CN104269844B - A kind of state of electric distribution network estimation abnormality recognition method and its device - Google Patents
A kind of state of electric distribution network estimation abnormality recognition method and its device Download PDFInfo
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- CN104269844B CN104269844B CN201410457311.0A CN201410457311A CN104269844B CN 104269844 B CN104269844 B CN 104269844B CN 201410457311 A CN201410457311 A CN 201410457311A CN 104269844 B CN104269844 B CN 104269844B
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- 238000000034 method Methods 0.000 title claims abstract description 19
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- 238000005259 measurement Methods 0.000 claims abstract description 29
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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Abstract
A kind of state of electric distribution network estimation abnormality recognition method and its device, are related to distribution automation field.At present, the efficiency that manually searches problem is low, the cycle is long, there is the shortcomings of identification is not comprehensive, can not effectively improve power distribution network and measure qualification rate.The present invention comprises the following steps：Automatically electric network model and section are read from SCADA system timing, generate Network Topology for Real-Time, data are handled using state of electric distribution network algorithm for estimating, identify unqualified measurement, according to preset rule, identifying causes to measure abnormal equipment, judges equipment owner, and short message is sent to equipment owner, in time operations staff is notified to do relevant treatment.The technical program reduces manually check and correction, checks link, reduces cost, improves efficiency.
Description
Technical field
The present invention relates to distribution automation field more particularly to a kind of abnormal method recognition methods of state of electric distribution network estimation and
Device.
Background technology
At present, electrical power distribution automatization system in the process of running, due to the influence of each link such as harvester, substation, passage,
Cause the measurement of acquisition and actual measurement there are deviations and mistake, such as channel disturbance to cause data distortion, mutual inductor or measurement
Equipment damage causes doomed dead evidence, and system maintenance causes measurement direction that the inverse problem beginning occurs not in time, these problems at present can only
It is solved by the mode that substation field is compared manually is timed to, the mode of on-the-spot testing can search, identify portion
Component measured data problem, such as doomed dead evidence；It inquires and can not be found at the scene for measurement direction mistake etc., and manually search problem
Efficiency is low, the cycle is long, there is the shortcomings of identification is not comprehensive, can not effectively improve power distribution network and measure qualification rate.
The content of the invention
In view of this, the present invention provides a kind of state of electric distribution network estimation abnormality recognition method and its device, looked into realizing
Look for the quick purpose of problem.To achieve the above object, the present invention takes following technical scheme：
A kind of state of electric distribution network estimates abnormality recognition method, it is characterised in that comprises the following steps：
1）Automatically electric network model and section are read from SCADA system timing；
2）Generate Network Topology for Real-Time；
3）Data are handled using state of electric distribution network algorithm for estimating；
4）Identify unqualified measurement, according to preset rule, identifying causes to measure abnormal equipment；
5）Judge equipment owner, and short message is sent to equipment owner.
Using general file mode, electric network model and section, weight are read from network equipment or automated system automatically
Structure electric network model determines Network Topology for Real-Time, and adoption status algorithm for estimating is processed data, identifies unqualified amount
It surveys, according to preset rule, judges to cause to measure abnormal equipment, send notification to related personnel, guidance is maked an inspection tour investigation and lacked
It falls into.
As further improving and supplementing to above-mentioned technical proposal, present invention additionally comprises following additional technical features.
Preferably, when reading electric network model and section from SCADA system timing automatically, using FTP technologies, electricity is read in timing
Pessimistic concurrency control and section.
When reading electric network model and section from SCADA system timing automatically, the electric network model and section of use are based on CIM/E
Form.
When being handled using state of electric distribution network algorithm for estimating data, born according to capacity of distribution transform as in unobservable area
Relative scale between lotus, by total sharing of load that measures in unobservable area to each distribution transformer, on this basis into once
Distribution power system load flow calculation, estimates the via net loss in each inconsiderable area accordingly, so to the load total amount in unobservable area into
Row is corrected, so as to obtain estimated value.
It is active that unqualified measurement includes active uneven, the active imbalance of circuit of transformer active imbalance, busbar, circuit
One or more in the active saltus step data of doomed dead evidence, circuit, the doomed dead evidence of transformer active.
A kind of state of electric distribution network estimates anomalous identification device, it is characterised in that including：
Electric network model and section reading unit directly read electric network model and section file, really using RJ-45 network interface modes
Determine Network Topology for Real-Time and flow state；
State estimation and anomalous identification unit, for carrying out state estimation calculation；Electric network model and section reading unit pass through
EMAC interfaces are connected with state estimation with anomalous identification unit；
Defect notification unit including communication chip, is equipped with GSM sending functions；
Power supply, for providing 3.3V direct currents.
Advantageous effect：The technical program set up from electric network model and metric data read, calculate, identification, notice it is complete
Whole chain realizes the automatic of measurement problem and finds to send with information, has the following advantages：
First, efficiently solve the problems, such as that related personnel can not in time, comprehensively have found metric data inaccuracy, reduce people
Work check and correction checks link, reduces cost, improves efficiency.
Secondly, a kind of State Estimation for Distribution Network for being divided in portion load is provided, power distribution network is solved and measures number
According to the state estimation practicability in the case of incomplete it is insufficient the problem of so that the measurement problem that estimated state is found is more comprehensive, promotees
It is effectively improved into measurement qualification rate.
Description of the drawings
Fig. 1 is the principle of the present invention figure；
Fig. 2 is structure diagram provided in an embodiment of the present invention.
In Fig. 1：1. reading model and section；2. reconstructed network model；3. carry out state estimation；4. identify warping apparatus；
5. abnormal notice.
In Fig. 2：1. model and section reading unit；2. state estimating unit；3. defect notification unit；4. power supply.
Specific embodiment
In order to understand persons skilled in the art and realize the present invention, in conjunction with the implementation of the attached drawing description present invention
Example.Obviously, described embodiments are only a part of embodiments of the present application, instead of all the embodiments, based on the application
In embodiment, all other implementation that those of ordinary skill in the art are obtained without making creative work
Example should all belong to the scope of the application protection.
The invention discloses a kind of state of electric distribution network to estimate abnormality recognition method, and step is as shown in Figure 1：
1st, electric network model and section are read.
By general file mode, electric network model and section are read in timing；
CIM/E forms may be employed in the electric network model and section file, and power grid CIM/E is a kind of expression electric network model
With a kind of file format of metric data, CIM/XML is inherited in terms of modeling, it defines grid equipment model class object,
Respective application attribute and its topological relation are contained in each class object.
CIM is International Electrotechnical Commissio (IEC, International Electrotechnical
Commission a set of common information model defined in), it contains all main objects of electric power enterprise, a kind of by providing
The standard method of power system resource is represented with object class and attribute and the relation between them.
More specifically, FTP technologies may be employed to read the electric network model and section of the CIM/E forms on Internet resources,
FTP paths and account can carry out Initialize installation by interface.
It should be noted that need based on CIM electric network model carry out Network topology, be converted into based on node-
Branch model, so that it is determined that flow state.
The Network topology mainly according to switch/disconnecting link point/conjunction state will be based on tie point-switch-branch
Equipment connecting relation model conversion be the network computing model based on topological point-branch
The Real-time Power Flow state mainly according to the network topology of CIM/E forms with switch measure come to power grid operating mode into
Row reappears.
The timing may be employed in the way of 10-15 minutes periodically to read file.
The Internet resources include SCADA data file server.
2nd, state estimation.
Data are handled using improved proportional assignment state estimation algorithm.Compared with power transmission network, power distribution network one
As radially or weakly loops shape, have that branch is more, branch impedance is than greatly, and have the characteristics that measurement is not complete, therefore, directly
It connects using power transmission network method for estimating state and improper, it is necessary to present situation for power distribution network measure configuration weakness, using in proportion
The state estimation algorithm of partition capacity, will not be considerable by the use of capacity of distribution transform as the relative scale between unobservable area's internal loading
Total sharing of load that measures in area is surveyed to each distribution transformer, on this basis into primary distribution Load flow calculation, is estimated accordingly
Go out the via net loss in each inconsiderable area, and then the load total amount in unobservable area is modified, so as to obtain estimated value.
The state estimation is based on the Statistic features of measurement error, and estimation is calculated with the method for mathematical statistics
Value, effect are the self-consistentencies for improving data precision and keeping data, and believable Real-time Power Flow number is provided for network analysis
According to.
3rd, it is bad to measure identification.
Automatic to find bad measurement using state estimation result, described badly measuring refers mainly to measuring value and is not inconsistent with actual value
Or deviation is excessive and can not use, and mainly includes：
Deviation measures, and current measurement is checked using active reactive measuring value, active and reactive, voltage, the shelves having found that it is likely that
The bad measurement of position.
Measurement direction, based on measurement regional balance degree inspection, the direction of tide mistake having found that it is likely that
Switch identification：The mistake switch remote signalling being had found that it is likely that with experts database is calculated using switching branches effective power flow.
After bad measurement identifies, bad measurement is exported with CIM/E modes.
4th, doomed dead evidence and saltus step data identify.
Using state estimation result, doomed dead evidence and saltus step data are found automatically, the doomed dead evidence is due to harvester
Or no longer changed data caused by a variety of causes such as passage；And saltus step data then refer to data and larger change occur suddenly
Change, and these data are virtually impossible to what is varied widely.
The doomed dead evidence is mainly just detectable no longer changed by the accumulative comparison to continuous multiple sections
Data.
The saltus step data mainly pass through the accidental data found more afterwards to continuous multiple sections.
After column data and saltus step data identify, exported with CIM/E modes.
5th, warping apparatus identifies.
According to the identifications such as adjustment location, type, quantity and primary equipment, passage, sensor relation, identifying may draw
Play abnormal equipment.For example, when finding that bad measure occurs in certain transformer substation switch, and switch attachments measure it is normal, then can be with
Illustrate that this is badly measured caused by the acquisition sensor reason that may thus switch
6th, defect notification.
After warping apparatus is identified, automation personnel is sent an SMS to automatically, and operations staff is instructed to make an inspection tour
The foregoing description of the disclosed embodiments enables professional and technical personnel in the field to realize or use the present invention.
A variety of modifications of these embodiments will be apparent for those skilled in the art, it is as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, it is of the invention
It is not intended to be limited to the embodiments shown herein and is to fit to and the principles and novel features disclosed herein phase one
The most wide scope caused.
As shown in Fig. 2, a kind of state of electric distribution network estimates that the corresponding device of abnormality recognition method includes：
1st, electric network model and section reading unit.
Electric network model and section file are directly read using RJ-45 network interface modes, determine Network Topology for Real-Time and trend shape
State.
2nd, state estimation and anomalous identification unit.
State estimation calculation is carried out using ARM926 processor chips；The ARM microprocessors are a kind of high-performance, low
The 32-bit microprocessor of power consumption.
The electric network model and section reading unit is connected by EMAC interfaces with state estimation with anomalous identification unit.
3rd, defect notification unit.
Using the OMAP730 GSM/GPRS communication chips of Ti companies, which has high speed WLAN, has GPIO port,
Its embedded ARM926TEJ processor, highest frequency 200MHZ;With 16kB instruction caches, 8Kb data high-speeds cache,
Possess GSM sending functions.
4th, power supply.
3.3V direct currents are provided to other power supplys.
The foregoing description of the disclosed embodiments enables professional and technical personnel in the field to realize or use the present invention.
A variety of modifications of these embodiments will be apparent for those skilled in the art, it is as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, it is of the invention
It is not intended to be limited to the embodiments shown herein and is to fit to and the principles and novel features disclosed herein phase one
The most wide scope caused.
Claims (6)
1. a kind of state of electric distribution network estimates abnormality recognition method, it is characterised in that comprises the following steps：
1）Automatically electric network model and section are read from SCADA system timing；
2）Generate Network Topology for Real-Time；
3）Data are handled using state of electric distribution network algorithm for estimating；
4）Identify unqualified measurement, according to preset rule, identifying causes to measure abnormal equipment；
5）Judge equipment owner, and short message or wechat are sent to equipment owner；
Using general file mode, electric network model and section, reconstruct electricity are read from network equipment or automated system automatically
Pessimistic concurrency control determines Network Topology for Real-Time, and adoption status algorithm for estimating is processed data, identifies unqualified measurement,
According to preset rule, judge to cause to measure abnormal equipment, send notification to related personnel, investigation defect is maked an inspection tour in guidance；
Wherein recognition methods includes：
It is A) bad to measure identification,
Using state estimation result, it is automatic find it is bad measure, described bad measure includes referring to measuring value and actual value is not inconsistent or partially
Difference is excessive and can not use, including：
Deviation measures, and current measurement is checked using active reactive measuring value, the active and reactive, voltage that has found that it is likely that, gear
It is bad to measure；
Measurement direction, based on measurement regional balance degree inspection, the direction of tide mistake having found that it is likely that
Switch identification：The mistake switch remote signalling being had found that it is likely that with experts database is calculated using switching branches effective power flow；
After bad measurement identifies, bad measurement is exported with CIM/E modes；
B) doomed dead evidence is identified with saltus step data；
Using state estimation result, doomed dead evidence and saltus step data are found automatically, and the doomed dead evidence is due to harvester or logical
No longer changed data caused by road a variety of causes；And saltus step data then refer to data and have greatly changed suddenly, and this
A little data are virtually impossible to what is varied widely；
The doomed dead evidence is included through the just detectable no longer changed data of the accumulative comparison to continuous multiple sections；
The saltus step data are included through the accidental data found more afterwards to continuous multiple sections；
After column data and saltus step data identify, exported with CIM/E modes；
C) warping apparatus identifies；
According to adjustment location, type, quantity identification with primary equipment, passage, sensor relation, exception may be caused by identifying
Equipment；When finding that bad measure occurs in certain transformer substation switch, and switch attachments measurement is normal, then illustrates that this bad measures may
Thus caused by the acquisition sensor reason switched.
2. a kind of state of electric distribution network estimation abnormality recognition method according to claim 1, it is characterised in that：Automatically from
When electric network model and section are read in SCADA system timing, using FTP technologies, electric network model and section are read in timing.
3. a kind of state of electric distribution network estimation abnormality recognition method according to claim 1, it is characterised in that：Automatically from
When electric network model and section are read in SCADA system timing, the electric network model and section of use are based on CIM/E forms.
4. a kind of state of electric distribution network estimation abnormality recognition method according to claim 1, it is characterised in that：Using power distribution network
It, will according to capacity of distribution transform as the relative scale between unobservable area's internal loading when state estimation algorithm handles data
Total in unobservable area measures sharing of load to each distribution transformer, on this basis into primary distribution Load flow calculation, according to
This estimates the via net loss in each inconsiderable area, and then the load total amount in unobservable area is modified, so as to be estimated
Calculation value.
5. a kind of state of electric distribution network estimation abnormality recognition method according to claim 1, it is characterised in that：Unqualified measurement
Including transformer active imbalance, the active imbalance of busbar, the active imbalance of circuit, the active doomed dead evidence of circuit, the active jump of circuit
Become the one or more in data, the doomed dead evidence of transformer active.
6. a kind of state of electric distribution network estimates anomalous identification device, matched somebody with somebody using one kind described in claim 1-5 any claims
Power Network Status Estimation abnormality recognition method carries out state of electric distribution network estimation anomalous identification；It is characterized by comprising：
Electric network model and section reading unit directly read electric network model and section file using RJ-45 network interface modes, determine real
When network topology and flow state；
State estimation and anomalous identification unit, for carrying out state estimation calculation；Electric network model and section reading unit pass through EMAC
Interface is connected with state estimation with anomalous identification unit；
Defect notification unit including communication chip, is equipped with GSM sending functions；
Power supply, for providing 3.3V direct currents.
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| CN106022972B (en) * | 2016-06-30 | 2022-10-21 | 中国电力科学研究院 | Power distribution network abnormal data identification method based on state matrix symmetry |
| CN106408204B (en) * | 2016-09-30 | 2019-11-22 | 许继电气股份有限公司 | A kind of plant stand bad data detection and device based on multisource data fusion |
| CN106897946A (en) * | 2017-03-14 | 2017-06-27 | 国网天津市电力公司 | A kind of monitoring system status information section comparison method |
| CN109752629B (en) * | 2017-11-07 | 2022-09-23 | 中国电力科学研究院有限公司 | Intelligent diagnosis method and system for power grid measurement problems |
| CN111812449A (en) * | 2020-05-26 | 2020-10-23 | 广西电网有限责任公司电力科学研究院 | Power distribution network state estimation abnormity identification method |
| CN112649696A (en) * | 2020-10-26 | 2021-04-13 | 国网河北省电力有限公司邢台供电分公司 | Power grid abnormal state identification method |
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| CN102184209A (en) * | 2011-04-29 | 2011-09-14 | 中国电力科学研究院 | Simulation data accessing method based on power grid CIM (Common Information Model) interface |
| CN103324858A (en) * | 2013-07-03 | 2013-09-25 | 国家电网公司 | Three-phase load flow state estimation method of power distribution network |
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| JP5501796B2 (en) * | 2010-02-24 | 2014-05-28 | 富士通株式会社 | Distribution network estimation apparatus and distribution network estimation method |
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| CN102184209A (en) * | 2011-04-29 | 2011-09-14 | 中国电力科学研究院 | Simulation data accessing method based on power grid CIM (Common Information Model) interface |
| CN103324858A (en) * | 2013-07-03 | 2013-09-25 | 国家电网公司 | Three-phase load flow state estimation method of power distribution network |
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