced; the power failure times of users are reduced, and the power supply quality is improved; ensuring the fairness and fairness of electric energy metering and creating great economic and social benefits.
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:
(1) 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 collecting test result data, environment variable data and electric energy meter state data information;
(2) constructing a regression model by using a partial least square method:
a. set up original characteristic data argument X i Is an N-m dimensional matrix, and the dependent variable Y i Is an N-N-dimensional matrix, and has N sample pairs and X 0 The middle sample is characterized by m dimension, Y 0 The middle sample is characterized by n dimensions;
b. x is to be i And Y i Performing standardization processing, and setting w 1 ，c 1 Is X i And Y i The first principal component axial quantity, w 1 ，c 1 Then X can be expressed i And Y i A first pair of principal components t 1 ，u 1 Wherein，/>；
c. Further solving for w by 1 ，c 1 ：
；
d. From steps b, c, t is determined 1 ，u 1 ：，/>；
e. X can be calculated according to principal component regression concept i 、Y i For their main components t respectively 1 ，u 1 Regression modeling was performed as follows:，/>wherein p is 1 ，q 1 Is an axial vector, E, G is a residual matrix;
f. by t 1 And u 1 Correlation construction between Y i For X i Is the main component t of (2) 1 Regression modeling is carried out, and the regression model is obtained through deduction and solving:
，
regression is carried out according to the steps, and the cycle is repeated until the residual error F reaches the precision requirement or the number of the main components is initially X i Rank of alreadyUntil the upper limit is reached;
g. finally, the original X can be obtained i 、Y i Expressed as:
，/>；
h. bonding of，/>The relation of (2) converts the above formula into a matrix form:
，/>i.e. X i →Y i Regression equation of>；
i. The values of W, R calculated in the algorithm process are collected and can be predicted by PLS, i.e. for a new input piece of data x, each principal component is calculated by W first, i.eThen substituting the formula to obtain the predicted value of the vector y, or directly substituting the predicted value of the vector y>Solving;
(3) aiming at all electric energy meters in a typical environment, obtaining 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;
(4) the regression model is used for 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 from the dimension of the single manufacturer, the single batch or the single electric energy meter, if the influence coefficient values of the single manufacturer, the single batch or the single electric energy meter exceed the standard coefficient range, the single manufacturer, the single batch or the single electric energy meter is weak in anti-interference capability and low in reliability;
the arguments include: temperature, humidity, air pressure, illumination, wind speed, run length, production lot, sample number, test voltage, test current, power factor, and load point; the dependent variable is the operation error of the electric energy meter.
2. The method for analyzing the reliability of the electric energy meter in the typical environment according to claim 1, wherein the step of comparing X is as follows i And Y i Normalization processing includes subtracting the mean value from the standard deviation.
3. The method for analyzing reliability of electric energy meter under typical environment according to claim 1, wherein in the process of constructing the model, considering linear output of the model and stability of the model, when modeling is performed by using a partial least square method, the selected input variables only comprise environmental factor data and time factor data, the electric energy meter type selects a single 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 under the typical environment according to claim 1, wherein if the collected test result data, environment variable data and electric energy meter state data information have abnormal data and missing data, deleting or interpolating missing values and directly rejecting abnormal values by combining test sample data, data quantity and data characteristics.
5. The method for analyzing reliability of an electric energy meter in a typical environment according to claim 4, wherein the processing rules of the abnormal data and the missing data are as follows: if the environment variable data is missing, the environment values of the linear model and the predicted time point of the last several hours are needed to be taken for missing value filling; if the error value is missing, the error value of the sample at the similar time point in the last few days is needed to be filled; for abnormal temperature data and error value data, the abnormal values are directly removed to avoid affecting the model effect.
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