CN109884469A - The determination method of distribution network failure section and fault moment - Google Patents
The determination method of distribution network failure section and fault moment Download PDFInfo
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- CN109884469A CN109884469A CN201910166586.1A CN201910166586A CN109884469A CN 109884469 A CN109884469 A CN 109884469A CN 201910166586 A CN201910166586 A CN 201910166586A CN 109884469 A CN109884469 A CN 109884469A
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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
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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
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Abstract
The invention discloses the determination methods of a kind of distribution network failure section and fault moment, comprising: building data set；Calculation Estimation index；The monitoring threshold of evaluation index fluctuation is set；Fault time and fault zone are determined based on the evaluation index of calculating；Determine failure phase.Determined according to the covariance matrix maximum singular value ratio of history data set and online data collection, each measuring point of the whole network is measured in real time, it determines after being out of order the moment occurring, measuring point data anomalies are determined according to the judgement result of each measuring point of fault moment, the moment occurs according to failure and anomalies ranking determines fault zone, and how far of the fault point from measuring point is determined using anomalies, failure phase is judged using fault zone end any point data.The present invention is suitable for any load level, and without obtaining the regularity of distribution of distributed generation resource and load, calculation amount is small, and the fault diagnosis of the distribution system or existing distribution system that can access for distributed generation resource provides theory and technology guarantee.
Description
Technical field
The present invention relates to the distribution protection technical fields containing distributed generation resource, are specifically related to a kind of distribution network failure section
With the determination method of fault moment.
Background technique
The access of distributed generation resource changes the power of power distribution network so that power distribution network is changed into active electric network by passive network
Flow direction, the difference so that fault signature changes cause the existing relay protection of existing power distribution network to be easy to happen malfunction and misaction, into
And the fault location to power distribution network, isolation, recovery etc. generate very important influence.It is effectively to reduce that fault section, which accurately identifies,
Fault location range, guarantee the reliable and stable operation of system one of important measures, and failure occur the moment accurately be determined as after
The guarantee of continuous fault localization, line maintenance offer simultaneously reduces the economic costs such as artificial.Therefore how to fast implement containing distribution
The distribution system fault section and failure of power supply occur the moment accurately identify and determine be operation of power networks protection staff very
Concern.
Moment identification problem occurs about fault section determination and failure, traditional method is usually with given load
Characteristic, network topology, route lumped parameter, the condition that the estimation of distribution parameter or hypothesis can be calculated current in the fault point
It is lower to be analyzed and calculate.If when unknown to above-mentioned condition or when not exclusively known fault section is determining and failure occurs
It is not applicable to carve identification.
In conclusion to overcome above-mentioned difficulties, it is necessary to provide a kind of quick, efficient, accurate believable determination method.
Summary of the invention
In order to solve the above-mentioned problems of the prior art, the object of the present invention is to provide a kind of distribution network failure section with
The determination method of fault moment can not only be solved in part throttle characteristics, network topology, route lumped parameter, route distribution parameter
Fault section under incomplete known conditions is determining and moment identification problem occurs for failure, and can satisfy to meet and actually answer
With the differentiation result of needs.
To achieve the above object, the invention adopts the following technical scheme:
On the one hand, the determination method of a kind of distribution network failure section and fault moment provided by the invention, the method packet
It includes:
Construct data set；
Calculation Estimation index；
The monitoring threshold of evaluation index fluctuation is set；
Fault time and fault zone are determined based on the evaluation index of calculating；
Determine failure phase.
As a kind of possible implementation of the present embodiment, the process for constructing data set includes:
It builds history data set: utilizing the data building history number after measurement equipment the last time fault recovery of each measuring point
According to collection, the maximum singular value σ of history data set covariance matrix is calculated by singular value decomposition1；
Building online data collection: the metric data collection at kth moment include the kth moment metric data and before M-1 when
The metric data at quarter, then kth moment online data collection may be expressed as: Y=[yk-M+1..., yk], wherein M includes by data set
The number of sampled value, ykFor the metric data at kth moment, yk-M+1For the metric data at M-1 moment before the kth moment；And to every
The online data collection at one moment acquires the maximum singular value σ ' of online data collection covariance matrix using singular value decomposition1。
As a kind of possible implementation of the present embodiment, evaluation index includes the evaluation index of single class metric data and more
The comprehensive evaluation index of class metric data, the evaluation index ψ of the list class metric data and the overall merit of multiclass metric data
Shown in the calculation formula of index Ψ such as formula (1) and formula (2):
Wherein, X is history data set, and Y is online measured data collection；Cov (X, Y) and cov (X, X) respectively indicate online reality
The covariance matrix of measured data collection and history data set；||·||2To seek 2 norms；σ1' and σ1Respectively online measured data collection
With the maximum singular value of the covariance matrix of history data set；L is the data class actually measured, the data class actually measured
Including voltage magnitude, voltage phase angle, current amplitude, current phase angle, each measuring point trend, on measuring point all kinds of switches switch state；
ψiFor the evaluation index of the i-th class data type；αiFor the weight and satisfaction of the i-th class data evaluation index
As a kind of possible implementation of the present embodiment, be arranged monitoring threshold range that evaluation index fluctuates 0.95~
Between 1.95, i.e.,
|ψ-1|≤0.05 (3)
|Ψ-1|≤0.05 (4)
ψ and Ψ is respectively the evaluation index of single class metric data and the comprehensive evaluation index of multiclass metric data.
As a kind of possible implementation of the present embodiment, fault time and faulty section are determined based on the evaluation index of calculating
The process in domain are as follows:
The evaluation index of single class metric data is calculated separately to power distribution network the whole network measurement point metric data and multiclass measures number
According to comprehensive evaluation index, if | ψ -1 | > 0.05 or | Ψ -1 | > 0.05 illustrates the corresponding faulty generation of measurement point；
The abnormal metric data time of occurrence for the measurement point that breaks down is ranked up, occurs abnormal metric data earliest
Time is the time that failure occurs；
The measurement point region of occurred abnormal amount measured data is marked into non-suspected malfunctions region；
Measuring point metric data each in suspected malfunctions region is ranked up according to out-of-limit degree, that is, is installed | ψ -1 | or | Ψ -1
| size sequence, be determined fault zone.
As a kind of possible implementation of the present embodiment, size is fluctuated according to the end measuring point data of fault zone and determines event
Barrier point with a distance from endpoint degree, that is, fluctuate it is bigger illustrate it is closer from the point.
As a kind of possible implementation of the present embodiment, determine that the process of failure phase is；According to fault zone end measuring point
The measurement data of every phase determines failure phase, i.e., calculates separately single class metric data to the measurement data of the every phase of fault zone end measuring point
Evaluation index and multiclass metric data comprehensive evaluation index, if | ψ -1 | > 0.05 or | Ψ -1 | > 0.05, it is determined that should
It is mutually failure phase.
As a kind of possible implementation of the present embodiment, the determination method further include:
Export result；The result of output includes that time of failure, fault section range, fault point are remote far from fault zone end
Short range degree and failure phase.
On the other hand, the determination method of a kind of distribution network failure section and fault moment provided by the invention, comprising:
History data set and online data collection are respectively constituted using historical measurement data and real-time online data；
The maximum singular value of history data set covariance matrix and online data collection covariance matrix is solved respectively；
Distribution network failure section is carried out according to the ratio of maximum singular value and fault moment determines.
As a kind of possible implementation of the present embodiment, distribution network failure section is carried out according to the ratio of maximum singular value
With fault moment determine process include:
Each measuring point of the whole network is measured in real time first, the moment occurs for determining be out of order；
Measuring point data anomalies are determined according to the judgement result of each measuring point of fault moment；
The moment occurs according to failure and anomalies ranking determines fault zone, and determines fault point using anomalies
How far from measuring point；
Finally failure phase is judged using fault zone end any point data.
What the technical solution of the embodiment of the present invention can have has the beneficial effect that:
The determination method of a kind of distribution network failure section and fault moment that the technical solution of the embodiment of the present invention provides, institute
The method of stating includes: building data set；Calculation Estimation index；The monitoring threshold of evaluation index fluctuation is set；Evaluation based on calculating
Index determines fault time and fault zone；Determine failure phase.The wherein amount that metric data is installed in existing distribution system
Survey device.Its judging result includes that section occurs for failure, and moment and failure phase occur for failure.Measured data structure before use
At history data set, current online data constitutes online data collection, and according to history data set covariance matrix and online data
The maximum singular value ratio of collection covariance matrix is determined, is measured in real time first to each measuring point of the whole network, determines event
After moment occurs for barrier, measuring point data anomalies are determined according to the judgement result of each measuring point of fault moment, when occurring according to failure
It carves and anomalies ranking determines fault zone, and determine how far of the fault point from measuring point using anomalies.Finally
Failure phase is judged using fault zone end any point data.The technical solution is suitable for any load level, divides without obtaining
The regularity of distribution of cloth power supply and load, calculation amount is small, can be the distribution system or existing distribution system of distributed generation resource access
Fault diagnosis provide theory and technology guarantee.This method is desirably integrated into existing measurement equipment software, equipment that no replacement is required,
It is worth with stronger practical application in industry.
The determination method of a kind of distribution network failure section and fault moment that the technical solution of the embodiment of the present invention provides, it is first
History data set and online data collection are respectively constituted first with historical measurement data and real-time online data；Then it solves and goes through respectively
The maximum singular value of history data set covariance matrix and online data collection covariance matrix；And according to the ratio of maximum singular value into
Row distribution network failure section and fault moment determine.To solve in part throttle characteristics, network topology, route lumped parameter, route point
Fault section under the incomplete known conditions of cloth parameter is determining and moment identification problem occurs for failure, and finally provides satisfaction symbol
The differentiation of practical application needs is closed as a result, the technical solution of the embodiment proposes a kind of two stage determination method: first stage
The related decision content of historical data is calculated, second stage is the online evaluation stage, under conditions of given threshold, second stage root
Maximum singular value ratio according to history data set covariance matrix and online data collection covariance matrix is determined, first to complete
It nets each measuring point to be measured in real time, determine after being out of order the moment occurring, determined according to the judgement result of each measuring point of fault moment
Measuring point data anomalies occur the moment according to failure and anomalies ranking determine fault zone, and using anomalies
Determine how far of the fault point from measuring point.Finally failure phase is judged using fault zone end any point data.By each measuring point
Data mutation is ranked up to determine fault time and fault section by out-of-limit degree, can satisfy under distributed generation resource access
Power distribution network relay protection requirement., this method is suitable for any load level, without obtaining point of distributed generation resource and load
Cloth rule, calculation amount is small, and the fault diagnosis of the distribution system or existing distribution system that can access for distributed generation resource provides theory
And technical guarantee.This method is desirably integrated into existing measurement equipment software, equipment that no replacement is required, has stronger actual industrial
Using
The judgment method that the technical solution of the embodiment of the present invention provides calculates simplicity, only uses the existing measurement equipment of system
Metric data overcomes the problems, such as the computationally intensive and credible result for directly using data, can be realized and opens up in part throttle characteristics, network
Flutter, under route lumped parameter, the incomplete known conditions of route distribution parameter to distribution network failure section and time of failure
Determine and identifies.
Detailed description of the invention
Fig. 1 is the determination method of a kind of distribution network failure section and fault moment shown according to an exemplary embodiment
Flow chart；
Fig. 2 is the determination method of a kind of the distribution network failure section and fault moment that show according to another exemplary embodiment
Flow chart.
Fig. 3 is a kind of rate the process figure of determination method proposed by the present invention；
Fig. 4 is that the flow chart for determining result is provided in Fig. 3.
Specific embodiment
The present invention will be further described with embodiment with reference to the accompanying drawing:
In order to clarify the technical characteristics of the invention, below by specific embodiment, and its attached drawing is combined, to this hair
It is bright to be described in detail.Following disclosure provides many different embodiments or example is used to realize different knots of the invention
Structure.In order to simplify disclosure of the invention, hereinafter the component of specific examples and setting are described.In addition, the present invention can be with
Repeat reference numerals and/or letter in different examples.This repetition is that for purposes of simplicity and clarity, itself is not indicated
Relationship between various embodiments and/or setting is discussed.It should be noted that illustrated component is not necessarily to scale in the accompanying drawings
It draws.Present invention omits the descriptions to known assemblies and treatment technology and process to avoid the present invention is unnecessarily limiting.
Using the metric data for having measurement equipment in existing network, although can be with direct solution fault section and failure
Moment identification occurs.But the calculation amount of direct solving method is huge.It solves difficult point and is:
(1) norm Solve problems: the metric data of measurement equipment very magnanimity, if directly solved by general norm
Method calculate, with the increase of processing data set, calculation amount is very huge.
(2) solving result determines Creditability Problems: if directly solved from data itself, due to measuring in actual motion
Equipment error in measurement, distributed generation resource access and power output different problems, so that direct basis data itself carry out judgement result
Credibility cannot meet the requirement of practical application.
It therefore, is solution in the not exclusively known item of part throttle characteristics, network topology, route lumped parameter, route distribution parameter
Fault section under part is determining and moment identification problem occurs for failure, and finally provides to meet and meet sentencing for practical application needs
Other result.It is an object of the invention to propose a kind of two stage determination method: the correlation that the first stage calculates historical data is sentenced
Quantitative, second stage is that under conditions of given threshold, each measuring point data mutation is carried out by out-of-limit degree for online data
Sequence is to determine fault time and fault section.
The present invention gives the determination methods of easy a distribution network failure section and fault moment, come when determining failure
Between and fault section and failure phase, this method, which can satisfy, wants the power distribution network relay protection under distributed generation resource access
It asks.
Embodiment 1
Fig. 1 is the determination method of a kind of distribution network failure section and fault moment shown according to an exemplary embodiment
Flow chart.As shown in Figure 1, provided in an embodiment of the present invention
A kind of determination method of distribution network failure section and fault moment, which comprises
Construct data set；
Calculation Estimation index；
The monitoring threshold of evaluation index fluctuation is set；
Fault time and fault zone are determined based on the evaluation index of calculating；
Determine failure phase.
In one possible implementation, the process for constructing data set includes:
It builds history data set: utilizing the data building history number after measurement equipment the last time fault recovery of each measuring point
According to collection, the maximum singular value σ of history data set covariance matrix is calculated by singular value decomposition1；
Building online data collection: the metric data collection at kth moment include the kth moment metric data and before M-1 when
The metric data at quarter, then kth moment online data collection may be expressed as: Y=[yk-M+1..., yk], wherein M includes by data set
The number of sampled value, ykFor the metric data at kth moment, yk-M+1For the metric data at M-1 moment before the kth moment；And to every
The online data collection at one moment acquires the maximum singular value σ ' of online data collection covariance matrix using singular value decomposition1。
In one possible implementation, evaluation index includes that the evaluation index of single class metric data and multiclass measure number
According to comprehensive evaluation index, the comprehensive evaluation index Ψ's of the evaluation index ψ and multiclass metric data of the list class metric data
Shown in calculation formula such as formula (1) and formula (2):
Wherein, X is history data set, and Y is online measured data collection；Cov (X, Y) and cov (X, X) respectively indicate online reality
The covariance matrix of measured data collection and history data set；||·||2To seek 2 norms；σ′1And σ1Respectively online measured data collection
With the maximum singular value of the covariance matrix of history data set；L is the data class actually measured, the data class actually measured
Including voltage magnitude, voltage phase angle, current amplitude, current phase angle, each measuring point trend, on measuring point all kinds of switches switch state；
ψiFor the evaluation index of the i-th class data type；αiFor the weight and satisfaction of the i-th class data evaluation index
In one possible implementation, setting evaluation index fluctuate monitoring threshold range 0.95~1.95 it
Between, i.e.,
|ψ-1|≤0.05 (3)
|Ψ-1|≤0.05 (4)
ψ and Ψ is respectively the evaluation index of single class metric data and the comprehensive evaluation index of multiclass metric data.
In one possible implementation, the process of fault time and fault zone is determined based on the evaluation index of calculating
Are as follows:
The evaluation index of single class metric data is calculated separately to power distribution network the whole network measurement point metric data and multiclass measures number
According to comprehensive evaluation index, if | ψ -1 | > 0.05 or | Ψ -1 | > 0.05 illustrates the corresponding faulty generation of measurement point；
The abnormal metric data time of occurrence for the measurement point that breaks down is ranked up, occurs abnormal metric data earliest
Time is the time that failure occurs；
The measurement point region of occurred abnormal amount measured data is marked into non-suspected malfunctions region；
Measuring point metric data each in suspected malfunctions region is ranked up according to out-of-limit degree, that is, is installed | ψ -1 | or | Ψ -1
| size sequence, be determined fault zone.
In one possible implementation, fluctuating size according to the end measuring point data of fault zone determines fault point far from end
Point apart from degree, that is, fluctuate it is bigger illustrate it is closer from the point.
In one possible implementation, the process of judgement failure phase is；According to the survey of the every phase of fault zone end measuring point
Amount data determine failure phase, i.e., the evaluation for calculating separately single class metric data to the measurement data of the every phase of fault zone end measuring point refers to
Mark and multiclass metric data comprehensive evaluation index, if | ψ -1 | > 0.05 or | Ψ -1 | > 0.05, it is determined that this is mutually failure
Phase.
In one possible implementation, the determination method further include:
Export result；Output result step is not shown in FIG. 1, and the result of output includes time of failure, faulty section
Segment limit, fault point are far from fault zone end how far and failure phase.
Measured data constitute history data set before the present embodiment use, and current online data constitutes online data collection,
And determined according to the maximum singular value ratio of history data set covariance matrix and online data collection covariance matrix, first
Each measuring point of the whole network is measured in real time, is determined after being out of order the moment occurring, according to the judgement result of each measuring point of fault moment
It determines measuring point data anomalies, the moment is occurred according to failure and anomalies ranking determines fault zone, and using mutation
Degree determines how far of the fault point from measuring point.Finally failure phase is judged using fault zone end any point data.
The technical solution is suitable for any load level, without obtaining the regularity of distribution of distributed generation resource and load, calculates
The fault diagnosis for measuring small, can to access for distributed generation resource distribution system or existing distribution system provides theory and technology guarantee.
This method is desirably integrated into existing measurement equipment software, equipment that no replacement is required, has stronger practical application in industry value.
Judgment method provided in this embodiment calculates easy, to only use the existing measurement equipment of system metric data, overcomes
The computationally intensive and credible result problem for directly using data, can be realized and join in part throttle characteristics, network topology, sets of lines
Judgement and identification under number, the incomplete known conditions of route distribution parameter to distribution network failure section and time of failure.
Embodiment 2
Fig. 2 is the determination method of a kind of the distribution network failure section and fault moment that show according to another exemplary embodiment
Flow chart.As shown in Fig. 2, provided in an embodiment of the present invention
A kind of determination method of distribution network failure section and fault moment, comprising:
History data set and online data collection are respectively constituted using historical measurement data and real-time online data；
The maximum singular value of history data set covariance matrix and online data collection covariance matrix is solved respectively；
Distribution network failure section is carried out according to the ratio of maximum singular value and fault moment determines.
In one possible implementation, when carrying out distribution network failure section and failure according to the ratio of maximum singular value
Carving the process determined includes:
Each measuring point of the whole network is measured in real time first, the moment occurs for determining be out of order；
Measuring point data anomalies are determined according to the judgement result of each measuring point of fault moment；
The moment occurs according to failure and anomalies ranking determines fault zone, and determines fault point using anomalies
How far from measuring point；
Finally failure phase is judged using fault zone end any point data.
Current embodiment require that the evaluation index of the single type metric data of calculating and the overall merit of polymorphic type metric data refer to
It marks, the monitoring threshold of evaluation index fluctuation determines regular in consideration system normal operation, considers fault time determination, the event of index
Hinder region, faulted phase decision method.
The comprehensive evaluation index of the evaluation index and polymorphic type metric data that calculate single type metric data is as follows:
Under any load level, the evaluation index of single class metric data and the comprehensive evaluation index of multiclass metric data
Are as follows:
Wherein, the comprehensive evaluation index of the evaluation index of the single class metric data of ψ and Ψ difference and multiclass metric data.X is
History data set, Y are online measured data collection；Cov (X, Y) and cov (X, X) respectively indicate online measured data collection and history number
According to the covariance matrix of collection；||·||2Seek 2 norms；σ′1And σ1The association side of respectively online measured data collection and history data set
The maximum singular value of poor matrix, the singular value can be acquired by singular value decomposition method；L is the data class actually measured,
Data class includes voltage magnitude, voltage phase angle, current amplitude, and current phase angle, each measuring point trend, all kinds of switches opens on measuring point
Off status；ψiFor the evaluation index of the i-th class data type；αiFor the weight and satisfaction of the i-th class data evaluation index
The monitoring threshold of evaluation index fluctuation determines rule in consideration system normal operation:
No matter load or distributed generation resource if access influences to meet national standard and state network mark is quasi- show the state
Lower system normal operation, it may be determined that monitoring threshold range when normal operation is between 0.95~1.95, i.e.,
|ψ-1|≤0.05 (3)
Or
|Ψ-1|≤0.05 (4)
Consider fault time determination, the fault zone, faulted phase decision method of index.
As shown in Figure 3 and Figure 4, the determination method of embodiment 2 includes two stages, in the first stage off-line calculation, is obtained
The maximum singular value of history data set；Second stage calculates maximum singular value according to online data collection, goes through with obtained by the first stage
Its step are as follows for the comparison result progress of history data set maximum singular value:
(1) stage one: constructing history data set using the data after measurement equipment the last time fault recovery of each measuring point,
Its maximum singular value σ is calculated by singular value decomposition1, require the data to report main website or list according to system power automation
Solely exist in each measurement equipment；
(2) stage two: requiring according to on-line evaluation, constructs online data collection by following way of example
The metric data collection at kth moment includes the metric data and the data at M-1 moment before at kth moment, then when kth
Online data collection is carved to be represented by
Y=[yk-M+l..., yk]
Wherein M by data set include sampled value number.
(3) maximum singular value σ ' stage two: is acquired using singular value decomposition to the online data at each moment1, according to being
Electric automation under unified central planning requires the data to report in main website or each measurement equipment of individualism；
(4) stage two: to the whole network measure point data, by formula (1) and formula (2) ratio calculated, if | ψ -1 | > 0.05 or | Ψ -
1 | > 0.05 then determines faulty generation；
(5) stage two: abnormal data time of occurrence sequence is ranked up the time that can determine generation of being out of order and
The preliminary judgement of fault zone；
(6) stage two: sorting according to the out-of-limit degree of each measuring point data, i.e., | ψ -1 | or | Ψ -1 | size sequence, and combine
The preliminary judging result of (5) step can determine fault zone and fluctuate the determining event of size according to fault zone end measuring point data
Barrier point with a distance from endpoint degree, that is, fluctuate it is bigger illustrate it is closer from the point；
(7) stage two: according to the measurement data of the every phase of fault zone end measuring point data according to two step of stage (4) to (6)
Process determine failure occur phase.
(8) output is as a result, include time of failure, fault section range, fault point far from fault zone end how far,
Failure phase.
So far, the fault section involved in the present invention is determining and failure occurs moment identification process and finishes.
When subsequent fault section is determining and failure generation moment identification process starts, it is transferred to the stage one: according to system
Data after restoring normal rebuild history data set, and calculate maximum singular value.
The present embodiment respectively constitutes history data set and in line number first with historical measurement data and real-time online data
According to collection；Then the maximum singular value of history data set covariance matrix and online data collection covariance matrix is solved respectively；And root
Distribution network failure section is carried out according to the ratio of maximum singular value and fault moment determines.The embodiment proposes that one kind is two stage and sentences
Determine method: the first stage calculates the related decision content of historical data, and second stage is the online evaluation stage, in the item of given threshold
Under part, second stage according to the maximum singular value ratio of history data set covariance matrix and online data collection covariance matrix into
Row determines, is measured in real time first to each measuring point of the whole network, determines after being out of order the moment occurring, according to each measuring point of fault moment
Judgement result determine measuring point data anomalies, moment and anomalies ranking are occurred according to failure and determine fault zone, and
How far of the fault point from measuring point is determined using anomalies.Finally event is judged using fault zone end any point data
Hinder phase.Each measuring point data mutation is ranked up by out-of-limit degree to determine fault time and fault section, can satisfy to point
The requirement of power distribution network relay protection under cloth plant-grid connection.This method is suitable for any load level, distributed without obtaining
The regularity of distribution of power supply and load, calculation amount is small, can be the event of the distribution system or existing distribution system of distributed generation resource access
Barrier diagnosis provides theory and technology guarantee.This method is desirably integrated into existing measurement equipment software, and equipment that no replacement is required has
Stronger practical application in industry
Judgment method provided in this embodiment calculates easy, to only use the existing measurement equipment of system metric data, overcomes
The computationally intensive and credible result problem for directly using data, can be realized and join in part throttle characteristics, network topology, sets of lines
Judgement and identification under number, the incomplete known conditions of route distribution parameter to distribution network failure section and time of failure.
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present invention
The limitation enclosed.To those of ordinary skill in the art, other different forms can also be made on the basis of the above description
Modification or deformation.There is no necessity and possibility to exhaust all the enbodiments.On the basis of technical solution of the present invention
On, the various modifications or variations that can be made by those skilled in the art with little creative work still in protection of the invention
Within range.
Claims (10)
1. a kind of determination method of distribution network failure section and fault moment, which is characterized in that the described method includes:
Construct data set；
Calculation Estimation index；
The monitoring threshold of evaluation index fluctuation is set；
Fault time and fault zone are determined based on the evaluation index of calculating；
Determine failure phase.
2. the determination method of distribution network failure section and fault moment according to claim 1, which is characterized in that building number
Include: according to the process of collection
It builds history data set: utilizing the data building historical data after measurement equipment the last time fault recovery of each measuring point
Collection, the maximum singular value σ of history data set covariance matrix is calculated by singular value decomposition1；
Building online data collection: the metric data and M-1 moment before that the metric data collection at kth moment includes the kth moment
Metric data, then kth moment online data collection may be expressed as: Y=[yk-M+1..., yk], wherein M includes sampling by data set
The number of value, ykFor the metric data at kth moment, yk-M+1For the metric data at M-1 moment before the kth moment；And to per a period of time
The online data collection at quarter acquires the maximum singular value σ ' of online data collection covariance matrix using singular value decomposition1。
3. the determination method of distribution network failure section and fault moment according to claim 2, which is characterized in that evaluation refers to
Mark includes the evaluation index of single class metric data and the comprehensive evaluation index of multiclass metric data, and the list class metric data is commented
Shown in the calculation formula such as formula (1) and formula (2) of the comprehensive evaluation index Ψ of valence index ψ and multiclass metric data:
Wherein, X is history data set, and Y is online measured data collection；Cov (X, Y) and cov (X, X) respectively indicates online actual measurement number
According to the covariance matrix of collection and history data set；||·||2To seek 2 norms；σ′1And σ1It respectively online measured data collection and goes through
The maximum singular value of the covariance matrix of history data set；L is the data class actually measured, and the data class actually measured includes
Voltage magnitude, voltage phase angle, current amplitude, current phase angle, each measuring point trend, on measuring point all kinds of switches switch state；ψiFor
The evaluation index of i-th class data type；αiFor the weight and satisfaction of the i-th class data evaluation index
4. the determination method of distribution network failure section and fault moment according to claim 3, which is characterized in that setting is commented
The monitoring threshold range of valence index fluctuation is between 0.95~1.95, i.e.,
|Ψ-1|≤0.05 (3)
|Ψ-1|≤0.05 (4)
ψ and Ψ is respectively the evaluation index of single class metric data and the comprehensive evaluation index of multiclass metric data.
5. the determination method of distribution network failure section and fault moment according to claim 4, which is characterized in that based on
The evaluation index of calculation determines the process of fault time and fault zone are as follows:
The evaluation index and multiclass metric data of single class metric data are calculated separately to power distribution network the whole network measurement point metric data
Comprehensive evaluation index, if | ψ -1 | > 0.05 or | Ψ -1 | > 0.05 illustrates the corresponding faulty generation of measurement point；
The abnormal metric data time of occurrence for the measurement point that breaks down is ranked up, occurs the time of abnormal metric data earliest
The time occurred for failure；
The measurement point region of occurred abnormal amount measured data is marked into non-suspected malfunctions region；
Measuring point metric data each in suspected malfunctions region is ranked up according to out-of-limit degree, that is, is installed | ψ -1 | or | Ψ -1 |
Size sequence, is determined fault zone.
6. the determination method of distribution network failure section and fault moment according to claim 5, which is characterized in that according to event
The end measuring point data fluctuation size in barrier region determines fault point degree with a distance from endpoint, that is, fluctuates bigger illustrate from the point more
Closely.
7. the determination method of distribution network failure section and fault moment according to claim 5, which is characterized in that determine event
Barrier phase process be；Determine failure phase according to the measurement data of the every phase of fault zone end measuring point, i.e., it is every to fault zone end measuring point
The measurement data of phase calculates separately the evaluation index of single class metric data and the comprehensive evaluation index of multiclass metric data, if |
ψ -1 | > 0.05 or | Ψ -1 | > 0.05, it is determined that this is mutually failure phase.
8. the determination method of distribution network failure section and fault moment described in -7 any one according to claim 1, feature
It is, further includes:
Export result；The result of output includes time of failure, fault section range, fault point far from fault zone end distance journey
Degree and failure phase.
9. a kind of determination method of distribution network failure section and fault moment characterized by comprising
History data set and online data collection are respectively constituted using historical measurement data and real-time online data；
The maximum singular value of history data set covariance matrix and online data collection covariance matrix is solved respectively；
Distribution network failure section is carried out according to the ratio of maximum singular value and fault moment determines.
10. the determination method of distribution network failure section and fault moment according to claim 9, which is characterized in that according to
The ratio of maximum singular value carries out the process that distribution network failure section determines with fault moment
Each measuring point of the whole network is measured in real time first, the moment occurs for determining be out of order；
Measuring point data anomalies are determined according to the judgement result of each measuring point of fault moment；
The moment occurs according to failure and anomalies ranking determines fault zone, and determines fault point from survey using anomalies
The how far of point；
Finally failure phase is judged using fault zone end any point data.
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