CN107330056A - Wind power plant SCADA system and its operation method based on big data cloud computing platform - Google Patents
Wind power plant SCADA system and its operation method based on big data cloud computing platform Download PDFInfo
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- CN107330056A CN107330056A CN201710512657.XA CN201710512657A CN107330056A CN 107330056 A CN107330056 A CN 107330056A CN 201710512657 A CN201710512657 A CN 201710512657A CN 107330056 A CN107330056 A CN 107330056A
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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
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2282—Tablespace storage structures; Management thereof
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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
- G06F16/10—File systems; File servers
- G06F16/18—File system types
- G06F16/182—Distributed file systems
- G06F16/1824—Distributed file systems implemented using Network-attached Storage [NAS] architecture
- G06F16/183—Provision of network file services by network file servers, e.g. by using NFS, CIFS
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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
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1097—Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
Abstract
The invention discloses a kind of wind power plant SCADA system and its operation method based on big data cloud computing platform.The system includes wind farm side, cloud computing platform and centralized control center side；The wind farm side and centralized control center side are connected by internet with cloud computing platform；The wind farm side includes blower fan, booster stations, case change, anemometer tower, kilowatt-hour meter, AGC, AVC, prudential sub-station, failure wave-recording, telemechanical apparatus, front server, fire wall and interchanger；The cloud computing platform includes big data server, senior application server and interchanger；The centralized control center side includes the network printer, blower fan collection control work station, booster stations monitor workstation, form and alarm work station and maintenance work station.This method uses Hadoop+MapReduce distributed data processing, not only greatly improves the space of data storage, and the cost of data storage fallen below it is minimum, while allowing the processing speed of data to obtain the lifting of matter.
Description
Technical field
The present invention relates to wind-powered electricity generation field, specifically a kind of wind power plant SCADA system based on big data cloud computing platform and
Its operation method.
Background technology
Wind energy increasingly causes the attention of countries in the world, wind generating technology as a kind of regenerative resource of cleaning
Tend to be ripe substantially.There are tens even up to a hundred wind energy conversion systems in large-scale wind power plant, how effectively to each wind energy conversion system
State is monitored, make whole wind electric field blower it is safe and reliable, economically operation become most important.Solve the above problems
Approach is to set up wind power plant SCADA (Supervisory ControlAnd Data Acquisition, data acquisition and monitoring
Control) system, realize wind power plant total system fan monitoring, information sharing and fault diagnosis and maintenance.SCADA system changes
The looks of wind power plant O＆M are become, have made the wind power plant O＆M pattern of " unattended, few man on duty, district management ", set up wind
Electric field remote control center, significantly improves the severe working environment of staff, has received more high-tech talents to add
Enter Wind Power Generation Industry, enhance the market competitiveness of wind-powered electricity generation company, the further development of Wind Power Generation Industry has been promoted significantly.
Although the appearance of SCADA system brings earth-shaking change for the O＆M of wind power plant, in view of wind power plant
The data collection layer of bottom has the characteristics of data acquisition amount is big, frequency acquisition is high, and history library will form the complicated, wind of isomery
TV university data, traditional SCADA system starts to face computer CPU upgrading, low memory, computer hardware expansion, cost increasing
Plus etc. a series of problem.Traditional data processing method, it is difficult to the wind power plant big data of quick processing magnanimity.Application number
201310471096.5 disclose wind power plant centralized monitoring system platform, including real-time system, data acquisition subsystem and MIS/
DMIS systems；Real-time system include data server group, SCADA servers, communication server, electronics on duty, remote maintenance and
Work station；Data acquisition subsystem includes data server, data acquisition server；MIS/DMIS systems take including MIS/DMIS
Business device, MIS/DMIS work stations and MIS work stations；Whole system is double web frame, database server, SCADA servers and
Data acquisition server is then dual-computer redundancy configuration, and the passage towards each wind power plant also uses an Ethernet for main channel,
Another Ethernet is the dual channel mode of standby passage.But the system uses traditional data acquisition and transmission method, to hard
The requirement of part equipment is higher, and system complex, cost is very high, but the speed and security of data acquisition and transmission is not high.More
It is that system only possesses basic wind power plant monitoring function for distinct issues, has that data processing speed is slow, quantity is small, data are dug
The problem of digging the data processings such as scarce capacity aspect, is a kind of wasting of resources, system for restricting exploitation in itself to wind power plant big data
Go out more senior applications, be unfavorable for the intellectuality of wind farm monitoring system.
The content of the invention
In view of the shortcomings of the prior art, the technical problem that the present invention is intended to solve is to provide a kind of based on big data cloud computing
The wind power plant SCADA system and its operation method of platform.The system is traditional wind power plant SCADA system and cloud computing platform knot
Close, by big data cloud computing platform framework is flexible and changeable, parallel data processing and the low advantage of cost, reduce system to hard
The requirement of part equipment, maximizes the effect of wind-powered electricity generation big data, and in data processing using the mark of MapReduceization
Quasi- K-Means algorithms, on the premise of system cost is reduced, significantly improve data processing speed.
It is a kind of based on big data cloud computing platform that the present invention solves the problems, such as that the technical scheme of the systems technology is to provide
Wind power plant SCADA system, it is characterised in that the system includes wind farm side, cloud computing platform and centralized control center side；The wind-powered electricity generation
Field side and centralized control center side are connected by internet with cloud computing platform；
The wind farm side includes blower fan, booster stations, case change, anemometer tower, kilowatt-hour meter, AGC, AVC, prudential sub-station, failure
Recording, telemechanical apparatus, front server, fire wall and interchanger；The blower fan is connected by Ethernet with front server；Institute
Booster stations, case change, anemometer tower, kilowatt-hour meter, AGC, AVC, prudential sub-station and failure wave-recording is stated to fill with telemechanical respectively by Ethernet
Put connection；The telemechanical apparatus is connected by Ethernet and front server, and front server is connected with fire wall, fire wall and
Interchanger is connected；
The cloud computing platform includes big data server, senior application server and interchanger；The big data service
It is connected with each other between device, senior application server and interchanger by Ethernet；
The centralized control center side includes the network printer, blower fan collection control work station, booster stations monitor workstation, form and announcement
Alert work station and maintenance work station；The network printer, blower fan collection control work station, booster stations monitor workstation, form and announcement
It is connected with each other between alert work station and maintenance work station by Ethernet.
The technical scheme of the present invention solution operation method technical problem is to provide a kind of flat based on big data cloud computing
The operation method of the wind power plant SCADA system of platform, it is characterised in that comprise the following steps：
(1) data acquisition and transmission：The data of blower fan are transferred directly to front server, booster stations, case change, anemometer tower,
Kilowatt-hour meter, AGC, AVC, the data of prudential sub-station and failure wave-recording are converted into the rule of standard by the stipulations conversion of telemechanical apparatus
About packet, then by being transferred to front server by Ethernet；The data of wind farm side are after front server collects, warp
Fire wall encryption is crossed, data are uploaded to by user's access interface by cloud computing platform by interchanger；
(2) data storage：Data acquisition and it is transferred to after cloud computing platform, cloud computing platform is first had to the big number of wind power plant
According to being stored, SC is the storage control being deployed on a big data server in storage architecture, with the virtual clothes built
Business device VM connections, VM quantity is variable as needed, and each VM associates a memory volume and comes extension storage, VM
Between data sharing, jointly access a big data storage region；VM is connected with cluster controller CC, and final connection is based on
Hadoop basic frameworks set up the HBase databases in HDFS files；
(3) data processing：Using the standard K-Means algorithms of MapReduceization in cloud computing platform to HBase databases
In data handled；Map/Reduce programs in MapReduce standard program models are by the Hive in Hadoop platform
Tool for Data Warehouse is divided into the Map functions and Reduce functions that order is performed, and an initial key-value pair passes through Map functions,
Generate one group of middle key-value pair as bridge, but key-value pair only in the middle of key assignments identical, it can just send Reduce letters to
Number；The effect of Reduce functions is to receive one of key assignments key assignments related with one group, is combined, forms smaller one group
Key assignments；The mass data storage of input is in distributed file system HDFS, and program is by the way of migration computing, Map/
Reduce tasks are downloaded to ready-portioned back end and performed parallel, and the final result of data processing still preserves HDFS files
In, centralized control center side receives the data after cloud computing platform is handled by user's access interface.
Compared with prior art, beneficial effect of the present invention is：By the advantage of internet cloud calculating platform instantly, mark
Accurate data processing method is combined with cloud computing platform, is not only greatly improved data processing speed, and facilitate system to open
The more senior applications of hair, are conducive to the intellectuality of wind power plant SCADA system.Cloud computing platform uses Hadoop+MapReduce
Distributed data processing, compared with data processing method traditional before wind-powered electricity generation industry, the platform be based on it is more flexible
Changeable Open Framework, needs to change component at any time according to systemic-function, and support level extends, with internet community, more
Increase and put safety.Whole cloud computing platform can complete a complete task, including data storage and processing, it is not necessary to tradition
The storage devices such as the disk array in SCADA system, not only greatly improve the space of data storage, and data storage
Cost fall below it is minimum, while allowing the processing speed of data to obtain the lifting of matter.
Brief description of the drawings
Fig. 1 is the present invention, and wind power plant SCADA system and its operation method one kind based on big data cloud computing platform are implemented
The integrated connection block diagram of example；
Fig. 2 is the present invention, and wind power plant SCADA system and its operation method one kind based on big data cloud computing platform are implemented
The cloud computing platform storage rack composition of example；
Fig. 3 is the present invention, and wind power plant SCADA system and its operation method one kind based on big data cloud computing platform are implemented
The system operation flow chart of example；
Fig. 4 is in wind power plant SCADA system of the present invention based on big data cloud computing platform and its operation method embodiment 1
The time-consuming schematic diagram of K-Means algorithm different pieces of information amounts after standard K-Means algorithms and MapReduceization；
Embodiment
The specific embodiment of the present invention is given below.Specific embodiment is only used for that the present invention is further described, and does not limit
The application scope of the claims processed.
The invention provides a kind of wind power plant SCADA system based on big data cloud computing platform (referring to Fig. 1-4, abbreviation
System), it is characterised in that the system includes wind farm side 1, cloud computing platform 2 and centralized control center side 3；The He of wind farm side 1
Centralized control center side 3 is connected by user's access interface of internet with cloud computing platform 2；
The wind farm side 1 include blower fan 11, booster stations 12, case become 13, anemometer tower 14, kilowatt-hour meter 15, AGC16,
AVC17, prudential sub-station 18, failure wave-recording 19, telemechanical apparatus 110, front server 111, fire wall 112 and interchanger 113；Institute
State blower fan 11 to be connected with front server 111 by Ethernet, the data of blower fan 11 are transferred directly to front server 111；
The booster stations 12, case become 13, anemometer tower 14, kilowatt-hour meter 15, AGC16, AVC17, prudential sub-station 18 and failure wave-recording 19 and passed through
Ethernet is connected with telemechanical apparatus 110 respectively, is changed by the stipulations of telemechanical apparatus 110, and booster stations 12, case are become into 13, wind is surveyed
Tower 14, kilowatt-hour meter 15, AGC16, AVC17, the data of prudential sub-station 18 and failure wave-recording 19 are converted into 104 stipulations numbers of standard
According to bag；The telemechanical apparatus 110 is connected by Ethernet and front server 111, and front server 111 connects with fire wall 112
Connect, fire wall 112 is connected with interchanger 113；The data of wind farm side 1 are after front server 111 collects, by fire wall
Data, cloud computing platform 2 is uploaded to by interchanger 113 by 112 encryptions by user's access interface of internet.
Blower fan 11 refers to blower fan actual in wind power plant；Booster stations 12 are used for making the electric boost that wind power plant is issued, it is therefore an objective to
Reduce line current so as to reducing electric loss of energy；It is a kind of high-tension switch gear, distribution transformer and low-voltage distribution that case, which becomes 13,
Device, is mainly used to change voltage；Anemometer tower 14 is used to wind power plant air motion situation is observed and recorded；AGC16 is controlled
Exerting oneself for frequency modulation unit is made, to meet the electricity needs for the user being continually changing；It is idle excellent that AVC17 can carry out on-Line Voltage
Change control, ensure the quality of power supply, improve power transmission efficiency, reduce network loss；Prudential sub-station 18 is by the telecontrol information of wind power plant, protection
Information and artwork information are uploaded；Failure wave-recording 19 can automatically and accurately record mistake before and after failure in system jam
The situation of change of the various electrical quantity of journey, by the way that original waveform is to the analysis of these electrical quantity and compares, analyzing and processing accident, sentences
It is disconnected protection whether correct operation, improve safe operation of power system level；Telemechanical apparatus 110 is used for the data to wind farm device
It is acquired and forwards, the model of the telemechanical apparatus 110 is PCS-9799；Front server 111 is used to show that wind power plant connects
Real time data, channel status, communication packet for receiving etc.；Fire wall 112 refers to a kind of by in-house network (such as Ethernet) and the public
The separated method of net (such as internet) is accessed, it is actually a kind of security isolation technology；Interchanger 113 is mainly in internet
The middle exchange for completing information.
The cloud computing platform 2 includes big data server 21, senior application server 22 and interchanger 23；The big number
It is connected with each other according between server 21, senior application server 22 and interchanger 23 by Ethernet；The interchanger 23 is used
Exchanged in the information of internet and cloud computing platform 2；The big data server 21 is used to storing and handling wind farm data, carries
For functions such as inquiry, renewal, transaction management, index, cache, query optimization, safety and multi-user access controls；The height
Level application server 22 is the more senior applications of intelligent development exploitation of wind power plant, and such as wind power prediction, blower fan shake
Monitoring, WEB issues, device predicted maintenance, the alarm of wind-powered electricity generation accident forecast etc..
The centralized control center side 3 include the network printer 31, blower fan collection control work station 32, booster stations monitor workstation 33,
Form and alarm work station 34 and maintenance work station 35；The network printer 31, blower fan collection control work station 32, booster stations monitoring
It is connected with each other between work station 33, form and alarm work station 34 and maintenance work station 35 by Ethernet；The network printer
31 refer to printer by printing server as independent equipment access to LAN or internet, are one arranged side by side with network
Network node and outlet terminal；Fan monitoring work station 32 is used for monitoring the real time execution situation of blower fan in wind power plant；Boosting
Monitor workstation 33 of standing is used for monitoring the real time execution situation of wind power plant booster stations；Form and alarm work station 34 provide customization
Data exhibiting function there is provided the generation of form, printing and reporting functions, be seamlessly connected with the reporting system of superior unit, together
When for the failure that occurs at any time, carry out sound and light alarm, alarm content is shown in foremost, be easy to operations staff to check in alarm
Hold；Maintenance work station 35 is timely repaired for staff to the failure that wind farm device occurs, and reduces the hair of accident
It is raw, it is ensured that the safe operation of wind power plant.
Cloud computing platform 2 is using Hadoop+MapReduce distributed data processing, the forward pass with wind-powered electricity generation industry
The data processing method of system is compared, and the platform needs to change at any time based on more flexible and changeable Open Framework according to systemic-function
Become component, and support level extends, with internet community, more open safety.Whole cloud computing platform can complete one
Complete task, including data storage and processing, it is not necessary to the storage device such as disk array in traditional SCADA system, not only
Greatly improve the space of data storage, and the cost of data storage fallen below it is minimum, while allowing the processing speed of data
Degree has obtained the lifting of matter.
Hadoop is the basic framework agreed with cloud computing platform, supports various data algorithms, including data sorting, is looked into
Inquiry, pattern analysis, clustering, statistical analysis, optimization, data mining, scheduling etc..Wind power plant cloud computing platform is with wind
TV university data are as input, under the rule of given algorithm, the given data of processing, and calculate final result.
Hadoop is the operation framework of typical distributed parallel, the algorithm with cloud computing platform parallel processing mass data
The mass data of input in simple terms, is exactly divided into different areas, the number in so each area by slitless connection, its operation principle
It will greatly reduce according to amount, originally total big task also just divide into several small tasks, it is corresponding that each small task handles oneself
Performed parallel between partition data, each small task.HBase is employed based on row memory module, can be easily database
Middle data provide physics adjacent memory cell, are read and storage mass data therefore, it is possible to quick, using HBase technologies significantly
The demand for building large-scale structureization storage to hardware is reduced, easy PC server can meet requirement.Hive is
The Tool for Data Warehouse of Hadoop platform, can be the data file of structuring in cloud computing platform using this instrument of hive
Database table is mapped as, hive instruments can also be converted to sql sentences the substep execution of MapReduce tasks.MapReduce has
The characteristics of having simple readily understood, flexible and changeable, high fault-tolerant, is the parallel processing that various big data Processing Algorithms can be applied mechanically
Standard program model.
A kind of operation method of the wind power plant SCADA system based on big data cloud computing platform, it is characterised in that including with
Lower step：
(1) data acquisition and transmission：The data of blower fan 11 are transferred directly to front server 111, and booster stations 12, case become
13rd, the data of anemometer tower 14, kilowatt-hour meter 15, AGC16, AVC17, prudential sub-station 18 and failure wave-recording 19 pass through telemechanical apparatus 110
Stipulations conversion be converted into 104 conventions data bags of standard, then by being transferred to front server 111 by Ethernet；Adopt
The variable of collection includes five classes, specially remote measurement amount, remote signalling amount, remote control amount, remote regulating amount and electricity, in order to ensure that data are accurate
Property, the frequency acquisition of telemechanical apparatus 110 cannot be less than 0.2.The data of wind farm side 1 are passed through after front server 111 collects
Fire wall 112 is encrypted, and data are uploaded to cloud computing platform 2 by user's access interface by interchanger 113.Data acquisition and biography
If defeated period encounters the situation of network connection interruption, will not also influence be produced on data acquisition, because interface routine can be repeated
Network connection state is detected, the data in the suspension period will not lose, only can form temporary transient cache file, once network connects
Recovery is connect, at once normal transmission.
(2) data storage：Data acquisition and it is transferred to after cloud computing platform 2, cloud computing platform 2 first has to big to wind power plant
Data are stored, with reference to the characteristics of wind power plant big data and cloud computing platform 2, using storage architecture as shown in Figure 2.This is deposited
It is the storage control being deployed on a big data server 21 to store up SC in framework, is connected with the virtual server VM built,
VM quantity is variable as needed, and one memory volume of each VM associations carrys out data between extension storage, VM and is total to
Enjoy, a big data storage region is accessed jointly；VM is connected with cluster controller CC, and final connection is based on Hadoop basic frameworks
Set up the HBase databases in HDFS files；
(3) data processing：Using the standard K-Means algorithms of MapReduceization in cloud computing platform 2 to HBase data
Data in storehouse are handled；Map/Reduce programs in MapReduce standard program models are by Hadoop platform
Hive Tool for Data Warehouse is divided into the Map functions and Reduce functions that order is performed, and an initial key-value pair passes through Map letters
Number, one group of middle key-value pair as bridge of generation, but key-value pair only in the middle of key assignments identical, it can just send Reduce to
Function；The effect of Reduce functions is to receive one of key assignments key assignments related with one group, is combined, forms smaller one
Group key assignments；The mass data storage of input is in distributed file system HDFS, and program is by the way of migration computing, Map/
Reduce tasks are downloaded to ready-portioned back end and performed parallel, and the final result of data processing still preserves HDFS files
In, centralized control center side 3 receives the data after cloud computing platform 2 is handled by user's access interface.
K-Means cluster algorithms are a kind of classical data processing algorithms, and the algorithm is using K as parameter, a number
According to N number of data tuple of concentration, K subset is split into, the basic demand of fractionation is the phase of the data tuple in each subset
It is high as far as possible like spending, but the similarity of the data tuple between different subsets is low as far as possible, the judge mark of similarity
Standard is the average value of object in subset.The execution step of standard K-Means algorithms is as follows：
(1) k initial cluster center of selection, such as cp [0]=D [0], cp [k-1]=D [k-1] ..., wherein D is number of transactions
According to collection, cp in general, the selection of initial center is random；
(2) D [n] for D [0] ..., calculates corresponding cp [0] ... cp [k-1] distance, closest person's note respectively
For c [i], c [i] total number is designated as Ci；
(3) for all c [i] of the 2nd step, new cluster centre cp [i]=(the corresponding D [j] of ∑ c [i])/C is calculatedi；
(4) 2,3 steps are repeated, until the data tuple in D [i] and current c [i] distance be less than given threshold value or
Untill each cluster no longer changes, algorithm performs are finished, and have obtained k cluster.
During standard K-Means algorithm performs, calculate D [0] and cp [0] ... cp [k-1] apart from while,
The distance for the cp [k-1] that D [1] and cp [0] can be calculated ..., this process is kissed with the framework that cloud computing platform distributed parallel is run
Close, the present invention combines standard K-Means algorithms with cloud computing platform, makes standard K-Means algorithm MapReduceization, significantly
Improve data processing speed.The execution step of standard K-Means algorithm MapReduceization is as follows：
(1) randomly choose k initial cluster center, such as cp [0]=D [0], cp [k-1]=D [k-1] ..., at the same by this
A little initial cluster centers are copied in initial clustering module OriginalCluster [], and by initial clustering module
OriginalCluster [] piecemeal, according to the situation of calculate node cluster, by initial clustering module OriginalCluster []
Distribute to each calculate node；
(2)Map:The D [n] for D [0] ..., the cp [n-1] that itself and cp [0] calculated respectively ... distance, closest person's note
For c [i], c [ic] total number be designated as Ci, while under MapReduce frameworks, key-value pair Key-Value Key and Value
I, D [k] are corresponded to respectively；
(3)Reduce:Because i is key-value pair Key in MapReduce frameworks, it ensure that same Key all D [k]
Same Reduce processes can be assigned to, then can calculate new cluster centre cp [i]=(∑ c [i] is right in this Reduce process
D [the j]/C answeredi), and this new cluster centre is stored in final cluster module DestinationCluster []；
(4) relatively more final cluster module DestinationCluster [] and initial clustering module OriginalCluster
[], if both changes are less than previously given threshold value, cluster is completed, otherwise by final cluster module
DestinationCluster [], which is copied to, to be jumped to the 2nd step and continues to hold after initial clustering module OriginalCluster []
OK；Centralized control center side 3 receives the data after cloud computing platform 2 is handled by user's access interface.
The MapReduceization of K-Means algorithms only needs to be available for Map and Reduce to be partially stripped out algorithm, structure
Key-value pair is made, the task such as other communications, monitoring, scheduling all gives the MapReduce frameworks based on Hadoop platform and goes completion.
Using the advantage of cloud computing platform, data processing speed substantially adds after standard K-Means algorithm MapReduceization
It hurry up, and data set scale is bigger, speed advantage is more obvious, be that wind power prediction, blower fan vibration monitoring, WEB issues, equipment are pre-
The exploitation for surveying more senior applications such as maintenance, the alarm of wind-powered electricity generation accident forecast is laid a good foundation, and is easy to the intellectuality in wind power plant future
Management.
Embodiment 1
Choose voltage, electric current, frequency, phase difference of voltage, the current phase angle in the remote measurement reference list of certain wind power plant booster stations
The suitable data set for doing clustering such as difference, calls 45,000,000 be stored in SCADA system history server and records conduct
Experimental data, is divided into 1,000,000,2,000,000,5,000,000,10,000,000,18,000,000,30,000,000 and 45,000,000 five groups, different pieces of information amount
Time-consuming (as shown in table 1), simulation result (as shown in Figure 4).
The time-consuming contrast of 1 different pieces of information amount of table, two kinds of algorithms
Experiment simulation is carried out by using different data volumes, two curves of contrast can be drawn the following conclusions, with standard
K-Means algorithms are compared, and are substantially added with the K-Means algorithm datas processing speed after cloud computing platform combination MapReduceization
It hurry up, and data set scale is bigger, and speed advantage is more obvious, demonstrates the feasibility and validity of system, is wind power plant future
More intellectuality is laid a good foundation.
The present invention does not address part and is applied to prior art.
Claims (5)
1. a kind of wind power plant SCADA system based on big data cloud computing platform, it is characterised in that the system include wind farm side,
Cloud computing platform and centralized control center side；The wind farm side and centralized control center side are connected by internet with cloud computing platform；
The wind farm side include blower fan, booster stations, case change, anemometer tower, kilowatt-hour meter, AGC, AVC, prudential sub-station, failure wave-recording,
Telemechanical apparatus, front server, fire wall and interchanger；The blower fan is connected by Ethernet with front server；The liter
Station, case change, anemometer tower, kilowatt-hour meter, AGC, AVC, prudential sub-station and failure wave-recording is pressed to connect respectively with telemechanical apparatus by Ethernet
Connect；The telemechanical apparatus is connected by Ethernet and front server, and front server is connected with fire wall, and fire wall is with exchanging
Machine is connected；
The cloud computing platform includes big data server, senior application server and interchanger；The big data server, height
It is connected with each other between level application server and interchanger by Ethernet；
The centralized control center side includes the network printer, blower fan collection control work station, booster stations monitor workstation, form and alarm work
Make station and maintenance work station；The network printer, blower fan collection control work station, booster stations monitor workstation, form and alarm work
Stand and be connected with each other between maintenance work station by Ethernet.
2. the wind power plant SCADA system according to claim 1 based on big data cloud computing platform, it is characterised in that described
The model of telemechanical apparatus is PCS-9799.
3. the wind power plant SCADA system according to claim 1 based on big data cloud computing platform, it is characterised in that telemechanical
The frequency acquisition of device cannot be less than 0.2.
4. a kind of operation method of the wind power plant SCADA system based on big data cloud computing platform, it is characterised in that including following
Step：
(1) data acquisition and transmission：The data of blower fan are transferred directly to front server, booster stations, case change, anemometer tower, electric degree
Table, AGC, AVC, the data of prudential sub-station and failure wave-recording are converted into the stipulations number of standard by the stipulations conversion of telemechanical apparatus
According to bag, then by being transferred to front server by Ethernet；The data of wind farm side are after front server collects, by anti-
Wall with flues is encrypted, and data are uploaded to cloud computing platform by user's access interface by interchanger；
(2) data storage：Data acquisition and it is transferred to after cloud computing platform, cloud computing platform first has to enter wind power plant big data
SC is the storage control being deployed on a big data server in row storage, storage architecture, with the virtual server built
VM connections, VM quantity is variable as needed, and each VM, which associates a memory volume, to be come between extension storage, VM
Data sharing, accesses a big data storage region jointly；VM is connected with cluster controller CC, and final connection is based on Hadoop bases
Plinth framework sets up the HBase databases in HDFS files；
(3) data processing：Using the standard K-Means algorithms of MapReduceization in cloud computing platform in HBase databases
Data are handled；Map/Reduce programs in MapReduce standard program models are by the Hive data in Hadoop platform
Warehouse instrument is divided into the Map functions and Reduce functions that order is performed, and an initial key-value pair passes through Map functions, generation
One group of middle key-value pair as bridge, but key-value pair only in the middle of key assignments identical, can just send Reduce functions to；
The effect of Reduce functions is to receive one of key assignments key assignments related with one group, is combined, forms one group of smaller key
Value；The mass data storage of input is in distributed file system HDFS, and program is by the way of migration computing, Map/Reduce
Task is downloaded to ready-portioned back end and performed parallel, and the final result of data processing is still preserved in HDFS files, collection control
Central side receives the data after cloud computing platform processing by user's access interface.
5. the operation method of the wind power plant SCADA system according to claim 4 based on big data cloud computing platform, it is special
Levy and be comprising the following steps that for the 3rd step data processing：
(1) k initial cluster center, such as cp [0]=D [0], cp [k-1]=D [k-1] ..., while by the beginning of these are randomly choosed
Beginning cluster centre is copied in initial clustering module OriginalCluster [], and by initial clustering module
OriginalCluster [] piecemeal, according to the situation of calculate node cluster, by initial clustering module OriginalCluster []
Distribute to each calculate node；
(2)Map:The D [n] for D [0] ..., the cp [n-1] that itself and cp [0] calculated respectively ... distance, closest person are designated as c
[i], c [ic] total number be designated as Ci, while under MapReduce frameworks, Key and Value points of key-value pair Key-Value
I, D [k] are not corresponded to；
(3)Reduce:Because i is key-value pair Key in MapReduce frameworks, it ensure that same Key all D [k] can divide
Same Reduce processes are fitted on, then can calculate new cluster centre cp [i]=(∑ c [i] is corresponding in this Reduce process
D[j]/Ci), and this new cluster centre is stored in final cluster module DestinationCluster []；
(4) relatively more final cluster module DestinationCluster [] and initial clustering module OriginalCluster [],
If both changes are less than previously given threshold value, cluster is completed, otherwise by final cluster module
DestinationCluster [], which is copied to, to be jumped to the 2nd step and continues to hold after initial clustering module OriginalCluster []
OK；Centralized control center side receives the data after cloud computing platform processing by user's access interface.
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