CN107909508A - A kind of distribution transformer load abnormality alarming method - Google Patents
A kind of distribution transformer load abnormality alarming method Download PDFInfo
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- CN107909508A CN107909508A CN201710880710.1A CN201710880710A CN107909508A CN 107909508 A CN107909508 A CN 107909508A CN 201710880710 A CN201710880710 A CN 201710880710A CN 107909508 A CN107909508 A CN 107909508A
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Classifications
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- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- 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
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
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- 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
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
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- 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
- G06F16/25—Integrating or interfacing systems involving database management systems
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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
Abstract
A kind of distribution transformer load abnormality alarming method of the present invention, belong to distribution transformer alarm field, information on load data of the invention by choosing the distribution transformer in power information acquisition system, such as the historical load data of distribution transformer, distribution transformer affiliated area data, facility information, customer profile data, current and voltage data etc., consider the cyclic fluctuation of distribution transformer electric load, build distribution transformer load Early-warning Model, overload to distribution transformer, overload, heavy duty, underloading, phenomena such as three-phase imbalance, carries out early warning analysis, to solve in the prior art, in processing magnanimity, it is fuzzy, mixed and disorderly data age rate is low, the problems such as big data analysis of distribution transformer cannot be supported the related application such as to excavate well.
Description
Technical field
The present invention relates to distribution transformer to alert field, more particularly to a kind of distribution transformer load abnormality alarming method.
Background technology
With the development of all trades and professions, the demand of power distribution network supply constantly meets urban construction, and scale expands rapidly, and
Distribution transformer plays a crucial role during power transmission, to ensure stability, the promptness of distribution, by advanced
Computer technology the load data of distribution transformer is analyzed with big data analysis, there is critically important guidance to anticipate
Justice.Current distribution transformer load Analysis is all based on traditional statistical analysis to carry out data standard, data storage, data
Calculate and data exhibiting, traditional statistical analysis refer to statistical method and the knowledge related with analysis object, from quantitative
Research activities with qualitatively being combined upper progress.Analyzed with traditional statistical analysis technique in application, need to be to data
Relation between distribution and variable is made it is assumed that determining what probability function to describe the relation between variable of, and how to examine ginseng
Several statistical significances, assumes whether set up with verification, and can not realize the relation or rule hidden between Automatic-searching variable, and
And traditional statistical analysis is in processing magnanimity, fuzzy, mixed and disorderly data age rate is low, it is impossible to supports distribution transformer well
The related applications such as the big data analysis excavation of device.Advanced technology develops to Distributed Calculation now, can effectively improve actual effect
And be conducive to big data excavation.
The content of the invention
The present invention by a kind of distribution transformer load abnormality alarming method, come solve in the prior art, processing magnanimity,
It is fuzzy, mixed and disorderly data age rate is low, it is impossible to the big data analysis of support distribution transformer such as excavates at the related application well
The problems such as.The present invention using stream calculation, is divided based on data such as distribution transformer varying duty information, essential information, user power utilization information
The technologies such as cloth calculating, data mining analysis, structure rate of load condensate Early-warning Model, apply to install active volume Early-warning Model, three-phase injustice
Weigh Early-warning Model, realizes the data mining to distribution transformer load.
Concrete implementation method is to extract electricity consumption acquisition system and marketing industry by real-time (kafka) or timing (sqoop)
Data in application system of being engaged in, relevant database (Mysql/ is stored data according to data type and various calculating demand
PostgreSql), in distributed file system (HDFS), non-relational database (HBase), by stream calculation (storm), criticize
Amount calculates (MapReduce), inquiry calculates (hive) technology and realizes the real-time and off-line calculations of data, and by data modeling and
Data mining component realizes the analysis and excavation of data, comprises the following steps that：
Step 1：By acquisition terminal by the data information acquisitions such as the load data of distribution transformer, current data to electricity consumption
Information acquisition system, imported by initial data, data inputting window, vocational window by user information, power supply unit information, become
The data message typing sales service application system such as power station information, transformer information；
Step 2：Power information acquisition system related data and sales service application system data are carried out Data Integration to deposit
Storage, especially by big data platform extraction assembly (sqoop), periodically by operation system data, (user message table, trade connection are believed
Cease table, power supply unit table, marketing coding schedule, day measurement point power curve table, substation information table, substation's coordinate information table,
Contingency table, substation's resource code mapping table etc.) it is drawn into the mysql storehouses progress data storage of big data platform；
Step 3：Big data platform analysis program periodically by the region information on load in mysql storehouses and is matched somebody with somebody by timed task
Piezoelectric transformer information on load, Current Voltage initial data are parsed, and write hive regions information on load resolution table, substation
Information on load resolution table write-in hive storehouses, offer data preparation is calculated in order to which big data platform carries out data；
Step 4：Big data platform is periodically carried out by stream calculation (strom), Distributed Calculation (map/reduce) mode
Data calculate, and by zone information table therein, trade information table, electricity consumption category information table, substation information table, user information
Table, zone user quantity information table, industry user's quantity information table, electricity consumption class users quantity information table, customer charge information
Table, industry information on load table, electricity consumption classification information on load table, rate of load condensate model middle table, apply to install active volume model middle table,
Three-phase imbalance model middle table etc. writes PostgreSQL databases；
Step 5：Using R language and Mahout etc., distributed data digging algorithms library is formed, among rate of load condensate model
Table, apply to install active volume model middle table, three-phase imbalance model middle table, carries out data exploration analysis；
Step 6：By the demonstration tool and TABLEAU demonstration tools of big data platform, carry out theme and show (including load
Rate theme, apply to install active volume theme, three-phase imbalance theme), and the alarm result based on displaying, pass through information alert interface
Load warning information is passed on to relevant departments.
The module frame built for above-mentioned stream compression step is as follows：
1st, data source：The main distribution transformer load data including electricity consumption acquisition system and sales service application system, distribution become
The data messages such as depressor affiliated area data, facility information, customer profile data, Current Voltage.
2nd, Data Integration：The technologies such as kafka real time datas Distributed Message Queue, the extraction of sqoop off-line datas are merged, it is real
Existing isomeric data quickly accesses, and builds distributed data integration function, the acquisition process ability for possessing timing/real time data,
Realize the configuration exploitation stored from data source to platform, process monitoring.
3rd, data store：Using storage skills such as relation number database, distributed file system, distributed online databases
Art, there is provided the data storage capacities such as relational data storage, distributed document storage, while uniform memory access interface is provided,
The ability extending transversely of data storage low cost is improved, the fast data access responding ability under the conditions of high concurrent is improved, expires
Sufficient mass data in real time with quasi real time storage demand.
4th, data calculate：The data processing techniques such as batch measurements, stream calculation are provided, and support SQL query, when meeting different
Effect property calculates demand.Batch, which calculates, supports high-volume Off-line data analysis, as historical data report is analyzed；Stream calculation is supported real-time
Processing, as electricity consumption data handles in real time, early warning；The query analysis technology of similar SQL is provided at the same time, query statement is translated to simultaneously
Capable distributed computing task.
5th, data analysis：Integrated R language and Mahout, form distributed data digging algorithms library, there is provided excavate modeling and set
Meter instrument, builds unified analysis modeling ability and runtime engine.Meanwhile by Promotion Transformation analysis decision platform, improve and divide
The abilities such as modeling, model running, model issue are analysed, increase the support to big data Distributed Calculation, meet real-time, offline application
Analysis mining demand, for analysis decision application build provide basic platform support.
6th, scene shows：Realized using integrated self-service analysis tool and tableau instruments with " distribution transformer load point
Analysis " scene application.
It is as follows that scene display module specifically shows method：Utilize matching somebody with somebody for electricity consumption acquisition system and sales service application system
The data such as varying duty data, distribution transformer affiliated area data, facility information, customer profile data, Current Voltage, structure are negative
(model includes lotus Early-warning Model：Load Analysis model, apply to install three submodules such as active volume model, three-phase imbalance model
Type), realize the analysis of all kinds of common situations of distribution transformer load, aspect early warning analysis single with business department or according to artificial warp
Anticipation is tested to compare, the distribution transformer load Analysis model analysis based on big data technology more comprehensively, more in time, it is more accurate.
Rate of load condensate analysis model：According to transformer capacity, with reference to distribution transformer load data, analysis overload, overload, be heavily loaded, just
Often, underloading even load situation, model rule are to gather situation according to transformer load, compare transformer capacity, determine transformer
Current load situation, loading condition can divide into overload, overload, heavy duty, normal, underloading by grade, its content includes public/special
Total number of terminals, overload number, overload number, heavily loaded number, underloading number, A overload of the phase number, B overload of the phase number, the C overload of the phase number information of change, its
Middle determination methods are as follows：
1st, the determination methods of overload：Exceed transformer capacity * K1 when total load continuous 2 is small；
2nd, the determination methods of overload：Transformer capacity * K2<When=total load continuous 2 is small<Transformer capacity * K1；
3rd, heavily loaded determination methods：Transformer capacity * K3<When=total load continuous 2 is small<Transformer capacity * K2；
4th, the determination methods of underloading：Maximum total load<Transformer capacity * K4；
5th, the determination methods of A/B/C one-phase overloads：A/B/C monophase currents exceed transformer rated current * K5；
Wherein the threshold values of K1, K2, K3, K4, K5 can be defined freely；
Apply to install active volume model：It is monitored by day power load, and is compared with rated capacity (C), analysis is each
The currently available of distribution transformer applies to install capacity, and determination methods Pv=Pavg/Pmax*100, wherein Pv are rate of load condensate, Pavg
For average load, Pmax is peak load, and model function point is distribution transformer rate of load condensate, currently available applies to install capacity.
Three-phase imbalance model：By the three-phase load of distribution transformer, three-phase current, tri-phase unbalance factor, mould are calculated
Type method is：Wherein Imax maximum currents, the minimum electricity of Imin
Stream, model function point count for current imbalance number.
Beneficial effect：Early warning analysis is monitored or according to artificial based on threshold method using single with traditional method
Experience anticipation is compared, and the present invention proposes a kind of distribution transformer load abnormality alarming method, can be more accurate, timely, is passed through
The short-term weight of model prediction, overload Early-warning Model carry out early warning, and accuracy can reach 85% or so, 15 are improved compared with current way
Percentage point, at utmost reduce distribution transforming overload, overload again, heavy duty, underloading it is horizontal, mitigate distribution transformer load abnormal belt and come
Harmful effect, lifted supplying power allocation ability.
Brief description of the drawings
Fig. 1 big data platform distribution transformers load data circulation figure
Fig. 2 big data platform distribution transformer load system figures
Specific embodiment
The present invention by a kind of distribution transformer load abnormality alarming method, come solve in the prior art, processing magnanimity,
It is fuzzy, mixed and disorderly data age rate is low, it is impossible to the big data analysis of support distribution transformer such as excavates at the related application well
The problems such as.The present invention using stream calculation, is divided based on data such as distribution transformer varying duty information, essential information, user power utilization information
The technologies such as cloth calculating, data mining analysis, structure rate of load condensate Early-warning Model, apply to install active volume Early-warning Model, three-phase injustice
Weigh Early-warning Model, realizes the data mining to distribution transformer load.
Concrete implementation method is to extract electricity consumption acquisition system and marketing industry by real-time (kafka) or timing (sqoop)
Data in application system of being engaged in, relevant database (Mysql/ is stored data according to data type and various calculating demand
PostgreSql), in distributed file system (HDFS), non-relational database (HBase), by stream calculation (storm), criticize
Amount calculates (MapReduce), inquiry calculates (hive) technology and realizes the real-time and off-line calculations of data, and by data modeling and
Data mining component realizes the analysis and excavation of data, comprises the following steps that：
Step 1：By acquisition terminal by the data information acquisitions such as the load data of distribution transformer, current data to electricity consumption
Information acquisition system, imported by initial data, data inputting window, vocational window by user information, power supply unit information, become
The data message typing sales service application system such as power station information, transformer information；
Step 2：Power information acquisition system related data and sales service application system data are carried out Data Integration to deposit
Storage, especially by big data platform extraction assembly (sqoop), periodically by operation system data, (user message table, trade connection are believed
Cease table, power supply unit table, marketing coding schedule, day measurement point power curve table, substation information table, substation's coordinate information table,
Contingency table, substation's resource code mapping table etc.) it is drawn into the mysql storehouses progress data storage of big data platform；
Step 3：Big data platform analysis program periodically by the region information on load in mysql storehouses and is matched somebody with somebody by timed task
Piezoelectric transformer information on load, Current Voltage initial data are parsed, and write hive regions information on load resolution table, substation
Information on load resolution table write-in hive storehouses, offer data preparation is calculated in order to which big data platform carries out data；
Step 4：Big data platform is periodically carried out by stream calculation (strom), Distributed Calculation (map/reduce) mode
Data calculate, and by zone information table therein, trade information table, electricity consumption category information table, substation information table, user information
Table, zone user quantity information table, industry user's quantity information table, electricity consumption class users quantity information table, customer charge information
Table, industry information on load table, electricity consumption classification information on load table, rate of load condensate model middle table, apply to install active volume model middle table,
Three-phase imbalance model middle table etc. writes PostgreSQL databases；
Step 5：Using R language and Mahout etc., distributed data digging algorithms library is formed, among rate of load condensate model
Table, apply to install active volume model middle table, three-phase imbalance model middle table, carries out data exploration analysis；
Step 6：By the demonstration tool and TABLEAU demonstration tools of big data platform, carry out theme and show (including load
Rate theme, apply to install active volume theme, three-phase imbalance theme), and the alarm result based on displaying, pass through information alert interface
Load warning information is passed on to relevant departments.
The module frame built for above-mentioned stream compression step is as follows：
1st, data source：The main distribution transformer load data including electricity consumption acquisition system and sales service application system, distribution become
The data messages such as depressor affiliated area data, facility information, customer profile data, Current Voltage.
2nd, Data Integration：The technologies such as kafka real time datas Distributed Message Queue, the extraction of sqoop off-line datas are merged, it is real
Existing isomeric data quickly accesses, and builds distributed data integration function, the acquisition process ability for possessing timing/real time data,
Realize the configuration exploitation stored from data source to platform, process monitoring.
3rd, data store：Using storage skills such as relation number database, distributed file system, distributed online databases
Art, there is provided the data storage capacities such as relational data storage, distributed document storage, while uniform memory access interface is provided,
The ability extending transversely of data storage low cost is improved, the fast data access responding ability under the conditions of high concurrent is improved, expires
Sufficient mass data in real time with quasi real time storage demand.
4th, data calculate：The data processing techniques such as batch measurements, stream calculation are provided, and support SQL query, when meeting different
Effect property calculates demand.Batch, which calculates, supports high-volume Off-line data analysis, as historical data report is analyzed；Stream calculation is supported real-time
Processing, as electricity consumption data handles in real time, early warning；The query analysis technology of similar SQL is provided at the same time, query statement is translated to simultaneously
Capable distributed computing task.
5th, data analysis：Integrated R language and Mahout, form distributed data digging algorithms library, there is provided excavate modeling and set
Meter instrument, builds unified analysis modeling ability and runtime engine.Meanwhile by Promotion Transformation analysis decision platform, improve and divide
The abilities such as modeling, model running, model issue are analysed, increase the support to big data Distributed Calculation, meet real-time, offline application
Analysis mining demand, for analysis decision application build provide basic platform support.
6th, scene shows：Realized using integrated self-service analysis tool and tableau instruments with " distribution transformer load point
Analysis " scene application.
It is as follows that scene display module specifically shows method：Utilize matching somebody with somebody for electricity consumption acquisition system and sales service application system
The data such as varying duty data, distribution transformer affiliated area data, facility information, customer profile data, Current Voltage, structure are negative
(model includes lotus Early-warning Model：Load Analysis model, apply to install three submodules such as active volume model, three-phase imbalance model
Type), realize the analysis of all kinds of common situations of distribution transformer load, aspect early warning analysis single with business department or according to artificial warp
Anticipation is tested to compare, the distribution transformer load Analysis model analysis based on big data technology more comprehensively, more in time, it is more accurate.
Rate of load condensate analysis model：According to transformer capacity, with reference to distribution transformer load data, analysis overload, overload, be heavily loaded, just
Often, underloading even load situation, model rule are to gather situation according to transformer load, compare transformer capacity, determine transformer
Current load situation, loading condition can divide into overload, overload, heavy duty, normal, underloading by grade, its content includes public/special
Total number of terminals, overload number, overload number, heavily loaded number, underloading number, A overload of the phase number, B overload of the phase number, the C overload of the phase number information of change, its
Middle determination methods are as follows：
Apply to install active volume model：It is monitored by day power load, and is compared with rated capacity (C), analysis is each
The currently available of distribution transformer applies to install capacity, and determination methods Pv=Pavg/Pmax*100, wherein Pv are rate of load condensate, Pavg
For average load, Pmax is peak load, and model function point is distribution transformer rate of load condensate, currently available applies to install capacity.
Three-phase imbalance model：By the three-phase load of distribution transformer, three-phase current, tri-phase unbalance factor, mould are calculated
Type method is：Wherein Imax maximum currents, the minimum electricity of Imin
Stream, model function point count for current imbalance number.
Claims (5)
1. a kind of distribution transformer load abnormality alarming method, it is characterised in that include the following steps：
Step 1 is by acquisition terminal by the data information acquisitions such as the load data of distribution transformer, current data to power information
Acquisition system, is imported, data inputting window, vocational window be by user information, power supply unit information, substation by initial data
The data message typing sales service application system such as information, transformer information；
Power information acquisition system related data and sales service application system data are carried out Data Integration storage, tool by step 2
Body by big data platform extraction assembly (sqoop) periodically by operation system data (user message table, customer contact information table,
Power supply unit table, marketing coding schedule, day measurement point power curve table, substation information table, substation's coordinate information table, association
Table, substation's resource code mapping table etc.) it is drawn into the mysql storehouses progress data storage of big data platform；
Step 3 big data platform analysis program is periodically become the region information on load in mysql storehouses and distribution by timed task
Depressor information on load, Current Voltage initial data are parsed, and write hive regions information on load resolution table, substation's load
Information resolution table write-in hive storehouses, offer data preparation is calculated in order to which big data platform carries out data；
Step 4 big data platform periodically carries out data meter by stream calculation (strom), distributed meter (map/reduce) mode
Calculate, and by zone information table therein, trade information table, electricity consumption category information table, substation information table, user message table, area
Domain number of users information table, industry user's quantity information table, electricity consumption class users quantity information table, customer charge information table, OK
Industry information on load table, electricity consumption classification information on load table, rate of load condensate model middle table, apply to install active volume model middle table, three-phase
Imbalance model middle table etc. writes PostgreSQL databases；
Step 5 forms distributed data digging algorithms library, based on rate of load condensate model middle table, report using R language and Mahout etc.
Active volume model middle table, three-phase imbalance model middle table are filled, carries out data exploration analysis；
Step 6 is carried out theme and is showed including rate of load condensate master by the demonstration tool and TABLEAU demonstration tools of big data platform
Inscribe, apply to install active volume theme, three-phase imbalance theme, and the alarm result based on displaying, by information alert interface to phase
Load warning information is passed on by pass department.
A kind of 2. distribution transformer load abnormality alarming method according to claim 1, it is characterised in that theme in step 6
Show that method is as follows, using belonging to the distribution transformer load data of electricity consumption acquisition system and sales service application system, distribution transformer
The data such as area data, facility information, customer profile data, Current Voltage, structure load Early-warning Model include：Load Analysis mould
Type, apply to install three submodels such as active volume model, three-phase imbalance model.
A kind of 3. distribution transformer load abnormality alarming method according to claim 2, it is characterised in that theme in step 6
Show the rate of load condensate analysis model in method：According to transformer capacity, with reference to distribution transformer load data, analysis overload, overload, again
Carry, normal, underloading even load situation, judge to gather situation according to transformer load, compare transformer capacity, determine that transformer is worked as
Preceding loading condition, loading condition can divide into overload, overload, heavy duty, normal, underloading by grade, its content includes public/specially change
Total number of terminals, overload number, overload number, heavily loaded number, underloading number, A overload of the phase number, B overload of the phase number, C overload of the phase number information；Overload
Determination methods：Exceed transformer capacity * K1 when total load continuous 2 is small；Embodiment：If Pn (n 1 ... 8)>=C*K1, then surpass
Carry, wherein C is transformer capacity, and P is total load, and n is 1~8 successive value (15 minutes 1 points, 2 hours, 8 points), and K1 is
Threshold value；Overload determination methods：Transformer capacity * K2<When=total load continuous 2 is small<Transformer capacity * K1；Embodiment：If C*
K2<=Pn (n 1 ... 8) and Pn (n 1 ... 8)<C*K1, then overload, and wherein C is transformer capacity, and P is total load, and n is 1~8
Successive value (15 minutes 1 points, 2 hours, 8 points), K2, K1 are threshold value；Heavily loaded determination methods：Transformer capacity * K3<=
When total load continuous 2 is small<Transformer capacity * K2；Implementation：If C*K3<=Pn (n 1 ... 8) and Pn (n 1 ... 8)<C*
K2, then heavily loaded, wherein C is transformer capacity, and P is total load, and n is 1~8 successive value (15 minutes 1 points, 2 hours 8
Point), K3, K2 are threshold value；Underloading judgment mode：Maximum total load<Transformer capacity * K4；Implementation：If Pmax<C*K4, then
Underloading, wherein Pmax are maximum total loads, and C transformer capacities, K4 is threshold value；A/B/C one-phase overload determination methods：A/B/C is mono-
Phase current exceedes transformer rated current * K5；Embodiment is as follows：If Ia ＞=I*K5, A overload of the phase；If Ib ＞=I*K5, B phases
Overload；If Ic ＞=I*K5, C overload of the phase；Ia is A phase currents, and Ib is B phase currents, and Ic is C phase currents, and I is rated current, and K5 is
Threshold value, the threshold values of K1, K2, K3, K4, K5 can be with self-definings.
A kind of 4. distribution transformer load abnormality alarming method according to claim 2, it is characterised in that theme in step 6
Show in method and apply to install active volume model：It is monitored, and is compared with rated capacity (C), analysis by day power load
Each the currently available of distribution transformer applies to install capacity, and determination methods Pv=Pavg/Pmax*100, wherein Pv are rate of load condensate,
Pavg is average load, and Pmax is peak load, and model function point is distribution transformer rate of load condensate, currently available applies to install capacity.
A kind of 5. distribution transformer load abnormality alarming method according to claim 2, it is characterised in that theme in step 6
Show the three-phase imbalance model in method：By the three-phase load of distribution transformer, three-phase current, three-phase imbalance is calculated
Degree, model method are：Wherein Imax maximum currents, Imin are minimum
Electric current, model function point count for current imbalance number.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201710880710.1A CN107909508A (en) | 2017-09-26 | 2017-09-26 | A kind of distribution transformer load abnormality alarming method |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201710880710.1A CN107909508A (en) | 2017-09-26 | 2017-09-26 | A kind of distribution transformer load abnormality alarming method |
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| CN107909508A true CN107909508A (en) | 2018-04-13 |
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