CN106452877A - Electric power information network fault locating method - Google Patents
Electric power information network fault locating method Download PDFInfo
- Publication number
- CN106452877A CN106452877A CN201610911531.5A CN201610911531A CN106452877A CN 106452877 A CN106452877 A CN 106452877A CN 201610911531 A CN201610911531 A CN 201610911531A CN 106452877 A CN106452877 A CN 106452877A
- Authority
- CN
- China
- Prior art keywords
- detection
- max
- network
- probes
- fault
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000001514 detection method Methods 0.000 claims abstract description 154
- 239000000523 sample Substances 0.000 claims description 113
- 239000013598 vector Substances 0.000 claims description 14
- 238000012217 deletion Methods 0.000 claims description 3
- 230000037430 deletion Effects 0.000 claims description 3
- 238000000926 separation method Methods 0.000 claims description 3
- ZHBOFZNNPZNWGB-UHFFFAOYSA-N 9,10-bis(phenylethynyl)anthracene Chemical compound C1=CC=CC=C1C#CC(C1=CC=CC=C11)=C(C=CC=C2)C2=C1C#CC1=CC=CC=C1 ZHBOFZNNPZNWGB-UHFFFAOYSA-N 0.000 description 6
- 238000004891 communication Methods 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 208000024891 symptom Diseases 0.000 description 3
- 230000002452 interceptive effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000012792 core layer Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
Classifications
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
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 invention relates to the technical field of electric power information, in particular to a method for positioning a fault of an electric power information network.
Background
In the smart grid, the power information network carries various power production services, and is an important channel for guaranteeing power communication services. However, a large network structure causes a fault, and positioning is difficult after the fault occurs, and if the fault cannot be quickly and accurately checked, an electric power accident is caused. Most of traditional network fault management uses the alarm information sent by northbound interface passive acquisition equipment to establish an alarm-fault association model to analyze the fault source, but passively waiting for alarm results in poor fault location timeliness, and once the alarm information is false, lost and redundant, the accuracy of fault location cannot be guaranteed. In order to make up for the defects of traditional network management, active measurement is gradually applied to operation and maintenance of the power information network, and the method has important application significance for improving the management operation and maintenance level of the power information network.
The research results of using network detection to improve the network operation and maintenance level in public networks at home and abroad are relatively more, most of the research results are that a detection packet is actively sent to acquire network and service quality data, the hidden danger of the network is found to perform fault location, and typical application scenes comprise fault detection, location, identification and the like. Active probing aims to select the best probing set, and generally comprises a pre-selection mode and an interactive selection mode. The pre-selection mode is simple in calculation method, but the execution process is low in efficiency, the accuracy is relatively low due to poor real-time performance, and the interactive detection set selection mode reduces the number of executed detections by using a mode of calculating and sending at the same time, so that the network load is lower.
The patent number CN103840967A is named as a method for positioning faults in a power communication network, and provides the method for positioning the faults in the power communication network, wherein the method is characterized in that the many-to-many uncertainty of faults and symptoms is modeled by a weighted bipartite graph, the fault influence weight is introduced, the probability of prior faults is converted into the conditional probability by utilizing the total probability and Bayesian thought under the bipartite graph model, the fault influence degree is calculated, the influence of suspected faults is controlled by adding a credible parameter, and the symptoms which are generated are completely and reasonably explained are selected by combining the coverage degree and the contribution degree. The theory involved in the method is too complicated, the realization is complex, the topology of the whole network needs to be analyzed, the collection of symptoms is too passive, and the positioning timeliness is low.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the existing method is complex to realize, and can not meet the requirement of higher fault positioning timeliness while ensuring the fault positioning accuracy.
The invention provides a power information network fault positioning method, which comprises the following steps:
s1, randomly selecting network nodes in the power information network to deploy probes, obtaining all probes from all the probes to all the network nodes, and forming an available probe set Tall；
S2, from available detection set TallSelects probes covering all nodes of the network, periodically sends probe packets to the information network, these probes form a fault detection set TobsWhile obtaining a candidate probe set Tsta＝Tall-TobsNow fault location set T consisting of detections capable of locating faultsdiaIs empty;
s3, establishing a candidate detection set TstaDetection of (1)jAnd network node xiWhere j 1,2, is 1,2, n, m is a candidate probe set TstaThe number of probes, n is the number of network nodes;
s4, detecting set T according to faultsobsDividing the Bayesian network model established in S3 into a plurality of subnets according to the return results of all the probes;
s5, calculating candidate detection set TstaGain of information per probe G (t)j)＝H(X|T)-H(X|T∪{S(tj) }) and the number of important nodes N detected eachIm(tj) To obtain the detection value V (t) of each detectionj)＝αG(tj)+βNIm(tj) Candidate probe set TstaThe middle detection is ranked from big to small according to the detection value, and t is takenmaxFor the current candidate probe set TstaDetecting with the maximum detection value; wherein S (t)j) To detect tjIn the state of (a) to (b),p (X, T) is the joint probability of X and T, P (X | T) is the conditional probability of X given T, X ═ S (X) T1),S(x2),...,S(xn) Is the state vector of the entire network node, T ═ S (T'1),S(t'2),...,S(t'h) Is a state vector of probes, t 'for currently transmitted probe packets'1,t'2,...,t'hH is the number of probes of the currently transmitted probe packet, and α and β are preset weights;
s6 cost C (T) of fault location setdia) If B is equal to or more than B, go to S9, otherwise detect tmaxSending the detection packet to the information network to obtain the current state S (t) of the detection in the information networkmax) While t will be detectedmaxAdd fault location set TdiaAnd from the candidate probe set TstaIn deletion, ifAt this time, candidate probe set TstaIf empty, go to S9; wherein B is a preset detection cost threshold;
s7, taking t'maxFor the current candidate probe set TstaDetection of medium detection value, if t'maxAnd tmaxWithin the same subnet, then probe t 'is recalculated'maxIs detected as a value V (t'max) Otherwise go directly to S8;
s8, gettingAs candidate probe set TstaMiddle row at probe t'maxThe latter bit is detected ifLet tmaxIs t'maxGo to S6, otherwise, judgeAnd tmaxIf the two sub-networks are not in the same subnet, t 'is compared'maxAndif the same subnet exists, recalculatingDetection value ofThen t 'is compared'maxAndthe detection value of (2); if it is notThen t 'is'maxIs composed ofGo to S8Otherwise, let tmaxIs t'maxGo to S6;
s9, calculating an example X of the overall network node state X*Get the most likely state vector X of the whole network node (X | ST) ═ argmaxP (X | ST)*Thereby locating the faulty node; wherein ST ═ S (T)1),S(T2) ,..) is the current fault detection set TobsAnd fault location set TdiaAll detected state vectors.
Further, step S4 specifically includes:
according to fault detection set TobsIn the return results of all probes, compute network node xiNumber of times to be detectedAnd overlay the network node xiAll probes of (2) return a consistent ratio of resultsIf a is satisfied simultaneouslyiλ and biIf > theta, defining the network node xiIn order to approximate observable nodes, a Bayesian network model established in S3 is divided into a plurality of mutually independent small-scale Bayesian sub-networks by using a D-separation method; wherein c' is the available probe set TallMedium coverage network node xiC is a fault detection set TobsMedium coverage network node xiThe detection of (a) is performed,e is a fault detection set TobsMedium coverage network node xiAnd the state is the detection of k, | · | represents the number of elements included in the solution set, and λ and θ are preset coefficients.
Further, the fault location set T in step S6diaCost C (T)dia) Set T for fault locationdiaThe number of probes in.
The invention has the beneficial effects that: (1) according to the method, a Bayesian network model is used for describing the relation between the detection in the candidate detection set and the nodes in the power information network, so that the problems are effectively simplified, and the fault positioning process is clearer due to the directed edges between the information nodes and the detection; (2) combining the information gain of single detection and the number of important nodes in a single detection path as detection values to measure the diagnostic capability of each detection in the candidate detection set, selecting the detection with the maximum detection value from the candidate detection set to form a fault positioning set, and improving the accuracy of fault positioning; (3) the Bayesian network is divided into a plurality of subnets by utilizing the condition independence of the Bayesian network, and only the detection of the same subnet is updated when the detection value is updated, so that the detection selection time is reduced, and the timeliness of fault positioning is improved; (4) the sub-models are expressed in the process of updating the detection value, the number of updating and detection is reduced, the detection selection time is shortened, and the timeliness of fault positioning is improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a graph comparing the fault location accuracy of the method of the present invention and the BPEA method.
FIG. 3 is a graph comparing the timeliness of the inventive process and the BPEA process.
Detailed Description
The concept of several probe sets used in the present invention is as follows:
1) available probe set Tall: a plurality of nodes are randomly selected in the information network to deploy probes, each probe can send an active detection packet to any network node in the information network, the probe is called as a detection, and the condition of the state of the network node can be inferred according to the result returned by the detection. The set of all probes from all probes to all network nodes is the available probe set TallThe probe set described below is a subset of this set.
2) Fault detection set Tobs: to ensure reliable operation of the power information network, the network operation needs to be monitored in real time without passively waiting for a user to issue a fault declaration, so that an available detection set T needs to be usedallIn which the probes are selected to periodically transmit probe packets to the network, the probes selected to be transmitted collectively have the ability to cover all nodes in the network, and the set of probes is a fault detection set Tobs。
3) Candidate probe set Tsta: available probe set TallMiddle or not belonging to fault detection set TobsOf (2) a set of probes, i.e. Tsta＝Tall-Tobs。
4) Fault location set Tdia: the fault location set is when the known fault detection set T isobsIn the case of returning a result from each probe, from the candidate probe set TstaThe selected detection group can achieve the purpose of positioning the fault.
The invention provides a power information network fault positioning method which comprises the following steps:
s1, randomly selecting network nodes in the power information network to deploy probes to obtain an available detection set Tall；
S2, from available detection set TallThe detection of all nodes of the coverage network is selected to form a fault detection set TobsWhile obtaining a candidate probe set Tsta；
S3, establishing a candidate detection set TstaDetection of (1)jAnd network node xiWhere j 1,2, is 1,2, n, m is a candidate probe set TstaThe number of probes, n is the number of network nodes;
s4, detecting set T according to faultsobsDividing the Bayesian network model established in S3 into a plurality of subnets according to the return results of all the probes;
s5, calculating candidate detection set TstaGain of information per probe G (t)j)＝H(X|T)-H(X|T∪{S(tj) }) and the number of important nodes N detected eachIm(tj) To obtain the detection value V (t) of each detectionj)＝αG(tj)+βNIm(tj) Candidate probe set TstaThe middle detection is ranked from big to small according to the detection value, and t is takenmaxFor the current candidate probe set TstaDetecting with the maximum detection value; wherein S (t)j) To detect tjIn the state of (a) to (b),p (X, T) is the joint probability of X and T, P (X | T) is the conditional probability of X given T, X ═ S (X) T1),S(x2),...,S(xn) Is the state vector of the entire network node, T ═ S (T'1),S(t'2),...,S(t'h) Is a state vector of probes, t 'for currently transmitted probe packets'1,t'2,...,t'hH is the number of probes of the currently transmitted probe packet, and α and β are preset weights;
s6 cost C (T) of fault location setdia) If B is equal to or more than B, go to S9, otherwise detect tmaxSending the detection packet to the information network to obtain the current state S (t) of the detection in the information networkmax) While t will be detectedmaxAdd fault location set TdiaAnd from the candidate probe set TstaIn the case of deletion, if the candidate probe set T is in the processstaIf empty, go to S9; wherein B is a preset detection cost threshold;
s7, taking t'maxFor the current candidate probe set TstaDetection of medium detection value, if t'maxAnd tmaxWithin the same subnet, then probe t 'is recalculated'maxIs detected as a value V (t'max) Otherwise go directly to S8;
s8, gettingAs candidate probe set TstaMiddle row at probe t'maxThe latter bit is detected ifLet tmaxIs t'maxGo to S6, otherwise, judgeAnd tmaxIf the two sub-networks are not in the same subnet, t 'is compared'maxAndif the same subnet exists, recalculatingDetection value ofThen t 'is compared'maxAndthe detection value of (2); if it is notThen t 'is'maxIs composed ofGo to S8, otherwise let tmaxIs t'maxGo to S6;
s9, calculating an example X of the overall network node state X*Get the most likely state vector X of the whole network node (X | ST) ═ argmaxP (X | ST)*Thereby locating the faulty node; wherein ST ═ S (T)1),S(T2) ,..) is the current fault detection set TobsAnd fault location set TdiaAll detected state vectors.
Step S3 establishesNode x in the Bayesian network modeli(i 1, 2.. times.n) denotes a node in the information network, and the node t denotes a node in the information networkj(j ═ 1, 2.. times, m) denotes the candidate probe set TstaThe directed edge between the network node and the probe indicates that the probe path of the probe includes the node. Node xiThere are two states, S (x)i) 0 means that the node is not faulty, S (x)i) 1 indicates that the node has a fault, and detects tjState S (t)j) The state S (t) of this probe is related to the states of all the network nodes pointing to it, as long as the state of one of the network nodes pointed to is 0j) Failure of any one node on the probe path results in a detected failure, i.e. whenever t is detectedjDirected to a state S (x) of one of the network nodesi) State S (t) of this detectionj) 0. The detection and network nodes are abstracted into nodes in a Bayesian network model, so that the problem is effectively simplified, and the fault positioning process is clearer due to the directed edges between the nodes and the detection.
Step S4 divides the sub-networks, specifically according to the failure detection set TobsWherein the result returned by all probes is defined as "approximate observable node" by the following formula to indicate the network node xiHas determined approximately:
wherein,
| DEG | represents the number of elements contained in the set, λ and theta are preset values, λ represents the minimum number of times that the node which can be observed approximately should be detected, and theta represents the detection return resultThe result of the agreement is at least the proportion that needs to be satisfied, theta is more than or equal to 0 and less than or equal to 1. If network node xiIf the formula (1) and the formula (2) are satisfied simultaneously, the system is considered to be approximately observable, the directional detection of the system meets the condition independence, a large-scale Bayesian network is converted into a plurality of mutually independent sub-networks by using a D-separation principle, and the other detections in the same sub-network are only required to be updated when the detection value of the candidate detection is updated.
From step S5 to step S8, the candidate probe set TstaWell-selected fault location set TdiaThe step (2).
In particular, to measure candidate probe sets TstaThe quality of the medium detection is characterized in that information entropy is introduced to quantify uncertainty of the state of the power information network, and a vector X is defined as (S (X)1),S(x2),...,S(xn) Denotes the state of the entire network node, where n is the number of network nodes, and a definition vector T ═ S (T'1),S(t'2),...,S(t'h) State of probing of transmitted probe packets, where h is the number of probes of the currently transmitted probe packet, the uncertainty of the state of the information network after obtaining the probe return result is expressed as:
in the formula (4), P (X, T) is a joint probability of X and T, and P (X | T) is a conditional probability of X in the case of known T. And (3) quantifying the advantages and disadvantages of the single detection t by using an information gain G (t):
G(t)＝H(X|T)-H(X|T∪{S(t)}) (5)
in order to ensure that more important network nodes are detected preferentially, the importance of the network nodes needs to be considered when comparing the advantages and disadvantages of two probes, V (t) is defined to represent the detection value of a single probe:
V(t)＝αG(t)+βNIm(t) (6)
n in formula (6)Im(t) representsThe number of important nodes in the detection path of the detection t is detected, the important nodes are core layer network nodes in the network center in the embodiment, α and β are weights, and the value of α is adjusted to balance the information gain and the weight of the important nodes in the detection value, so that the requirements of different information network structures are met.
Using equations (4) to (6), candidate probe set T is calculatedstaThe detection value of each of the probes. From candidate probe set TstaContinuously selecting the detection joining set T with the maximum detection value*When C (T)*) When B is satisfied, the selection is finished and a fault location set T is obtaineddia＝T*. Wherein C (T)*) Representation set T*The cost of (C) (T) in this embodiment, which is generally the price of the probe or the time required to complete the probe*) As a set T*The number of middle detections, B is a detection cost threshold set according to actual conditions.
In the detection process of selecting the detection value with the maximum detection value, after a new detection result is observed, the detection values of all the detections in the remaining candidate detection sets are reduced after updating, so that the detection with the maximum detection value at present only needs to be updated, and if the detection value of the detection after updating is larger than that of the detection before updating of other detections, the detection can be determined to be the best selection of the next round without updating of other detections, so that the time is greatly saved.
Step S9 is specifically to find the fault location set TdiaThen, according to the fault detection set TobsAnd fault location set TdiaAnd analyzing the node state in the power information network by using the result returned by the middle detection, namely solving the following steps:
x*＝argmaxP(X|ST) (8)
wherein ST ═ S (T)1),S(T2) ,..) is the set of states returned by the sent probe, x*Is an example of network state X. x is the number of*If the state of any network node is 0, the network node is indicated to be faulty. The node state of the information network can be comprehensively judged by using a fault detection setThe burden of a fault positioning set is reduced, and a better fault positioning result can be obtained under the condition that a detection cost threshold value is fixed.
In fig. 2 and fig. 3, IPCA is the method of the present invention, BPEA is the traditional multi-fault network detection selection algorithm, fig. 2 is a comparison graph of fault location accuracy between the method of the present invention and the BPEA method, and fig. 3 is a comparison graph of timeliness between the method of the present invention and the BPEA method. As can be seen from fig. 2, as the number of network nodes increases, the accuracy of the method of the present invention is approximately the same as that of the BPEA method, indicating that the method of the present invention ensures the accuracy of fault location. As can be seen from fig. 3, as the number of nodes increases, the time improvement effect of the method of the present invention is significant, and the time efficiency of fault diagnosis is improved.
Claims (3)
1. A power information network fault positioning method is characterized by comprising the following steps:
s1, randomly selecting network nodes in the power information network to deploy probes, obtaining all probes from all the probes to all the network nodes, and forming an available probe set Tall；
S2, from available detection set TallSelects probes covering all nodes of the network, periodically sends probe packets to the information network, these probes form a fault detection set TobsWhile obtaining a candidate probe set Tsta＝Tall-TobsNow fault location set T consisting of detections capable of locating faultsdiaIs empty;
s3, establishing a candidate detection set TstaDetection of (1)jAnd network node xiWhere j 1,2, is 1,2, n, m is a candidate probe set TstaThe number of probes, n is the number of network nodes;
s4, detecting set T according to faultsobsDividing the Bayesian network model established in S3 into a plurality of subnets according to the return results of all the probes;
s5, calculating candidate detection set TstaGain of information per probe G (t)j)＝H(X|T)-H(X|T∪{S(tj) }) and the number of important nodes N detected eachIm(tj) To obtain the detection value V (t) of each detectionj)＝αG(tj)+βNIm(tj) Candidate probe set TstaThe middle detection is ranked from big to small according to the detection value, and t is takenmaxFor the current candidate probe set TstaDetecting with the maximum detection value; wherein S (t)j) To detect tjIn the state of (a) to (b),p (X, T) is the joint probability of X and T, P (X | T) is the conditional probability of X given T, X ═ S (X) T1),S(x2),...,S(xn) Is the state vector of the entire network node, T ═ S (T'1),S(t'2),...,S(t'h) Is a state vector of probes, t 'for currently transmitted probe packets'1,t'2,...,t'hH is the number of probes of the currently transmitted probe packet, and α and β are preset weights;
s6 cost C (T) of fault location setdia) If B is equal to or more than B, go to S9, otherwise detect tmaxSending the detection packet to the information network to obtain the current state S (t) of the detection in the information networkmax) While t will be detectedmaxAdd fault location set TdiaAnd from the candidate probe set TstaIn the case of deletion, if the candidate probe set T is in the processstaIf empty, go to S9; wherein B is a preset detection cost threshold;
s7, taking t'maxFor the current candidate probe set TstaDetection of medium detection value, if t'maxAnd tmaxWithin the same subnet, then probe t 'is recalculated'maxIs detected as a value V (t'max) Otherwise go directly to S8;
s8, gettingAs candidate probe set TstaMiddle row at probe t'maxThe latter bit is detected ifLet tmaxIs t'maxGo to S6, otherwise, judgeAnd tmaxIf the two sub-networks are not in the same subnet, t 'is compared'maxAndif the same subnet exists, recalculatingDetection value ofThen t 'is compared'maxAndthe detection value of (2); if it is notThen t 'is'maxIs composed ofGo to S8, otherwise let tmaxIs t'maxGo to S6;
s9, calculating an example X of the overall network node state X*Get the most likely state vector X of the whole network node (X | ST) ═ argmaxP (X | ST)*Thereby locating the faulty node; wherein ST ═ S (T)1),S(T2) ,..) is the current fault detection set TobsAnd fault location set TdiaAll detected state vectors.
2. The method according to claim 1, wherein the step S4 specifically includes:
according to fault detection set TobsIn the return results of all probes, compute network node xiNumber of times to be detectedAnd overlay the network node xiAll probes of (2) return a consistent ratio of resultsIf a is satisfied simultaneouslyiλ and biIf > theta, defining the network node xiIn order to approximate observable nodes, a Bayesian network model established in S3 is divided into a plurality of mutually independent small-scale Bayesian sub-networks by using a D-separation method; wherein c' is the available probe set TallMedium coverage network node xiC is a fault detection set TobsMedium coverage network node xiThe detection of (a) is performed,k is 0,1, i is 1,2obsMedium coverage network node xiAnd the state is the detection of k, | · | represents the number of elements included in the solution set, and λ and θ are preset coefficients.
3. Method for locating faults in an electrical power information network according to claim 1 or 2, characterized in that in step S6, a fault location set T is setdiaCost C (T)dia) Set T for fault locationdiaThe number of probes in.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610911531.5A CN106452877B (en) | 2016-10-19 | 2016-10-19 | Fault positioning method for power information network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610911531.5A CN106452877B (en) | 2016-10-19 | 2016-10-19 | Fault positioning method for power information network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106452877A true CN106452877A (en) | 2017-02-22 |
| CN106452877B CN106452877B (en) | 2021-09-24 |
Family
ID=58175301
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610911531.5A Active CN106452877B (en) | 2016-10-19 | 2016-10-19 | Fault positioning method for power information network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106452877B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106685742A (en) * | 2017-03-02 | 2017-05-17 | 北京邮电大学 | Network fault diagnosing method and apparatus |
| CN107135100A (en) * | 2017-05-02 | 2017-09-05 | 国网辽宁省电力有限公司 | A kind of malfunctioning node detection method of SDN |
| CN107147534A (en) * | 2017-05-31 | 2017-09-08 | 国家电网公司 | A kind of probe deployment method of quantity optimization for power telecom network fault detect |
| CN107171832A (en) * | 2017-05-02 | 2017-09-15 | 国网辽宁省电力有限公司 | A kind of malfunctioning node detection system of SDN |
| CN110048901A (en) * | 2019-06-04 | 2019-07-23 | 广东电网有限责任公司 | A kind of Fault Locating Method of power telecom network, device and equipment |
| CN110380903A (en) * | 2019-07-23 | 2019-10-25 | 广东电网有限责任公司 | A kind of power telecom network fault detection method, device and equipment |
| CN112436954A (en) * | 2020-10-10 | 2021-03-02 | 西安电子科技大学 | Probability probe selection method, system, equipment and application for fault diagnosis |
| CN113300868A (en) * | 2020-07-13 | 2021-08-24 | 阿里巴巴集团控股有限公司 | Method and device for positioning fault network equipment node and network communication method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120116721A1 (en) * | 2004-04-16 | 2012-05-10 | International Business Machines Corporation | Active probing for real-time diagnosis |
| CN102684902A (en) * | 2011-03-18 | 2012-09-19 | 北京邮电大学 | Network fault positioning method based on probe prediction |
| CN103501257A (en) * | 2013-10-11 | 2014-01-08 | 北京邮电大学 | Method for selecting IP (Internet Protocol) network fault probe |
| CN103840967A (en) * | 2013-12-23 | 2014-06-04 | 北京邮电大学 | Method for locating faults in power communication network |
| CN105721209A (en) * | 2016-02-19 | 2016-06-29 | 国网河南省电力公司信息通信公司 | Fault detection method for noisy network |
-
2016
- 2016-10-19 CN CN201610911531.5A patent/CN106452877B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120116721A1 (en) * | 2004-04-16 | 2012-05-10 | International Business Machines Corporation | Active probing for real-time diagnosis |
| CN102684902A (en) * | 2011-03-18 | 2012-09-19 | 北京邮电大学 | Network fault positioning method based on probe prediction |
| CN103501257A (en) * | 2013-10-11 | 2014-01-08 | 北京邮电大学 | Method for selecting IP (Internet Protocol) network fault probe |
| CN103840967A (en) * | 2013-12-23 | 2014-06-04 | 北京邮电大学 | Method for locating faults in power communication network |
| CN105721209A (en) * | 2016-02-19 | 2016-06-29 | 国网河南省电力公司信息通信公司 | Fault detection method for noisy network |
Non-Patent Citations (1)
| Title |
|---|
| 时庆宾: "基于PMS和GIS系统的10kV配电网故障快速定位方法研究", 《电子制作》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106685742A (en) * | 2017-03-02 | 2017-05-17 | 北京邮电大学 | Network fault diagnosing method and apparatus |
| CN106685742B (en) * | 2017-03-02 | 2020-04-10 | 北京邮电大学 | Network fault diagnosis method and device |
| CN107135100A (en) * | 2017-05-02 | 2017-09-05 | 国网辽宁省电力有限公司 | A kind of malfunctioning node detection method of SDN |
| CN107171832A (en) * | 2017-05-02 | 2017-09-15 | 国网辽宁省电力有限公司 | A kind of malfunctioning node detection system of SDN |
| CN107147534A (en) * | 2017-05-31 | 2017-09-08 | 国家电网公司 | A kind of probe deployment method of quantity optimization for power telecom network fault detect |
| CN110048901A (en) * | 2019-06-04 | 2019-07-23 | 广东电网有限责任公司 | A kind of Fault Locating Method of power telecom network, device and equipment |
| CN110048901B (en) * | 2019-06-04 | 2022-03-22 | 广东电网有限责任公司 | Fault positioning method, device and equipment for power communication network |
| CN110380903A (en) * | 2019-07-23 | 2019-10-25 | 广东电网有限责任公司 | A kind of power telecom network fault detection method, device and equipment |
| CN113300868A (en) * | 2020-07-13 | 2021-08-24 | 阿里巴巴集团控股有限公司 | Method and device for positioning fault network equipment node and network communication method |
| CN113300868B (en) * | 2020-07-13 | 2024-04-30 | 阿里巴巴集团控股有限公司 | Positioning method and device for fault network equipment node and network communication method |
| CN112436954A (en) * | 2020-10-10 | 2021-03-02 | 西安电子科技大学 | Probability probe selection method, system, equipment and application for fault diagnosis |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106452877B (en) | 2021-09-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106452877B (en) | Fault positioning method for power information network | |
| CN106685742B (en) | Network fault diagnosis method and device | |
| CN104270268A (en) | Network performance analysis and fault diagnosis method of distributed system | |
| CN103428852B (en) | A kind of wireless terminal location method and device | |
| BR102012001055A2 (en) | generation of a network map | |
| CN110380903B (en) | Power communication network fault detection method, device and equipment | |
| CN102684902B (en) | Based on the network failure locating method of probe prediction | |
| Lampe et al. | Power grid topology inference using power line communications | |
| CN105187273A (en) | Probe deployment method and device for power communication private network service monitoring | |
| JP2023516486A (en) | Topology and phase detection for power grids | |
| JP2017041883A (en) | Fault detection device and fault detection system | |
| CN111884859B (en) | Network fault diagnosis method and device and readable storage medium | |
| CN112383934A (en) | Multi-domain cooperation service fault diagnosis method under 5G network slice | |
| CN104471902A (en) | Deterministic network failure detection | |
| JP2021522722A5 (en) | ||
| CN111600805A (en) | Bayes-based power data network congestion link inference algorithm | |
| CN106066415B (en) | Method and device for detecting fraud in an electrical power supply network, storage means | |
| Pourahmadi et al. | Indoor positioning and distance-aware graph-based semi-supervised learning method | |
| CN111683377B (en) | Real-time reliable relay deployment method for power distribution network | |
| Tagyo et al. | Network tomography using routing probability for undeterministic routing | |
| Li | Fully distributed state estimation of smart grids | |
| US9158871B2 (en) | Graph modeling systems and methods | |
| Qi et al. | Probabilistic probe selection algorithm for fault diagnosis in communication networks | |
| CN105898778B (en) | A kind of communication blind district confirmation method, communication blind district confirm device | |
| CN107426044B (en) | Serial line detection method and device and operation and maintenance server |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |