CN106059838A - Relay protection reliability calculation method and device - Google Patents
Relay protection reliability calculation method and device Download PDFInfo
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- CN106059838A CN106059838A CN201610617812.XA CN201610617812A CN106059838A CN 106059838 A CN106059838 A CN 106059838A CN 201610617812 A CN201610617812 A CN 201610617812A CN 106059838 A CN106059838 A CN 106059838A
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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
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
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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
- H04L41/08—Configuration management of networks or network elements
- H04L41/0803—Configuration setting
- H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
- H04L41/0836—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability to enhance reliability, e.g. reduce downtime
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Abstract
The invention discloses a relay protection reliability calculation method and device. The relay protection reliability calculation method comprises the following steps of: obtaining a multi-mode capacity constraint matrix and a probability set of various pre-set real object components working in various modes according to the historical flow of various pre-set real object components in an intelligent transformer substation; constructing a stochastic flow network model, the reliable node edge of which is invalid; obtaining a corresponding effective-state set based on the stochastic flow network model; and calculating the relay protection reliability under the stochastic flow network model according to the effective-state set. On the basis of the method disclosed by the invention, the relay protection reliability is calculated by adopting the stochastic flow network model; and thus, a purpose of quantitatively calculating the reliability of a complex power system is achieved.
Description
Technical field
The present invention relates to reliability assessment technical field, particularly relate to a kind of reliability of relay protection computational methods and dress
Put.
Background technology
Protective relaying device as one of critical elements in secondary equipment in power system, can and alarm by direct shadow
Ring safety and the reliability of Operation of Electric Systems.Longtime running practice have shown that, the hardware and software of protective relaying device own loses
The self-diagnostic function that the probability of effect is the least and built-in often can detect this kind of failure event, therefore believes under intelligent grid background
Breath stream becomes the key factor of restriction protective relaying device reliability.
At present, existing reliability of relay protection appraisal procedure is based primarily upon instantaneous state probability, half markoff process
Or monte carlo simulation methodology relay protection is carried out quantitative analysis and utilize reliability block diagram and minimal path sets algorithm to carry out can
Calculate by property.But this appraisal procedure only considered flow of information connectedness from source point to meeting point, the running status of acquisition is only
Complete failure and properly functioning two kinds, and do not take into account the factor affecting element manipulation, the most potential network failure and information
Current mass declines the reliability decrease caused and does not embodies.
In view of this, existing technical scheme, when calculating reliability of relay protection, exists that result of calculation is inaccurate asks
Topic.
Summary of the invention
In view of this, the present invention provides a kind of reliability of relay protection computational methods and device, to solve existing technology
The inaccurate problem of reliability of relay protection result of computation schemes.Technical scheme is as follows:
A kind of reliability of relay protection computational methods, are applied to reliability of relay protection and calculate device, including:
Multi-modal capacity-constrained matrix is obtained with each according to each historical traffic presetting element in kind in intelligent substation
Individual described default element manipulation Making by Probability Sets under each mode in kind；
Build stochastic-flow networks model G=(V, E, C, P) that node can keep to the side to lose efficacy；
Wherein, node V is the physical connection cluster tool of each described default element in kind, and limit E is described default material object
The set of element, C is described multi-modal capacity-constrained matrix, and P is that each described default element manipulation in kind is under each mode
Making by Probability Sets；
Corresponding effective status set W is obtained based on described stochastic-flow networks model G；
According to described effective status set W, calculate the reliability of relay protection under described stochastic-flow networks model GWherein, lower boundary point Y during l is described effective status set WdNumber.
Preferably, described based on described stochastic-flow networks model G obtain corresponding effective status set W, including:
Obtain each network state based on described stochastic-flow networks model G, and generate state space tree, wherein, each institute
State the node that network state is described state space tree；
Zero network state is generated based on described network state, and using described zero network state as described state space tree
First father node；
Determine that a described default element in kind of network state described in described state space tree is as current search unit
Part；
Determine that in described state space tree, next network state is current search node, and according to default minimal path sets meter
Calculation method obtains information source node and the minimal path sets of information meeting point of described state space tree；
Judge whether described information source node connects with the network topology of described information meeting point according to described minimal path sets；
If do not connected, perform back tracking operation, and return that execution is described determines in described state space tree next network
State is current search node, the step for；
If connection, start max-flow according to default maximum-flow algorithm and calculate, obtain the network of described current search node
State capacity also judges that whether described network state capacity is less than preset need capacity；
If described network state capacity is not less than preset need capacity, described network state capacity is defined as described lower bound
Point YdIt is stored in state set L, performs described back tracking operation, and return that execution is described determines next in described state space tree
Individual network state is current search node, the step for；
If described network state capacity is less than preset need capacity, it is judged that whether the mode sequence number of described current search node
Less than preset mode sequence number maximum；
If the mode sequence number of described current search node is less than preset mode sequence number maximum, return execution is described determines institute
Stating next network state in state space tree is current search node, the step for；
If the mode sequence number of described current search node is not less than preset mode sequence number maximum, perform described backtracking behaviour
Make, and return that execution is described determines in described state space tree that next network state is current search node, the step for；
Wherein, described back tracking operation includes:
Judge that whether described current search element is the final search element of described state space tree；
If described current search element is not the final search element of described state space tree, by described state space tree
In a upper network state be described current search node, and determine in described state space tree next described default unit in kind
Part is as described current search element；
If described current search element is the final search element of described state space tree, by described state space tree
A upper network state is described current search node, and judges whether described current search node is described zero network state；
If described current search node is not described zero network state, determine in described state space tree next described pre-
If element in kind is as described current search element；
If described current search node is described zero network state, described state set L is defined as described effective status
Set W, and terminate max-flow calculating.
Preferably, the span of each described default element manipulation Probability p under each mode in kind is 0～1.
Preferably, described default minimal path sets computational methods include: connection matrix method.
Preferably, described default maximum-flow algorithm includes: extensions path Ford-Fulkerson algorithm.
A kind of reliability of relay protection calculates device, including: acquisition module, model construction module, effective status set obtain
Delivery block and Calculation of Reliability module；
Described acquisition module, for obtaining multi-modal according to each historical traffic presetting element in kind in intelligent substation
Capacity-constrained matrix and each described default element manipulation Making by Probability Sets under each mode in kind；
Described model construction module, stochastic-flow networks model G=(V, E, C, P) lost efficacy for building node to keep to the side；
Wherein, node V is the physical connection cluster tool of each described default element in kind, and limit E is the collection of described default element in kind
Closing, C is described multi-modal capacity-constrained matrix, and P is each described default element manipulation Making by Probability Sets under each mode in kind；
Described effective status set acquisition module, for obtaining corresponding effectively shape based on described stochastic-flow networks model G
State set W；
Described Calculation of Reliability module, for according to described effective status set W, calculates described stochastic-flow networks model G
Under reliability of relay protectionWherein, lower boundary point Y during l is described effective status set Wd
Number.
Preferably, described effective status set acquisition module includes: state space tree signal generating unit, zero network state generate
Unit, search for element determination unit, determine computing unit, the first judging unit, the first performance element, backtracking performance element, the
Two judging units, the second performance element, the 3rd judging unit and the 3rd performance element；
Described state space tree signal generating unit, for obtaining each network state based on described stochastic-flow networks model G, and
Generating state space tree, wherein, each described network state is the node of described state space tree；
Described zero network state signal generating unit, for generating zero network state based on described network state, and by described zero
Network state is as the first father node of described state space tree；
Described search element determination unit, is used for determining that of network state described in described state space tree is described pre-
If element in kind is as current search element；
Described determine computing unit, be used for determining in described state space tree that next network state is current search joint
Point, and information source node and the minimal path of information meeting point of described state space tree is obtained according to default minimal path sets computational methods
Collection；
Described first judging unit, for judging described information source node and described information meeting point according to described minimal path sets
Network topology whether connect；If it is, trigger described second judging unit；If it does not, trigger described first performance element；
Described first performance element, is used for triggering backtracking performance element, and enters and described determine computing unit；
Described second judging unit, for starting max-flow and calculate according to presetting maximum-flow algorithm, obtains and described currently searches
The network state capacity of socket point also judges that whether described network state capacity is less than preset need capacity；If it is not, trigger described
Second performance element；If so, described 3rd judging unit is triggered；
Described second performance element, for being defined as described lower boundary point Y by described network state capacitydIt is stored in state set
Close in L, trigger described backtracking performance element, and trigger and described determine computing unit；
Described 3rd judging unit, for judging that whether the mode sequence number of described current search node is less than preset mode sequence
Number maximum；It is to trigger and described determine computing unit；No, trigger described 3rd performance element；
Described 3rd performance element, is used for triggering described backtracking performance element, and enters and described determine computing unit；
Described backtracking performance element, including: the 4th judging unit, the 4th performance element, the 5th judging unit, the 5th execution
Unit and the 6th performance element；
Described 4th judging unit, for judging whether described current search element is finally searching of described state space tree
Rope element；If it does not, trigger described 4th performance element；If it is, trigger described 5th judging unit；
Described 4th performance element, being used for a network state upper in described state space tree is described current search joint
Point, and determine that in described state space tree, next described default element in kind is as described current search element；
Described 5th judging unit, being used for a network state upper in described state space tree is described current search joint
Point, and judge whether described current search node is described zero network state；If it is not, trigger described 5th performance element；If so,
Trigger described 6th performance element；
Described 5th performance element, is used for determining in described state space tree that next described default element in kind is as institute
State current search element；
Described 6th performance element, for described state set L is defined as described effective status set W, and terminates
Big stream calculation.
Comparing and prior art, what the present invention realized has the beneficial effect that
A kind of reliability of relay protection computational methods the most provided by the present invention and device, according to each in intelligent substation
The historical traffic of individual default element in kind obtains multi-modal capacity-constrained matrix and each presets element manipulation in kind in each mode
Under Making by Probability Sets；Build the stochastic-flow networks model that node can keep to the side to lose efficacy；Obtain corresponding based on stochastic-flow networks model
Effective status set；According to effective status set, calculate the reliability of relay protection under stochastic-flow networks model.Based on above-mentioned public affairs
The method opened, uses the stochastic-flow networks model Calculation of Reliability to relay protection, thus reaches reliable to complicated electric power system
Property carries out the purpose of quantitative Analysis.
Accompanying drawing explanation
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
In having technology to describe, the required accompanying drawing used is briefly described, it should be apparent that, the accompanying drawing in describing below is only this
Inventive embodiment, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to according to
The accompanying drawing provided obtains other accompanying drawing.
Fig. 1 is a kind of reliability of relay protection computational methods flow chart disclosed in the embodiment of the present invention one；
Fig. 2 is a kind of reliability of relay protection computational methods partial process view disclosed in the embodiment of the present invention two；
Fig. 3 is a kind of reliability of relay protection computational methods partial process view disclosed in the embodiment of the present invention two；
Fig. 4 is a kind of reliability of relay protection computing device structure schematic diagram disclosed in the embodiment of the present invention three；
Fig. 5 is that disclosed in the embodiment of the present invention four, a kind of reliability of relay protection calculates device section separation structure schematic diagram；
Fig. 6 is that disclosed in the embodiment of the present invention four, a kind of reliability of relay protection calculates device section separation structure schematic diagram.
Detailed description of the invention
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Describe, it is clear that described embodiment is only a part of embodiment of the present invention rather than whole embodiments wholely.Based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under not making creative work premise
Embodiment, broadly falls into the scope of protection of the invention.
Embodiment one
Disclosed in the embodiment of the present invention one, a kind of reliability of relay protection computational methods, are applied to reliability of relay protection meter
Calculating device, flow chart is as it is shown in figure 1, reliability of relay protection computational methods include:
S101, obtains multi-modal capacity-constrained matrix according to each historical traffic presetting element in kind in intelligent substation
Element manipulation Making by Probability Sets under each mode in kind is preset with each；
S102, builds stochastic-flow networks model G=(V, E, C, P) that node can keep to the side to lose efficacy；
Wherein, node V is the physical connection cluster tool of each described default element in kind, and limit E is for presetting element in kind
Set, C is multi-modal capacity-constrained matrix, and P is that each presets element manipulation Making by Probability Sets under each mode in kind；
During performing step S102, during building the stochastic-flow networks model that node can keep to the side to lose efficacy,
Each presets physical connection cluster tool V={v of element in kindi| 1≤i≤n} is as the node of stochastic-flow networks model, in advance
If the set E={e of element in kindi| 1≤i≤a} is as the limit of stochastic-flow networks model, C={cij|1≤i≤n+a,0≤j≤
hiAs multi-modal capacity-constrained matrix, P={pij|1≤i≤n+a,0≤j≤hiIt is that each default element manipulation in kind exists
Making by Probability Sets under each mode, wherein, hiFor the mode sequence number that i-th element in element is corresponding；
S103, obtains corresponding effective status set W based on stochastic-flow networks model G；
S104, according to effective status set W, calculates the reliability of relay protection under stochastic-flow networks model GWherein, lower boundary point Y during l is effective status set WdNumber.
It should be noted that each span presetting element manipulation Probability p under each mode in kind is 0～1, institute
Have and preset the element manipulation in kind Probability p under each mode and be 1.
Reliability of relay protection computational methods disclosed in the embodiment of the present invention, according in intelligent substation, each presets material object
The historical traffic of element obtains multi-modal capacity-constrained matrix and each presets element manipulation probability set under each mode in kind
Close；Build the stochastic-flow networks model that node can keep to the side to lose efficacy；Corresponding effective status collection is obtained based on stochastic-flow networks model
Close；According to effective status set, calculate the reliability of relay protection under stochastic-flow networks model.Based on method disclosed above,
Use the stochastic-flow networks model Calculation of Reliability to relay protection, thus reach complicated electric power system reliability is carried out quantitatively
The purpose calculated.
Embodiment two
Learning method is represented, as illustrated in FIG. 1 based on multi-modal data a kind of disclosed in the invention described above embodiment one
In step S103, obtain the process that specifically performs of corresponding effective status set W based on stochastic-flow networks model G, such as Fig. 2 institute
Show, including:
S201, obtains each network state based on stochastic-flow networks model G, and generates state space tree, wherein, and each net
Network state is the node of described state space tree；
During performing step S201, obtain corresponding state space Ω=(Y based on stochastic-flow networks model G1,
Y2,…,Yi,Ym), and generate state space tree, wherein, each network state Y according to each network stateiEmpty for described state
Between tree node.
S202, generates zero network state based on network state, and using zero network state as the first father of state space tree
Node；
During performing step S202, based on each network state Y obtainediGenerate zero network state, and by zero
Network state is as the first father node i.e. initiating searches point of state space tree.
S203, determines that one of network state described in state space tree is preset element in kind as current search element；
S204, determines that in state space tree, next network state is current search node, and according to default minimal path sets
Computational methods obtain information source node and the minimal path sets of information meeting point of state space tree；
According to minimal path sets, S205, judges whether information source node connects with the network topology of described information meeting point；
S206, if do not connected, performs back tracking operation, and returns execution and determine in state space tree next network state
For current search node, the step for；
S207, if connection, starts max-flow according to default maximum-flow algorithm and calculates, obtain the network of current search node
State capacity also judges that whether network state capacity is less than preset need capacity；
S208, if network state capacity is not less than preset need capacity, is defined as lower boundary point Y by network state capacitydDeposit
It is stored in state set L, performs back tracking operation, and return execution and determine in state space tree that next network state is for currently to search
Socket point, the step for；
S209, if network state capacity is less than preset need capacity, it is judged that the mode sequence number of current search node is the least
In preset mode sequence number maximum；
S210, if the mode sequence number of described current search node is less than preset mode sequence number maximum, returns described in performing
Determine that in described state space tree, next network state is current search node, the step for；
S211, if the mode sequence number of current search node is not less than preset mode sequence number maximum, performs back tracking operation, and
Return execution and determine that in described state space tree, next network state is current search node, the step for；
Wherein, the concrete execution process of back tracking operation is as it is shown on figure 3, include:
S301, it is judged that whether current search element is the final search element of state space tree；
S302, if current search element is not the final search element of state space tree, by state space tree upper one
Individual network state is current search node, and determines that in state space tree, next material object element of presetting is as current search unit
Part；
S303, if current search element is the final search element of state space tree, by state space tree upper one
Network state is current search node, and judges whether current search node is zero network state；
S304, if current search node is not zero network state, determines next default element in kind in state space tree
As current search element；
S305, if current search node is zero network state, is defined as state set L effective status set W, and terminates
Max-flow calculates.
It should be noted that default minimal path sets computational methods include but are not limited to connection matrix method, can be according to reality
Need the minimal path sets computational methods selecting to be suitable for；Default maximum-flow algorithm includes but is not limited to extensions path Ford-
Fulkerson algorithm, the method that the calculating max-flow being suitable for can be selected according to actual needs.
Reliability of relay protection computational methods disclosed in the embodiment of the present invention, according in intelligent substation, each presets material object
The historical traffic of element obtains multi-modal capacity-constrained matrix and each presets element manipulation probability set under each mode in kind
Close；Build the stochastic-flow networks model that node can keep to the side to lose efficacy；Corresponding effective status collection is obtained based on stochastic-flow networks model
Close；According to effective status set, calculate the reliability of relay protection under stochastic-flow networks model.Based on method disclosed above,
Use the stochastic-flow networks model Calculation of Reliability to relay protection, thus reach complicated electric power system reliability is carried out quantitatively
The purpose calculated.
Embodiment three
The reliability of relay protection computational methods provided based on each embodiment of the invention described above, the present embodiment three is then corresponding public
The reliability of relay protection having opened execution above-mentioned reliability of relay protection computational methods calculates device, its structural representation such as Fig. 4
Shown in, reliability of relay protection calculates device and includes 400: acquisition module 401, model construction module 402, effective status set obtain
Delivery block 403 and Calculation of Reliability module 404；Wherein,
Acquisition module 401, for obtaining multi-modal according to each historical traffic presetting element in kind in intelligent substation
Capacity-constrained matrix and each default element manipulation Making by Probability Sets under each mode in kind；
Model construction module 402, stochastic-flow networks model G=(V, E, C, P) lost efficacy for building node to keep to the side；Its
In, node V is the physical connection cluster tool of each described default element in kind, and limit E is the set of described default element in kind,
C is described multi-modal capacity-constrained matrix, and P is each described default element manipulation Making by Probability Sets under each mode in kind；
Effective status set acquisition module 403, for obtaining corresponding effective status set based on stochastic-flow networks model G
W；
Calculation of Reliability module 404, for according to effective status set W, calculates the relay under stochastic-flow networks model G and protects
Protect reliabilityWherein, lower boundary point Y during l is effective status set WdNumber.
Disclosed in the embodiment of the present invention reliability of relay protection calculate device, acquisition module according in intelligent substation each
The historical traffic presetting element in kind obtains multi-modal capacity-constrained matrix and each default element manipulation in kind under each mode
Making by Probability Sets；Model construction module builds the stochastic-flow networks model that node can keep to the side to lose efficacy；Effective status set obtains mould
Block obtains corresponding effective status set based on stochastic-flow networks model；Calculation of Reliability module acquisition module is according to effective status
Set, calculates the reliability of relay protection under stochastic-flow networks model.Based on device disclosed above, use stochastic-flow networks mould
The type Calculation of Reliability to relay protection, thus reach complicated electric power system reliability is carried out the purpose of quantitative Analysis.
Embodiment four
Calculating device and accompanying drawing 4 in conjunction with reliability of relay protection disclosed in above-described embodiment three, the present embodiment four is also disclosed
A kind of reliability of relay protection calculates device, wherein, effective status set acquisition module 403 structural representation as it is shown in figure 5,
Effective status set acquisition module 403 includes: state space tree signal generating unit 501, zero network state signal generating unit
502, search for element determination unit 503, determine computing unit the 504, first judging unit the 505, first performance element 506, backtracking
Performance element the 507, second judging unit the 508, second performance element the 509, the 3rd judging unit 510 and the 3rd performance element 511；
State space tree signal generating unit 501, for obtaining each network state based on stochastic-flow networks model G, and generates
State space tree, wherein, each network state is the node of described state space tree；
Zero network state signal generating unit 502, for generating zero network state based on network state, and makees zero network state
First father node for state space tree；
Search element determination unit 503, for determining that presetting element in kind for one of network state in state space tree makees
For current search element；
Determine computing unit 504, be used for determining in state space tree that next network state is current search node, and root
Information source node and the minimal path sets of information meeting point of state space tree is obtained according to default minimal path sets computational methods；
According to minimal path sets, first judging unit 505, for judging that the network of described information source node and information meeting point is opened up
Flutter and whether connect；If it is, trigger the second judging unit 508；If it does not, trigger the first performance element 506；
First performance element 506, is used for triggering backtracking performance element 507, and entrance determines computing unit 504；
Second judging unit 508, for according to presetting maximum-flow algorithm startup max-flow calculating, obtaining current search node
Network state capacity and judge that network state capacity is whether less than preset need capacity；If it is not, trigger the second performance element
509；If so, the 3rd judging unit 510 is triggered；
Second performance element 509, for being defined as lower boundary point Y by network state capacitydIt is stored in state set L, touches
Beam back the performance element 507 that traces back, and entrance determines computing unit 504；
3rd judging unit 510, for judging that whether less than preset mode sequence number the mode sequence number of current search node
Big value；It is to trigger and determine computing unit 504；No, trigger the 3rd performance element 511；
3rd performance element 511, is used for triggering described backtracking performance element, and entrance determines computing unit 504；
Wherein, recall the structural representation of performance element 507 as shown in Figure 6, including: the 4th judging unit the 601, the 4th is held
Row unit the 602, the 5th judging unit the 603, the 5th performance element 604 and the 6th performance element 605；
4th judging unit 601, for judging that whether current search element is the final search element of state space tree；As
The most no, trigger the 4th performance element 602；If it is, trigger the 5th judging unit 603；
4th performance element 602, being used for a network state upper in state space tree is current search node, and determines
In state space tree, next material object element of presetting is as described current search element；
5th judging unit 603, being used for a network state upper in state space tree is current search node, and judges
Whether current search node is zero network state；If it is not, trigger the 5th performance element 604；If so, the 6th performance element is triggered
605；
5th performance element 604, is used for determining in state space tree that next material object element of presetting is as current search unit
Part；
6th performance element 605, for state set L is defined as effective status set W, and terminates max-flow calculating.
Disclosed in the embodiment of the present invention reliability of relay protection calculate device, acquisition module according in intelligent substation each
The historical traffic presetting element in kind obtains multi-modal capacity-constrained matrix and each default element manipulation in kind under each mode
Making by Probability Sets；Model construction module builds the stochastic-flow networks model that node can keep to the side to lose efficacy；Effective status set obtains mould
Each unit in block obtains corresponding effective status set based on stochastic-flow networks model；Calculation of Reliability module acquisition module root
According to effective status set, calculate the reliability of relay protection under stochastic-flow networks model.Based on device disclosed above, use with
The machine flow network model Calculation of Reliability to relay protection, thus reach complicated electric power system reliability is carried out quantitative Analysis
Purpose.
Above a kind of reliability of relay protection computational methods provided by the present invention and device are described in detail, this
Applying specific case in literary composition to be set forth principle and the embodiment of the present invention, the explanation of above example is only intended to
Help to understand method and the core concept thereof of the present invention；Simultaneously for one of ordinary skill in the art, according to the think of of the present invention
Thinking, the most all will change, in sum, it is right that this specification content should not be construed as
The restriction of the present invention.
It should be noted that each embodiment in this specification all uses the mode gone forward one by one to describe, each embodiment weight
Point explanation is all the difference with other embodiments, and between each embodiment, identical similar part sees mutually.
For device disclosed in embodiment, owing to it corresponds to the method disclosed in Example, so describe is fairly simple, phase
See method part in place of pass to illustrate.
Also, it should be noted in this article, the relational terms of such as first and second or the like is used merely to one
Entity or operation separate with another entity or operating space, and not necessarily require or imply between these entities or operation
There is relation or the order of any this reality.And, term " includes ", " comprising " or its any other variant are intended to contain
Comprising of lid nonexcludability, so that include the key element that the process of a series of key element, method, article or equipment are intrinsic,
Or also include the key element intrinsic for these processes, method, article or equipment.In the case of there is no more restriction,
The key element limited by statement " including ... ", it is not excluded that including the process of described key element, method, article or equipment
In there is also other identical element.
Described above to the disclosed embodiments, makes professional and technical personnel in the field be capable of or uses the present invention.
Multiple amendment to these embodiments will be apparent from for those skilled in the art, as defined herein
General Principle can realize without departing from the spirit or scope of the present invention in other embodiments.Therefore, the present invention
It is not intended to be limited to the embodiments shown herein, and is to fit to and principles disclosed herein and features of novelty phase one
The widest scope caused.
Claims (7)
1. reliability of relay protection computational methods, it is characterised in that be applied to reliability of relay protection and calculate device, bag
Include:
Multi-modal capacity-constrained matrix and each institute is obtained according to each historical traffic presetting element in kind in intelligent substation
State and preset element manipulation Making by Probability Sets under each mode in kind；
Build stochastic-flow networks model G=(V, E, C, P) that node can keep to the side to lose efficacy；
Wherein, node V is the physical connection cluster tool of each described default element in kind, and limit E is described default element in kind
Set, C is described multi-modal capacity-constrained matrix, and P is each described default element manipulation probability under each mode in kind
Set；
Corresponding effective status set W is obtained based on described stochastic-flow networks model G；
According to described effective status set W, calculate the reliability of relay protection under described stochastic-flow networks model GWherein, lower boundary point Y during l is described effective status set WdNumber.
Method the most according to claim 1, it is characterised in that described corresponding based on described stochastic-flow networks model G acquisition
Effective status set W, including:
Obtain each network state based on described stochastic-flow networks model G, and generate state space tree, wherein, each described net
Network state is the node of described state space tree；
Zero network state is generated based on described network state, and using first as described state space tree of described zero network state
Father node；
Determine that a described default element in kind of network state described in described state space tree is as current search element；
Determine that in described state space tree, next network state is current search node, and according to default minimal path sets calculating side
Method obtains information source node and the minimal path sets of information meeting point of described state space tree；
Judge whether described information source node connects with the network topology of described information meeting point according to described minimal path sets；
If do not connected, perform back tracking operation, and return that execution is described determines in described state space tree next network state
For current search node, the step for；
If connection, start max-flow according to default maximum-flow algorithm and calculate, obtain the network state of described current search node
Capacity also judges that whether described network state capacity is less than preset need capacity；
If described network state capacity is not less than preset need capacity, described network state capacity is defined as described lower boundary point Yd
It is stored in state set L, performs described back tracking operation, and return that execution is described determines next net in described state space tree
Network state is current search node, the step for；
If described network state capacity is less than preset need capacity, it is judged that whether the mode sequence number of described current search node is less than
Preset mode sequence number maximum；
If the mode sequence number of described current search node is less than preset mode sequence number maximum, return execution is described determines described shape
In state space tree, next network state is current search node, the step for；
If the mode sequence number of described current search node is not less than preset mode sequence number maximum, perform described back tracking operation, and
Return execution is described determines that in described state space tree, next network state is current search node, the step for；
Wherein, described back tracking operation includes:
Judge that whether described current search element is the final search element of described state space tree；
If described current search element is not the final search element of described state space tree, in described state space tree
One network state is described current search node, and determines that in described state space tree, next described default element in kind is made
For described current search element；
If described current search element is the final search element of described state space tree, by described state space tree upper one
Individual network state is described current search node, and judges whether described current search node is described zero network state；
If described current search node is not described zero network state, determine next described default reality in described state space tree
Construction element is as described current search element；
If described current search node is described zero network state, described state set L is defined as described effective status set
W, and terminate max-flow calculating.
Method the most according to claim 1, it is characterised in that each described default element manipulation in kind is under each mode
The span of Probability p is 0～1.
Method the most according to claim 2, it is characterised in that described default minimal path sets computational methods include: contact square
The tactical deployment of troops.
Method described in 2 the most as requested, it is characterised in that described default maximum-flow algorithm includes: extensions path Ford-
Fulkerson algorithm.
6. a reliability of relay protection calculates device, it is characterised in that including: acquisition module, model construction module, effective shape
State set acquisition module and Calculation of Reliability module；
Described acquisition module, for obtaining multi-modal capacity according to each historical traffic presetting element in kind in intelligent substation
Constraint matrix and each described default element manipulation Making by Probability Sets under each mode in kind；
Described model construction module, stochastic-flow networks model G=(V, E, C, P) lost efficacy for building node to keep to the side；Wherein,
Node V is the physical connection cluster tool of each described default element in kind, and limit E is the set of described default element in kind, and C is
Described multi-modal capacity-constrained matrix, P is each described default element manipulation Making by Probability Sets under each mode in kind；
Described effective status set acquisition module, for obtaining corresponding effective status collection based on described stochastic-flow networks model G
Close W；
Described Calculation of Reliability module, for according to described effective status set W, calculates under described stochastic-flow networks model G
Reliability of relay protectionWherein, lower boundary point Y during l is described effective status set Wd?
Number.
Device the most according to claim 6, it is characterised in that described effective status set acquisition module includes: state is empty
Between set signal generating unit, zero network state signal generating unit, search for element determination unit, determine computing unit, the first judging unit, the
One performance element, backtracking performance element, the second judging unit, the second performance element, the 3rd judging unit and the 3rd performance element；
Described state space tree signal generating unit, for obtaining each network state based on described stochastic-flow networks model G, and generates
State space tree, wherein, each described network state is the node of described state space tree；
Described zero network state signal generating unit, for generating zero network state based on described network state, and by described zero network
State is as the first father node of described state space tree；
Described search element determination unit, for determining a described default reality of network state described in described state space tree
Construction element is as current search element；
Described determine computing unit, be used for determining in described state space tree that next network state is current search node, and
Information source node and the minimal path sets of information meeting point of described state space tree is obtained according to default minimal path sets computational methods；
Described first judging unit, for judging described information source node and the net of described information meeting point according to described minimal path sets
Whether network topology connects；If it is, trigger described second judging unit；If it does not, trigger described first performance element；
Described first performance element, is used for triggering backtracking performance element, and enters and described determine computing unit；
Described second judging unit, for according to presetting maximum-flow algorithm startup max-flow calculating, obtaining described current search joint
The network state capacity of point also judges that described network state capacity is whether less than preset need capacity；If it is not, trigger described second
Performance element；If so, described 3rd judging unit is triggered；
Described second performance element, for being defined as described lower boundary point Y by described network state capacitydIt is stored in state set L
In, trigger described backtracking performance element, and trigger and described determine computing unit；
Described 3rd judging unit, for judging that whether less than preset mode sequence number the mode sequence number of described current search node
Big value；It is to trigger and described determine computing unit；No, trigger described 3rd performance element；
Described 3rd performance element, is used for triggering described backtracking performance element, and enters and described determine computing unit；
Described backtracking performance element, including: the 4th judging unit, the 4th performance element, the 5th judging unit, the 5th performance element
With the 6th performance element；
Described 4th judging unit, for judging that whether described current search element is the final search unit of described state space tree
Part；If it does not, trigger described 4th performance element；If it is, trigger described 5th judging unit；
Described 4th performance element, being used for a network state upper in described state space tree is described current search node,
And determine that in described state space tree, next described default element in kind is as described current search element；
Described 5th judging unit, being used for a network state upper in described state space tree is described current search node,
And judge whether described current search node is described zero network state；If it is not, trigger described 5th performance element；If so, touch
Send out the 6th performance element described；
Described 5th performance element, is used for determining in described state space tree that next described default element in kind is worked as described
Front search element；
Described 6th performance element, for described state set L is defined as described effective status set W, and terminates max-flow
Calculate.
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