CN106373032B - The high-incidence region discrimination method of Distribution Network Failure based on big data - Google Patents
The high-incidence region discrimination method of Distribution Network Failure based on big data Download PDFInfo
- Publication number
- CN106373032B CN106373032B CN201610814151.XA CN201610814151A CN106373032B CN 106373032 B CN106373032 B CN 106373032B CN 201610814151 A CN201610814151 A CN 201610814151A CN 106373032 B CN106373032 B CN 106373032B
- Authority
- CN
- China
- Prior art keywords
- failure
- grid
- fault
- distribution
- geographic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 93
- 238000012850 discrimination method Methods 0.000 title claims abstract description 14
- 238000013507 mapping Methods 0.000 claims abstract description 7
- 238000003745 diagnosis Methods 0.000 claims abstract description 5
- 230000001131 transforming effect Effects 0.000 claims description 21
- 238000000034 method Methods 0.000 claims description 14
- 230000032683 aging Effects 0.000 claims description 7
- 230000015556 catabolic process Effects 0.000 claims description 5
- 230000004888 barrier function Effects 0.000 claims description 4
- 238000007689 inspection Methods 0.000 claims description 4
- 230000003014 reinforcing effect Effects 0.000 claims description 4
- 230000005540 biological transmission Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000004891 communication Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 3
- 238000005520 cutting process Methods 0.000 claims description 3
- 230000006378 damage Effects 0.000 claims description 3
- 230000014509 gene expression Effects 0.000 claims description 3
- 238000011835 investigation Methods 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 claims description 3
- 230000011664 signaling Effects 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 238000009966 trimming Methods 0.000 claims description 3
- 230000007547 defect Effects 0.000 claims description 2
- 230000011218 segmentation Effects 0.000 claims 1
- 238000013139 quantization Methods 0.000 abstract description 4
- 238000012423 maintenance Methods 0.000 abstract description 2
- 238000007619 statistical method Methods 0.000 abstract description 2
- 230000008859 change Effects 0.000 description 5
- 230000005611 electricity Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000003780 insertion Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000004087 circulation Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 235000019580 granularity Nutrition 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 230000002787 reinforcement Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Databases & Information Systems (AREA)
- Economics (AREA)
- General Engineering & Computer Science (AREA)
- Water Supply & Treatment (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Geometry (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Computer Hardware Design (AREA)
- Remote Sensing (AREA)
- Data Mining & Analysis (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
- Locating Faults (AREA)
Abstract
The invention discloses the high-incidence region discrimination method of Distribution Network Failure based on big data, including the following steps: fuzzy diagnosis is carried out to Distribution Network Failure position based on multi-source data；The assessment of Distribution Network Failure power failure load loss is carried out based on historical load data, weather data；Grid dividing is carried out to geographic view, and realizes the mapping of failure and geographic grid；The failure for calculating each geographic grid in given time period influences statistical indicator, quantifies on the influence of each net region internal fault；It influences statistical indicator according to each grid failure to be coloured, so that generating failure influences color spot figure；Region high-incidence for failure proposes corresponding aid decision suggestion.The present invention has carried out loss of outage quantization and fuzzy positioning by the Distribution Network Failure to magnanimity, and it is mapped with geographic grid, and then it is influenced for the failure of geographic grid for statistical analysis, forming failure influences color spot figure, to provide the aid decision suggestion of fining and customization for distribution scheduling, O&M, maintenance.
Description
Technical field
The present invention relates to a kind of high-incidence region discrimination method of Distribution Network Failure based on big data, belong to software safety with can
By property analysis field.
Background technique
In traditional Distribution Network Failure management work, often studied and judged just for single failure, and on this basis into
Row region dimension (province, city, county) and time dimension (year, month, day) Distribution Network Failure quantity statistics.These statistical result granularities compared with
Slightly, inspection can not be transported to distribution scheduling and effective aid decision is provided.
The generation of single electric network fault has certain contingency, however from the point of view of one section of long period, distribution rack is thin
The high failure rate in the region that weak or O&M falls behind shows certain regularity in average level.Therefore, by history event
Barrier carries out big data analysis, effectively identifies the high risk zone of Frequent Troubles, and corresponding technology or management means is taken
The probability that failure risk occurs is reduced, is had a very important significance for improving Reliability of Power Supplying Net Work.
With the continuous propulsion that smart grid is built, the level of IT application of power distribution network constantly promoted, and Jiangsu Power Grid has been at present
EMS (Energy Management System), PMS (production management system), DMS (distribution management system) and electricity consumption acquisition system has been done step-by-step
Comprehensive covering.
Summary of the invention
In view of the deficienciess of the prior art, it is an object of the present invention to provide a kind of Distribution Network Failure district occurred frequently based on big data
Domain discrimination method has carried out loss of outage quantization and fuzzy positioning by the Distribution Network Failure to magnanimity, and has carried out with geographic grid
Mapping, and then influence for the failure of geographic grid for statistical analysis, forming failure influences color spot figure, to be distribution tune
The aid decision suggestion that degree, O&M, maintenance provide fining and customize.
To achieve the goals above, the present invention is to realize by the following technical solutions:
The high-incidence region discrimination method of Distribution Network Failure based on big data of the invention, including the following steps:
(1) for per failure together, fuzzy diagnosis is carried out to Distribution Network Failure position based on multi-source data；Based on historical load
Data, weather data carry out the assessment of Distribution Network Failure power failure load loss；
(2) grid dividing is carried out to geographic view, and realizes the mapping of failure and geographic grid；
(3) given time period [t is calculateds,te] in each geographic grid failure influence statistical indicator, to each net region
Internal fault influence is quantified；
(4) it influences value of statistical indicant according to each grid failure to colour geographic grid, so that generating failure influences color
Spot figure；
(5) region high-incidence for failure proposes corresponding aid decision suggestion.
In step (1), the method for carrying out fuzzy diagnosis to the Distribution Network Failure position is as follows:
(1a) according to regular expressions, the abort situation text that districts and cities' sole duty is made a report on from electric network fault statistical report system
This information is parsed, and identifies device numbering and device name, and corresponding equipment is searched into production management system PMS, from
And determine abort situation (pos_x, pos_y), and marking abort situation type is 0, if can not identify, go to step
(2a)；
(2a) searches the block switch remote signalling quartile record of faulty line from distribution management system DMS, if the route
All block switches are mounted with distributing automation apparatus, find out in line fault time range two block switches of separating brake at first
ss1、ss2, and marking abort situation type is 1；If route does not install distributing automation apparatus, step (3a) is gone to；
(3a) searches the load data that fault feeder has all distribution transformers under its command from power information acquisition system, according to
Electric current falls zero duration, determines the power failure duration of each distribution transforming, and the longest distribution transforming of the duration that will have a power failure is added to distribution transforming set pb_
In set, and marking abort situation type is 2.
In step (2), the method for carrying out grid dividing to the geographic view is as follows:
Geographic area is based on first quartile coordinate system, and geographic area is carried out cutting with the square that side length is len, it is assumed that
Total m column n row square covers given geographic area, wherein and m > 3, n > 3 number the grid in the lower left corner for g (0,0),
Then it is located at xth column, the grid number that y line position is set is g (x, y).
In step (2), the mapping method of the failure and geographic grid is as follows:
The event of failure list ft_list (x, y) for defining geographic grid g (x, y) occurs in the grid for storing
Event of failure, each record is comprising with properties: time of failure occure_time, fault feeder feeder_info,
Abort situation GPS coordinate x-axis pos_x, abort situation GPS coordinate y-axis pos_y, fault outage lose load loss, fault type
Type, abort situation fuzziness amb and breakdown loss weighted value weight；
Distribution Network Failure is traversed, for failure fti,
Case1: the location of fault type is 0, if position (pos_x occurs for its failurei,pos_yi) ∈ g (x, y), then
Ft will be recordediIt is inserted into ft_list (x, y), wherein fti.Amb=0, fti.Weight=1, fti.pos_x=pos_xi,
fti.pos_y=pos_yi；
Case2: the location of fault type is 1,
Case2.1: if two block switch ss1、ss2And the route between them is all located among g (x, y), then will
Record ftiIt is inserted into ft_list (x, y), wherein fti.Amb=0, fti.Weight=1；
Case2.2: by two block switch ss1、ss2And the route between them is known as ft_section, if ft_
Section is located at different geographic grid g (x1,y1)~g (xn,yn) in, failure is inserted into these ground according to different weights
In the error listing for managing grid；For geographic grid g (xj,yj), ft will be recordediIt is inserted into ft_list (xj,yj), wherein fti。
Amb=1, fti。
Case3: the location of fault type is 2,
Case3.1: if pb_set and the route between them are all located among g (x, y), ft will be recordediInsertion
Ft_list (x, y), wherein fti.Amb=0, fti.Weight=1；
Case3.2: pb_set and the track section for connecting these distribution transformings are known as ft_section, if ft_
Section is located at different geographic grid g (x1,y1)~g (xn,yn) in, failure is inserted into these ground according to different weights
In the error listing for managing grid；For geographic grid g (xj,yj), ft will be recordediIt is inserted into ft_list (xj,yj), wherein fti。
Amb=1, fti。
In step (3), it includes failure frequency, fault outage influence accumulated value, event that the failure, which influences statistical indicator,
Barrier, which has a power failure, influences line length mean value and fault outage influence 10kV capacity of distribution transform mean value；
For grid g (x, y), the calculation method that the failure influences statistical indicator is as follows:
(1b) failure frequency
Wherein, n ' is to occur
Primary fault number in grid；
(2b) fault outage influences accumulated value
(3b) fault outage influences line length mean value
Wherein, length_grid (linej) it is length of the route j in grid g (x, y)；
M ' is the sum for the distribution transforming for including or the sum of feeder line staggered with grid, line in gridjFor j-th strip in grid
Feeder line；
(4b) fault outage influences 10kV capacity of distribution transform mean value
Wherein, volumn (trj) be distribution transformer j capacity, wherein trjFor jth platform distribution transforming in grid.
In step (4), it is as follows that the failure influences color spot map generalization method:
After selected statistical regions, statistical time range, fault type, statistical indicator, the fault statistics value of each geographic grid is calculated
Two-dimensional array g_stat [x] [y]；
Statistics obtains 95 probability values, 85 probability values and 30 probability values of the array, respectively as high-risk, serious, common and
The threshold value that lower region divides；
Each geographic grid is coloured according to corresponding grade, so that forming failure influences color spot figure.
In step (5), the aid decision is suggested as follows:
The high risk zone (1c), and based on overhead transmission line, then suggest carrying out insulating, cabling reconstructing or carries out distribution certainly
Dynamicization upgrading；
High risk zone (2c), and based on cable but power distribution automation degree is low then suggests carrying out distribution to route automatic
Change upgrading；
High risk zone in (3c), and statistical indicator is that fault outage influences line length mean value, then it is recommended to increase the regions
Line sectionalizing；
High risk zone in (4c), and fault type is external force destruction, then suggests reinforcing the communication with unit in charge of construction, and increase
The dynamics of region tour inspection；
High risk zone in (5c), and fault type is ageing equipment, then suggests reinforcing area equipment investigation, timely update
Defect or aging equipment；
High risk zone in (6c), and fault type is tree line contradiction, then suggests periodically carrying out the region tree trimming.
Distribution Network Failure zone location may be implemented in the present invention, and the loss of outage caused by failure carries out accurate evaluation.?
On the basis of this, Distribution Network Failure and geographic grid are mapped, realize the accumulative quantization of the breakdown loss value based on geographic grid, and
It renders geographic grid in the way of color spot figure to be prompted, to provide upgrading, transformation, reinforcement for scheduling, Yun Jiandeng department
The aid decision suggestion that spy precision such as patrols and customizes.
Detailed description of the invention
Fig. 1 is the high-incidence region discrimination method work flow diagram of the Distribution Network Failure of the invention based on big data；
Fig. 2 is the schematic diagram of geographic grid division and position mark of the present invention.
Specific embodiment
To be easy to understand the technical means, the creative features, the aims and the efficiencies achieved by the present invention, below with reference to
Specific embodiment, the present invention is further explained.
Referring to Fig. 1, the high-incidence domain discrimination method of Distribution Network Failure based on big data carries out fuzzy positioning to Distribution Network Failure, and
The loss of outage caused by failure carries out accurate evaluation；On this basis, Distribution Network Failure and geographic grid are mapped, is realized
The accumulative quantization of breakdown loss value based on geographic grid, and render geographic grid in the way of color spot figure and prompted, to adjust
Degree, Yun Jiandeng department, which provide upgrading, transformation, reinforces spy the precision and the aid decision customized support such as patrols.
In order to effectively use this method, the geographic view for having certain region, power distribution network topological structure, distribution event should ensure that
Barrier list of thing, Distribution Network Failure report information, feeder line and distribution transforming machine account information, feeder line and distribution transformer load data, weather monitoring number
According to etc..
This method includes following five steps:
(1) the intelligent fuzzy identification of Distribution Network Failure position and the accurate assessment of fault outage loss.
The identification of Distribution Network Failure position intelligent fuzzy, specifically:
(1a) according to regular expressions, the abort situation text made a report on from electric network fault reporting system according to districts and cities' sole duty
Information is parsed, and mode includes: " #xx ring network cabinet ", " #xx bar ", " #xx on-pole switch ", " #xx cut cable ", " #xx matches
Change ", " #xx feeder pillar " etc., identify key message (such as device numbering, title), and carry out searching corresponding set into PMS
It is standby, so that it is determined that failure approximate location (pos_x, pos_y), and marking abort situation type is 0.If can not identify, go to
Step (2a)；
(2a) searches the block switch remote signalling quartile record of faulty line from DMS, if all block switches of the route
It is mounted with two distant (or three distant) device, it is assumed that line fault time range is [t1,t2], find out in the period separating brake at first
Two block switch ss1、ss2, and marking abort situation type is 1.If route does not install distributing automation apparatus, go to
Step (3a)；
(3a) searches the load data that the feeder line has all distribution transformers under its command from power information acquisition system, according to electricity
The duration that stream is zero determines its power failure duration, and the longest distribution transforming of the duration that will have a power failure is added in distribution transforming set pb_set, and
Marking abort situation type is 2.Power failure duration is judged in addition to falling zero according to electric current, can also stop telegram in reply event according to distribution transforming come really
Determine power failure duration (if having event acquisition and report mechanism).
The assessment of Distribution Network Failure power failure load loss specifically:
Using history distribution transformer load, weather history live data, constructed according to the similar day algorithm based on human comfort
Distribution transformer load Short-term Forecasting Model；According to the weather data of failure period of right time, period of being out of order each distribution transforming loss is estimated
Load curve, and integrate calculate loss of outage electricity.Each distribution transforming loss load is added up, feeder line power failure load loss is obtained
Value.It specifically can refer to patent: the Distribution Network Failure loss of outage appraisal procedure of meter and distributed new, application No. is:
201610151200.6。
(2) geographic view carries out grid dividing and geographic grid and fault correlation maps.
Geographic view grid dividing, specifically:
Geographic area is subjected to cutting with the square that side length is len, dynamic adjustment can be carried out according to actual needs, it is proposed that
Value is 1km~10km.Assuming that total m column n row square covers given geographic area (m > 3, n > 3), by the net in the lower left corner
Lattice number is (0,0) g.For geographic area g (x, y), x ∈ (0, m), y ∈ (0, n), eight grids of direct neighbor can be with
It is represented sequentially as g (x-1, y), grid (x-1, y-1), g (x, y-1), g (x-1, y+1), g (x+1, y), grid (x+1, y-1), g
(x, y+1), g (x+1, y+1) are specific as shown in Figure 2.State's net GIS platform or Baidu map, Amap can be used in geographic view
The equal open map platform of socializations.
Fault Mapping based on geographic grid, specifically:
The event of failure list ft_list (x, y) of geographic grid g (x, y) is defined first, it, which is stored, occurs in the grid
Event of failure, each record is comprising with properties:
1) time of failure occure_time；
2) fault feeder feeder_info；
3) abort situation GPS coordinate x-axis pos_x
4) abort situation GPS coordinate y-axis pos_y
5) fault outage loses load loss；
6) fault type type.Remarks: comprising bad weather, tree line contradiction, ageing equipment, external force destruction, user's reason,
Design the types such as Rig up error, reason be unknown
7) abort situation fuzziness amb, value range 0,1 two kinds
8) breakdown loss weighted value weight, value range 0~1.
Distribution Network Failure is traversed, for failure fti,
Case1: the location of fault type is 0, illustrates that the abort situation is precisely reliable, if position occurs for its failure
Set (pos_xi,pos_yi) ∈ g (x, y), then it will record ftiIt is inserted into ft_list (x, y), wherein fti.Amb=0, fti。
Weight=1；
Case2: the location of fault type be 1, illustrate the abort situation be it is fuzzy, for it is all include faulty line
The affiliated grid of section assigns each corresponding weight of grid according to length accounting:
Case2.1: if two block switch ss1、ss2And the route (containing branch) between them is all located at g (x, y)
Among, then it will record ftiIt is inserted into ft_list (x, y),
Wherein, fti.Amb=0, fti.Weight=1；
Case2.2: if two block switch ss1、ss2And the route between them (contains branch, referred to as ft_
Section) it is located at different geographic grid g (x1,y1)~g (xn,yn) in, with geographic grid g (x thereinj,yj) for, it will
Record ftiIt is inserted into ft_list (xj,yj),
Wherein fti.Amb=1, fti。
Case3: the location of fault type is 2,
Case3.1:, will record if pb_set and the route (containing branch) between them are all located among g (x, y)
ftiIt is inserted into ft_list (x, y), wherein fti.Amb=0, fti.Weight=1；
Case3.2: if pb_set and connecting track section (containing branch, referred to as ft_section) positions of these distribution transformings
In different geographic grid g (x1,y1)~g (xn,yn) in.With geographic grid g (x thereinj,yj) for, ft will be recordediInsertion
ft_list(xj,yj),
Wherein fti.Amb=1, fti。
(3) geographic grid failure influences statistic quantification, specifically:
Firstly, period [the t of selected statisticss,te], it is proposed that Shi Changwei 1 year or more.Duration no more than 3 years because
Larger change may occur for overlong time, distribution network frame topology
Secondly, quantifying on the influence of each net region internal fault.For grid g (x, y), statistical indicator is as follows:
(1b) failure frequency
Wherein, n ' is to occur
Primary fault number in grid；
(2b) fault outage influences accumulated value
(3b) fault outage influences line length mean value
Wherein, length_grid (linej) it is length of the route j in grid g (x, y)；
Wherein, m ' is the sum for the distribution transforming for including or the sum of feeder line staggered with grid, line in gridjFor in grid
J-th strip feeder line；
(4b) fault outage influences 10kV capacity of distribution transform mean value
Wherein, volumn (trj) be distribution transformer j capacity, wherein trjFor jth platform distribution transforming in grid.
It is worth noting that, These parameters are the overall performane regardless of fault type.Can also on the basis of failure modes,
The statistics of These parameters is carried out, in favor of more fine accident analysis and aid decision.
(4) statistical value is influenced according to each grid failure and generates failure influence color spot figure:
After selected statistical regions, statistical time range, fault type, statistical indicator, system generates each according to step 1~3 operations
(calculation method is that two circulations is divided to successively to calculate each grid to the fault statistics value two-dimensional array g_stat [x] [y] of geographic grid
Numerical value, i.e., calculated always from g_stat [0] [0] to g_stat [m] [n], be existing method details are not described herein again).It counts
To 95 probability values, 85 probability values and 30 probability values of the array, respectively as high-risk, serious, common, more low region division
Threshold value (corresponding method are as follows: statistical value be higher than 95 probability values be it is high-risk, be greater than 85 probability values and less than 95 probability values be it is serious, greatly
It is common in 30 probability values and less than 85 probability values, is lower lower than 30 probability values).Field color is defined as follows: high-risk ---
Red, middle danger --- it is orange, common --- yellow, lower --- green.Each geographic grid is carried out according to corresponding grade
Color, so that forming failure influences color spot figure.When user's mouse clicks certain geographic grid, it is all which is popped up with list mode
(or certain class) fault message, shows its details after clicking specific fault message.
(5) corresponding aid decision suggestion is proposed for the higher region of failure influence degree
The high risk zone (1c), and based on overhead transmission line, it is proposed that it is automatic to carry out insulating, cabling reconstructing or progress distribution
Change upgrading；
The high risk zone (2c), and based on cable but power distribution automation degree is low, it is proposed that power distribution automation is carried out to route
Upgrading；
High risk zone (statistical indicator are as follows: fault outage influences line length mean value) in (3c), it is proposed that increase the region
Line sectionalizing；
High risk zone (fault type are as follows: external force is destroyed) in (4c), it is proposed that reinforce the communication with unit in charge of construction, and increasing should
The dynamics of region tour inspection；
High risk zone (fault type are as follows: ageing equipment) in (5c), it is proposed that reinforce area equipment investigation, timely update scarce
Sunken or aging equipment；
High risk zone (fault type are as follows: tree line contradiction) in (6c), it is proposed that periodically carry out the region tree trimming.
The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the above embodiments and description only describe this
The principle of invention, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appended claims and its
Equivalent thereof.
Claims (6)
1. the high-incidence region discrimination method of Distribution Network Failure based on big data, which is characterized in that including the following steps:
(1) for per failure together, fuzzy diagnosis is carried out to Distribution Network Failure position based on multi-source data；Based on historical load number
The assessment of Distribution Network Failure power failure load loss is carried out according to, weather data；
(2) grid dividing is carried out to geographic view, and realizes the mapping of failure and geographic grid；
(3) given time period [t is calculateds,te] in the failure of each geographic grid influence statistical indicator, in each net region therefore
Barrier influences to be quantified；
(4) it influences value of statistical indicant according to each grid failure to colour geographic grid, so that generating failure influences color spot figure；
(5) region high-incidence for failure proposes corresponding aid decision suggestion；
In step (1), the method for carrying out fuzzy diagnosis to the Distribution Network Failure position is as follows:
(1a) according to regular expressions, the abort situation text envelope that districts and cities' sole duty is made a report on from electric network fault statistical report system
Breath is parsed, and identifies device numbering and device name, and corresponding equipment is searched into production management system PMS, thus really
Determine abort situation (pos_x, pos_y), and marking abort situation type is 0, if can not identify, goes to step (2a)；
(2a) searches the block switch remote signalling quartile record of faulty line from distribution management system DMS, if the route is all
Block switch is mounted with distributing automation apparatus, finds out in line fault time range two block switch ss of separating brake at first1、
ss2, and marking abort situation type is 1；If route does not install distributing automation apparatus, step (3a) is gone to；
(3a) searches the load data that fault feeder has all distribution transformers under its command from power information acquisition system, according to electric current
The duration for falling zero, determines the power failure duration of each distribution transforming, and the longest distribution transforming of the duration that will have a power failure is added to distribution transforming set pb_set
In, and marking abort situation type is 2.
2. the high-incidence region discrimination method of the Distribution Network Failure according to claim 1 based on big data, which is characterized in that step
(2) in, the method for carrying out grid dividing to the geographic view is as follows:
Geographic area is based on first quartile coordinate system, geographic area is carried out cutting with the square that side length is len, it is assumed that total
M column n row square covers given geographic area, wherein m > 3, n > 3 number the grid in the lower left corner for g (0,0), then position
Numbering in the grid that xth column, y line position are set is g (x, y).
3. the high-incidence region discrimination method of the Distribution Network Failure according to claim 2 based on big data, which is characterized in that step
(2) in, the mapping method of the failure and geographic grid is as follows:
The event of failure list ft_list (x, y) for defining geographic grid g (x, y), for storing the failure occurred in the grid
Event, each record is comprising with properties: time of failure occure_time, fault feeder feeder_info, failure
Position GPS coordinate x-axis pos_x, abort situation GPS coordinate y-axis pos_y, fault outage lose load loss, fault type
Type, abort situation fuzziness amb and breakdown loss weighted value weight；
Distribution Network Failure is traversed, for failure fti,
Case1: the location of fault type is 0, if position (pos_x occurs for its failurei,pos_yi) ∈ g (x, y), then it will note
Record ftiIt is inserted into ft_list (x, y), wherein fti.amb=0, fti.weight=1, fti.pos_x=pos_xi,fti.pos_y
=pos_yi；
Case2: the location of fault type is 1,
Case2.1: if two block switch ss1、ss2And the route between them is all located among g (x, y), then it will record
ftiIt is inserted into ft_list (x, y), wherein fti.amb=0, fti.weight=1；
Case2.2: by two block switch ss1、ss2And the route between them is known as ft_section, if ft_
Section is located at different geographic grid g (x1,y1)~g (xn,yn) in, failure is inserted into these ground according to different weights
In the error listing for managing grid；For geographic grid g (xj,yj), ft will be recordediIt is inserted into ft_list (xj,yj), wherein
fti.amb=1,
Case3: the location of fault type is 2,
Case3.1: if pb_set and the route between them are all located among g (x, y), ft will be recordediIt is inserted into ft_
List (x, y), wherein fti.amb=0, fti.weight=1；
Case3.2: being known as ft_section for pb_set and the track section for connecting these distribution transformings, if ft_section
In different geographic grid g (x1,y1)~g (xn,yn) in, failure is inserted into these geographic grids according to different weights
In error listing；For geographic grid g (xj,yj), ft will be recordediIt is inserted into ft_list (xj,yj), wherein fti.amb=1,
4. the high-incidence region discrimination method of the Distribution Network Failure according to claim 3 based on big data, which is characterized in that step
(3) in, it includes failure frequency, fault outage influence accumulated value, fault outage influence line that the failure, which influences statistical indicator,
Road length mean value and fault outage influence 10kV capacity of distribution transform mean value；
For grid g (x, y), the calculation method that the failure influences statistical indicator is as follows:
(1b) failure frequency
Wherein, n ' is to occur in grid
Interior primary fault number；
(2b) fault outage influences accumulated value
(3b) fault outage influences line length mean value
Wherein, length_grid (linej) it is length of the route j in grid g (x, y)；
M ' is the sum for the distribution transforming for including or the sum of feeder line staggered with grid, line in gridjFor j-th strip feeder line in grid；
(4b) fault outage influences 10kV capacity of distribution transform mean value
Wherein, volumn (trj) be distribution transformer j capacity, wherein trjFor jth platform distribution transforming in grid.
5. the high-incidence region discrimination method of the Distribution Network Failure according to claim 1 based on big data, which is characterized in that step
(4) in, it is as follows that the failure influences color spot map generalization method:
After selected statistical regions, statistical time range, fault type, statistical indicator, the fault statistics value two dimension of each geographic grid is calculated
Array g_stat [x] [y]；
Statistics obtains 95 probability values, 85 probability values and 30 probability values of the array, respectively as high-risk, serious, common and lower
The threshold value of region division；
Each geographic grid is coloured according to corresponding grade, so that forming failure influences color spot figure.
6. the high-incidence region discrimination method of the Distribution Network Failure according to claim 1 based on big data, which is characterized in that step
(5) in, the aid decision is suggested as follows:
The high risk zone (1c), and based on overhead transmission line, then suggest carrying out insulating, cabling reconstructing or carries out power distribution automation
Upgrading；
The high risk zone (2c), and based on cable but power distribution automation degree is low, then suggest carrying out power distribution automation liter to route
Grade transformation；
High risk zone in (3c), and statistical indicator is that fault outage influences line length mean value, then it is recommended to increase the lines in the region
Road segmentation；
High risk zone in (4c), and fault type is external force destruction, then suggests reinforcing the communication with unit in charge of construction, and increase the area
The dynamics of domain tour inspection；
High risk zone in (5c), and fault type is ageing equipment, then suggests reinforcing area equipment investigation, timely update defect
Or aging equipment；
High risk zone in (6c), and fault type is tree line contradiction, then suggests periodically carrying out the region tree trimming.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610814151.XA CN106373032B (en) | 2016-09-09 | 2016-09-09 | The high-incidence region discrimination method of Distribution Network Failure based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610814151.XA CN106373032B (en) | 2016-09-09 | 2016-09-09 | The high-incidence region discrimination method of Distribution Network Failure based on big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106373032A CN106373032A (en) | 2017-02-01 |
| CN106373032B true CN106373032B (en) | 2019-07-19 |
Family
ID=57900280
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610814151.XA Active CN106373032B (en) | 2016-09-09 | 2016-09-09 | The high-incidence region discrimination method of Distribution Network Failure based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106373032B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108051725B (en) * | 2017-10-31 | 2019-12-06 | 国网浙江省电力有限公司检修分公司 | Converter station valve tower thyristor positioning system and working method thereof |
| CN108182194A (en) * | 2017-11-22 | 2018-06-19 | 国电南瑞科技股份有限公司 | The management method that a kind of gridding based on power grid GIS divides |
| CN109344219A (en) * | 2018-10-26 | 2019-02-15 | 苏州百卓网络技术有限公司 | Method and device based on the space lattice technology analysis high-incidence region of accident |
| CN110555532A (en) * | 2019-07-18 | 2019-12-10 | 国网浙江台州市椒江区供电有限公司 | Internal fault first-aid repair service system for electricity customer |
| CN110990792B (en) * | 2019-12-05 | 2023-05-16 | 国网湖南省电力有限公司 | Power grid equipment icing fault rate calculation method considering meteorological factors |
| CN112421767B (en) * | 2020-09-30 | 2023-04-07 | 云南电网有限责任公司西双版纳供电局 | Power grid disaster prevention emergency panoramic monitoring method and device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103728536A (en) * | 2013-12-11 | 2014-04-16 | 国家电网公司 | Multi-source data grid fault identification method |
| CN104504525A (en) * | 2014-12-26 | 2015-04-08 | 国家电网公司 | Method for realizing power-grid equipment failure prewarning through big data mining technology |
| CN105762793A (en) * | 2016-03-16 | 2016-07-13 | 国网江苏省电力公司电力科学研究院 | Distribution network fault power failure loss assessment method considering distributed new energy |
-
2016
- 2016-09-09 CN CN201610814151.XA patent/CN106373032B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103728536A (en) * | 2013-12-11 | 2014-04-16 | 国家电网公司 | Multi-source data grid fault identification method |
| CN104504525A (en) * | 2014-12-26 | 2015-04-08 | 国家电网公司 | Method for realizing power-grid equipment failure prewarning through big data mining technology |
| CN105762793A (en) * | 2016-03-16 | 2016-07-13 | 国网江苏省电力公司电力科学研究院 | Distribution network fault power failure loss assessment method considering distributed new energy |
Non-Patent Citations (1)
| Title |
|---|
| 气象灾害风险智能分析及预警***;杨笑宇等;《山东电力技术》;20160725;第21-24页 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106373032A (en) | 2017-02-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106373032B (en) | The high-incidence region discrimination method of Distribution Network Failure based on big data | |
| CN105005827B (en) | A kind of emergency response method for early warning based on electric power accident | |
| Pant et al. | Vulnerability assessment framework for interdependent critical infrastructures: case-study for Great Britain’s rail network | |
| CN112070286B (en) | Precipitation forecast and early warning system for complex terrain river basin | |
| CN109215347A (en) | A kind of traffic data quality control method based on crowdsourcing track data | |
| CN106980071B (en) | Visual first-aid repair system based on power grid GIS and working method thereof | |
| CN113189451B (en) | Power distribution network fault positioning and judging method, system, computer equipment and storage medium | |
| CN104778263B (en) | A kind of charging station system for electric vehicle emulates data digging method | |
| CN109828182A (en) | A kind of network system accident analysis method for early warning based on failure modes processing | |
| CN101043152A (en) | Intelligent decision support system for urban power grid accidents | |
| CN105069690A (en) | Power grid operating data processing method | |
| CN106814286B (en) | Power distribution network fault positioning system, method and server based on multi-element fault acquisition | |
| CN107290623A (en) | A kind of transmission line malfunction reason recognition methods based on Auto-matching | |
| CN112381321B (en) | Power distribution network operation state sensing method based on gridding division | |
| CN104655986A (en) | Method for judging lightning stroke fault point of tripped transmission line | |
| CN110380514A (en) | A kind of intelligent substation relay protection secondary circuit method for diagnosing faults | |
| CN105528671B (en) | A kind of power grid Multidimensional Awareness based on big data and safety estimation system and method | |
| CN108398934A (en) | The system that a kind of equipment fault for rail traffic monitors | |
| CN110690699B (en) | Transformer substation intelligent detection system based on ubiquitous power Internet of things | |
| CN106250570A (en) | A kind of subdrainage pipe network intelligent management system | |
| CN106846168A (en) | The processing unit and system of transmission line of electricity scissors crossing | |
| CN102289582A (en) | Method for recognizing risk state of power distribution network | |
| CN113177858B (en) | Multi-dimensional power grid typhoon resistance evaluation method | |
| CN110334127A (en) | A kind of distribution network line fault law mining method, system and storage medium | |
| CN113902583A (en) | Distribution network side operation and maintenance method and system using low-voltage network equipment data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |