CN108414896A - A kind of electric network failure diagnosis method - Google Patents
A kind of electric network failure diagnosis method Download PDFInfo
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- CN108414896A CN108414896A CN201810565254.6A CN201810565254A CN108414896A CN 108414896 A CN108414896 A CN 108414896A CN 201810565254 A CN201810565254 A CN 201810565254A CN 108414896 A CN108414896 A CN 108414896A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
Abstract
The present invention discloses a kind of electric network failure diagnosis method, belongs to the technical field of electric network failure diagnosis.Power supply interrupted district is obtained by protection and breaker information, takes circuit therein as suspected malfunctions circuit, their combination current is constructed, as sample sequence；Using DTW algorithms, the DTW distances between the sample sequence of each suspected malfunctions circuit and the reference sequences of power supply interrupted district External Reference circuit are sought, lateral variation degree is configured to；Meanwhile using DTW algorithms, the DTW distances between the sample sequence before and after each suspected malfunctions line fault are sought, longitudinal difference degree is configured to.The transverse and longitudinal diversity factor of N suspected malfunctions circuit is formed to the similarity matrix of 2 × N, the matrix is clustered using artificial fish-swarm clustering algorithm, it is divided into failure classes and normal class, the larger one kind of cluster centre value is diagnosed as failure classes, circuit in failure classes is judged as faulty line.The present invention can Accurate Diagnosis faulty line, do not influenced by abort situation, transition resistance, fault type etc., it is functional.
Description
Technical field
The invention belongs to the technical fields of electric network failure diagnosis.
Background technology
Continuous development of the correlative study of electric network failure diagnosis Jing Guo recent decades, experts and scholars both domestic and external propose perhaps
More new theories, such as neural network, expert system, Petri network, rough set, these methods all rely on greatly protection, breaker
Correctly act or receive complete warning information.Go out when crossing in more complicated failure or related warning information occurs for system
Active, it is possible to influence the accuracy of diagnostic result.
But with application of the new technology in power grid, the acquisition of electrical quantity is more and more convenient, using including in electrical quantity
Abundant information carries out fault diagnosis, becomes the hot spot of electric network failure diagnosis research.Electric quantity information is believed compared to switching value simultaneously
Breath has the characteristics that accuracy height, strong interference immunity, but also carrying out troubleshooting circuit using electric quantity information becomes one
Important fault diagnosis direction.
Therefore, this method is directed to electric simulation amount information, using the current information of circuit, using dynamic time warping DTW
(Dynamic Time Warping) algorithm is combined with artificial fish-swarm clustering algorithm to diagnose fault circuit.
Invention content
The object of the present invention is to provide a kind of electric network failure diagnosis methods, it can efficiently solve existing electric network failure diagnosis
Method depends on switching value information, less the problem of using electric quantity information more.
The purpose of the present invention is be achieved through the following technical solutions：
1. a kind of electric network failure diagnosis method, step include：
Step 1: according to Grid network topology structure, each breaker actuation information in the region is collected respectively, obtains electricity
The power supply interrupted district of net using each circuit in power supply interrupted district as suspected malfunctions circuit, and selects a reference line outside power supply interrupted district
Road；
Step 2: being directed to suspected malfunctions circuit j, the combination current sample sequence X of first three cycle of its fault moment is obtainedjb
With the combination current sample sequence X of three cycles after fault momentja, using dynamic time consolidation algorithm DTW, find out sequence Xjb
With XjaBetween DTW distances, which is configured to the longitudinal difference degree d of the circuitzj；
Step 3: finding out the sample sequence X of the circuit using DTW algorithms for suspected malfunctions circuit jjWith reference line
Reference sequences X0Between DTW distances, which is configured to the lateral variation degree d of the circuithj；
Step 4: according to Step 2: step 3, finds out the lateral variation of N suspected malfunctions circuit in power supply interrupted district respectively
Degree, longitudinal difference degree form the similarity matrix of a 2 × N-dimensional；For the matrix, carried out using artificial fish-swarm clustering algorithm
Optimization Solution and cluster are two major classes, i.e. failure classes and normal class each suspected malfunctions route clustering, and per in a kind of cluster
The heart is the vector of two rows 1 row, the big one kind of cluster centre numerical value is judged as failure classes, another kind of is normal class, by failure
Circuit is judged as faulty line in class.
After the power supply interrupted district for obtaining power grid, collect in grid power blackout region outside each suspected malfunctions circuit and power supply interrupted district
The three-phase current sampled value of one reference line constructs the combination current sequence of each circuit：
F in Fig. 21, f2Two position of failure point are indicated respectively, and transmission line of electricity busbar one end is set as transmitting terminal, is marked with S,
The other end is line receiver end, is marked with R, iSExpression flows to the electric current of circuit, i from the sending end of circuitRIndicate the receiving end from circuit
Flow to the electric current of circuit, iaS、ibS、icSFor the instantaneous value of the three-phase current of circuit transmitting terminal, electric current positive direction is defined as from busbar
Transmitting terminal is directed toward line receiver end；
The difference current for obtaining A phase currents is：
ia=iaS+iaR (1)
The difference current for obtaining other two-phase again is ib、ic, the combination current for defining the circuit is：
iz=ia+2ib-3ic (2)
The combination current of each three cycles is as sample sequence X before and after the fault moment of selection suspected malfunctions circuitj, will join
The combination current of each three cycles, which is used as, before and after " the virtual faults moment " of examining circuit refers to sequence X0。
It is directed to j-th strip suspected malfunctions circuit described in step 2, electricity is obtained using traditional current break quantity measuring method
Net breaks down moment tl, respectively obtained before the line fault moment by recorder data and each three cycles after fault moment
Current data；By circuit combination current sequence constructing method, the combination current sequence x before the line fault moment is constructedj
(k1), wherein k1=tl-3T,…,tl- 1 is each moment before failure, constructs the combination current sequence after the line fault moment
xj(k2), wherein k2=tl,…,tl+ 3T is each moment after failure；Then it is directed to the circuit, using DTW algorithms, when finding out failure
Combination current sequence x after quarterj(k2) with fault moment before combination current sequence xj(k1) between minimal path value, i.e. longitudinal direction DTW
Distance γzj, by longitudinal direction DTW distances γzjIt is configured to the longitudinal difference degree d of j-th strip circuitzj。
A reference line is selected outside power supply interrupted district, and the combination current sample of reference line is constructed using above-mentioned steps
Sequence X0, for j-th strip suspected malfunctions circuit in power supply interrupted district, construct the combination current sample sequence X of the circuitj；Then sharp
With DTW algorithms, the sequence X of the circuit is solvedjWith the sequence X of reference line0Between minimal path value, be configured to lateral DTW
Distance γhj, transverse direction DTW distances are configured to the lateral variation degree d of j-th strip circuithj。
The longitudinal difference degree d of N suspected malfunctions circuit in the power supply interrupted districtzjWith lateral variation degree dhjIt is poor to form 2 × N
Different degree matrix d=[x1,x2,…,xN；y1,y2,…,yN], wherein the lateral variation degree of each circuit of the first behavior, the second behavior are each
The longitudinal difference degree of circuit；This matrix is original sample set to be sorted, the input quantity as artificial fish-swarm clustering algorithm：
First, the parameter of initialization algorithm, every Artificial Fish include classification results cluster centre corresponding with them, together
When, by each element in matrix d and the distance between cluster centre and it is reciprocal as the adaptation for evaluating every Artificial Fish quality
Angle value, i.e., the cluster centre value of every Artificial Fish；
Secondly, three behaviors are executed to several Artificial Fishs, behavior of bunching is first carried out, i.e., by owner in present viewing field
Matrix d is divided by cluster centre of the mean values of the cluster centre of work fish as shoal of fish center according to the cluster centre
Each element is assigned to cluster centre with the element apart from nearest one kind by two classes when specifically executing；Fish is obtained after classification
The fitness value of group center, if the fitness value meets：fitc＞ fitm&&fitc/nf＞ δ fitm, wherein fitmIt is the m articles
The fitness value of Artificial Fish, fitcFor the fitness value of shoal of fish center, nfFor the item number of fish in the visual field, δ be crowding because
Son；Then fish moves towards the position and moves a step, and classifies according to new cluster centre；
Then, behavior of knocking into the back is executed, the Artificial Fish with maximum adaptation angle value in present viewing field is found, if the position is suitable
Angle value is answered to meet fitx＞ fitm&&fitx/nf＞ δ fitm, wherein fitxFor the fitness value of the fish with maximum adaptation angle value；
It moves and moves a step towards the position, and classify according to new cluster centre；
The size for comparing knock into the back behavior and behaviour adaptation angle value of bunching retains the big behavior of wherein fitness value, and will
Updated Artificial Fish is assigned to bulletin board, as this action selection as a result, if being not carried out above two behavior, holds
Row foraging behavior；
Foraging behavior is to find the big Artificial Fish of field range internal ratio itself fitness value, towards its direction movement one
Step, if not possessing the Artificial Fish of larger fitness value, one step of random movement, and updated Artificial Fish is assigned to announce
Plate；So far the update of the m articles Artificial Fish terminates；
After all Artificial Fishs update once like this, the cluster centre of every Artificial Fish is updated, completes an iteration；
It iterates according to above-mentioned steps, reaches iterations and terminate；
Obtain the classification results and cluster centre of optimal Artificial Fish recorded in bulletin board, classification results include failure classes and
Two class of normal class；Obtain which kind of every circuit belongs to by the classification results；Choose that big a kind of conduct of cluster centre numerical value
Failure classes, the line diagnosis for then including by failure classes are faulty line.
Present invention beneficial effect compared with prior art：
1) present invention carries out electric network failure diagnosis using DTW and artificial fish-swarm clustering method, only utilizes the electricity of line related
Flow data, the difficulty for obtaining data are relatively low；
2) artificial fish-swarm clustering method of the present invention is failure classes and normal class each route clustering, and then to be diagnosed to be event
Hinder circuit, fault threshold need not be set in this way, avoided because the improper diagnostic error that may be brought is arranged in fault threshold；
3) this method can be diagnosed fault accurately, not influenced by abort situation, transition resistance, fault type etc., performance
Well.
Description of the drawings
Fig. 1 is flow chart of the present invention
Fig. 2 is the simple conspectus of the present invention
Specific implementation mode
The specific implementation mode of the present invention is described below, in order to facilitate understanding by those skilled in the art this hair
It is bright：
1. a kind of electric network failure diagnosis method, step include：
Step 1: according to Grid network topology structure, each breaker actuation information in the region is collected respectively, obtains electricity
The power supply interrupted district of net using each circuit in power supply interrupted district as suspected malfunctions circuit, and selects a reference line outside power supply interrupted district
Road；
Step 2: being directed to suspected malfunctions circuit j, the combination current sample sequence X of first three cycle of its fault moment is obtainedjb
With the combination current sample sequence X of three cycles after fault momentja, using DTW algorithms, find out XjbWith XjaBetween DTW away from
From the distance to be configured to the longitudinal difference degree d of the circuitzj；
Step 3: finding out the sample sequence X of the circuit using DTW algorithms for suspected malfunctions circuit jjWith reference line
Reference sequences X0Between DTW distances, which is configured to the lateral variation degree d of the circuithj；
Step 4: according to Step 2: step 3, finds out the lateral variation of N suspected malfunctions circuit in power supply interrupted district respectively
Degree, longitudinal difference degree form the similarity matrix of a 2 × N-dimensional；For the matrix, carried out using artificial fish-swarm clustering algorithm
Optimization Solution and cluster are two major classes, i.e. failure classes and normal class each suspected malfunctions route clustering, and per in a kind of cluster
The heart is the vector of two rows 1 row, the big one kind of the numerical value of cluster centre is judged as failure classes, another kind of is normal class, by event
Circuit is judged as faulty line in barrier class.
After the power supply interrupted district for obtaining power grid, collect in grid power blackout region outside each suspected malfunctions circuit and power supply interrupted district
The three-phase current sampled value of one reference line constructs the combination current sequence of each circuit：
Transmission line of electricity busbar one end is set as transmitting terminal, is marked with S, the other end is line receiver end, is marked with R, iSTable
Show the electric current that circuit is flowed to from the sending end of circuit, iRExpression flows to the electric current of circuit, i from the receiving end of circuitaS、ibS、icSFor circuit
The instantaneous value of the three-phase current of transmitting terminal, electric current positive direction are defined as being directed toward line receiver end from busbar transmitting terminal：
The difference current for obtaining A phase currents is：
ia=iaS+iaR (1)
The difference current for obtaining other two-phase again is ib、ic, the combination current for defining the circuit is：
iz=ia+2ib-3ic (2)
The combination current of each three cycles is as sample sequence X before and after the failure of selection suspected malfunctions circuit jj, will refer to
The combination current of each three cycles, which is used as, before and after " the virtual faults moment " of circuit refers to sequence X0。
It is directed to i-th suspected malfunctions circuit described in step 2, electricity is obtained using traditional current break quantity measuring method
Net breaks down moment tl, respectively obtained before the line fault moment by recorder data and each three cycles after fault moment
Current data；By the circuit combination current sequence constructing method, the combination current before the line fault moment is constructed
Sequence xj(k1), wherein k1=tl-3T,…,tl- 1 is each moment before failure, constructs the comprehensive electricity after the line fault moment
Flow sequence xj(k2), wherein k2=tl,…,tl+ 3T is each moment after failure；Then it is directed to suspected malfunctions circuit j, is calculated using DTW
Method finds out combination current sequence x after fault momentj(k2) with fault moment before combination current sequence xj(k1) between minimal path
Diameter value, i.e. longitudinal direction DTW distances γzj, by longitudinal direction DTW distances γzjIt is configured to the longitudinal difference degree d of the circuitzj。
A reference line is selected outside power supply interrupted district, and the combination current sample of reference line is constructed using above-mentioned steps
Sequence X0, for j-th strip suspected malfunctions circuit in power supply interrupted district, construct the combination current sample sequence X of suspected malfunctions circuitj；
Then DTW algorithms are utilized, the sequence X of the circuit is solvedjWith the sequence X of reference line0Between minimal path value, be configured to
Lateral DTW distances γ hj, transverse direction DTW distances are configured to the lateral variation degree d of the circuithj。
The longitudinal difference degree d of N circuit in the power supply interrupted districtzjWith lateral variation degree dhjForm 2 × N similarity matrix d
=[x1,x2,...,xN；y1,y2,…,yN], wherein the lateral variation degree of each circuit of the first behavior, each circuit of the second behavior are indulged
To diversity factor；This matrix is original sample set to be sorted, the input quantity as artificial fish-swarm clustering algorithm：
First, the parameter of initialization algorithm, every Artificial Fish include classification results cluster centre corresponding with them, together
When, by each element in matrix d and the distance between cluster centre and it is reciprocal as the adaptation for evaluating every Artificial Fish quality
Angle value, i.e., the cluster centre of every Artificial Fish；
Secondly, three behaviors are executed to several Artificial Fishs, behavior of bunching is first carried out, i.e., it is present viewing field is all artificial
Matrix d is divided into two by cluster centre of the mean values of the cluster centre of fish as shoal of fish center according to the cluster centre
Each element is assigned to cluster centre with the element apart from nearest one kind by class when specifically executing；The shoal of fish is obtained after classification
The fitness value at center, if the fitness value meets：fitc＞ fitm&&fitc/nf＞ δ fitm, wherein fitiFor the m articles people
The fitness value of work fish, fitcFor the fitness value of shoal of fish center, nfFor the item number of fish in the visual field, δ is the crowding factor；
Then fish moves towards the position and moves a step, and classifies according to new cluster centre；
Then, behavior of knocking into the back is executed, the Artificial Fish with maximum adaptation angle value in present viewing field is found, if the position is suitable
Angle value is answered to meet fitx＞ fitm&&fitx/nf＞ δ fitm, wherein fitxFor the fitness value of the fish with maximum adaptation angle value；
It moves and moves a step towards the position, and classify according to new cluster centre；
The size for comparing knock into the back behavior and behaviour adaptation angle value of bunching retains the big behavior of wherein fitness value, and will
Updated Artificial Fish is assigned to bulletin board, as this action selection as a result, if being not carried out above two behavior, holds
Row foraging behavior；
Foraging behavior is to find the big Artificial Fish of field range internal ratio itself fitness value, towards its direction movement one
Step, if not possessing the Artificial Fish of larger fitness value, one step of random movement, and updated Artificial Fish is assigned to announce
Plate；So far the update of the m articles Artificial Fish terminates；
After all Artificial Fishs update once like this, the cluster centre of every Artificial Fish is updated, completes an iteration；
It iterates according to above-mentioned steps, reaches iterations and terminate；
Obtain the classification results and cluster centre of optimal Artificial Fish recorded in bulletin board, classification results include failure classes and
Two class of normal class；Obtain which kind of every circuit belongs to by the classification results；Choose that big one kind work of the numerical value of cluster centre
For failure classes, the line diagnosis for then including by failure classes is faulty line.
Embodiment
According to an embodiment of the present application, using PSCAD build 39 node power system emulation of standard IEEE test system into
Row emulation experiment.
In order to examine effectiveness of the invention, the different location of failure generation, size, the different faults of transition resistance are considered
Type generates fault diagnosis different influences, the transverse and longitudinal diversity factor of each circuit when various fault conditions occur for circuit 16-17
As shown in table 1.
The transverse and longitudinal diversity factor of each circuit when various types failure occurs for 1 circuit 16-17 of table
Note：The data underlined indicate abnormality degree larger in regular link.
In table 1, the circuit of number 16-17 is faulty line, number 15-16,16-19,16-24,17-18,17-27,3-
18 equal circuits are randomly selected regular link (similarly hereinafter).As can be seen from Table 1, when in different location event occurs for circuit 16-17
When barrier, no matter which kind of failure occurs, the lateral variation degree of faulty line will be more than the lateral variation degree of each regular link, be worth
It is noted that when AB high resistance grounds occur, difference between faulty line and regular link and not as good as other fault types that
Apparent, diversity factor magnitude is closer to, if simple given threshold, it is difficult to an appropriate numerical value is set, such as
When AB two-phase high resistance grounds occur, the diversity factor of faulty line 16-17 is 69.2267, and in regular link comprising 6.0769 this
The larger data of kind, increase the difficulty of given threshold.
The cluster and diagnostic result of 2 circuit 16-17 of table each circuits when breaking down
Table 2 is clusters of the circuit 16-17 when various fault types occur for different location and diagnostic result table, is seen by table 2
Go out, the cluster centre value that class 1 is obtained by the classification results is larger, chooses class 1 and is used as failure classes, the circuit 16- that failure classes include
17 are diagnosed as faulty line.Diagnostic result is correct.
No matter which kind of type is the failure that electric system occurs be, no matter the size of transition resistance is how many, the equal energy of the present invention
Identify faulty line 16-17.As seen from Table 2, the method that the present invention uses is not by fault type, abort situation, transition resistance
The influence of size can correctly identify faulty line.
The lateral variation degree that each circuit under twin failure occurs for circuit 16-17 and 3-18 is as shown in table 3.
The lateral variation degree of each circuit when twin failure occurs for 3 circuit 16-17 of table and 3-18
Table 3 is that circuit 16-17, circuit 3-18 occur under various types failure respectively under different location, different transition resistances
Circuit transverse and longitudinal diversity factor, as can be seen from Table 3, the diversity factor of two lines road 16-17,3-18 for breaking down are obviously more each normal
Circuit is big, especially under line to line fault, three-phase shortcircuit situation, difference clearly, even if numerical value is smaller under high-impedance state,
But still it is enough to distinguish faulty line and regular link.
The longitudinal difference degree of regular link wants small compared to lateral variation degree, this is consistent under substance malfunction,
This shows which kind of situation no matter carried algorithm encounter, and can obtain one and stablize output, although from can numerically distinguish those
It is the circuit of failure, but this is unfavorable for the efficient processing work of operating personnel, while the problem of with substance failure when encounters
Similar, fault threshold is set as how many suitable, is not easy to determine, so same method is taken, by two species diversity degree of transverse and longitudinal
The clustering processing that carries out next step is sent into as 2 × 7 dimension matrixes in artificial fish-swarm clustering algorithm.
The cluster and diagnostic result of 4 circuit 16-17 of table and 3-18 each circuits when breaking down
Table 4 is that in different location cluster result table when twin failure occurs occurs for circuit 16-17 and 3-18, is seen by table 4
Go out, the cluster centre value that class 1 is obtained by the classification results is larger, chooses class 1 and is used as failure classes, the circuit for including by failure classes
16-17,3-18 are diagnosed as faulty line.
Diagnostic result is correct.And result is simple and clear, can Accurate Diagnosis go out under different location, different transition resistance situation
The faulty line of different types of faults occurs.
The above substance experiment and dual experiment illustrate that the method recognition efficiency of the present invention is high, and the result provided is succinctly bright
.It should be apparent that the present invention is not limited to the ranges of specific implementation mode, for those skilled in the art,
As long as various change is in the spirit and scope of the present invention that the attached claims limit and determine, these variations are aobvious and easy
See, all are using the innovation and creation of present inventive concept in the row of protection.
Claims (5)
1. a kind of electric network failure diagnosis method, step include：
Step 1: according to Grid network topology structure, each breaker actuation information in the region is collected respectively, obtains power grid
Power supply interrupted district using each circuit in power supply interrupted district as suspected malfunctions circuit, and selects a reference line outside power supply interrupted district；
Step 2: being directed to suspected malfunctions circuit j, the combination current sample sequence X of first three cycle of its fault moment is obtainedjbAnd therefore
The combination current sample sequence X of three cycles after the barrier momentja, using dynamic time consolidation algorithm DTW, find out sequence XjbWith Xja
Between DTW distances, which is configured to the longitudinal difference degree d of the circuitzj；
Step 3: finding out the sample sequence X of the circuit using DTW algorithms for suspected malfunctions circuit jjWith the ginseng of reference line
Examine sequence X0Between DTW distances, which is configured to the lateral variation degree d of the circuithj；
Step 4: according to Step 2: step 3, finds out the lateral variation degree of N suspected malfunctions circuit in power supply interrupted district, indulges respectively
To diversity factor, the similarity matrix of a 2 × N-dimensional is formed；For the matrix, is optimized and asked using artificial fish-swarm clustering algorithm
Solution and cluster are two major classes, i.e. failure classes and normal class each suspected malfunctions route clustering, and are per a kind of cluster centre
The big one kind of cluster centre numerical value is judged as failure classes by the vector of two rows 1 row, and another kind of is normal class, by failure classes center line
Road is judged as faulty line.
2. a kind of electric network failure diagnosis method according to claim 1, which is characterized in that in the power supply interrupted district for obtaining power grid
Afterwards, the three-phase current sampled value of each suspected malfunctions circuit and the outer reference line of power supply interrupted district in grid power blackout region is collected,
Construct the combination current sequence of each circuit：
Transmission line of electricity busbar one end is set as transmitting terminal, is marked with S, the other end is line receiver end, is marked with R, iSIt indicates from line
The sending end on road flows to the electric current of circuit, iRExpression flows to the electric current of circuit, i from the receiving end of circuitaS、ibS、icSFor circuit transmitting terminal
Three-phase current instantaneous value, electric current positive direction is defined as being directed toward line receiver end from busbar transmitting terminal：
Construction A phase currents difference current be：
ia=iaS+iaR (1)
The difference current for constructing other two-phases is ib、ic, the combination current for defining the circuit is：
iz=ia+2ib-3ic (2)
The combination current of each three cycles is as sample sequence X before and after the fault moment of selection suspected malfunctions circuit jj, by reference line
The combination current of each three cycles, which is used as, before and after " the virtual faults moment " on road refers to sequence X0。
3. a kind of electric network failure diagnosis method according to claim 1, which is characterized in that be directed to jth described in step 2
Suspected malfunctions circuit, grid collapses moment t is obtained using traditional current break quantity measuring methodl, pass through recording number
According to respectively obtain before the suspected malfunctions circuit j fault moments and after fault moment each three cycles current data；By above-mentioned
Circuit combination current sequence constructing method, construct the combination current sequence x before the line fault momentj(k1), wherein k1
=tl-3T,…,tl- 1 is each moment before failure, constructs the combination current sequence x after the line fault momentj(k2), wherein
k2=tl,…,tl+ 3T is each moment after failure；Then it is directed to suspected malfunctions circuit j, using DTW algorithms, after finding out fault moment
Combination current sequence xj(k2) with fault moment before combination current sequence xj(k1) between minimal path value, i.e. longitudinal direction DTW distances
γzj, by longitudinal direction DTW distances γzjIt is configured to the longitudinal difference degree d of j-th strip circuitzj。
4. a kind of electric network failure diagnosis method according to claim 1, which is characterized in that select one outside power supply interrupted district
Reference line constructs the combination current sample sequence X of reference line using above-mentioned steps0, doubted for j-th strip in power supply interrupted district
Like faulty line, the combination current sample sequence X of the circuit is constructedj；Then DTW algorithms are utilized, the sequence of the circuit is solved
Arrange XjWith the sequence X of reference line0Between minimal path value, be configured to lateral DTW distances γhj, by transverse direction DTW apart from structure
Make the lateral variation degree d for j-th strip suspected malfunctions circuithj。
5. a kind of electric network failure diagnosis method according to claim 1, which is characterized in that N line in the power supply interrupted district
Longitudinal difference degree and lateral variation degree composition 2 × N similarity matrix d=[x on road1,x2,...,xN；y1,y2,...,yN],
In, the lateral variation degree of each circuit of the first behavior, the longitudinal difference degree of each circuit of the second behavior；This matrix is to be sorted original
Sample set, the input quantity as artificial fish-swarm clustering algorithm：
First, the parameter of initialization algorithm, every Artificial Fish include classification results cluster centre corresponding with them, meanwhile, it will
In matrix d the distance between each element and cluster centre and it is reciprocal as the fitness value for evaluating every Artificial Fish quality, i.e.,
The cluster centre value of every Artificial Fish；
Secondly, three behaviors are executed to several Artificial Fishs, behavior of bunching is first carried out, i.e., by all Artificial Fishs in present viewing field
Cluster centre cluster centre of the mean values as shoal of fish center, matrix d is divided into two classes according to the cluster centre,
Each element is assigned into cluster centre with the element apart from nearest one kind when specific execution；Shoal of fish center is obtained after classification
Fitness value, if the fitness value meet：fitc＞ fitm&&fitc/nf＞ δ fitm, wherein fitmFor the m articles Artificial Fish
Fitness value, fitcFor the fitness value of shoal of fish center, nfFor the item number of fish in the visual field, δ is the crowding factor；Then fish
It moves and moves a step towards the position, and classify according to new cluster centre；
Then, behavior of knocking into the back is executed, the Artificial Fish with maximum adaptation angle value in present viewing field is found, if the fitness of the position
Value meets fitx＞ fitm&&fitx/nf＞ δ fitm, wherein fitxFor the fitness value of the fish with maximum adaptation angle value；Towards this
Position is moved and moves a step, and classifies according to new cluster centre；
The size for comparing knock into the back behavior and behaviour adaptation angle value of bunching retains the big behavior of wherein fitness value, and will update
Artificial Fish afterwards is assigned to bulletin board, and as this action selection as a result, if being not carried out above two behavior, execution is looked for
Food behavior；
Foraging behavior is to find the big Artificial Fish of field range internal ratio itself fitness value, is moved a step towards the shifting of its direction, such as
Fruit does not possess the Artificial Fish of larger fitness value, then one step of random movement, and updated Artificial Fish is assigned to bulletin board；Extremely
The update of this m articles Artificial Fish terminates；
After all Artificial Fishs update once like this, the cluster centre of every Artificial Fish is updated, completes an iteration；
It iterates according to above-mentioned steps, reaches iterations and terminate；
The classification results and cluster centre of the optimal Artificial Fish recorded in bulletin board are obtained, classification results are comprising failure classes and normally
Two class of class；Obtain which kind of every circuit belongs to by the classification results；It chooses that big one kind of cluster centre numerical value and is used as failure
Class, the line diagnosis for then including by failure classes are faulty line.
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Effective date of registration: 20221018 Address after: 136000 No. 999, Zhongyang West Road, Tiexi District, Siping City, Jilin Province Patentee after: SIPING POWER SUPPLY COMPANY OF STATE GRID JILINSHENG ELECTRIC POWER SUPPLY Co. Address before: 610031 No. two, section 111, ring road, Chengdu, Sichuan, China Patentee before: SOUTHWEST JIAOTONG University |