CN109038552A - Distribution net equipment running state analysis method and device based on big data - Google Patents
Distribution net equipment running state analysis method and device based on big data Download PDFInfo
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- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/62—The condition being non-electrical, e.g. temperature
- H02J2310/64—The condition being economic, e.g. tariff based load management
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- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
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Abstract
The distribution net equipment running state analysis method based on big data that the invention discloses a kind of, comprising: acquisition distribution load data are synchronized to Hive data warehouse；Quality verification is carried out to the distribution transformer load data that power information acquires based on big data technology；From each working node that fortune inspection equipment account data, marketing equipment account data and power information distribution transformer load data are loaded onto big data platform according to load balancing strategy in Hive data warehouse；Each working node is according to heavy-overload, low-voltage, three-phase imbalance analysis of calculation models distribution net equipment operating status；According to the index analysis result that each working node is completed, distribution overall operation state analysis is completed.Additionally provide the device for implementing the above method, comprising: data acquisition, synchronous, reparation, load and analysis module.The present invention controls distribution macro operation state by the calculating to operating index such as distribution transforming heavy-overload, low-voltage, three-phase imbalances in real time, improves power grid power supply reliability and power supply quality.
Description
Technical field
The invention belongs to power distribution network index analysis fields, and in particular to a kind of distribution net equipment operating status based on big data
Analysis method and device.
Background technique
Distribution is in the tip of entire power grid, is the window that electric power enterprise gears to the needs of the society, and the operational management of distribution is directly closed
It is huge numbers of families, social responsibility and influence are huge.With the continuous development of society, the lean management of distribution is proposed more next
Higher requirement.Power distribution network has the characteristics that a little more, wire length, wide, with the development of power information system, the day of acquisition device
Benefit is advanced, and most of public distribution transforming of distribution has the condition of acquisition electric current, voltage, power, effective to run number using public become
According to for statistical analysis to index, for find early it is public become be operating abnormally, the acquisition quality of data, transmission channel problem have weight
The practical significance wanted.
Currently, public become the traditional index such as three-phase imbalance, low-voltage, heavy-overload statistical analysis, can preferably reflect
The operating condition that power distribution network public affairs become, can be used for carrying out renovation in time.However as the gradually accumulation of distribution transforming operation data,
Traditional analysis seems increasingly out of strength, and therefore, it is necessary to one kind to control overall condition from data source header, be more advantageous to development
The analysis method of renovation and specified management measure.
Summary of the invention
The purpose of the present invention is to provide the distribution net equipment running state analysis method and device based on big data, Neng Gouyou
Effect improves the efficiency of data analysis, while constructing distribution transforming heavy-overload, low overvoltage, three-phase imbalance analysis model, to entirety
Distribution transforming operating condition and the quality of data are analyzed, foundation is provided to formulate management measure, further promotes power distribution network operation level.
In order to solve the above-mentioned technical problem, the present invention is achieved by the following technical solutions: the distribution based on big data
Equipment running status analysis method acquires distribution load data, by fortune inspection equipment account data, the battalion for Base data platform of marketing
Pin equipment account data and distribution transformer load data are synchronized to Hive data warehouse；
It is perfect to distribution transformer load data progress quality verification, reparation；
From Hive data warehouse according to load balancing strategy will fortune inspection equipment account data, marketing equipment account data and
Distribution transformer load data are loaded onto each working node；
Distribution transforming heavy-overload is established respectively based on fortune inspection equipment account data, marketing equipment account data and distribution transformer load data
Analysis model, low-voltage analysis model and three-phase imbalance analysis model, each working node are matched according to established model analysis
Net equipment running status obtains index analysis result, completes distribution overall operation state analysis.
Preferably, the power grid account data of the asset of equipments lean operation management system includes: fortune inspection side distribution transforming platform
Account information；
The marketing equipment account data of the marketing Base data platform include: marketing side stage area information, marketing side stage
Area's information and fortune inspection side distribution transforming information corresponding relationship.
Preferably, described to equal the power grid account data of asset of equipments lean operation management system, marketing basic data
The distribution transformer load data of marketing equipment account data and the power information acquisition of platform are synchronized to the detailed process of Hive data warehouse
Are as follows:
By Spark distributed synchronization program by asset of equipments lean operation management system transport inspection side distribution transforming account information,
Marketing Base data platform marketing side stage area information, marketing side stage area information and fortune inspection side distribution transforming information corresponding relationship use telecommunications
Ceasing acquisition system load data is mainly the electric current, voltage and power extraction of distribution transforming into memory；
The asset of equipments lean operation management system stored based on relational data is transported into inspection side distribution transforming by Spark program
Account information, marketing Base data platform are marketed, and side stage area information, marketing side stage area information are corresponding with fortune inspection side distribution transforming information to close
And power information acquisition system load data model conversion is the model stored based on Hive, and data are stored in Hive data bins
In library.
Preferably, described that quality verification is carried out to the distribution transformer load data that power information acquires based on big data technology, is repaired
It is multiple perfect, specifically includes the following steps:
First by the distribution transformer load data and power-off event of power information acquisition；
Then distribution transforming each current data is traversed, if the period of electric current leak source not in power off time section, seeks leaking
Current average at electricity；
Secondly traversal distribution transforming each voltage data, for leak source situation in voltage data, if the period of voltage leak source
Not in power off time section, average voltage at electric leakage is sought, and repair to current voltage based on average voltage；
Then distribution transforming each power data is traversed, if the period of power leak source seeks in power off time section just
Power average value at electric leakage, and current power is repaired based on power average value；
The distribution transformer load data including the voltage after electric current, reparation and the power after reparation are finally updated to Hive
Data warehouse.
Preferably, described to set fortune inspection equipment account data, marketing according to load balancing strategy from Hive data warehouse
Standby account data and power information distribution transformer load data are loaded onto the detailed process in each working node of big data platform are as follows:
Fortune inspection side apparatus account data, marketing equipment account data and power information distribution transformer load data are divided into several
A data block, and according to each calculate node loading condition, data block is distributed into the node of setting according to the rule of setting；
Distribution transforming running state analysis logic is distributed in the data block in each node.
It is preferably, described to establish distribution transforming heavy-overload analysis model, distribution transforming heavy-overload model specifically:
Distribution transforming heavy-overload analysis model: acquiring load power curve according to distribution transforming, calculate every corresponding load factor value, if
Continuous 8 points persistently occur 2 hours, load factor value [80%, 100%) between, it is defined as distribution transforming heavy duty；If continuous 8
Point persistently occurs 2 hours, load factor value [100%, 150%) between, it is defined as distribution transforming overload；Wherein, load factor=distribution transforming
General power * integrates multiplying power/distribution transforming rated capacity；Distribution transforming refers to the transformer for being in operating status and non-user assets.
Preferably, the foundation is described with the overvoltage model that is lower with the Over-voltage Analysis model that is lower specifically:
With the Over-voltage Analysis model that is lower: load voltage curve is acquired according to distribution transforming, analyzes every corresponding voltage value, if
Continuous 4 points persistently occur 1 hour, voltage value [150V, 198V) between, it is defined as distribution transforming low-voltage；If continuous 4 points,
Persistently occur 1 hour, voltage value [235.4V, 280V) between, it is defined as distribution transforming overvoltage；
Wherein, 198V is that take 90%, the 235.4V of nominal voltage 220V be take nominal voltage 220V 107%.
It is preferably, described to establish distribution transforming three-phase imbalance analysis model specifically:
Distribution transforming three-phase imbalance analysis model: according to distribution transforming acquisition load current and power curve, analysis every is corresponding
Three-phase electricity flow valuve persistently occurs 2 hours if continuous 8 points, three-phase current unbalance degree > 25% and load factor > 60% are fixed
Justice is distribution transforming three-phase equilibrium；
Wherein, the comprehensive multiplying power/distribution transforming rated capacity of load factor=distribution transforming general power *；Three-phase current unbalance degree=(maximum
Electric current-minimum current)/maximum current.
It is preferably, described according to established analysis of calculation models distribution net equipment operating status, detailed process are as follows:
Concentrator mainly includes carrier module, infrared module, communication module, ammeter meter and pulse module, according to DL/T
698.34 and DL/T 698.34 is standardized, negative in the distribution transforming of acquisition by RS485 mouthfuls of acquisition distribution transforming electric currents, voltages, power curve
Based on lotus curve, according to distribution transforming heavy-overload, low overvoltage, three-phase imbalance analysis model, analysis marketing side stage area runs shape
State；
According to marketing side stage area information, marketing side stage area and fortune inspection side distribution transforming corresponding relationship, fortune inspection side distribution transforming information realization
The one-to-one correspondence in platform area and distribution transforming, to realize fortune inspection side distribution transforming running state analysis.
Distribution net equipment running state analysis device based on big data, comprising:
Data acquisition module acquires distribution load data；
Data simultaneous module, will fortune inspection equipment account data, market Base data platform marketing equipment account data and
Distribution transformer load data are synchronized to Hive data warehouse；
Data repair module is perfect to distribution transformer load data progress quality verification, reparation based on big data technology；
Fortune is examined equipment account data, marketing according to load balancing strategy from Hive data warehouse by data loading module
Equipment account data and distribution transformer load data are loaded onto each working node of big data platform；
Data analysis module, based on fortune inspection equipment account data, marketing equipment account data and distribution transformer load data difference
Distribution transforming heavy-overload analysis model, low-voltage analysis model and three-phase imbalance analysis model are established, each working node is according to built
Vertical model analysis distribution net equipment operating status obtains index analysis result, completes distribution overall operation state analysis.
Compared with prior art, the invention has the advantages that the public power transformation of present invention combination power information acquisition system acquisition
The operation datas such as stream, voltage, power, rely on big data technology to distribution transforming operation data, according to distribution transforming heavy-overload, low excessively electric
Pressure, three-phase imbalance analysis model are quickly and effectively analyzed, and distribution transforming heavy-overload, low overvoltage, three-phase imbalance etc. are passed through
The control of key index, not only can macroscopic view control the operation health condition of transformer, moreover it is possible to find that acquisition device and data pass
Defeated channel defect, to the potential potential faults of discovering device, it is ensured that the stable operation of power system security has important reality
Meaning.
Specific embodiment
By the way that the following examples are exemplary, it is intended to be used to explain the present invention, and should not be understood as to of the invention
Limitation.
With the public gradually accumulation for becoming operation data, traditional analysis seems increasingly out of strength, and therefore, the present invention proposes one
Distribution net equipment running state analysis method and device of the kind based on big data, can effectively improve the efficiency of data analysis, simultaneously
Distribution transforming heavy-overload, low overvoltage, three-phase imbalance analysis model are constructed, to global analysis distribution transforming operating condition and data matter
Amount provides foundation to formulate management measure, further promotes power distribution network operation level.
Embodiment one:
A kind of distribution net equipment running state analysis method based on big data, comprising:
Step S1:
Standardized according to DL/T 698.34 and DL/T 698.34, by RS485 mouthfuls of acquisition distribution transforming electric currents of concentrator, voltage,
Power curve, the distribution transformer load data progress quality verification acquired based on big data technology to power information, reparation are perfect；
The power grid account data data of asset of equipments lean operation management system, marketing basic data are put down by Spark
The distribution transformer load data (mainly including electric current, voltage, power) of marketing equipment account data and the power information acquisition of platform are synchronous
To Hive data warehouse；
The step S1 specifically includes the following steps:
S1.1: distribution transforming electric current, voltage, the power of acquisition are sent to power information acquisition main website by power information acquisition terminal
System, power information acquire main station system and load data are synchronized to marketing Base data platform；
S1.2: distribution transforming in one day will be specified from certain unit marketing Base data platform by Spark distributed synchronization program
Electric current, voltage power extracts into memory；
S1.3: by Spark distributed synchronization program by the marketing of the unit from certain unit marketing Base data platform
Side stage area information, marketing side stage area information and fortune inspection side distribution transforming information corresponding relationship are extracted into memory；
S1.4: fortune is examined from certain unit equipment assets lean operation management system by Spark distributed synchronization program
Distribution transforming account information extraction in side is into memory；
S1.5: the fortune stored based on relational data is examined into side distribution transforming account information, battalion using Spark program in memory
Pin Base data platform marketing side stage area information, marketing side stage area information pass corresponding with fortune inspection side distribution transforming information and power information are adopted
Collecting system load data model conversion is the model stored based on Hive, and data are stored in Hive data warehouse.
Step S2: quality verification is carried out to the distribution transformer load data that power information acquires based on big data technology, has been repaired
It is kind；
Specifically:
S2.1: from the Allocation transformer profile table of Hive data warehouse, by stored certain day current data extract to
In memory；
96 current values of S2.2:Spark program pass each current curve, leak source situation if it exists, in conjunction with the day
Practical power-off event analysis leak source reasonability.If it is because the problem of channel or terminal acquire, takes reclamation activities, presses
According to It=(It+1+It-1The rule process of)/2.Such as last moment point current value is 2.1A, subsequent time current value is 2.3A, then
Present current value is 2.2A, replaces leak source current value with 2.2A, improves distribution transforming three-phase imbalance and analyzes correctness, reasonability；
S2.3: each leak source current value is updated by S2.2 method, is stored into Hive data warehouse；
S2.4: from the power distribution voltage curve table of Hive data warehouse, by stored certain day voltage data extract to
In memory；
96 voltage values of S2.5:Spark program pass each voltage curve, leak source situation if it exists, in conjunction with the day
Practical power-off event analysis leak source reasonability.If it is because the problem of channel or terminal acquire, takes reclamation activities, presses
According to Ut=(Ut+1+Ut-1The rule process of)/2.Such as last moment point voltage value is 221.3V, subsequent time voltage value is
220.3V, then current voltage value is 220.8V, replaces leak source voltage value with 220.8V；Each electric voltage exception point is analyzed simultaneously,
If voltage value>280V or voltage value<150V is defined as electric voltage exception point, also according to above method reparation, raising, which is matched, to be lower
Voltage analysis correctness, reasonability；
S2.6: each leak source or abnormal voltage value are updated by S2.5 method, is stored into Hive data warehouse；
S2.7: from the distribution transforming power curve table of Hive data warehouse, by stored certain day power data extract to
In memory；
96 performance numbers of S2.8:Spark program pass each power curve, leak source situation if it exists, in conjunction with the day
Practical power-off event analysis leak source reasonability.If it is because the problem of channel or terminal acquire, takes reclamation activities, presses
According to Pt=Pt-1+(Pt-1-Pt-2) rule process.Such as last moment point performance number is 380.8KW, upper two moment performance number is
370.6KW, then current power value is 391KW, replaces leak source performance number with 391KW；Each power abnormal point is analyzed simultaneously, if
The numerical value that the power data of terminal acquisition multiplies comprehensive multiplying power is greater than 10 times of user's contract capacity, is defined as power abnormal point, together
Sample is repaired according to the method described above, is improved distribution transforming heavy-overload and is analyzed correctness, reasonability.
Step S3: being based on big data platform, establish distribution net equipment running state analysis model, examines side apparatus account in conjunction with fortune
The distribution transformer load data of data, Marketing-side equipment account data and power information acquisition calculate separately out distribution net equipment operating status
The value of each analysis indexes；That is: distribution transforming heavy-overload, low overvoltage, three-phase imbalance analysis model are distributed to often by big data platform
A working node, each working node analyze distribution transforming heavy-overload, low overvoltage, three-phase imbalance, and evaluation distribution net equipment runs shape
State；
In embodiments of the present invention, the distribution net equipment running state analysis model includes distribution transforming heavy-overload model, matches
The overvoltage that is lower model and distribution transforming three-phase imbalance model；
The distribution transforming heavy-overload model, specifically:
Distribution transforming heavy-overload analysis model: acquiring load power curve according to distribution transforming, calculate every corresponding load factor value, if
Continuous 8 points (persistently occur 2 hours) load factor value [80%, 100%) between, be defined as distribution transforming heavy duty；If continuous 8 points
(persistently occurring 2 hours) load factor value [100%, 150%) between, it is defined as distribution transforming overload；Wherein, load factor=distribution transforming is total
Power * integrates multiplying power/distribution transforming rated capacity；Distribution transforming refers to the transformer for being in operating status and non-user assets.
The support big data platform, the power grid account data of bonding apparatus assets lean operation management system, marketing base
The marketing equipment account data of plinth data platform calculate the preferred implementation process of distribution transforming heavy-overload are as follows:
Power information acquisition load data is loaded onto memory by big data platform, clear using Spark program according to step 2
It washes, repair with Variable power abnormal data or the leak source data due to caused by terminal, channel problems, successively traverse power data, if
Current time power data point analysis go out load factor be in [80%, 100%) between, continue analyze subsequent time power data
Point, if continuous 8 or more point load factor values [80%, 100%) between, then judge that heavy duty occurs for the distribution transforming, and record
First point corresponds to the time and the last one puts the corresponding time, is denoted as distribution transforming heavy duty time of origin and heavily loaded end time；If working as
Preceding moment power data point analysis go out load factor be in [100%, 150%) between, continue analyze subsequent time power data
Point, if continuous 8 or more point load factor values [100%, 150%) between, then judge that the distribution transforming is overloaded, and record
First point corresponds to the time and the last one puts the corresponding time, is denoted as distribution transforming overload time of origin and overload end time.By institute
There is the logout that heavy-overload distribution transforming occurs to pass through marketing side stage area data, marketing side stage area data and fortune inspection side distribution transforming account number
According to corresponding relationship, fortune inspection side distribution transforming account data be associated, analyze to meet the tendency of inspection distribution transforming heavy-overload situation.
It is described to match the overvoltage model that is lower specifically:
With the Over-voltage Analysis model that is lower: load voltage curve is acquired according to distribution transforming, analyzes every corresponding voltage value, if
Continuous 4 points (persistently occur 1 hour) voltage value [150V, 198V) between, be defined as distribution transforming low-voltage；If continuous 4 points
(persistently occurring 1 hour) voltage value [235.4V, 280V) between, it is defined as distribution transforming overvoltage；Wherein, 198V is to take nominal electricity
90%, 235.4V of pressure value (220V) is take nominal voltage (220V) 107%.
The support big data platform, the power grid account data of bonding apparatus assets lean operation management system, marketing base
The marketing equipment account data of plinth data platform calculate the preferred implementation process with the overvoltage that is lower are as follows:
Power information acquisition load data is loaded onto memory by big data platform, using Spark Program Purge, is repaired and is matched
Time variant voltage abnormal data or the leak source data due to caused by terminal, channel problems, successively traverse voltage data, if current time is electric
Press data value be in [150V, 198V) between, continue analyze subsequent time voltage data point, if continuous 4 or more are put voltage
Value [150V, 198V) between, then judge the distribution transforming occur low-voltage, and record first point correspond to the time and the last one
The point corresponding time, it is denoted as distribution transforming low-voltage time of origin and low-voltage end time；If current time voltage value is in
[235.4V, 280V) between, continue to analyze subsequent time voltage data point, if continuous 4 or more point voltage values exist
[235.4V, 280V) between, then judge that overvoltage occurs for the distribution transforming, and record first point and correspond to time and the last one point pair
Between seasonable, it is denoted as distribution transforming overvoltage time of origin and overvoltage end time.By all event notes that low overvoltage distribution transforming occurs
Record passes through marketing side stage area data, marketing side stage area data and fortune inspection side distribution transforming account data corresponding relationship, fortune inspection side distribution transforming platform
Account data are associated, and are analyzed to meeting the tendency of inspection with being lower over-voltage condition.
The distribution transforming three-phase imbalance model specifically:
Distribution transforming three-phase imbalance analysis model: according to distribution transforming acquisition load current and power curve, analysis every is corresponding
Three-phase electricity flow valuve, it is fixed if three-phase current unbalance degree > 25% and load factor > 60% (persistently occur 2 hours) for continuous 8 points
Justice is distribution transforming three-phase equilibrium；Wherein, the comprehensive multiplying power/distribution transforming rated capacity of load factor=distribution transforming general power *；Three-phase current unbalance
Degree=(maximum current-minimum current)/maximum current.
The support big data platform, the power grid account data of bonding apparatus assets lean operation management system, marketing base
The marketing equipment account data of plinth data platform calculate the preferred implementation process of distribution transforming three-phase imbalance are as follows:
Power information acquisition load data is loaded onto memory by big data platform, clear using Spark program according to step 2
It washes, repair distribution transforming electric current leak source data due to caused by terminal, channel problems, current data is successively traversed, if current time is electric
Corresponding current unbalance factor > 25% of flow valuve and current power value analyze load factor > 60% come, continue to analyze subsequent time
Current data point judges that the distribution transforming occurs three if continuous 8 or more are put current unbalance factor > 25% and load factor > 60%
Phase is uneven, and records first point and correspond to time and the last one point corresponding time, when being denoted as the generation of distribution transforming three-phase imbalance
Between and the three-phase imbalance end time, by it is all occur three-phase imbalance distribution transformings logouts by marketing side stage area data,
Marketing side stage area data are associated with fortune inspection side distribution transforming account data corresponding relationship, fortune inspection side distribution transforming account data, are analyzed
Distribution transforming three-phase imbalance situation is examined to meeting the tendency of.
Step S4: the index analysis result completed according to each working node completes distribution overall operation state analysis, specifically
Are as follows:
According to marketing side stage area information (critical field: platform area mark), marketing side stage area and fortune inspection side distribution transforming corresponding relationship
(critical field: platform area mark-distribution transforming mark), fortune inspection side distribution transforming information (critical field: distribution transforming mark) realize platform area and distribution transforming
One-to-one correspondence, thus realize fortune inspection side distribution transforming running state analysis.
Embodiment two:
A kind of distribution net equipment running state analysis method based on big data, comprising:
The power grid account data data of asset of equipments lean operation management system, marketing basic data are put down by Spark
The distribution transformer load data (mainly including electric current, voltage, power) of marketing equipment account data and the power information acquisition of platform are synchronous
To Hive data warehouse；
The distribution transformer load data that power information is acquired based on big data technology carry out quality verification, repair it is perfect；
From Hive data warehouse according to load balancing strategy will fortune inspection equipment account data, marketing equipment account data and
Power information distribution transformer load data are loaded onto each working node of big data platform；
Distribution transforming heavy-overload, low-voltage, three-phase imbalance analysis model are established, each working node relies on big data platform, root
According to established analysis of calculation models distribution net equipment operating status；
According to the index analysis result that each working node is completed, distribution overall operation state analysis is completed.
In embodiments of the present invention, the distribution net equipment running state analysis model includes distribution transforming heavy-overload model, matches
The overvoltage that is lower model and distribution transforming three-phase imbalance model；
The distribution transforming heavy-overload model, specifically:
Distribution transforming heavy-overload analysis model: acquiring load power curve according to distribution transforming, calculate every corresponding load factor value, if
Continuous 8 points (persistently occur 2 hours) load factor value [80%, 100%) between, be defined as distribution transforming heavy duty；If continuous 8 points
(persistently occurring 2 hours) load factor value [100%, 150%) between, it is defined as distribution transforming overload；Wherein, load factor=distribution transforming is total
Power * integrates multiplying power/distribution transforming rated capacity；Distribution transforming refers to the transformer for being in operating status and non-user assets.
The support big data platform, the power grid account data of bonding apparatus assets lean operation management system, marketing base
The marketing equipment account data of plinth data platform calculate the preferred implementation process of distribution transforming heavy-overload are as follows:
Power information acquisition load data is loaded onto memory by big data platform, clear using Spark program according to step 2
It washes, repair with Variable power abnormal data or the leak source data due to caused by terminal, channel problems, successively traverse power data, if
Current time power data point analysis go out load factor be in [80%, 100%) between, continue analyze subsequent time power data
Point, if continuous 8 or more point load factor values [80%, 100%) between, then judge that heavy duty occurs for the distribution transforming, and record
First point corresponds to the time and the last one puts the corresponding time, is denoted as distribution transforming heavy duty time of origin and heavily loaded end time；If working as
Preceding moment power data point analysis go out load factor be in [100%, 150%) between, continue analyze subsequent time power data
Point, if continuous 8 or more point load factor values [100%, 150%) between, then judge that the distribution transforming is overloaded, and record
First point corresponds to the time and the last one puts the corresponding time, is denoted as distribution transforming overload time of origin and overload end time.By institute
There is the logout that heavy-overload distribution transforming occurs to pass through marketing side stage area data, marketing side stage area data and fortune inspection side distribution transforming account number
According to corresponding relationship, fortune inspection side distribution transforming account data be associated, analyze to meet the tendency of inspection distribution transforming heavy-overload situation.
It is described to match the overvoltage model that is lower specifically:
With the Over-voltage Analysis model that is lower: load voltage curve is acquired according to distribution transforming, analyzes every corresponding voltage value, if
Continuous 4 points (persistently occur 1 hour) voltage value [150V, 198V) between, be defined as distribution transforming low-voltage；If continuous 4 points
(persistently occurring 1 hour) voltage value [235.4V, 280V) between, it is defined as distribution transforming overvoltage；Wherein, 198V is to take nominal electricity
90%, 235.4V of pressure value (220V) is take nominal voltage (220V) 107%.
The support big data platform, the power grid account data of bonding apparatus assets lean operation management system, marketing base
The marketing equipment account data of plinth data platform calculate the preferred implementation process with the overvoltage that is lower are as follows:
Power information acquisition load data is loaded onto memory by big data platform, clear using Spark program according to step 2
It washes, repair power distribution voltage abnormal data or the leak source data due to caused by terminal, channel problems, successively traverse voltage data, if
Current time voltage data value be in [150V, 198V) between, continue analyze subsequent time voltage data point, if continuous 4 and
The above point voltage value [150V, 198V) between, then judge that low-voltage occurs for the distribution transforming, and record first point and correspond to the time
And the last one puts the corresponding time, is denoted as distribution transforming low-voltage time of origin and low-voltage end time；If current time voltage value
In [235.4V, 280V) between, continue to analyze subsequent time voltage data point, if continuous 4 or more point voltage values exist
[235.4V, 280V) between, then judge that overvoltage occurs for the distribution transforming, and record first point and correspond to time and the last one point pair
Between seasonable, it is denoted as distribution transforming overvoltage time of origin and overvoltage end time.By all event notes that low overvoltage distribution transforming occurs
Record passes through marketing side stage area data, marketing side stage area data and fortune inspection side distribution transforming account data corresponding relationship, fortune inspection side distribution transforming platform
Account data are associated, and are analyzed to meeting the tendency of inspection with being lower over-voltage condition.
The distribution transforming three-phase imbalance model specifically:
Distribution transforming three-phase imbalance analysis model: according to distribution transforming acquisition load current and power curve, analysis every is corresponding
Three-phase electricity flow valuve, it is fixed if three-phase current unbalance degree > 25% and load factor > 60% (persistently occur 2 hours) for continuous 8 points
Justice is distribution transforming three-phase equilibrium；Wherein, the comprehensive multiplying power/distribution transforming rated capacity of load factor=distribution transforming general power *；Three-phase current unbalance
Degree=(maximum current-minimum current)/maximum current.
The support big data platform, the power grid account data of bonding apparatus assets lean operation management system, marketing base
The marketing equipment account data of plinth data platform calculate the preferred implementation process of distribution transforming three-phase imbalance are as follows:
Power information acquisition load data is loaded onto memory by big data platform, clear using Spark program according to step 2
It washes, repair distribution transforming electric current leak source data due to caused by terminal, channel problems, current data is successively traversed, if current time is electric
Corresponding current unbalance factor > 25% of flow valuve and current power value analyze load factor > 60% come, continue to analyze subsequent time
Current data point judges that the distribution transforming occurs three if continuous 8 or more are put current unbalance factor > 25% and load factor > 60%
Phase is uneven, and records first point and correspond to time and the last one point corresponding time, when being denoted as the generation of distribution transforming three-phase imbalance
Between and the three-phase imbalance end time, by it is all occur three-phase imbalance distribution transformings logouts by marketing side stage area data,
Marketing side stage area data are associated with fortune inspection side distribution transforming account data corresponding relationship, fortune inspection side distribution transforming account data, are analyzed
Distribution transforming three-phase imbalance situation is examined to meeting the tendency of.
The distribution net equipment running state analysis device based on big data that the above method uses, comprising:
Data acquisition module acquires distribution load data；
Data simultaneous module, will fortune inspection equipment account data, market Base data platform marketing equipment account data and
Distribution transformer load data are synchronized to Hive data warehouse；
Data repair module is perfect to distribution transformer load data progress quality verification, reparation based on big data technology；
Fortune is examined equipment account data, marketing according to load balancing strategy from Hive data warehouse by data loading module
Equipment account data and distribution transformer load data are loaded onto each working node of big data platform；
Data analysis module, based on fortune inspection equipment account data, marketing equipment account data and distribution transformer load data difference
Distribution transforming heavy-overload analysis model, low-voltage analysis model and three-phase imbalance analysis model are established, each working node is according to built
Vertical model analysis distribution net equipment operating status obtains index analysis result, completes distribution overall operation state analysis.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Finally it should be noted that: the above embodiments are merely illustrative of the technical scheme of the present invention and are not intended to be limiting thereof, to the greatest extent
Invention is explained in detail referring to above-described embodiment for pipe, it should be understood by those ordinary skilled in the art that: still
It can be with modifications or equivalent substitutions are made to specific embodiments of the invention, and without departing from any of spirit and scope of the invention
Modification or equivalent replacement, should all cover within the scope of the claims of the present invention.
Claims (10)
1. the distribution net equipment running state analysis method based on big data, it is characterised in that:
Acquire distribution load data, will fortune inspection equipment account data, market Base data platform marketing equipment account data and
Distribution transformer load data are synchronized to Hive data warehouse；
It is perfect to distribution transformer load data progress quality verification, reparation；
Fortune is examined into equipment account data, marketing equipment account data and distribution transforming according to load balancing strategy from Hive data warehouse
Load data is loaded onto each working node；
The analysis of distribution transforming heavy-overload is established respectively based on fortune inspection equipment account data, marketing equipment account data and distribution transformer load data
Model, low-voltage analysis model and three-phase imbalance analysis model, each working node are set according to established model analysis distribution
Standby operating status, obtains index analysis result, completes distribution overall operation state analysis.
2. the distribution net equipment running state analysis method based on big data as described in claim 1, it is characterised in that: described
The power grid account data of asset of equipments lean operation management system includes: fortune inspection side distribution transforming account information；
The marketing equipment account data of the marketing Base data platform include: marketing side stage area information, marketing side stage area letter
Breath and fortune inspection side distribution transforming information corresponding relationship.
3. the distribution net equipment running state analysis method based on big data as claimed in claim 1 or 2, it is characterised in that: institute
State by the power grid account data of asset of equipments lean operation management system, market Base data platform marketing equipment account number
According to and the distribution transformer load data of power information acquisition be synchronized to the detailed process of Hive data warehouse are as follows:
Asset of equipments lean operation management system is transported into inspection side distribution transforming account information, marketing by Spark distributed synchronization program
Base data platform marketing side stage area information, marketing side stage area information and fortune inspection side distribution transforming information corresponding relationship, power information are adopted
Collecting system load data is mainly the electric current, voltage and power extraction of distribution transforming into memory；Relationship will be based on by Spark program
The asset of equipments lean operation management system fortune inspection side distribution transforming account information of type data storage, marketing Base data platform Marketing-side
Platform area information, marketing side stage area information pass corresponding with fortune inspection side distribution transforming information and power information acquisition system load data model turn
It is changed to the model based on Hive storage, and data are stored in Hive data warehouse.
4. the distribution net equipment running state analysis method based on big data as claimed in claim 1 or 2, it is characterised in that: institute
State the distribution transformer load data that power information is acquired based on big data technology carry out quality verification, repair it is perfect, specifically include with
Lower step:
First by the distribution transformer load data and power-off event of power information acquisition；
Then distribution transforming each current data is traversed, if the period of electric current leak source does not seek at electric leakage in power off time section
Current average；
Secondly traversal distribution transforming each voltage data, if the period of voltage leak source does not seek at electric leakage in power off time section
Average voltage, and current voltage is repaired based on average voltage；
Then distribution transforming each power data is traversed, if the period of power leak source does not seek at electric leakage in power off time section
Power average value, and current power is repaired based on power average value；
The distribution transformer load data including the voltage after electric current, reparation and the power after reparation are finally updated to Hive data
Warehouse.
5. the distribution net equipment running state analysis method based on big data as described in claim 1, it is characterised in that: it is described from
Fortune inspection equipment account data, marketing equipment account data and power information are matched according to load balancing strategy in Hive data warehouse
Varying duty data are loaded onto the detailed process in each working node of big data platform are as follows:
Fortune inspection side apparatus account data, marketing equipment account data and power information distribution transformer load data are divided into several numbers
According to block, and according to each calculate node loading condition, data block is distributed into the node of setting according to the rule of setting；
Distribution transforming running state analysis logic is distributed in the data block in each node.
6. the distribution net equipment running state analysis method based on big data as claimed in claim 1 or 2, it is characterised in that: institute
That states establishes distribution transforming heavy-overload analysis model, distribution transforming heavy-overload model specifically:
Distribution transforming heavy-overload analysis model: load power curve is acquired according to distribution transforming, every corresponding load factor value is calculated, if continuously
8 points persistently occur 2 hours, load factor value [80%, 100%) between, it is defined as distribution transforming heavy duty；If continuous 8 points, i.e.,
Persistently occur 2 hours, load factor value [100%, 150%) between, it is defined as distribution transforming overload；Wherein, load factor=distribution transforming total work
Rate * integrates multiplying power/distribution transforming rated capacity；Distribution transforming refers to the transformer for being in operating status and non-user assets.
7. the distribution net equipment running state analysis method based on big data as claimed in claim 1 or 2, it is characterised in that: institute
The foundation stated is described with the overvoltage model that is lower specifically: with the Over-voltage Analysis model that is lower with the Over-voltage Analysis model that is lower:
Load voltage curve is acquired according to distribution transforming, analyzes every corresponding voltage value, if continuous 4 points, i.e., is persistently occurred 1 hour, electricity
Pressure value [150V, 198V) between, it is defined as distribution transforming low-voltage；If continuous 4 points, i.e., persistently occur 1 hour, voltage value
[235.4V, 280V) between, it is defined as distribution transforming overvoltage；Wherein, 198V is to take 90%, the 235.4V of nominal voltage 220V to be
Take the 107% of nominal voltage 220V.
8. the distribution net equipment running state analysis method based on big data as claimed in claim 1 or 2, it is characterised in that: institute
That states establishes distribution transforming three-phase imbalance analysis model specifically: distribution transforming three-phase imbalance analysis model: acquiring load according to distribution transforming
Electric current and power curve analyze every corresponding three-phase electricity flow valuve, if continuous 8 points, i.e., persistently occur 2 hours, three-phase current
Degree of unbalancedness > 25% and load factor > 60% are defined as distribution transforming three-phase equilibrium；
Wherein, the comprehensive multiplying power/distribution transforming rated capacity of load factor=distribution transforming general power *；Three-phase current unbalance degree=(maximum electricity
Stream-minimum current)/maximum current.
9. the distribution net equipment running state analysis method according to claim 1 or 2 based on big data, it is characterised in that:
It is described according to established analysis of calculation models distribution net equipment operating status, detailed process are as follows: concentrator mainly includes carrier wave
Module, infrared module, communication module, ammeter meter and pulse module standardize according to DL/T 698.34 and DL/T 698.34, pass through
RS485 mouthfuls of acquisition distribution transforming electric currents, voltages, power curve, based on the distribution transformer load curve of acquisition, according to distribution transforming heavy-overload,
Low overvoltage, three-phase imbalance analysis model, analysis marketing side stage area operating status；
According to marketing side stage area information, marketing side stage area and fortune inspection side distribution transforming corresponding relationship, fortune inspection side distribution transforming information realization platform area
With the one-to-one correspondence of distribution transforming, inspection side distribution transforming running state analysis is transported to realize.
10. the distribution net equipment running state analysis device based on big data, it is characterised in that: include:
Data acquisition module acquires distribution load data；
Data simultaneous module, by fortune inspection equipment account data, the marketing equipment account data and distribution transforming of Base data platform of marketing
Load data is synchronized to Hive data warehouse；
Data repair module is perfect to distribution transformer load data progress quality verification, reparation based on big data technology；
Fortune is examined equipment account data, marketing equipment according to load balancing strategy from Hive data warehouse by data loading module
Account data and distribution transformer load data are loaded onto each working node of big data platform；
Data analysis module is established respectively based on fortune inspection equipment account data, marketing equipment account data and distribution transformer load data
Distribution transforming heavy-overload analysis model, low-voltage analysis model and three-phase imbalance analysis model, each working node is according to established
Model analysis distribution net equipment operating status obtains index analysis result, completes distribution overall operation state analysis.
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