CN108132423B - A kind of method for rapidly positioning based on state transition probability power system monitoring data distortion - Google Patents
A kind of method for rapidly positioning based on state transition probability power system monitoring data distortion Download PDFInfo
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Abstract
The present invention relates to a kind of method for rapidly positioning based on state transition probability power system monitoring data distortion, this method pays close attention to the state change probability of monitoring data, it is compared by the threshold value of the feature and equipment runtime data normal variation of the probability distribution after repeatedly shifting, export distortion positional matrix, quickly positions the position of distortion data.Entirety is divided into four step 1. monitoring data attribute entities and divides, the monitoring data transition probability of 2. power equipments, the multiple monitoring transfer matrix of power equipment, and the positioning of 4. distortion datas is measured with subsystem distortion level.Various data formats or data structure can be unified into state transition probability by data of the present invention acquisition rank, therefore evaded different data format in multi-source heterogeneous data and reduced the complexity of analysis system to influence caused by data analysis.
Description
Technical field
The invention belongs to the application studies that Electric Power Communication Data and big data technology blend, by the shape that will test data
State changes probability words, by the uniform format of data each in power communication system at the transfering probability distribution of data mode, then passes through
Qie Puman-kolmogorov equation repeatedly shifts initial transfer distribution, and the theoretical distribution repeatedly shifted passes through
The change threshold of each data limits, and calculates the distortion positional matrix that can directly position distortion data.
Background technique
Power communication system is the concept of a broad sense, refer to each subsystem related to electric power network and they generate
Data information, with the continuous development of China's electric power network, the continuous expansion of power demand, the number generated in power communication system
According to also increasingly huge, while the speed that data generate also is getting faster, and the data structure between different sub-systems also has very big
Difference, the data that power communication system generates become typical big data.
Power communication system is the important system for ensureing electric system and operating normally, and is carried out by various kinds of sensors to equipment
Monitoring, provides decision for equipment fault, provides foundation for maintenance of equipment.Large-scale power communication system generates the monitoring number of magnanimity
According to these data inevitably will appear data distortion phenomenon during acquisition, typing, transmission, exchange and storage.Reality
In, these distortion datas have become positioning and analyze the important hindering factor of electrical equipment fault.Improve power communication system
The quality of data be to improve the important link of electric power grid system.Domestic and international expert proposes the detection of distortion data in electric system
A variety of solutions are gone out, have occurred the reason of data distortion, then setting about from reason in certain research Energy Management System, solve number
The problem of according to distortion.Certain research is to set about attempting to improve the quality of data from data platform.Certain research comes from the angle of interpolation fitting
Prediction data quality research is handed over based on the high speed model of common information model (Common Information Model, CIM)
The data check technology between the not homologous ray that format CIM/E text is carrier is changed, is screened using improved multi-source data more high-quality
The means of data, and the method fed back according to main website state estimation to field data are measured, power network dispatching system is improved
Overall data quality.
Raw data detection above based on status of electric power estimation is in the Quality advance for treating office system local data
When the multi-source heterogeneous big data that has a certain effect, but generated for entire power communication system do not have good be applicable in still
Property, and it is relatively high for establishing the cost of corresponding knowledge base for each data distortion.The present invention proposes to be based on state
The quick positioning of transition probability power system monitoring data distortion is not concerned with the multi-source heterogeneous format of electric power monitoring data, then
The state change of data is paid close attention to, whether the probability of high spot reviews monitoring data variation is consistent with equipment actual change, passes through
National grid real data set verification algorithm, the experimental results showed that this method is suitable for the power communication system under big data environment
The quick positioning of the multi-source heterogeneous data distortion generated.
Summary of the invention
Power communication system is to ensure the necessary information Transmission system of electric system normal table operation, passes through all kinds of sensings
Device carries out real-time monitoring to electrical equipment, reports abnormal data to ensure equipment stable operation in time, provides decision for equipment fault
It supports, provides basis on location for maintenance of equipment.Currently, as the scale of network system is increasing and all kinds of power equipments are supervised
Survey sensor type gradually increase, power communication system daily all generate magnanimity monitoring data, these data acquisition,
It inevitably will appear data distortion phenomenon during typing, transmission, exchange and storage.In fact, in big data era, this
Class distortion data has become positioning and analyzes one of the important hindering factor of electrical equipment fault.Occur in power communication system
Data distortion mainly include following two in terms of:
1. violating monitoring data consistency, monitoring data unanimously refer to, system physical record to data whether meet one
Fixed functional dependence or logical relation, if having the data beyond attribute definition domain.
2. violating monitoring data integrality, monitoring data integrality is that the data that power information system is actually typing are to exist
Missing, if the completely recorded total data recorded by design requirement.
It is a kind of automatic right the present invention is directed to establish for the relatively low problem of the quality of data occurred in current power communication system
Electric power data carries out the random process quick discrimination method of distortion identification, positioning and distortion level measurement, the concern weight of this method
Point is the consistency of variation and the variation of physical device operating status of electric power data state.By data each in power communication system
Uniform format at data mode transfering probability distribution, then by Qie Puman-kolmogorov equation to initial transfer distribution
It is repeatedly shifted, the theoretical distribution repeatedly shifted, is limited by the change threshold of each data, calculating directly to determine
The distortion positional matrix of position distortion data.
To complete the above target, the present invention integrally includes four steps, and overall flow figure is shown in attached drawing 1
Step 1 monitoring data attribute entities divide
Monitoring data acquisition essentially refers in electric power grid system, and various kinds of sensors is monitored equipment and will monitoring
The process that data transmission is stored to designated position.Monitoring data have a variety of storage mechanisms, this step in different subsystems
Purpose be so that collected data are divided into solid data set according to the entity device of data source.This step is divided into 3
Sub-step:
The acquisition of step 1.1 primary monitoring data
Define 1 original data set
D={ d11,d12,...,d21,d22,...,dn1,dn2,...,dnk}
Wherein, in D expression system collected all kinds of initial data attribute set, dijIndicate the kth category of the i-th equipment
Property, in actual information acquisition, since the data acquisition modes of each subsystem of power grid are not quite similar, so each subsystem is acquired
It is often mixed and disorderly data acquisition system after data summarization afterwards.
Step 1.2 monitoring data are classified by entity source address
In data acquisition, data are classified according to source index field, this step is divided into two kinds of forms.
Form one: the classification of data has been acquired
For shaped like collected data in 1 are defined, needing according to the data source index field classification in data attribute,
One group will be divided into from the monitoring data of identical entity
Form two: monitoring data are acquired by entity
For the subsystem of entity output monitoring data can be pressed, its monitoring data is directly acquired, and indicate in fact in data
The unique attribute of body.
Define 2 device data vectors
di=(di1,di2,...,dik)
Wherein, diIndicate the monitoring data vector from i-th (1≤i≤n) equipment, dijIndicate the jth of the i-th equipment, (1
≤ j≤k) attribute.The data sorted out in this way by the data source of any entity device i are in vector form by diRecord
Step 1.3 structure attribute extended matrix
In power communication system, electric power network includes each subsystem, and various kinds of equipment supports one by cooperating
The data variation of the normal operation of a subsystem, set of subsystem can be formalized by attribute extension matrix.
Define 3 attribute extension matrixes
Wherein, M (t) indicates the attribute extension matrix of t moment, dij(t) jth of i-th (1≤i≤n) equipment, (1≤j are indicated
≤ki≤ k) monitor value of the attribute in moment t.The upper bound k for the number of attributes that equipment possesses is defined in above formula, wherein k=
max(k1,k2...) and indicate the attribute number for possessing most attribute equipment in subsystem, it defines the columns of extended matrix.Electricity
The attribute number of distinct device can be different in power subsystem, and most row vectors in such extended matrix M (t) are not fixed
The attribute value of justice, such undefined attribute is referred to as extended attribute, their effect is to keep the rectangle knot of extended matrix
Structure is in order to next Mathematical treatment.Extended matrix M (t) is completely contained in power subsystem in the attribute value of moment t.
The monitoring data transition probability of step 2 power equipment
This step is divided into 3 sub-steps
The state demarcation of step 2.1 data
The value of each attribute of power equipment has a certain range normal operation in normal domain, when a certain attribute value exceeds its normal operation in normal domain, claims
There is exceptional value in the attribute.For discrete type attribute value, domain is denumerable discrete point.For continuous type attribute value,
Its domain is continuous section.It, can be by the different data values monitored according to its definition according to specific power equipment
Domain is divided into different states.When equipment is in normal condition, the state of monitoring data is known as stable state.For discrete
Different points can be classified as one kind according to specific data attribute feature, form a state by type data value.It can also directly will be every
One point is considered as a state.For continuous data value, serial number section can be divided into segment by specific features, often
A segment is a state.
The effect of the step is that the data of equipment are described with discrete state, with monitoring data turning in the status
In-migration portrays the variation of data.
The state of step 2.2 equipment
Power equipment may operate in different states, and different equipment states represents the phase characteristic of equipment operation.
For example, the operating status of transformer can be divided into normal operation, hot operation, unit exception.Different equipment possesses different
Operating status, and the inconsistent essence performance of electric power data is the different of operating status and the monitoring data state of physical device
It causing, i.e., monitoring data cannot really reflect equipment practical operation situation, and the operating status of equipment is varied, and data mode
Type is more.A usual equipment state corresponds to the specific combination of volume of data state.In the past, find wherein inconsistent
Corresponding relationship be complicated, and the technical approach that the present invention takes is, is not concerned with the state itself of equipment and data, but logical
The variation for investigating these states is crossed, inconsistent corresponding relationship is navigated to.
When the equipment stable operation in electric system, all kinds of monitoring data values of equipment should also be stablized in a certain range
In, while it changes rule also with stability.When equipment, which runs out present condition, to be changed, a part of monitoring data will be bigger
Probability deviate original state, into new state, thus this stability regular before breaking.
The step establishes with probabilistic manner the channel for describing data mode transfer, i.e. the transfer of data mode can be by
According to the mathematical form description of probability distribution.
The state transition frequency of step 2.3 statistical history monitoring data
Define 4 data mode transition frequencies
Wherein, fijIt indicates to equipment monitoring Ni+NjData are transferred to the frequency of state j, N from state i after secondaryiExpression is in
The number of state i, NjIndicate the number for being in state j.
1 Bernoulli Jacob's law of large numbers of law
Wherein, P indicates that probability, ε indicate a positive number, and Bernoulli Jacob's law of large numbers shows by largely collecting power equipment
The state of monitoring data shifts data, and the frequency for the state transfer being calculated can convergence in (with)probability.The law shows to lead to
The mode of monitoring current-period data or direct statistical history data is crossed repeatedly to estimate the state transition probability of power equipment.
The multiple monitoring transfer matrix of step 3 power equipment
This step is divided into 4 sub-steps
The Markov property and time homogeneity of step 3.1 data transition probability
Since each monitoring data essence is the sampling on discrete time point, while according to the state demarcation of step 2.2
Method, monitoring data are also divided into discrete state, so the state transfer of electric power monitoring data is substantially a time
The all discrete random process with state.Due to monitoring every time, data state in which is only related to last state in which
(carrying out probability transfer by initial state of state where the last time), so this random process has Markov property, essence is
Markov Chain.To data each time monitoring sampling obtain result with for the first time monitor state in which be it is unrelated, this
Illustrate that the state transition probability of data also has time homogeneity.This step establishes state transition probability and markovian
System, theoretically illustrates that the transition probability of equipment monitoring data can construct the transfer matrix with Markov property.
The multiple transfer matrix of step 3.2 electric power data
Define 6 single state-transition matrixes
Wherein, E indicates the single state-transition matrix of electric power data, element pijIndicate that data are primary from state i experience
The probability of state j is transferred to behind monitoring time interval.Its probability value can by method statistic described in step 2.3 come.It is single
Next state transfer matrix E be fully described after the primary monitoring of any electric power data experience stateful transfer probability distribution feelings
Condition.According to Qie Puman-kolmogorov equation
Transfer matrix of the available electric power data after the transfer of any next state,
By Qie Puman-kolmogorov equation it is found that need to only pass through the single shape of step 3.2 method construct electric power data
State transfer matrix, can directly calculate it is m+n times any after state-transition matrix, E(n+m)The element representation data of middle i row j column
Monitored with original state i, after then undergoing m+n monitoring, data are in the probability of state j.It is obtained by this calculation method
The practical significance of multiple transition probability out refers to when power equipment is in stable state, the theoretical shape that data should reach
State transition probability.
Locating theoretical probability distribution after step 3.4 time transfer
Define 7 electric power data initial distributions
Wherein, the initial distribution vector of φ (0) table monitoring registration evidence, element(0≤j≤n) indicates data at the beginning
Each shape probability of state locating for carving 0.According to Qie Puman-kolmogorov equation, the theory at available any m+n moment
Probability distribution.
The theoretical probability of more than 1 transfering state of theorem is distributed
φ (m+n)=φ (0) Em+n
Wherein, φ (m+n) indicates theoretical probability distribution locating after shifting calculated electric power data experience m+n times
The positioning of step 4 distortion data is measured with subsystem distortion level
Equipment is in the change that stable state refers to equipment non-generating state during this period of time within a certain period of time, right
The probability distribution over states for the monitoring data answered has reflected equipment due data mode conceptual schema feature in this state.One
When denier power communication system detects that the state of current power equipment does not meet the distribution characteristics of corresponding state transition probability, then
The system of may infer that has been likely to occur monitoring data distortion.In monitoring cycle, if equipment running status does not change, and phase
Data mode is answered to change, such case is referred to as first kind distortion.If equipment running status changes, and data mode
Corresponding change does not occur, such case is referred to as the distortion of the second class.This step is divided into 5 sub-steps
The irrelevance of step 4.1 electric power data state transfer distribution
Define the irrelevance of 9 electric power data states transfer distribution
δ > v (m+n)=| | φ ' (m+n)-φ (m+n) | |2
Wherein v (m+n) indicates that the irrelevance of electric power data state transfer distribution, φ ' (m+n) are power equipments in experience m+
The actual transfering probability distribution counted after n times monitoring, φ (m+n) are that power equipment undergoes the theory after m+n monitoring general
Rate distribution passes through the irrelevance of both distributions of the 2- norm measure of vector.δ > 0, which is one, to be defined according to the actual situation
Deviation threshold value, it is determined by the intrinsic state transfer characteristic of the reality of equipment in subsystem.
As δ > v (m+n), illustrate the virtual condition transfer distribution departure degree of device data in the reasonable scope.
As δ≤v (m+n), illustrate the actual transfer Deviation of device data beyond threshold value.
When equipment stable operation, there is δ≤v (m+n), shows that first kind distortion sign occurs in device data, because of number
It has been appeared in the state seldom occurred according at certain moment with greater probability.When changing in equipment running process, out
Existing δ > v (m+n) shows that the second class distortion sign occurs in equipment, because of the variation of the operating status with equipment, monitoring data
Corresponding state variation ought to occur, and virtual condition distribution does not vary widely.
The positioning of distortion data in step 4.2 subsystem
Define the monitoring data departure matrix of 10 subsystems
Power grid subsystem is the organic whole comprising system relevant device, can quick positioning subsystem by departure matrix
The data of middle distortion.
Wherein, V (t) indicates that the monitoring data of subsystem undergo the departure matrix after t normal period, element vij(t) table
Show the irrelevance of the data mode transfer distribution of j-th of attribute of i-th of equipment in subsystem after undergoing t normal period.Root
According to the property of extended matrix in step 2.1, if the attribute, command element value is not present in the corresponding equipment in matrix corresponding position
It is -1.
Step 4.3 carries out distortion positioning, is based on following formula:
Define 11 distortion positional matrixs
Wherein,Indicate that the distortion positional matrix of a n × k, element are only made of numerical value 0,1 and -1, F representing matrix
0-1 transforming function transformation function, its function are that element in matrix is carried out to 0-1 transformation, and Δ (t) indicates to deviate threshold matrix, element δij(t)
It indicates to correspond to irrelevance vij(t) deviation threshold value, function f (zij∈ V (t)-Δ (t)) it indicates to carry out 0-1 change to its independent variable
It changes, is defined as follows and defines 11 element 0-1 transforming function transformation functions
The practical significance of distortion positional matrix is all rubidium markings beyond threshold value to be less than for 1 the mark of threshold value
It is denoted as 0, the position of element in a matrix corresponds the position in data in subsystem again.Such a distortion positional matrix
Just contain the variable condition and location information of distortion data in a subsystem.
The processing of step 4.4 missing data
Shortage of data is a kind of performance of also data distortion, regardless of the true operating status of equipment, as long as monitoring number
According to there is data null value, then it is assumed that be data distortion, execute missing data transformation at this time
Wherein, g (dij) indicate missing transforming function transformation function, it by M (t) the element of promising null value be mapped as 1, and enterIt is mapped as when assigning null data lacks transforming function transformation function againIn 1 element when, show its distortion attribute.Completely holding
After the primary distortion positioning of row, need to be updated state transition frequency using the initial data of epicycle as historical data.In this way
It is ensured that the probability element of the single state-transition matrix constructed in step 3.2 has real-time accuracy.
The positioning of step 4.5 data distortion, is measured with power subsystem distortion level.
Traversal distortion positional matrix it is primary, record it is each be equal to 1 element institute in a matrix position corresponded one
Distortion data position, rower represent device number.Attribute number of the list for the equipment.Taking out element accounts for all elements simultaneously
Ratio quantitative response power subsystem distortion level.
It is to be noted that being related to geneva in step 3.1 proves
It proves: electric power data state migration procedure { Xn, n=0,1,2 ... } it is Markov Chain
Due to n be it is limited arrange, andAnd state i, j, i0,i1,…,in-1There is always conditional probability P (Xn+1
=j | X0=i0,X1=i1,…,Xn-1=in-1,Xn=i) make P (Xn+1=j | X0=i0,X1=i1,…,Xn-1=in-1,Xn=
I)=P (Xn+1=j | Xn=i)
I.e. sampling process meets geneva, { Xn, n=0,1,2 ... } it is a Markov chain.
Therefore, the present invention has the advantage that 1, data of the present invention acquisition rank can be by various data formats or data structure
It is unified into state transition probability, therefore has evaded in multi-source heterogeneous data different data format to influence caused by data analysis,
Reduce the complexity of analysis system.2, the distortion differentiation of data can be carried out dynamically in real time, as long as theoretically giving certain
As soon as the data mode initial distribution at moment can calculate data distortion situation at this time.The data being distorted simultaneously can be returned again
It returns and updates historical data.3, data distortion, which differentiates, has simultaneity, after data distortion is determined, the position of distortion with positioning
It is determined simultaneously according to distortion positional matrix, the emergency repair time of equipment fault can be shortened.
Detailed description of the invention
Attached drawing 1 is overall flow schematic diagram of the invention.
Specific embodiment
Step 1 monitoring data attribute entities divide
Monitoring data acquisition essentially refers in electric power grid system, and various kinds of sensors is monitored equipment and will monitoring
The process that data transmission is stored to designated position.Monitoring data have a variety of storage mechanisms, this step in different subsystems
Purpose be so that collected data are divided into solid data set according to the entity device of data source.This step is divided into 3
Sub-step
The acquisition of step 1.1 primary monitoring data
Define 1 original data set
D={ d11,d12,...,d21,d22,...,dn1,dn2,...,dnk}
Wherein, in D expression system collected all kinds of initial data attribute set, dijIndicate the kth category of the i-th equipment
Property, in actual information acquisition, since the data acquisition modes of each subsystem of power grid are not quite similar, so each subsystem is acquired
It is often mixed and disorderly data acquisition system after data summarization afterwards.
Step 1.2 monitoring data are classified by entity source address
In data acquisition, data are classified according to source index field, this step is divided into two kinds of forms.Form
One: having acquired the classification of data
For shaped like collected data in 1 are defined, needing according to the data source index field classification in data attribute,
One group will be divided into from the monitoring data of identical entity
Form two: monitoring data are acquired by entity
For the subsystem of entity output monitoring data can be pressed, its monitoring data is directly acquired, and indicate in fact in data
The unique attribute of body.
Define 2 device data vectors
di=(di1,di2,...,dik)
Wherein, diIndicate the monitoring data vector from i-th (1≤i≤n) equipment, dijIndicate the jth of the i-th equipment, (1
≤ j≤k) attribute.The data sorted out in this way by the data source of any entity i equipment are in vector form by diRecord
Step 1.3 structure attribute extended matrix
In power communication system, electric power network includes each subsystem, and various kinds of equipment supports one by cooperating
The data variation of the normal operation of a subsystem, set of subsystem can be formalized by attribute extension matrix.
Define 3 attribute extension matrixes
Wherein, M (t) indicates the attribute extension matrix of t moment, dij(t) jth of i-th (1≤i≤n) equipment, (1≤j are indicated
≤ki≤ k) monitor value of the attribute in moment t.The upper bound k for the number of attributes that equipment possesses is defined in above formula, wherein k=
max(k1,k2...) and indicate the attribute number for possessing most attribute equipment in subsystem, it defines the columns of extended matrix.Electricity
The attribute number of distinct device can be different in power subsystem, and most row vectors in such extended matrix M (t) are not fixed
The attribute value of justice, such undefined attribute is referred to as extended attribute, their effect is to keep the rectangle knot of extended matrix
Structure is in order to next Mathematical treatment.Extended matrix M (t) is completely contained in power subsystem in the attribute value of moment t.
The monitoring data transition probability of step 2 power equipment
This step is divided into 3 sub-steps
The state demarcation of step 2.1 data
The value of each attribute of power equipment has a certain range normal operation in normal domain, when a certain attribute value exceeds its normal operation in normal domain, claims
There is exceptional value in the attribute.For discrete type attribute value, domain is denumerable discrete point.For continuous type attribute value,
Its domain is continuous section.It, can be by the different data values monitored according to its definition according to specific power equipment
Domain is divided into different states.When equipment is in normal condition, the state of monitoring data is known as stable state.For discrete
Different points can be classified as one kind according to specific data attribute feature, form a state by type data value.It can also directly will be every
One point is considered as a state.For continuous data value, serial number section can be divided into segment by specific features, often
A segment is a state.
The effect of the step is that the data of equipment are described with discrete state, with monitoring data turning in the status
In-migration portrays the variation of data.
The state of step 2.2 equipment
Power equipment may operate in different states, and different equipment states represents the phase characteristic of equipment operation.
For example, the operating status of transformer can be divided into normal operation, hot operation, unit exception.Different equipment possesses different
Operating status, and the inconsistent essence performance of electric power data is the different of operating status and the monitoring data state of physical device
It causing, i.e., monitoring data cannot really reflect equipment practical operation situation, and the operating status of equipment is varied, and data mode
Type is more.A usual equipment state corresponds to the specific combination of volume of data state.In the past, find wherein inconsistent
Corresponding relationship be complicated, and the technical approach that the present invention takes is, is not concerned with the state itself of equipment and data, but logical
The variation for investigating these states is crossed, inconsistent corresponding relationship is navigated to.
When the equipment stable operation in electric system, all kinds of monitoring data values of equipment should also be stablized in a certain range
In, while it changes rule also with stability.When equipment, which runs out present condition, to be changed, a part of monitoring data will be bigger
Probability deviate original state, into new state, thus this stability regular before breaking.
The step establishes with probabilistic manner the channel for describing data mode transfer, i.e. the transfer of data mode can be by
According to the mathematical form description of probability distribution.
The state transition frequency of step 2.3 statistical history monitoring data
Define 4 data mode transition frequencies
Wherein, fijIt indicates to equipment monitoring Ni+NjData are transferred to the frequency of state j, N from state i after secondaryiExpression is in
The number of state i, NjIndicate the number for being in state j.
1 Bernoulli Jacob's law of large numbers of law
Wherein, P indicates that probability, ε indicate a positive number, and Bernoulli Jacob's law of large numbers shows by largely collecting power equipment
The state of monitoring data shifts data, and the frequency for the state transfer being calculated can convergence in (with)probability.The law shows to lead to
The mode of monitoring current-period data or direct statistical history data is crossed repeatedly to estimate the state transition probability of power equipment.
The multiple monitoring transfer matrix of step 3 power equipment
This step is divided into 4 sub-steps
The Markov property and time homogeneity of step 3.1 data transition probability
Since each monitoring data essence is the sampling on discrete time point, while according to the state demarcation of step 2.2
Method, monitoring data are also divided into discrete state, so the state transfer of electric power monitoring data is substantially a time
The all discrete random process with state.Due to monitoring every time, data state in which is only related to last state in which
(carrying out probability transfer by initial state of state where the last time), so this random process has Markov property, essence is
Markov Chain.To data each time monitoring sampling obtain result with for the first time monitor state in which be it is unrelated, this
Illustrate that the state transition probability of data also has time homogeneity.This step establishes state transition probability and markovian
System, theoretically illustrates that the transition probability of equipment monitoring data can construct the transfer matrix with Markov property.
The multiple transfer matrix of step 3.2 electric power data
Define 6 single state-transition matrixes
Wherein, E indicates the single state-transition matrix of electric power data, element pijIndicate that data are primary from state i experience
The probability of state j is transferred to behind monitoring time interval.Its probability value can by method statistic described in step 2.3 come.It is single
Next state transfer matrix E be fully described after the primary monitoring of any electric power data experience stateful transfer probability distribution feelings
Condition.According to Qie Puman-kolmogorov equation
Transfer matrix of the available electric power data after the transfer of any next state,
By Qie Puman-kolmogorov equation it is found that need to only pass through the single shape of step 3.2 method construct electric power data
State transfer matrix, can directly calculate it is m+n times any after state-transition matrix, E(n+m)The element representation data of middle i row j column
Monitored with original state i, after then undergoing m+n monitoring, data are in the probability of state j.It is obtained by this calculation method
The practical significance of multiple transition probability out refers to when power equipment is in stable state, the theoretical shape that data should reach
State transition probability.
Locating theoretical probability distribution after step 3.4 time transfer
Define 7 electric power data initial distributions
Wherein, the initial distribution vector of φ (0) table monitoring registration evidence, element(0≤j≤n) indicates data at the beginning
Each shape probability of state locating for carving 0.According to Qie Puman-kolmogorov equation, the theory at available any m+n moment
Probability distribution.
The theoretical probability of more than 1 transfering state of theorem is distributed
φ (m+n)=φ (0) Em+n
Wherein, φ (m+n) indicates theoretical probability distribution locating after shifting calculated electric power data experience m+n times.
The positioning of step 4 distortion data
Equipment is in the change that stable state refers to equipment non-generating state during this period of time within a certain period of time, right
The probability distribution over states for the monitoring data answered has reflected equipment due data mode conceptual schema feature in this state.One
When denier power communication system detects that the state of current power equipment does not meet the distribution characteristics of corresponding state transition probability, then
The system of may infer that has been likely to occur monitoring data distortion.In monitoring cycle, if equipment running status does not change, and phase
Data mode is answered to change, such case is referred to as first kind distortion.If equipment running status changes, and data mode
Corresponding change does not occur, such case is referred to as the distortion of the second class.This step is divided into 5 sub-steps
The irrelevance of step 4.1 electric power data state transfer distribution
Define the irrelevance of 9 electric power data states transfer distribution
δ > v (m+n)=| | φ ' (m+n)-φ (m+n) | |2
Wherein v (m+n) indicates that the irrelevance of electric power data state transfer distribution, φ ' (m+n) are power equipments in experience m+
The actual transfering probability distribution counted after n times monitoring, φ (m+n) are that power equipment undergoes the theory after m+n monitoring general
Rate distribution passes through the irrelevance of both distributions of the 2- norm measure of vector.δ > 0, which is one, to be defined according to the actual situation
Deviation threshold value, as δ > v (m+n), illustrate device data virtual condition transfer distribution departure degree in the reasonable scope,
As δ≤v (m+n), illustrate the actual transfer Deviation of device data beyond threshold value.
When equipment stable operation, there is δ≤v (m+n), shows that first kind distortion sign occurs in device data, because of number
It has been appeared in the state seldom occurred according at certain moment with greater probability.When changing in equipment running process, out
Existing δ > v (m+n) shows that the second class distortion sign occurs in equipment, because of the variation of the operating status with equipment, monitoring data
Corresponding state variation ought to occur, and virtual condition distribution does not vary widely.
The positioning of distortion data in step 4.2 subsystem
Define the monitoring data departure matrix of 10 subsystems
Power grid subsystem is the organic whole comprising system relevant device, can quick positioning subsystem by departure matrix
The data of middle distortion.
Wherein, V (t) indicates that the monitoring data of subsystem undergo the departure matrix after t normal period, element vij(t) table
Show the irrelevance of the data mode transfer distribution of j-th of attribute of i-th of equipment in subsystem after undergoing t normal period.Root
According to the property of extended matrix in step 2.1, if the attribute, command element value is not present in the corresponding equipment in matrix corresponding position
It is -1.
Step 4.3
Define 11 distortion positional matrixs
Wherein,Indicate that the distortion positional matrix of a n × k, element are only made of numerical value 0,1 and -1, F representing matrix
0-1 transforming function transformation function, its function are that element in matrix is carried out to 0-1 transformation, and Δ (t) indicates to deviate threshold matrix, element δij(t)
It indicates to correspond to irrelevance vij(t) deviation threshold value, function f (zij∈ V (t)-Δ (t)) it indicates to carry out 0-1 change to its independent variable
It changes, is defined as follows and defines 11 element 0-1 transforming function transformation functions
The practical significance of distortion positional matrix is all rubidium markings beyond threshold value to be less than for 1 the mark of threshold value
It is denoted as 0, the position of element in a matrix corresponds the position in data in subsystem again.Such a distortion positional matrix
Just contain the variable condition and location information of distortion data in a subsystem.
The processing of step 4.4 missing data
Shortage of data is a kind of performance of also data distortion, regardless of the true operating status of equipment, as long as monitoring number
According to there is data null value, then it is assumed that be data distortion, execute missing data transformation at this time
Wherein, g (dij) indicate missing transforming function transformation function, it by M (t) the element of promising null value be mapped as 1, and enterIt is mapped as when assigning null data lacks transforming function transformation function againIn 1 element when, show its distortion attribute.Completely holding
After the primary distortion positioning of row, need to be updated state transition frequency using the initial data of epicycle as historical data.In this way
It is ensured that the probability element of the single state-transition matrix constructed in step 3.2 has real-time accuracy.
The positioning of step 4.5 data distortion, is measured with power subsystem distortion level.
Traversal distortion positional matrix it is primary, record it is each be equal to 1 element institute in a matrix position corresponded one
Distortion data position, rower represent device number.Attribute number of the list for the equipment.Taking out element accounts for all elements simultaneously
Ratio quantitative response power subsystem distortion level.
Specific embodiment described herein is only an example for the spirit of the invention.The neck of technology belonging to the present invention
The technical staff in domain can make various modifications or additions to the described embodiments or replace by a similar method
In generation, however, it does not deviate from the spirit of the invention or beyond the scope of the appended claims.
Claims (1)
1. a kind of method for rapidly positioning based on state transition probability power system monitoring data distortion, which is characterized in that including
Following steps:
Step 1, monitoring data attribute entities divide, and acquire the primary monitoring data of equipment, and by collected raw monitored number
It is divided into solid data set according to the entity device according to data source, is specifically included:
Step 1.1, primary monitoring data acquisition, and collected raw sensor data is attributed in set according to defined below:
Define 1, original data set
D={ d11,d12,...,d21,d22,...,dn1,dn2,...,dnk}
Wherein, in D expression system collected all kinds of initial data attribute set, dijIndicate the jth attribute of the i-th equipment, In
In actual information acquisition, since the data acquisition modes of each subsystem of power grid are not quite similar, so by after the acquisition of each subsystem
It is often mixed and disorderly data acquisition system after data summarization；
Step 1.2 classifies the primary monitoring data of acquisition by entity source address, in data acquisition, by data according to
Source index field is classified, and classification selects following form to classify according to the type of data:
Classification form one: the classification for having acquired data is needed for collected data in 1 are defined according to data category
Property in the classification of data source index field, one group will be divided into from the monitoring data of identical entity
Classification form two: it is directly adopted by entity acquisition monitoring data classification for the subsystem of entity output monitoring data can be pressed
Collect its monitoring data, and indicates the unique attribute of entity in data；
Define 2 device data vectors
di=(di1,di2,...,dik)
Wherein, diIndicate the monitoring data vector from i-th (1≤i≤n) equipment, dijIndicate the jth of the i-th equipment, (1≤j
≤ k) attribute；The data sorted out by the data source of any entity device i are in vector form by diRecord
Step 1.3, structure attribute extended matrix: in power communication system, electric power network includes each subsystem, various kinds of equipment
The normal operation of a subsystem is supported by cooperating, the data variation of set of subsystem can pass through attribute extension square
Battle array formalizes, and wherein attribute extension matrix is based on defined below
Define 3 attribute extension matrixes
Wherein, M (t) indicates the attribute extension matrix of t moment, dij(t) jth of i-th (1≤i≤n) equipment, (1≤j≤k are indicatedi
≤ k) monitor value of the attribute in moment t；The upper bound k for the number of attributes that equipment possesses is defined in above formula, wherein k=max
(k1,k2...) and indicate the attribute number for possessing most attribute equipment in subsystem, it defines the columns of extended matrix；Electric power
The attribute number of distinct device can be different in subsystem, and most row vectors in such extended matrix M (t) do not define
Attribute value, such undefined attribute is referred to as extended attribute, their effect be keep extended matrix rectangular configuration
In order to next Mathematical treatment；Extended matrix M (t) is completely contained in power subsystem in the attribute value of moment t；
Step 2, the monitoring data transition probability for obtaining power equipment, specifically include:
The state demarcation of step 2.1 data
The value of each attribute of power equipment has a certain range normal operation in normal domain, when a certain attribute value exceeds its normal operation in normal domain, claims the category
There is exceptional value in property；For discrete type attribute value, domain is denumerable discrete point；For continuous type attribute value, determine
Adopted domain is continuous section；
It is divided into different states according to specific power equipment by the different data values monitored according to its domain；
When equipment is in normal condition, the state of monitoring data is known as stable state；
For discrete data value, different points can be classified as one kind according to specific data attribute feature, form a state, or
Each point is directly considered as a state by person；It is drawn for continuous data value, or by serial number section by specific features
It is divided into segment, each segment is a state；
The state of step 2.2 equipment
Power equipment may operate in different states, and different equipment states represents the phase characteristic of equipment operation；It is different
Equipment possess different operating statuses, and the inconsistent essence performance of electric power data is the operating status and monitoring of physical device
Data mode it is inconsistent, i.e., monitoring data cannot really reflect that equipment practical operation situation, the operating status of equipment are a variety of more
Sample, and the type of data mode is more；One equipment state corresponds to the specific combination of volume of data state；
When the equipment stable operation in electric system, all kinds of monitoring data values of equipment should also be stablized in a certain range,
It, which changes rule, simultaneously also has stability；When equipment, which runs out present condition, to be changed, a part of monitoring data will be bigger
Probability deviates original state, into new state, thus this stability regular before breaking；
The state transition frequency of step 2.3, statistical history monitoring data is based on acquisition defined below:
Define 4 data mode transition frequencies
Wherein, fijIt indicates to equipment monitoring Ni+NjData are transferred to the frequency of state j, N from state i after secondaryiIt indicates to be in state i
Number, NjIndicate the number for being in state j；
Then electricity is estimated by way of carrying out repeatedly monitoring current-period data or direct statistical history data based on following formula
The state transition probability of power equipment
Wherein, P indicates that probability, ε indicate a positive number, and the state by largely collecting power equipment monitoring data shifts data,
The frequency for the state transfer being calculated can convergence in (with)probability；It can be by repeatedly monitoring current-period data or direct statistical history number
According to mode estimate the state transition probability of power equipment；
Step 3, the multiple monitoring transfer matrix for obtaining power equipment, specifically include:
The Markov property and time homogeneity of step 3.1 data transition probability
Since each monitoring data essence is the sampling on discrete time point, while according to the state demarcation method of step 2.2,
Monitoring data are also divided into discrete state, so the state transfer of electric power monitoring data is substantially a time and state
All discrete random process；Due to monitoring every time, data state in which is only related to last state in which, so this
Random process has Markov property, and essence is Markov Chain；The result and that the sampling of monitoring each time of data is obtained
Once monitoring state in which is unrelated, this illustrates that the state transition probability of data also has time homogeneity；
Step 3.2, the multiple transfer matrix for obtaining electric power data, based on defined below:
Define 6 single state-transition matrixes
Wherein, E indicates the single state-transition matrix of electric power data, element pijIndicate that data undergo primary monitoring from state i
The probability of state j is transferred to after time interval；Its probability value can by method statistic described in step 2.3 come；Single shape
State transfer matrix E be fully described after the primary monitoring of any electric power data experience stateful transfer probability distribution；Root
According to Qie Puman-kolmogorov equation
Transfer matrix of the electric power data after the transfer of any next state is obtained,
By Qie Puman-kolmogorov equation it is found that need to only be turned by the single state of step 3.2 method construct electric power data
Move matrix, can directly calculate it is m+n times any after state-transition matrix, E(n+m)The element representation data of middle i row j column are with first
Beginning state i is monitored, and after then undergoing m+n monitoring, data are in the probability of state j；It is drawn by this calculation method
The practical significance of multiple transition probability refer to that when power equipment is in stable state, the theory state that data should reach turns
Move probability；
Locating theoretical probability distribution after step 3.4, acquisition time transfer, is based on defined below:
Define 7 electric power data initial distributions
Wherein, the initial distribution vector of φ (0) table monitoring registration evidence, element(0≤j≤n) indicates that data carve 0 institute at the beginning
Each shape probability of state at place；According to Qie Puman-kolmogorov equation, the theoretical probability at available any m+n moment
Distribution；
The theoretical probability of more than 1 transfering state of theorem is distributed
φ (m+n)=φ (0) Em+n
Wherein, φ (m+n) indicates theoretical probability distribution locating after shifting calculated electric power data experience m+n times
Step 4 obtains distortion data positioning and subsystem distortion level measurement, specifically includes:
Step 4.1, the irrelevance for calculating the transfer distribution of electric power data state, based on defined below:
Define the irrelevance of 9 electric power data states transfer distribution
δ > v (m+n)=| | φ ' (m+n)-φ (m+n) | |2
Wherein v (m+n) indicates that the irrelevance of electric power data state transfer distribution, φ ' (m+n) are power equipments at experience m+n times
The actual transfering probability distribution counted after monitoring, φ (m+n) are that power equipment undergoes the theoretical probability point after m+n monitoring
Cloth passes through the irrelevance of both distributions of the 2- norm measure of vector；δ > 0 be one can define according to the actual situation it is inclined
From threshold value, as δ > v (m+n), illustrate device data virtual condition transfer distribution departure degree in the reasonable scope, when δ≤
When v (m+n), illustrate the actual transfer Deviation of device data beyond threshold value；
When equipment stable operation, there is δ≤v (m+n), shows that first kind distortion sign occurs in device data, because data exist
Certain moment have been appeared in the state seldom occurred with greater probability；When changing in equipment running process, there is δ >
V (m+n) shows that the second class distortion sign occurs in equipment, because of the variation of the operating status with equipment, monitoring data ought to be sent out
Raw corresponding state variation, and virtual condition distribution does not vary widely；
Step 4.2, the positioning for carrying out distortion data in subsystem, based on defined below:
Define the monitoring data departure matrix of 10 subsystems
Power grid subsystem is the organic whole comprising system relevant device, can be lost in quick positioning subsystem by departure matrix
Genuine data；
Wherein, V (t) indicates that the monitoring data of subsystem undergo the departure matrix after t normal period, element vij(t) warp is indicated
The irrelevance that the data mode transfer of j-th of attribute of i-th of equipment is distributed in subsystem after going through t normal period；According to step
The property of extended matrix in rapid 2.1, if the corresponding equipment in matrix corresponding position be not present the attribute, command element value be-
1；
Step 4.3 carries out distortion positioning, is based on following formula:
Define 11 distortion positional matrixs
Wherein,Indicate that the distortion positional matrix of a n × k, element are only made of numerical value 0,1 and -1, F representing matrix 0-1 becomes
Exchange the letters number, its function are that element in matrix is carried out to 0-1 transformation, and Δ (t) indicates to deviate threshold matrix, element δij(t) it indicates
Corresponding to irrelevance vij(t) deviation threshold value, function f (zij∈ V (t)-Δ (t)) it indicates to carry out 0-1 transformation to its independent variable,
It is defined as follows
Define 11 element 0-1 transforming function transformation functions
The practical significance of distortion positional matrix is all rubidium markings beyond threshold value be 1 and be less than the label of threshold value for
0, the position of element in a matrix corresponds the position in data in subsystem again；Such a distortion positional matrix just wraps
The variable condition and location information of distortion data in a subsystem are contained；
Step 4.4, the processing for carrying out missing data, the specifically transformation of execution missing data, are based on following formula
Wherein, g (dij) indicate missing transforming function transformation function, it by M (t) the element of promising null value be mapped as 1, and enter
It is mapped as when assigning null data lacks transforming function transformation function againIn 1 element when, show its distortion attribute；It is executed once complete
After distortion positioning, need to be updated state transition frequency using the initial data of epicycle as historical data；It can thus protect
The probability element of the single state-transition matrix constructed in card step 3.2 has real-time accuracy；
Step 4.5 carries out data distortion positioning, and calculates and measure with power subsystem distortion level, specifically: traversal distortion is fixed
Bit matrix is primary, record it is each be equal to 1 element institute in a matrix position corresponded a distortion data position,
Rower represents device number；Attribute number of the list for the equipment；The ratio quantitative response electricity that element accounts for all elements is taken out simultaneously
Power subsystem distortion level.
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