CN106452877A - Electric power information network fault locating method - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0677—Localisation of faults
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Abstract
The invention discloses an electric power information network fault locating method. The relation between detection in a candidate detection set and nodes in an electric power information network is described by using a Bayesian network model. The information gain of single detection and the number of important nodes in a single detection path are combined to act as detection values to measure the diagnostic capacity of each detection in the candidate detection set. The detection of the maximum detection value is selected out of the candidate detection set to form a fault locating set. The most possible state information of the electric power information network is obtained by the returned detection result so as to locate fault nodes. The method is simple in theory, the fault locating process is enabled to be clearer by the directed edge between the information nodes and detection, and the new detection values are defined to act as the standard of selecting the fault locating set so that the accuracy of fault locating can be enhanced; and the Bayesian network is divided into multiple sub-networks by using the conditional independence of the Bayesian network, and the detection of the same sub-network is updated when the detection values are updated so that detection selection time can be reduced and the timeliness of fault locating can be enhanced.
Description
Technical field
The present invention relates to technical field of electric power is and in particular to a kind of power information network fault positioning method.
Background technology
In intelligent grid, Electricity Information Network carries various electric power services, is the important of guarantee energy communication service
Passage.But huge network structure causing trouble happens occasionally, and location difficulty after breaking down, if cannot quickly, accurately
Investigation is out of order, and can cause electric power accident.Legacy network fault management is used mostly what the passive collecting device of northbound interface sent
Warning information, sets up alarm-fault correlation model to analyze fault rootstock, but passively waits alarm can causing trouble position
Poor in timeliness, once and warning information occur false, lose and redundancy phenomena, the accuracy of fault location cannot be ensured.In order to
Make up the deficiency of legacy network management, actively measurement is progressively applied in Electricity Information Network maintenance work, believe for improving electric power
The management O＆M level of breath net, has important application value.
In public network, the achievement in research using network detection raising network O＆M level is relatively many both at home and abroad, is logical mostly
Cross actively to send and detect the acquisition that bag carries out network and business quality data, find that the hidden danger of network carries out fault location, typical case
Application scenarios include fault detect, positioning and identification etc..Active probe is intended to choose optimal detection collection, generally comprises in advance
Selection mode and interactive selection mode.Mode computational methods simple still implementation procedure is pre-selected poorly efficient, and real-time is not
Good lead to accuracy rate relatively low, interactive probing collection selection mode is decreased using the mode sending in calculating and executes detection
Number so that offered load is lower.
Entitled " a kind of method of fault location in power telecom network " patent of patent No. CN103840967A, there is provided
A kind of method of fault location in power telecom network, the uncertainty of fault and the multi-to-multi of symptom is built with weighting bipartite graph
Mould, introduces fault weighing factor and utilizes full probability and Bayes's thought under bipartite model, priori probability of malfunction is converted into
Conditional probability, calculates fault disturbance degree, adds the impact that authentication parameters control suspected malfunctions, in conjunction with coverage and contribution degree choosing
Go out the symptom that entirely sensible explanation is occurred.The theory that the method is related to is excessively loaded down with trivial details, implements more complicated, needs to divide
The topology of analysis the whole network, and the collection of symptom is excessively passive, positioning is ageing low.
Content of the invention
The technical problem to be solved in the present invention is：Existing method is realized complicated, while guaranteeing the fault location degree of accuracy
Higher fault location can not be met ageing.
The present invention proposes a kind of power information network fault positioning method, comprises the following steps：
S1, randomly select in Power Information Network network node deployment probe, obtain by all probes to all-network
Whole detections of node, composition is available to detect collection Tall；
S2, from available detect collection TallThe detection of all nodes of middle selection energy overlay network, periodically send detect bag to
In Information Network, these detect and constitute fault detect collection Tobs, obtain candidate simultaneously and detect collection Tsta=Tall-Tobs, now by can
The fault location collection T detecting composition of positioning failurediaFor sky；
S3, set up candidate detect collection TstaIn detection tjWith network node xiBetween Bayesian network model, wherein j=
1,2 ..., m, i=1,2 ..., n, m are that candidate detects collection TstaThe number of middle detection, n is the number of network node；
S4, according to fault detect collection TobsIn all detections returning result, the Bayesian network model that S3 is set up divides
Become some subnets；
S5, calculating candidate detect collection TstaIn each detect information gain G (tj)=H (X | T)-H (X | T ∪ { S (tj)})
And important node number N of each detectionIm(tj), show that the detection that each detects is worth V (tj)=α G (tj)+βNIm(tj), will
Candidate detects collection TstaIn detection according to detect be worth sort from big to small, take tmaxDetect collection T for current candidatestaMiddle detection
The detection of Maximum Value；Wherein S (tj) for detecting tjState,P (X, T) is X
With the joint probability of T, P (X | T) is the conditional probability of X in the case of known T, X=(S (x1),S(x2),...,S(xn)) it is overall
The state vector of network node, T=(S (t'1),S(t'2),...,S(t'h)) it is the current state sending the detection detecting bag
Vector, t'1,t'2,...,t'hSend the detection detecting bag for current, h is the current number sending the detection detecting bag, α
It is preset weights with β；
If the cost C (T of S6 fault location collectiondia) >=B, then go to S9, otherwise detects tmaxSend and detect bag to Information Network
In, obtain the state S (t of Information Network this detection currentmax), t will be detected simultaneouslymaxAdd fault location collection TdiaAnd by it from time
Choosing detects collection TstaMiddle deletion, if now candidate detects collection TstaFor sky, then go to S9；Wherein B is default detection cost threshold value；
S7, take t'maxDetect collection T for current candidatestaThe middle detection detecting Maximum Value, if t'maxWith tmaxIn same son
In net, then recalculate detection t'maxDetection be worth V (t'max), otherwise pass directly to S8；
S8, takeDetect collection T for candidatestaIn come detection t'maxRear one detection, ifThen make
tmaxFor t'max, go to S6, otherwise judgeWith tmaxWhether in same subnet, if not in same subnet, compare t'maxWithDetection be worth, if in same subnet, recalculateDetection be worthCompare t' againmaxWithDetection
It is worth；IfThen make t'maxForGo to S8, otherwise make tmaxFor t'max, go to S6；
S9, an example x of calculating overall network node state X*=argmaxP (X | ST), draw overall network node
Most possible state vector x*, thus positioning failure node；Wherein ST=(S (T1),S(T2) ...) it is current failure detection
Collection TobsWith fault location collection TdiaIn all detections state vector.
Further, step S4 specifically includes：
According to fault detect collection TobsIn all detections returning result, calculating network node xiThe number of times that should be detectedWith covering this network node xiThe consistent ratio of all detection returning resultsIf meet a simultaneouslyi＞ λ and bi＞ θ, then define this network node xiFor approximate Observable node, using D-
The Bayesian network model that S3 sets up is divided into several separate small-scale Bayes's sub-networks by separation method；Wherein
C' is that available detection collects TallMiddle overlay network node xiDetection, c be fault detect collection TobsMiddle overlay network node xiSpy
Survey,E is fault detect collection TobsMiddle overlay network node xiAnd
State is the detection of k, | | represent the number asking set to comprise element, λ and θ is predetermined coefficient.
Further, fault location collection T in step S6diaCost C (Tdia) it is fault location collection TdiaThe number of middle detection.
Beneficial effects of the present invention：(1) the inventive method is described candidate and detects concentration using Bayesian network model
Detect the relation with the node in Power Information Network, effectively simplify problem, the directed edge between information node and detection
Make fault location process apparent；(2) combine the important node in the information gain and single detective path of single detection
Number, to weigh, as detecting to be worth, the diagnosis capability that candidate's detection concentrates each to detect, to detect from candidate and concentrate selection detection valency
The maximum detection composition fault location collection of value, improves the degree of accuracy of fault location；(3) utilize the conditional sampling of Bayesian network
Property, it is divided into some subnets, update detection only updates the detection of same subnet when being worth, reduces detecting access time, carry
Rise the ageing of fault location；(4) detect to be worth in renewal process and show submodel, reduce the number of update detection, accelerate to visit
Survey access time, lift the ageing of fault location.
Brief description
Fig. 1 is the flow chart of the inventive method.
Fig. 2 is the fault location degree of accuracy comparison diagram of the inventive method and BPEA method.
Fig. 3 is the ageing comparison diagram of the inventive method and BPEA method.
Specific embodiment
Used in the present invention, the concept of several detection collection is as follows：
1) can use and detect collection Tall：Randomly choose some node deployment probes in Information Network, each probe can be sent out
Send active probe bag to network node arbitrary in Information Network, referred to as one detection, can push away according to detecting the result returning
The quality of circuit network node state.Available spy is combined into by the collection that whole detections that all probes to all-network node form are constituted
Survey collection Tall, detection collection hereinafter described is all the subset that this gathers.
2) fault detect collection Tobs：The reliability service of Electricity Information Network to be ensured is it is impossible to passively wait user to send fault
Shen is accused, and needs real time monitoring network ruuning situation, so collection T will be detected from availableallMiddle with selecting some detection cycles transmission
Detect bag in network, each of selected transmission detects all nodes integrating in overlay network to be had the ability, by this
A little set detecting composition are fault detect collection Tobs.
3) candidate detects collection Tsta：Available detection collects TallIn be not belonging to fault detect collection TobsDetect composition set, that is,
Tsta=Tall-Tobs.
4) fault location collection Tdia：Fault location collection is to have learned that fault detect collection TobsIn each returning result that detects
In the case of, detect collection T from candidatestaThe positioning failure purpose that can reach of middle selection detects the set forming.
A kind of power information network fault positioning method that the present invention provides comprises the following steps：
S1, randomly select network node deployment probe in Power Information Network, obtain and available detect collection Tall；
S2, from available detect collection TallThe detection of all nodes of middle selection energy overlay network, constitutes fault detect collection Tobs, with
When obtain candidate detect collection Tsta；
S3, set up candidate detect collection TstaIn detection tjWith network node xiBetween Bayesian network model, wherein j=
1,2 ..., m, i=1,2 ..., n, m are that candidate detects collection TstaThe number of middle detection, n is the number of network node；
S4, according to fault detect collection TobsIn all detections returning result, the Bayesian network model that S3 is set up divides
Become some subnets；
S5, calculating candidate detect collection TstaIn each detect information gain G (tj)=H (X | T)-H (X | T ∪ { S (tj)})
And important node number N of each detectionIm(tj), show that the detection that each detects is worth V (tj)=α G (tj)+βNIm(tj), will
Candidate detects collection TstaIn detection according to detect be worth sort from big to small, take tmaxDetect collection T for current candidatestaMiddle detection
The detection of Maximum Value；Wherein S (tj) for detecting tjState,P (X, T) is X
With the joint probability of T, P (X | T) is the conditional probability of X in the case of known T, X=(S (x1),S(x2),...,S(xn)) it is overall
The state vector of network node, T=(S (t'1),S(t'2),...,S(t'h)) it is the current state sending the detection detecting bag
Vector, t'1,t'2,...,t'hSend the detection detecting bag for current, h is the current number sending the detection detecting bag, α
It is preset weights with β；
If the cost C (T of S6 fault location collectiondia) >=B, then go to S9, otherwise detects tmaxSend and detect bag to Information Network
In, obtain the state S (t of Information Network this detection currentmax), t will be detected simultaneouslymaxAdd fault location collection TdiaAnd by it from time
Choosing detects collection TstaMiddle deletion, if now candidate detects collection TstaFor sky, then go to S9；Wherein B is default detection cost threshold value；
S7, take t'maxDetect collection T for current candidatestaThe middle detection detecting Maximum Value, if t'maxWith tmaxIn same son
In net, then recalculate detection t'maxDetection be worth V (t'max), otherwise pass directly to S8；
S8, takeDetect collection T for candidatestaIn come detection t'maxRear one detection, ifThen make
tmaxFor t'max, go to S6, otherwise judgeWith tmaxWhether in same subnet, if not in same subnet, compare t'maxWithDetection be worth, if in same subnet, recalculateDetection be worthCompare t' againmaxWithDetection
It is worth；IfThen make t'maxForGo to S8, otherwise make tmaxFor t'max, go to S6；
S9, an example x of calculating overall network node state X*=argmaxP (X | ST), draw overall network node
Most possible state vector x*, thus positioning failure node；Wherein ST=(S (T1),S(T2) ...) it is current failure detection
Collection TobsWith fault location collection TdiaIn all detections state vector.
The Bayesian network model interior joint x that step S3 is set upi(i=1,2 ..., n) represent information network in node,
Node tj(j=1,2 ..., m) represent that candidate detects collection TstaIn detection, network node and detect between directed edge represent
This node is contained in the detective path of this detection.Node xiThere are two states, S (xi)=0 represents that this node does not have fault, S
(xi)=1 represents that this node has fault, detects tjState S (tj) relevant with the state pointing to its all-network node, only
The state wanting a network node of its sensing is 0, this state S (t detectingj), any one node on detective path goes out
Fault all can lead to detection to be out of order, as long as detecting tjThere is the state S (x of a node in the network node pointing toi)=0, this
State S (the t detectingj)=0.It is abstracted into one of Bayesian network model node by detecting with network node, effectively
Simplify problem, and the directed edge between node and detection makes fault location process apparent.
Step S4 subnet division network, specially according to fault detect collection TobsIn all detect return results, by following formula
Represent this network node x to define " approximate Observable node "iState approximately determine：
Wherein,
| | represent the number that set comprises element, λ and θ is preset value, λ represents what approximate Observable node should be detected
Minimum number, θ represents that detecting consistent results in returning result at least needs the ratio of satisfaction, 0≤θ≤1.If network node xiWith
When meet formula (1) and formula (2) then it is assumed that it is approximately observable, its point to detection between just meet conditional independence, make
With D- separation principle, large-scale Bayesian network is converted into several separate sub-networks, is updating what candidate detected
Detect remaining only needing to when being worth in renewal same subnet to detect.
Step S5 to step S8 is to detect collection T from candidatestaMiddle selection fault location collection TdiaStep.
Specifically, detect collection T to weigh candidatestaThe quality of middle detection, introduces comentropy and quantifies power information net state
Uncertainty, definition vector X=(S (x1),S(x2),...,S(xn)) represent overall network node state, wherein n be net
The number of network node, definition vector T=(S (t'1),S(t'2),...,S(t'h)) represent the shape sending the detection detecting bag
State, wherein h are the current numbers sending the detection detecting bag, then, after obtaining detection returning result, information net state is not
Certainty is expressed as：
In formula (4), P (X, T) is the joint probability of X and T, and P (X | T) is the conditional probability of X in the case of known T.Using information
Gain G (t) quantifies the quality of single detection t：
G (t)=H (X | T)-H (X | T ∪ { S (t) }) (5)
In order to ensure that prior network node is preferentially detected, compare two probes good and bad when need to consider network
The importance degree of node, the inventive method defines V (t) and represents that the detection of single detection is worth：
V (t)=α G (t)+β NIm(t) (6)
N in formula (6)ImT () represents the number of important node in the detective path detecting t, in the present embodiment, important node is
It is in the core layer network node of network center；α and β is weights, adjusts α, the value of β can be with balancing information gain and important node
Detect the weight in being worth, meet the needs of different information network structures.
Using formula (4) to formula (6), calculate candidate and detect collection TstaIn each detect detection be worth.Detect collection from candidate
TstaIn constantly select detect Maximum Value detection add set T*, as C (T*During)=B, selection terminates, and obtains fault location
Collection Tdia=T*.Wherein C (T*) represent set T*Cost, the usually price of probe or complete to detect the time etc. needing,
C (T in the present embodiment*) it is set T*The number of middle detection, B is the detection cost threshold value according to actual conditions setting.
Select in the detection process detect Maximum Value, after observing a new result of detection, remaining candidate detects
The detection concentrating all detections is worth and all diminishes in the updated, therefore only needs to update and currently has the detection of maximum probe value and be
Can, if other detections of the detection value ratio that after updating, this detects are also big before updating, in the case of not updating remaining detection
Just can determine that this detection is exactly the optimal selection of next round, cost savings the time.
Step S9 is specific, obtains fault location collection TdiaAfterwards, according to fault detect collection TobsWith fault location collection TdiaMiddle spy
Survey the interpretation of result Electricity Information Network interior joint state returning, that is, ask：
x*=argmaxP (X | ST) (8)
Wherein ST=(S (T1),S(T2) ...) it is to have sent to detect the state set returning, x*It is the one of network state X
Individual example.x*If in have network node state be 0, show that this network node is faulty.Comprehensively sentenced using fault detect collection
Disconnected Information Network node state can reduce the burden of fault location collection, can obtain more preferably in the case that detection cost threshold value is certain
Fault location result.
In Fig. 2 and Fig. 3, IPCA is the inventive method, and BPEA is traditional multiple faults network detection selection algorithm, and Fig. 2 show
The fault location degree of accuracy comparison diagram of the inventive method and BPEA method, Fig. 3 show the inventive method and BPEA method when
Effect property comparison diagram.From figure 2 it can be seen that the number with network node increases, the degree of accuracy of the inventive method and BPEA side
Method is approximately the same, shows that the inventive method ensure that the degree of accuracy of fault location.From figure 3, it can be seen that the number with node
Increase, the inventive method to the improvement effect of time substantially, improve can fault diagnosis time efficiency.
Claims (3)
1. a kind of power information network fault positioning method is it is characterised in that comprise the following steps：
S1, randomly select in Power Information Network network node deployment probe, obtain by all probes to all-network node
Whole detections, composition is available to detect collection Tall；
S2, from available detect collection TallThe detection of all nodes of middle selection energy overlay network, periodically sends and detects bag and arrive information
In net, these detect and constitute fault detect collection Tobs, obtain candidate simultaneously and detect collection Tsta=Tall-Tobs, now by can position
The fault location collection T detecting composition of faultdiaFor sky；
S3, set up candidate detect collection TstaIn detection tjWith network node xiBetween Bayesian network model, wherein j=1,
2 ..., m, i=1,2 ..., n, m are that candidate detects collection TstaThe number of middle detection, n is the number of network node；
S4, according to fault detect collection TobsIn all detections returning result, if the Bayesian network model that S3 is set up is divided into
Dry subnet；
S5, calculating candidate detect collection TstaIn each detect information gain G (tj)=H (X | T)-H (X | T ∪ { S (tj)) and every
Important node number N of individual detectionIm(tj), show that the detection that each detects is worth V (tj)=α G (tj)+βNIm(tj), by candidate
Detect collection TstaIn detection according to detect be worth sort from big to small, take tmaxDetect collection T for current candidatestaMiddle detection is worth
Maximum detection；Wherein S (tj) for detecting tjState,P (X, T) is X and T
Joint probability, P (X | T) is the conditional probability of X in the case of known T, X=(S (x1),S(x2),...,S(xn)) it is overall network section
The state vector of point, T=(S (t'1),S(t'2),...,S(t'h)) it is the current state vector sending the detection detecting bag,
t'1,t'2,...,t'hSend the detection detecting bag for current, h is the current number sending the detection detecting bag, α and β is
Preset weights；
If the cost C (T of S6 fault location collectiondia) >=B, then go to S9, otherwise detects tmaxSend and detect bag in Information Network,
Obtain the state S (t of Information Network this detection currentmax), t will be detected simultaneouslymaxAdd fault location collection TdiaAnd it is visited from candidate
Survey collection TstaMiddle deletion, if now candidate detects collection TstaFor sky, then go to S9；Wherein B is default detection cost threshold value；
S7, take t'maxDetect collection T for current candidatestaThe middle detection detecting Maximum Value, if t'maxWith tmaxIn same subnet,
Then recalculate detection t'maxDetection be worth V (t'max), otherwise pass directly to S8；
S8, takeDetect collection T for candidatestaIn come detection t'maxRear one detection, ifThen make tmaxFor
t'max, go to S6, otherwise judgeWith tmaxWhether in same subnet, if not in same subnet, compare t'maxWith's
Detect and be worth, if in same subnet, recalculateDetection be worthCompare t' againmaxWithDetection be worth；
IfThen make t'maxForGo to S8, otherwise make tmaxFor t'max, go to S6；
S9, an example x of calculating overall network node state X*=argmaxP (X | ST), showing that overall network node has most can
State vector x of energy*, thus positioning failure node；Wherein ST=(S (T1),S(T2) ...) it is current failure detection collection TobsWith
Fault location collection TdiaIn all detections state vector.
2. power information network fault positioning method according to claim 1 is it is characterised in that step S4 specifically includes：
According to fault detect collection TobsIn all detections returning result, calculating network node xiThe number of times that should be detectedWith covering this network node xiThe consistent ratio of all detection returning resultsIf meet a simultaneouslyi＞ λ and bi＞ θ, then define this network node xiFor approximate Observable node, using D-
The Bayesian network model that S3 sets up is divided into several separate small-scale Bayes's sub-networks by separation method；Wherein
C' is that available detection collects TallMiddle overlay network node xiDetection, c be fault detect collection TobsMiddle overlay network node xiSpy
Survey,K=0,1, i=1,2 ..., n, e are fault detect collection TobsMiddle overlay network node
xiAnd state is the detection of k, | | represent the number asking set to comprise element, λ and θ is predetermined coefficient.
3. power information network fault positioning method according to claim 1 and 2 it is characterised in that in step S6 fault fixed
Position collection TdiaCost C (Tdia) it is fault location collection TdiaThe number of middle detection.
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