CN107330056B - Wind power plant SCADA system based on big data cloud computing platform and operation method thereof - Google Patents
Wind power plant SCADA system based on big data cloud computing platform and operation method thereof Download PDFInfo
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Abstract
The invention discloses a wind power plant SCADA system based on a big data cloud computing platform and an operation method thereof. The system comprises a wind power plant side, a cloud computing platform and a centralized control center side; the wind power plant side and the centralized control center side are connected with the cloud computing platform through the internet; the wind power plant side comprises a fan, a booster station, a box transformer substation, a wind measuring tower, an electric meter, AGC (automatic gain control), AVC (automatic voltage control), a protection substation, a fault wave recording device, a telecontrol device, a front server, a firewall and a switch; the cloud computing platform comprises a big data server, an advanced application server and a switch; the centralized control center side comprises a network printer, a fan centralized control workstation, a booster station monitoring workstation, a report form, an alarm workstation and a maintenance workstation. The method adopts a Hadoop + MapReduce distributed data processing technology, so that the data storage space is greatly improved, the cost of data storage is reduced to the minimum, and the data processing speed is improved qualitatively.
Description
Technical Field
The invention relates to the field of wind power, in particular to a wind power plant SCADA system based on a big data cloud computing platform and an operation method thereof.
Background
Wind energy has attracted increasing attention from countries around the world as a clean renewable energy source, and wind power generation technology has substantially matured. Dozens or even hundreds of wind turbines exist in a large wind farm, and how to effectively monitor the states of the wind turbines makes the safe, reliable and economic operation of the wind turbines in the whole wind farm become important. The approach for solving the above problems is to establish a Supervisory control and Data Acquisition (SCADA) system of the wind power plant, so as to realize wind turbine monitoring, information sharing, fault diagnosis and maintenance of the whole system of the wind power plant. The appearance of the SCADA system changes the operation and maintenance face of a wind power plant, creates an unattended, unattended and regional management wind power plant operation and maintenance mode, establishes a remote centralized control center of the wind power plant, greatly improves the severe working environment of workers, absorbs more high-tech talents, adds to the wind power industry, enhances the market competitiveness of the wind power company, and greatly promotes the further development of the wind power industry.
Although the appearance of the SCADA system brings the change of the coverage of the sky to the operation and maintenance of the wind power plant, the historical library must form complex and heterogeneous wind power big data in view of the characteristics of large data acquisition amount and high acquisition frequency of a data acquisition layer at the bottom of the wind power plant, and the traditional SCADA system initially faces a series of problems of computer CPU upgrading, insufficient memory, computer hardware expansion, cost increase and the like. The traditional data processing mode is difficult to rapidly process massive wind power plant big data. Application number 201310471096.5 discloses a wind farm centralized monitoring system platform, which comprises a real-time system, a data acquisition subsystem and a MIS/DMIS system; the real-time system comprises a data server group, an SCADA server, a communication server, an electronic duty, a remote maintenance and a workstation; the data acquisition subsystem comprises a data server and a data acquisition server; the MIS/DMIS system comprises an MIS/DMIS server, an MIS/DMIS workstation and an MIS workstation; the whole system is of a dual-network structure, the database server, the SCADA server and the data acquisition server are of dual-machine redundancy configuration, and a dual-channel mode that one Ethernet is used as a main channel and the other Ethernet is used as a standby channel is adopted for channels facing each wind power plant. However, the system adopts the traditional data acquisition and transmission method, has high requirements on hardware equipment, is complex and has high cost, but has low data acquisition and transmission rate and safety. The system has the more outstanding problems that the system only has a basic wind power plant monitoring function, the problems of low data processing speed, small quantity, insufficient data mining capability and the like in data processing are solved, the large data of the wind power plant is a resource waste, the system is restricted from developing more advanced applications, and the intelligentization of the wind power plant monitoring system is not facilitated.
Disclosure of Invention
Aiming at the defects in the prior art, the technical problem to be solved by the invention is to provide a wind power plant SCADA system based on a big data cloud computing platform and an operation method thereof. According to the system, a traditional wind power plant SCADA system is combined with a cloud computing platform, the requirements of the system on hardware equipment are reduced by virtue of the advantages of flexible and changeable structure, data parallel processing and low cost of a big data cloud computing platform, the effect of wind power big data is maximized, and the data processing speed is greatly increased on the premise of reducing the system cost by adopting a MapReduced standard K-Means algorithm in the data processing process.
The technical scheme for solving the technical problem of the system is that the wind power plant SCADA system based on the big data cloud computing platform is provided, and the wind power plant SCADA system is characterized by comprising a wind power plant side, a cloud computing platform and a centralized control center side; the wind power plant side and the centralized control center side are connected with the cloud computing platform through the internet;
the wind power plant side comprises a fan, a booster station, a box transformer substation, a wind measuring tower, an electric meter, AGC (automatic gain control), AVC (automatic voltage control), a protection substation, a fault wave recording device, a telecontrol device, a front server, a firewall and a switch; the fan is connected with the front-end server through the Ethernet; the booster station, the box transformer substation, the anemometer tower, the electric meter, the AGC, the AVC, the information protection substation and the fault recording are respectively connected with the telemechanical device through the Ethernet; the telecontrol device is connected with the front-end server through the Ethernet, the front-end server is connected with the firewall, and the firewall is connected with the switch;
the cloud computing platform comprises a big data server, an advanced application server and a switch; the big data server, the advanced application server and the switch are all connected with each other through Ethernet;
the centralized control center side comprises a network printer, a fan centralized control workstation, a booster station monitoring workstation, a report form, an alarm workstation and a maintenance workstation; the network printer, the fan centralized control workstation, the booster station monitoring workstation, the report form and the alarm workstation are all connected with the maintenance workstation through Ethernet.
The technical scheme for solving the technical problem of the operation method is to provide an operation method of a wind power plant SCADA system based on a big data cloud computing platform, and the operation method is characterized by comprising the following steps of:
(1) data acquisition and transmission: the data of the fan is directly transmitted to the front-end server, and the data of the booster station, the box transformer substation, the anemometer tower, the watt-hour meter, the AGC, the AVC, the information protection substation and the fault recording are converted into standard protocol data packets through protocol conversion of the telecontrol device and then transmitted to the front-end server through the Ethernet; after the data on the wind power plant side are collected by the front-end server, the data are encrypted through a firewall and uploaded to the cloud computing platform through a user access interface by the switch;
(2) data storage: after data are collected and transmitted to a cloud computing platform, the cloud computing platform firstly stores wind power plant big data, an SC in a storage framework is a storage controller deployed on a big data server and connected with a built virtual server VM, the number of VMs is variable according to requirements, each VM is associated with a storage volume to expand and store, data are shared among the VMs, and a big data storage area is accessed together; the VM is connected with a cluster controller CC, and finally connected with an HBase database which is established in an HDFS file based on a Hadoop basic framework;
(3) data processing: processing data in the HBase database by adopting a MapReduce standard K-Means algorithm in a cloud computing platform; a Map/Reduce program in the MapReduce standard programming model is divided into a Map function and a Reduce function which are sequentially executed by a Hive data warehouse tool in a Hadoop platform, an initial key value pair generates a group of intermediate key value pairs serving as a bridge through the Map function, and the intermediate key value pairs can be transmitted to the Reduce function only if the intermediate key value pairs have the same key value; the Reduce function is used for receiving one key value and a group of related key values, and combining the key values to form a smaller group of key values; the input mass data are stored in a distributed file system (HDFS), a program adopts a migration operation mode, Map/Reduce tasks are downloaded to divided data nodes to be executed in parallel, the final result of data processing is still stored in the HDFS, and a centralized control center side receives data processed by a cloud computing platform through a user access interface.
Compared with the prior art, the invention has the beneficial effects that: by means of the advantages of the existing Internet cloud computing platform, a standard data processing method is combined with the cloud computing platform, so that the data processing speed is greatly increased, more advanced applications are conveniently developed by the system, and the wind power plant SCADA system is intelligentized. The cloud computing platform adopts a Hadoop + MapReduce distributed data processing technology, and compared with the traditional data processing mode in the wind power industry, the cloud computing platform is based on a more flexible and changeable open-source framework, can change components at any time according to system function requirements, supports horizontal expansion, has internet attributes, and is more open and safe. The whole cloud computing platform can complete a complete task, including data storage and processing, storage devices such as a disk array in a traditional SCADA system are not needed, the space for data storage is greatly increased, the cost for storing data is reduced to the minimum, and meanwhile the processing speed of the data is improved qualitatively.
Drawings
FIG. 1 is an overall connection block diagram of an embodiment of a wind power plant SCADA system based on a big data cloud computing platform and an operation method thereof;
FIG. 2 is a cloud computing platform storage architecture diagram of an embodiment of a wind farm SCADA system based on a big data cloud computing platform and an operation method thereof;
FIG. 3 is a system operation flow chart of an embodiment of a wind power plant SCADA system based on a big data cloud computing platform and an operation method thereof;
FIG. 4 is a schematic time consumption diagram of different data volumes of a standard K-Means algorithm and a MapReduced K-Means algorithm in embodiment 1 of the wind power plant SCADA system based on the big data cloud computing platform and the operation method thereof;
Detailed Description
Specific examples of the present invention are given below. The specific examples are only intended to illustrate the invention in further detail and do not limit the scope of protection of the claims of the present application.
The invention provides a wind power plant SCADA system (see figures 1-4, system for short) based on a big data cloud computing platform, which is characterized by comprising a wind power plant side 1, a cloud computing platform 2 and a centralized control center side 3; the wind power plant side 1 and the centralized control center side 3 are both connected with the cloud computing platform 2 through user access interfaces of the internet;
the wind power plant side 1 comprises a fan 11, a booster station 12, a box transformer substation 13, a wind measuring tower 14, an electric meter 15, AGC16, AVC17, a letter protection substation 18, a fault recording station 19, a telecontrol device 110, a front server 111, a firewall 112 and a switch 113; the fan 11 is connected with the front server 111 through the ethernet, and directly transmits data of the fan 11 to the front server 111; the booster station 12, the box transformer substation 13, the anemometer tower 14, the watt-hour meter 15, the AGC16, the AVC17, the information protection substation 18 and the fault recording 19 are respectively connected with the telecontrol device 110 through Ethernet, and data of the booster station 12, the box transformer substation 13, the anemometer tower 14, the watt-hour meter 15, the AGC16, the AVC17, the information protection substation 18 and the fault recording 19 are converted into standard 104 protocol data packets through protocol conversion of the telecontrol device 110; the telecontrol device 110 is connected with a front-end server 111 through Ethernet, the front-end server 111 is connected with a firewall 112, and the firewall 112 is connected with a switch 113; after the data of the wind farm side 1 is collected by the front-end server 111, the data is encrypted through the firewall 112, and the data is uploaded to the cloud computing platform 2 through the user access interface of the internet by the switch 113.
The fan 11 refers to an actual fan in the wind farm; the booster station 12 is used for boosting the electricity generated by the wind farm in order to reduce the line current and thereby reduce the loss of electric energy; the box transformer substation 13 is a high-voltage switchgear, a distribution transformer and a low-voltage distribution device, and is mainly used for changing voltage; the anemometer tower 14 is used for observing and recording the airflow motion condition of the wind power plant; the AGC16 controls the output of the frequency modulation unit to meet the power demand of the constantly changing users; AVC17 can perform online voltage reactive power optimization control, guarantee electric energy quality, improve transmission efficiency and reduce network loss; the information protection substation 18 uploads telemechanical information, protection information and graph model information of the wind power plant; the fault recording 19 can automatically and accurately record the change conditions of various electrical quantities before and after a fault when the system has a fault, analyze and compare the electrical quantities through original waveforms, analyze and process the fault, judge whether protection acts correctly or not, and improve the safe operation level of the power system; the telecontrol device 110 is used for collecting and transmitting data of wind power plant equipment, and the model of the telecontrol device 110 is PCS-9799; the front server 111 is used for displaying real-time data, channel states, communication messages and the like received by the wind power plant; firewall 112 refers to a method of separating an intranet (e.g., ethernet) from a public access network (e.g., the internet), which is effectively a security isolation technique; the switch 113 completes the exchange of information mainly in the internet.
The cloud computing platform 2 comprises a big data server 21, an advanced application server 22 and a switch 23; the big data server 21, the advanced application server 22 and the switch 23 are all connected with each other through Ethernet; the switch 23 is used for information exchange between the internet and the cloud computing platform 2; the big data server 21 is used for storing and processing wind power plant data, and providing functions of query, update, transaction management, indexing, cache, query optimization, security, multi-user access control and the like; the advanced application server 22 develops more advanced applications for the intelligent development of wind farms, such as wind power prediction, wind vibration monitoring, WEB release, equipment predictive maintenance, wind accident predictive alarm, and so on.
The centralized control center side 3 comprises a network printer 31, a fan centralized control workstation 32, a booster station monitoring workstation 33, a report and alarm workstation 34 and a maintenance workstation 35; the network printer 31, the fan centralized control workstation 32, the booster station monitoring workstation 33, the report and alarm workstation 34 and the maintenance workstation 35 are all connected with each other through Ethernet; the network printer 31 is a network node and an output terminal which are parallel to the network, and the printer is used as an independent device to access a local area network or the internet through a print server; the fan monitoring workstation 32 is used for monitoring the real-time operation condition of the fans in the wind power plant; the booster station monitoring work station 33 is used for monitoring the real-time operation condition of the booster station of the wind power plant; the report and alarm workstation 34 provides a customized data display function, provides report generation, printing and reporting functions, is in seamless connection with a report system of a superior unit, and simultaneously performs sound-light alarm on faults occurring at any time, and alarm content is displayed at the forefront end, so that operating personnel can conveniently check the alarm content; the maintenance workstation 35 is used for timely maintenance of faults of the wind power plant equipment by workers, so that accidents are reduced, and safe operation of the wind power plant is guaranteed.
The cloud computing platform 2 adopts a Hadoop + MapReduce distributed data processing technology, compared with the traditional data processing mode in the wind power industry, the cloud computing platform is based on a more flexible and changeable open-source framework, can change components at any time according to system function requirements, supports horizontal expansion, has internet attributes, and is more open and safe. The whole cloud computing platform can complete a complete task, including data storage and processing, storage devices such as a disk array in a traditional SCADA system are not needed, the space for data storage is greatly increased, the cost for storing data is reduced to the minimum, and meanwhile the processing speed of the data is improved qualitatively.
Hadoop is a basic framework matched with a cloud computing platform and supports various data algorithms including data sorting, query, graph analysis, cluster analysis, statistical analysis, optimization, data mining, scheduling and the like. The wind power plant cloud computing platform takes wind power big data as input, processes given data under the rule of a given algorithm, and calculates a final result.
Hadoop is a typical distributed parallel operation architecture, is seamlessly butted with an algorithm for processing mass data in parallel by a cloud computing platform, and has a simple working principle that input mass data is divided into different areas, so that the data volume of each area is greatly reduced, an original total large task is divided into a plurality of small tasks, each small task processes partition data corresponding to the small task, and each small task is executed in parallel. The HBase adopts a column storage mode, and can conveniently provide physically adjacent storage units for data in a database, so that mass data can be quickly read and stored, the requirement of building large-scale structured storage on hardware is greatly reduced by adopting the HBase technology, and the requirement can be met by a simple PC server. Hive is a data warehouse tool of the Hadoop platform, structured data files in the cloud computing platform can be mapped into database tables by the Hive tool, and sql sentences can be converted into MapReduce tasks to be executed step by the Hive tool. The MapReduce has the characteristics of simplicity, easiness in understanding, flexibility, changeability and high fault tolerance, and is a parallel processing standard programming model which can be applied to various big data processing algorithms.
An operation method of a wind power plant SCADA system based on a big data cloud computing platform is characterized by comprising the following steps:
(1) data acquisition and transmission: data of the fan 11 are directly transmitted to the front-end server 111, data of the booster station 12, the box transformer substation 13, the anemometer tower 14, the electric meter 15, the AGC16, the AVC17, the information protection substation 18 and the fault recording 19 are converted into standard 104 protocol data packets through protocol conversion of the telecontrol device 110, and then transmitted to the front-end server 111 through the Ethernet; the acquired variables include five types, specifically, telemetering, teletransmission, telemodulation, and electrometric, and in order to ensure data accuracy, the acquisition frequency of the telemechanical device 110 should not be less than 0.2. After the data of the wind farm side 1 is collected by the front-end server 111, the data is encrypted through the firewall 112, and the data is uploaded to the cloud computing platform 2 through the user access interface by the switch 113. If the condition of network connection interruption is met during data acquisition and transmission, the data acquisition cannot be influenced, because the interface program can repeatedly detect the network connection state, the data in the network interruption time period cannot be lost, only a temporary cache file can be formed, and once the network connection is recovered, the data can be normally transmitted immediately.
(2) Data storage: after the data is acquired and transmitted to the cloud computing platform 2, the cloud computing platform 2 firstly stores the wind farm big data, and combines the wind farm big data and the characteristics of the cloud computing platform 2 to adopt the storage architecture as shown in fig. 2. In the storage architecture, an SC is a storage controller deployed on a big data server 21 and connected with a built virtual server VM, the number of VMs is variable according to needs, each VM is associated with a memory volume to expand storage, data sharing is performed among the VMs, and a big data storage area is accessed together; the VM is connected with a cluster controller CC, and finally connected with an HBase database which is established in an HDFS file based on a Hadoop basic framework;
(3) data processing: processing data in the HBase database by adopting a MapReduce standard K-Means algorithm in the cloud computing platform 2; a Map/Reduce program in the MapReduce standard programming model is divided into a Map function and a Reduce function which are sequentially executed by a Hive data warehouse tool in a Hadoop platform, an initial key value pair generates a group of intermediate key value pairs serving as a bridge through the Map function, and the intermediate key value pairs can be transmitted to the Reduce function only if the intermediate key value pairs have the same key value; the Reduce function is used for receiving one key value and a group of related key values, and combining the key values to form a smaller group of key values; the input mass data is stored in a distributed file system (HDFS), a program adopts a migration operation mode, Map/Reduce tasks are downloaded to divided data nodes to be executed in parallel, the final result of data processing is still stored in the HDFS, and a centralized control center side 3 receives data processed by a cloud computing platform 2 through a user access interface.
The K-Means cluster analysis algorithm is a classical data processing algorithm, wherein K is used as a parameter, N data tuples in one data set are divided into K subsets, the basic requirement of the division is that the similarity of the data tuples in each subset is as high as possible, but the similarity of the data tuples among different subsets is as low as possible, and the judgment standard of the similarity is the average value of objects in the subsets. The standard K-Means algorithm is performed as follows:
(1) selecting k initial cluster centers, such as cp [0] ═ D [0], cp [ k-1] ═ D [ k-1., where D is the transactional dataset, cp in general, the selection of initial centers is random;
(2) for D [0]...D[n]Respectively calculate cp [0] corresponding thereto]...cp[k-1]Distance, the closest is denoted c [ i ]]，c[i]Total number of (2) is denoted as Ci；
(3) For all c [ i ] s of step 2]Calculating a new cluster center cp [ i ]]＝(∑c[i]Corresponding D [ j ]])/Ci；
(4) And (5) repeatedly executing the steps (2) and (3) until the distance between the data tuple in the D [ i ] and the current c [ i ] is smaller than a given threshold value or each cluster is not changed any more, finishing the algorithm execution and obtaining k clusters.
In the execution process of the standard K-Means algorithm, the distance between D [0] and cp [0]. cp [ K-1] can be calculated, and meanwhile, the distance between D [1] and cp [0]. cp [ K-1] can be calculated, and the process is consistent with the framework of distributed parallel operation of a cloud computing platform. The standard K-Means algorithm MapReduce is implemented as follows:
(1) randomly selecting k initial clustering centers, such as cp [0] ═ D [0], cp [ k-1] ═ D [ k-1]. at the same time, copying the initial clustering centers into an initial clustering module OriginalCluster [ ] and partitioning the initial clustering module OriginalCluster [ ], and according to the condition of the computing node clusters, allocating the initial clustering module OriginalCluster [ ] to each computing node;
(2) map for D [0]]...D[n]Separately calculate it and cp [0]]...cp[n-1]The distance of (d) is c [ i ] when the distance is the closest]，c[ic]Total number of (2) is denoted as CiSimultaneously, under the MapReduce framework, the Key and Value of the Key Value pair Key-Value are respectively corresponding to i and D [ k ]]；
(3) Reduce since i is MapReduceKey-value pairs in the frame, which guarantees all D [ k ] s of the same Key]Will be assigned to the same Reduce process, and the Reduce process can calculate a new cluster center cp [ i [ [ i ])]＝(∑c[i]Corresponding D [ j ]]/Ci) And storing the new clustering center in the final clustering module DestinationCluster]Performing the following steps;
(4) comparing the final clustering module DestinationCluster [ ] with the initial clustering module OriginalCluster [ ], if the change of the two modules is less than a preset threshold value, finishing clustering, otherwise copying the final clustering module DestinationCluster [ ] to the initial clustering module OriginalCluster [ ] and then jumping to the step 2 for continuous execution; and the centralized control center side 3 receives the data processed by the cloud computing platform 2 through a user access interface.
The MapReduce of the K-Means algorithm only needs to strip part of the algorithm for Map and Reduce, construct key value pairs, and complete other tasks of communication, monitoring, scheduling and the like to a MapReduce framework based on a Hadoop platform.
By utilizing the advantages of a cloud computing platform, the data processing speed is obviously accelerated after the standard K-Means algorithm MapReduce is implemented, and the speed advantage is more obvious when the data set scale is larger, so that a foundation is laid for the development of more advanced applications such as wind power prediction, fan vibration monitoring, WEB release, equipment prediction and maintenance, wind power accident prediction and alarm and the like, and the future intelligent management of a wind power plant is facilitated.
Example 1
Selecting a data set suitable for cluster analysis, such as voltage, current, frequency, voltage phase angle difference, current phase angle difference and the like in a telemetering reference table of a certain wind power plant booster station, calling 4500 ten thousand records stored in an SCADA system history server as experimental data, dividing the experimental data into 100 ten thousand, 200 ten thousand, 500 ten thousand, 1000 ten thousand, 1800 ten thousand, 3000 ten thousand and 4500 ten thousand groups, consuming time of different data volumes (shown in table 1), and simulating results (shown in fig. 4).
TABLE 1 comparison of time consumption of two algorithms with different data volumes
Compared with the standard K-Means algorithm, the speed of data processing of the K-Means algorithm combined with the MapReduce of the cloud computing platform is obviously increased, the speed advantage is more obvious when the data set is large in scale, the feasibility and the effectiveness of the system are verified, and a foundation is laid for future intelligentization of a wind power plant.
Nothing in this specification is said to apply to the prior art.
Claims (4)
1. A wind power plant SCADA system based on a big data cloud computing platform is characterized by comprising a wind power plant side, a cloud computing platform and a centralized control center side; the wind power plant side and the centralized control center side are connected with the cloud computing platform through the internet;
the wind power plant side comprises a fan, a booster station, a box transformer substation, a wind measuring tower, an electric meter, AGC (automatic gain control), AVC (automatic voltage control), a protection substation, a fault wave recording device, a telecontrol device, a front server, a firewall and a switch; the fan is connected with the front-end server through the Ethernet; the fan refers to an actual fan in the wind power plant; the booster station, the box transformer substation, the anemometer tower, the electric meter, the AGC, the AVC, the information protection substation and the fault recording are respectively connected with the telemechanical device through the Ethernet; the booster station is used for boosting the electricity generated by the wind power plant, and aims to reduce the line current so as to reduce the loss of electric energy; the box transformer is a high-voltage switch device, a distribution transformer and a low-voltage distribution device, and is mainly used for changing voltage; the anemometer tower is used for observing and recording the airflow motion condition of the wind power plant; the AGC controls the output of the frequency modulation unit to meet the power demand of a constantly changing user; AVC can perform online voltage reactive power optimization control, guarantee the electric energy quality, improve the transmission efficiency and reduce the network loss; the information protection substation uploads telemechanical information, protection information and graph model information of the wind power plant; the fault recording can automatically and accurately record the change conditions of various electrical quantities in the processes before and after the fault when the system has the fault, analyze and compare the electrical quantities through the original waveform, analyze and process the fault, judge whether the protection acts correctly or not and improve the safe operation level of the power system; the telecontrol device is connected with the front-end server through the Ethernet, the front-end server is connected with the firewall, and the firewall is connected with the switch; the telemechanical device is used for acquiring and forwarding data of the wind power plant equipment; the front-end server is used for displaying real-time data, channel states and communication messages received by the wind power plant; a firewall refers to a method of separating an intranet from a public access network; the exchanger completes the information exchange in the internet;
the cloud computing platform comprises a big data server, an advanced application server and a switch; the big data server, the advanced application server and the switch are all connected with each other through Ethernet; the switch is used for information exchange between the Internet and the cloud computing platform; the big data server is used for storing and processing wind power plant data and providing functions of query, update, transaction management, indexing, cache, query optimization, safety and multi-user access control; the advanced application server develops advanced applications of wind power prediction, fan vibration monitoring, WEB release, equipment prediction and maintenance and wind power accident prediction and alarm for the intelligent development of the wind power plant;
the centralized control center side comprises a network printer, a fan centralized control workstation, a booster station monitoring workstation, a report form, an alarm workstation and a maintenance workstation; the network printer, the fan centralized control workstation, the booster station monitoring workstation, the report and alarm workstation and the maintenance workstation are all connected with each other through Ethernet; the network printer is connected to a local area network or the Internet as an independent device through a print server, and is a network node and an output terminal which are parallel to the network; the fan monitoring workstation is used for monitoring the real-time operation condition of a fan in the wind power plant; the booster station monitoring workstation is used for monitoring the real-time operation condition of the booster station of the wind power plant; the report and alarm workstation provides a customized data display function, provides report generation, printing and reporting functions, is in seamless connection with a report system of a superior unit, and performs sound-light alarm on faults occurring at any time, alarm content is displayed at the forefront end, and operating personnel can conveniently check the alarm content; the maintenance workstation is used for timely maintaining the faults of the wind power plant equipment by workers, so that accidents are reduced, and the safe operation of the wind power plant is ensured;
the operation of the wind power plant SCADA system based on the big data cloud computing platform comprises the following steps:
(1) data acquisition and transmission: the data of the fan is directly transmitted to the front-end server, and the data of the booster station, the box transformer substation, the anemometer tower, the watt-hour meter, the AGC, the AVC, the information protection substation and the fault recording are converted into standard protocol data packets through protocol conversion of the telecontrol device and then transmitted to the front-end server through the Ethernet; after the data on the wind power plant side are collected by the front-end server, the data are encrypted through a firewall and uploaded to the cloud computing platform through a user access interface by the switch; if the network connection is interrupted during the data acquisition and transmission, the data acquisition is not influenced, because the interface program can repeatedly detect the network connection state, the data in the network interruption time period can not be lost, only a temporary cache file can be formed, and once the network connection is recovered, the data can be normally transmitted immediately;
(2) data storage: after data are collected and transmitted to a cloud computing platform, the cloud computing platform firstly stores wind power plant big data, an SC in a storage framework is a storage controller deployed on a big data server and connected with a built virtual server VM, the number of VMs is variable according to requirements, each VM is associated with a storage volume to expand and store, data are shared among the VMs, and a big data storage area is accessed together; the VM is connected with a cluster controller CC, and finally connected with an HBase database which is established in an HDFS file based on a Hadoop basic framework;
(3) data processing: processing data in the HBase database by adopting a MapReduce standard K-Means algorithm in a cloud computing platform; a Map/Reduce program in the MapReduce standard programming model is divided into a Map function and a Reduce function which are sequentially executed by a Hive data warehouse tool in a Hadoop platform, an initial key value pair generates a group of intermediate key value pairs serving as a bridge through the Map function, and the intermediate key value pairs can be transmitted to the Reduce function only if the intermediate key value pairs have the same key value; the Reduce function is used for receiving one key value and a group of related key values, and combining the key values to form a smaller group of key values; the input mass data are stored in a distributed file system (HDFS), a program adopts a migration operation mode, Map/Reduce tasks are downloaded to divided data nodes to be executed in parallel, the final result of data processing is still stored in the HDFS, and a centralized control center side receives data processed by a cloud computing platform through a user access interface.
2. A big data cloud computing platform based wind farm SCADA system according to claim 1, characterized in that the model number of the telemechanical device is PCS-9799.
3. A wind farm SCADA system based on big data cloud computing platform according to claim 1 characterized in that the collection frequency of telemechanical device is not less than 0.2.
4. A wind farm SCADA system based on a big data cloud computing platform according to claim 1, characterized in that the specific steps of the data processing of the 3 rd step are as follows:
(1) randomly selecting k initial clustering centers, such as cp [0] ═ D [0], cp [ k-1] ═ D [ k-1]. at the same time, copying the initial clustering centers into an initial clustering module OriginalCluster [ ] and partitioning the initial clustering module OriginalCluster [ ], and according to the condition of the computing node clusters, allocating the initial clustering module OriginalCluster [ ] to each computing node; d is a transaction data set;
(2) map for D [0]]...D[n]Separately calculate it and cp [0]]...cp[n-1]The distance of (d) is c [ i ] when the distance is the closest]，c[i]Total number of (2) is denoted as CiSimultaneously, under the MapReduce framework, the Key and Value of the Key Value pair Key-Value are respectively corresponding to i and D [ k ]]；
(3) Reduce, i is Key-value pair Key in MapReduce frame, which ensures all D [ k ] of the same Key]Will be assigned to the same Reduce process, and the Reduce process can calculate a new cluster center cp [ i [ [ i ])]＝(∑c[i]Corresponding D [ j ]])/CiAnd storing the new clustering center in the final clustering module DestinationCluster]Performing the following steps;
(4) comparing the final clustering module DestinationCluster [ ] with the initial clustering module OriginalCluster [ ], if the change of the two modules is less than a preset threshold value, finishing clustering, otherwise copying the final clustering module DestinationCluster [ ] to the initial clustering module OriginalCluster [ ] and then jumping to the step 2 for continuous execution; and the centralized control center side receives the data processed by the cloud computing platform through the user access interface.
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