CN113837466A - Localized intelligent power generation power prediction system based on multiple meteorological sources - Google Patents
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Abstract
The invention relates to the technical field of new energy, and discloses a localized intelligent power generation power prediction system based on multiple meteorological sources. The localized intelligent power generation power prediction system based on multiple meteorological sources adopts multiple prediction algorithms such as multiple neural networks, machine learning, a time sequence method and a vector method, takes active power data, power generation equipment historical data, historical wind and light measurement data meteorological data and future 0-72h numerical weather forecast data and the like which are collected by a power station end in real time as input, calculates multiple short-term prediction power data through the multiple prediction algorithms, calculates, analyzes and compares the accuracy precision of the multiple short-term prediction data through an intelligent statistical analysis algorithm, selects a result with higher prediction precision, and switches power prediction data sources in time, so that the effect of continuously improving the prediction precision is achieved.
Description
Technical Field
The invention relates to the technical field of new energy, in particular to a localized intelligent power generation power prediction system based on multiple meteorological sources.
Background
In the current power prediction system of the new energy power station, short-term prediction algorithms are deployed on cloud servers of respective prediction manufacturers, and comprise numerical weather and power prediction algorithms for use prediction and the like.
Because the short-term prediction needs to modify the model regularly, the data of the power station end needs to be collected regularly, the collection period is long, and human participation is needed, for example, if the data cannot be collected regularly due to objective reasons, the power prediction precision of the power station end can be affected adversely.
Under the background, the requirement of real-time performance and effectiveness of prediction model repairing data is considered, the development of an artificial intelligence technology is combined, software, hardware, an algorithm and real-time data services are effectively combined, and more stable, efficient and intelligent prediction services are provided for customers through local deployment of a new energy power station end, so that a system capable of improving the accuracy, timeliness and intelligence of a power prediction system and reducing prediction and assessment deviation is needed.
Disclosure of Invention
The invention aims to provide a localized intelligent power generation prediction system based on multiple meteorological sources, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a localized intelligent power generation power prediction system based on multiple meteorological sources is provided to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a localized intelligent power generation power prediction system based on multiple meteorological sources comprises a prediction cloud platform, a real-time anemometry tower and a wind farm, wherein a transmitting end of the prediction cloud platform is in signal connection with a receiving end of a weather forecast numerical module, a transmitting end of the real-time anemometry tower is in signal connection with a receiving end of a real-time anemometry tower data module, transmitting ends of the weather forecast numerical module and the real-time anemometry tower data module are in signal connection with a receiving end of a server user III, the transmitting end of the server user III is in signal connection with a receiving end of a firewall module, the transmitting end of the firewall module is in signal connection with a receiving end of a reverse isolation device module, the transmitting end of the reverse isolation device module is in signal connection with a meteorological server module receiving end, a high-performance algorithm server receiving end, a database server module receiving end, a user workstation module receiving end and a WEB server receiving end, and the transmitting end, the high-performance algorithm server transmitting end, the real-time anemometry tower and the wind farm are all connected with a signal connection with a weather server, The system comprises a database server module transmitting end, a user workstation module transmitting end and a WEB server transmitting end, wherein the database server module transmitting end, the user workstation module transmitting end and the WEB server transmitting end are all in signal connection with a local area network receiving end, the wind power plant transmitting end is in signal connection with a fan monitoring system module, the fan monitoring system module transmitting end is in signal connection with the local area network receiving end, and the local area network transmitting end is in signal connection with a dispatching center receiving end.
Preferably, the local area network receiving end is in signal connection with the transmitting end of the security equipment between the field station and the centralized control room, so that the security equipment between the field station and the centralized control room can send data to the local area network.
Preferably, the dispatching center transmitting terminal is in signal connection with the distributed database cluster storage module receiving terminal, and can upload the dispatching center data into the distributed database cluster storage module.
Preferably, the performance of the server of the dispatching center at least meets the requirements of 64G memory, 2TGb hard disk storage and CPU processor with more than 8 cores, and the dispatching center can run the hardware with the lowest requirement.
Preferably, the fan monitoring system module needs the firewall module to determine when sending data to the local area network, and safety protection can be performed through the firewall module.
Preferably, the transmitting end of the distributed database cluster storage module is in signal connection with a receiving end of a data encryption module, the transmitting end of the data encryption module is in signal connection with a receiving end of a cloud data storage library module, and the cloud data storage library module is a hundred-degree cloud network disk or a micro cloud.
Preferably, the receiving end of the security equipment in the station and the centralized control room is in signal connection with a transmitting end of a security machine room system module, a transmitting end of a building intercom system module, a transmitting end of a closed-circuit monitoring system module and a transmitting end of an anti-theft alarm system module.
Compared with the prior art, the invention provides a localized intelligent power generation power prediction system based on multiple meteorological sources, which has the following beneficial effects:
1. the localized intelligent power generation power prediction system based on multiple meteorological sources adopts multiple prediction algorithms such as multiple neural networks, machine learning, a time sequence method and a vector method, takes active power data, power generation equipment historical data, historical wind and light measurement data meteorological data and future 0-72h numerical weather forecast data and the like which are collected by a power station end in real time as input, obtains multiple short-term predicted power data through multiple prediction algorithms, calculates, analyzes and compares the accuracy precision of the multiple short-term predicted data through an intelligent statistical analysis algorithm, selects a result with higher prediction precision, and switches power prediction data sources in time, so that the effect of continuously improving the prediction precision is achieved.
2. According to the localized intelligent power generation prediction system based on multiple meteorological sources, high-quality numerical meteorological source selection is a key means for improving prediction accuracy, for each power station project, different meteorological sources are selected, prediction results are greatly different, meanwhile, different prediction algorithms can be selected, each algorithm can be dynamically combined with different meteorological sources, therefore, when it cannot be determined which algorithm is more suitable for the actual environment of a certain station, each algorithm and multiple meteorological sources need to be calculated and operated simultaneously, data are accumulated continuously, the algorithm is more advantageous to be judged by data accumulation for a period of time, meanwhile, the results of different algorithms and meteorological sources need to be dynamically used as final prediction report data according to seasonal changes, and therefore the effects of improving prediction accuracy and reducing power station assessment are achieved.
3. The localized intelligent power generation power prediction system based on the multiple meteorological sources combines intelligent model repair and manual model repair, model revision is required to be carried out regularly for short-term power prediction, the former mode is that manual model repair is carried out after data acquisition, after localized deployment, through precision comparison of various short-term power data in a historical period of time, which algorithm is more accurate can be found at the first time, meanwhile, through an artificial intelligence technology, regular performance analysis is carried out on a prediction curve, power generation equipment and the like in the historical period of time, fine adjustment can be carried out on parameters of each prediction model, when a power station end system can acquire the periods of maintenance, power limitation and the like of future power generation equipment, the states can be used as algorithm input, and the algorithm can be used for carrying out more accurate calculation results on the short-term prediction.
4. The localization service of the new energy prediction power is that relevant prediction links such as data acquisition, algorithm calculation, automatic model repair, intelligent comparison and the like are deployed in an electric field end server, an artificial intelligent algorithm model repair service, a real-time database and a big data cluster service are set up by configuring a high-performance computer, various prediction algorithm models are dynamically combined to generate various algorithm type short-term prediction data, accuracy precision comparison is carried out on prediction results, and efficient, accurate and timely short-term power prediction results are achieved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive labor:
FIG. 1 is a block diagram of an overall system according to the present invention;
FIG. 2 is a schematic view of the overall flow structure of the present invention;
FIG. 3 is a diagram illustrating a system configuration in a LAN according to the present invention;
FIG. 4 is a schematic diagram of a system coordination structure of security equipment in a station and a centralized control room according to the present invention;
FIG. 5 is a diagram illustrating a system coordination structure at a storage module of a distributed database cluster according to the present invention.
In the figure: 1. predicting a cloud platform; 2. a real-time anemometer tower; 3. a weather forecast value module; 4. a real-time anemometer tower data module; 5. III server user; 6. a firewall module; 7. a reverse isolation device module; 8. a weather server module; 9. a high performance algorithm server; 10. a database server module; 11. a user workstation module; 12. a WEB server; 13. security equipment in a station and a centralized control room; 14. a local area network; 15. a wind farm; 16. a fan monitoring system module; 17. and a dispatching center.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1-5, the present invention provides a technical solution: a localized intelligent power generation prediction system based on multiple meteorological sources comprises a prediction cloud platform 1, a real-time anemometer tower 2 and a wind farm 15, wherein a transmitting end of the prediction cloud platform 1 is in signal connection with a receiving end of a weather forecast numerical module 3, a transmitting end of the real-time anemometer tower 2 is in signal connection with a receiving end of a real-time anemometer tower data module 4, transmitting ends of the weather forecast numerical module 3 and the real-time anemometer tower data module 4 are both in signal connection with a receiving end of a III server user 5, a transmitting end of the III server user 5 is in signal connection with a receiving end of a firewall module 6, a transmitting end of the firewall module 6 is in signal connection with a receiving end of a reverse isolation device module 7, a transmitting end of the reverse isolation device module 7 is in signal connection with a receiving end of a meteorological server module 8, a receiving end of a high-performance algorithm server 9, a receiving end of a database server module 10, a receiving end of a user workstation module 11 and a receiving end of a WEB server 12, the transmitting end of a high performance algorithm server 9, the transmitting end of a database server module 10, the transmitting end of a user workstation module 11 and the transmitting end of a WEB server 12 are all in signal connection with a local area network 14 receiving end, the transmitting end of a wind farm 15 is in signal connection with a fan monitoring system module 16, the receiving end of the local area network 16 is in signal connection with the transmitting end of a field station and a centralized control room security device 13, the field station and the centralized control room security device 13 can send data to the local area network 16, the transmitting end of the fan monitoring system module 16 is in signal connection with the receiving end of the local area network 14, the transmitting end of the local area network 14 is in signal connection with the receiving end of a dispatching center 17, the transmitting end of the dispatching center 17 is in signal connection with the receiving end of a distributed database cluster storage module, the data of the dispatching center 17 can be uploaded to the distributed database cluster storage module, the performance of the dispatching center 17 server at least meets 64G memory, 2TGb hard disk storage, and more than 8-core CPU processors, the dispatching center 17 can operate and need minimum hardware requirement, and the fan monitored control system module 16 needs to prevent hot wall module 6 when sending data to LAN 14 and confirms, can carry out safety protection through preventing hot wall module 6, and distributed database cluster storage module transmitting terminal signal connection has the data encryption module receiving terminal, data encryption module transmitting terminal signal connection has cloud data storage library module receiving terminal, just cloud data storage library module is hundred degree cloud net dishes or cloudiness, and the security protection equipment 13 receiving terminal signal connection has security protection computer lab system module transmitting terminal, building intercom system module transmitting terminal, closed circuit monitored control system module transmitting terminal and burglar alarm system module transmitting terminal between field station, centralized control.
The new energy power prediction data localization prediction algorithm is intelligently switched, various neural networks, machine learning, time sequence methods, vector methods and other prediction algorithms are adopted, active power data, historical data of power generation equipment, historical wind and light measuring data meteorological data, future 0-72h numerical weather forecast data and the like which are collected by a power station end in real time are taken as input, various short-term prediction power data are obtained through various prediction algorithms, accuracy precision calculation, analysis and comparison are carried out on the various short-term prediction data through an intelligent statistical analysis algorithm, a result with higher prediction precision is selected, a power prediction data source is switched in time, therefore, the effect of continuously improving the prediction precision is achieved, and the high-performance hardware server is mainly applied to the workload needing high-performance sequential read-write access to the large data set in the local storage, such as: establishing distributed database cluster storage, establishing real-time database cluster management (a memory bank such as Redis can be used), collecting, analyzing, processing and applying large-scale parallel data, carrying out algorithm collocation on various numerical meteorological data, calculating different results, requiring that the performance of a server at least meets 64G memory, storing a 2TGb hard disk, intelligently switching a CPU (central processing unit) with more than 8 cores and a new energy power prediction data localization prediction algorithm, adopting various prediction algorithms such as various neural networks, machine learning, a time sequence method, a vector method and the like, taking active power data, historical data of power generation equipment, historical wind metering data meteorological data and future 0-72h numerical weather forecast data and the like collected in real time at a power station end as input, obtaining various short-term prediction power data through various prediction algorithms, carrying out accuracy precision calculation and analysis comparison on various short-term prediction data through an intelligent statistical analysis algorithm, selecting a result with higher prediction precision, switching a power prediction data source in time, thereby achieving the effect of continuously improving the prediction precision, selecting a numerical weather source with high quality is a key means for improving the prediction precision, selecting different weather sources for each power station project, wherein the prediction results are very different, and simultaneously selecting different prediction algorithms, each algorithm can be dynamically combined with different weather sources, so that when the algorithm is not determined to be more suitable for the actual environment of a certain station, each algorithm and a plurality of weather sources need to be simultaneously calculated and operated, data is continuously accumulated, the algorithm is judged to be more advantageous by data accumulation for a period of time, and meanwhile, the results of different algorithms and weather sources need to be dynamically used as final prediction report data according to seasonal changes, thereby achieving the improvement of the prediction precision, the method has the advantages that the effect of power station examination is reduced, intelligent model repair and manual model repair are combined, model revision is required to be carried out regularly for short-term power prediction, manual model repair is carried out after data are collected in the former mode, after localized deployment, through precision comparison of various short-term power data in a historical period of time, which algorithm is more accurate can be found at the first time, meanwhile, through an artificial intelligence technology, regular performance analysis of a prediction curve and power generation equipment in the historical period of time and the like can be carried out, fine adjustment can be carried out on parameters of each prediction model, when a power station end system can acquire the time periods of future power generation equipment maintenance, power limitation and the like, the states can be used as algorithm input, and the algorithm can be used for carrying out more accurate calculation results on short-term prediction.
The power prediction result satisfies:
1. the power prediction accuracy rate of the wind power plant 0-24h day before is more than or equal to 85%, and when the power prediction accuracy rate is less than 85%, the power prediction accuracy rate is assessed according to the following formula:
day-ahead accuracy daily assessment electric quantity (80% accuracy) x PNX 1 (hours)
In the formula: pMiIs the actual power at time i, PPiIs a day-ahead power predicted value at the moment i, Cap is the available capacity of the wind power plant, n is the number of samples, PNRated capacity of the wind farm.
2. The power prediction accuracy of the photovoltaic power station 0-24h day before is more than or equal to 90%, and when the power prediction accuracy is less than 90%, the power prediction accuracy is evaluated according to the following formula:
day-ahead accuracy daily assessment electric quantity (85% one accuracy) x PNX 1.5 (hours)
In the formula: pMiIs the actual power at time i, PPiIs a power predicted value at the moment i, Cap is the available capacity of the photovoltaic power station, n is the number of samples in the power generation period, PNFor photovoltaic power stationsRated capacity.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. The term "comprising", without further limitation, means that the element so defined is not excluded from the group consisting of additional identical elements in the process, method, article, or apparatus that comprises the element.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. The utility model provides a localized intelligent generated power prediction system based on many meteorological sources, includes prediction cloud platform (1), real-time anemometer tower (2) and wind farm (15), its characterized in that: the system comprises a forecast cloud platform (1), a transmitting end of a real-time anemometer tower (2), a real-time anemometer tower data module (4), a receiving end of a weather forecast numerical module (3), transmitting ends of the weather forecast numerical module (3) and the real-time anemometer tower data module (4) are in signal connection with a receiving end of a III server user (5), the transmitting end of the III server user (5) is in signal connection with the receiving end of a firewall module (6), the transmitting end of the firewall module (6) is in signal connection with the receiving end of a reverse isolation device module (7), the transmitting end of the reverse isolation device module (7) is in signal connection with a meteorological server module (8), the receiving end of a high-performance algorithm server (9), the receiving end of a database server module (10), the receiving end of a user workstation module (11) and the receiving end of a WEB server (12), the high-performance algorithm server comprises a high-performance algorithm server (9) transmitting end, a database server module (10) transmitting end, a user workstation module (11) transmitting end and a WEB server (12) transmitting end which are all in signal connection with a local area network (14) receiving end, a wind power plant (15) transmitting end is in signal connection with a fan monitoring system module (16), the fan monitoring system module (16) transmitting end is in signal connection with the local area network (14) receiving end, and the local area network (14) transmitting end is in signal connection with a scheduling center (17) receiving end.
2. The system according to claim 1, wherein said system comprises: and the receiving end of the local area network (16) is in signal connection with the transmitting end of the security equipment (13) between the station and the centralized control room.
3. The multi-meteorological-source-based localized intelligent generated power prediction system of claim 2, wherein: and the transmitting end of the dispatching center (17) is in signal connection with the receiving end of the distributed database cluster storage module.
4. The system of claim 3, wherein the system comprises: the performance of the server of the dispatching center (17) at least meets 64G memory, 2TGb hard disk storage and CPU processor with more than 8 cores.
5. The system according to claim 4, wherein said system comprises: the fan monitoring system module (16) needs the firewall module (6) to determine when sending data to the local area network (14).
6. The system according to claim 5, wherein said system comprises: the distributed database cluster storage module transmitting terminal is in signal connection with a data encryption module receiving terminal, the data encryption module transmitting terminal is in signal connection with a cloud data storage library module receiving terminal, and the cloud data storage library module is a hundred-degree cloud network disk or a micro cloud.
7. The system of claim 6, wherein the system comprises: and the receiving end (13) of the security equipment between the field station and the centralized control room is in signal connection with a transmitting end of a security machine room system module, a transmitting end of a building intercom system module, a transmitting end of a closed-circuit monitoring system module and a transmitting end of an anti-theft alarm system module.
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| CN114462722A (en) * | 2022-04-12 | 2022-05-10 | 南方电网数字电网研究院有限公司 | New energy power generation light-weight high-precision cloud prediction system, method and device |
| CN114977503A (en) * | 2022-06-02 | 2022-08-30 | 中交机电工程局有限公司 | Full-chain monitoring system and method for running state of integrated system |
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| CN114462722A (en) * | 2022-04-12 | 2022-05-10 | 南方电网数字电网研究院有限公司 | New energy power generation light-weight high-precision cloud prediction system, method and device |
| CN114462722B (en) * | 2022-04-12 | 2022-07-29 | 南方电网数字电网研究院有限公司 | New energy power generation light-weight high-precision cloud prediction system, method and device |
| CN114977503A (en) * | 2022-06-02 | 2022-08-30 | 中交机电工程局有限公司 | Full-chain monitoring system and method for running state of integrated system |
| CN114977503B (en) * | 2022-06-02 | 2023-09-08 | 中交机电工程局有限公司 | Full-chain monitoring system and method for running state of integrated system |
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