CN116842684A - Electric energy meter, evaluation method and system of operation reliability of electric energy meter and electric energy meter processor - Google Patents
Electric energy meter, evaluation method and system of operation reliability of electric energy meter and electric energy meter processor Download PDFInfo
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Abstract
The application discloses an electric energy meter, an evaluation method and a system for operation reliability of the electric energy meter, and an electric energy meter processor, wherein a first model for fusing time stress and electric stress in a nonlinear manner, a second model for fusing all stresses in a linear regression manner, a third model for fusing two nonlinear forms and internal interactions of the two nonlinear forms, a fourth model for conducting sectional preprocessing on input environment stress, and the time stress is fused in a quadratic polynomial manner, parameters of each model are calculated, and WAIC indexes are used for obtaining the evaluation model. The method can quantitatively explain and analyze the influence of the environmental stress and the electric stress on the electric energy meter in the operation environment, and greatly improves the accuracy and objectivity of the operation reliability evaluation of the electric energy meter.
Description
Technical Field
The application relates to the field of reliability data evaluation, in particular to an evaluation method and system for operation reliability of an electric energy meter and an electric energy meter processor.
Background
In the operation process of the electric energy meter, the electric energy meter is influenced by environmental factors, particularly temperature, humidity and the like, and the measurement accuracy and the operation reliability are gradually reduced. In addition, over time, components of the electric energy meter age and the performance is significantly degraded. The metering accuracy and reliability of the terminal nerve serving as the intelligent power grid of the electric energy meter are important as follows:
the application patent with the application number of CN109767061B discloses a state evaluation and replacement method, a state evaluation and replacement device and a terminal of an electric energy meter. The method comprises the following steps: acquiring data of an electric energy meter in a working state to obtain data of the electric energy meter in operation; inputting the data of the on-operation electric energy meter into a fault prediction model to obtain a prediction fault rate; according to the predicted failure rate and the on-line electricity meter data, health evaluation is carried out on the on-line electricity meter, and a health evaluation score is obtained; and determining whether to replace the on-line electricity meter according to the predicted failure rate and the health evaluation score. The index of CN109767061B is the failure rate of the electric energy meter, the data is inconvenient to count and acquire, and the failure rate data is the rotation data of the electric energy meter when the electric energy meter is hung on a net to operate, but the rotation of the electric energy meter does not indicate that the electric energy meter fails.
The application patent with the application number of CN109375143B discloses a method for determining the residual life of an intelligent electric energy meter, which comprises the following steps: sampling all Y intelligent electric energy meters of the production batch to be tested to determine intelligent electric energy meter samples for performing multi-level constant stress acceleration tests; acquiring values of performance parameters of an intelligent electric energy meter sample at a plurality of preset monitoring moments when a multi-level constant stress acceleration test is performed; according to the obtained values of the performance parameters of the intelligent electric energy meter samples, the bivariate constant stress acceleration model and the exponential performance degradation model, determining the pseudo life value of each intelligent electric energy meter in all M multiplied by Q intelligent electric energy meters of the intelligent electric energy meter samples under the normal stress level; according to the pseudo life value of the intelligent electric energy meter sample, determining the reliable life value of the intelligent electric energy meter of the production batch to be tested under the appointed reliability value r; and determining the residual life value of the intelligent electric energy meter of the production batch to be tested after the intelligent electric energy meter is put into service for N years.
The application patent application number CN114925938B discloses an electric energy meter running state prediction method and device based on a self-adaptive SVM model, wherein the method comprises the following steps: acquiring operation and maintenance data of a plurality of electric energy meters, and constructing equipment characteristics, statistical characteristics and time sequence characteristics related to faults of the intelligent electric energy meters from the operation and maintenance data; based on the trained neural network, calculating first-class abnormal probabilities of the electric energy meter in different periods according to the time sequence characteristics; based on the machine learning classifier optimized by the group intelligent optimization algorithm, calculating the second class abnormal probability of the electric energy meter under different statistical characteristics and equipment characteristics; training an adaptive SVM model according to the first class abnormal probability and the second class abnormal probability; and carrying out state prediction on the electric energy meter to be evaluated by using the self-adaptive SVM model after training.
According to the scheme, the environmental stress and the electric stress in the running environment of the electric energy meter are not considered, quantitative interpretation and analysis are not available, dynamic trend fluctuation analysis cannot be conducted on the reliability index, and therefore the running reliability analysis accuracy of the electric energy meter is low.
Disclosure of Invention
Aiming at the defects of the prior art, the application provides an electric energy meter, an evaluation method and system of the operation reliability of the electric energy meter, and an electric energy meter processor, and the accuracy of the operation reliability analysis of the electric energy meter is improved.
In order to solve the technical problems, the application adopts the following technical scheme: an evaluation method for the operation reliability of an electric energy meter comprises the following steps:
s1, constructing the following model:
a first model: y is t ＝β 0 +β 1,1 t+β 1,2 ln(t)+β 2 T t +β 3 R t +β 4 lnIP t +εB(t)；
And (3) a second model: y is t ＝β 0 +β 1 t+β 2 T t +β 3 R t +β 4 IP t +εB(t)；
Third model:
fourth model: y is t ＝β 0 +β 1,1 t+β 1,2 t 2 +φ(x)+β 4 lnIP t +εB(t)；
Wherein y is t Basic error BE for the detection of the electric energy meter at t time is shown; beta 0 Is regression bias; t (T) t ，R t ，IP t The temperature, the humidity and the electric stress are detected at the time t respectively; beta i The regression coefficient corresponding to the ith stress, i=1, 2,3,4; beta i，j J=1, 2,3, which is the j-th regression component of the i-th regression coefficient; b (t) is standard Brownian motion; epsilon is a strictly non-negative coefficient; phi (x) is an Eyring factor,a, b are temperature stress coefficient and humidity stress coefficient respectively, c is the regression coefficient of the index;
s2, calculating parameters epsilon and beta of the model by using a NUTs sampler i ；
S3, calculating WAIC indexes of each model:wherein (1)>Inputting a basic error of a Bayesian model for t time; n is the number of samples of the model, and the model parameter set (u z ，σ z ，ε,β i ) Sampling, wherein the sampling is performed by utilizing NUTs, setting is performed in advance, and an empirical value of 500 is generally selected for N; p (y) * t ) Posterior distribution probability of the Bayesian generalized regression model; v (lovp (y) * t |θ)) substitutionThe variance of the table likelihood function; θ represents a parameter set (u) z ，σ z ，ε,β i )；σ z ～Half-Cauchy(υ z )，/> υ z For a constant, normal () represents a Normal distribution, and halo-Cauchy () represents a Half Cauchy distribution;
s4, acquiring a model with the minimum WAIC index value, wherein the model is an optimal model, and the optimal model is used as an evaluation model.
The method comprises the steps of respectively constructing a first model for fusing time stress and electric stress in a nonlinear mode, a second model for fusing all stresses in a linear regression mode, a third model for fusing two nonlinear modes and internal interactions of the two nonlinear modes, a fourth model for conducting piecewise pretreatment on input environment stress, fusing the time stress in a quadratic polynomial mode, calculating parameters of the models, and further obtaining an evaluation model by using WAIC indexes. The method can quantitatively explain and analyze the influence of all environmental stresses and electric stresses on the electric energy meter in the operation environment, greatly improves the accuracy and objectivity of the operation reliability evaluation of the electric energy meter, and can track the real-time operation state of the electric energy meter and realize the dynamic trend fluctuation analysis of the reliability index because the evaluation model is selected to be constructed based on the environmental data and the electric stress data acquired in real time.
The application also includes: s5, calculating the operation reliability of the electric energy meter by using the evaluation model.
In step S5, the reliability calculation formula is: representing integration of the reliability probability density distribution function f (t), the +.> Representing the pair m (t; beta) 1,j ) Integration is performed, m (t; beta 1,j ) To evaluate the model as a function of time t, y l For a specified failure threshold value of the electric energy meter, y d Is a bias term under basic bias, environmental stress, electrical stress.
As an inventive concept, the present application also provides an electric energy meter processor, comprising:
a model construction unit for constructing the following models:
a first model: y is t ＝β 0 +β 1,1 t+β 1,2 ln(t)+β 2 T t +β 3 R t +β 4 lnIP t +εB(t)；
And (3) a second model: y is t ＝β 0 +β 1 t+β 2 T t +β 3 R t +β 4 IP t +εB(t)；
Third model:
fourth model: y is t ＝β 0 +β 1,1 t+β 1,2 t 2 +φ(x)+β 4 lnIP t +εB(t)；
Wherein y is t Basic error BE for the detection of the electric energy meter at t time is shown; beta 0 Is regression bias; t (T) t ，R t ，IP t The temperature, the humidity and the electric stress are detected at the time t respectively; beta i The regression coefficient corresponding to the ith stress, i=1, 2,3,4; beta i，j J=1, 2,3, which is the j-th regression component of the i-th regression coefficient; b (t) is standard Brownian motion; epsilon is a strictly non-negative coefficient; phi (x) is an Eyring factor,a, b are temperature stress coefficient and humidity stress coefficient respectively, c is the regression coefficient of the index;
a parameter calculation unit for calculating parameters epsilon and beta of the first model to the fourth model by using a NUTs sampler i ；
An index calculation unit for calculating the WAIC index of each model:wherein N is the sampling number of the model; p (y) * t ) Posterior distribution probability of the Bayesian generalized regression model; v (lovp (y) * t |θ)) represents the variance of the likelihood function; θ represents a parameter set (u) z ，σ z ，ε,β i )；/>σ z ～Half-Cauchy(υ z )；/>υ z For a constant, normal () represents a Normal distribution, and halo-Cauchy () represents a Half Cauchy distribution;
and the output unit is used for acquiring the model with the minimum WAIC index value, setting the model as an optimal model, and taking the optimal model as an evaluation model.
According to the application, the electric energy metering value of the electric energy meter in one metering period is compensated by using the model parameters of the optimal model, so that more accurate electric energy metering value (namely, the compensated electric energy metering value) is obtained. The compensation value being equal to the offset term y d 。
The electric energy meter processor is embedded with four built models, and the optimal model can be screened out to serve as an evaluation model, so that the influence of all environmental stresses and electric stresses on the electric energy meter in the operating environment can be quantitatively explained and analyzed, the electric energy meter has an automatic analysis function, and the practicability of the electric energy meter is improved.
The electric energy meter processor further comprises an evaluation unit for calculating the operation reliability of the electric energy meter by using the evaluation model.
The application also provides an evaluation system for the operation reliability of the electric energy meter, which comprises a memory and a processor; the memory stores a computer program, and the processor implements the steps of the above-described evaluation method of the present application when executing the computer program; alternatively, the processor is the electric energy meter processor.
In order to facilitate the real-time display of reliability analysis data or the rest of data acquired by the processor, the processor of the application is connected with a display unit to display the dynamic fluctuation trend of the reliability index.
In order to obtain data in real time, the processor is communicated with the electric energy sampling unit and the environment collecting unit; the electric energy sampling unit is used for acquiring electric stress; the environment acquisition unit is used for acquiring environment temperature and humidity data, namely temperature stress and humidity stress.
The application also provides an electric energy meter which adopts the electric energy meter processor.
Compared with the prior art, the application has the following beneficial effects: the application can quantitatively explain and analyze the influence of all environmental stresses and electric stresses on the electric energy meter in the running environment and display the dynamic trend fluctuation of the reliability index. The application selects the key parameter basic error of the electric energy meter as the reliability index, directly monitors the measurement accuracy of the electric energy meter, and can dynamically evaluate the measurement reliability of the electric energy meter by using an input model.
Drawings
FIG. 1 is a flow chart of a method according to embodiment 1 of the present application;
fig. 2 is a diagram showing a processor configuration according to embodiment 2 of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first," "second," and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The terms "a," "an," and other similar words are not intended to mean that there is only one of the things, but rather that the description is directed to only one of the things, which may have one or more. In this document, the terms "comprise," "include," and other similar words are intended to denote a logical relationship, but not to be construed as implying a spatial structural relationship. For example, "a includes B" is intended to mean that logically B belongs to a, and not that spatially B is located inside a. In addition, the terms "comprising," "including," and other similar terms should be construed as open-ended, rather than closed-ended. For example, "a includes B" is intended to mean that B belongs to a, but B does not necessarily constitute all of a, and a may also include other elements such as C, D, E.
The basic error of the intelligent ammeter specified in the national metering verification procedure of JJG 596-2012 electronic AC electric energy meter adopts relative error expression, and the expression is that
Wherein P is ref Represents standard value, P test Representing the measured value, BE represents the base error. Basic error acquisition: generated by a standard signal unit, the standard signal value and the measured value are acquired by a processor, andand (5) calculating by using a formula (1) to obtain a basic error.
The Bayesian statistical method integrates sample information and prior information, and updates the prior information by using posterior information, so that the estimation result is closer to a true value. The Bayesian model is a probability method and is commonly used for fusion of multiple types of variables, and in addition, the model can be parameterized, and quantitative analysis is carried out on the model through solving parameters. BE is obviously affected by environmental stress, time stress, electric stress and the like, and the data distribution has the characteristics of certain randomness and obvious non-monotonicity, so that the BE of the electric energy meter is simulated and modeled by utilizing Gaussian distribution.
Example 1
The embodiment provides a method for evaluating the operation reliability of an electric energy meter, as shown in fig. 1. The implementation process of the evaluation method of the embodiment comprises the following steps:
s1, constructing the following model:
model 1: fusing the time stress and the electric stress in a nonlinear manner, fusing the environmental stress in a linear manner,
y t ＝β 0 +β 1,1 t+β 1,2 ln(t)+β 2 T t +β 3 R t +β 4 lnIP t +εB(t) (2)
wherein y is t BE for indicating the detection of the electric energy meter at t time; beta 0 Is regression bias; t (T) t ，R t ，IP t Temperature, humidity and electric stress detected at time t; when i>0，β i Regression coefficient, beta, corresponding to the ith stress i，j The j-th regression component that is the i-th regression coefficient; b (t) is standard Brownian motion (see: peng Baohua. Reliability modeling method based on Wiener process study [ D ]]University of national defense science and technology, 2010); epsilon is its coefficient and is strictly non-negative.
Model 2: all stresses are fused into the model 2 in a linear regression form, and the expression is that
y t ＝β 0 +β 1 t+β 2 T t +β 3 R t +β 4 IP t +εB(t) (3)
Model 3: for time stress, model 3 fuses two non-linear forms and their internal interactions, expressed as follows:
model 4: on the premise of integrating the Eyring factor and ensuring the stable operation of the model, the input environmental stress is required to be subjected to sectional pretreatment, and the time stress is fused in a quadratic polynomial mode, wherein the expression is
y t ＝β 0 +β 1,1 t+β 1,2 t 2 +φ(x)+β 4 lnIP t +εB(t) (5)
Wherein; phi (x) is an Eyring factor, and the expression is:the Eyring factor fuses temperature stress and humidity stress in an exponential form; a, b is the stress coefficient of temperature and humidity, c is the regression coefficient of the index;
the reasonable prior distribution is beneficial to the rapid convergence and stable operation of the model. However, most of the parameters of the model are fuzzy a priori distributions. For the applicability of the model, the parameter distribution should have a strong adaptability. The above 4 models all adopt 3-layer hierarchical Bayesian models, and formulas (2) - (5) are the first layer of each hierarchical model, namely likelihood functions of each model. For the middle and bottom layers of each model, model 1 is taken as an example:
in the intermediate layer epsilon is strictly non-negative and is a weak information prior distribution, so Half Cauchy () is chosen as the prior distribution, and the scale parameter v=10, i.e. epsilon-Half-Cauchy (v=10). To ensure fairness and standardization of the experiment, fuzzy prior distribution is selected. In addition, the stress coefficient beta i Is unlimited and therefore designated as Normal distribution Normal (), as follows
Wherein u is z 、σ z The mean and variance of the z-th normal distribution parameter.
In the bottom layer, u z Sum sigma z There is no a priori information. To ensure generalization and stability of the model, the distribution is selected to be normal distribution and half Cauchy distribution
σ z ～Half-Cauchy(υ z ) (8)
Where Z represents the Z-th normal distribution parameter, half-Cauchy () is a Half Cauchy distribution,υ z the meaning of (1) is a constant, taking larger values; in the processes of model operation and parameter updating, model sampling failure cannot be caused. After the prior distribution of the model parameters is set, new data is observed by combining with the Bayesian theorem, and posterior distribution probability of the model parameters is continuously updated. The associated posterior probability distribution is updated as:
wherein P (y) t |ε,β i (ii) likelihood function; p (ε) is the ε probability distribution; p (beta) i |u z ,σ z ) Is P (beta) i ) In u z 、σ z Probability distribution under joint conditions; p (u) z ) Is u z Probability distribution; p (sigma) z ) Is sigma (sigma) z Probability distribution; p (y) t ) Data edge distribution density is calculated as
P(y t )＝∫∫P(y t |ε,β i )P(ε,β i )dεdβ (10)
Wherein P (ε, β) i ) Is epsilon, beta i A joint probability density distribution; then, by integrating the other parameters, each parameter (u z ，σ z ，ε，β i ) Posterior edge distribution of (c). For example, the edge posterior density function for ε is calculated as follows:
P(ε|y t )＝∫P(β i |y t )P(ε|β i )dβ i (11)
wherein P (beta) i |y t ) Is P (beta) i ) In y t Probability distribution under conditions; p (ε|beta) i ) Is P (epsilon) at beta i Probability distribution under conditions; after updating the epsilon parameters, other parameters are continuously updated by using a similar formula (10), and then a new observation distribution is predicted by combining a formula (11). And finally, solving the parameter distribution of each variable one by one. In the model solving process, when the prior distribution and the posterior distribution of model parameters are unconjugated distribution, an MCMC method is usually adopted (reference: liu Xuming. Intelligent ammeter reliability estimation [ D ] based on Bayesian ZINB-GLM]University of hunan, 2019.). The application uses a NUTs sampler in the MCMC to analyze model parameters.
The model comparison index selects an information criterion (WAIC) with wide application of overall posterior distribution. Replication information in the model can be proposed, and the smaller the WAIC value, the higher the model fitting effect and stability. WAIC can be expressed as
Wherein N is the sampling number of the model; p (y) * t ) Posterior distribution probability of the Bayesian generalized regression model; v (lovp (y) * t |θ)) represents the variance of the likelihood function; θ represents a parameter set (u) z ，σ z ，ε,β i ). The simulation environment and the codes adopt a PyMC3 Python probability programming library, and the codes have portability.
Embedding the multiple models into the electric energy meter processor, fusing the Bayesian hierarchical models by utilizing the collected data, then synchronously and parallelly training the multiple models, and selecting an optimal model by comparing WAIC values of the models. And (3) carrying out corresponding compensation on the electric energy measurement of the electric energy meter by utilizing the optimal model parameters solved by the NUTs sampler, and simultaneously, carrying out reliability calculation and prediction by utilizing corresponding reliability formulas of the models. The reliability probability density distribution function of each model is estimated as follows:
in the method, in the process of the application,m(t；β 1,j ) A function of the time stress t is included for each model. y is l For a specified failure threshold value of the electric energy meter, y d Is a bias term under basic bias, environmental stress, electrical stress. For example in model 1
According to the parameters of the current solving, calculating the reliability of the current electric energy meter, and when the set time t is the future time amount, realizing the reliability prediction of the electric energy meter; in addition, the reliability and the electric energy compensation value of the electric energy meter are transmitted, stored in the background and displayed by the display unit. The reliability of the electric energy meter is referred, and guidance is provided for relevant departments to purchase, bid and bid the electric energy meter, and rotate and update the electric energy meter on the net.
The Bayesian model is suitable for small sample data, so that the triggering period of a standard signal source can be prolonged, the sample size can be reduced, and the hardware cost can be reduced. In addition, the standard signal source should have high accuracy and high stability.
Example 2
The present embodiment provides a power meter processor, as shown in fig. 2.
A model construction unit for constructing the following models:
a first model: y is t ＝β 0 +β 1,1 t+β 1,2 ln(t)+β 2 T t +β 3 R t +β 4 lnIP t +εB(t)；
And (3) a second model: y is t ＝β 0 +β 1 t+β 2 T t +β 3 R t +β 4 IP t +εB(t)；
Third model:
fourth model: y is t ＝β 0 +β 1,1 t+β 1,2 t 2 +φ(x)+β 4 lnIP t +εB(t)；
Wherein y is t Basic error BE for the detection of the electric energy meter at t time is shown; beta 0 Is regression bias; t (T) t ，R t ，IP t The temperature, the humidity and the electric stress are detected at the time t respectively; beta i The regression coefficient corresponding to the ith stress, i=1, 2,3,4; beta i，j J=1, 2,3, which is the j-th regression component of the i-th regression coefficient; b (t) is standard Brownian motion; epsilon is a strictly non-negative coefficient; phi (x) is an Eyring factor,a and b are temperature stress coefficient and humidity stress coefficient respectively, and c is an exponential regression coefficient.
A parameter calculation unit for calculating parameters epsilon and beta of the first model to the fourth model by using a NUTs sampler i 。
An index calculation unit for calculating the WAIC index of each model:wherein N is the sampling number of the model; p (y) * t ) Posterior distribution probability of the Bayesian generalized regression model; v (lovp (y) * t |θ)) represents the variance of the likelihood function; θ represents a parameter set (u) z ，σ z ，ε,β i )；/>σ z ～Half-Cauchy(υ z )；/>υ z For a constant, normal () represents a Normal distribution, and halo-Cauchy () represents a Half Cauchy distribution.
And the output unit is used for acquiring the model with the minimum WAIC index value, setting the model as an optimal model, and taking the optimal model as an evaluation model.
And the standard signal unit adopts periodic triggering to act on the electric stress of the electric energy meter. Transmitting standard signal sources at intervals, wherein the signal source signals are output by a control variable method, such as control voltage, power factor invariance, current change and the like, and the signal sources are represented by variable IP; the standard signal unit is in physical connection with the electric energy sampling unit besides bidirectional data transmission with the processor unit.
The environment acquisition unit acquires environmental temperature and humidity data, and the temperature and humidity values are respectively represented by T, R, namely temperature and humidity stress; the unit and the processor are unidirectional data transmission.
The clock unit has a time setting function, provides a reference for triggering of standard signal output, can represent the time length, and is denoted by t, namely, the time stress; the unit and the processor have bi-directional data transmission.
In this embodiment, the specific calculation process of each unit is the same as that of embodiment 1, and will not be described here again.
Example 3
Embodiment 3 of the present application provides an electric energy meter operation reliability evaluation system corresponding to embodiment 1 or embodiment 2, including a memory, a processor, and a computer program stored on the memory; the processor executes the computer program on the memory to implement the steps of the method of embodiment 1 described above; alternatively, the processor is the processor of embodiment 2 described above.
In some implementations, the memory may be high-speed random access memory (RAM: random Access Memory), and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
In other implementations, the processor may be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), or other general-purpose processor, which is not limited herein.
Example 4
Embodiment 4 of the present application provides an electric energy meter corresponding to embodiment 2 above, where the electric energy meter employs the processor of embodiment 2 above.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be realized by adopting various computer languages, such as object-oriented programming language Java, an transliteration script language JavaScript and the like.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The method for evaluating the operation reliability of the electric energy meter is characterized by comprising the following steps of:
s1, constructing the following model:
a first model: y is t ＝β 0 +β 1,1 t+β 1,2 ln(t)+β 2 T t +β 3 R t +β 4 ln IP t +εB(t)；
And (3) a second model: y is t ＝β 0 +β 1 t+β 2 T t +β 3 R t +β 4 IP t +εB(t)；
Third model:
fourth model: y is t ＝β 0 +β 1,1 t+β 1,2 t 2 +φ(x)+β 4 ln IP t +εB(t)；
Wherein y is t Basic error BE for the detection of the electric energy meter at t time is shown; beta 0 Is regression bias; t (T) t ，R t ，IP t The temperature, the humidity and the electric stress are detected at the time t respectively; beta i The regression coefficient corresponding to the ith stress, i=1, 2,3,4; beta i，j J=1, 2, … n, which is the j-th regression component of the i-th regression coefficient; b (t) is standard Brownian motion; epsilon is a strictly non-negative coefficient; phi (x) is an Eyring factor,a, b are temperature stress coefficient and humidity stress coefficient respectively, c is the regression coefficient of the index;
s2, calculating parameters epsilon and beta of the model by using a NUTs sampler i ；
S3, calculating WAIC indexes of each model:wherein (1)>Inputting a basic error of a Bayesian model for t time; n is the sampling number; p (y) * t ) Posterior distribution probability of the Bayesian generalized regression model; v (lovp (y) * t |θ)) represents the variance of the likelihood function; θ represents a parameter set (u) z ，σ z ，ε,β i )；σ z ～Half-Cauchy(υ z )，/> υ z For a constant, normal () represents a Normal distribution, and halo-Cauchy () represents a Half Cauchy distribution;
s4, acquiring a model with the minimum WAIC index value, wherein the model is an optimal model, and the optimal model is used as an evaluation model.
2. The method for evaluating the operational reliability of an electric energy meter according to claim 1, further comprising:
s5, calculating the operation reliability of the electric energy meter by using the evaluation model.
3. The method for evaluating the operation reliability of an electric energy meter according to claim 2, wherein in step S5, the reliability calculation formula is:representing integration of the reliability probability density distribution function f (t), the +.> Represents a group represented by the general formula (i) for m (t; 1,j ) Integration is performed, m (t; 1,j ) To evaluate the model as a function of time t, y l For a specified failure threshold value of the electric energy meter, y d Is a bias term under basic bias, environmental stress, electrical stress.
4. The method for evaluating the operational reliability of an electric energy meter according to claim 3, further comprising: compensating the electric energy metering value of the electric energy meter in one metering period by utilizing the parameters of the optimal model to obtain a compensated electric energy metering value; wherein the compensation value is equal to the offset term y d Equal.
5. A power meter processor, comprising:
a model construction unit for constructing the following models:
a first model: y is t ＝β 0 +β 1,1 t+β 1,2 ln(t)+β 2 T t +β 3 R t +β 4 ln IP t +εB(t)；
And (3) a second model: y is t ＝β 0 +β 1 t+β 2 T t +β 3 R t +β 4 IP t +εB(t)；
Third model:
fourth model: y is t ＝β 0 +β 1,1 t+β 1,2 t 2 +φ(x)+β 4 ln IP t +εB(t)；
Wherein y is t Basic error BE for the detection of the electric energy meter at t time is shown; beta 0 Is regression bias; t (T) t ，R t ，IP t The temperature, the humidity and the electric stress are detected at the time t respectively; beta i The regression coefficient corresponding to the ith stress, i=1, 2,3,4; beta i，j J=1, 2,3, which is the j-th regression component of the i-th regression coefficient; b (t) is standard Brownian motion; epsilon is a strictly non-negative coefficient; phi (x) is an Eyring factor,a, b are temperature stress coefficient and humidity stress coefficient respectively, c is the regression coefficient of the index;
a parameter calculation unit for calculating parameters epsilon and beta of the first model to the fourth model by using a NUTs sampler i The method comprises the steps of carrying out a first treatment on the surface of the An index calculation unit for calculating the WAIC index of each model:wherein N is the sampling number; p (y) * t ) Posterior distribution probability of the Bayesian generalized regression model; v (lovp (y) * t |θ)) represents the variance of the likelihood function; θ represents a parameter set (u) z， σ z， ε,β i )；/>σ z ～Half-Cauchy(υ z )；/>υ z For a constant, normal () represents a Normal distribution, and halo-Cauchy () represents a Half Cauchy distribution;
and the output unit is used for acquiring the model with the minimum WAIC index value, setting the model as an optimal model, and taking the optimal model as an evaluation model.
6. The electric energy meter processor of claim 5, further comprising an evaluation unit for calculating an operational reliability of the electric energy meter using the evaluation model.
7. An evaluation system for the operation reliability of an electric energy meter comprises a memory and a processor; characterized in that the memory stores a computer program and that the processor, when executing the computer program, carries out the steps of the method according to one of claims 1 to 4; alternatively, the processor is a processor as claimed in claim 5 or 6.
8. The system for evaluating the operational reliability of an electric energy meter of claim 7, wherein the processor is coupled to a display unit.
9. The system for evaluating operational reliability of an electric energy meter of claim 7, wherein the processor is in communication with an electric energy sampling unit, an environmental collection unit; the electric energy sampling unit is used for acquiring electric stress; the environment acquisition unit is used for acquiring environment temperature and humidity data, namely temperature stress and humidity stress.
10. An electric energy meter, characterized in that it employs a processor according to claim 5 or 6.
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| CN117932501B (en) * | 2024-03-22 | 2024-05-28 | 深圳市江机实业有限公司 | Electric energy meter running state management method and system |
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