CN109359101A - Wind power plant cluster big data distributed analysis computing system - Google Patents
Wind power plant cluster big data distributed analysis computing system Download PDFInfo
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- CN109359101A CN109359101A CN201811504651.9A CN201811504651A CN109359101A CN 109359101 A CN109359101 A CN 109359101A CN 201811504651 A CN201811504651 A CN 201811504651A CN 109359101 A CN109359101 A CN 109359101A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention relates to a kind of wind power plant cluster big data distributed analysis computing systems.The system includes: distributed storage server, for executing distributed storage to the file；Name server is connect by network with the distributed storage server, and the title of file and file for storing to needs is managed；Client accesses server, is connected by network respectively at the distributed storage server and the name server, for being managed to the human-computer interaction between client.Wind power plant cluster big data distributed analysis computing system provided by the invention is it can be found that the principal element of influence wind power generating set operation reduces system operation cost to improve running efficiency of system.
Description
Technical field
The present invention relates to Wind turbines technical fields, more particularly to a kind of wind power plant cluster big data distributed analysis meter
Calculation system.
Background technique
Currently, the data of wind power plant predominantly stay in the monitoring memory phase of single wind farm data, it can only be to single wind
The data of several ten Wind turbines of electric field are stored, and can only carry out storage analysis to low volume data.Currently, there is minority
The data of wind power plant have been carried out a large amount of storages by wind-power electricity generation enterprise construction centralized control center, cannot be formed to mass data into
The efficient complicated analysis of row and calculating, consuming cost is higher, fluctuation of service；With the increase of data volume, the performance of server
It needs constantly to improve, needs constantly the storage of data could be maintained to calculate analysis server update, need periodically to lead
Enter to export mass data, is easy to cause loss of data；Mass data operates on single server, needs the higher service of performance
Device has increased significantly operation cost, when server goes wrong, will affect the operation of entire data system, causes systemic breakdown,
It is be easy to cause the loss of data, influences the hardware device and software equipment of system, causes unnecessary loss for company.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of wind power plant cluster big data distributed analysis computing systems, make
It can be found that influence the principal element of wind power generating set operation, to improve running efficiency of system, reduce system operation at
This.
In order to solve the above technical problems, the present invention provides a kind of wind power plant cluster big data distributed analysis to calculate system
System, the system comprises: distributed storage server, for executing distributed storage to the file；Name server passes through
Network is connect with the distributed storage server, and the title of file and file for storing to needs is managed；Visitor
Family end accesses server, is connected by network respectively at the distributed storage server and the name server, for pair
Human-computer interaction between client is managed.
As an improvement of the present invention, the human-computer interaction includes: that the issuing of order, the results of running show,
And the operation control of cluster.
As an improvement of the present invention, the name server is connected to the distribution by industrial grade switch and deposits
It stores up server and client accesses server；The client access server is connected to described point by industrial grade switch
Cloth storage server and the name server.
As an improvement of the present invention, further includes: backup name server, for being provided the name server
Service backed up.
As an improvement of the present invention, the distributed storage server, the name server and the backup name
Claim to run on server and has resource manager.
As an improvement of the present invention, operation has resource on the distributed storage server and the name server
Management program, the resource manager include: resource manager and management node.
As an improvement of the present invention, the resource manager is used for the management and distribution of system resource, and task is equal
Weighing apparatus is distributed to each management node and the calling of system-computed program；The management node is for book server
System resource management, starting, operation, the stopping of book server calculation procedure.
As an improvement of the present invention, the reading data process of the system includes: client call name server
Interior file obtains the address where file by name server；Client passes through reading order from distributed storage service
Device reads data；After current distributed storage server reads completion, which is automatically closed, automatic to read
Next distributed storage server is taken, until searching out the address where file.
As an improvement of the present invention, the write-in process of the system includes: in client call name server
File；To new file designation in the name file of name server；Name server calls distributed storage service automatically
Device, to complete data write-in.
As an improvement of the present invention, the resource management process of system includes: client terminal start-up resource manager；Resource
Manager is communicated with management node, to start the computation process in management node；Computation process passes through inside
Network is to resource manager application resource；Resource manager is issued to management node and is ordered, and management node is made to start corresponding task.
By adopting such a design, the present invention has at least the following advantages:
It is carried out by mass data of the wind power plant cluster big data distributed storage analytical calculation system to wind power plant cluster
Analytical calculation, it was found that influence the principal element of wind power generating set operation, we can become more apparent upon the demand of owner, improve
The satisfaction of owner, we can become more apparent upon the developing direction in wind power generating set future, improve company's production efficiency, life
Yield and quality provides outstanding platform for our wind-powered electricity generation industry big data calculating, which is researched and developed based on linux system, fortune
Row cost is lower, more efficient, more stable.
Detailed description of the invention
The above is merely an overview of the technical solutions of the present invention, in order to better understand the technical means of the present invention, below
In conjunction with attached drawing, the present invention is described in further detail with specific embodiment.
Fig. 1 is the system architecture diagram of wind power plant cluster big data distributed analysis computing system of the present invention.
Fig. 2 is the schematic diagram of data read process in wind power plant cluster big data distributed analysis computing system of the present invention.
Fig. 3 is the schematic diagram of data writing process in wind power plant cluster big data distributed analysis computing system of the present invention.
Fig. 4 is the schematic diagram of wind power plant cluster big data distributed analysis computing system node administration of the present invention.
Specific embodiment
Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings, it should be understood that preferred reality described herein
Apply example only for the purpose of illustrating and explaining the present invention and is not intended to limit the present invention.
Fig. 1 is the structure chart of wind power plant cluster big data distributed analysis computing system of the present invention.Referring to Fig. 1, the system
System includes: a windows server, an industrial grade switch and 26 linux servers.Windows server and quilt
Referred to as client accesses server 1, is deployed with wind power plant client access process thereon.The wind power plant client access process is used
Interaction in the access to wind power plant cluster big data distributed storage analytical calculation system, for man-machine interface.Distributed number
According to storing process major deployments on linux server, for the distributed storage to mass data, the reading of data and write
Enter.Resource management process major deployments are mainly used for the management of system resource, the distribution of task, data on linux server
The tracking of calculating process.
Mounting means, windows server are connected by cable with industrial grade switch 2 with 26 linux servers,
Windows operating system is disposed on windows server system, disposes linux operation system respectively on 26 linux servers
This 27 servers are arranged in same network segment, can communicate between each other by system.Firstly, wind power plant client is accessed
Program is deployed on windows server.Secondly, Distributed Storage process is deployed on linux server.It will wherein
One linux server is set as name server 3, wherein will be set as alternative name server by another linux server
4, wherein will be set as distributed storage server 5 by another platform linux server, and so on by other linux servers
It is disposed as distributed storage server 5.Finally, resource management process is deployed on linux server, it will wherein one point
Cloth storage server 5 is set as corresponding node management, wherein another distributed storage server 5 will be set as corresponding node
Management, and so on set corresponding node management for other distributed storage servers 5.
In terms of working principle, wind farm group client access process is mainly used for human-computer interaction, issuing including order and
The results of running shows, operation control of server cluster etc..Distributed Storage process is mainly used for wind power plant
The storage of group's great mass of data, the write-in and reading of data.Reading process and the writing process difference of data are as shown in Figures 2 and 3.
Distributed Storage process includes 26 nodes, respectively name node, alternative name node and back end.It
Correspond respectively to name server, alternative name server and distributed storage server.The effect of name node is main
It is to be managed to the title for needing the file and file that store, back end is mainly used for the distributed storage of file.Money
Source control process is mainly used for executing the task that client issues, and resource management process mainly includes resource manager and management section
Point.Resource manager is mainly used for the management and distribution of system resource, and task balance is distributed to each management node, system meter
Calculate the calling of program.The main function of management node is mainly responsible for the management of this node system resource, this node calculation procedure
Starting, stops operation.
Distributed Storage process operation process reads process, the name of client call Distributed Storage process
Claim the file in node, the address where file is obtained by name node, name node returns address of node where file
After client, client reads data by reading order since distributed storage server 5, when having read this section
After point, the file of appropriate address is not found, this node can be automatically closed, and system can read next node automatically, until seeking
The address where file is found, after client has read file, system flow is automatically closed.Process, client tune is written
With the file in the name node of Distributed Storage process, new file is ordered in the name file of name node
Name, determine that this title was not applied in this system, otherwise by occur name repeat situation, when client in name node to file
After the completion of name, name node calls distributed storage server 5, that is, back end to write file for client automatically
Enter.As shown in figure 3, the loss of file, this system have prepared four Backup Data nodes in order to prevent, when first back end
After the completion of write-in, the confirmation write-in of node return information is completed, and system automatically writes second node, it is known that completes write-in the 4th
Node, system are automatically performed write-in process.
As shown in figure 4, the operational process of resource management process is as follows: client accesses resource management process, to resource pipe
Reason process sends order, starts the resource manager in resource management process, and resource manager is communicated with management node, opened
Computation process in dynamic management node, the computation process, to resource manager application resource, are somebody's turn to do by internal network
After computation process application to resource, resource manager can be issued to corresponding management node and be ordered, it is desirable that management node opens
Corresponding task is moved, while resource manager coordinates the distribution that each management node carries out task by the quantity and size of task
And execution.During system operation, client can be by the execution state of resource manager monitors task, until task terminates.When
After the completion of computation process calculates, resource manager will end automatically task.
It is calculated by the big data analysis to wind power plant cluster, we can understand the Wind turbines of production from more high angle
Performance, find influence unit performance principal element；It is calculated by the big data analysis to wind power plant cluster, we can be more
Add the demand for understanding owner, provides direction for the research and development of wind power generating set；Company can be helped to optimize production procedure, improved
Efficiency of service and quality；By the analytical calculation to national wind-resources data everywhere, provided for our exploitations of wind-resources in future
Quick reliable platform finds more outstanding wind power plant；It is calculated by the big data analysis to wind power plant cluster, Wo Menke
To provide more good after-sale service for client, the after-sale service satisfaction of customer service is improved；By to the big of wind power plant cluster
Data analytical calculation, we can constantly optimize wind power plant and its auxiliary facility, be built more with lower cost
Outstanding wind power plant.
The above described is only a preferred embodiment of the present invention, be not intended to limit the present invention in any form, this
Field technical staff makes a little simple modification, equivalent variations or modification using the technology contents of the disclosure above, all falls within this hair
In bright protection scope.
Claims (10)
1. a kind of wind power plant cluster big data distributed analysis computing system characterized by comprising
Distributed storage server, for executing distributed storage to the file；
Name server is connect by network with the distributed storage server, the file and text for storing to needs
The title of part is managed；
Client accesses server, is connected by network respectively at the distributed storage server and the name server,
For being managed to the human-computer interaction between client.
2. wind power plant cluster big data distributed analysis computing system according to claim 1, which is characterized in that the people
Machine interaction include: the issuing of order, the results of running show and the operation of cluster control.
3. wind power plant cluster big data distributed analysis computing system according to claim 1, which is characterized in that the name
Server is claimed to be connected to the distributed storage server and client access server by industrial grade switch；
The client access server is connected to the distributed storage server and the title by industrial grade switch
Server.
4. wind power plant cluster big data distributed analysis computing system according to claim 1, which is characterized in that also wrap
It includes:
Backup name server, for being backed up to service provided by the name server.
5. wind power plant cluster big data distributed analysis computing system according to claim 4, which is characterized in that described point
Operation has resource manager on cloth storage server, the name server and the backup name server.
6. wind power plant cluster big data distributed analysis computing system according to claim 1, which is characterized in that described point
Operation has resource manager in cloth storage server and the name server, and the resource manager includes: resource
Manager and management node.
7. wind power plant cluster big data distributed analysis computing system according to claim 6, which is characterized in that the money
Source manager is used for the management and distribution of system resource, and task balance is distributed to each management node and system meter
Calculate the calling of program；
The management node is used for the system resource management of book server, starting, operation, the stopping of book server calculation procedure.
8. wind power plant cluster big data distributed analysis computing system according to claim 1, which is characterized in that the system
The reading data process of system includes: the file in client call name server, obtains file place by name server
Address；Client reads data from distributed storage server by reading order；Current distributed storage server is read
After completion, which is automatically closed, and reads next distributed storage server automatically, until searching out
Address where file.
9. wind power plant cluster big data distributed analysis computing system according to claim 1, which is characterized in that the system
The write-in process of system includes: the file in client call name server；To new in the name file of name server
File designation；Name server calls distributed storage server automatically, to complete data write-in.
10. wind power plant cluster big data distributed analysis computing system according to claim 6, which is characterized in that system
Resource management process include: client terminal start-up resource manager；Resource manager is communicated with management node, to start pipe
Manage the computation process in node；Computation process passes through internal network to resource manager application resource；Resource management
Device is issued to management node and is ordered, and management node is made to start corresponding task.
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| CN201811504651.9A CN109359101A (en) | 2018-12-10 | 2018-12-10 | Wind power plant cluster big data distributed analysis computing system |
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| CN201811504651.9A CN109359101A (en) | 2018-12-10 | 2018-12-10 | Wind power plant cluster big data distributed analysis computing system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109725592A (en) * | 2019-03-13 | 2019-05-07 | 国电联合动力技术有限公司 | Wind power plant wisdom analog acquisition test macro |
| CN112579531A (en) * | 2020-12-15 | 2021-03-30 | 东方电气风电有限公司 | Method for downloading and storing wind power plant data |
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| CN107330056A (en) * | 2017-06-29 | 2017-11-07 | 河北工业大学 | Wind power plant SCADA system and its operation method based on big data cloud computing platform |
| CN107547653A (en) * | 2017-09-11 | 2018-01-05 | 华北水利水电大学 | A kind of distributed file storage system |
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- 2018-12-10 CN CN201811504651.9A patent/CN109359101A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| US20120158882A1 (en) * | 2010-12-17 | 2012-06-21 | International Business Machines Corporation | Highly scalable and distributed data sharing and storage |
| CN107330056A (en) * | 2017-06-29 | 2017-11-07 | 河北工业大学 | Wind power plant SCADA system and its operation method based on big data cloud computing platform |
| CN107547653A (en) * | 2017-09-11 | 2018-01-05 | 华北水利水电大学 | A kind of distributed file storage system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109725592A (en) * | 2019-03-13 | 2019-05-07 | 国电联合动力技术有限公司 | Wind power plant wisdom analog acquisition test macro |
| CN112579531A (en) * | 2020-12-15 | 2021-03-30 | 东方电气风电有限公司 | Method for downloading and storing wind power plant data |
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