CN110488218A - A kind of electric energy meter operating status appraisal procedure and assessment device - Google Patents
A kind of electric energy meter operating status appraisal procedure and assessment device Download PDFInfo
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Abstract
The invention belongs to electric-power metering and checkup measures fields, and in particular to a kind of electric energy meter operating status appraisal procedure and assessment device.The present invention is unified to electric energy meter indices data first to be pre-processed, deadline sequence data characterization；Then time series data is integrated using separation window setting technique in real time, is based on Random Matrices Theory, various dimensions electric energy meter time series data is calculated in real time, analyzes statistic temporal aspect；Time series data similarity is further calculated using DTW clustering algorithm, thus to random matrix statistic classification；Ultimate analysis cluster result obtains electric energy meter operating status evaluation grade range, completes the assessment of electric energy meter real-time running state.Method of the invention does not depend on score but the similarity between sequence carries out status categories division, has good noise immunity and timeliness, can more accurately divide state interval；Meanwhile space-time characterisation for data has better applicability.
Description
Technical field
The invention belongs to electric-power metering and checkup measures fields, and in particular to a kind of electric energy meter operating status appraisal procedure and
Assess device.
Background technique
With the continuous development of Intelligent power distribution network scale, the continuous expansion of power supply area and the intelligent journey of gauge table
Degree is constantly promoted, and the information acquisition system coverage rate of intelligent meter is gradually increased, and is examined during operation of power networks, monitoring data amount
Exponentially magnitude increases.The operating status of electric energy meter also shows the characteristics of more complicated and burst as a result,.Currently, for electricity
The evaluation and disposition of energy table operating status, Utilities Electric Co. mainly judge the operation shape of electric energy meter by way of on-site test
State.However, electric energy meter operating status is affected in practice examining process by factors such as atmospheric environments, meanwhile, load
Change also brings along large error to the measurement of electric energy meter, or will be unable to obtain the accurate operating status of electric energy meter.In this regard, in order to
Such dynamic problem is effectively solved, the correlation between various factors and electric energy meter operating status need to be researched and analysed, with this
Comprehensive assessment electric energy meter operating status.
As the driving of big data produces, it is applied in all trades and professions based on the analysis method that big data is excavated, engineering
Habit etc. is gradually available for more Disciplinary Frontiers by the analysis method of data-driven.In novel electric network composition, big data is utilized
Technology analyzes electric power network real-time running state, and predicts that its operation trend becomes new research hotspot.
Currently, the multi objective correlation analysis for electric energy meter operating status is broadly divided into two classes: being referred to based on theoretical mechanism
Target evaluation model and the appraisal procedure driven based on big data.For the index model constructed based on theoretical mechanism, analysis
As a result validity depends primarily on the accuracy of the index system of mechanism model.Therefore, more complicated or special for assessing
Operating status, practical versatility is lower.And the intelligent evaluation method based on big data can efficiently solve such problem.
The characteristics of based on electric energy meter time series data, makes full use of the diversity and synchronism of time series data, can manage random matrix
By the novel analysis method as electric power big data, the problems such as to research and solve more complicated timing dependence influence factor.It is existing
There is the algorithm in technology more single, it cannot be from entirety to local comprehensively analyzing and processing data, in addition in counting accuracy, Shandong
It is apparent that stick, reliability and timeliness etc. are insufficient.
Summary of the invention
The technical problem to be solved in the present invention is that overcoming the deficiencies of the prior art and provide a kind of electric energy meter operation shape
State appraisal procedure and assessment device.
The application proposes a kind of electric energy meter operating status appraisal procedure.It pre-processes, completes firstly, unified to electric power big data
Time series data characterization.Then time series data is integrated using separation window setting technique (moving-split window, MSW) in real time.
Secondly, being based on Random Matrices Theory, various dimensions electric energy meter time series data is calculated in real time, analyzes statistic temporal aspect.
Further, for this problem, time series data similarity is calculated using improved DTW clustering algorithm, to count to random matrix
Measure classification.Finally, analysis cluster result, obtains electric energy meter operating status evaluation grade and range, it is real-time to complete electric energy meter
Operating status assessment.It is worth noting that, the application constructs two kinds of stochastic matrix models, it is respectively applied to entire batch electricity
The real-time assessment of energy table operating status and single electric energy meter operating status.Therefore, from entirety to from the comprehensive time series data in part
Reason and analysis can carry out effective assessment in real time, analysis and prediction to electric energy meter operating status.Finally, the application is by proposition
Electric energy meter operating status appraisal procedure be applied to corresponding instance analysis in, and with traditional pivot analysis (principal
Component analysis, PCA) appraisal procedure compares research, and a series of experiments analysis and result demonstrate this method
Robustness, reliability and timeliness provide new approaches for electric power network detection technique application study.Specifically include following step
It is rapid:
A. sampling instant T is collectediThe time series data of electric energy meter acquisition, is completed using big data principle and real-time split window method
The characterization of time series data generates the standard product matrix for respectively indicating whole and single electric energy meter operating status；
B. feature visualization, and the operation to electric energy meter are carried out to above two standard product matrix using stochastic matrix models
State stability is monitored, and calculates linear character statistic, output timing feature evaluation curve；
C. curve classification assessed to the temporal aspect of output using improved DTW clustering algorithm, building from entirety to
The evaluation system of part realizes comprehensive real-time monitoring and assessment to the operating status of electric energy meter, generates network analysis result；
D. according to network analysis as a result, the individually real-time running state with whole electric energy meter is exported, so as to equipment management people
Member carries out respective handling to electric energy meter according to feedback information.
Further, the time series data in the step A include electric energy meter day measurement point electric current, voltage, electric flux, electricity
Power and voltage phase angle.
Further, the step A specifically includes the following steps:
A.1, the space sample of n electric energy meter as a whole is chosen, by the correlation of the collected time series data of electric energy meter energy
Influence factor number of dimensions is denoted as v, under the dimension of a certain Correlative Influence Factors, acquires sampling instant TiData xnIt is constituted
Column vector be
xn(Ti)=[x1,x2,...,xn]T(1)；
Wherein, TiSampling instant section is set according to actual acquisition demand, therefore under the dimension of all Correlative Influence Factors
Complete time series matrixIt is expressed as
A.2, for some electric energy meter, v relative influence dimension is in TiThe data x of sampling instant acquisitionvThe column constituted
Vector is
xv(Ti)=[x1,x2,...,xv]T (3)
Wherein, xn(Ti) and xv(Ti) it is the identical T based on all electric energy metersiSampling instant data, therefore xv(Ti)
Complete time sequence matrix is represented by
A.3, willWithIt is replaced with matrix X；
A.4, time series matrix is generated using real-time split window method, used moving window is w=iw×jw, wherein
W=iw×jw(iw=1,2 ..., N, jw=1,2 ..., N), matrix X is generated into window time sequence matrix Xw, to respectively obtain
WithWindow time sequence matrix be respectivelyWith
A.5, by matrix XwIn element be standardized according to following formula (5), obtain the non-Hermitian of standard
Matrix
Wherein,It is respectivelyAverage and standard deviation；It is respectivelyIt is flat
Mean value and standard deviation, andTherefore it obtainsWithNon- Hermitian be respectivelyWith
A.6, (6) calculate according to the following formulaWithUnusual equivalent matriceWith
Wherein, U is Haar unitary matrice；
A.7, (7) calculating matrix product respectively obtains standard product matrix and is according to the following formulaWith
Wherein, matrix productL represents non-Hermitian matrix number, and variance meets σ2(Z)=1/iw,
WithThe canonical matrix that will be analyzed as stochastic matrix models.
Important tool of the Random Matrices Theory (random matrix theory, RMT) as mathematical statistics, by multiple
The data characteristics of miscellaneous system is for statistical analysis, assesses the stochastic behaviour of data, so that analysis obtains the whole row of real data
It is characterized, to explain the structure and property of complication system.In recent years, Random Matrices Theory is as a forward position research hotspot, In
The various fields such as finance, traffic and communication have carried out being widely applied research.The big number of electric power based on Random Matrices Theory model
Apply in electric power grid system according to analysis method.
Further, the step B specifically includes the following steps:
B.1, described two standard product matrixes are replaced to be with matrix MWith
B.2, setting M is the non-Hermitian matrix of s rank matrix, characteristic value λi(i=1,2 ..., s), for all
It is as follows to define one-dimensional experience spectral distribution function for characteristic value:
Wherein, #Set refers to the number of element in set Set, due to most of eigenvalue λiFor plural number, therefore, definition
The two-dimensional empirical spectral distribution function of matrix M are as follows:
B.3, matrix M={ m is setijIt is the non-Hermitian random matrix of s rank, and all elements in matrix meet independently
With distribution, it is expected that E (mij)=0, variance E (| mij|2)=1 obtains standard according to formula (7) for L non-Hermitian matrixes
Product matrix works as i, when j tends to infinity, and when ranks ratio p=i/j (p ∈ (0,1]) keeps constant constant, standard product matrix's
Experience spectral distribution function obeys the uniform convergence of monocycle law, and probability density function f can be indicated are as follows:
B.4, for meeting the non-Hermitian random matrix M={ m of independent identically distributed s rankij, it is expected that E (mij)=0,
Variance E (| mij|2)=1, according to the covariance matrix S of formula (11) calculating matrix M,
Work as i, when j tends to infinity, and when ranks ratio p=i/j (p ∈ (0,1]) keeps constant constant, covariance matrix S
Meet M-P law, i.e. the ESD of matrix S converges on the probability density function f of formula (12)M-P:
Wherein,WithThe respectively supremum and infimum of covariance matrix S；
B.5, average spectral radius system MSR metering is introduced to be analyzed:
The definition of MSR be random matrix M all characteristic values on a complex plane, the average distance of each point to central point,
Definition is as follows:
Wherein, RMSRIt is averaged spectral radius for all characteristic values, calculate point based on average spectral radius clock synchronization sequence characterization matrix
Analysis, output timing feature evaluation curve.
It is a kind of basic problem in Clustering in Data Mining, while is also one of machine learning important algorithm.Poly-
In class problem, for sample data difference and propose a variety of clustering algorithms.Time series data is gathered around compared to static data
There are higher dimension and structure complexity, while to guarantee to cluster the integrality of time series, there is bigger difficulty in cluster.When
Between the target that clusters of sequence data be each sequence in time series data set to be divided into different subsets, and require same
Sequence similarity in one subset is larger, differing greatly between subset.Dynamic time warping algorithm (dynamic time
Warping, DTW) it is proposed earliest by Japanese scholars Itakura as processing Time Series Clustering matching problem, it is that one kind is based on
The algorithm of Dynamic Programming Idea be used to solve the problems, such as the Speed variation in speech recognition earliest.J.Bemdt and lifford exist
It is applied within 1994 the analysis of time series for the first time.On the basis of DTW algorithm, for different problems there has been proposed
A variety of innovatory algorithms.Pentitjean etc. is based on this and proposes DTW center of gravity average algorithm, when acquiring the center of time series set
Between sequence.Yao Peiyang etc. combine DTW algorithm and hidden Markov model carry out aerial target flight path carry out sequential extraction procedures and
Sequence.Tang Min etc. proposes the Sequence clustering based on DTW and part close to figure and designs, and can accurately determine the number of clusters of cluster, and
Mark off core point and marginal point.
For the temporal aspect for the average spectral radius (MSR) that random matrix is obtained, further progress is needed to analyze.This Shen
Please MSR data divide electric energy meter operating status by way of cluster, and exports different evaluation status grades and draws
Divide range.
Further, the step C specifically includes the following steps:
C.1, assume there is two time series datas S and O, length is respectively i and j, is indicated are as follows:
Two different time sequential element si, OjThe distance between measurement δ have the following two kinds definition mode:
Wherein, δ is the distance between two time series elements measurement；
C.2, using a time series as reference template, each point each point into another time series in reference template is calculated
The shortest distance, most short point may make up regular path W to set:
W=w1,w2,…,wk (16)
Wherein, wkFor the shortest distance point pair of each point pair in sequence, it is iterated based on this and can be calculated two time sequences
The similarity DTW (S, T) of column:
C.3, further spread out to obtain:
C.4, penalty coefficient α is acquired in conjunction with the DTW algorithm of penalty coefficient and be multiplied with the result of former algorithm, after obtaining update
Sequence between distance:
Wherein, Num (W) is apart from shortest point to number, comLeniLength of the point between when expression i=j；
C.5, the similarity using binary traversal algorithm according to distance between sequence carries out different grades of classification.
Further, the step C.5 in classification include good, normal, early warning and abnormal four kinds of grades.
The present invention also provides a kind of electric energy meter operating statuses to assess device, utilizes above-mentioned appraisal procedure, comprising:
Data acquisition unit, for acquiring sampling instant TiThe time series data of electric energy meter；
Data processing unit, the time series data for acquiring to the data acquisition unit are handled, thus building from
The whole evaluation system to part realizes comprehensive real-time monitoring and assessment to the operating status of electric energy meter, generates network analysis
As a result.
Further, further includes:
Data display unit, for according to network analysis as a result, output individually and the real-time running state of whole electric energy meter,
So that equipment management personnel carries out respective handling to electric energy meter according to feedback information.
Further, the time series data includes quantity, current phase angle, voltage phase angle, the electrical power, electricity of electric energy meter
Pressure, electric current, electric flux, frequency fluctuation value, power factor (PF), power direction, load current and sampling instant.
The beneficial effects of the present invention are:
1, the present invention is unified to electric energy meter indices data first pre-processes, deadline sequence data characterization；Then
Time series data is integrated using real-time separation window setting technique, is based on Random Matrices Theory, it is real to various dimensions electric energy meter time series data
When calculate, analysis statistic temporal aspect；Time series data similarity is further calculated using DTW clustering algorithm, thus to random
Matrix statistic classification；Finally, analysis cluster result, obtains electric energy meter operating status evaluation grade range, electric energy is completed
The assessment of table real-time running state.This method has good robustness, reliability and timeliness；
2, the present invention constructs two kinds of stochastic matrix models, is respectively applied to entire batch electric energy meter operating status and list
The real-time assessment of a electric energy meter operating status, therefore, from entirety to part comprehensively to electric energy meter carry out time series data processing and
Analysis can carry out effective assessment in real time, analysis and prediction to electric energy meter operating status, have to the space-time characterisation of data good
Applicability.
Detailed description of the invention
It, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical solution in the prior art
Embodiment or attached drawing needed to be used in the description of the prior art be briefly described, it should be apparent that, it is described below
Attached drawing is some embodiments of the present invention, for those of ordinary skill in the art, before not making the creative labor
It puts, is also possible to obtain other drawings based on these drawings.
Fig. 1 is a kind of flow diagram of electric energy meter operating status appraisal procedure of the present invention；
Fig. 2 is the flow diagram of time series data processing method in Fig. 1；
Fig. 3 is the time series data matrix structure schematic diagram of a certain index of whole electric energy meter in Fig. 2；
Fig. 4 is the time series matrix structure schematic diagram of single electric energy meter indices in Fig. 2；
Fig. 5 is characterized spectral distribution graph of the value under stable state and unsteady state；
Fig. 6 is the basic effect figure of M-P law under different load；
Fig. 7 is time series data linear character statistics distribution figure；
Fig. 8 is DTW clustering algorithm schematic diagram；
Fig. 9 is time series data monocycle law distribution map described in specific embodiment；
Figure 10 is time series data M-P law distribution map described in specific embodiment；
Figure 11 is timing linear character statistics distribution figure described in specific embodiment；
Figure 12 is DTW Clustering Effect figure described in specific embodiment；
Figure 13 is the big logotype of distance of time series data described in specific embodiment；
Figure 14 is single electric energy meter timing LES schematic diagram described in specific embodiment.
Specific embodiment
It is described in detail below in conjunction with embodiment of the attached drawing to technical solution of the present invention.Following embodiment is only used for
Clearly illustrate technical solution of the present invention, therefore be only used as example, and cannot be used as a limitation and limit protection model of the invention
It encloses.
It should be noted that unless otherwise indicated, technical term or scientific term used in this application should be this hair
The ordinary meaning that bright one of ordinary skill in the art are understood.
In the description of the present application, it is to be understood that term " center ", " longitudinal direction ", " transverse direction ", " length ", " width ",
" thickness ", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom" "inner", "outside", " up time
The orientation or positional relationship of the instructions such as needle ", " counterclockwise ", " axial direction ", " radial direction ", " circumferential direction " be orientation based on the figure or
Positional relationship is merely for convenience of description of the present invention and simplification of the description, rather than the device or element of indication or suggestion meaning must
There must be specific orientation, be constructed and operated in a specific orientation, therefore be not considered as limiting the invention.
In this application unless specifically defined or limited otherwise, term " installation ", " connected ", " connection ", " fixation " etc.
Term shall be understood in a broad sense, for example, it may be being fixedly connected, may be a detachable connection, or integral；It can be mechanical connect
It connects, is also possible to be electrically connected；It can be directly connected, can also can be in two elements indirectly connected through an intermediary
The interaction relationship of the connection in portion or two elements.It for the ordinary skill in the art, can be according to specific feelings
Condition understands the concrete meaning of above-mentioned term in the present invention.
The application proposes a kind of electric energy meter operating status appraisal procedure.Specifically includes the following steps: as shown in Figure 1,
1) firstly, electric energy meter can acquire numerous dimension time series datas as an intelligent metering instrument simultaneously.To n electricity
, it is specified that the particular sample moment is Ti (i=1,2 ..., N), (this can be manually set in sampling interval for v timing variable analysis of energy table
System is directed to 24,48 and 96 point datas of day sampling and is analyzed).Therefore, window (is separated based on big data principle and technology in real time
Technology), multi-dimensional data is handled in real time, completes the time series data characterization of electric energy meter.Wherein generate two kinds of standard product matrixes and,
Respectively as the canonical product random matrix of whole and single electric energy meter operating status.
2) secondly, Random Matrices Theory is based on, to two kinds of canonical product matrix analyses.After data normalization, random square is analyzed
The monocycle law of battle array, M-P law characteristic complete electric energy meter time series data feature visualization, compare inner ring radius, can be to electric energy meter
Operating status stability is monitored.Then, linear character statistic (MSR) is calculated, output timing feature evaluation curve.Wherein
The feature of analysis is calculated, main includes changing character of the whole n electric energy meter in v index, and the v of single electric energy meter
The timing of a index becomes feature.
3) then, to the whole timing curve signature analysis of multiple dimensions of output, i.e., system is according to the MSR timing of matrix
Variation, using improved DTW clustering algorithm classification, with this operating status assessment, prediction to n ammeter.Further,
To the timing curve feature of v index of single electric energy meter, i.e. the MSR timing variations of matrix, clustering is carried out, is constructed from entirety
To the evaluation system of part, comprehensive real-time monitoring and assessment are realized to the operating status of electric energy meter.Generally speaking, for the square
The changing character curve of battle array, the result that the electric energy meter of different conditions obtains have larger difference, are in particular in the whole of curve
Body trend: electric energy meter good for operation conditions, slope approximation is constant after the smoothed processing of indicatrix；And operation conditions
The slope decline after smooth of the indicatrix of poorer electric energy meter is bigger.Therefore for the timing of the electric energy meter of different operating statuses
Curve distance is obvious, different conditions can be clustered very well using DTW clustering algorithm is improved.It is based further on exemplary power
Timing curve feature carries out threshold value setting to curve distance, so that cluster result be made to represent different status assessments.Further,
Timing curve feature and template sequence for the indices state of single ammeter carry out Cluster Evaluation, with this to single electric energy
The multinomial measurement index of table carries out real-time monitoring and assessment.For big data quantity, Moving split-window technique is equally applied to by the application
It improves in DTW method, is influenced with solving the high bring computation rate of time complexity.
4) finally, according to network analysis as a result, the real-time running state of the electric energy meter of each and whole batch of output.This
When, according to feedback information, equipment management personnel needs that electric energy meter is overhauled or replaced in time.
The processing of 1 electric energy meter time series data
Big data principle is the Mathematical original for being calculated, being analyzed and being handled for all data involved in problem
Reason, wherein big data has the characteristics of 5V (Volume, Velocity, Variety, Value, Veracity).With data-driven
For the big data principle of core concept, substantially it is a kind of methodology in quantitative statistics, signature analysis and behaviour decision making and recognizes
The combination for knowing opinion, is the Cognitive Explanation to data subject mechanism and trend.The key effect of big data technology is magnanimity
Grade data information specialization and visual analyzing and processing, must rely on distributed treatment, the distributed data of cloud computing
Library and cloud storage, virtualization technology.As the driving of big data develops, the application study based on big data principle and technology is each
Field plays wide application.In recent years, as the magnitude of electric power big data increases, the power train based on magnanimity electric power big data
The analysis method for operation situation of uniting provides brand-new thinking for power grid application research.
Specifically: as shown in Fig. 2,
Firstly, the time series data for electric energy meter operating status total evaluation is handled, n electric energy meter node is chosen, as
Whole space sample.Meanwhile by electric energy meter can collected time series data Correlative Influence Factors (timing indicator) number of dimensions
Amount is denoted as v.Therefore, under the dimension of a certain Correlative Influence Factors, n electric energy meter node is in TiThe data x of sampling instant acquisitionn
The column vector constituted are as follows:
xn(Ti)=[x1,x2,...,xn]T (1)
Wherein, TiSampling instant section is set according to actual acquisition demand.Therefore complete time series matrix Xnv × T
It may be expressed as:
Secondly, handling the time series data of single electric energy meter operating status.Equally, to n electric energy meter node and v
The T of a Correlative Influence Factors dimensioniSampling instant characterizes time series data.Different from the data processing method of total evaluation, to list
The assessment of a electric energy meter operating status, the variable for needing to control are n.Therefore, for some ammeter, v relative influence dimension
In TiThe data x of sampling instant acquisitionvThe column vector constituted are as follows:
xv(Ti)=[x1,x2,...,xv]T (3)
Wherein, xn(Ti) and xv(Ti) it is the identical T based on all electric energy metersiSampling instant data.Therefore xv(Ti)
Complete time sequence matrix may be expressed as:
Above two data matrix generation form is as shown in Figure 3-4, wherein Fig. 3 indicates a certain index of whole electric energy meter
Time series data matrix structure schematic diagram；Fig. 4 indicates the time series matrix structure schematic diagram of single electric energy meter indices.
For above-mentioned two time series matrix, subsequent normalization method is identical.Here, uniformly will in calculating stepWithIt is replaced with matrix X.Using real-time separation window setting technique w=i is used for time sequence matrixw×jwThe movement of size
Window generates window time sequence matrix Xw, wherein w=iw×jw(iw=1,2 ..., N, jw=1,2 ..., N), to respectively obtainWithWindow time sequence matrix be respectivelyWithThis method is by historical data and real-time data synchronization meter
It calculates, can effectively characterize time series data.Next according to formula (5), by matrix XwIt is converted into the non-Hermitian matrix of standard
Wherein,It is respectivelyAverage and standard deviation；It is respectivelyIt is flat
Mean value and standard deviation, andTherefore it obtainsWithNon- Hermitian be respectivelyWithThen, it is calculated according to formula (6)WithUnusual equivalent matriceWith
Wherein, U is Haar unitary matrice.
Finally, respectively obtaining standard product matrix according to formula (7) calculating matrix product and beingWith
Wherein, matrix productL represents non-Hermitian matrix number, and variance meets σ2(Z)=1/iw。
WithThe canonical matrix that will be analyzed as stochastic matrix models.Above procedure, completely present for electric energy meter data when
Sequence data characterization carries out data preparation for stochastic matrix models building.
Electric energy meter itself includes static data information and dynamic acquisition data information as a power system device,
Middle electric energy meter static data information mainly includes the attribute numbers such as model, production batch, production firm, power supply unit and installation month
According to, electric energy meter institute can collected time series data information include day measurement point electric current, voltage, electric flux, electrical power and voltage phase
The sequential combinations data such as parallactic angle.The application carries out assessment tool to electric energy meter operating status effectively in real time to time series data analysis
There are more superior timeliness and reliability.Meanwhile total evaluation and individual assessment can be divided into the assessment of electric energy meter operating status, i.e.,
From entirety to the comprehensive assessment system of individual.
The building of 2 stochastic matrix models
In mathematical statistics principle, random matrix refers to the matrix containing multiple stochastic variables, is widely used in data system
In meter analysis.Nineteen fifty-one, Random Matrices Theory (random matrix theory, RMT) are that physicist Wigner is being carried out
It proposes when spectrum interpretations and develops.For more complicated quantum physics system, the signature analysis of Random Matrices Theory is predicted
The trend of whole system, reflects being averaged for the interaction of actual capabilities.By the development of over half a century, random matrix
Theory has been used for solving the great number of issues of scientific research and engineering practice.Wherein, the Random Matrices Theory based on big data is in recent years
As new research hotspot.
Difference of the random matrix according to dimension size, can be divided into infinite dimension random matrix and finite dimension random matrix.Wherein,
The theory analysis of wireless dimension random matrix has been pushed to the practical application of finite dimension, the i.e. dimension of matrix by finite dimension Random Matrices Theory
Degree is no longer limited by the infinite dimension requirement of finite dimension Random Matrices Theory.Therefore, when the ranks dimension ratio of random matrix is kept
When constant, the timing linear character of random matrix, such as experience spectral distribution function (empirical spectral
Distribution, ESD) meet a variety of statistics theorems, including semicircle rate (semi-circle law), M-P law
(Marchenko-Pastur law), monocycle law (single ring theorem) etc..Therefore, based on first part when
Sequence data characterization, further constructs stochastic matrix models, based on the specificity analysis of the above law, calculates ASSOCIATE STATISTICS amount.Finally,
Analysis random matrix temporal characteristics in real time, to obtain the statistics measure feature of data.In terms of real data treatment effect, it is based on
The analysis method of Random Matrices Theory has good robustness, can be adaptive to phenomena such as bad data or loss of data, exception
It should stablize.
2.1 experience spectral distribution functions
Because the method for subsequent processing for standard product matrix is identical, therefore is by above-mentioned two standard product matrix herein
WithIt is replaced with matrix M, if M is the non-Hermitian matrix of s rank matrix, characteristic value λi(i=1,2 ..., s).For
All characteristic values, it is as follows that we define one-dimensional experience spectral distribution function:
Wherein, #Set refers to the number of element in set Set.Due to most of eigenvalue λiFor plural number, therefore, definition
The two-dimensional empirical spectral distribution function of matrix M are as follows:
The theoretical goal in research of random matrix is to study given random matrix M its experience spectral distribution function FM(x,
Y) convergence.And the limit Spectral structure (limit spectrum distribution, LSD) of matrix has stochastic behaviour.
2.2 monocycle laws
Assuming that matrix M={ mijIt is the non-Hermitian random matrix (non-square matrix) of s rank, and all elements in matrix are full
Sufficient independent same distribution (independent identically distributed, IID), it is expected that E (mij)=0, variance E (| mij
|2)=1.For L non-Hermitian matrixes, standard product matrix is obtained according to formula (7).Work as i, when j tends to infinity, and ranks
When keeping constant constant than p=i/j (p ∈ (0,1]), standard product matrixExperience spectral distribution function obey monocycle law one
Convergence is caused, probability density function (probability density function, PDF) f can be indicated are as follows:
According to monocycle law it is found that eigenvalue λiBeing distributed in outer ring and the radius that radius is 1 on a complex plane is (1-p)L/2
Between inner ring, effect diagram is as shown in Figure 5.Characteristic value distribution situation is made a concrete analysis of it is found that being special as shown in 5 (a)
The spatial distribution of value indicative at steady state；As shown in 5 (b), it is characterized spatial distribution of the value under unsteady state, when being
System receives signal, and when load increases and plays pendulum, partial feature value will be distributed in inner ring (Inner
Circle), i.e. the system instability status that certain probability will occurs.When most of characteristic value is in inner ring, system will meet
Face the risk of collapse.
2.3Marchenko-Pastur law
Similarly, for meeting the non-Hermitian random matrix M={ m of independent identically distributed s rankij, it is expected that E (mij)=
0, variance E (| mij|2)=1.According to the covariance matrix S of formula (11) calculating matrix M.
Work as i, when j tends to infinity, and when ranks ratio p=i/j (p ∈ (0,1]) keeps constant constant, covariance matrix S
Meet M-P law, i.e. the ESD of matrix S converges on the probability density function f of formula 12M-P:
Wherein,WithThe respectively supremum and infimum of covariance matrix S.M-P is fixed
The basic effect figure of rule is as shown in Figure 6.
The probability density function curve of characteristic value is calculated, when not having additional effect load, association is variance matrix
Characteristic value meets the distribution of M-P law；When load increases, system is in unsteady state, and it is fixed that feature Distribution value no longer meets M-P
Rule.
2.4 timing linear character statistics
According to monocycle law with M-P law it is found that EDS when system is in different conditions is different, and for temporal aspect
Reflection need further to select the statistic of more real-time.Therefore, for electric energy meter time series data, linear character Data-Statistics
The more effective statistical indicator of random matrix M will be used as by measuring (linear eigenvalue statistic, LES), to a greater extent
Reflect its statistical law.It is analyzed in this regard, introducing average spectral radius (mean spectral radius, MSR) statistic.
The definition of MSR be random matrix M all characteristic values on a complex plane, the average distance of each point to central point,
Definition is as follows:
Wherein, RMSRIt is averaged spectral radius for all characteristic values.As shown in fig. 7, the timing curve of average spectral radius known to analysis
Effectively analysis system overall development trend.Calculating analysis is carried out based on average spectral radius clock synchronization sequence characterization matrix, is obtained average
Spectral radius timing variations curve further needs to divide state range to timing curve clustering.
3 carry out classification using improved DWT clustering algorithm
Dynamic time warping (DTW) clustering algorithm is a kind of typical recursive optimization algorithm, by will in time series it is each
Point, which corresponds, solves distance to compress or stretch data segment, excludes the interference of time irreversibility, thus two time sequences of matching
The similarity of column.
Assuming that having two time series datas S and O, length is respectively i and j, is indicated are as follows:
Two different time sequential element si, OjThe distance between measurement δ have the following two kinds definition mode:
Wherein, δ is the distance between two time series elements measurement.After defining distance, Time Series Clustering problem is just
The matching that distance is nearest between iteration time arrangement set midpoint pair can be converted into, i.e., using a sequence as reference template, meter
The shortest distance of each point each point into another sequence in reference template is calculated, most short point may make up regular path W to set:
W=w1,w2,…,wk (16)
Wherein, wkFor the shortest distance point pair of each point pair in sequence.It is iterated based on this and can be calculated two time sequences
The similarity DTW (S, T) of column:
It further spreads out to obtain:
When occurring and when template sequence monotonicity similar sequences, the same sequence distance of out of phase can be generated interference from
And influence the Clustering Effect of DTW algorithm.Optimum path planning principle is referred to based on this problem the application, using in conjunction with penalty coefficient
DTW algorithm.It acquires penalty coefficient α and is multiplied with the result of former algorithm, obtain distance between updated sequence.
Wherein, Num (W) is apart from shortest point to number, comLeniLength of the point between when expression i=j.If α
Smaller, point when this illustrates i=j is more to quantity, then two sequences tend to the same data sequence of out of phase, the two
Distance is also closer, reduces the interference of monotonicity similar sequences.It is directed to the high complexity of ammeter big data bring simultaneously, in conjunction with
Fast-DTW algorithm maintenance data presses the retraction one-step optimization cluster speed of service.For its meter of the normal electric energy meter of working condition
It is smaller to calculate linear character statistic (MSR) fluctuation, therefore works as MSR time series data SMSRIn meet SMSR[j]-SMSR[j-1] < ε, can
It is approximately considered SMSR[j]=SMSR[j-1] and by SMSR[j] and SMSR[j-1] point merges with compression time sequence length, accelerometer
It calculates.Overall improvement DTW algorithm effect schematic diagram is as shown in Figure 8.
The application is based on Random Matrices Theory and clustering algorithm, analyzes time series data feature, assesses electric energy meter real time execution
State.For electric energy meter operating status, analysis time series data has more timeliness.Compared to analysis associated static relative influence because
Element, the signature analysis of time series data can reflect the real-time running state of electric energy meter, while make trend to electric energy meter operating status
Prediction.
The present invention also provides a kind of electric energy meter operating statuses to assess device, utilizes above-mentioned appraisal procedure, comprising:
Data acquisition unit, for acquiring sampling instant TiThe time series data of electric energy meter；
Data processing unit, the time series data for acquiring to the data acquisition unit are handled, thus building from
The whole evaluation system to part realizes comprehensive real-time monitoring and assessment to the operating status of electric energy meter, generates network analysis
As a result.
Further, further includes:
Data display unit, for according to network analysis as a result, output individually and the real-time running state of whole electric energy meter,
So that equipment management personnel carries out respective handling to electric energy meter according to feedback information.
Further, the time series data includes quantity, current phase angle, voltage phase angle, the electrical power, electricity of electric energy meter
Pressure, electric current, electric flux, frequency fluctuation value, power factor (PF), power direction, load current and sampling instant.
Embodiment
Selection is analyzed with a batch of 100 electric energy meters (n=100), and index for selection is respectively current phase angle, electricity
Press phase angle, electrical power, voltage, electric current, electric flux, frequency fluctuation, 10 power factor (PF), power direction and load current indexs
(v=10), wherein data include normal electric energy meter measurement data and abnormal electric energy meter data.This instance analysis, which is directed to, adopts day
96 point data of sample is analyzed (Ti=15min), in order to embody the timing feature of data characteristics and make full use of history number
According to feature, the time ordered interval that moving distance is less than moving window is set, i.e. window w size is 100 × 4, and moving distance 2 is moved
Dynamic number 1000 times.Appraisal procedure based on proposition successively carries out monocycle law, M-P Thermodynamics Law Analysts to time series data, and
LES calculating, cluster and state analysis.
For the running state analysis of whole electric energy meter, the monocycle law feature of time series data is calculated using python 3.7
It is worth Spectral structure and the distribution of M-P law, analysis two of them the index (voltage and electrical power) of exceptional value occur in T50Moment and
T500The monocycle law and M-P law distribution situation at moment, the p of two of them law keep constant (p=4/100=0.04 <
1), distribution map is as shown in Figure 9 and Figure 10.
According to the feature Distribution value of monocycle law it is found that in T50The voltage data and electrical power data of moment electric energy meter measurement
Monolithic stability, individual electric energy meters at the beginning between there is the abnormal situation of measurement；When in T500Moment, voltage and electrical power are surveyed
There are 4 exceptional values in amount data, that is, multiple electric energy meters occur and be operating abnormally situation.Further, T is analyzed50Moment and T500Moment
M-P law electric energy meter, analyze time series data characteristic value distribution situation.
Similarly, T is analyzed using calculating50Moment and T500The characteristic value probability density distribution of the covariance matrix at moment, point
Analyse the M-P law distribution situation of time series data.Comparison discovery, in T50Moment covariance matrix characteristic value meets M-P law probability
Density Distribution, i.e. overall operation are normal；In T500There is certain deviation, i.e. part electric energy compared with M-P Ddensity in moment
Table measures voltage and exception occurs in the operating status of electrical power.
Monocycle law and M-P law tentatively assess electric energy meter overall operation state, during real-time monitoring, can show
Show T0Moment is to T1000The situation of change at moment makes assessment to overall operation state in real time.Further, calculate analysis LES when
Sequence characteristics position the disconnected abnormal electric energy meter of disease with this, and the LES result figure of this example is as shown in figure 11.
It observes 100 electric energy meters (n=100) timing linear character statistic under voltage and electrical power index and changes feelings
Condition, whole MSR steady change, and trend is identical, part electric energy meter MSR occurs deviateing whole situation.It follows that electric energy meter
Generally in steady operational status, while also there are multiple electric energy meters and measuring abnormal situation during timing.Compare monocycle
Law and M-P Thermodynamics Law Analysts also demonstrate the variation of electric energy meter operating status, complete qualitative analysis.Further, quantitative analysis,
Fast-DTW clustering is carried out based on electric energy meter temporal aspect statistic, interval range, assessment are demarcated to time series data
State.
Time window W is clustered by settingtSize is 500, step-length 500, by 100 ammeters from T0Moment is to T1000Moment
MSR history time series data SMSR1,SMSR2,...,SMSR100Input improves DTW algorithm, clusters to it and uses binary tree according to distance
Similarity carries out arrangement output[i], it can be achieved that requiring to carry out data into cluster output according to different brackets and realize to ammeter state
Grading divide.Use well, normally, early warning and abnormal four kinds of level models in the application.With voltage indexes number on the left of Figure 12
For, the binary tree form of classification results in the MSR time series data of 100 ammeters of the second layer as shown, be wherein divided into
Four classes, enlarged drawing as shown among Figure 12, chosen from every class one calculate its with the template data of good ammeter away from
From DTW (S, O)n(n ∈ [1,2,3,4]), smaller then such electric energy table status of DTW (S, O) are better.When ordinal number on the right side of Figure 12
According to apart from size are as follows: DTW (S, O)2>DTW(S,O)1>DTW(S,O)3>DTW(S,O)4, enlarged drawing is as shown in figure 13, can obtain
It is 2. out good class, is 1. normal class, 3. for early warning class and be 4. exception class.
The obtained electric energy meter state classification of cluster result is as shown in table 1, and improper serial number is respectively 25,37,69 Hes
82, complete the evaluation grade division to ammeter state.The real inspection of comparison is as a result, improper electric energy meter serial number and above-mentioned analysis result
It is identical.Electrical power index can be analyzed similarly.Multiple groups index compares orientable specific abnormal electric energy meter and specifically refers to extremely
Mark.Finally, further analyzing the timing linear character statistic variation of all indexs of single electric energy meter.
1 DTW of table clusters range and distribution
According to cluster analysis result, abnormal electric energy meter is analyzed for No. 69.As shown in figure 14,10 of No. 69 electric energy meters
Occurs the average spectral radius abnormal phenomenon of many index in index, in conjunction with monocycle law and the distribution of M-P law it is found that the electric energy meter
Voltage, electrical power and electric energy measurement it is abnormal.With this, complete to assess the operating status of single ammeter.
Above example analysis and research are distributed according to monocycle law and M-P law, are done on the whole to electric energy meter operating status
Pre- judgement.It is special using timing of the fast-DTW clustering algorithm to electric energy meter according to the timing variations of linear character statistic (MSR)
Point is analyzed, and is assessed the timing operating status of whole electric energy meter.Meanwhile LES is carried out to the abnormal electric energy meter of diagnosis
Time-Series analysis shows specific abnormal measurement index.With this, complete to electric energy meter from entirety to the comprehensive assessment of individual
And prediction.
A kind of validity of electric energy meter operating status appraisal procedure of examples detailed above result verification.This section will compare pivot point
Analysis method (principal component analysis, PCA), verifies the superiority and timeliness of the application method.PCA essence
On be a kind of dimensionality reduction reduced chemical reaction kinetics model based on data, be the algorithms most in use of current processing multidimensional index Data Analysis Services.
Therefore, it is similarly based on examples detailed above, assessment marking is carried out to electric energy meter operating status using principle component analysis.For electric energy meter
It is as shown in table 2 need to construct PCA pivot index weight value system for the assessment of operating status:
2 PCA index weights of table
As shown in table 2, in order to control variable, PCA time series data is assessed into the appraisal procedure that dimension and the application are proposed
Unanimously, i.e., 10 timing indicators are identical.Meanwhile it is electric energy meter registration is abnormal also as one of evaluation index.This comparative study
The electric energy meter of test assessment is carried out, is 100 electric energy meters of instance analysis.With PCA to 100 electric energy meters in experiment into
Row weighting marking, Score Lists are as shown in table 3:
3 PCA score of table
The experimental result based on this research is compared, due to there are not electric energy meter registration abnormal conditions, PCA assesses the model of score
It is not high to enclose accuracy.It is convenient that the advantage of PCA is to evaluate, and calculation amount is smaller, and disadvantage is that timeliness is low, can not be sufficiently sharp
It is assessed, only current state is assessed, while may be unexpected by electric energy meter follow-up operation state with history time series data, furthermore
PCA relies on electric energy meter measured data to obtain grade marking, and the presence of noise then has very big shadow to the PCA algorithm based on marking
It rings.And the assessment of actual power table operating status needs better timing assessment, the application clustering algorithm does not depend on score but sequence
Similarity between column carries out status categories division, has good noise immunity and timeliness, can more accurately divide state
Section.Meanwhile space-time characterisation for data has better applicability.
The application devises a kind of electric energy meter operating status assessment side based on Random Matrices Theory and DTW clustering algorithm
Method.It is pre-processed firstly, unified to electric energy meter indices data, deadline sequence data characterization.Then using separation in real time
Window setting technique integrates time series data, is based on Random Matrices Theory, is calculated in real time various dimensions electric energy meter time series data, analyzes system
Measure temporal aspect.Further, time series data similarity is calculated using DTW clustering algorithm, thus poly- to random matrix statistic
Class classification.Finally, analysis cluster result, obtains electric energy meter operating status evaluation grade range, electric energy meter real time execution shape is completed
State assessment.
The innovative point of the application is that constructing two kinds of stochastic matrix models is respectively applied to transport entire batch electric energy meter
Row state and single electric energy meter operating status are assessed, therefore, can be right from entirety to the comprehensive data processing in part and analysis
Electric energy meter operating status carries out effective assessment in real time, analysis, has good applicability to the space-time characterisation of data.In example point
It is proposed method and traditional PCA are judged that appraisal procedure compares research in analysis.The experimental results showed that the application method can be effective
Divide electric energy meter operating status and grade.
Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent
Pipe present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: its according to
So be possible to modify the technical solutions described in the foregoing embodiments, or to some or all of the technical features into
Row equivalent replacement；And these are modified or replaceed, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution
The range of scheme.
Claims (9)
1. a kind of electric energy meter operating status appraisal procedure, it is characterised in that: specifically includes the following steps:
A. sampling instant T is collectediThe time series data of electric energy meter acquisition, completes the characterization of time series data, generation respectively indicate it is whole and
The standard product matrix of single electric energy meter operating status；
B. feature visualization is carried out to above two standard product matrix, and the operating status stability of electric energy meter is monitored,
Calculate linear character statistic, output timing feature evaluation curve；
C. curve classification is assessed to the temporal aspect of output, the evaluation system from entirety to part is constructed, to electric energy meter
Operating status realizes comprehensive real-time monitoring and assessment, generates network analysis result.
2. a kind of electric energy meter operating status appraisal procedure according to claim 1, it is characterised in that: further include following step
It is rapid:
D. according to network analysis as a result, the individually real-time running state with whole electric energy meter is exported, so as to equipment management personnel root
Respective handling is carried out to electric energy meter according to feedback information.
3. a kind of electric energy meter operating status appraisal procedure according to claim 2, it is characterised in that: the step A is specific
The following steps are included:
A.1, the space sample of n electric energy meter as a whole is chosen, by the relative influence of the collected time series data of electric energy meter energy
Factorial dimensions quantity is denoted as v, sets sampling instant as Ti, n electric energy meter is in TiThe data x of sampling instant acquisitionnThe column constituted
Vector is xn(Ti), under the dimension of Correlative Influence Factors, complete time series matrixIt is expressed as
A.2, for some electric energy meter, v relative influence dimension is in TiThe data x of sampling instant acquisitionvThe column vector constituted
For xv(Ti), the time series matrix of single electric energy meter is expressed as
A.3, willWithIt is replaced with matrix X；
A.4, time series matrix is generated using real-time split window method, used moving window is w=iw×jw, wherein w=
iw×jw(iw=1,2 ..., N, jw=1,2 ..., N), matrix X is generated into window time sequence matrix Xw；
A.5, by matrix XwIn element be standardized according to following formula (3), obtain the non-Hermitian matrix of standard
Wherein,It is respectivelyAverage and standard deviation；It is respectivelyAverage value and
Standard deviation, and
A.6, (4) calculate according to the following formulaUnusual equivalent matrice
Wherein, U is Haar unitary matrice；
A.7, (5) calculating matrix product obtains standard product matrix and is according to the following formula
Wherein, matrix productL represents non-Hermitian matrix number, and variance meets σ2(Z)=1/iw,By conduct
The canonical matrix of stochastic matrix models analysis.
4. a kind of electric energy meter operating status appraisal procedure according to claim 3, it is characterised in that: the step B is specific
The following steps are included:
B.1, the standard product matrix is replaced to be with matrix M
B.2, setting M is the non-Hermitian matrix of s rank matrix, characteristic value λi(i=1,2 ..., s), for all features
Value, it is as follows to define one-dimensional experience spectral distribution function:
Wherein, #Set refers to the number of element in set Set, defines the two-dimensional empirical spectral distribution function of matrix M are as follows:
B.3, matrix M={ m is setijIt is the non-Hermitian random matrix of s rank, and all elements in matrix meet independent same point
Cloth, it is expected that E (mij)=0, variance E (| mij|2)=1 obtains standard product moment according to formula (7) for L non-Hermitian matrixes
Battle array, works as i, when j tends to infinity, and when ranks ratio p=i/j (p ∈ (0,1]) keeps constant constant, standard product matrixExperience
Spectral distribution function obeys the uniform convergence of monocycle law, and probability density function f is indicated are as follows:
B.4, for meeting the non-Hermitian random matrix M={ m of independent identically distributed s rankij, it is expected that E (mij)=0, variance E
(|mij|2)=1, according to the covariance matrix S of formula (9) calculating matrix M,
Work as i, when j tends to infinity, and when ranks ratio p=i/j (p ∈ (0,1]) keeps constant constant, covariance matrix S meets
M-P law, the i.e. ESD of matrix S converge on the probability density function f of formula (10)M-P:
Wherein,WithThe respectively supremum and infimum of covariance matrix S；
B.5, average spectral radius system MSR metering is introduced to be analyzed:
The definition of MSR be random matrix M all characteristic values on a complex plane, the average distance of each point to central point, define
Formula is as follows:
Wherein, RMSRIt is averaged spectral radius for all characteristic values, calculating analysis is carried out based on average spectral radius clock synchronization sequence characterization matrix,
Output timing feature evaluation curve.
5. a kind of electric energy meter operating status appraisal procedure according to claim 4, it is characterised in that: the step C is specific
The following steps are included:
C.1, assume there is two time series datas S and O, length is respectively i and j, is indicated are as follows:
Two different time sequential element Si, OjThe distance between measurement δ have the following two kinds definition mode:
Wherein, δ is the distance between two time series elements measurement；
C.2, the regular path W that building shortest distance point constitutes set:
W=w1,w2,…,wk (14)
Wherein, wkFor the shortest distance point pair of each point pair in two time serieses；
C.3, it is iterated to obtain the similarity DTW (S, T) of two time serieses based on above-mentioned regular path:
C.4, penalty coefficient α is acquired in conjunction with the DTW algorithm of penalty coefficient and be multiplied with the result of former algorithm, obtain updated sequence
Column pitch from:
Wherein, Num (W) is apart from shortest point to number, comLeniLength of the point between when expression i=j；
C.5, the similarity using binary traversal algorithm according to distance between sequence carries out different grades of classification.
6. a kind of electric energy meter operating status appraisal procedure according to claim 5, it is characterised in that: the step C.5 in
Classification include good, normal, early warning and abnormal four kinds of grades.
7. utilizing a kind of assessment device of electric energy meter operating status appraisal procedure described in any one of claims 1-6, feature
It is: includes:
Data acquisition unit, for acquiring sampling instant TiThe time series data of electric energy meter；
Data processing unit, the time series data for acquiring to the data acquisition unit are handled, to construct from entirety
To the evaluation system of part, comprehensive real-time monitoring and assessment are realized to the operating status of electric energy meter, generate network analysis result.
8. a kind of electric energy meter operating status according to claim 7 assesses device, it is characterised in that: further include:
Data display unit, for according to network analysis as a result, output individually and the real-time running state of whole electric energy meter, so as to
Equipment management personnel carries out respective handling to electric energy meter according to feedback information.
9. a kind of electric energy meter operating status according to claim 7 assesses device, it is characterised in that: the time series data packet
Include quantity, current phase angle, voltage phase angle, electrical power, voltage, electric current, electric flux, the frequency fluctuation value, power of electric energy meter
Factor, power direction, load current and sampling instant.
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