CN112329977A - Wind power prediction system for extreme scene - Google Patents
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Abstract
The invention discloses an extreme scene-oriented wind power prediction system in the field of wind power prediction, which comprises a data acquisition server, a database server, a switch, a PC (personal computer) workstation, a wind power prediction server and a power grid dispatching center, wherein the data acquisition server is connected with a numerical weather forecast system, a wind measuring tower, a booster station SCADA (supervisory control and data acquisition) server, a fan SCADA (supervisory control and data acquisition) server and a three-dimensional laser radar measurement system through a communication network, the data acquisition server is respectively connected with the PC workstation, the wind power prediction server and the database server through the switch through the communication network, and the switch is connected with the power grid dispatching center through a data interface server. By increasing the input information quantity and reducing the prediction deviation, a combined prediction model with small prediction error and high calculation efficiency is established by adopting a prediction method combining physics, statistics and learning, the calculation of economic dispatch is enhanced by selecting an extreme scene of load, and the economical efficiency of the system is ensured by considering a central scene sample.
Description
Technical Field
The invention relates to the technical field of wind power prediction, in particular to a wind power prediction system for extreme scenes.
Background
Wind power generation is an energy utilization technology for converting wind energy into electric energy through a wind turbine generator, and is the 'green energy' which is developed fastest and has the highest potential in the field of new energy development at present. The wind power prediction technology is used for predicting the power output by a wind power place in a future period of time so as to arrange a scheduling plan. This is because wind energy belongs to unstable energy with random fluctuation, and large-scale wind power is incorporated into the system, which inevitably brings new challenges to the stability of the system. The power generation dispatching mechanism needs to know the wind power output power for hours in the future. The method is divided according to the output prediction time scale of the wind power plant, and comprises long-term prediction, medium-term prediction, short-term prediction and ultra-short-term prediction.
Foreign wind power prediction research starts earlier, wind power prediction technologies of countries such as Denmark, Spain, Germany and the United states are mature, and wind power prediction systems researched by various countries are launched to operate.
The European Union funded "SafeWind" project develops research from different time and space scales for wind power prediction in extreme weather conditions. Compared with a land wind power plant, the offshore wind power plant has flat and smooth terrain, the change of wind speed and heat effect is relatively sensitive, and the existing prediction model is mostly aimed at the land wind power plant, so that the establishment of a proper offshore wind power plant prediction model is awaited for further research.
In the latest theoretical research, related research calculates errors of wind power prediction of a region and a single wind power plant, analysis finds that the reduction of the errors depends on the size of the region, and a new thought for reducing the errors is provided by predicting the wind power of the region by using a spatial smoothing effect. There are also studies to apply a time-adaptive quantile-copula method to wind power probability prediction, and discuss how to select kernel functions modeling different variables. In addition, the research firstly carries out wavelet decomposition on the wind speed sequence, adopts a self-adaptive wavelet neural network to carry out regression prediction on each decomposition signal, and then converts a wind speed predicted value into a wind power predicted value through a feedforward neural network. There are documents that wind turbine data and numerical weather forecast data are converted into wind speed vectors, wind speed and wind direction are predicted through a plurality of observation points, and then a wind speed predicted value is converted into a wind power predicted value through a power curve.
Current state of wind power forecast in China
The research institutions of the wind power prediction system in China mainly comprise China institute of Electrical Power science, China Meteorological bureau national Meteorological center, North China university of electric Power, Jinfeng science and technology, Inc., and the like, and simultaneously, China also has international cooperation with Germany Institute of Solar Energy (ISET), Danish Rise national laboratory and Norway WindSim. The wind power prediction system based on the numerical weather forecast and the artificial neural network is established in research and applied to the Jilin power grid dispatching center, has a good man-machine interface and realizes seamless connection with an energy management system ((EMS), and a hybrid wind power prediction system based on the numerical weather forecast and the artificial neural network is also proposed in literature, and can correct output data according to historical data of the generated energy of a specific wind turbine generator or a wind farm.
In order to further improve the prediction precision, domestic scholars carry out deeply-owned theoretical research on aspects such as prediction models and algorithms. And analyzing main factors influencing wind speed prediction by using a rough set theory, taking the main factors as an additional input person of the medium-long term wind speed prediction model, and establishing a rough set chaotic neural network prediction model. In the literature, the wind speed of the wind power plant is predicted by taking accuracy and efficiency into consideration and selecting proper grid resolution by using a WRF mesoscale numerical prediction mode.
The biggest problem of wind power prediction is that the prediction error is large, so that the practicability of each prediction method is greatly reduced, the error source of the whole process is analyzed, and the existing problems of wind power prediction are as follows:
1. the input data is single, many current prediction systems mainly comprise a numerical weather forecast processing module and a wind power module variable as input data, and wind power is also influenced by surrounding environment factors, physical factors and the like, so that some deviation is brought to prediction.
2. The single prediction model only considers general scene information and is not considered due to some possible load scenes, particularly some extreme load scenes, and does not consider uncertainty caused by load prediction errors of the extreme load scenes. It presents a huge challenge to the economy and reliability of the grid, such as frequency droop, transmission line overload, cascading failures even when the system is in a heavy load condition, etc.
Based on the above, the invention designs the wind power prediction system facing the extreme scene to solve the above mentioned problems.
Disclosure of Invention
The invention aims to provide a wind power prediction system for an extreme scene, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: the utility model provides a wind power prediction system towards extreme scene, includes data acquisition server, database server, switch, PC workstation, wind power prediction server and electric wire netting dispatch center, data acquisition server is connected with numerical value weather forecast system, anemometer tower, booster station SCADA server, fan SCADA server and three-dimensional laser radar measurement system respectively through communication network, booster station SCADA server, fan SCADA server send and are connected fan monitoring box and booster station monitoring box respectively, data acquisition server passes through the switch and passes through communication network with PC workstation, wind power prediction server and database server respectively and be connected, the switch is connected with electric wire netting dispatch center through data interface server.
Preferably, the data acquisition server comprises a data acquisition module and a data processing module, the data acquisition module is used for acquiring data monitored in real time by a numerical weather forecast system, a wind measuring tower, a booster station SCADA server, a fan SCADA server and a three-dimensional laser radar measurement system, the data processing module is used for carrying out the processing of engineering quantity transformation, return-to-zero inspection, dead zone inspection, limit inspection, change rate inspection, correlation inspection, mean value and standard deviation inspection and the like on the real-time monitoring data acquired by the data acquisition module, carrying out interpolation and substitution on unreasonable and lack-of-measurement data, and finally uploading the processed data to a switch.
Preferably, the real-time monitoring data collected by the data collection module comprises
Weather forecast prediction data collected by a numerical weather forecast system: including but not limited to wind speed, wind pressure, air temperature, air pressure, humidity;
real-time wind data collected by the anemometer tower: including but not limited to wind direction, air density, atmospheric humidity;
the real-time power data of the wind power plant collected by the booster station are as follows: including but not limited to total useful work, total useless work, reactive compensation, feeder data;
real-time operation data collected by the fan: including but not limited to wind speed, wind direction, power, turbulence;
data collected by the laser radar measuring system: including but not limited to geographic coordinates, spatial coordinates, density, and topographical features.
Preferably, the three-dimensional laser radar measuring system is carried by an aircraft, and scans fans in the area through the three-dimensional laser radar measuring system, such as geographical coordinates, space coordinates, density, terrain and landform, and the three-dimensional laser radar measuring system comprises a laser scanner, a GPS (global positioning system) and an inertia measuring device.
Preferably, the communication network is a local area network, and the anemometer tower, the booster station SCADA server, the fan SCADA server, the three-dimensional laser radar measurement system, the PC workstation, the wind power prediction server and the database server are all connected with the switch through the communication network through serial port communication protocols.
Preferably, a reverse isolation device is connected between the data weather forecast system and the switch, the data weather forecast system comprises a weather research institution, and NWP data of the weather research institution is transmitted to the data weather forecast system in a unidirectional mode through the internet and the reverse isolation device through a TCP/IP protocol.
Preferably, the database server is configured to perform statistics on historical power data, historical wind measurement data, geographic position data, numerical weather forecast data, and wind farm operation data, and store the historical power data, the wind measurement data, the geographic position data, and the numerical weather forecast data.
Preferably, the PC workstation includes a parameter maintenance module, a graph generation display module, and a report module, and is configured to perform parameter maintenance, graph generation display, and report production and printing on the prediction system, and provide a wind power oriented monitoring screen, such as a geographical map, a system working condition monitoring map, a wind farm primary wiring map, a wind farm working condition monitoring map, a fan list monitoring map, a real-time and historical trend curve, a wind speed-power curve, a wind direction rose map, a wind frequency map, a comparison curve of a wind farm actual average wind speed and a predicted average wind speed, a real-time comparison curve of a wind farm actual output and a predicted value, a historical comparison curve of a wind farm actual output and a predicted value, a daily prediction accuracy statistical chart, and a monthly prediction accuracy statistical chart.
Preferably, the wind power prediction server comprises a sample division module, a load sample reconstruction module and a calculation module,
the system comprises a sample dividing module, a central scene sample processing module and a load analyzing module, wherein the sample dividing module is used for dividing historical load samples stored by a database server into a common scene sample, an extreme scene sample and a central scene sample;
the load sample reconstruction module is used for reconstructing historical load sample data of all divided samples;
and the calculation module is used for calculating whether the extreme scene sample in the load sample reconstruction module has a unit combination scheme, calculating whether each core sample (a general name of a central sample and a common sample) has a corresponding economic dispatching scheme, and recording the score of the extreme scene sample having the unit combination scheme.
Preferably, the unit combination scheme has an economic scheduling solution in the core scenario, the scores of the corresponding unit combination schemes are increased (SI is SI +1), the feasibility ratio of each unit combination scheme is calculated by using SI/NJ, SI is the total score of each unit combination scheme, NJ is the total core sample number, and then the unit combination scheme with the largest SI score is selected for economic scheduling calculation.
Compared with the prior art, the invention has the beneficial effects that:
1. besides weather forecast prediction data acquired by a numerical weather forecast system, real-time wind data acquired by a wind measuring tower and real-time operation data acquired by a fan, wind power plant real-time power data acquired by a booster station are also considered: the method includes the steps of not only limiting to total useful work, total useless work, reactive compensation and feeder line data, most importantly, collecting data of geographic coordinates, space coordinates, density and landform by a laser radar measuring system, fully considering factors influencing wind power prediction, reducing prediction deviation by increasing input information quantity, and reasonably arranging a power generation plan and a scheduling task by a scheduling center through high-precision wind power prediction so as to reduce the influence of a wind power generator set in a wind power plant on a power grid during large-scale grid connection and improve the safety and stability of power grid operation.
2. The invention establishes a combined prediction model with small prediction error and high calculation efficiency by adopting a prediction method combining physics, statistics and learning, thereby improving the prediction precision of the prediction model.
3. In order to avoid the uncertainty possibly occurring in the wind power plant in the extreme scene, the prediction data in the extreme scene is fully considered, the robustness of the unit combination scheme is proved to be ensured by a small number of extreme scenes, the calculation of economic dispatching is enhanced by selecting the extreme scenes of loads, and the economical efficiency of the system is ensured by considering a central scene sample.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic block diagram of the present invention;
FIG. 2 is a schematic diagram of a data acquisition module according to the present invention;
FIG. 3 is a flow chart of the wind power forecast server according to the present invention.
In the drawings, the components represented by the respective reference numerals are listed below:
1. a data acquisition server; 2. a switch; 3. a database server; 4. a wind power prediction server; 5. a PC workstation; 6. a numerical weather forecast system; 7. a fan SCADA server; 8. a three-dimensional lidar measurement system; 9. a booster station SCADA server; 10. a anemometer tower; 11. a fan monitoring box; 12. a booster station monitoring box; 13. a reverse isolation device; 14. a meteorological research institution; 15. a data interface server; 16. and a power grid 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.
Referring to fig. 1-2, the present invention provides a technical solution: a wind power prediction system facing extreme scenes comprises a data acquisition server 1, a database server 3, an exchanger 2, a PC workstation 5, a wind power prediction server and a power grid dispatching center 16, wherein the data acquisition server 1 is respectively connected with a numerical weather forecast system 6, a wind measuring tower 10, a booster SCADA server 9, a fan SCADA server 7 and a three-dimensional laser radar measuring system 8 through a communication network, the booster SCADA server 9 and the fan SCADA server 7 are respectively connected with a fan monitoring box 11 and a booster monitoring box 12, the data acquisition server 1 is respectively connected with the PC workstation 5, the wind power prediction server and the database server 3 through the exchanger 2, the exchanger 2 is connected with the power grid dispatching center 16 through a data interface server 15, and the dispatching center reasonably arranges power generation planning and dispatching tasks, the influence on a power grid when a wind generating set in the wind power plant is connected to the power grid in a large scale is reduced, and the safety and the stability of the operation of the power grid are improved.
In this embodiment, the data acquisition server 1 includes a data acquisition module and a data processing module, the data acquisition module is used for acquiring data monitored by a numerical weather forecast system 6, a wind measuring tower 10, a booster station SCADA server 9, a fan SCADA server 7 and a three-dimensional laser radar measurement system 8 in real time, the data processing module is used for processing real-time monitoring data acquired by the data acquisition module, such as engineering quantity conversion, return-to-zero inspection, dead zone inspection, limit inspection, change rate inspection, correlation inspection, mean value and standard deviation inspection, and the like, interpolating and replacing unreasonable and missing data, and finally uploading the processed data to the switch 2, the switch 2 is a network device for forwarding electric signals, and can provide an exclusive electric signal path for any two network nodes accessing the switch 2, the common switch 2 is an ethernet switch 2 or an optical fiber switch 2, the invention prefers the Ethernet exchanger 2 to carry out mutual communication and real information sharing through the local area network.
The real-time monitoring data collected by the data collection module comprises
Weather forecast prediction data acquired by the numerical weather forecast system 6: including but not limited to wind speed, wind pressure, air temperature, air pressure, humidity;
real-time wind data collected by the anemometer tower 10: including but not limited to wind direction, air density, atmospheric humidity;
the real-time power data of the wind power plant collected by the booster station are as follows: including but not limited to total useful work, total useless work, reactive compensation, feeder data;
real-time operation data collected by the fan: including but not limited to wind speed, wind direction, power, turbulence;
data collected by the laser radar measuring system: including but not limited to geographic coordinates, spatial coordinates, density, and topographical features.
The invention considers the wind power field real-time power data collected by the booster station besides the weather forecast prediction data collected by the numerical weather forecast system 6, the real-time wind data collected by the anemometer tower 10 and the real-time operation data collected by the fan: the method includes but is not limited to total useful work, total useless work, reactive power compensation and feeder line data, and most importantly, data of geographic coordinates, spatial coordinates, density and terrain and landform are collected through a laser radar measurement system, factors influencing wind power prediction are fully considered, and prediction deviation is reduced by increasing input information quantity.
In the above embodiment, the three-dimensional lidar measurement system 8 is carried by an aircraft, and the three-dimensional lidar measurement system 8 scans fans in an area, such as geographical coordinates, spatial coordinates, density, and terrain, and the three-dimensional lidar measurement system 8 includes a laser scanner, a GPS positioning system, and an inertial measurement unit. The three-dimensional laser radar measuring system 8 is carried by the aircraft to obtain the space coordinate, the geographic coordinate, the density and the topographic and geomorphic data file of each wind turbine, and the data of the position of each wind turbine is used as a control parameter to be provided for the physical model of the wind power prediction server 4.
The spatial coordinates and the geographic coordinates may include longitude and latitude of the wind turbines and altitude of the wind turbines, or a three-dimensional coordinate system is established, a horizontal plane is taken as a plane where an X axis and a Y axis are located, a direction perpendicular to the horizontal plane is taken as a direction where a Z axis is located, a certain point in a wind farm area is taken as a coordinate origin, and spatial coordinates of positions of the wind turbines are positions relative to the coordinate origin.
The topographic features may include geodetic coordinate values of a number of points within the wind farm area, contour lines within and around the wind farm area within a certain range of areas, surface roughness and stability within and around the wind farm area within a certain range of areas, and the like.
The density may include a distribution of the number of wind turbines within the wind farm area.
In this embodiment, the communication network is a local area network, and the anemometer tower 10, the booster station SCADA server, the fan SCADA server, the three-dimensional laser radar measurement system 8, the PC workstation 5, the wind power prediction server, and the database server 3 are all connected to the switch 2 via the communication network through a serial port communication protocol.
In this embodiment, a reverse isolation device 13 is connected between the data weather forecast system and the switch 2, the data weather forecast system includes a weather research institution 14, NWP data of the weather research institution 14 is transmitted to the data weather forecast system through the internet and the reverse isolation device 13 in a unidirectional manner through a TCP/IP protocol, and the reverse isolation device 13 is a network security device that is capable of cutting off link layer connections between networks on a circuit by dedicated hardware with various control functions and performing application data exchange with moderate security between networks.
The data weather forecast system can download weather data of each region in the global scope from the weather research institution 14, including air pressure, temperature, humidity, wind, cloud and precipitation, ground temperature, radiation and the like, and adopts a numerical weather forecast module to forecast relevant weather parameters of a wind electric field region in a future period, wherein the weather parameters mainly include wind speed, wind direction, air pressure, temperature, humidity and the like of the wind power plant region.
In this embodiment, the database server 3 is configured to perform statistics on historical power data, historical wind measurement data, geographic position data, numerical weather forecast data, and wind farm operation data, store the historical power data, the wind measurement data, the geographic position data, and the numerical weather forecast data, and may retrieve the stored data through the database server 3.
In this embodiment, the PC workstation 5 includes a parameter maintenance module, a graph generation display module, and a report module, and is used for maintaining parameters of a prediction system, generating and displaying graphs, and printing reports, and providing a wind power oriented monitoring picture such as a geographical map, a system working condition monitoring map, a wind farm primary wiring map, a wind farm working condition monitoring map, a fan list monitoring map, a real-time and historical trend curve, a wind speed-power curve, a wind direction rose map, a wind frequency map, a comparison curve of an actual average wind speed of a wind farm and a predicted average wind speed, a real-time comparison curve of an actual output of a wind farm and a predicted value, a historical comparison curve of an actual output of a wind farm and a predicted value, a daily prediction accuracy statistical chart, and a monthly prediction accuracy statistical chart, and performing parameter maintenance, graph generation display and report printing on parameters of wind power prediction, and the wind power prediction server adopts a, the accuracy of power prediction is improved, and the prediction model has good adaptability.
As shown in fig. 3, in this embodiment, in order to avoid uncertainty that may occur to the wind farm in an extreme scene, prediction data in the extreme scene is fully considered, the wind power prediction server includes a sample division module, a load sample reconstruction module and a calculation module, the wind power prediction server itself includes a plurality of combined prediction models, including physical models such as a neural network and a clustering algorithm, which all have respective advantages, the load sample is obtained by using one of the above methods, the load sample in a period of time is stored in the database server 3, the wind power prediction server only needs to call the historical load sample obtained by using the physical model in the database server 3,
dividing historical load samples stored in the database server 3 into a common scene sample, an extreme scene sample and a central scene sample through a sample dividing module; the method adopted by the method is CSFDP, namely fast searching and density peak value finding clustering, and can be used for separating scenes because the method has an important parameter, namely local density. The degree of closeness between the scene and other surrounding scenes is represented, which reflects the occurrence probability of the scene, so that the occurrence probability of each scene can be calculated through the parameter, and the scenes can be divided into common scenes, extreme scenes and central scenes according to the difference of local densities (the magnitude of the occurrence probability). CSFDP is a method for clustering data with similar information by local density and distance, and calculates local density using gaussian kernel:
wherein d iscRepresents the truncation distance, djkRepresents the distance, p, between the jth and kth datajIs the local density of the jth scene. If the local density of a certain historical load sample is greater than the average density, the sample is identified as a core sample (the general name of a center sample and a common sample), otherwise, the sample is an edge sample, namely an extreme sample, the cluster center in the core sample is the center sample, and the other samples are common samples.
Then, reconstructing historical load sample data of all divided samples through a load sample reconstruction module; the historical load samples are reconstructed using the following equation:
wherein,
Ssfor the s-th load scenario, N is load scenario data, Λm＝[e1，e2，……，em]，e1，e2，……，eτIs a covariance matrix Δ STThe feature vector matrix of (a) is,
ys＝Λt m·ΔSS。
and the calculation module is used for calculating whether the extreme scene sample in the load sample reconstruction module has a unit combination scheme, calculating whether each core sample (a general name of a central sample and a common sample) has a corresponding economic dispatching scheme, and recording the score of the extreme scene sample having the unit combination scheme.
Judging whether a unit combination scheme exists through the following unit combination model:
pmin,iIits≤pits≤pmax,iIits 
|SF·(KpPts-KD·PDts)|≤PLmax 
wherein, Fi(pits) Shows the coal burning cost, SU, at the t-th moment of the ith unit in the scene sitsRepresenting the startup cost of the ith unit at the tth moment under the scene s, NG representing the number of coal-fired units, NT representing the planned time scale, and pitsRepresents the output and PD of the ith unit at the t-th moment in the scene stsRepresenting the load value, R, at time t under scene siRepresents the upper limit of the slope climbing of the ith unit, pmin,i，pmax,iRespectively representing the minimum and maximum output of the ith unit, IitsRepresenting the state of the unit i at the t-th moment in the scene s, Xonf,i(t-1)s,Xoff,i(t-1)sRespectively representing the cumulative opening and closing time of the ith unit at the T-1 th moment under the scene s, Ton,i，Toff,iRespectively representing the minimum startup and shutdown time required by the ith unit, wherein SF represents a system transfer factor matrix, Kp，KDRespectively representing the unit and the composite node connection matrix, PLmaxRepresenting the upper limit of the line power flow; pts,PDtsRespectively representing the output of the coal-fired unit and the system load at the time t of the scene s.
Economic dispatch calculations were performed using the following formula:
pmin,iIits≤pits≤pmax,iIits 
|SF·(KpPts-KD·PDts)|≤PLmax 
wherein NS represents the number of central scenes, πSIndicating the probability of the occurrence of the s-th central scene.
In this embodiment, if an economic scheduling solution exists in the unit combination scheme in the core scenario, the scores of the corresponding unit combination schemes are increased (SI is SI +1), the feasibility proportion of each unit combination scheme is calculated by using SI/NJ, SI is the total score of each unit combination scheme, NJ is the total core sample number, and then the unit combination scheme with the largest SI score is selected for economic scheduling calculation.
The method has the advantages that the method can generate a scheduling scheme with better economical efficiency while ensuring the robustness, and a large number of simulation experiments prove that the robustness of the unit combination scheme can be ensured by a small number of extreme scenes.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (10)
1. The wind power prediction system for the extreme scene is characterized in that: the system comprises a data acquisition server, a database server, a switch, a PC workstation, a wind power prediction server and a power grid dispatching center, wherein the data acquisition server is respectively connected with a numerical weather forecast system, a wind measuring tower, a booster station SCADA server, a fan SCADA server and a three-dimensional laser radar measuring system through a communication network, the booster station SCADA server and the fan SCADA server are respectively connected with a fan monitoring box and a booster station monitoring box, the data acquisition server is respectively connected with the PC workstation, the wind power prediction server and the database server through the communication network through the switch, and the switch is connected with the power grid dispatching center through a data interface server.
2. The extreme scenario-oriented wind power prediction system of claim 1, wherein: the data acquisition server comprises a data acquisition module and a data processing module, the data acquisition module is used for acquiring data monitored in real time by a numerical weather forecast system, a wind measuring tower, a booster station SCADA server, a fan SCADA server and a three-dimensional laser radar measurement system, the data processing module is used for carrying out engineering quantity transformation, zeroing inspection, dead zone inspection, limit inspection, change rate inspection, correlation inspection, mean value and standard deviation inspection and the like on the real-time monitoring data acquired by the data acquisition module, carrying out interpolation and substitution on unreasonable and lack-of-measurement data, and finally uploading the processed data to a switch.
3. The extreme scenario-oriented wind power prediction system according to claim 2, wherein: the real-time monitoring data collected by the data collection module comprises
Weather forecast prediction data collected by a numerical weather forecast system: including but not limited to wind speed, wind pressure, air temperature, air pressure, humidity;
real-time wind data collected by the anemometer tower: including but not limited to wind direction, air density, atmospheric humidity;
the real-time power data of the wind power plant collected by the booster station are as follows: including but not limited to total useful work, total useless work, reactive compensation, feeder data;
real-time operation data collected by the fan: including but not limited to wind speed, wind direction, power, turbulence;
data collected by the laser radar measuring system: including but not limited to geographic coordinates, spatial coordinates, density, and topographical features.
4. The extreme scenario-oriented wind power prediction system according to claim 2, wherein: the three-dimensional laser radar measuring system is carried by an aircraft, scans fans in an area through the three-dimensional laser radar measuring system, and comprises a geographical coordinate, a space coordinate, density and a terrain, a laser scanner of the three-dimensional laser radar measuring system, a GPS positioning system and an inertia measuring device.
5. The extreme scenario-oriented wind power prediction system of claim 1, wherein: the communication network is a local area network, and the anemometer tower, the booster station SCADA server, the fan SCADA server, the three-dimensional laser radar measuring system, the PC workstation, the wind power prediction server and the database server are all connected with the switch through a serial port communication protocol via the communication network.
6. The extreme scenario-oriented wind power prediction system of claim 1, wherein: the data weather forecast system comprises a weather research institution, and NWP data of the weather research institution is transmitted to the data weather forecast system in a one-way mode through the Internet and the reverse isolation device through a TCP/IP protocol.
7. The extreme scenario-oriented wind power prediction system of claim 1, wherein: the database server is used for counting historical power data, historical wind measuring data, geographical position data, numerical weather forecast data and wind power plant operation data, and storing the historical power data, the wind measuring data, the geographical position data and the numerical weather forecast data.
8. The extreme scenario-oriented wind power prediction system of claim 1, wherein: the PC workstation comprises a parameter maintenance module, a graph generation display module and a report module, and is used for maintaining parameters of a prediction system, generating and displaying graphs and making and printing reports, and providing wind power oriented monitoring pictures such as a geographic map, a system working condition monitoring map, a wind power plant primary wiring map, a wind power plant working condition monitoring map, a fan list monitoring map, a real-time and historical trend curve, a wind speed-power curve, a wind direction rose map, a wind frequency map, a comparison curve of the actual average wind speed of the wind power plant and the predicted average wind speed, a real-time comparison curve of the actual output of the wind power plant and the predicted value, a historical comparison curve of the actual output of the wind power plant and the predicted value, a daily prediction precision statistical chart and a monthly prediction precision statistical.
9. The extreme scenario-oriented wind power prediction system of claim 1, wherein: the wind power prediction server comprises a sample division module, a load sample reconstruction module and a calculation module,
the system comprises a sample dividing module, a central scene sample processing module and a load analyzing module, wherein the sample dividing module is used for dividing historical load samples stored by a database server into a common scene sample, an extreme scene sample and a central scene sample;
the load sample reconstruction module is used for reconstructing historical load sample data of all divided samples;
and the calculation module is used for calculating whether the extreme scene sample in the load sample reconstruction module has a unit combination scheme, calculating whether each core sample (a general name of a central sample and a common sample) has a corresponding economic dispatching scheme, and recording the score of the extreme scene sample having the unit combination scheme.
10. The extreme scenario-oriented wind power prediction system of claim 9, wherein: the unit combination scheme has an economic scheduling solution in the core scene, the fraction of the corresponding unit combination scheme is increased (SI +1), the feasibility proportion of each unit combination scheme is calculated by using SI/NJ, SI is the total fraction of each unit combination scheme, NJ is the total core sample number, and then the unit combination scheme with the maximum SI fraction is selected for economic scheduling calculation.
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