CN112417371A - Method for monitoring running state of intelligent electric energy meter in distribution network area - Google Patents
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Abstract
The invention provides a method for monitoring the running state of an intelligent electric energy meter in a distribution network area, which comprises the following steps: s1, collecting the operation electrical data of each intelligent electric energy meter corresponding to a bus in the distribution room; s2, preprocessing the acquired electrical operation data, splicing and combining the preprocessed data, and constructing a time-space data set; s3, establishing a high-dimensional factor model of the time-space data set, acquiring a residual error matrix from the high-dimensional factor model for analysis, and determining an optimal residual error matrix; and S4, mining statistical information of the optimal residual error matrix, and monitoring the running state of each intelligent electric energy meter according to a preset index system. The intelligent electric energy meter monitoring system can monitor the running state of the intelligent electric energy meter and improve the efficiency and accuracy of intelligent electric energy meter measurement monitoring.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a method for monitoring the running state of an intelligent electric energy meter in a distribution network area, electronic equipment and a readable storage medium.
Background
The intelligent electric energy meter is an element of an electricity utilization information acquisition system and is an important data source for user behavior perception. The metering accuracy of the intelligent electric energy meter is related to rights and interests of both an electric power enterprise and a user, an effective means is still lacked in remote monitoring of the metering performance of the intelligent electric energy meter at present, particularly after comprehensive popularization and application of state change and misalignment change of the electric energy meter, a management mode of the electric energy meter is changed from 'limited-term use and due alternation' into dynamic adjustment according to the integral operation quality level of the electric energy meter, and the change of the management mode needs an effective monitoring means for supporting.
Therefore, it is needed to monitor the operation state of the intelligent electric energy meter, and improve the efficiency and accuracy of the measurement monitoring of the intelligent electric energy meter.
Disclosure of Invention
The invention aims to provide a method for monitoring the running state of an intelligent electric energy meter in a distribution network area, electronic equipment and a readable storage medium, wherein characteristic analysis is carried out on a high-dimensional space-time data set, the running state of the electric energy meter is monitored and evaluated by carrying out deep mining on the running characteristic statistical information of the intelligent electric energy meter, and carrying out operations such as high-dimensional factor model analysis and residual matrix random matrix model analysis on the built space-time data set.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
a method for monitoring the running state of an intelligent electric energy meter in a distribution network area comprises the following steps:
s1, collecting the operation electrical data of each intelligent electric energy meter corresponding to a bus in the distribution room;
s2, preprocessing the acquired electrical operation data, splicing and combining the preprocessed data, and constructing a time-space data set;
s3, establishing a high-dimensional factor model of the time-space data set, acquiring a residual error matrix from the high-dimensional factor model for analysis, and determining an optimal residual error matrix;
and S4, mining statistical information of the optimal residual error matrix, and monitoring the running state of each intelligent electric energy meter according to a preset index system.
Further, in the method for monitoring the operation state of the intelligent electric energy meter in the distribution network area, the operation electrical data includes: one or more of voltage, current, power usage, and power.
Further, in the method for monitoring the operation state of the intelligent electric energy meter in the distribution network area, the step S2 includes the following steps: missing value population, outlier cleansing, data smoothing, and normalization operations.
Further, in the method for monitoring the operation state of the intelligent electric energy meter in the distribution network area, S3 sets up a high-dimensional factor model of the time-space data set, and obtains a residual matrix from the high-dimensional factor model for analysis, where the method includes:
performing sliding window selection at each sampling point in the spatio-temporal data set;
and constructing a factor model for the data matrix of each sliding window selection area, removing principal component factors of the data matrix, and acquiring a residual error matrix for analysis.
Further, in the method for monitoring the operation state of the intelligent electric energy meter in the distribution network area, S3 determines an optimal residual error matrix, including:
and estimating the optimal parameter combination of the number of factors and the residual error coefficient of each factor model by using Jensen-Shannon divergence, and determining the optimal residual error matrix corresponding to each sliding window selection area according to the optimal parameter combination.
Further, in the method for monitoring the operation state of the intelligent electric energy meter in the distribution network area, S4 finds statistical information of the residual error matrix, including:
and analyzing the empirical spectrum distribution of the residual error matrix through a random matrix model, and judging whether an abnormal value appears through an M-P law and a circular law.
Further, in the method for monitoring the operation state of the intelligent electric energy meters in the distribution network area, S4 monitors the operation state of each intelligent electric energy meter according to a preset index system, and includes:
and selecting an ammeter running state index from a pre-constructed linear characteristic value index system, and judging whether the index exceeds a threshold value when an abnormal state occurs.
An electronic device comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method for monitoring the running state of the intelligent electric energy meter in the distribution network area is realized.
A readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for monitoring the running state of the intelligent electric energy meter in the distribution network area is realized.
Compared with the traditional intelligent electric meter sampling inspection mode, the intelligent electric meter sampling inspection method has the following advantages:
1) the method comprises the steps that a factor model is adopted, an original data set is decomposed into main factors and a residual matrix, the residual matrix is analyzed, data flow information of the intelligent ammeter is fully utilized, and efficient and accurate monitoring of the running state of the ammeter is achieved based on a data driving mode;
2) by constructing the characteristic value index system, a reasonable characteristic value function can be better adjusted and constructed according to the performance evaluation threshold value of the running state of the electric meter.
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In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description will be briefly introduced, and it is obvious that the drawings in the following description are an embodiment of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts according to the drawings:
fig. 1 is a flowchart of a method for monitoring an operation state of an intelligent electric energy meter in a distribution network area according to an embodiment of the present invention.
Detailed Description
The following describes the operation state monitoring method, the electronic device, and the readable storage medium of the intelligent electric energy meter in the distribution network area according to the present invention in detail with reference to the accompanying drawings and the detailed description. The advantages and features of the present invention will become more apparent from the following description. It is to be noted that the drawings are in a very simplified form and are all used in a non-precise scale for the purpose of facilitating and distinctly aiding in the description of the embodiments of the present invention. To make the objects, features and advantages of the present invention comprehensible, reference is made to the accompanying drawings. It should be understood that the structures, ratios, sizes, and the like shown in the drawings and described in the specification are only used for matching with the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the implementation conditions of the present invention, so that the present invention has no technical significance, and any structural modification, ratio relationship change or size adjustment should still fall within the scope of the present invention without affecting the efficacy and the achievable purpose of the present invention.
The invention carries out characteristic monitoring, analysis and evaluation on the space-time data set based on the high-dimensional statistical characteristic analysis and evaluation technology, and can provide an effective monitoring and evaluation means for the metering characteristic of the intelligent electric energy meter by deeply mining the running characteristic statistical information of the intelligent electric energy meter. The method and the device are beneficial to improving the efficiency and the accuracy of the metering monitoring of the intelligent electric energy meter, timely and accurately identifying the metering misalignment condition of the electric energy meter, accurately analyzing the reasons caused by the deviation of the metering data, deeply excavating the electricity consumption behavior characteristics of the user, enabling an electric power enterprise to better provide differentiated high-quality service for the user, and having important significance on the development of the work of metering characteristic monitoring, transformer area line loss management, electricity stealing prevention and the like.
The theoretical basis, data model and algorithm to which the present invention relates are first introduced below:
1. factor model and residual detection thereof
Factor models (factor models) are used as an effective mathematical tool for statistical and economic analysis. It models the observation data as:
wherein, XitIs an element of the observation matrix at time t; p is the number of factors, FjtIs the jth factor of time t, LijAs a coefficient thereof, UitI.e. the residual (residual) of the observation matrix. Written in matrix form as:
X＝LF+U
the residual matrix can be modeled by AR (1), i.e.
Uit＝bUi,t-1+ξit
Wherein b is AR (1) model coefficient, ξitIs a gaussian random variable.
And collecting a plurality of wave recording data in the same period of time in the electric meter to form an NxT space-time matrix. And estimating the number of factors and residual coefficients by Jensen-Shannon divergence, and decomposing the observation matrix, thereby capturing the change of the running state of the electric meter according to the empirical spectrum distribution of the residual matrix.
Furthermore, statistical information of a residual error matrix is deeply mined by introducing tools such as random matrix modeling, eigenvalue (spectrum) analysis, random matrix splicing, free probability and the like, and then the statistical information is associated with the concerned engineering problem, and a corresponding index system is designed to assist in functional design.
And taking the label data set as a source, and constructing a factor model and a random matrix model of the data by considering the characteristics of diversification, periodicity, uncertainty and the like of the main body.
2. Random matrix theoretical model
And analyzing inherent uncertainty and periodicity in the data by using a random matrix model, and refining related indexes for subsequent signal detection, correlation analysis and the like.
For large engineering data, the space dimension N and the time dimension T are often different, and a Wishart matrix and linear characteristic values thereof which accord with the characteristics are researched to statistically measure the law of majority, the central limit theorem and the like of LES. Based on the above studies, the limits of LES are obtained a priori and the empirical spectral density convergence rate of the data matrix is evaluated. They can be used as reference and analysis basis for comparison of experimental data, and are the entry points for realizing data-driven object cognition.
And analyzing the acquired space-time data by using an M-P law and a circular law, monitoring the occurrence of an abnormal state, and introducing a linear characteristic value evaluation system to evaluate the abnormal state.
M-P law: for an NxT LUE matrix, when N, T → ∞ and
the spectral density function satisfies:
wherein the content of the first and second substances,
circle law: singular value equivalence matrix multiplicative multiplication considering L independent random matrices
Wherein
Further, Z can be normalized to
When N, T → ∞ and N/T ∈ (0, 1)]When the temperature of the water is higher than the set temperature,
almost certainly (alomost recovery) converges to
And developing an analysis algorithm of the data model, extracting statistics of the data model, calculating theoretical limits of the data model, and further extracting statistical information contained in the data by combining means such as hypothesis test and the like.
3. Linear eigenvalue statistical LES
The definition of linear characteristic value statistical measure LES is given and the statistical characteristics of the linear characteristic value statistical measure LES are researched, namely a law of maximums and a central limit theorem of LES.
Defining:
wherein the content of the first and second substances,
for continuous test function (testing function), lambdaiIs the matrix eigenvalue.
4. Testing functions
Design (2) of
Testing functions
Is the core of optimizing the performance of the LES,
it is sufficient to be continuous enough. A common test function is as follows:
chebyshev Polynomials T2：2x2-1
5. Mosaic matrix construction
The system state depends on a number of influencing factors. Assuming that the state of a certain electric meter collecting parameter is a variable of an N-dimensional parameter and has M potential influencing factors, the method comprises the following steps ofi(i-1, 2, …, T), the N-dimensional vectors contained in the meter parameters may naturally constitute the basis state matrix
The value of the time interval can be obtained from each influence factor, and a factor vector is formed
Two matrices (vectors) having the same length can form a new matrix through a splicing operation. Based on the common knowledge, the basic state matrix B and the factor vector c are combinedjSpliced to form a composite matrix Aj。
In order to facilitate the analysis of the influence of its influencing factors on the base state, it is necessary to amplify the influence of the influencing factors. Vector of selected factors cjThe factor vector is replicated K times (K can be 0.4 XN) in a certain way to form a matrix D which is matched with the state matrix sizejAs follows:
next, in DjIntroducing white noise to eliminate internal phaseThe relationship is shown as the following formula:
Cj＝Dj+ηjR(j＝1,2,...,m)
wherein R is a standard Gaussian random matrix, ηjRelated to the signal-to-noise ratio (SNR) p:
thus, each factor vector c can be applied in paralleljForming a composite matrix A by matrix splicingj：
The following description is provided for a method for monitoring the running state of an intelligent electric energy meter in a distribution network area based on a factor model and residual error detection thereof, as shown in fig. 1, and the method comprises the following steps:
and S1, collecting the operation electrical data of each intelligent electric energy meter corresponding to a bus in the distribution room.
Preferably, the operational electrical data includes: one or more of voltage, current, power usage, and power. The sampling frequency may be 15 min/time.
Each data can be processed by adopting the method of the invention, so as to realize the monitoring of the running state of the intelligent electric energy meter.
And S2, preprocessing the acquired electrical operation data, splicing and combining the preprocessed data, and constructing a time-space data set.
The preprocessing mode comprises missing value filling, abnormal value cleaning, smoothing processing by a moving sliding window method and the like, and the normalization of the data can be improved through preprocessing.
S3, establishing a high-dimensional factor model of the time-space data set, obtaining a residual error matrix from the high-dimensional factor model for analysis, and determining an optimal residual error matrix.
Specifically, a sliding window selection is performed at each sampling point in the spatio-temporal data set; and constructing a factor model for the data matrix of each sliding window selection area, removing principal component factors of the data matrix, and acquiring a residual error matrix for analysis.
And then estimating the optimal parameter combination of the number of factors and the residual error coefficient of each factor model by using Jensen-Shannon divergence, and determining the optimal residual error matrix corresponding to each sliding window selection area according to the optimal parameter combination.
And S4, mining statistical information of the optimal residual error matrix, and monitoring the running state of each intelligent electric energy meter according to a preset index system.
And deeply mining statistical information of the optimal residual error matrix by introducing tools such as a random matrix model, eigenvalue (spectrum) analysis, free probability and the like, further associating the statistical information with the running state of the electric meter, and designing a corresponding index system to assist in functional design.
Specifically, empirical spectrum distribution of the residual matrix is analyzed through a random matrix model, and whether an abnormal value appears is judged through an M-P law and a circular law.
And selecting an ammeter running state index from a pre-constructed linear characteristic value index system, and judging whether the index exceeds a threshold value when an abnormal state occurs.
The technical solution of the present invention is explained below with a specific example.
Step 1: data preprocessing, namely preprocessing voltage, current, power and power consumption data, wherein the preprocessing comprises missing value filling, abnormal value cleaning, data standardization, smoothing processing by a moving sliding window method and the like, and a time-space data set D is constructed;
step 2: at each sampling point tjTaking the shape of a sliding window with the shape of NxT
Data set D ofj；
And step 3: for the number of residual factors p to be removed 1,2(p)＝R-L(p)F(p)Acquiring a residual error matrix of p-level;
and 4, step 4: standardizing the residual error matrix obtained in the step 3, and converting the residual error matrix into a standardized matrix with a mean value of 0 and a variance of 1;
and 5: calculating a covariance matrix of the standardized matrix obtained in the step 4;
step 6: calculating the empirical spectrum distribution of the covariance matrix obtained in the step 5;
and 7: calculating a model-based empirical spectrum distribution for the autoregressive ratios b-U [0,1] by the following formula;
wherein the content of the first and second substances,
it is indicated that the imaginary part operation is performed,
is a green function, λ is a eigenvalue variable, and ε is an imaginary part;
and 8: calculating the spectral distance between the real data matrix and the model matrix;
and step 9: the spectral distance is minimized by Jensen-Shannon divergence,
obtaining optimal parameter combination of factor number and residual error coefficient
Step 10: and analyzing the empirical spectrum distribution of the optimal residual error matrix through the M-P law and the circular law of the random matrix model, and detecting whether an abnormal value occurs.
Step 11: computing linear eigenvalue index system
And monitoring and evaluating the running state of the electric energy meter by combining with the running state threshold value of the electric energy meter.
Based on the same inventive concept, the invention further provides electronic equipment which comprises a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to realize the method for monitoring the running state of the intelligent electric energy meter in the distribution network area.
The processor may be, in some embodiments, a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor (e.g., a GPU), or other data Processing chip. The processor is typically used to control the overall operation of the electronic device. In this embodiment, the processor is configured to run a program code stored in the memory or process data, for example, a program code of a method for monitoring an operating state of an intelligent electric energy meter in a distribution network area.
The memory includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage may be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device. In other embodiments, the memory may also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Memory Card (SMC), a Secure Digital (SD) Card, a flash memory Card (FlashCard), and the like, provided on the electronic device. Of course, the memory may also include both internal and external memory units of the electronic device. In this embodiment, the memory is generally used to store an operation method installed in the electronic device and various application software, for example, a program code of a method for monitoring an operation state of an intelligent electric energy meter in a distribution network area. In addition, the memory may also be used to temporarily store various types of data that have been output or are to be output.
Based on the same inventive concept, the present embodiment further provides a readable storage medium, where a computer program is stored in the readable storage medium, and when the computer program is executed by a processor, the method for monitoring the operation state of the intelligent electric energy meter in the distribution network area is implemented.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.
Claims (9)
1. A method for monitoring the running state of an intelligent electric energy meter in a distribution network area is characterized by comprising the following steps:
s1, collecting the operation electrical data of each intelligent electric energy meter corresponding to a bus in the distribution room;
s2, preprocessing the acquired electrical operation data, splicing and combining the preprocessed data, and constructing a time-space data set;
s3, establishing a high-dimensional factor model of the time-space data set, acquiring a residual error matrix from the high-dimensional factor model for analysis, and determining an optimal residual error matrix;
and S4, mining statistical information of the optimal residual error matrix, and monitoring the running state of each intelligent electric energy meter according to a preset index system.
2. The method for monitoring the operation state of the intelligent electric energy meter in the distribution network area according to claim 1, wherein the operation electrical data comprises: one or more of voltage, current, power usage, and power.
3. The method for monitoring the operation state of the intelligent electric energy meter in the distribution network area according to claim 1, wherein the step of preprocessing the acquired electrical operation data by the step of S2 comprises the steps of: missing value population, outlier cleansing, data smoothing, and normalization operations.
4. The method for monitoring the operating state of the intelligent electric energy meters in the distribution network area as claimed in claim 1, wherein S3 establishes a high-dimensional factor model of the time-space data set, and acquires a residual matrix from the high-dimensional factor model for analysis, and the method comprises:
performing sliding window selection at each sampling point in the spatio-temporal data set;
and constructing a factor model for the data matrix of each sliding window selection area, removing principal component factors of the data matrix, and acquiring a residual error matrix for analysis.
5. The method for monitoring the operation state of the intelligent electric energy meter in the distribution network area as claimed in claim 4, wherein the step S3 of determining the optimal residual error matrix comprises the steps of:
and estimating the optimal parameter combination of the number of factors and the residual error coefficient of each factor model by using Jensen-Shannon divergence, and determining the optimal residual error matrix corresponding to each sliding window selection area according to the optimal parameter combination.
6. The method for monitoring the operation state of the intelligent electric energy meter in the distribution network area according to claim 1, wherein the step of S4 mining the statistical information of the residual error matrix comprises the following steps:
and analyzing the empirical spectrum distribution of the residual error matrix through a random matrix model, and judging whether an abnormal value appears through an M-P law and a circular law.
7. The method for monitoring the operating states of the intelligent electric energy meters in the distribution network area according to claim 1, wherein the step S4 of monitoring the operating states of the intelligent electric energy meters according to a preset index system comprises:
and selecting an ammeter running state index from a pre-constructed linear characteristic value index system, and judging whether the index exceeds a threshold value when an abnormal state occurs.
8. An electronic device comprising a processor and a memory, the memory having stored thereon a computer program which, when executed by the processor, implements the method of any of claims 1 to 7.
9. A readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1 to 7.
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| CN114034978B (en) * | 2021-11-11 | 2023-11-10 | 四川中电启明星信息技术有限公司 | Automatic model detection method and system for distribution network project |
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